WorldWideScience

Sample records for helpful learning features

  1. Feature Inference Learning and Eyetracking

    Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

    2009-01-01

    Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

  2. Helping Your Child Learn Geography

    ,

    1996-01-01

    By the year 2000, all students will leave grades 4, 8, and 12 having demonstrated competency over challenging subject matter including English, mathematics, science, foreign languages, civics and government, economics, arts, history, and geography, and every school in America will ensure that all students learn to use their minds well, so they may be prepared for responsible citizenship, further learning, and productive employment in our Nation's modern economy.

  3. Help Your Child Learn To Write Well.

    Office of Educational Research and Improvement (ED), Washington, DC.

    Addressing parents, this pamphlet describes ways to help children learn to write well and thereby excel in school, enjoy self-expression, and become more self-reliant. Writing is discussed as a practical, job-related, stimulating, social, and therapeutic activity that receives inadequate attention in many schools. It is emphasized that writing is…

  4. Features Students Really Expect from Learning Analytics

    Schumacher, Clara; Ifenthaler, Dirk

    2016-01-01

    In higher education settings more and more learning is facilitated through online learning environments. To support and understand students' learning processes better, learning analytics offers a promising approach. The purpose of this study was to investigate students' expectations toward features of learning analytics systems. In a first…

  5. Video Scene Parsing with Predictive Feature Learning

    Jin, Xiaojie; Li, Xin; Xiao, Huaxin; Shen, Xiaohui; Lin, Zhe; Yang, Jimei; Chen, Yunpeng; Dong, Jian; Liu, Luoqi; Jie, Zequn; Feng, Jiashi; Yan, Shuicheng

    2016-01-01

    In this work, we address the challenging video scene parsing problem by developing effective representation learning methods given limited parsing annotations. In particular, we contribute two novel methods that constitute a unified parsing framework. (1) \\textbf{Predictive feature learning}} from nearly unlimited unlabeled video data. Different from existing methods learning features from single frame parsing, we learn spatiotemporal discriminative features by enforcing a parsing network to ...

  6. Embedded Incremental Feature Selection for Reinforcement Learning

    2012-05-01

    Prior to this work, feature selection for reinforce- ment learning has focused on linear value function ap- proximation ( Kolter and Ng, 2009; Parr et al...InProceed- ings of the the 23rd International Conference on Ma- chine Learning, pages 449–456. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature

  7. Helping Education Students Understand Learning through Designing

    Ronen-Fuhrmann, Tamar; Kali, Yael; Hoadley, Christopher

    2008-01-01

    This article describes a course in which graduate students in education learn practical and theoretical aspects of educational design by creating technologies for learning. The course was built around three themes: "Analyzing technologies," in which students study state-of- the-art technologies and interview their designers; "design studio," in…

  8. Learning slow features for behavior analysis

    Zafeiriou, Lazaros; Nicolaou, Mihalis A.; Zafeiriou, Stefanos; Nikitids, Symeon; Pantic, Maja

    2013-01-01

    A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative

  9. Don't Want to Look Dumb? The Role of Theories of Intelligence and Humanlike Features in Online Help Seeking.

    Kim, Sara; Zhang, Ke; Park, Daeun

    2018-02-01

    Numerous studies have shown that individuals' help-seeking behavior increases when a computerized helper is endowed with humanlike features in nonachievement contexts. In contrast, the current research suggests that anthropomorphic helpers are not universally conducive to help-seeking behavior in contexts of achievement, particularly among individuals who construe help seeking as a display of incompetence (i.e., entity theorists). Study 1 demonstrated that when entity theorists received help from an anthropomorphized (vs. a nonanthropomorphized) helper, they were more concerned about negative judgments from other people, whereas incremental theorists were not affected by anthropomorphic features. Study 2 showed that when help was provided by an anthropomorphized (vs. a nonanthropomorphized) helper, entity theorists were less likely to seek help, even at the cost of lower performance. In contrast, incremental theorists' help-seeking behavior and task performance were not affected by anthropomorphism. This research deepens the current understanding of the role of anthropomorphic computerized helpers in online learning contexts.

  10. Learning Transferable Features with Deep Adaptation Networks

    Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.

    2015-01-01

    Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...

  11. New learning resource features CERN

    Katarina Anthony

    2011-01-01

    A new educational website, STEM Works, has been launched this month, presenting science and technology in an industrial context for students aged 11-14. Developed with contributions from CERN, the site highlights the Laboratory as a “real-world” example of the opportunities available to science graduates. While the site was developed in Northern Ireland, STEM Works addresses issues of global relevance.   Students share their projects with Steve Myers, Richard Hanna (CCEA), and Catriona Ruane (Education Minister). STEM stands for Science, Technology, Engineering and Mathematics – the four cornerstones of the curriculum featured on the STEM Works website. It is part of a nationwide push in Northern Ireland to highlight how important STEM subjects are to both academia and industry. CERN worked closely with the Northern Ireland Council for the Curriculum, Examinations and Assessment (CCEA) to develop educational content for the site. “The CCEA STEM Works site i...

  12. Enhanced Resource Descriptions Help Learning Matrix Users.

    Roempler, Kimberly S.

    2003-01-01

    Describes the Learning Matrix digital library which focuses on improving the preparation of math and science teachers by supporting faculty who teach introductory math and science courses in two- and four-year colleges. Suggests it is a valuable resource for school library media specialists to support new science and math teachers. (LRW)

  13. Pizza and Pasta Help Students Learn Metabolism

    Passos, Renato M.; Se, Alexandre B.; Wolff, Vanessa L.; Nobrega, Yanna K. M.; Hermes-Lima, Marcelo

    2006-01-01

    In this article, we report on an experiment designed to improve the learning of metabolic biochemistry by nutrition and medical undergraduate students. Twelve students participated in a monitored lunch and had their blood extracted for analysis: (1) before lunch; (2) 30 min after lunch; and (3) 3 h after lunch. The subjects were divided in two…

  14. How Computer Games Help Children Learn

    Shaffer, David Williamson

    2008-01-01

    This book looks at how particular video and computer games--such as "Digital Zoo", "The Pandora Project", "SodaConstructor", and more--can help teach children and students to think like doctors, lawyers, engineers, urban planners, journalists, and other professionals. In the process, new "smart games" will give them the knowledge and skills they…

  15. Learning radiological appearances of diseases: Does comparison help?

    Kok, Ellen M.; de Bruin, Anique B H; Robben, Simon C. F.; van Merrienboer, Jeroen J. G.

    Comparison learning is a promising approach for learning complex real-life visual tasks. When medical students study radiological appearances of diseases, comparison of images showing diseases with images showing no abnormalities could help them learn to discriminate relevant, disease-related

  16. Feature and Region Selection for Visual Learning.

    Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

    2016-03-01

    Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

  17. Slow feature analysis: unsupervised learning of invariances.

    Wiskott, Laurenz; Sejnowski, Terrence J

    2002-04-01

    Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

  18. Using tactile features to help functionally blind individuals denominate banknotes.

    Lederman, Susan J; Hamilton, Cheryl

    2002-01-01

    This study, which was conducted for the Bank of Canada, assessed the feasibility of presenting a raised texture feature together with a tactile denomination code on the next Canadian banknote series ($5, $10, $20, $50, and $100). Adding information accessible by hand would permit functionally blind individuals to independently denominate banknotes. In Experiment 1, 20 blindfolded, sighted university students denominated a set of 8 alternate tactile feature designs. Across the 8 design series, the proportion of correct responses never fell below .97; the mean response time per banknote ranged from 11.4 to 13.1 s. In Experiment 2, 27 functionally blind participants denominated 4 of the previous 8 candidate sets of banknotes. The proportion of correct responses never fell below .92; the corresponding mean response time per banknote ranged from 11.7 to 13.0 s. The Bank of Canada selected one of the four raised-texture designs for inclusion on its new banknote series. Other potential applications include designing haptic displays for teleoperation and virtual environment systems.

  19. Machine learning spatial geometry from entanglement features

    You, Yi-Zhuang; Yang, Zhao; Qi, Xiao-Liang

    2018-02-01

    Motivated by the close relations of the renormalization group with both the holography duality and the deep learning, we propose that the holographic geometry can emerge from deep learning the entanglement feature of a quantum many-body state. We develop a concrete algorithm, call the entanglement feature learning (EFL), based on the random tensor network (RTN) model for the tensor network holography. We show that each RTN can be mapped to a Boltzmann machine, trained by the entanglement entropies over all subregions of a given quantum many-body state. The goal is to construct the optimal RTN that best reproduce the entanglement feature. The RTN geometry can then be interpreted as the emergent holographic geometry. We demonstrate the EFL algorithm on a 1D free fermion system and observe the emergence of the hyperbolic geometry (AdS3 spatial geometry) as we tune the fermion system towards the gapless critical point (CFT2 point).

  20. Learning to recommend helpful hotel reviews

    O'Mahony, Michael P.; Smyth, Barry

    2009-01-01

    User-generated reviews are a common and valuable source of product information, yet little attention has been paid as to how best to present them to end-users. In this paper, we describe a classification-based recommender system that is designed to recommend the most helpful reviews for a given product. We present a large-scale evaluation of our approach using TripAdvisor hotel reviews, and we show that our approach is capable of suggesting superior reviews compared to a number of alternat...

  1. Features and characteristics of problem based learning

    Eser Ceker

    2016-12-01

    Full Text Available Throughout the years, there appears to be an increase in Problem Based Learning applications in education; and Problem Based Learning related research areas. The main aim of this research is to underline the fundamentals (basic elements of Problem Based Learning, investigate the dimensions of research approached to PBL oriented areas (with a look for the latest technology supported tools of Problem Based Learning. This research showed that the most researched characteristics of PBL are; teacher and student assessments on Problem Based Learning, Variety of disciplines in which Problem Based Learning strategies were tried and success evaluated, Using Problem Based Learning alone or with other strategies (Hybrid or Mix methods, Comparing Problem Based Learning with other strategies, and new trends and tendencies in Problem Based Learning related research. Our research may help us to identify the latest trends and tendencies referred to in the published studies related to “problem based learning” areas. In this research, Science Direct and Ulakbim were used as our main database resources. The sample of this study consists of 150 articles.

  2. Text feature extraction based on deep learning: a review.

    Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

    2017-01-01

    Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

  3. Multimodal Feature Learning for Video Captioning

    Sujin Lee

    2018-01-01

    Full Text Available Video captioning refers to the task of generating a natural language sentence that explains the content of the input video clips. This study proposes a deep neural network model for effective video captioning. Apart from visual features, the proposed model learns additionally semantic features that describe the video content effectively. In our model, visual features of the input video are extracted using convolutional neural networks such as C3D and ResNet, while semantic features are obtained using recurrent neural networks such as LSTM. In addition, our model includes an attention-based caption generation network to generate the correct natural language captions based on the multimodal video feature sequences. Various experiments, conducted with the two large benchmark datasets, Microsoft Video Description (MSVD and Microsoft Research Video-to-Text (MSR-VTT, demonstrate the performance of the proposed model.

  4. Visual attention to features by associative learning.

    Gozli, Davood G; Moskowitz, Joshua B; Pratt, Jay

    2014-11-01

    Expecting a particular stimulus can facilitate processing of that stimulus over others, but what is the fate of other stimuli that are known to co-occur with the expected stimulus? This study examined the impact of learned association on feature-based attention. The findings show that the effectiveness of an uninformative color transient in orienting attention can change by learned associations between colors and the expected target shape. In an initial acquisition phase, participants learned two distinct sequences of stimulus-response-outcome, where stimuli were defined by shape ('S' vs. 'H'), responses were localized key-presses (left vs. right), and outcomes were colors (red vs. green). Next, in a test phase, while expecting a target shape (80% probable), participants showed reliable attentional orienting to the color transient associated with the target shape, and showed no attentional orienting with the color associated with the alternative target shape. This bias seemed to be driven by learned association between shapes and colors, and not modulated by the response. In addition, the bias seemed to depend on observing target-color conjunctions, since encountering the two features disjunctively (without spatiotemporal overlap) did not replicate the findings. We conclude that associative learning - likely mediated by mechanisms underlying visual object representation - can extend the impact of goal-driven attention to features associated with a target stimulus. Copyright © 2014 Elsevier B.V. All rights reserved.

  5. Helping While Learning: A Skilled Group Helper Training Program.

    Smaby, Marlowe H.; Tamminen, Armas W.

    1983-01-01

    Describes a developmental group training workshop for training experienced counselors to do group counseling. Discusses stages of training including exploration, understanding, and action, which can help counselors learn helping skills for counseling that can often transfer to their own interpersonal lives and interactions with others. (JAC)

  6. Mathematic anxiety, help seeking behavior and cooperative learning

    Masoud Gholamali Lavasani; Farah Khandan

    2011-01-01

    Present project assess the effectiveness of cooperative learning over the mathematic anxiety and review the behavior of help seeking in first grade high school girl students. The experimental research procedure was in the form of pre-post tests after a period of 8 sessions of teaching. To measure the variables, the questionnaire of mathematic anxiety (Shokrani, 2002) and the questionnaire of help seeking technique (Ghadampour, 1998) were practiced (accepting or avoiding help seeking).To perfo...

  7. Helping Children Learn Vocabulary during Computer-Assisted Oral Reading

    Gregory Aist

    2002-04-01

    Full Text Available This paper addresses an indispensable skill using a unique method to teach a critical component: helping children learn to read by using computer-assisted oral reading to help children learn vocabulary. We build on Project LISTEN’s Reading Tutor, a computer program that adapts automatic speech recognition to listen to children read aloud, and helps them learn to read (http://www.cs.cmu.edu/~listen. To learn a word from reading with the Reading Tutor, students must encounter the word and learn the meaning of the word in context. We modified the Reading Tutor first to help students encounter new words and then to help them learn the meanings of new words. We then compared the Reading Tutor to classroom instruction and to human-assisted oral reading as part of a yearlong study with 144 second and third graders. The result: Second graders did about the same on word comprehension in all three conditions. However, third graders who read with the 1999 Reading Tutor, modified as described in this paper, performed statistically significantly better than other third graders in a classroom control on word comprehension gains – and even comparably with other third graders who read one-on-one with human tutors.

  8. Unsupervised feature learning for autonomous rock image classification

    Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

    2017-09-01

    Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

  9. Child Care Helps America Work and Learn. Issue No. 1

    Child Care Bureau, 2010

    2010-01-01

    "Child Care Helps America Work and Learn" is a new publication produced by the Child Care Bureau. This new series will highlight some of the many Recovery Act-funded child care success stories from communities across the country that illustrate how the Bureau is working toward the shared goal of supporting children and families. This…

  10. Pocket Electronic Dictionaries for Second Language Learning: Help or Hindrance?

    Tang, Gloria M.

    1997-01-01

    Reports on the concerns of English-as-a-Second-Language (ESL) teachers in Canada regarding their students' use of pocket bilingual electronic dictionaries (EDs). The article highlights the ED's features, uses, and effectiveness as a tool for learning ESL at the secondary level and ESL students' perceptions of the ED's usefulness. (nine references)…

  11. Manifold regularized multitask feature learning for multimodality disease classification.

    Jie, Biao; Zhang, Daoqiang; Cheng, Bo; Shen, Dinggang

    2015-02-01

    Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single task, and use group-sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly select the common features from multiple tasks. Furthermore, we introduce a new manifold-based Laplacian regularizer to preserve the data distribution information from each task. Finally, we use the multikernel support vector machine method to fuse multimodality data for eventual classification. Conversely, we also extend our method to the semisupervised setting, where only partial data are labeled. We evaluate our method using the baseline magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative database. The experimental results demonstrate that our proposed method can not only achieve improved classification performance, but also help to discover the disease-related brain regions useful for disease diagnosis. © 2014 Wiley Periodicals, Inc.

  12. Infrared image enhancement with learned features

    Fan, Zunlin; Bi, Duyan; Ding, Wenshan

    2017-11-01

    Due to the variation of imaging environment and limitations of infrared imaging sensors, infrared images usually have some drawbacks: low contrast, few details and indistinct edges. Hence, to promote the applications of infrared imaging technology, it is essential to improve the qualities of infrared images. To enhance image details and edges adaptively, we propose an infrared image enhancement method under the proposed image enhancement scheme. On the one hand, on the assumption of high-quality image taking more evident structure singularities than low-quality images, we propose an image enhancement scheme that depends on the extractions of structure features. On the other hand, different from the current image enhancement algorithms based on deep learning networks that try to train and build the end-to-end mappings on improving image quality, we analyze the significance of first layer in Stacked Sparse Denoising Auto-encoder and propose a novel feature extraction for the proposed image enhancement scheme. Experiment results prove that the novel feature extraction is free from some artifacts on the edges such as blocking artifacts, ;gradient reversal;, and pseudo contours. Compared with other enhancement methods, the proposed method achieves the best performance in infrared image enhancement.

  13. Feature selection is the ReliefF for multiple instance learning

    Zafra, A.; Pechenizkiy, M.; Ventura, S.

    2010-01-01

    Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature selection techniques have been proposed for the traditional settings, where each instance is expected to have a label. In

  14. An Educational System to Help Students Assess Website Features and Identify High-Risk Websites

    Kajiyama, Tomoko; Echizen, Isao

    2015-01-01

    Purpose: The purpose of this paper is to propose an effective educational system to help students assess Web site risk by providing an environment in which students can better understand a Web site's features and determine the risks of accessing the Web site for themselves. Design/methodology/approach: The authors have enhanced a prototype…

  15. Can learning health systems help organisations deliver personalised care?

    Nwaru, Bright I; Friedman, Charles; Halamka, John; Sheikh, Aziz

    2017-10-02

    There is increasing international policy and clinical interest in developing learning health systems and delivering precision medicine, which it is hoped will help reduce variation in the quality and safety of care, improve efficiency, and lead to increasing the personalisation of healthcare. Although reliant on similar policies, informatics tools, and data science and implementation research capabilities, these two major initiatives have thus far largely progressed in parallel. In this opinion piece, we argue that they should be considered as complementary, synergistic initiatives whereby the creation of learning health systems infrastructure can support and catalyse the delivery of precision medicine that maximises the benefits and minimises the risks associated with treatments for individual patients. We illustrate this synergy by considering the example of treatments for asthma, which is now recognised as an umbrella term for a heterogeneous group of related conditions.

  16. Learning Biochemistry through Manga--Helping Students Learn and Remember, and Making Lectures More Exciting.

    Nagata, Ryoichi

    1999-01-01

    Uses panels taken from manga, Japanese comics and cartoons, to supplement explanations of biochemical terms and topics in biochemistry classes. Results indicate that the use of manga helped students remember what they had learned. (Author/CCM)

  17. A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

    Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

    2015-12-01

    Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

  18. Features and Characteristics of Problem Based Learning

    Ceker, Eser; Ozdamli, Fezile

    2016-01-01

    Throughout the years, there appears to be an increase in Problem Based Learning applications in education; and Problem Based Learning related research areas. The main aim of this research is to underline the fundamentals (basic elements) of Problem Based Learning, investigate the dimensions of research approached to PBL oriented areas (with a look…

  19. Unsupervised Learning of Spatiotemporal Features by Video Completion

    Nallabolu, Adithya Reddy

    2017-01-01

    In this work, we present an unsupervised representation learning approach for learning rich spatiotemporal features from videos without the supervision from semantic labels. We propose to learn the spatiotemporal features by training a 3D convolutional neural network (CNN) using video completion as a surrogate task. Using a large collection of unlabeled videos, we train the CNN to predict the missing pixels of a spatiotemporal hole given the remaining parts of the video through minimizing per...

  20. Feature selection for domain knowledge representation through multitask learning

    Rosman, Benjamin S

    2014-10-01

    Full Text Available represent stimuli of interest, and rich feature sets which increase the dimensionality of the space and thus the difficulty of the learning problem. We focus on a multitask reinforcement learning setting, where the agent is learning domain knowledge...

  1. Towards a Serious Game to Help Students Learn Computer Programming

    Mathieu Muratet

    2009-01-01

    Full Text Available Video games are part of our culture like TV, movies, and books. We believe that this kind of software can be used to increase students' interest in computer science. Video games with other goals than entertainment, serious games, are present, today, in several fields such as education, government, health, defence, industry, civil security, and science. This paper presents a study around a serious game dedicated to strengthening programming skills. Real-Time Strategy, which is a popular game genre, seems to be the most suitable kind of game to support such a serious game. From programming teaching features to video game characteristics, we define a teaching organisation to experiment if a serious game can be adapted to learn programming.

  2. Joint Feature Selection and Classification for Multilabel Learning.

    Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

    2018-03-01

    Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

  3. Multi-task feature learning by using trace norm regularization

    Jiangmei Zhang

    2017-11-01

    Full Text Available Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.

  4. Inferring feature relevances from metric learning

    Schulz, Alexander; Mokbel, Bassam; Biehl, Michael

    2015-01-01

    Powerful metric learning algorithms have been proposed in the last years which do not only greatly enhance the accuracy of distance-based classifiers and nearest neighbor database retrieval, but which also enable the interpretability of these operations by assigning explicit relevance weights...

  5. Help My House Program Profile

    Learn about Help My House, a program that helps participants reduce their utility bills by nearly 35 percent through low-cost loans for EE improvements. Learn more about the key features, approaches, funding sources, and achievements of this program.

  6. Which assessment features shape students' learning? A review study

    Joosten-ten Brinke, Desirée; Sluijsmans, Dominique; Van der Vleuten, Cees

    2013-01-01

    Joosten-ten Brinke, D., Sluijsmans, D., & Van der Vleuten, C. (2012, 28 November). Which assessment features shape students’ learning? A review study. Presentation at the Eapril conference, Jyväskylä, Finland.

  7. Do Interactive Globes and Games Help Students Learn Planetary Science?

    Coba, Filis; Burgin, Stephen; De Paor, Declan; Georgen, Jennifer

    2016-01-01

    The popularity of animations and interactive visualizations in undergraduate science education might lead one to assume that these teaching aids enhance student learning. We tested this assumption for the case of the Google Earth virtual globe with a comparison of control and treatment student groups in a general education class of over 370 students at a large public university. Earth and Planetary Science course content was developed in two formats: using Keyhole Markup Language (KML) to create interactive tours in Google Earth (the treatment group) and Portable Document Format (PDF) for on-screen reading (the control group). The PDF documents contained identical text and images to the placemark balloons or "tour stops" in the Google Earth version. Some significant differences were noted between the two groups based on the immediate post-questionnaire with the KML students out-performing the PDF students, but not on the delayed measure. In a separate but related project, we undertake preliminary investigations into methods of teaching basic concepts in planetary mantle convection using numerical simulations. The goal of this project is to develop an interface with a two-dimensional finite element model that will allow students to vary parameters such as the temperatures assigned to the boundaries of the model domain, to help them actively explore important variables that control convection.

  8. Learning to Learn Online: Using Locus of Control to Help Students Become Successful Online Learners

    Lowes, Susan; Lin, Peiyi

    2015-01-01

    In this study, approximately 600 online high school students were asked to take Rotter's locus of control questionnaire and then reflect on the results, with the goal of helping them think about their ability to regulate their learning in this new environment. In addition, it was hoped that the results could provide a diagnostic for teachers who…

  9. Learning Features of Music from Scratch

    Thickstun, John; Harchaoui, Zaid; Kakade, Sham

    2016-01-01

    This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument/note annotations resulting in over 1 million temporal labels on 34 hours of chamber music performances under various studio and microphone conditions. The paper defines a multi-label clas...

  10. Intelligent Learning Management Systems: Definition, Features and Measurement of Intelligence

    Fardinpour, Ali; Pedram, Mir Mohsen; Burkle, Martha

    2014-01-01

    Virtual Learning Environments have been the center of attention in the last few decades and help educators tremendously with providing students with educational resources. Since artificial intelligence was used for educational proposes, learning management system developers showed much interest in making their products smarter and more…

  11. Sonographic features of thyroid nodules that may help distinguish clinically atypical subacute thyroiditis from thyroid malignancy.

    Pan, Fu-shun; Wang, Wei; Wang, Yan; Xu, Ming; Liang, Jin-yu; Zheng, Yan-ling; Xie, Xiao-yan; Li, Xiao-xi

    2015-04-01

    The purpose of this study was to evaluate sonographic features for distinguishing clinically atypical subacute thyroiditis from malignant thyroid nodules. A total of 165 hypoechoic thyroid nodules without calcification in 135 patients with histologic diagnosis were included in this study. These nodules were classified into 2 groups: a thyroiditis group (55 nodules in 36 patients) and a malignancy group (110 nodules in 99 patients). The sonographic features of the groups were retrospectively reviewed. No significant differences were detected for the variables of marked echogenicity, a taller-than-wide shape, and mixed vascularity. However, a poorly defined margin was detected more frequently in the thyroiditis group than the malignancy group (P thyroiditis, with sensitivity and specificity of 87.3% and 80.9%, respectively. Centripetal reduction echogenicity was observed exclusively in the thyroiditis group, with high specificity (100%) but low sensitivity (21.8%) for atypical subacute thyroiditis diagnosis. All of the thyroiditis nodules with a positive color signal showed noninternal vascularity (negative predictive value, 100%). There is a considerable overlap between the sonographic features of atypical subacute thyroiditis and thyroid malignancy. However, the margin, echogenicity, and vascularity type are helpful indicators for differential diagnosis of atypical subacute thyroiditis. © 2015 by the American Institute of Ultrasound in Medicine.

  12. The Effectiveness of Three Serious Games Measuring Generic Learning Features

    Bakhuys Roozeboom, Maartje; Visschedijk, Gillian; Oprins, Esther

    2017-01-01

    Although serious games are more and more used for learning goals, high-quality empirical studies to prove the effectiveness of serious games are relatively scarce. In this paper, three empirical studies are presented that investigate the effectiveness of serious games as opposed to traditional classroom instruction on learning features as well as…

  13. Learning Hierarchical Feature Extractors for Image Recognition

    2012-09-01

    feature space . . . . . . . . . . . . . . . 85 5.3.1 Preserving neighborhood relationships during coding . . . . . . 86 5.3.2 Letting only neighbors vote ...Letting only neighbors vote during pooling Pooling involves extracting an ensemble statistic from a potentially large group of in- puts. However...element. For slicing the 4D tensor S we adopt the MATLAB notation for simplicity of notation. function ConvCoD(x,D, α) Set: S = DT ∗ D Initialize: z = 0; β

  14. Learning Disorders: Know the Signs, How to Help

    Healthy Lifestyle Children's health Learning disorders can make it hard for a child to read, write or do simple math. Understand the signs and what ... By Mayo Clinic Staff Many children who have learning disorders, also known as learning disabilities, struggle for ...

  15. Skype me! Socially contingent interactions help toddlers learn language.

    Roseberry, Sarah; Hirsh-Pasek, Kathy; Golinkoff, Roberta M

    2014-01-01

    Language learning takes place in the context of social interactions, yet the mechanisms that render social interactions useful for learning language remain unclear. This study focuses on whether social contingency might support word learning. Toddlers aged 24-30 months (N = 36) were exposed to novel verbs in one of three conditions: live interaction training, socially contingent video training over video chat, and noncontingent video training (yoked video). Results suggest that children only learned novel verbs in socially contingent interactions (live interactions and video chat). This study highlights the importance of social contingency in interactions for language learning and informs the literature on learning through screen media as the first study to examine word learning through video chat technology. © 2013 The Authors. Child Development © 2013 Society for Research in Child Development, Inc.

  16. Skype me! Socially Contingent Interactions Help Toddlers Learn Language

    Roseberry, Sarah; Hirsh-Pasek, Kathy; Golinkoff, Roberta Michnick

    2013-01-01

    Language learning takes place in the context of social interactions, yet the mechanisms that render social interactions useful for learning language remain unclear. This paper focuses on whether social contingency might support word learning. Toddlers aged 24- to 30-months (N=36) were exposed to novel verbs in one of three conditions: live interaction training, socially contingent video training over video chat, and non-contingent video training (yoked video). Results suggest that children only learned novel verbs in socially contingent interactions (live interactions and video chat). The current study highlights the importance of social contingency in interactions for language learning and informs the literature on learning through screen media as the first study to examine word learning through video chat technology. PMID:24112079

  17. Skype me! Socially Contingent Interactions Help Toddlers Learn Language

    Roseberry, Sarah; Hirsh-Pasek, Kathy; Golinkoff, Roberta Michnick

    2013-01-01

    Language learning takes place in the context of social interactions, yet the mechanisms that render social interactions useful for learning language remain unclear. This paper focuses on whether social contingency might support word learning. Toddlers aged 24- to 30-months (N=36) were exposed to novel verbs in one of three conditions: live interaction training, socially contingent video training over video chat, and non-contingent video training (yoked video). Results sugges...

  18. Challenge of Helping Introductory Physics Students Transfer Their Learning by Engaging with a Self-Paced Learning Tutorial

    Emily Megan Marshman

    2018-03-01

    Full Text Available With advances in digital technology, research-validated self-paced learning tools can play an increasingly important role in helping students with diverse backgrounds become good problem solvers and independent learners. Thus, it is important to ensure that all students engage with self-paced learning tools effectively in order to learn the content deeply, develop good problem-solving skills, and transfer their learning from one context to another. Here, we first provide an overview of a holistic framework for engaging students with self-paced learning tools so that they can transfer their learning to solve novel problems. The framework not only takes into account the features of the self-paced learning tools but also how those tools are implemented, the extent to which the tools take into account student characteristics, and whether factors related to students’ social environments are accounted for appropriately in the implementation of those tools. We then describe an investigation in which we interpret the findings using the framework. In this study, a research-validated self-paced physics tutorial was implemented in both controlled one-on-one interviews and in large enrollment, introductory calculus-based physics courses as a self-paced learning tool. We find that students who used the tutorial in a controlled one-on-one interview situation performed significantly better on transfer problems than those who used it as a self-paced learning tool in the large-scale implementation. The findings suggest that critically examining and taking into account how the self-paced tools are implemented and incentivized, student characteristics including their self-regulation and time-management skills, and social and environmental factors can greatly impact the extent and manner in which students engage with these learning tools. Getting buy in from students about the value of these tools and providing appropriate support while implementing them is

  19. E-Learning in Universities: Supporting Help-Seeking Processes by Instructional Prompts

    Schworm, Silke; Gruber, Hans

    2012-01-01

    University students are more responsible than school students for their own learning. The role of self-regulated learning increases in virtual e-learning course environments. Academic help-seeking is an important strategy of self-regulated learning, but many students fail to use this strategy appropriately. A lack of information and a perceived…

  20. Can a Multimedia Tool Help Students' Learning Performance in ...

    Hennie

    2015-11-25

    Nov 25, 2015 ... During the research process, an experimental design with two groups, TCbio (n = 22) and Mbio (n = 26), were used. The results of .... as well as their views towards learning approaches. Based on this ..... image, number, logic, rhythm, colour and spatial .... /learning process can be better supported via multi-.

  1. Deep Learning Methods for Underwater Target Feature Extraction and Recognition

    Gang Hu

    2018-01-01

    Full Text Available The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

  2. Peer-Led Team Learning Helps Minority Students Succeed.

    Snyder, Julia J; Sloane, Jeremy D; Dunk, Ryan D P; Wiles, Jason R

    2016-03-01

    Active learning methods have been shown to be superior to traditional lecture in terms of student achievement, and our findings on the use of Peer-Led Team Learning (PLTL) concur. Students in our introductory biology course performed significantly better if they engaged in PLTL. There was also a drastic reduction in the failure rate for underrepresented minority (URM) students with PLTL, which further resulted in closing the achievement gap between URM and non-URM students. With such compelling findings, we strongly encourage the adoption of Peer-Led Team Learning in undergraduate Science, Technology, Engineering, and Mathematics (STEM) courses.

  3. Neighbors Based Discriminative Feature Difference Learning for Kinship Verification

    Duan, Xiaodong; Tan, Zheng-Hua

    2015-01-01

    In this paper, we present a discriminative feature difference learning method for facial image based kinship verification. To transform feature difference of an image pair to be discriminative for kinship verification, a linear transformation matrix for feature difference between an image pair...... than the commonly used feature concatenation, leading to a low complexity. Furthermore, there is no positive semi-definitive constrain on the transformation matrix while there is in metric learning methods, leading to an easy solution for the transformation matrix. Experimental results on two public...... databases show that the proposed method combined with a SVM classification method outperforms or is comparable to state-of-the-art kinship verification methods. © Springer International Publishing AG, Part of Springer Science+Business Media...

  4. Using appreciative inquiry to help students identify strategies to overcome handicaps of their learning styles.

    Kumar, Latha Rajendra; Chacko, Thomas Vengail

    2012-01-01

    In India, as in some other neighboring Asian countries, students and teachers are generally unaware of the differences in the learning styles among learners, which can handicap students with learning styles alien to the common teaching/learning modality within the institution. This study aims to find out whether making students aware of their learning styles and then using the Appreciative Inquiry approach to help them discover learning strategies that worked for them and others with similar learning styles within the institution made them perceive that this experience improved their learning and performance in exams. The visual, auditory, read-write, and kinesthetic (VARK) inventory of learning styles questionnaire was administered to all 100 first-year medical students of the Father Muller's Medical College in Mangalore India to make them aware of their individual learning styles. An Appreciate Inquiry intervention was administered to 62 student volunteers who were counseled about the different learning styles and their adaptive strategies. Pre and post intervention change in student's perception about usefulness of knowing learning styles on their learning, learning behavior, and performance in examinations was collected from the students using a prevalidated questionnaire. Post intervention mean scores showed a significant change (P learning style and discovering strategies that worked within the institutional environment. There was agreement among students that the intervention helped them become more confident in learning (84%), facilitating learning in general (100%), and in understanding concepts (100%). However, only 29% of the students agreed that the intervention has brought about their capability improvement in application of learning and 31% felt it improved their performance in exams. Appreciate Inquiry was perceived as useful in helping students discover learning strategies that work for different individual learning styles and sharing them within

  5. Cross-Domain Semi-Supervised Learning Using Feature Formulation.

    Xingquan Zhu

    2011-12-01

    Semi-Supervised Learning (SSL) traditionally makes use of unlabeled samples by including them into the training set through an automated labeling process. Such a primitive Semi-Supervised Learning (pSSL) approach suffers from a number of disadvantages including false labeling and incapable of utilizing out-of-domain samples. In this paper, we propose a formative Semi-Supervised Learning (fSSL) framework which explores hidden features between labeled and unlabeled samples to achieve semi-supervised learning. fSSL regards that both labeled and unlabeled samples are generated from some hidden concepts with labeling information partially observable for some samples. The key of the fSSL is to recover the hidden concepts, and take them as new features to link labeled and unlabeled samples for semi-supervised learning. Because unlabeled samples are only used to generate new features, but not to be explicitly included in the training set like pSSL does, fSSL overcomes the inherent disadvantages of the traditional pSSL methods, especially for samples not within the same domain as the labeled instances. Experimental results and comparisons demonstrate that fSSL significantly outperforms pSSL-based methods for both within-domain and cross-domain semi-supervised learning.

  6. Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference

    Bao, Ruying; Liang, Sihang; Wang, Qingcan

    2018-01-01

    Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations are intentionally added to the original inputs to fool the classifier. In this paper, we propose a defense method, Featurized Bidirectional Generative Adversarial Networks (FBGAN), to capture the semantic features of the input and filter the non-semantic perturbation. FBGAN is pre-trained on the clean dataset in an unsupervised manner, adversarially learning a bidirectional mapping b...

  7. Zero-Shot Learning by Generating Pseudo Feature Representations

    Lu, Jiang; Li, Jin; Yan, Ziang; Zhang, Changshui

    2017-01-01

    Zero-shot learning (ZSL) is a challenging task aiming at recognizing novel classes without any training instances. In this paper we present a simple but high-performance ZSL approach by generating pseudo feature representations (GPFR). Given the dataset of seen classes and side information of unseen classes (e.g. attributes), we synthesize feature-level pseudo representations for novel concepts, which allows us access to the formulation of unseen class predictor. Firstly we design a Joint Att...

  8. Breast image feature learning with adaptive deconvolutional networks

    Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

    2012-03-01

    Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

  9. The Teaching and Learning Environment SAIDA: Some Features and Lessons.

    Grandbastien, Monique; Morinet-Lambert, Josette

    Written in ADA language, SAIDA, a Help System for Data Implementation, is an experimental teaching and learning environment which uses artificial intelligence techniques to teach a computer science course on abstract data representations. The application domain is teaching advanced programming concepts which have not received much attention from…

  10. The Foundations for Learning Campaign: helping hand or hurdle ...

    One initiative taken by the Department was to launch the Foundations for Learning Campaign, a four-year national literacy and numeracy programme, in 2008. The Campaign entails amongst other things providing teachers with lesson plans and the resources needed for effective teaching and assessment. In view of the ...

  11. An Expert System Helps Students Learn Database Design

    Post, Gerald V.; Whisenand, Thomas G.

    2005-01-01

    Teaching and learning database design is difficult for both instructors and students. Students need to solve many problems with feedback and corrections. A Web-based specialized expert system was created to enable students to create designs online and receive immediate feedback. An experiment testing the system shows that it significantly enhances…

  12. Learning about Posterior Probability: Do Diagrams and Elaborative Interrogation Help?

    Clinton, Virginia; Alibali, Martha W.; Nathan, Mitchell J.

    2016-01-01

    To learn from a text, students must make meaningful connections among related ideas in that text. This study examined the effectiveness of two methods of improving connections--elaborative interrogation and diagrams--in written lessons about posterior probability. Undergraduate students (N = 198) read a lesson in one of three questioning…

  13. Can a Multimedia Tool Help Students' Learning Performance in ...

    The aim of the present study was to determine the effects of multimedia-based biology teaching (Mbio) and teacher-centered biology (TCbio) instruction approaches on learners' biology achievements, as well as their views towards learning approaches. During the research process, an experimental design with two groups, ...

  14. Improving EEG signal peak detection using feature weight learning ...

    Therefore, we aimed to develop a general procedure for eye event-related applications based on feature weight learning (FWL), through the use of a neural network with random weights (NNRW) as the classifier. The FWL is performed using a particle swarm optimization algorithm, applied to the well-studied Dumpala, Acir, ...

  15. Feature economy vs. logical complexity in phonological pattern learning

    Seinhorst, K.T.

    Complexity has been linked to ease of learning. This article explores the roles of two measures of complexity – feature economy and logical complexity – in the acquisition of sets of signs, taken from a small sign language that serves as an analogue of plosive inventories in spoken language. In a

  16. Pairwise Constraint-Guided Sparse Learning for Feature Selection.

    Liu, Mingxia; Zhang, Daoqiang

    2016-01-01

    Feature selection aims to identify the most informative features for a compact and accurate data representation. As typical supervised feature selection methods, Lasso and its variants using L1-norm-based regularization terms have received much attention in recent studies, most of which use class labels as supervised information. Besides class labels, there are other types of supervised information, e.g., pairwise constraints that specify whether a pair of data samples belong to the same class (must-link constraint) or different classes (cannot-link constraint). However, most of existing L1-norm-based sparse learning methods do not take advantage of the pairwise constraints that provide us weak and more general supervised information. For addressing that problem, we propose a pairwise constraint-guided sparse (CGS) learning method for feature selection, where the must-link and the cannot-link constraints are used as discriminative regularization terms that directly concentrate on the local discriminative structure of data. Furthermore, we develop two variants of CGS, including: 1) semi-supervised CGS that utilizes labeled data, pairwise constraints, and unlabeled data and 2) ensemble CGS that uses the ensemble of pairwise constraint sets. We conduct a series of experiments on a number of data sets from University of California-Irvine machine learning repository, a gene expression data set, two real-world neuroimaging-based classification tasks, and two large-scale attribute classification tasks. Experimental results demonstrate the efficacy of our proposed methods, compared with several established feature selection methods.

  17. Unsupervised Feature Learning for Heart Sounds Classification Using Autoencoder

    Hu, Wei; Lv, Jiancheng; Liu, Dongbo; Chen, Yao

    2018-04-01

    Cardiovascular disease seriously threatens the health of many people. It is usually diagnosed during cardiac auscultation, which is a fast and efficient method of cardiovascular disease diagnosis. In recent years, deep learning approach using unsupervised learning has made significant breakthroughs in many fields. However, to our knowledge, deep learning has not yet been used for heart sound classification. In this paper, we first use the average Shannon energy to extract the envelope of the heart sounds, then find the highest point of S1 to extract the cardiac cycle. We convert the time-domain signals of the cardiac cycle into spectrograms and apply principal component analysis whitening to reduce the dimensionality of the spectrogram. Finally, we apply a two-layer autoencoder to extract the features of the spectrogram. The experimental results demonstrate that the features from the autoencoder are suitable for heart sound classification.

  18. Automatic feature extraction in large fusion databases by using deep learning approach

    Farias, Gonzalo, E-mail: gonzalo.farias@ucv.cl [Pontificia Universidad Católica de Valparaíso, Valparaíso (Chile); Dormido-Canto, Sebastián [Departamento de Informática y Automática, UNED, Madrid (Spain); Vega, Jesús; Rattá, Giuseppe [Asociación EURATOM/CIEMAT Para Fusión, CIEMAT, Madrid (Spain); Vargas, Héctor; Hermosilla, Gabriel; Alfaro, Luis; Valencia, Agustín [Pontificia Universidad Católica de Valparaíso, Valparaíso (Chile)

    2016-11-15

    Highlights: • Feature extraction is a very critical stage in any machine learning algorithm. • The problem dimensionality can be reduced enormously when selecting suitable attributes. • Despite the importance of feature extraction, the process is commonly done manually by trial and error. • Fortunately, recent advances in deep learning approach have proposed an encouraging way to find a good feature representation automatically. • In this article, deep learning is applied to the TJ-II fusion database to get more robust and accurate classifiers in comparison to previous work. - Abstract: Feature extraction is one of the most important machine learning issues. Finding suitable attributes of datasets can enormously reduce the dimensionality of the input space, and from a computational point of view can help all of the following steps of pattern recognition problems, such as classification or information retrieval. However, the feature extraction step is usually performed manually. Moreover, depending on the type of data, we can face a wide range of methods to extract features. In this sense, the process to select appropriate techniques normally takes a long time. This work describes the use of recent advances in deep learning approach in order to find a good feature representation automatically. The implementation of a special neural network called sparse autoencoder and its application to two classification problems of the TJ-II fusion database is shown in detail. Results have shown that it is possible to get robust classifiers with a high successful rate, in spite of the fact that the feature space is reduced to less than 0.02% from the original one.

  19. Automatic feature extraction in large fusion databases by using deep learning approach

    Farias, Gonzalo; Dormido-Canto, Sebastián; Vega, Jesús; Rattá, Giuseppe; Vargas, Héctor; Hermosilla, Gabriel; Alfaro, Luis; Valencia, Agustín

    2016-01-01

    Highlights: • Feature extraction is a very critical stage in any machine learning algorithm. • The problem dimensionality can be reduced enormously when selecting suitable attributes. • Despite the importance of feature extraction, the process is commonly done manually by trial and error. • Fortunately, recent advances in deep learning approach have proposed an encouraging way to find a good feature representation automatically. • In this article, deep learning is applied to the TJ-II fusion database to get more robust and accurate classifiers in comparison to previous work. - Abstract: Feature extraction is one of the most important machine learning issues. Finding suitable attributes of datasets can enormously reduce the dimensionality of the input space, and from a computational point of view can help all of the following steps of pattern recognition problems, such as classification or information retrieval. However, the feature extraction step is usually performed manually. Moreover, depending on the type of data, we can face a wide range of methods to extract features. In this sense, the process to select appropriate techniques normally takes a long time. This work describes the use of recent advances in deep learning approach in order to find a good feature representation automatically. The implementation of a special neural network called sparse autoencoder and its application to two classification problems of the TJ-II fusion database is shown in detail. Results have shown that it is possible to get robust classifiers with a high successful rate, in spite of the fact that the feature space is reduced to less than 0.02% from the original one.

  20. E-learning benefits nurse education and helps shape students' professional identity.

    McKenzie, Karen; Murray, Aja

    E-learning is increasingly used in nurse education and practice development. This method can enhance learning opportunities for students and qualified nurses. This article examines the features of this technology and the ways in which it can be harnessed to maximise learning opportunities.

  1. E-learning benefits nurse education and helps shape students' professional identity

    McKenzie, Karen; Murray, Aja

    2010-01-01

    E-learning is increasingly used in nurse education and practice development. This method can enhance learning opportunities for students and qualified nurses. This article examines the features of this technology and the ways in which it can be harnessed to maximise learning opportunities.

  2. Watching Subtitled Films Can Help Learning Foreign Languages.

    Birulés-Muntané, J; Soto-Faraco, S

    2016-01-01

    Watching English-spoken films with subtitles is becoming increasingly popular throughout the world. One reason for this trend is the assumption that perceptual learning of the sounds of a foreign language, English, will improve perception skills in non-English speakers. Yet, solid proof for this is scarce. In order to test the potential learning effects derived from watching subtitled media, a group of intermediate Spanish students of English as a foreign language watched a 1h-long episode of a TV drama in its original English version, with English, Spanish or no subtitles overlaid. Before and after the viewing, participants took a listening and vocabulary test to evaluate their speech perception and vocabulary acquisition in English, plus a final plot comprehension test. The results of the listening skills tests revealed that after watching the English subtitled version, participants improved these skills significantly more than after watching the Spanish subtitled or no-subtitles versions. The vocabulary test showed no reliable differences between subtitled conditions. Finally, as one could expect, plot comprehension was best under native, Spanish subtitles. These learning effects with just 1 hour exposure might have major implications with longer exposure times.

  3. Listening to Music: Helping Children Regulate Their Emotions and Improve Learning in the Classroom

    Foran, Lucille M.

    2009-01-01

    Early education teachers are familiar with using music and rhythm as tools for learning language and building memory. However, the potential of music to help across all special education settings is largely unexplored. Work with music has been widely judged helpful in cases of psychological trauma, yet people do not know why it is helpful. The…

  4. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

    Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

    2017-07-01

    Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

  5. Mobile Learning: How Smartphones Help Illiterate Farmers in Rural India

    Knoche, Hendrik

    2012-01-01

    about agriculture, causing schemes to fail. Computer scientist Hendrik, at the Swiss Federal Institute of Technology in Lausanne, is aiming to change that. He has designed a new smart-phone interface for farmers especially so that both illiterate and literate can share ideas and vital information about...... agriculture, helping them, and 62% of the world’s food supply, to stay in business. Digital Diversity is a series of blog posts from FrontlineSMS about how mobile phones and other appropriate technologies are being used throughout the world to improve, enrich, and empower billions of lives. This article...

  6. Local Feature Learning for Face Recognition under Varying Poses

    Duan, Xiaodong; Tan, Zheng-Hua

    2015-01-01

    In this paper, we present a local feature learning method for face recognition to deal with varying poses. As opposed to the commonly used approaches of recovering frontal face images from profile views, the proposed method extracts the subject related part from a local feature by removing the pose...... related part in it on the basis of a pose feature. The method has a closed-form solution, hence being time efficient. For performance evaluation, cross pose face recognition experiments are conducted on two public face recognition databases FERET and FEI. The proposed method shows a significant...... recognition improvement under varying poses over general local feature approaches and outperforms or is comparable with related state-of-the-art pose invariant face recognition approaches. Copyright ©2015 by IEEE....

  7. Enhanced HMAX model with feedforward feature learning for multiclass categorization

    Yinlin eLi

    2015-10-01

    Full Text Available In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 milliseconds of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: 1 To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; 2 To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; 3 Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

  8. Enhanced HMAX model with feedforward feature learning for multiclass categorization.

    Li, Yinlin; Wu, Wei; Zhang, Bo; Li, Fengfu

    2015-01-01

    In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT) layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 ms of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: (1) To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; (2) To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; (3) Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

  9. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

    Choi, Ickwon; Chung, Amy W; Suscovich, Todd J; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J; Francis, Donald; Robb, Merlin L; Michael, Nelson L; Kim, Jerome H; Alter, Galit; Ackerman, Margaret E; Bailey-Kellogg, Chris

    2015-04-01

    The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

  10. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

    Ickwon Choi

    2015-04-01

    Full Text Available The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release. We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

  11. Can "YouTube" help students in learning surface anatomy?

    Azer, Samy A

    2012-07-01

    In a problem-based learning curriculum, most medical students research the Internet for information for their "learning issues." Internet sites such as "YouTube" have become a useful resource for information. This study aimed at assessing YouTube videos covering surface anatomy. A search of YouTube was conducted from November 8 to 30, 2010 using research terms "surface anatomy," "anatomy body painting," "living anatomy," "bone landmarks," and "dermatomes" for surface anatomy-related videos. Only relevant video clips in the English language were identified and related URL recorded. For each videotape the following information were collected: title, authors, duration, number of viewers, posted comments, and total number of days on YouTube. The data were statistically analyzed and videos were grouped into educationally useful and non-useful videos on the basis of major and minor criteria covering technical, content, authority, and pedagogy parameters. A total of 235 YouTube videos were screened and 57 were found to have relevant information to surface anatomy. Analysis revealed that 15 (27%) of the videos provided useful information on surface anatomy. These videos scored (mean ± SD, 14.0 ± 0.7) and mainly covered surface anatomy of the shoulder, knee, muscles of the back, leg, and ankle, carotid artery, dermatomes, and anatomical positions. The other 42 (73%) videos were not useful educationally, scoring (mean ± SD, 7.4 ± 1.8). The total viewers of all videos were 1,058,634. Useful videos were viewed by 497,925 (47% of total viewers). The total viewership per day was 750 for useful videos and 652 for non-useful videos. No video clips covering surface anatomy of the head and neck, blood vessels and nerves of upper and lower limbs, chest and abdominal organs/structures were found. Currently, YouTube is an inadequate source of information for learning surface anatomy. More work is needed from medical schools and educators to add useful videos on You

  12. Learning deep features with adaptive triplet loss for person reidentification

    Li, Zhiqiang; Sang, Nong; Chen, Kezhou; Gao, Changxin; Wang, Ruolin

    2018-03-01

    Person reidentification (re-id) aims to match a specified person across non-overlapping cameras, which remains a very challenging problem. While previous methods mostly focus on feature extraction or metric learning, this paper makes the attempt in jointly learning both the global full-body and local body-parts features of the input persons with a multichannel convolutional neural network (CNN) model, which is trained by an adaptive triplet loss function that serves to minimize the distance between the same person and maximize the distance between different persons. The experimental results show that our approach achieves very promising results on the large-scale Market-1501 and DukeMTMC-reID datasets.

  13. Deep Feature Learning and Cascaded Classifier for Large Scale Data

    Prasoon, Adhish

    from data rather than having a predefined feature set. We explore deep learning approach of convolutional neural network (CNN) for segmenting three dimensional medical images. We propose a novel system integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D......This thesis focuses on voxel/pixel classification based approaches for image segmentation. The main application is segmentation of articular cartilage in knee MRIs. The first major contribution of the thesis deals with large scale machine learning problems. Many medical imaging problems need huge...... amount of training data to cover sufficient biological variability. Learning methods scaling badly with number of training data points cannot be used in such scenarios. This may restrict the usage of many powerful classifiers having excellent generalization ability. We propose a cascaded classifier which...

  14. Advancing Affect Modeling via Preference Learning and Unsupervised Feature Extraction

    Martínez, Héctor Pérez

    strategies (error functions and training algorithms) for artificial neural networks are examined across synthetic and psycho-physiological datasets, and compared against support vector machines and Cohen’s method. Results reveal the best training strategies for neural networks and suggest their superiority...... difficulties, ordinal reports such as rankings and ratings can yield more reliable affect annotations than alternative tools. This thesis explores preference learning methods to automatically learn computational models from ordinal annotations of affect. In particular, an extensive collection of training...... over the other examined methods. The second challenge addressed in this thesis refers to the extraction of relevant information from physiological modalities. Deep learning is proposed as an automatic approach to extract input features for models of affect from physiological signals. Experiments...

  15. Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning

    Shi, Bibo; Hou, Rui; Mazurowski, Maciej A.; Grimm, Lars J.; Ren, Yinhao; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

    2018-02-01

    Purpose: To determine whether domain transfer learning can improve the performance of deep features extracted from digital mammograms using a pre-trained deep convolutional neural network (CNN) in the prediction of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. Method: In this study, we collected digital mammography magnification views for 140 patients with DCIS at biopsy, 35 of which were subsequently upstaged to invasive cancer. We utilized a deep CNN model that was pre-trained on two natural image data sets (ImageNet and DTD) and one mammographic data set (INbreast) as the feature extractor, hypothesizing that these data sets are increasingly more similar to our target task and will lead to better representations of deep features to describe DCIS lesions. Through a statistical pooling strategy, three sets of deep features were extracted using the CNNs at different levels of convolutional layers from the lesion areas. A logistic regression classifier was then trained to predict which tumors contain occult invasive disease. The generalization performance was assessed and compared using repeated random sub-sampling validation and receiver operating characteristic (ROC) curve analysis. Result: The best performance of deep features was from CNN model pre-trained on INbreast, and the proposed classifier using this set of deep features was able to achieve a median classification performance of ROC-AUC equal to 0.75, which is significantly better (p<=0.05) than the performance of deep features extracted using ImageNet data set (ROCAUC = 0.68). Conclusion: Transfer learning is helpful for learning a better representation of deep features, and improves the prediction of occult invasive disease in DCIS.

  16. Allowing the Voices of Parents To Help Shape Teaching and Learning.

    Nicholson, Karen; Evans, Judith F.; Tellier-Robinson, Dora; Aviles, Leticia

    2001-01-01

    Three teachers describe how parents of deaf, severely disabled, and bilingual children participated in their children's learning. Qualitative research methods were used to help parents share their knowledge with teachers. (SK)

  17. Clustering-based Feature Learning on Variable Stars

    Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

    2016-04-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

  18. CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

    Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

    2016-01-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline

  19. CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

    Mackenzie, Cristóbal; Pichara, Karim [Computer Science Department, Pontificia Universidad Católica de Chile, Santiago (Chile); Protopapas, Pavlos [Institute for Applied Computational Science, Harvard University, Cambridge, MA (United States)

    2016-04-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

  20. Designing a mobile learning game to investigate the impact of role-playing on helping behavior

    Schmitz, Birgit; Ternier, Stefaan; Klemke, Roland; Kalz, Marco; Specht, Marcus

    2013-01-01

    Schmitz, B., Ternier, S., Klemke, R., Kalz, M., & Specht, M. (2013). Designing a mobile learning game to investigate the impact of role-playing on helping behavior. In D. Hernández-Leo et al. (Eds.), Scaling up Learning for Sustained Impact. Proceedings of European Conference on Technology Enhanced

  1. The Use of Help Options in Multimedia Listening Environments to Aid Language Learning: A Review

    Mohsen, Mohammed Ali

    2016-01-01

    This paper provides a comprehensive review on the use of help options (HOs) in the multimedia listening context to aid listening comprehension (LC) and improve incidental vocabulary learning. The paper also aims to synthesize the research findings obtained from the use of HOs in Computer-Assisted Language Learning (CALL) literature and reveals the…

  2. Developing iPad-Based Physics Simulations That Can Help People Learn Newtonian Physics Concepts

    Lee, Young-Jin

    2015-01-01

    The aims of this study are: (1) to develop iPad-based computer simulations called iSimPhysics that can help people learn Newtonian physics concepts; and (2) to assess its educational benefits and pedagogical usefulness. To facilitate learning, iSimPhysics visualizes abstract physics concepts, and allows for conducting a series of computer…

  3. Self-Help Training System for Nursing Students to Learn Patient Transfer Skills

    Huang, Zhifeng; Nagata, Ayanori; Kanai-Pak, Masako; Maeda, Jukai; Kitajima, Yasuko; Nakamura, Mitsuhiro; Aida, Kyoko; Kuwahara, Noriaki; Ogata, Taiki; Ota, Jun

    2014-01-01

    This paper describes the construction and evaluation of a self-help skill training system for assisting student nurses in learning skills involving the transfer of patients from beds to wheelchairs. We have proposed a feedback method that is based on a checklist and video demonstrations. To help trainees efficiently check their performance and…

  4. Is LabTutor a helpful component of the blended learning approach to biosciences?

    Swift, Amelia; Efstathiou, Nikolaos; Lameu, Paula

    2016-09-01

    To evaluate the use of LabTutor (a physiological data capture and e-learning package) in bioscience education for student nurses. Knowledge of biosciences is important for nurses the world over, who have to monitor and assess their patient's clinical condition, and interpret that information to determine the most appropriate course of action. Nursing students have long been known to find acquiring useable bioscience knowledge challenging. Blended learning strategies are common in bioscience teaching to address the difficulties students have. Student nurses have a preference for hands-on learning, small group sessions and are helped by close juxtaposition of theory and practice. An evaluation of a new teaching method using in-classroom voluntary questionnaire. A structured survey instrument including statements and visual analogue response format and open questions was given to students who participated in Labtutor sessions. The students provided feedback in about the equipment, the learning and the session itself. First year (n = 93) and third year (n = 36) students completed the evaluation forms. The majority of students were confident about the equipment and using it to learn although a few felt anxious about computer-based learning. They all found the equipment helpful as part of their bioscience education and they all enjoyed the sessions. This equipment provides a helpful way to encourage guided independent learning through practice and discovery and because each session is case study based and the relationship of the data to the patient is made clear. Our students helped to evaluate our initial use of LabTutor and found the sessions enjoyable and helpful. LabTutor provides an effective learning tool as part of a blended learning strategy for biosciences teaching. Improving bioscience knowledge will lead to a greater understanding of pathophysiology, treatments and interventions and monitoring. © 2016 John Wiley & Sons Ltd.

  5. More than one kind of inference: re-examining what's learned in feature inference and classification.

    Sweller, Naomi; Hayes, Brett K

    2010-08-01

    Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

  6. Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

    Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

    2015-04-01

    A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

  7. Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

    Kai Olav Ellefsen

    2015-04-01

    Full Text Available A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand. To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1 that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2 that one benefit of the modularity ubiquitous in the brains of natural animals

  8. Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

    Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

    2015-01-01

    A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

  9. Learning probabilistic features for robotic navigation using laser sensors.

    Fidel Aznar

    Full Text Available SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N to O(N(2, where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.

  10. Learning probabilistic features for robotic navigation using laser sensors.

    Aznar, Fidel; Pujol, Francisco A; Pujol, Mar; Rizo, Ramón; Pujol, María-José

    2014-01-01

    SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N(2)), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.

  11. RELATIONSHIP AMONG BRAIN HEMISPHERIC DOMINANCE, ATTITUDE TOWARDS L1 AND L2, GENDER, AND LEARNING SUPRASEGMENTAL FEATURES

    Mohammad Hadi Mahmoodi

    2016-07-01

    Full Text Available Oral skills are important components of language competence. To have good and acceptable listening and speaking, one must have good pronunciation, which encompasses segmental and suprasegmental features. Despite extensive studies on the role of segmental features and related issues in listening and speaking, there is paucity of research on the role of suprasegmental features in the same domain. Conducting studies which aim at shedding light on the issues related to learning suprasegmental features can help language teachers and learners in the process of teaching/learning English as a foreign language. To this end, this study was designed to investigate the relationship among brain hemispheric dominance, gender, attitudes towards L1 and L2, and learning suprasegmental features in Iranian EFL learners. First, 200 Intermediate EFL learners were selected from different English language teaching institutes in Hamedan and Isfahan, two provinces in Iran, as the sample. Prior to the main stage of the study, Oxford Placement Test (OPT was used to homogenize the proficiency level of all the participants. Then, the participants were asked to complete the Edinburgh Handedness Questionnaire to determine their dominant hemisphere. They were also required to answer two questionnaires regarding their attitudes towards L1 and L2. Finally, the participants took suprasegmental features test. The results of the independent samples t-tests indicated left-brained language learners’ superiority in observing and learning suprasegmental features. It was also found that females are better than males in producing suprasegmental features. Furthermore, the results of Pearson Product Moment Correlations indicated that there is significant relationship between attitude towards L2 and learning suprasegmental features. However, no significant relationship was found between attitude towards L1 and learning English suprasegmental features. The findings of this study can

  12. Simultenious binary hash and features learning for image retrieval

    Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

    2016-05-01

    Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

  13. Constructing the Syllabus: Devising a Framework for Helping Students Learn to Think like Historians

    Estes, Todd

    2007-01-01

    In this article, the author describes a syllabus which he designed in his United States history survey courses to help his students learn to think like historians. It contains important information about the way historians work and think, along with descriptions of the reading materials the student will use to further their practice of history.…

  14. Explaining Helping Behavior in a Cooperative Learning Classroom Setting Using Attribution Theory

    Ahles, Paula M.; Contento, Jann M.

    2006-01-01

    This recently completed study examined whether attribution theory can explain helping behavior in an interdependent classroom environment that utilized a cooperative-learning model. The study focused on student participants enrolled in 6 community college communication classes taught by the same instructor. Three levels of cooperative-learning…

  15. Help-Seeking Decisions of Battered Women: A Test of Learned Helplessness and Two Stress Theories.

    Wauchope, Barbara A.

    This study tested the learned helplessness theory, stress theory, and a modified stress theory to determine the best model for predicting the probability that a woman would seek help when she experienced severe violence from a male partner. The probability was hypothesized to increase as the stress of the violence experienced increased. Data were…

  16. How Can I Help My Students with Learning Disabilities in Mathematics?

    Jiménez-Fernández, Gracia

    2016-01-01

    Learning Disabilities in Mathematics (LDM) or dyscalculia are a frequent and disruptive problem within schools. Nevertheless, this problem has received little attention from researchers and practitioners, if compared with the number of studies published on disabilities in reading. Therefore, teachers do not have enough guidance to help children…

  17. Context-Dependent Help for the DynaLearn Modelling and Simulation Workbench

    Beek, W.; Bredeweg, B.; Latour, S.; Biswas, G.; Bull, S.; Kay, J.; Mitrovic, A.

    2011-01-01

    We implemented three kinds of context-dependent help for a qualitative modelling and simulation workbench called DynaLearn. We show that it is possible to generate and select assistance knowledge based on the current model, simulation results and workbench state.

  18. Learning by Helping? Undergraduate Communication Outcomes Associated with Training or Service-Learning Experiences

    Katz, Jennifer; DuBois, Melinda; Wigderson, Sara

    2014-01-01

    This study investigated communication outcomes after training or applied service-learning experiences. Pre-practicum trainees learned active listening skills over 10 weeks. Practicum students were successful trainees who staffed a helpline. Community interns were trained and supervised at community agencies. Undergraduate students in psychology…

  19. Grounded Learning Experience: Helping Students Learn Physics through Visuo-Haptic Priming and Instruction

    Huang, Shih-Chieh Douglas

    2013-01-01

    In this dissertation, I investigate the effects of a grounded learning experience on college students' mental models of physics systems. The grounded learning experience consisted of a priming stage and an instruction stage, and within each stage, one of two different types of visuo-haptic representation was applied: visuo-gestural simulation…

  20. Alteration of Occlusal Plane in Orthognathic Surgery: Clinical Features to Help Treatment Planning on Class III Patients

    Daniel Amaral Alves Marlière

    2018-01-01

    Full Text Available Dentofacial deformities (DFD presenting mainly as Class III malocclusions that require orthognathic surgery as a part of definitive treatment. Class III patients can have obvious signs such as increasing the chin projection and chin throat length, nasolabial folds, reverse overjet, and lack of upper lip support. However, Class III patients can present different facial patterns depending on the angulation of occlusal plane (OP, and only bite correction does not always lead to the improvement of the facial esthetic. We described two Class III patients with different clinical features and inclination of OP and had undergone different treatment planning based on 6 clinical features: (I facial type; (II upper incisor display at rest; (III dental and gingival display on smile; (IV soft tissue support; (V chin projection; and (VI lower lip projection. These patients were submitted to orthognathic surgery with different treatment plannings: a clockwise rotation and counterclockwise rotation of OP according to their facial features. The clinical features and OP inclination helped to define treatment planning by clockwise and counterclockwise rotations of the maxillomandibular complex, and two patients undergone to bimaxillary orthognathic surgery showed harmonic outcomes and stables after 2 years of follow-up.

  1. Associative learning in baboons (Papio papio) and humans (Homo sapiens): species differences in learned attention to visual features.

    Fagot, J; Kruschke, J K; Dépy, D; Vauclair, J

    1998-10-01

    We examined attention shifting in baboons and humans during the learning of visual categories. Within a conditional matching-to-sample task, participants of the two species sequentially learned two two-feature categories which shared a common feature. Results showed that humans encoded both features of the initially learned category, but predominantly only the distinctive feature of the subsequently learned category. Although baboons initially encoded both features of the first category, they ultimately retained only the distinctive features of each category. Empirical data from the two species were analyzed with the 1996 ADIT connectionist model of Kruschke. ADIT fits the baboon data when the attentional shift rate is zero, and the human data when the attentional shift rate is not zero. These empirical and modeling results suggest species differences in learned attention to visual features.

  2. Enhancing the Pronunciation of English Suprasegmental Features through Reflective Learning Method

    Suwartono

    2014-01-01

    Suprasegmental features are of paramount importance in spoken English. Yet, these pronunciation features are marginalised in EFL/ESL teaching-learning. This article reported a study that was aimed at improving the students' mastery of English suprasegmental features through the use of reflective learning method. The study adopted Kemmis and…

  3. Categorical Structure among Shared Features in Networks of Early-Learned Nouns

    Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

    2009-01-01

    The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

  4. Feature Selection and Kernel Learning for Local Learning-Based Clustering.

    Zeng, Hong; Cheung, Yiu-ming

    2011-08-01

    The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

  5. Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.

    Du, Tianchuan; Liao, Li; Wu, Cathy H; Sun, Bilin

    2016-11-01

    Protein-protein interactions play essential roles in many biological processes. Acquiring knowledge of the residue-residue contact information of two interacting proteins is not only helpful in annotating functions for proteins, but also critical for structure-based drug design. The prediction of the protein residue-residue contact matrix of the interfacial regions is challenging. In this work, we introduced deep learning techniques (specifically, stacked autoencoders) to build deep neural network models to tackled the residue-residue contact prediction problem. In tandem with interaction profile Hidden Markov Models, which was used first to extract Fisher score features from protein sequences, stacked autoencoders were deployed to extract and learn hidden abstract features. The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%. We showed that the stacked autoencoders could extract novel features, which can be utilized by deep neural networks and other classifiers to enhance learning, out of the Fisher score features. It is further shown that deep neural networks have significant advantages over SVM in making use of the newly extracted features. Copyright © 2016. Published by Elsevier Inc.

  6. AGSuite: Software to conduct feature analysis of artificial grammar learning performance.

    Cook, Matthew T; Chubala, Chrissy M; Jamieson, Randall K

    2017-10-01

    To simplify the problem of studying how people learn natural language, researchers use the artificial grammar learning (AGL) task. In this task, participants study letter strings constructed according to the rules of an artificial grammar and subsequently attempt to discriminate grammatical from ungrammatical test strings. Although the data from these experiments are usually analyzed by comparing the mean discrimination performance between experimental conditions, this practice discards information about the individual items and participants that could otherwise help uncover the particular features of strings associated with grammaticality judgments. However, feature analysis is tedious to compute, often complicated, and ill-defined in the literature. Moreover, the data violate the assumption of independence underlying standard linear regression models, leading to Type I error inflation. To solve these problems, we present AGSuite, a free Shiny application for researchers studying AGL. The suite's intuitive Web-based user interface allows researchers to generate strings from a database of published grammars, compute feature measures (e.g., Levenshtein distance) for each letter string, and conduct a feature analysis on the strings using linear mixed effects (LME) analyses. The LME analysis solves the inflation of Type I errors that afflicts more common methods of repeated measures regression analysis. Finally, the software can generate a number of graphical representations of the data to support an accurate interpretation of results. We hope the ease and availability of these tools will encourage researchers to take full advantage of item-level variance in their datasets in the study of AGL. We moreover discuss the broader applicability of the tools for researchers looking to conduct feature analysis in any field.

  7. How do we help students as newcomers to create and develop better communities of practice for learning in a Project based learning environment?

    Jensen, Lars Peter

    2007-01-01

    The question for debate in this paper, is how to help students creating and developing good communities of practice for learning in a Project based learning environment? At Aalborg University it has proven very helpful for students to have both a course addressing communication, collaboration......, learning and project management (CLP) and a reflection on these issues in a written process analysis....

  8. Getting Help

    ... Parents & Students Home > Special Features > Getting Help Getting Help Resources from NIAAA Treatment for Alcohol Problems: Finding ... and find ways to make a change. Professional help Your doctor. Primary care and mental health practitioners ...

  9. Can e-learning help you to connect compassionately? Commentary on a palliative care e-learning resource for India.

    Datta, Soumitra Shankar; Agrawal, Sanjit

    2017-01-01

    e-learning resources need to be customised to the audience and learners to make them culturally relevant. The ' Palliative care e-learning resource for health care professionals in India' has been developed by the Karunashraya Hospice, Bengaluru in collaboration with the Cardiff Palliative Care Education Team, Wales to address the training needs of professionals in India. The resource, comprising over 20 modules, integrates psychological, social and medical care for patients requiring palliative care for cancer and other diseases. With increased internet usage, it would help in training a large number of professionals and volunteers in India who want to work in the field of palliative care.

  10. Learning about the internal structure of categories through classification and feature inference.

    Jee, Benjamin D; Wiley, Jennifer

    2014-01-01

    Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

  11. Novel Automatic Filter-Class Feature Selection for Machine Learning Regression

    Wollsen, Morten Gill; Hallam, John; Jørgensen, Bo Nørregaard

    2017-01-01

    With the increased focus on application of Big Data in all sectors of society, the performance of machine learning becomes essential. Efficient machine learning depends on efficient feature selection algorithms. Filter feature selection algorithms are model-free and therefore very fast, but require...... model in the feature selection process. PCA is often used in machine learning litterature and can be considered the default feature selection method. RDESF outperformed PCA in both experiments in both prediction error and computational speed. RDESF is a new step into filter-based automatic feature...

  12. Aggregate-then-Curate: how digital learning champions help communities nurture online content

    Andrew Whitworth

    2012-12-01

    Full Text Available Informational resources are essential for communities, rooting them in their own history, helping them learn and solve problems, giving them a voice in decision-making and so on. For digital inclusion – and inclusion in the informational and democratic processes of society more generally – it is essential that communities retain the skills, awareness and motivation to create and manage their own informational resources.This article explores a model for the creation of online content that incorporates the different ways in which the quality and relevance of information can be assured. This model, “Aggregate-then-Curate” (A/C, was developed from earlier work concerning digital inclusion in UK online centres, models of informal e-learning and ecologies of resources. A/C shows how creating online content can be viewed as a 7-step process, initiated by individuals but bringing in “digital learning champions”, other community members and formal educational institutions at different stages. A/C can be used to design training to help build the capacity to manage community informational resources in an inclusive way. The article then discusses and evaluates MOSI-ALONG, a Joint Information Systems Committee (JISC funded project founded on these ideas, which illustrates how A/C can be used to design training to help build the capacity to manage community informational resources in an inclusive way. This conclusion is supported by evaluations of the work done so far in MOSI-ALONG.

  13. Understanding psychiatric disorder by capturing ecologically relevant features of learning and decision-making.

    Scholl, Jacqueline; Klein-Flügge, Miriam

    2017-09-28

    Recent research in cognitive neuroscience has begun to uncover the processes underlying increasingly complex voluntary behaviours, including learning and decision-making. Partly this success has been possible by progressing from simple experimental tasks to paradigms that incorporate more ecological features. More specifically, the premise is that to understand cognitions and brain functions relevant for real life, we need to introduce some of the ecological challenges that we have evolved to solve. This often entails an increase in task complexity, which can be managed by using computational models to help parse complex behaviours into specific component mechanisms. Here we propose that using computational models with tasks that capture ecologically relevant learning and decision-making processes may provide a critical advantage for capturing the mechanisms underlying symptoms of disorders in psychiatry. As a result, it may help develop mechanistic approaches towards diagnosis and treatment. We begin this review by mapping out the basic concepts and models of learning and decision-making. We then move on to consider specific challenges that emerge in realistic environments and describe how they can be captured by tasks. These include changes of context, uncertainty, reflexive/emotional biases, cost-benefit decision-making, and balancing exploration and exploitation. Where appropriate we highlight future or current links to psychiatry. We particularly draw examples from research on clinical depression, a disorder that greatly compromises motivated behaviours in real-life, but where simpler paradigms have yielded mixed results. Finally, we highlight several paradigms that could be used to help provide new insights into the mechanisms of psychiatric disorders. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  14. Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

    Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

    2017-06-09

    In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

  15. Cinemeducation: A pilot student project using movies to help students learn medical professionalism.

    Lumlertgul, Nuttha; Kijpaisalratana, Naruchorn; Pityaratstian, Nuttorn; Wangsaturaka, Danai

    2009-07-01

    Using movies has been accepted worldwide as a tool to help students learn medical professionalism. In the second year, a group of medical students conducted the "Cinemeducation" project to promote professionalism in the "Medical Ethics and Critical Thinking" course. Five movies with professionalism issues were screened with 20-30 students attending each session. After the show, participants then were asked to reflect on what they had learned in terms of professionalism. Two students led group discussion emphasizing questioning and argumentation for 60 min. Additional learning issues emerging from each session were also explored in more depth and arranged into a report. In the Cinemeducation Project, medical students have learned five main ethical issues in each film, which were the doctor-patient relationship, informed consent and clinical trials in patients, management of genetic disorders, patient management, and brain death and organ transplantation. In addition to issues of professionalism, they also developed critical thinking and moral reasoning skills. Using a case-based scenario in movies has proven to be an effective and entertaining method of facilitating students with learning on professionalism.

  16. Opening the Learning Process: The Potential Role of Feature Film in Teaching Employment Relations

    Lafferty, George

    2016-01-01

    This paper explores the potential of feature film to encourage more inclusive, participatory and open learning in the area of employment relations. Evaluations of student responses in a single postgraduate course over a five-year period revealed how feature film could encourage participatory learning processes in which students reexamined their…

  17. Learning features for tissue classification with the classification restricted Boltzmann machine

    van Tulder, Gijs; de Bruijne, Marleen

    2014-01-01

    Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce the convo...... outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy....

  18. Towards Stable Adversarial Feature Learning for LiDAR based Loop Closure Detection

    Xu, Lingyun; Yin, Peng; Luo, Haibo; Liu, Yunhui; Han, Jianda

    2017-01-01

    Stable feature extraction is the key for the Loop closure detection (LCD) task in the simultaneously localization and mapping (SLAM) framework. In our paper, the feature extraction is operated by using a generative adversarial networks (GANs) based unsupervised learning. GANs are powerful generative models, however, GANs based adversarial learning suffers from training instability. We find that the data-code joint distribution in the adversarial learning is a more complex manifold than in the...

  19. The importance of internal facial features in learning new faces.

    Longmore, Christopher A; Liu, Chang Hong; Young, Andrew W

    2015-01-01

    For familiar faces, the internal features (eyes, nose, and mouth) are known to be differentially salient for recognition compared to external features such as hairstyle. Two experiments are reported that investigate how this internal feature advantage accrues as a face becomes familiar. In Experiment 1, we tested the contribution of internal and external features to the ability to generalize from a single studied photograph to different views of the same face. A recognition advantage for the internal features over the external features was found after a change of viewpoint, whereas there was no internal feature advantage when the same image was used at study and test. In Experiment 2, we removed the most salient external feature (hairstyle) from studied photographs and looked at how this affected generalization to a novel viewpoint. Removing the hair from images of the face assisted generalization to novel viewpoints, and this was especially the case when photographs showing more than one viewpoint were studied. The results suggest that the internal features play an important role in the generalization between different images of an individual's face by enabling the viewer to detect the common identity-diagnostic elements across non-identical instances of the face.

  20. The helpfulness of category labels in semi-supervised learning depends on category structure.

    Vong, Wai Keen; Navarro, Daniel J; Perfors, Amy

    2016-02-01

    The study of semi-supervised category learning has generally focused on how additional unlabeled information with given labeled information might benefit category learning. The literature is also somewhat contradictory, sometimes appearing to show a benefit to unlabeled information and sometimes not. In this paper, we frame the problem differently, focusing on when labels might be helpful to a learner who has access to lots of unlabeled information. Using an unconstrained free-sorting categorization experiment, we show that labels are useful to participants only when the category structure is ambiguous and that people's responses are driven by the specific set of labels they see. We present an extension of Anderson's Rational Model of Categorization that captures this effect.

  1. Building Knowledge Structures by Testing Helps Children With Mathematical Learning Difficulty.

    Zhang, Yiyun; Zhou, Xinlin

    2016-01-01

    Mathematical learning difficulty (MLD) is prevalent in the development of mathematical abilities. Previous interventions for children with MLD have focused on number sense or basic mathematical skills. This study investigated whether mathematical performance of fifth grade children with MLD could be improved by developing knowledge structures by testing using a web-based curriculum learning system. A total of 142 children with MLD were recruited; half of the children were in the experimental group (using the system), and the other half were in the control group (not using the system). The children were encouraged to use the web-based learning system at home for at least a 15-min session, at least once a week, for one and a half months. The mean accumulated time of testing on the system for children in the experimental group was 56.2 min. Children in the experimental group had significantly higher scores on their final mathematical examination compared to the control group. The results suggest that web-based curriculum learning through testing that promotes the building of knowledge structures for a mathematical course was helpful for children with MLD. © Hammill Institute on Disabilities 2014.

  2. Helping students with learning difficulties in medical and health-care education.

    Coles, C R

    1990-05-01

    In health profession education many more students than is currently acknowledged experience often extreme difficulties with their studying. This booklet is intended to help them. It outlines an approach being adopted in the Faculty of Medicine at the University of Southampton by which students are encouraged to reflect on and discuss their approaches to studying, identifying their perception of their task and where necessary changing this. It is shown that students need to elaborate their knowledge, that is to structure the factual information they are receiving and to relate it to their practical experiences. A number of suggestions are made to encourage this, and their theoretical underpinnings are discussed. It is concluded that while inappropriate curricula and teaching methods and not some weakness on the part of students are largely the cause of learning difficulties, it will take time to change these. Establishing a kind of 'clinic' for helping students cope can be of value immediately.

  3. Internal attention to features in visual short-term memory guides object learning.

    Fan, Judith E; Turk-Browne, Nicholas B

    2013-11-01

    Attending to objects in the world affects how we perceive and remember them. What are the consequences of attending to an object in mind? In particular, how does reporting the features of a recently seen object guide visual learning? In three experiments, observers were presented with abstract shapes in a particular color, orientation, and location. After viewing each object, observers were cued to report one feature from visual short-term memory (VSTM). In a subsequent test, observers were cued to report features of the same objects from visual long-term memory (VLTM). We tested whether reporting a feature from VSTM: (1) enhances VLTM for just that feature (practice-benefit hypothesis), (2) enhances VLTM for all features (object-based hypothesis), or (3) simultaneously enhances VLTM for that feature and suppresses VLTM for unreported features (feature-competition hypothesis). The results provided support for the feature-competition hypothesis, whereby the representation of an object in VLTM was biased towards features reported from VSTM and away from unreported features (Experiment 1). This bias could not be explained by the amount of sensory exposure or response learning (Experiment 2) and was amplified by the reporting of multiple features (Experiment 3). Taken together, these results suggest that selective internal attention induces competitive dynamics among features during visual learning, flexibly tuning object representations to align with prior mnemonic goals. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Paralog-divergent Features May Help Reduce Off-target Effects of Drugs: Hints from Glucagon Subfamily Analysis

    Zhining Sa

    2017-08-01

    Full Text Available Side effects from targeted drugs remain a serious concern. One reason is the nonselective binding of a drug to unintended proteins such as its paralogs, which are highly homologous in sequences and have similar structures and drug-binding pockets. To identify targetable differences between paralogs, we analyzed two types (type-I and type-II of functional divergence between two paralogs in the known target protein receptor family G-protein coupled receptors (GPCRs at the amino acid level. Paralogous protein receptors in glucagon-like subfamily, glucagon receptor (GCGR and glucagon-like peptide-1 receptor (GLP-1R, exhibit divergence in ligands and are clinically validated drug targets for type 2 diabetes. Our data showed that type-II amino acids were significantly enriched in the binding sites of antagonist MK-0893 to GCGR, which had a radical shift in physicochemical properties between GCGR and GLP-1R. We also examined the role of type-I amino acids between GCGR and GLP-1R. The divergent features between GCGR and GLP-1R paralogs may be helpful in their discrimination, thus enabling the identification of binding sites to reduce undesirable side effects and increase the target specificity of drugs.

  5. Learning to Recognize Features of Valid Textual Entailments

    MacCartney, Bill; Grenager, Trond; Marneffe, Marie-Catherine de; Cer, Daniel; Manning, Christopher D

    2006-01-01

    .... Instead we propose a pipelined approach where alignment is followed by a classification step, in which we extract features representing high-level characteristics of the entailment problem, and pass the resulting feature vector to a statistical classifier trained on development data. We report results on data from the 2005 Pascal RTE Challenge which surpass previously reported results for alignment-based systems.

  6. Multiscale deep features learning for land-use scene recognition

    Yuan, Baohua; Li, Shijin; Li, Ning

    2018-01-01

    The features extracted from deep convolutional neural networks (CNNs) have shown their promise as generic descriptors for land-use scene recognition. However, most of the work directly adopts the deep features for the classification of remote sensing images, and does not encode the deep features for improving their discriminative power, which can affect the performance of deep feature representations. To address this issue, we propose an effective framework, LASC-CNN, obtained by locality-constrained affine subspace coding (LASC) pooling of a CNN filter bank. LASC-CNN obtains more discriminative deep features than directly extracted from CNNs. Furthermore, LASC-CNN builds on the top convolutional layers of CNNs, which can incorporate multiscale information and regions of arbitrary resolution and sizes. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

  7. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching.

    Guo, Yanrong; Gao, Yaozong; Shen, Dinggang

    2016-04-01

    Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods.

  8. Feature-Learning-Based Printed Circuit Board Inspection via Speeded-Up Robust Features and Random Forest

    Eun Hye Yuk

    2018-06-01

    Full Text Available With the coming of the 4th industrial revolution era, manufacturers produce high-tech products. As the production process is refined, inspection technologies become more important. Specifically, the inspection of a printed circuit board (PCB, which is an indispensable part of electronic products, is an essential step to improve the quality of the process and yield. Image processing techniques are utilized for inspection, but there are limitations because the backgrounds of images are different and the kinds of defects increase. In order to overcome these limitations, methods based on machine learning have been used recently. These methods can inspect without a normal image by learning fault patterns. Therefore, this paper proposes a method can detect various types of defects using machine learning. The proposed method first extracts features through speeded-up robust features (SURF, then learns the fault pattern and calculates probabilities. After that, we generate a weighted kernel density estimation (WKDE map weighted by the probabilities to consider the density of the features. Because the probability of the WKDE map can detect an area where the defects are concentrated, it improves the performance of the inspection. To verify the proposed method, we apply the method to PCB images and confirm the performance of the method.

  9. Peculiarities of learning style in adolescents with the features of hyperactivity

    Nasvytienė, Dalia; Trakimavičiūtė, Rasa

    2010-01-01

    The aim of research was to investigate the learning style of adolescents with the features of hyperactivity. The participants were selected as quite common group in the educational practice exceeding by number the pure clinical disorder group of hyperactive children. Their learning style is still under discussion in regard to the efficiency and dynamics of learning process. Learning style questionnaire was created for this purpose. The participants came from a consecutive sample of 30 adolesc...

  10. Virtual and physical toys: open-ended features for non-formal learning.

    Petersson, Eva; Brooks, Anthony

    2006-04-01

    This paper examines the integrated toy--both physical and virtual--as an essential resource for collaborative learning. This learning incorporates rehabilitation, training, and education. The data derived from two different cases. Pedagogical issues related to non-formal learning and open-ended features of design are discussed. Findings suggest that social, material, and expressive affordances constitute a base for an alterative interface to encourage children's play and learning.

  11. Neural correlates of context-dependent feature conjunction learning in visual search tasks.

    Reavis, Eric A; Frank, Sebastian M; Greenlee, Mark W; Tse, Peter U

    2016-06-01

    Many perceptual learning experiments show that repeated exposure to a basic visual feature such as a specific orientation or spatial frequency can modify perception of that feature, and that those perceptual changes are associated with changes in neural tuning early in visual processing. Such perceptual learning effects thus exert a bottom-up influence on subsequent stimulus processing, independent of task-demands or endogenous influences (e.g., volitional attention). However, it is unclear whether such bottom-up changes in perception can occur as more complex stimuli such as conjunctions of visual features are learned. It is not known whether changes in the efficiency with which people learn to process feature conjunctions in a task (e.g., visual search) reflect true bottom-up perceptual learning versus top-down, task-related learning (e.g., learning better control of endogenous attention). Here we show that feature conjunction learning in visual search leads to bottom-up changes in stimulus processing. First, using fMRI, we demonstrate that conjunction learning in visual search has a distinct neural signature: an increase in target-evoked activity relative to distractor-evoked activity (i.e., a relative increase in target salience). Second, we demonstrate that after learning, this neural signature is still evident even when participants passively view learned stimuli while performing an unrelated, attention-demanding task. This suggests that conjunction learning results in altered bottom-up perceptual processing of the learned conjunction stimuli (i.e., a perceptual change independent of the task). We further show that the acquired change in target-evoked activity is contextually dependent on the presence of distractors, suggesting that search array Gestalts are learned. Hum Brain Mapp 37:2319-2330, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  12. Caudate nucleus reactivity predicts perceptual learning rate for visual feature conjunctions.

    Reavis, Eric A; Frank, Sebastian M; Tse, Peter U

    2015-04-15

    Useful information in the visual environment is often contained in specific conjunctions of visual features (e.g., color and shape). The ability to quickly and accurately process such conjunctions can be learned. However, the neural mechanisms responsible for such learning remain largely unknown. It has been suggested that some forms of visual learning might involve the dopaminergic neuromodulatory system (Roelfsema et al., 2010; Seitz and Watanabe, 2005), but this hypothesis has not yet been directly tested. Here we test the hypothesis that learning visual feature conjunctions involves the dopaminergic system, using functional neuroimaging, genetic assays, and behavioral testing techniques. We use a correlative approach to evaluate potential associations between individual differences in visual feature conjunction learning rate and individual differences in dopaminergic function as indexed by neuroimaging and genetic markers. We find a significant correlation between activity in the caudate nucleus (a component of the dopaminergic system connected to visual areas of the brain) and visual feature conjunction learning rate. Specifically, individuals who showed a larger difference in activity between positive and negative feedback on an unrelated cognitive task, indicative of a more reactive dopaminergic system, learned visual feature conjunctions more quickly than those who showed a smaller activity difference. This finding supports the hypothesis that the dopaminergic system is involved in visual learning, and suggests that visual feature conjunction learning could be closely related to associative learning. However, no significant, reliable correlations were found between feature conjunction learning and genotype or dopaminergic activity in any other regions of interest. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

    Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

    2018-04-01

    In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

  14. ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

    L. Ding

    2018-04-01

    Full Text Available In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

  15. Using Problem-Based Learning to help Portuguese students make the Bologna transition

    Manuel Cabral Reis

    2013-08-01

    Full Text Available The Bologna Declaration has opened a stage of big and deep changes in the internal university organization, external cooperation, teaching models and methods, among other., all over the European countries. Here we will present and discuss a pilot experience conducted at the Engineering Department of the University of Trás-os-Montes e Alto Douro, Portugal, during the second year of that transition period. In brief, we will present a set of non-mandatory courses proposed to the students of each individual syllabus, with one hundred hours duration, each, approximately seven hours/week, fifteen weeks long, with the permanent help of a specialized trainer to aid the students in their "homework". The formal bureaucratic transition is also presented. Design and implementation issues, supported on problem-based learning and experimental lab learning classes, final assessment results, as well as the opinion of the students, are presented and analyzed. We believe that this methodology helped to make the transition smoother to the students, but also to the teaching staff.

  16. Improving EEG signal peak detection using feature weight learning ...

    Asrul Adam

    4 School of Psychology and Counseling, Queensland University of Technology, Brisbane 4000, Australia. 5 QIMR ... The groups of Acir et al .... difference between the peak and the floating mean, which is ..... Thus, the individual features were.

  17. A Bridge to Active Learning: A Summer Bridge Program Helps Students Maximize Their Active-Learning Experiences and the Active-Learning Experiences of Others

    Cooper, Katelyn M.; Ashley, Michael; Brownell, Sara E.

    2017-01-01

    National calls to improve student academic success in college have sparked the development of bridge programs designed to help students transition from high school to college. We designed a 2-week Summer Bridge program that taught introductory biology content in an active-learning way. Through a set of exploratory interviews, we unexpectedly…

  18. Cooperative learning that features a culturally appropriate pedagogy

    Phuong-Mai, Nguyen; Terlouw, C.; Pilot, Albert; Elliott, Julian

    2009-01-01

    Many recent intercultural studies have shown that people cooperate with each other differently across cultures. We argue that cooperative learning (CL), an educational method originating in the USA and with fundamental psychological assumptions based on Western values, should be adjusted to be

  19. Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

    Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

    2018-04-01

    Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

  20. Learning Combinations of Multiple Feature Representations for Music Emotion Prediction

    Madsen, Jens; Jensen, Bjørn Sand; Larsen, Jan

    2015-01-01

    Music consists of several structures and patterns evolving through time which greatly influences the human decoding of higher-level cognitive aspects of music like the emotions expressed in music. For tasks, such as genre, tag and emotion recognition, these structures have often been identified...... and used as individual and non-temporal features and representations. In this work, we address the hypothesis whether using multiple temporal and non-temporal representations of different features is beneficial for modeling music structure with the aim to predict the emotions expressed in music. We test...

  1. Avoiding Misinterpretations of Piaget and Vygotsky: Mathematical Teaching without Learning, Learning without Teaching, or Helpful Learning-Path Teaching?

    Fuson, Karen C.

    2009-01-01

    This article provides an overview of some perspectives about special issues in classroom mathematical teaching and learning that have stemmed from the huge explosion of research in children's mathematical thinking stimulated by Piaget. It concentrates on issues that are particularly important for less-advanced learners and for those who might be…

  2. Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2013-01-01

    their power in this field. Two important issues of bag-of-feature strategy for tissue classification are investigated in this paper: the visual vocabulary learning and weighting, which are always considered independently in traditional methods by neglecting

  3. Hybrid image representation learning model with invariant features for basal cell carcinoma detection

    Arevalo, John; Cruz-Roa, Angel; González, Fabio A.

    2013-11-01

    This paper presents a novel method for basal-cell carcinoma detection, which combines state-of-the-art methods for unsupervised feature learning (UFL) and bag of features (BOF) representation. BOF, which is a form of representation learning, has shown a good performance in automatic histopathology image classi cation. In BOF, patches are usually represented using descriptors such as SIFT and DCT. We propose to use UFL to learn the patch representation itself. This is accomplished by applying a topographic UFL method (T-RICA), which automatically learns visual invariance properties of color, scale and rotation from an image collection. These learned features also reveals these visual properties associated to cancerous and healthy tissues and improves carcinoma detection results by 7% with respect to traditional autoencoders, and 6% with respect to standard DCT representations obtaining in average 92% in terms of F-score and 93% of balanced accuracy.

  4. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

    Malta, Tathiane M.; Sokolov, Artem; Gentles, Andrew J.; Burzykowski, Tomasz; Poisson, Laila; Weinstein, John N.; Kamińska, Bożena; Huelsken, Joerg; Omberg, Larsson; Gevaert, Olivier; Colaprico, Antonio; Czerwińska, Patrycja; Mazurek, Sylwia; Mishra, Lopa; Heyn, Holger; Krasnitz, Alex; Godwin, Andrew K.; Lazar, Alexander J.; Caesar-Johnson, Samantha J.; Demchok, John A.; Felau, Ina; Kasapi, Melpomeni; Ferguson, Martin L.; Hutter, Carolyn M.; Sofia, Heidi J.; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean C.; Zhang, Jiashan (Julia); Chudamani, Sudha; Liu, Jia; Lolla, Laxmi; Naresh, Rashi; Pihl, Todd; Sun, Qiang; Wan, Yunhu; Wu, Ye; Cho, Juok; DeFreitas, Timothy; Frazer, Scott; Gehlenborg, Nils; Getz, Gad; Heiman, David I.; Kim, Jaegil; Lawrence, Michael S.; Lin, Pei; Meier, Sam; Noble, Michael S.; Saksena, Gordon; Voet, Doug; Zhang, Hailei; Bernard, Brady; Chambwe, Nyasha; Dhankani, Varsha; Knijnenburg, Theo; Kramer, Roger; Leinonen, Kalle; Liu, Yuexin; Miller, Michael; Reynolds, Sheila; Shmulevich, Ilya; Thorsson, Vesteinn; Zhang, Wei; Akbani, Rehan; Broom, Bradley M.; Hegde, Apurva M.; Ju, Zhenlin; Kanchi, Rupa S.; Korkut, Anil; Li, Jun; Liang, Han; Ling, Shiyun; Liu, Wenbin; Lu, Yiling; Mills, Gordon B.; Ng, Kwok Shing; Rao, Arvind; Ryan, Michael; Wang, Jing; Weinstein, John N.; Zhang, Jiexin; Abeshouse, Adam; Armenia, Joshua; Chakravarty, Debyani; Chatila, Walid K.; de Bruijn, Ino; Gao, Jianjiong; Gross, Benjamin E.; Heins, Zachary J.; Kundra, Ritika; La, Konnor; Ladanyi, Marc; Luna, Augustin; Nissan, Moriah G.; Ochoa, Angelica; Phillips, Sarah M.; Reznik, Ed; Sanchez-Vega, Francisco; Sander, Chris; Schultz, Nikolaus; Sheridan, Robert; Sumer, S. Onur; Sun, Yichao; Taylor, Barry S.; Wang, Jioajiao; Zhang, Hongxin; Anur, Pavana; Peto, Myron; Spellman, Paul; Benz, Christopher; Stuart, Joshua M.; Wong, Christopher K.; Yau, Christina; Hayes, D. Neil; Parker, Joel S.; Wilkerson, Matthew D.; Ally, Adrian; Balasundaram, Miruna; Bowlby, Reanne; Brooks, Denise; Carlsen, Rebecca; Chuah, Eric; Dhalla, Noreen; Holt, Robert; Jones, Steven J.M.; Kasaian, Katayoon; Lee, Darlene; Ma, Yussanne; Marra, Marco A.; Mayo, Michael; Moore, Richard A.; Mungall, Andrew J.; Mungall, Karen; Robertson, A. Gordon; Sadeghi, Sara; Schein, Jacqueline E.; Sipahimalani, Payal; Tam, Angela; Thiessen, Nina; Tse, Kane; Wong, Tina; Berger, Ashton C.; Beroukhim, Rameen; Cherniack, Andrew D.; Cibulskis, Carrie; Gabriel, Stacey B.; Gao, Galen F.; Ha, Gavin; Meyerson, Matthew; Schumacher, Steven E.; Shih, Juliann; Kucherlapati, Melanie H.; Kucherlapati, Raju S.; Baylin, Stephen; Cope, Leslie; Danilova, Ludmila; Bootwalla, Moiz S.; Lai, Phillip H.; Maglinte, Dennis T.; Van Den Berg, David J.; Weisenberger, Daniel J.; Auman, J. Todd; Balu, Saianand; Bodenheimer, Tom; Fan, Cheng; Hoadley, Katherine A.; Hoyle, Alan P.; Jefferys, Stuart R.; Jones, Corbin D.; Meng, Shaowu; Mieczkowski, Piotr A.; Mose, Lisle E.; Perou, Amy H.; Perou, Charles M.; Roach, Jeffrey; Shi, Yan; Simons, Janae V.; Skelly, Tara; Soloway, Matthew G.; Tan, Donghui; Veluvolu, Umadevi; Fan, Huihui; Hinoue, Toshinori; Laird, Peter W.; Shen, Hui; Zhou, Wanding; Bellair, Michelle; Chang, Kyle; Covington, Kyle; Creighton, Chad J.; Dinh, Huyen; Doddapaneni, Harsha Vardhan; Donehower, Lawrence A.; Drummond, Jennifer; Gibbs, Richard A.; Glenn, Robert; Hale, Walker; Han, Yi; Hu, Jianhong; Korchina, Viktoriya; Lee, Sandra; Lewis, Lora; Li, Wei; Liu, Xiuping; Morgan, Margaret; Morton, Donna; Muzny, Donna; Santibanez, Jireh; Sheth, Margi; Shinbrot, Eve; Wang, Linghua; Wang, Min; Wheeler, David A.; Xi, Liu; Zhao, Fengmei; Hess, Julian; Appelbaum, Elizabeth L.; Bailey, Matthew; Cordes, Matthew G.; Ding, Li; Fronick, Catrina C.; Fulton, Lucinda A.; Fulton, Robert S.; Kandoth, Cyriac; Mardis, Elaine R.; McLellan, Michael D.; Miller, Christopher A.; Schmidt, Heather K.; Wilson, Richard K.; Crain, Daniel; Curley, Erin; Gardner, Johanna; Lau, Kevin; Mallery, David; Morris, Scott; Paulauskis, Joseph; Penny, Robert; Shelton, Candace; Shelton, Troy; Sherman, Mark; Thompson, Eric; Yena, Peggy; Bowen, Jay; Gastier-Foster, Julie M.; Gerken, Mark; Leraas, Kristen M.; Lichtenberg, Tara M.; Ramirez, Nilsa C.; Wise, Lisa; Zmuda, Erik; Corcoran, Niall; Costello, Tony; Hovens, Christopher; Carvalho, Andre L.; de Carvalho, Ana C.; Fregnani, José H.; Longatto-Filho, Adhemar; Reis, Rui M.; Scapulatempo-Neto, Cristovam; Silveira, Henrique C.S.; Vidal, Daniel O.; Burnette, Andrew; Eschbacher, Jennifer; Hermes, Beth; Noss, Ardene; Singh, Rosy; Anderson, Matthew L.; Castro, Patricia D.; Ittmann, Michael; Huntsman, David; Kohl, Bernard; Le, Xuan; Thorp, Richard; Andry, Chris; Duffy, Elizabeth R.; Lyadov, Vladimir; Paklina, Oxana; Setdikova, Galiya; Shabunin, Alexey; Tavobilov, Mikhail; McPherson, Christopher; Warnick, Ronald; Berkowitz, Ross; Cramer, Daniel; Feltmate, Colleen; Horowitz, Neil; Kibel, Adam; Muto, Michael; Raut, Chandrajit P.; Malykh, Andrei; Barnholtz-Sloan, Jill S.; Barrett, Wendi; Devine, Karen; Fulop, Jordonna; Ostrom, Quinn T.; Shimmel, Kristen; Wolinsky, Yingli; Sloan, Andrew E.; De Rose, Agostino; Giuliante, Felice; Goodman, Marc; Karlan, Beth Y.; Hagedorn, Curt H.; Eckman, John; Harr, Jodi; Myers, Jerome; Tucker, Kelinda; Zach, Leigh Anne; Deyarmin, Brenda; Hu, Hai; Kvecher, Leonid; Larson, Caroline; Mural, Richard J.; Somiari, Stella; Vicha, Ales; Zelinka, Tomas; Bennett, Joseph; Iacocca, Mary; Rabeno, Brenda; Swanson, Patricia; Latour, Mathieu; Lacombe, Louis; Têtu, Bernard; Bergeron, Alain; McGraw, Mary; Staugaitis, Susan M.; Chabot, John; Hibshoosh, Hanina; Sepulveda, Antonia; Su, Tao; Wang, Timothy; Potapova, Olga; Voronina, Olga; Desjardins, Laurence; Mariani, Odette; Roman-Roman, Sergio; Sastre, Xavier; Stern, Marc Henri; Cheng, Feixiong; Signoretti, Sabina; Berchuck, Andrew; Bigner, Darell; Lipp, Eric; Marks, Jeffrey; McCall, Shannon; McLendon, Roger; Secord, Angeles; Sharp, Alexis; Behera, Madhusmita; Brat, Daniel J.; Chen, Amy; Delman, Keith; Force, Seth; Khuri, Fadlo; Magliocca, Kelly; Maithel, Shishir; Olson, Jeffrey J.; Owonikoko, Taofeek; Pickens, Alan; Ramalingam, Suresh; Shin, Dong M.; Sica, Gabriel; Van Meir, Erwin G.; Zhang, Hongzheng; Eijckenboom, Wil; Gillis, Ad; Korpershoek, Esther; Looijenga, Leendert; Oosterhuis, Wolter; Stoop, Hans; van Kessel, Kim E.; Zwarthoff, Ellen C.; Calatozzolo, Chiara; Cuppini, Lucia; Cuzzubbo, Stefania; DiMeco, Francesco; Finocchiaro, Gaetano; Mattei, Luca; Perin, Alessandro; Pollo, Bianca; Chen, Chu; Houck, John; Lohavanichbutr, Pawadee; Hartmann, Arndt; Stoehr, Christine; Stoehr, Robert; Taubert, Helge; Wach, Sven; Wullich, Bernd; Kycler, Witold; Murawa, Dawid; Wiznerowicz, Maciej; Chung, Ki; Edenfield, W. Jeffrey; Martin, Julie; Baudin, Eric; Bubley, Glenn; Bueno, Raphael; De Rienzo, Assunta; Richards, William G.; Kalkanis, Steven; Mikkelsen, Tom; Noushmehr, Houtan; Scarpace, Lisa; Girard, Nicolas; Aymerich, Marta; Campo, Elias; Giné, Eva; Guillermo, Armando López; Van Bang, Nguyen; Hanh, Phan Thi; Phu, Bui Duc; Tang, Yufang; Colman, Howard; Evason, Kimberley; Dottino, Peter R.; Martignetti, John A.; Gabra, Hani; Juhl, Hartmut; Akeredolu, Teniola; Stepa, Serghei; Hoon, Dave; Ahn, Keunsoo; Kang, Koo Jeong; Beuschlein, Felix; Breggia, Anne; Birrer, Michael; Bell, Debra; Borad, Mitesh; Bryce, Alan H.; Castle, Erik; Chandan, Vishal; Cheville, John; Copland, John A.; Farnell, Michael; Flotte, Thomas; Giama, Nasra; Ho, Thai; Kendrick, Michael; Kocher, Jean Pierre; Kopp, Karla; Moser, Catherine; Nagorney, David; O'Brien, Daniel; O'Neill, Brian Patrick; Patel, Tushar; Petersen, Gloria; Que, Florencia; Rivera, Michael; Roberts, Lewis; Smallridge, Robert; Smyrk, Thomas; Stanton, Melissa; Thompson, R. Houston; Torbenson, Michael; Yang, Ju Dong; Zhang, Lizhi; Brimo, Fadi; Ajani, Jaffer A.; Gonzalez, Ana Maria Angulo; Behrens, Carmen; Bondaruk, Jolanta; Broaddus, Russell; Czerniak, Bogdan; Esmaeli, Bita; Fujimoto, Junya; Gershenwald, Jeffrey; Guo, Charles; Lazar, Alexander J.; Logothetis, Christopher; Meric-Bernstam, Funda; Moran, Cesar; Ramondetta, Lois; Rice, David; Sood, Anil; Tamboli, Pheroze; Thompson, Timothy; Troncoso, Patricia; Tsao, Anne; Wistuba, Ignacio; Carter, Candace; Haydu, Lauren; Hersey, Peter; Jakrot, Valerie; Kakavand, Hojabr; Kefford, Richard; Lee, Kenneth; Long, Georgina; Mann, Graham; Quinn, Michael; Saw, Robyn; Scolyer, Richard; Shannon, Kerwin; Spillane, Andrew; Stretch, Jonathan; Synott, Maria; Thompson, John; Wilmott, James; Al-Ahmadie, Hikmat; Chan, Timothy A.; Ghossein, Ronald; Gopalan, Anuradha; Levine, Douglas A.; Reuter, Victor; Singer, Samuel; Singh, Bhuvanesh; Tien, Nguyen Viet; Broudy, Thomas; Mirsaidi, Cyrus; Nair, Praveen; Drwiega, Paul; Miller, Judy; Smith, Jennifer; Zaren, Howard; Park, Joong Won; Hung, Nguyen Phi; Kebebew, Electron; Linehan, W. Marston; Metwalli, Adam R.; Pacak, Karel; Pinto, Peter A.; Schiffman, Mark; Schmidt, Laura S.; Vocke, Cathy D.; Wentzensen, Nicolas; Worrell, Robert; Yang, Hannah; Moncrieff, Marc; Goparaju, Chandra; Melamed, Jonathan; Pass, Harvey; Botnariuc, Natalia; Caraman, Irina; Cernat, Mircea; Chemencedji, Inga; Clipca, Adrian; Doruc, Serghei; Gorincioi, Ghenadie; Mura, Sergiu; Pirtac, Maria; Stancul, Irina; Tcaciuc, Diana; Albert, Monique; Alexopoulou, Iakovina; Arnaout, Angel; Bartlett, John; Engel, Jay; Gilbert, Sebastien; Parfitt, Jeremy; Sekhon, Harman; Thomas, George; Rassl, Doris M.; Rintoul, Robert C.; Bifulco, Carlo; Tamakawa, Raina; Urba, Walter; Hayward, Nicholas; Timmers, Henri; Antenucci, Anna; Facciolo, Francesco; Grazi, Gianluca; Marino, Mirella; Merola, Roberta; de Krijger, Ronald; Gimenez-Roqueplo, Anne Paule; Piché, Alain; Chevalier, Simone; McKercher, Ginette; Birsoy, Kivanc; Barnett, Gene; Brewer, Cathy; Farver, Carol; Naska, Theresa; Pennell, Nathan A.; Raymond, Daniel; Schilero, Cathy; Smolenski, Kathy; Williams, Felicia; Morrison, Carl; Borgia, Jeffrey A.; Liptay, Michael J.; Pool, Mark; Seder, Christopher W.; Junker, Kerstin; Omberg, Larsson; Dinkin, Mikhail; Manikhas, George; Alvaro, Domenico; Bragazzi, Maria Consiglia; Cardinale, Vincenzo; Carpino, Guido; Gaudio, Eugenio; Chesla, David; Cottingham, Sandra; Dubina, Michael; Moiseenko, Fedor; Dhanasekaran, Renumathy; Becker, Karl Friedrich; Janssen, Klaus Peter; Slotta-Huspenina, Julia; Abdel-Rahman, Mohamed H.; Aziz, Dina; Bell, Sue; Cebulla, Colleen M.; Davis, Amy; Duell, Rebecca; Elder, J. Bradley; Hilty, Joe; Kumar, Bahavna; Lang, James; Lehman, Norman L.; Mandt, Randy; Nguyen, Phuong; Pilarski, Robert; Rai, Karan; Schoenfield, Lynn; Senecal, Kelly; Wakely, Paul; Hansen, Paul; Lechan, Ronald; Powers, James; Tischler, Arthur; Grizzle, William E.; Sexton, Katherine C.; Kastl, Alison; Henderson, Joel; Porten, Sima; Waldmann, Jens; Fassnacht, Martin; Asa, Sylvia L.; Schadendorf, Dirk; Couce, Marta; Graefen, Markus; Huland, Hartwig; Sauter, Guido; Schlomm, Thorsten; Simon, Ronald; Tennstedt, Pierre; Olabode, Oluwole; Nelson, Mark; Bathe, Oliver; Carroll, Peter R.; Chan, June M.; Disaia, Philip; Glenn, Pat; Kelley, Robin K.; Landen, Charles N.; Phillips, Joanna; Prados, Michael; Simko, Jeffry; Smith-McCune, Karen; VandenBerg, Scott; Roggin, Kevin; Fehrenbach, Ashley; Kendler, Ady; Sifri, Suzanne; Steele, Ruth; Jimeno, Antonio; Carey, Francis; Forgie, Ian; Mannelli, Massimo; Carney, Michael; Hernandez, Brenda; Campos, Benito; Herold-Mende, Christel; Jungk, Christin; Unterberg, Andreas; von Deimling, Andreas; Bossler, Aaron; Galbraith, Joseph; Jacobus, Laura; Knudson, Michael; Knutson, Tina; Ma, Deqin; Milhem, Mohammed; Sigmund, Rita; Godwin, Andrew K.; Madan, Rashna; Rosenthal, Howard G.; Adebamowo, Clement; Adebamowo, Sally N.; Boussioutas, Alex; Beer, David; Giordano, Thomas; Mes-Masson, Anne Marie; Saad, Fred; Bocklage, Therese; Landrum, Lisa; Mannel, Robert; Moore, Kathleen; Moxley, Katherine; Postier, Russel; Walker, Joan; Zuna, Rosemary; Feldman, Michael; Valdivieso, Federico; Dhir, Rajiv; Luketich, James; Pinero, Edna M.Mora; Quintero-Aguilo, Mario; Carlotti, Carlos Gilberto; Dos Santos, Jose Sebastião; Kemp, Rafael; Sankarankuty, Ajith; Tirapelli, Daniela; Catto, James; Agnew, Kathy; Swisher, Elizabeth; Creaney, Jenette; Robinson, Bruce; Shelley, Carl Simon; Godwin, Eryn M.; Kendall, Sara; Shipman, Cassaundra; Bradford, Carol; Carey, Thomas; Haddad, Andrea; Moyer, Jeffey; Peterson, Lisa; Prince, Mark; Rozek, Laura; Wolf, Gregory; Bowman, Rayleen; Fong, Kwun M.; Yang, Ian; Korst, Robert; Rathmell, W. Kimryn; Fantacone-Campbell, J. Leigh; Hooke, Jeffrey A.; Kovatich, Albert J.; Shriver, Craig D.; DiPersio, John; Drake, Bettina; Govindan, Ramaswamy; Heath, Sharon; Ley, Timothy; Van Tine, Brian; Westervelt, Peter; Rubin, Mark A.; Lee, Jung Il; Aredes, Natália D.; Mariamidze, Armaz; Stuart, Joshua M.; Hoadley, Katherine A.; Laird, Peter W.; Noushmehr, Houtan; Wiznerowicz, Maciej

    2018-01-01

    Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR)

  5. Helping Autism-Diagnosed Teenagers Navigate and Develop Socially Using E-Learning Based on Mobile Persuasion

    Ohrstrom, Peter

    2011-01-01

    The HANDS (Helping Autism-diagnosed teenagers Navigate and Develop Socially) research project involves the creation of an e-learning toolset that can be used to develop individualized tools to support the social development of teenagers with an autism diagnosis. The e-learning toolset is based on ideas from persuasive technology. This paper…

  6. Relationship between Chinese Learning Motivation types and demographic features among Danish Students

    Zhang, Chun

    The purpose of this study is to investigate the relationship between Chinese learning motivation types and the various demographic features among students at lower and upper secondary schools in Denmark. The basis of the analysis is survey data collected in Denmark from 204 students from 6 upper......) in mind, the motivational types in Chinese learning demonstrate the distinct features of the context. Theoretical and pedagogical implications for the findings are discussed....

  7. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

    Shan, Juan; Alam, S Kaisar; Garra, Brian; Zhang, Yingtao; Ahmed, Tahira

    2016-04-01

    This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. All rights reserved.

  8. An exploration of learning to link with Wikipedia: features, methods and training collection

    He, J.; de Rijke, M.

    2010-01-01

    We describe our participation in the Link-the-Wiki track at INEX 2009. We apply machine learning methods to the anchor-to-best-entry-point task and explore the impact of the following aspects of our approaches: features, learning methods as well as the collection used for training the models. We

  9. Acquiring concepts and features of novel words by two types of learning: direct mapping and inference.

    Chen, Shuang; Wang, Lin; Yang, Yufang

    2014-04-01

    This study examined the semantic representation of novel words learnt in two conditions: directly mapping a novel word to a concept (Direct mapping: DM) and inferring the concept from provided features (Inferred learning: IF). A condition where no definite concept could be inferred (No basic-level meaning: NM) served as a baseline. The semantic representation of the novel word was assessed via a semantic-relatedness judgment task. In this task, the learned novel word served as a prime, while the corresponding concept, an unlearned feature of the concept, and an unrelated word served as targets. ERP responses to the targets, primed by the novel words in the three learning conditions, were compared. For the corresponding concept, smaller N400s were elicited in the DM and IF conditions than in the NM condition, indicating that the concept could be obtained in both learning conditions. However, for the unlearned feature, the targets in the IF condition produced an N400 effect while in the DM condition elicited an LPC effect relative to the NM learning condition. No ERP difference was observed among the three learning conditions for the unrelated words. The results indicate that conditions of learning affect the semantic representation of novel word, and that the unlearned feature was only activated by the novel word in the IF learning condition. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Delaware Longitudinal Study of Fraction Learning: Implications for Helping Children With Mathematics Difficulties.

    Jordan, Nancy C; Resnick, Ilyse; Rodrigues, Jessica; Hansen, Nicole; Dyson, Nancy

    The goal of the present article is to synthesize findings to date from the Delaware Longitudinal Study of Fraction Learning. The study followed a large cohort of children ( N = 536) between Grades 3 and 6. The findings showed that many students, especially those with diagnosed learning disabilities, made minimal growth in fraction knowledge and that some showed only a basic grasp of the meaning of a fraction even after several years of instruction. Children with low growth in fraction knowledge during the intermediate grades were much more likely to fail to meet state standards on a broad mathematics measure at the end of Grade 6. Although a range of general and mathematics-specific competencies predicted fraction outcomes, the ability to estimate numerical magnitudes on a number line was a uniquely important marker of fraction success. Many children with mathematics difficulties have deep-seated problems related to whole number magnitude representations that are complicated by the introduction of fractions into the curriculum. Implications for helping students with mathematics difficulties are discussed.

  11. Creating a learning organization to help meet the needs of multihospital health systems.

    Ward, Angela; Berensen, Nannette; Daniels, Rowell

    2018-04-01

    The considerations that leaders of multihospital health systems must take into account in developing and implementing initiatives to build and maintain an exceptional pharmacy workforce are described. Significant changes that require constant individual and organizational learning are occurring throughout healthcare and within the profession of pharmacy. These considerations include understanding why it is important to have a succession plan and determining what types of education and training are important to support that plan. Other considerations include strategies for leveraging learners, dealing with a large geographic footprint, adjusting training opportunities to accommodate the ever-evolving demands on pharmacy staffs in terms of skill mix, and determining ways to either budget for or internally develop content for staff development. All of these methods are critically important to ensuring an optimized workforce. Especially for large health systems operating multiple sites across large distances, the use of technology-enabled solutions to provide effective delivery of programming to multiple sites is critical. Commonly used tools include live webinars, live "telepresence" programs, prerecorded programming that is available through an on-demand repository, and computer-based training modules. A learning management system is helpful to assign and document completion of educational requirements, especially those related to regulatory requirements (e.g., controlled substances management, sterile and nonsterile compounding, competency assessment). Creating and sustaining an environment where all pharmacy caregivers feel invested in and connected to ongoing learning is a powerful motivator for performance, engagement, and retention. Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

  12. Teaching the Sociology of Popular Music with the Help of Feature Films: A Selected and Annotated Videography.

    Groce, Stephen B.

    1992-01-01

    Discusses the use of feature films for courses on popular culture and the sociology of popular music. Suggests that films can illustrate topics such as culture, social groups, deviant behavior, racism, and sexism. Lists a selection of Hollywood feature films with accompanying readings and students' evaluations. (DK)

  13. Helping students learn effective problem solving strategies by reflecting with peers

    Mason, Andrew; Singh, Chandralekha

    2010-07-01

    We study how introductory physics students engage in reflection with peers about problem solving. The recitations for an introductory physics course with 200 students were broken into a "peer reflection" (PR) group and a traditional group. Each week in recitation, small teams of students in the PR group reflected on selected problems from the homework and discussed why the solutions of some students employed better problem solving strategies than others. The graduate and undergraduate teaching assistants in the PR recitations provided guidance and coaching to help students learn effective problem solving heuristics. In the traditional group recitations students could ask the graduate TA questions about the homework before they took a weekly quiz. The traditional group recitation quiz questions were similar to the homework questions selected for peer reflection in the PR group recitations. As one measure of the impact of this intervention, we investigated how likely students were to draw diagrams to help with problem solving on the final exam with only multiple-choice questions. We found that the PR group drew diagrams on more problems than the traditional group even when there was no explicit reward for doing so. Also, students who drew more diagrams for the multiple-choice questions outperformed those who did not, regardless of which group they were a member.

  14. Deep-learning derived features for lung nodule classification with limited datasets

    Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

    2018-02-01

    Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.

  15. Perceptual learning of basic visual features remains task specific with Training-Plus-Exposure (TPE) training.

    Cong, Lin-Juan; Wang, Ru-Jie; Yu, Cong; Zhang, Jun-Yun

    2016-01-01

    Visual perceptual learning is known to be specific to the trained retinal location, feature, and task. However, location and feature specificity can be eliminated by double-training or TPE training protocols, in which observers receive additional exposure to the transfer location or feature dimension via an irrelevant task besides the primary learning task Here we tested whether these new training protocols could even make learning transfer across different tasks involving discrimination of basic visual features (e.g., orientation and contrast). Observers practiced a near-threshold orientation (or contrast) discrimination task. Following a TPE training protocol, they also received exposure to the transfer task via performing suprathreshold contrast (or orientation) discrimination in alternating blocks of trials in the same sessions. The results showed no evidence for significant learning transfer to the untrained near-threshold contrast (or orientation) discrimination task after discounting the pretest effects and the suprathreshold practice effects. These results thus do not support a hypothetical task-independent component in perceptual learning of basic visual features. They also set the boundary of the new training protocols in their capability to enable learning transfer.

  16. Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

    Ma, Ling; Liu, Xiabi; Fei, Baowei

    2017-01-01

    Common CT imaging signs of lung diseases (CISLs) are defined as the imaging signs that frequently appear in lung CT images from patients. CISLs play important roles in the diagnosis of lung diseases. This paper proposes a novel learning method, namely learning with distribution of optimized feature (DOF), to effectively recognize the characteristics of CISLs. We improve the classification performance by learning the optimized features under different distributions. Specifically, we adopt the minimum spanning tree algorithm to capture the relationship between features and discriminant ability of features for selecting the most important features. To overcome the problem of various distributions in one CISL, we propose a hierarchical learning method. First, we use an unsupervised learning method to cluster samples into groups based on their distribution. Second, in each group, we use a supervised learning method to train a model based on their categories of CISLs. Finally, we obtain multiple classification decisions from multiple trained models and use majority voting to achieve the final decision. The proposed approach has been implemented on a set of 511 samples captured from human lung CT images and achieves a classification accuracy of 91.96%. The proposed DOF method is effective and can provide a useful tool for computer-aided diagnosis of lung diseases on CT images.

  17. Using Relational Histogram Features and Action Labelled Data to Learn Preconditions for Means-End Actions

    Fichtl, Severin; Kraft, Dirk; Krüger, Norbert

    2015-01-01

    The outcome of many complex manipulation ac- tions is contingent on the spatial relationships among pairs of objects, e.g. if an object is “inside” or “on top” of another. Recognising these spatial relationships requires a vision system which can extract appropriate features from the vision input...... that capture and represent the spatial relationships in an easily accessible way. We are interested in learning to predict the success of “means end” actions that manipulate two objects at once, from exploratory actions, and the observed sensorimo- tor contingencies. In this paper, we use relational histogram...... features and illustrate their effect on learning to predict a variety of “means end” actions’ outcomes. The results show that our vision features can make the learning problem significantly easier, leading to increased learning rates and higher maximum performance. This work is in particular important...

  18. Maximum entropy methods for extracting the learned features of deep neural networks.

    Finnegan, Alex; Song, Jun S

    2017-10-01

    New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

  19. Less is More: How manipulative features affect children's learning from picture books.

    Tare, Medha; Chiong, Cynthia; Ganea, Patricia; Deloache, Judy

    2010-09-01

    Picture books are ubiquitous in young children's lives and are assumed to support children's acquisition of information about the world. Given their importance, relatively little research has directly examined children's learning from picture books. We report two studies examining children's acquisition of labels and facts from picture books that vary on two dimensions: iconicity of the pictures and presence of manipulative features (or "pop-ups"). In Study 1, 20-month-old children generalized novel labels less well when taught from a book with manipulative features than from standard picture books without such elements. In Study 2, 30- and 36-month-old children learned fewer facts when taught from a manipulative picture book with drawings than from a standard picture book with realistic images and no manipulative features. The results of the two studies indicate that children's learning from picture books is facilitated by realistic illustrations, but impeded by manipulative features.

  20. Does language help regularity learning? The influence of verbalizations on implicit sequential regularity learning and the emergence of explicit knowledge in children, younger and older adults.

    Ferdinand, Nicola K; Kray, Jutta

    2017-03-01

    This study aimed at investigating the ability to learn regularities across the life span and examine whether this learning process can be supported or hampered by verbalizations. For this purpose, children (aged 8-10 years) and younger (aged 19-30 years) and older (aged 70-80 years) adults took part in a sequence learning experiment. We found that verbalizing sequence-congruent information during learning is a powerful tool to generate explicit knowledge and it is especially helpful for younger adults. Although recent research suggests that implicit learning can be influenced by directing the participants' attention to relevant aspects of the task, verbalizations had a much weaker influence on implicit than explicit learning. Our results show that verbalizing during learning slows down reaction times (RTs) but does not influence the amount of implicit learning. Especially older adults were not able to overcome the cost of the dual-task situation. Younger adults, in contrast, show an initial dual-tasking cost that, in the case of a helpful verbalization, is overcome with practice and turns into a RT and learning benefit. However, when the verbalization is omitted this benefit is lost, that is, better implicit learning seems to be confined to situations in which the supporting verbalization is maintained. Additionally, we did not find reliable age differences in implicit learning in the no verbalization groups, which speaks in favor of age-invariant models of implicit learning across the life span. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  1. Object learning improves feature extraction but does not improve feature selection.

    Linus Holm

    Full Text Available A single glance at your crowded desk is enough to locate your favorite cup. But finding an unfamiliar object requires more effort. This superiority in recognition performance for learned objects has at least two possible sources. For familiar objects observers might: 1 select more informative image locations upon which to fixate their eyes, or 2 extract more information from a given eye fixation. To test these possibilities, we had observers localize fragmented objects embedded in dense displays of random contour fragments. Eight participants searched for objects in 600 images while their eye movements were recorded in three daily sessions. Performance improved as subjects trained with the objects: The number of fixations required to find an object decreased by 64% across the 3 sessions. An ideal observer model that included measures of fragment confusability was used to calculate the information available from a single fixation. Comparing human performance to the model suggested that across sessions information extraction at each eye fixation increased markedly, by an amount roughly equal to the extra information that would be extracted following a 100% increase in functional field of view. Selection of fixation locations, on the other hand, did not improve with practice.

  2. SVM and PCA Based Learning Feature Classification Approaches for E-Learning System

    Khamparia, Aditya; Pandey, Babita

    2018-01-01

    E-learning and online education has made great improvements in the recent past. It has shifted the teaching paradigm from conventional classroom learning to dynamic web based learning. Due to this, a dynamic learning material has been delivered to learners, instead ofstatic content, according to their skills, needs and preferences. In this…

  3. Learning Behaviours of Low-Achieving Children's Mathematics Learning in Using of Helping Tools in a Synchronous Peer-Tutoring System

    Tsuei, Mengping

    2017-01-01

    This study examined the effects of low-achieving children's use of helping tools in a synchronous mathematics peer-tutoring system on the children's mathematics learning and their learning behaviours. In a remedial class, 16 third-grade students in a remedial class engaged in peer tutoring in a face-to-face synchronous online environment during a…

  4. Jointly Feature Learning and Selection for Robust Tracking via a Gating Mechanism.

    Bineng Zhong

    Full Text Available To achieve effective visual tracking, a robust feature representation composed of two separate components (i.e., feature learning and selection for an object is one of the key issues. Typically, a common assumption used in visual tracking is that the raw video sequences are clear, while real-world data is with significant noise and irrelevant patterns. Consequently, the learned features may be not all relevant and noisy. To address this problem, we propose a novel visual tracking method via a point-wise gated convolutional deep network (CPGDN that jointly performs the feature learning and feature selection in a unified framework. The proposed method performs dynamic feature selection on raw features through a gating mechanism. Therefore, the proposed method can adaptively focus on the task-relevant patterns (i.e., a target object, while ignoring the task-irrelevant patterns (i.e., the surrounding background of a target object. Specifically, inspired by transfer learning, we firstly pre-train an object appearance model offline to learn generic image features and then transfer rich feature hierarchies from an offline pre-trained CPGDN into online tracking. In online tracking, the pre-trained CPGDN model is fine-tuned to adapt to the tracking specific objects. Finally, to alleviate the tracker drifting problem, inspired by an observation that a visual target should be an object rather than not, we combine an edge box-based object proposal method to further improve the tracking accuracy. Extensive evaluation on the widely used CVPR2013 tracking benchmark validates the robustness and effectiveness of the proposed method.

  5. Systemverilog for verification a guide to learning the testbench language features

    Spear, Chris

    2012-01-01

    Based on the highly successful second edition, this extended edition of SystemVerilog for Verification: A Guide to Learning the Testbench Language Features teaches all verification features of the SystemVerilog language, providing hundreds of examples to clearly explain the concepts and basic fundamentals. It contains materials for both the full-time verification engineer and the student learning this valuable skill. In the third edition, authors Chris Spear and Greg Tumbush start with how to verify a design, and then use that context to demonstrate the language features,  including the advantages and disadvantages of different styles, allowing readers to choose between alternatives. This textbook contains end-of-chapter exercises designed to enhance students’ understanding of the material. Other features of this revision include: New sections on static variables, print specifiers, and DPI from the 2009 IEEE language standard Descriptions of UVM features such as factories, the test registry, and the config...

  6. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

    Sun Xun

    2016-12-01

    Full Text Available In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR images using multiple-feature fusion and ensemble learning. First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images. Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results. Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results. Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.

  7. Realising Potential: Helping Homeless and Disenchanted Young People Back into Learning.

    Maxted, Peter

    This guide shows how "Foyers" (safe residences for working/learning youth) and other organizations provide routes back into learning for young people. Chapter 1, "Young People and the Current Learning Agenda," provides a summary of encouraging developments from government, ushering in new learning opportunities for young people. Chapter 2,…

  8. Can Executive Functions Help to Understand Children with Mathematical Learning Disorders and to Improve Instruction?

    Desoete, Annemie; De Weerdt, Frauke

    2013-01-01

    Working memory, inhibition and naming speed was assessed in 22 children with mathematical learning disorders (MD), 17 children with a reading learning disorder (RD), and 45 children without any learning problems between 8 and 12 years old. All subjects with learning disorders performed poorly on working memory tasks, providing evidence that they…

  9. Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

    Frédéric Li

    2018-02-01

    Full Text Available Getting a good feature representation of data is paramount for Human Activity Recognition (HAR using wearable sensors. An increasing number of feature learning approaches—in particular deep-learning based—have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM to obtain features characterising both short- and long-term time dependencies in the data.

  10. Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors.

    Li, Frédéric; Shirahama, Kimiaki; Nisar, Muhammad Adeel; Köping, Lukas; Grzegorzek, Marcin

    2018-02-24

    Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches-in particular deep-learning based-have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.

  11. The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.

    Ma, Teng; Li, Hui; Yang, Hao; Lv, Xulin; Li, Peiyang; Liu, Tiejun; Yao, Dezhong; Xu, Peng

    2017-01-01

    Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control. In the current study, we combine deep learning with compressed sensing to mine discriminative mVEP information to improve the mVEP BCI performance. The deep learning and compressed sensing approach can generate the multi-modality features which can effectively improve the BCI performance with approximately 3.5% accuracy incensement over all 11 subjects and is more effective for those subjects with relatively poor performance when using the conventional features. Compared with the conventional amplitude-based mVEP feature extraction approach, the deep learning and compressed sensing approach has a higher classification accuracy and is more effective for subjects with relatively poor performance. According to the results, the deep learning and compressed sensing approach is more effective for extracting the mVEP feature to construct the corresponding BCI system, and the proposed feature extraction framework is easy to extend to other types of BCIs, such as motor imagery (MI), steady-state visual evoked potential (SSVEP)and P300. Copyright © 2016 Elsevier B.V. All rights reserved.

  12. Abstract feature codes: The building blocks of the implicit learning system.

    Eberhardt, Katharina; Esser, Sarah; Haider, Hilde

    2017-07-01

    According to the Theory of Event Coding (TEC; Hommel, Müsseler, Aschersleben, & Prinz, 2001), action and perception are represented in a shared format in the cognitive system by means of feature codes. In implicit sequence learning research, it is still common to make a conceptual difference between independent motor and perceptual sequences. This supposedly independent learning takes place in encapsulated modules (Keele, Ivry, Mayr, Hazeltine, & Heuer 2003) that process information along single dimensions. These dimensions have remained underspecified so far. It is especially not clear whether stimulus and response characteristics are processed in separate modules. Here, we suggest that feature dimensions as they are described in the TEC should be viewed as the basic content of modules of implicit learning. This means that the modules process all stimulus and response information related to certain feature dimensions of the perceptual environment. In 3 experiments, we investigated by means of a serial reaction time task the nature of the basic units of implicit learning. As a test case, we used stimulus location sequence learning. The results show that a stimulus location sequence and a response location sequence cannot be learned without interference (Experiment 2) unless one of the sequences can be coded via an alternative, nonspatial dimension (Experiment 3). These results support the notion that spatial location is one module of the implicit learning system and, consequently, that there are no separate processing units for stimulus versus response locations. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  13. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

    Shao, Haidong; Jiang, Hongkai; Zhang, Haizhou; Duan, Wenjing; Liang, Tianchen; Wu, Shuaipeng

    2018-02-01

    The vibration signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative fault features of the collected vibration signals. In this paper, a novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing. Firstly, CS is adopted for reducing the vibration data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed deep model. The developed method is applied to analyze the experimental rolling bearing vibration signals. The results confirm that the developed method is more effective than the traditional methods.

  14. When it hurts (and helps to try: the role of effort in language learning.

    Amy S Finn

    Full Text Available Compared to children, adults are bad at learning language. This is counterintuitive; adults outperform children on most measures of cognition, especially those that involve effort (which continue to mature into early adulthood. The present study asks whether these mature effortful abilities interfere with language learning in adults and further, whether interference occurs equally for aspects of language that adults are good (word-segmentation versus bad (grammar at learning. Learners were exposed to an artificial language comprised of statistically defined words that belong to phonologically defined categories (grammar. Exposure occurred under passive or effortful conditions. Passive learners were told to listen while effortful learners were instructed to try to 1 learn the words, 2 learn the categories, or 3 learn the category-order. Effortful learners showed an advantage for learning words while passive learners showed an advantage for learning the categories. Effort can therefore hurt the learning of categories.

  15. When It Hurts (and Helps) to Try: The Role of Effort in Language Learning

    Finn, Amy S.; Lee, Taraz; Kraus, Allison; Hudson Kam, Carla L.

    2014-01-01

    Compared to children, adults are bad at learning language. This is counterintuitive; adults outperform children on most measures of cognition, especially those that involve effort (which continue to mature into early adulthood). The present study asks whether these mature effortful abilities interfere with language learning in adults and further, whether interference occurs equally for aspects of language that adults are good (word-segmentation) versus bad (grammar) at learning. Learners were exposed to an artificial language comprised of statistically defined words that belong to phonologically defined categories (grammar). Exposure occurred under passive or effortful conditions. Passive learners were told to listen while effortful learners were instructed to try to 1) learn the words, 2) learn the categories, or 3) learn the category-order. Effortful learners showed an advantage for learning words while passive learners showed an advantage for learning the categories. Effort can therefore hurt the learning of categories. PMID:25047901

  16. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

    Chuan Li

    2016-06-01

    Full Text Available Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM. The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

  17. Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

    Wang, Jim Jing-Yan

    2014-09-20

    Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.

  18. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

    Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

    2016-06-17

    Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

  19. Learning Rich Features from RGB-D Images for Object Detection and Segmentation

    Gupta, Saurabh; Girshick, Ross; Arbeláez, Pablo; Malik, Jitendra

    2014-01-01

    In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an av...

  20. Using Education Diplomacy to Help 15 Learning Champions Rethink Educational Assessment

    Anderson, Kate

    2018-01-01

    Learning assessment is essential for education systems to provide quality and equitable education. Education partners, both national and international, are supporting education systems around the world in their efforts to develop and implement holistic learning assessment strategies and mechanisms. In many cases, examining how learning is being…

  1. Learning Study: Helping Teachers to Use Theory, Develop Professionally, and Produce New Knowledge to Be Shared

    Pang, Ming Fai; Ling, Lo Mun

    2012-01-01

    The lesson study approach is a systematic process for producing professional knowledge about teaching by teachers, and has spread rapidly and extensively in the United States. The learning study approach is essentially a kind of lesson study with an explicit learning theory--the variation theory of learning. In this paper, we argue that having an…

  2. An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

    Liu, Jie; Hu, Youmin; Wang, Yan; Wu, Bo; Fan, Jikai; Hu, Zhongxu

    2018-05-01

    The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.

  3. Multi-level gene/MiRNA feature selection using deep belief nets and active learning.

    Ibrahim, Rania; Yousri, Noha A; Ismail, Mohamed A; El-Makky, Nagwa M

    2014-01-01

    Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting the most informative genes/miRNAs. Recently, deep learning has proven its ability in representing the data in multiple levels of abstraction, allowing for better discrimination between different classes. However, the idea of using deep learning for feature selection is not widely used in the bioinformatics field yet. In this paper, a novel multi-level feature selection approach named MLFS is proposed for selecting genes/miRNAs based on expression profiles. The approach is based on both deep and active learning. Moreover, an extension to use the technique for miRNAs is presented by considering the biological relation between miRNAs and genes. Experimental results show that the approach was able to outperform classical feature selection methods in hepatocellular carcinoma (HCC) by 9%, lung cancer by 6% and breast cancer by around 10% in F1-measure. Results also show the enhancement in F1-measure of our approach over recently related work in [1] and [2].

  4. Helping or hindering: the role of nurse managers in the transfer of practice development learning.

    Currie, Kay; Tolson, Debbie; Booth, Jo

    2007-09-01

    This paper reports selected findings from a recent PhD study exploring how graduates from a BSc Specialist Nursing programme, with an NMC-approved Specialist Practitioner Qualification, engage in practice development during their subsequent careers. The UKCC (1998) defines specialist practice as requiring higher levels of judgement, discretion and decision-making, with leadership in clinical practice development forming a core dimension of this level of practice. However, there is little evidence in the published literature that describes or evaluates the practice development role of graduate specialist practitioners. This study applied a modified Glaserian approach to grounded theory methods. A preliminary descriptive survey questionnaire was posted to all graduates from the programme, response rate of 45% (n=102). From these respondents, theoretical sampling decisions directed the selection of 20 participants for interview, permitting data saturation. The grounded theory generated by this study discovered a basic social process labelled 'making a difference', whereby graduate specialist practitioners are increasingly able to impact in developing patient care at a strategic level by coming to own the identity of an expert practitioner (Currie, 2006). Contextual factors strongly influence the practitioner journey, with organizational position and other people presenting enabling or blocking conditions. The line manager plays a crucial role in helping or hindering graduate specialist practitioners to transfer their learning to the clinical setting and become active in practice development. Recommendations to enhance managerial support for the practice development role of graduate specialist practitioners are proposed. ADDING TO CURRENT KNOWLEDGE: This work adds to currently limited knowledge of the graduate specialist practitioners' role in the leadership of clinical practice development. In addition, the findings emphasize the potential influence of the workplace

  5. How Academic Libraries Help Faculty Teach and Students Learn: The 2005 Colorado Academic Library Impact Study. A Closer Look

    Dickenson, Don

    2006-01-01

    This study examined academic library usage and outcomes. The objective of the study was to understand how academic libraries help students learn and assist faculty with teaching and research. From March to May 2005, nine Colorado institutions administered two online questionnaires--one to undergraduate students and another to faculty members who…

  6. Seven Affordances of Computer-Supported Collaborative Learning: How to Support Collaborative Learning? How Can Technologies Help?

    Jeong, Heisawn; Hmelo-Silver, Cindy E.

    2016-01-01

    This article proposes 7 core affordances of technology for collaborative learning based on theories of collaborative learning and CSCL (Computer-Supported Collaborative Learning) practices. Technology affords learner opportunities to (1) engage in a joint task, (2) communicate, (3) share resources, (4) engage in productive collaborative learning…

  7. I Help, Therefore, I Learn: Service Learning on Web 2.0 in an EFL Speaking Class

    Sun, Yu-Chih; Yang, Fang-Ying

    2015-01-01

    The present study integrates service learning into English as a Foreign Language (EFL) speaking class using Web 2.0 tools--YouTube and Facebook--as platforms. Fourteen undergraduate students participated in the study. The purpose of the service-learning project was to link service learning with oral communication training in an EFL speaking class…

  8. Feature Selection Methods for Zero-Shot Learning of Neural Activity

    Carlos A. Caceres

    2017-06-01

    Full Text Available Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

  9. Como Ayudar a sus Hijos a Aprender Ciencia (Helping Your Child Learn Science).

    Paulu, Nancy; Martin, Margery

    Because most parents say they do not or cannot help their children with science, this booklet was designed to help them do so, easily and with pleasure for both parent and child. The introduction presents information on why and how parents should help their children and provides a general orientation to the ideas and activities offered in the…

  10. Differential influences of achievement approach goals and intrinsic/extrinsic motivation on help-seeking in e-learning

    Yan Yang

    2013-06-01

    Full Text Available Considering the importance yet paucity of help-seeking in e-learning, the present study investigated the motivational antecedents of help-seeking among online college students. We explored and compared the influences of achievement approach goals from the old and new achievement motivation models (Elliot & McGregor, 2001; Elliot, Murayama, & Pekrun, 2011 on online students’ help-seeking through intrinsic/extrinsic motivation. Path analyses were used to test two models of help-seeking among college students from four online educational psychology classes (N = 93 based on the two models of achievement goals. Our results showed that the new 3 × 2 model was a better fit than the old 2 × 2 model, suggesting that the achievement approach goals of the new model differ from those of the old model conceptually as Elliot, Murayama, and Pekrun (2011 posited. Second, our results revealed both unexpected direct and indirect positive influence of performance- and other-approach goals on online students’ help-seeking behaviour through extrinsic motivation. Third, while mastery-approach goals indirectly predicted help-seeking through intrinsic motivation, self- and task-approach predicted help-seeking in a dramatically different manner. Self-approach goals displayed indirect influence on help-seeking through intrinsic motivation similar to mastery-approach, yet task-approach displayed a negative direct influence on help-seeking. These results suggested the potential positive impact of self-approach and the detrimental influence of task-approach goals on help-seeking in e-learning environment. Conceptual issues and pedagogical implications for online instructions are discussed.

  11. Help me if I can't: Social interaction effects in adult contextual word learning.

    Verga, Laura; Kotz, Sonja A

    2017-11-01

    A major challenge in second language acquisition is to build up new vocabulary. How is it possible to identify the meaning of a new word among several possible referents? Adult learners typically use contextual information, which reduces the number of possible referents a new word can have. Alternatively, a social partner may facilitate word learning by directing the learner's attention toward the correct new word meaning. While much is known about the role of this form of 'joint attention' in first language acquisition, little is known about its efficacy in second language acquisition. Consequently, we introduce and validate a novel visual word learning game to evaluate how joint attention affects the contextual learning of new words in a second language. Adult learners either acquired new words in a constant or variable sentence context by playing the game with a knowledgeable partner, or by playing the game alone on a computer. Results clearly show that participants who learned new words in social interaction (i) are faster in identifying a correct new word referent in variable sentence contexts, and (ii) temporally coordinate their behavior with a social partner. Testing the learned words in a post-learning recall or recognition task showed that participants, who learned interactively, better recognized words originally learned in a variable context. While this result may suggest that interactive learning facilitates the allocation of attention to a target referent, the differences in the performance during recognition and recall call for further studies investigating the effect of social interaction on learning performance. In summary, we provide first evidence on the role joint attention in second language learning. Furthermore, the new interactive learning game offers itself to further testing in complex neuroimaging research, where the lack of appropriate experimental set-ups has so far limited the investigation of the neural basis of adult word learning in

  12. Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

    Qingshan Liu

    2017-12-01

    Full Text Available This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM network to automatically learn the spectral-spatial features from hyperspectral images (HSIs. In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN, a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center. The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN.

  13. Simultaneous and Sequential Feature Negative Discriminations: Elemental Learning and Occasion Setting in Human Pavlovian Conditioning

    Baeyens, Frank; Vervliet, Bram; Vansteenwegen, Debora; Beckers, Tom; Hermans, Dirk; Eelen, Paul

    2004-01-01

    Using a conditioned suppression task, we investigated simultaneous (XA-/A+) vs. sequential (X [right arrow] A-/A+) Feature Negative (FN) discrimination learning in humans. We expected the simultaneous discrimination to result in X (or alternatively the XA configuration) becoming an inhibitor acting directly on the US, and the sequential…

  14. A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features

    Sturm, Bob L.; Kereliuk, Corey; Pikrakis, Aggelos

    2014-01-01

    Systems built using deep learning neural networks trained on low-level spectral periodicity features (DeSPerF) reproduced the most “ground truth” of the systems submitted to the MIREX 2013 task, “Audio Latin Genre Classification.” To answer why this was the case, we take a closer look...

  15. On equivalent parameter learning in simplified feature space based on Bayesian asymptotic analysis.

    Yamazaki, Keisuke

    2012-07-01

    Parametric models for sequential data, such as hidden Markov models, stochastic context-free grammars, and linear dynamical systems, are widely used in time-series analysis and structural data analysis. Computation of the likelihood function is one of primary considerations in many learning methods. Iterative calculation of the likelihood such as the model selection is still time-consuming though there are effective algorithms based on dynamic programming. The present paper studies parameter learning in a simplified feature space to reduce the computational cost. Simplifying data is a common technique seen in feature selection and dimension reduction though an oversimplified space causes adverse learning results. Therefore, we mathematically investigate a condition of the feature map to have an asymptotically equivalent convergence point of estimated parameters, referred to as the vicarious map. As a demonstration to find vicarious maps, we consider the feature space, which limits the length of data, and derive a necessary length for parameter learning in hidden Markov models. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. Learning effective color features for content based image retrieval in dermatology

    Bunte, Kerstin; Biehl, Michael; Jonkman, Marcel F.; Petkov, Nicolai

    We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn

  17. SPECIAL FEATURES OF VIRTUAL PRACTICE INTERACTIVE MEDIA DISCIPLINES FOR DISTANCE LEARNING

    M.P. Mazur

    2010-08-01

    Full Text Available The features of the development of interactive virtual practical training courses for distance learning are examined in the article. The authors propose their own methods of development tools such disciplines as virtual simulation or video-on labs.

  18. Learning Motion Features for Example-Based Finger Motion Estimation for Virtual Characters

    Mousas, Christos; Anagnostopoulos, Christos-Nikolaos

    2017-09-01

    This paper presents a methodology for estimating the motion of a character's fingers based on the use of motion features provided by a virtual character's hand. In the presented methodology, firstly, the motion data is segmented into discrete phases. Then, a number of motion features are computed for each motion segment of a character's hand. The motion features are pre-processed using restricted Boltzmann machines, and by using the different variations of semantically similar finger gestures in a support vector machine learning mechanism, the optimal weights for each feature assigned to a metric are computed. The advantages of the presented methodology in comparison to previous solutions are the following: First, we automate the computation of optimal weights that are assigned to each motion feature counted in our metric. Second, the presented methodology achieves an increase (about 17%) in correctly estimated finger gestures in comparison to a previous method.

  19. Linguistic labels, dynamic visual features, and attention in infant category learning.

    Deng, Wei Sophia; Sloutsky, Vladimir M

    2015-06-01

    How do words affect categorization? According to some accounts, even early in development words are category markers and are different from other features. According to other accounts, early in development words are part of the input and are akin to other features. The current study addressed this issue by examining the role of words and dynamic visual features in category learning in 8- to 12-month-old infants. Infants were familiarized with exemplars from one category in a label-defined or motion-defined condition and then tested with prototypes from the studied category and from a novel contrast category. Eye-tracking results indicated that infants exhibited better category learning in the motion-defined condition than in the label-defined condition, and their attention was more distributed among different features when there was a dynamic visual feature compared with the label-defined condition. These results provide little evidence for the idea that linguistic labels are category markers that facilitate category learning. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Feature selection and multi-kernel learning for sparse representation on a manifold

    Wang, Jim Jing-Yan

    2014-03-01

    Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. © 2013 Elsevier Ltd.

  1. Feature selection and multi-kernel learning for sparse representation on a manifold.

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2014-03-01

    Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. Feature extraction and learning using context cue and Rényi entropy based mutual information

    Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

    2015-01-01

    information. In particular, for feature extraction, we develop a new set of kernel descriptors−Context Kernel Descriptors (CKD), which enhance the original KDES by embedding the spatial context into the descriptors. Context cues contained in the context kernel enforce some degree of spatial consistency, thus...... improving the robustness of CKD. For feature learning and reduction, we propose a novel codebook learning method, based on a Rényi quadratic entropy based mutual information measure called Cauchy-Schwarz Quadratic Mutual Information (CSQMI), to learn a compact and discriminative CKD codebook. Projecting...... as the information about the underlying labels of the CKD using CSQMI. Thus the resulting codebook and reduced CKD are discriminative. We verify the effectiveness of our method on several public image benchmark datasets such as YaleB, Caltech-101 and CIFAR-10, as well as a challenging chicken feet dataset of our own...

  3. How do postgraduate GP trainees regulate their learning and what helps and hinders them? A qualitative study.

    Sagasser, Margaretha H; Kramer, Anneke W M; van der Vleuten, Cees P M

    2012-08-06

    Self-regulation is essential for professional development. It involves monitoring of performance, identifying domains for improvement, undertaking learning activities, applying newly learned knowledge and skills and self-assessing performance. Since self-assessment alone is ineffective in identifying weaknesses, learners should seek external feedback too. Externally regulated educational interventions, like reflection, learning portfolios, assessments and progress meetings, are increasingly used to scaffold self-regulation.The aim of this study is to explore how postgraduate trainees regulate their learning in the workplace, how external regulation promotes self-regulation and which elements facilitate or impede self-regulation and learning. In a qualitative study with a phenomenologic approach we interviewed first- and third-year GP trainees from two universities in the Netherlands. Twenty-one verbatim transcripts were coded. Through iterative discussion the researchers agreed on the interpretation of the data and saturation was reached. Trainees used a short and a long self-regulation loop. The short loop took one week at most and was focused on problems that were easy to resolve and needed minor learning activities. The long loop was focused on complex or recurring problems needing multiple and planned longitudinal learning activities. External assessments and formal training affected the long but not the short loop. The supervisor had a facilitating role in both loops. Self-confidence was used to gauge competence.Elements influencing self-regulation were classified into three dimensions: personal (strong motivation to become a good doctor), interpersonal (stimulation from others) and contextual (organizational and educational features). Trainees did purposefully self-regulate their learning. Learning in the short loop may not be visible to others. Trainees should be encouraged to actively seek and use external feedback in both loops. An important question for

  4. How do postgraduate GP trainees regulate their learning and what helps and hinders them? A qualitative study

    2012-01-01

    Background Self-regulation is essential for professional development. It involves monitoring of performance, identifying domains for improvement, undertaking learning activities, applying newly learned knowledge and skills and self-assessing performance. Since self-assessment alone is ineffective in identifying weaknesses, learners should seek external feedback too. Externally regulated educational interventions, like reflection, learning portfolios, assessments and progress meetings, are increasingly used to scaffold self-regulation. The aim of this study is to explore how postgraduate trainees regulate their learning in the workplace, how external regulation promotes self-regulation and which elements facilitate or impede self-regulation and learning. Methods In a qualitative study with a phenomenologic approach we interviewed first- and third-year GP trainees from two universities in the Netherlands. Twenty-one verbatim transcripts were coded. Through iterative discussion the researchers agreed on the interpretation of the data and saturation was reached. Results Trainees used a short and a long self-regulation loop. The short loop took one week at most and was focused on problems that were easy to resolve and needed minor learning activities. The long loop was focused on complex or recurring problems needing multiple and planned longitudinal learning activities. External assessments and formal training affected the long but not the short loop. The supervisor had a facilitating role in both loops. Self-confidence was used to gauge competence.Elements influencing self-regulation were classified into three dimensions: personal (strong motivation to become a good doctor), interpersonal (stimulation from others) and contextual (organizational and educational features). Conclusions Trainees did purposefully self-regulate their learning. Learning in the short loop may not be visible to others. Trainees should be encouraged to actively seek and use external feedback in

  5. Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification

    Sarkar, Achintya Kumar; Tan, Zheng-Hua

    2017-01-01

    In this paper, we present a time-contrastive learning (TCL) based bottleneck (BN) feature extraction method for speech signals with an application to text-dependent (TD) speaker verification (SV). It is well-known that speech signals exhibit quasi-stationary behavior in and only in a short interval......, and the TCL method aims to exploit this temporal structure. More specifically, it trains deep neural networks (DNNs) to discriminate temporal events obtained by uniformly segmenting speech signals, in contrast to existing DNN based BN feature extraction methods that train DNNs using labeled data...... to discriminate speakers or pass-phrases or phones or a combination of them. In the context of speaker verification, speech data of fixed pass-phrases are used for TCL-BN training, while the pass-phrases used for TCL-BN training are excluded from being used for SV, so that the learned features can be considered...

  6. Joint Concept Correlation and Feature-Concept Relevance Learning for Multilabel Classification.

    Zhao, Xiaowei; Ma, Zhigang; Li, Zhi; Li, Zhihui

    2018-02-01

    In recent years, multilabel classification has attracted significant attention in multimedia annotation. However, most of the multilabel classification methods focus only on the inherent correlations existing among multiple labels and concepts and ignore the relevance between features and the target concepts. To obtain more robust multilabel classification results, we propose a new multilabel classification method aiming to capture the correlations among multiple concepts by leveraging hypergraph that is proved to be beneficial for relational learning. Moreover, we consider mining feature-concept relevance, which is often overlooked by many multilabel learning algorithms. To better show the feature-concept relevance, we impose a sparsity constraint on the proposed method. We compare the proposed method with several other multilabel classification methods and evaluate the classification performance by mean average precision on several data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.

  7. Embedded Library Guides in Learning Management Systems Help Students Get Started on Research Assignments

    Dominique Daniel

    2016-03-01

    Full Text Available Objective – To determine whether library guides embedded in learning management systems (LMS get used by students, and to identify best practices for the creation and promotion of these guides by librarians. Design – Mixed methods combining quantitative and qualitative data collection and analysis (survey, interviews, and statistical analysis. Setting – A large public university in the United States of America. Subjects – 100 undergraduate students and 14 librarians. Methods – The researchers surveyed undergraduate students who were participating in a Project Information Literacy study about their use of library guides in the learning management system (LMS for a given quarter. At that university, all course pages in the LMS are automatically assigned a library guide. In addition, web usage data about the course-embedded guides was analyzed and high use guides were identified, namely guides that received an average of at least two visits per student enrolled in a course. The researchers also conducted a qualitative analysis of the layout of the high use guides, including the number of widgets (or boxes and links. Finally, librarians who created high use library guides were interviewed. These mixed methods were designed to address four research questions: 1 Were students finding the guides in the LMS, and did they find the guides useful? 2 Did high use guides differ in design and composition? 3 Were the guides designed for a specific course, or for an entire department or college? and, 4 How did the librarians promote use? Main Results – Only 33% of the students said they noticed the library guide in the LMS course page, and 21% reported using the guide. Among those who used the guide, the majority were freshmen (possibly because embedding of library guides in the LMS had just started at the university. Library guides with high use in relation to class enrollment did not significantly differ from low use guides in terms of numbers of

  8. Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

    Ramesh Kumar Lama

    2017-01-01

    Full Text Available Alzheimer’s disease (AD is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR images to discriminate AD, mild cognitive impairment (MCI, and healthy control (HC subjects using a support vector machine (SVM, an import vector machine (IVM, and a regularized extreme learning machine (RELM. The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

  9. Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

    Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

    2015-04-01

    With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  10. Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning

    Li, Songfeng; Wei, Jun; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir M.; Samala, Ravi K.

    2018-01-01

    Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input ‘for processing’ DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice’s coefficient (DC) of 0.79  ±  0.13 and Pearson’s correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as

  11. Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

    Li, Songfeng; Wei, Jun; Chan, Heang-Ping; Helvie, Mark A; Roubidoux, Marilyn A; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir M; Samala, Ravi K

    2018-01-09

    Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input 'for processing' DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79  ±  0.13 and Pearson's correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as

  12. Reinforcement learning on slow features of high-dimensional input streams.

    Robert Legenstein

    Full Text Available Humans and animals are able to learn complex behaviors based on a massive stream of sensory information from different modalities. Early animal studies have identified learning mechanisms that are based on reward and punishment such that animals tend to avoid actions that lead to punishment whereas rewarded actions are reinforced. However, most algorithms for reward-based learning are only applicable if the dimensionality of the state-space is sufficiently small or its structure is sufficiently simple. Therefore, the question arises how the problem of learning on high-dimensional data is solved in the brain. In this article, we propose a biologically plausible generic two-stage learning system that can directly be applied to raw high-dimensional input streams. The system is composed of a hierarchical slow feature analysis (SFA network for preprocessing and a simple neural network on top that is trained based on rewards. We demonstrate by computer simulations that this generic architecture is able to learn quite demanding reinforcement learning tasks on high-dimensional visual input streams in a time that is comparable to the time needed when an explicit highly informative low-dimensional state-space representation is given instead of the high-dimensional visual input. The learning speed of the proposed architecture in a task similar to the Morris water maze task is comparable to that found in experimental studies with rats. This study thus supports the hypothesis that slowness learning is one important unsupervised learning principle utilized in the brain to form efficient state representations for behavioral learning.

  13. Automatic plankton image classification combining multiple view features via multiple kernel learning.

    Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

    2017-12-28

    Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

  14. Students' Reactions to Undergraduate Science. Higher Education Learning Project (h.e.l.p.) - Physics.

    Ogborn, Jon, Ed.; And Others

    The transcripts of interviews with 115 physics students from ten different British universities are analyzed. Each student was encouraged to tell about one good learning experience and one bad learning experience. The characteristics of the good and bad stories are discussed and some general comments are made. The interview model explained in this…

  15. How Dispositional Learning Analytics helps understanding the worked-example principle

    Tempelaar, Dirk; Sampson, Demetrios G.; Spector, J. Michael; Ifenthaler, Dirk; Isaías, Pedro

    2017-01-01

    This empirical study aims to demonstrate how Dispositional Learning Analytics can contribute in the investigation of the effectiveness of didactical scenarios in authentic settings, where previous research has mostly been laboratory based. Using a showcase based on learning processes of 1080

  16. Do Peer Tutors Help Teach ESL Students to Learn English as a Second Language More Successfully?

    Lyttle, LeighAnne

    2011-01-01

    This research study tries to understand the information processing model and social learning theory in regards to teaching English as a Second Language (ESL) to Spanish speakers by using peer teaching methods. This study will examine each theory's concepts and frameworks to better comprehend what teaching methods support English language learning.…

  17. Finding the Right Fit: Helping Students Apply Theory to Service-Learning Contexts

    Ricke, Audrey

    2018-01-01

    Background: Although past studies of service-learning focus on assessing student growth, few studies address how to support students in applying theory to their service-learning experiences. Yet, the task of applying theory is a central component of critical reflections within the social sciences in higher education and often causes anxiety among…

  18. Looking Forward: Games, Rhymes and Exercises To Help Children Develop Their Learning Abilities.

    von Heider, Molly

    The range of games, rhymes, songs, and exercises for children collected in this book are based on Rudolf Steiner's educational philosophy and are designed to lay the foundation for sound later learning. The book's chapters are: (1) "Learning Aids"; (2) "The Early Years"; (3) "Foot Exercises: Kindergarten or Class I, 5-7…

  19. Help&Learn: A peer-to-peer architecture to support knowledge management in collaborative learning communities

    Guizzardi-Silva Souza, R.; Aroyo, L.M.; Wagner, G.

    Collaborative learning motivates active participation of individuals in their learning process, which often results in the attaining of creative and critical thinking skills. This way, students and teachers are viewed as both providers and consumers of knowledge gathered in environments where

  20. Can Task-based Learning Approach Help Attract Students with Diverse Backgrounds Learn Chinese at A Danish University?

    Ruan, Youjin; Duan, Xiaoju; Wang, Li

    2013-01-01

    Task-based method is regarded as a meaningful approach for promoting interaction and collaboration in language learning. In an elective Chinese language beginner course at Aalborg University, Denmark, a selection of tasks are designed and used to attract the students’ interests in learning a new...... and study programs showed good interests in this method and the course itself. Nevertheless, it is necessary to study the concrete effect of various types of tasks to maximize the learning outcome....... foreign language. Chinese culture elements are also integrated into the tasks and the learning process. By analyzing seven items of a post-course survey, this paper investigates the learners’ opinions towards the Task-based language teaching and learning method and toward the method of integrating culture...

  1. Less is More: How manipulative features affect children’s learning from picture books

    Tare, Medha; Chiong, Cynthia; Ganea, Patricia; DeLoache, Judy

    2010-01-01

    Picture books are ubiquitous in young children’s lives and are assumed to support children’s acquisition of information about the world. Given their importance, relatively little research has directly examined children’s learning from picture books. We report two studies examining children’s acquisition of labels and facts from picture books that vary on two dimensions: iconicity of the pictures and presence of manipulative features (or “pop-ups”). In Study 1, 20-month-old children generalized novel labels less well when taught from a book with manipulative features than from standard picture books without such elements. In Study 2, 30- and 36-month-old children learned fewer facts when taught from a manipulative picture book with drawings than from a standard picture book with realistic images and no manipulative features. The results of the two studies indicate that children’s learning from picture books is facilitated by realistic illustrations, but impeded by manipulative features. PMID:20948970

  2. Sparse feature learning for instrument identification: Effects of sampling and pooling methods.

    Han, Yoonchang; Lee, Subin; Nam, Juhan; Lee, Kyogu

    2016-05-01

    Feature learning for music applications has recently received considerable attention from many researchers. This paper reports on the sparse feature learning algorithm for musical instrument identification, and in particular, focuses on the effects of the frame sampling techniques for dictionary learning and the pooling methods for feature aggregation. To this end, two frame sampling techniques are examined that are fixed and proportional random sampling. Furthermore, the effect of using onset frame was analyzed for both of proposed sampling methods. Regarding summarization of the feature activation, a standard deviation pooling method is used and compared with the commonly used max- and average-pooling techniques. Using more than 47 000 recordings of 24 instruments from various performers, playing styles, and dynamics, a number of tuning parameters are experimented including the analysis frame size, the dictionary size, and the type of frequency scaling as well as the different sampling and pooling methods. The results show that the combination of proportional sampling and standard deviation pooling achieve the best overall performance of 95.62% while the optimal parameter set varies among the instrument classes.

  3. Learning object location predictors with boosting and grammar-guided feature extraction

    Eads, Damian Ryan [Los Alamos National Laboratory; Rosten, Edward [UNIV OF CAMBRIDGE; Helmbold, David [UC/SANTA CRUZ

    2009-01-01

    The authors present BEAMER: a new spatially exploitative approach to learning object detectors which shows excellent results when applied to the task of detecting objects in greyscale aerial imagery in the presence of ambiguous and noisy data. There are four main contributions used to produce these results. First, they introduce a grammar-guided feature extraction system, enabling the exploration of a richer feature space while constraining the features to a useful subset. This is specified with a rule-based generative grammer crafted by a human expert. Second, they learn a classifier on this data using a newly proposed variant of AdaBoost which takes into account the spatially correlated nature of the data. Third, they perform another round of training to optimize the method of converting the pixel classifications generated by boosting into a high quality set of (x,y) locations. lastly, they carefully define three common problems in object detection and define two evaluation criteria that are tightly matched to these problems. Major strengths of this approach are: (1) a way of randomly searching a broad feature space, (2) its performance when evaluated on well-matched evaluation criteria, and (3) its use of the location prediction domain to learn object detectors as well as to generate detections that perform well on several tasks: object counting, tracking, and target detection. They demonstrate the efficacy of BEAMER with a comprehensive experimental evaluation on a challenging data set.

  4. Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

    Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

    2017-12-01

    Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

  5. When Do Pictures Help Learning from Expository Text? Multimedia and Modality Effects in Primary Schools

    Herrlinger, Simone; Höffler, Tim N.; Opfermann, Maria; Leutner, Detlev

    2017-06-01

    Adding pictures to a text is very common in today's education and might be especially beneficial for elementary school children, whose abilities to read and understand pure text have not yet been fully developed. Our study examined whether adding pictures supports learning of a biology text in fourth grade and whether the text modality (spoken or written) plays a role. Results indicate that overall, pictures enhanced learning but that the text should be spoken rather than written. These results are in line with instructional design principles derived from common multimedia learning theories. In addition, for elementary school children, it might be advisable to read texts out to the children. Reading by themselves and looking at pictures might overload children's cognitive capacities and especially their visual channel. In this case, text and pictures would not be integrated into one coherent mental model, and effective learning would not take place.

  6. Can a multimedia tool help students' learning performance in complex biology subjects?

    Pinar Koseoglu

    2015-11-01

    Full Text Available The aim of the present study was to determine the effects of multimedia-based biology teaching (Mbio and teacher-centered biology (TCbio instruction approaches on learners' biology achievements, as well as their views towards learning approaches. During the research process, an experimental design with two groups, TCbio (n = 22 and Mbio (n = 26, were used. The results of the study proved that the Mbio approach was more effective than the TCbio approach with regard to supporting meaningful learning, academic achievement, enjoyment and motivation. Moreover, the TCbio approach is ineffective in terms of time management, engaging attention, and the need for repetition of subjects. Additionally, the results were discussed in terms of teaching, learning, multimedia design as well as biology teaching/learning.

  7. When increasing distraction helps learning: Distractor number and content interact in their effects on memory.

    Nussenbaum, Kate; Amso, Dima; Markant, Julie

    2017-11-01

    Previous work has demonstrated that increasing the number of distractors in a search array can reduce interference from distractor content during target processing. However, it is unclear how this reduced interference influences learning of target information. Here, we investigated how varying the amount and content of distraction present in a learning environment affects visual search and subsequent memory for target items. In two experiments, we demonstrate that the number and content of competing distractors interact in their influence on target selection and memory. Specifically, while increasing the number of distractors present in a search array made target detection more effortful, it did not impair learning and memory for target content. Instead, when the distractors contained category information that conflicted with the target, increasing the number of distractors from one to three actually benefitted learning and memory. These data suggest that increasing numbers of distractors may reduce interference from conflicting conceptual information during encoding.

  8. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

    Sun, Wenqing; Zheng, Bin; Qian, Wei

    2017-10-01

    This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

  9. The things we learned on Liberty Island: designing games to help people become competent game players

    Pelletier, Caroline

    2005-01-01

    Although interest in the use of games to support education is growing (e.g. Dawes & Dumbleton, 2001; McFarlane et al, 2002; Mitchell & Savill-Smith, 2004), there is relatively little research into how people learn to play games. This is surprising, since VanDeventer and White (2002) have demonstrated that game players demonstrate characteristics of expert behaviour and Gee (2003) argues that highly successful implicit theories of learning are embedded in well-designed games. In spite ...

  10. Helping HSE Team in Learning from Accident by Using the Management Oversight and Risk Tree Analysis Method

    Iraj Mohammadfam

    2016-09-01

    Conclusion: The analysis using MORT method helped the organization with learning lessons from the accident especially at the management level. In order to prevent the similar and dissimilar accidents, the inappropriate informational network within the organization, inappropriate operational readiness, lack of proper implementation of work permit, the inappropriate and lack of updated technical information systems regarding equipments and working process, and the inappropriate barriers should be considered in a special way.

  11. HOW CAN DYNAMIC RIGID-BODY MODELING BE HELPFUL IN MOTOR LEARNING? - DIAGNOSING PERFORMANCE USING DYNAMIC MODELING

    Shan, Gongbing; Sust, Martin; Simard, Stephane; Bohn, Christina; Nicol, Klaus

    2004-01-01

    There are two main problems for biomechanists in motor learning practice. One is theory vs. experience, the other is the determination of dominative information directly helpful in the practice. This project aimed at addressing these problems from a quantitative aspect by using motion capture and biomechanical rigid body modeling. The purposes were to identify differences in the description of movements amongst motion analysists (external view), athletes (internal sight) and coaches (internal...

  12. Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

    Sadeghi, Zahra; Testolin, Alberto

    2017-08-01

    In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

  13. Video-recorded simulated patient interactions: can they help develop clinical and communication skills in today's learning environment?

    Seif, Gretchen A; Brown, Debora

    2013-01-01

    It is difficult to provide real-world learning experiences for students to master clinical and communication skills. The purpose of this paper is to describe a novel instructional method using self- and peer-assessment, reflection, and technology to help students develop effective interpersonal and clinical skills. The teaching method is described by the constructivist learning theory and incorporates the use of educational technology. The learning activities were incorporated into the pre-clinical didactic curriculum. The students participated in two video-recording assignments and performed self-assessments on each and had a peer-assessment on the second video-recording. The learning activity was evaluated through the self- and peer-assessments and an instructor-designed survey. This evaluation identified several themes related to the assignment, student performance, clinical behaviors and establishing rapport. Overall the students perceived that the learning activities assisted in the development of clinical and communication skills prior to direct patient care. The use of video recordings of a simulated history and examination is a unique learning activity for preclinical PT students in the development of clinical and communication skills.

  14. A recommendation module to help teachers build courses through the Moodle Learning Management System

    Limongelli, Carla; Lombardi, Matteo; Marani, Alessandro; Sciarrone, Filippo; Temperini, Marco

    2016-01-01

    In traditional e-learning, teachers design sets of Learning Objects (LOs) and organize their sequencing; the material implementing the LOs could be either built anew or adopted from elsewhere (e.g. from standard-compliant repositories) and reused. This task is applicable also when the teacher works in a system for personalized e-learning. In this case, the burden actually increases: for instance, the LOs may need adaptation to the system, through additional metadata. This paper presents a module that gives some support to the operations of retrieving, analyzing, and importing LOs from a set of standard Learning Objects Repositories, acting as a recommending system. In particular, it is designed to support the teacher in the phases of (i) retrieval of LOs, through a keyword-based search mechanism applied to the selected repositories; (ii) analysis of the returned LOs, whose information is enriched by a concept of relevance metric, based on both the results of the searching operation and the data related to the previous use of the LOs in the courses managed by the Learning Management System; and (iii) LO importation into the course under construction.

  15. Learning representative features for facial images based on a modified principal component analysis

    Averkin, Anton; Potapov, Alexey

    2013-05-01

    The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

  16. Feature weighting using particle swarm optimization for learning vector quantization classifier

    Dongoran, A.; Rahmadani, S.; Zarlis, M.; Zakarias

    2018-03-01

    This paper discusses and proposes a method of feature weighting in classification assignments on competitive learning artificial neural network LVQ. The weighting feature method is the search for the weight of an attribute using the PSO so as to give effect to the resulting output. This method is then applied to the LVQ-Classifier and tested on the 3 datasets obtained from the UCI Machine Learning repository. Then an accuracy analysis will be generated by two approaches. The first approach using LVQ1, referred to as LVQ-Classifier and the second approach referred to as PSOFW-LVQ, is a proposed model. The result shows that the PSO algorithm is capable of finding attribute weights that increase LVQ-classifier accuracy.

  17. The Pedagogical, Linguistic, and Content Features of Popular English Language Learning Websites in China: A Framework for Analysis and Design

    Kettle, Margaret; Yuan, Yifeng; Luke, Allan; Ewing, Robyn; Shen, Huizhong

    2012-01-01

    As increasing numbers of Chinese language learners choose to learn English online, there is a need to investigate popular websites and their language learning designs. This paper reports on the first stage of a study that analyzed the pedagogical, linguistic, and content features of 25 Chinese English Language Learning (ELL) websites ranked…

  18. F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation

    Wu, Xiaohe; Zuo, Wangmeng; Zhu, Yuanyuan; Lin, Liang

    2015-01-01

    The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignores the minimization of the radius. Several approaches have been proposed to integrate radius and margin for joint learning of feature transformation and SVM classifier. However, most of them either require the form of the transformation matrix to be diagonal, or are non-convex and computationally expensive. In this ...

  19. Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer

    Wang, Jing; Wu, Chen-Jiang; Zhang, Jing; Wang, Xiao-Ning; Zhang, Yu-Dong; Bao, Mei-Ling

    2017-01-01

    To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis. For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]). Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa. (orig.)

  20. Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer

    Wang, Jing [CFDA, Center for Medical Device Evaluation, Beijing (China); Wu, Chen-Jiang; Zhang, Jing; Wang, Xiao-Ning; Zhang, Yu-Dong [First Affiliated Hospital with Nanjing Medical University, Department of Radiology, Nanjing, Jiangsu Province (China); Bao, Mei-Ling [First Affiliated Hospital with Nanjing Medical University, Department of Pathology, Nanjing (China)

    2017-10-15

    To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis. For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]). Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa. (orig.)

  1. Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

    Guangjun Zhao

    2016-01-01

    Full Text Available Cryosection brain images in Chinese Visible Human (CVH dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel. Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.

  2. Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification

    Wang, Jim Jing-Yan

    2013-12-01

    Automated classification of tissue types of Region of Interest (ROI) in medical images has been an important application in Computer-Aided Diagnosis (CAD). Recently, bag-of-feature methods which treat each ROI as a set of local features have shown their power in this field. Two important issues of bag-of-feature strategy for tissue classification are investigated in this paper: the visual vocabulary learning and weighting, which are always considered independently in traditional methods by neglecting the inner relationship between the visual words and their weights. To overcome this problem, we develop a novel algorithm, Joint-ViVo, which learns the vocabulary and visual word weights jointly. A unified objective function based on large margin is defined for learning of both visual vocabulary and visual word weights, and optimized alternately in the iterative algorithm. We test our algorithm on three tissue classification tasks: classifying breast tissue density in mammograms, classifying lung tissue in High-Resolution Computed Tomography (HRCT) images, and identifying brain tissue type in Magnetic Resonance Imaging (MRI). The results show that Joint-ViVo outperforms the state-of-art methods on tissue classification problems. © 2013 Elsevier Ltd.

  3. Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

    Bouboulis, Pantelis; Chouvardas, Symeon; Theodoridis, Sergios

    2018-04-01

    We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources, in a distributed setting. In contrast, the proposed method approximates the solution as a fixed-size vector (of larger dimension than the input space) using Random Fourier Features. This paves the way to use standard linear combine-then-adapt techniques. To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented. Conditions for asymptotic convergence and boundness of the networkwise regret are also provided. The simulated tests illustrate the performance of the proposed scheme.

  4. Tech-Assisted Language Learning Tasks in an EFL Setting: Use of Hand phone Recording Feature

    Alireza Shakarami

    2014-09-01

    Full Text Available Technology with its speedy great leaps forward has undeniable impact on every aspect of our life in the new millennium. It has supplied us with different affordances almost daily or more precisely in a matter of hours. Technology and Computer seems to be a break through as for their roles in the Twenty-First century educational system. Examples are numerous, among which CALL, CMC, and Virtual learning spaces come to mind instantly. Amongst the newly developed gadgets of today are the sophisticated smart Hand phones which are far more ahead of a communication tool once designed for. Development of Hand phone as a wide-spread multi-tasking gadget has urged researchers to investigate its effect on every aspect of learning process including language learning. This study attempts to explore the effects of using cell phone audio recording feature, by Iranian EFL learners, on the development of their speaking skills. Thirty-five sophomore students were enrolled in a pre-posttest designed study. Data on their English speaking experience using audio–recording features of their Hand phones were collected. At the end of the semester, the performance of both groups, treatment and control, were observed, evaluated, and analyzed; thereafter procured qualitatively at the next phase. The quantitative outcome lent support to integrating Hand phones as part of the language learning curriculum. Keywords:

  5. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

    Patel, Meenal J; Andreescu, Carmen; Price, Julie C; Edelman, Kathryn L; Reynolds, Charles F; Aizenstein, Howard J

    2015-10-01

    Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features. Late-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models. A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity. Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures-rather than region-based differences-are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps toward personalized late-life depression treatment. Copyright © 2015 John Wiley

  6. Can a Multimedia Tool Help Students' Learning Performance in Complex Biology Subjects?

    Koseoglu, Pinar; Efendioglu, Akin

    2015-01-01

    The aim of the present study was to determine the effects of multimedia-based biology teaching (Mbio) and teacher-centered biology (TCbio) instruction approaches on learners' biology achievements, as well as their views towards learning approaches. During the research process, an experimental design with two groups, TCbio (n = 22) and Mbio (n =…

  7. Does ERP Hands-On Experience Help Students Learning Business Process Concepts?

    Rienzo, Thomas; Han, Bernard

    2011-01-01

    Over the past decade, more and more business schools are attempting to teach business processes (BPs) by using enterprise resource planning (ERP) software in their curricula. Currently, most studies involving ERP software in the academy have concentrated on learning and teaching via self-assessment surveys or curriculum integration. This research…

  8. Participation and Collaborative Learning in Large Class Sizes: Wiki, Can You Help Me?

    de Arriba, Raúl

    2017-01-01

    Collaborative learning has a long tradition within higher education. However, its application in classes with a large number of students is complicated, since it is a teaching method that requires a high level of participation from the students and careful monitoring of the process by the educator. This article presents an experience in…

  9. Does Spatial Ability Help the Learning of Anatomy in a Biomedical Science Course?

    Sweeney, Kevin; Hayes, Jennifer A.; Chiavaroli, Neville

    2014-01-01

    A three-dimensional appreciation of the human body is the cornerstone of clinical anatomy. Spatial ability has previously been found to be associated with students' ability to learn anatomy and their examination performance. The teaching of anatomy has been the subject of major change over the last two decades with the reduction in time spent…

  10. Finding Moments of Meaning in Undergraduate Education: How the Scholarship of Teaching and Learning Can Help

    Bernstein, Jeffrey L.

    2018-01-01

    I argue for the value of high-impact educational practices as tools to minimize the commoditization of higher education. As a vehicle for doing so, I discuss a travel course to Washington, D.C., that which I have led. This course is a significant and meaningful learning experience for the students who participate. In reflecting upon the value of…

  11. Deep Learning Questions Can Help Selection of High Ability Candidates for Universities

    Mellanby, Jane; Cortina-Borja, Mario; Stein, John

    2009-01-01

    Selection of students for places at universities mainly depends on GCSE grades and predictions of A-level grades, both of which tend to favour applicants from independent schools. We have therefore developed a new type of test that would measure candidates' "deep learning" approach since this assesses the motivation and creative thinking…

  12. Enhancing Undergraduate Chemistry Learning by Helping Students Make Connections among Multiple Graphical Representations

    Rau, Martina A.

    2015-01-01

    Multiple representations are ubiquitous in chemistry education. To benefit from multiple representations, students have to make connections between them. However, connection making is a difficult task for students. Prior research shows that supporting connection making enhances students' learning in math and science domains. Most prior research…

  13. Giving Learning a Helping Hand: Finger Tracing of Temperature Graphs on an iPad

    Agostinho, Shirley; Tindall-Ford, Sharon; Ginns, Paul; Howard, Steven J.; Leahy, Wayne; Paas, Fred

    2015-01-01

    Gesturally controlled information and communication technologies, such as tablet devices, are becoming increasingly popular tools for teaching and learning. Based on the theoretical frameworks of cognitive load and embodied cognition, this study investigated the impact of explicit instructions to trace out elements of tablet-based worked examples…

  14. Helping Children Learn Mathematics through Multiple Intelligences and Standards for School Mathematics.

    Adams, Thomasenia Lott

    2001-01-01

    Focuses on the National Council of Teachers of Mathematics 2000 process-oriented standards of problem solving, reasoning and proof, communication, connections, and representation as providing a framework for using the multiple intelligences that children bring to mathematics learning. Presents ideas for mathematics lessons and activities to…

  15. Did "The Beaver" Question My Authority? Helping Children Learn about Respect

    Meidl, Christopher; Meidl, Tynisha

    2009-01-01

    In trying to make sense of how to navigate the duality of approaches to how children learn respect toward others--the "takes a village" community-oriented approach (that includes teachers) or the "I know my child best/go it alone" family autonomy approach--teachers need to understand that families are trying to navigate "parenting" their children…

  16. Helping Teams Work: Lessons Learned from the University of Arizona Library Reorganization.

    Diaz, Joseph R.; Pintozzi, Chestalene

    1999-01-01

    Describes library reorganization at the University of Arizona resulting from fiscal challenges and the need for current technology. Highlights include: the restructuring process and customer focus; team functioning and the learning organization, including training issues, communication, empowerment, and evaluation/assessment; current challenges,…

  17. Feedback Both Helps and Hinders Learning: The Causal Role of Prior Knowledge

    Fyfe, Emily R.; Rittle-Johnson, Bethany

    2016-01-01

    Feedback can be a powerful learning tool, but its effects vary widely. Research has suggested that learners' prior knowledge may moderate the effects of feedback; however, no causal link has been established. In Experiment 1, we randomly assigned elementary school children (N = 108) to a condition based on a crossing of 2 factors: induced strategy…

  18. Practical data mining and machine learning for optics applications: introduction to the feature issue.

    Abdulla, Ghaleb; Awwal, Abdul; Borne, Kirk; Ho, Tin Kam; Vestrand, W Thomas

    2011-08-01

    Data mining algorithms utilize search techniques to explore hidden patterns and correlations in the data, which otherwise require a tremendous amount of human time to explore. This feature issue explores the use of such techniques to help understand the data, build better simulators, explain outlier behavior, and build better predictive models. We hope that this issue will spur discussions and expose a set of tools that can be useful to the optics community.

  19. Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

    Aiming Liu

    2017-11-01

    Full Text Available Motor Imagery (MI electroencephalography (EEG is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP and local characteristic-scale decomposition (LCD algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems.

  20. Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata.

    Liu, Aiming; Chen, Kun; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

    2017-11-08

    Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain-computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain-computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain-computer interface systems.

  1. Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis

    Liu, Guo-Ping; Yan, Jian-Jun; Wang, Yi-Qin; Fu, Jing-Jing; Xu, Zhao-Xia; Guo, Rui; Qian, Peng

    2012-01-01

    Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:22719781

  2. Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis

    Guo-Ping Liu

    2012-01-01

    Full Text Available Background. In Traditional Chinese Medicine (TCM, most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs. Methods. We employed a multilabel learning using the relevant feature for each label (REAL algorithm to construct a syndrome diagnostic model for chronic gastritis (CG in TCM. REAL combines feature selection methods to select the significant symptoms (signs of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL, whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

  3. Joint Facial Action Unit Detection and Feature Fusion: A Multi-conditional Learning Approach.

    Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja

    2016-10-05

    Automated analysis of facial expressions can benefit many domains, from marketing to clinical diagnosis of neurodevelopmental disorders. Facial expressions are typically encoded as a combination of facial muscle activations, i.e., action units. Depending on context, these action units co-occur in specific patterns, and rarely in isolation. Yet, most existing methods for automatic action unit detection fail to exploit dependencies among them, and the corresponding facial features. To address this, we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection. Specifically, the proposed model performs feature fusion in a generative fashion via a low-dimensional shared subspace, while simultaneously performing action unit detection using a discriminative classification approach. We show that by combining the merits of both approaches, the proposed methodology outperforms existing purely discriminative/generative methods for the target task. To reduce the number of parameters, and avoid overfitting, a novel Bayesian learning approach based on Monte Carlo sampling is proposed, to integrate out the shared subspace. We validate the proposed method on posed and spontaneous data from three publicly available datasets (CK+, DISFA and Shoulder-pain), and show that both feature fusion and joint learning of action units leads to improved performance compared to the state-of-the-art methods for the task.

  4. Feature extraction for SAR target recognition based on supervised manifold learning

    Du, C; Zhou, S; Sun, J; Zhao, J

    2014-01-01

    On the basis of manifold learning theory, a new feature extraction method for Synthetic aperture radar (SAR) target recognition is proposed. First, the proposed algorithm estimates the within-class and between-class local neighbourhood surrounding each SAR sample. After computing the local tangent space for each neighbourhood, the proposed algorithm seeks for the optimal projecting matrix by preserving the local within-class property and simultaneously maximizing the local between-class separability. The use of uncorrelated constraint can also enhance the discriminating power of the optimal projecting matrix. Finally, the nearest neighbour classifier is applied to recognize SAR targets in the projected feature subspace. Experimental results on MSTAR datasets demonstrate that the proposed method can provide a higher recognition rate than traditional feature extraction algorithms in SAR target recognition

  5. How do postgraduate GP trainees regulate their learning and what helps and hinders them? A qualitative study

    Sagasser Margaretha H

    2012-08-01

    Full Text Available Abstract Background Self-regulation is essential for professional development. It involves monitoring of performance, identifying domains for improvement, undertaking learning activities, applying newly learned knowledge and skills and self-assessing performance. Since self-assessment alone is ineffective in identifying weaknesses, learners should seek external feedback too. Externally regulated educational interventions, like reflection, learning portfolios, assessments and progress meetings, are increasingly used to scaffold self-regulation. The aim of this study is to explore how postgraduate trainees regulate their learning in the workplace, how external regulation promotes self-regulation and which elements facilitate or impede self-regulation and learning. Methods In a qualitative study with a phenomenologic approach we interviewed first- and third-year GP trainees from two universities in the Netherlands. Twenty-one verbatim transcripts were coded. Through iterative discussion the researchers agreed on the interpretation of the data and saturation was reached. Results Trainees used a short and a long self-regulation loop. The short loop took one week at most and was focused on problems that were easy to resolve and needed minor learning activities. The long loop was focused on complex or recurring problems needing multiple and planned longitudinal learning activities. External assessments and formal training affected the long but not the short loop. The supervisor had a facilitating role in both loops. Self-confidence was used to gauge competence.Elements influencing self-regulation were classified into three dimensions: personal (strong motivation to become a good doctor, interpersonal (stimulation from others and contextual (organizational and educational features. Conclusions Trainees did purposefully self-regulate their learning. Learning in the short loop may not be visible to others. Trainees should be encouraged to actively seek

  6. Is It Cheating or Learning the Craft of Writing? Using Turnitin to Help Students Avoid Plagiarism

    Graham-Matheson, Lynne; Starr, Simon

    2013-01-01

    Plagiarism is a growing problem for universities, many of which are turning to software detection for help in detecting and dealing with it. This paper explores issues around plagiarism and reports on a study of the use of Turnitin in a new university. The purpose of the study was to inform the senior management team about the plagiarism policy…

  7. Technology for curriculum and teacher development : Software to help educators learn while designing teacher guides

    McKenney, Susan

    2005-01-01

    This article describes research on the quality of a computer program designed to help secondary level science teachers in southern Africa create exemplary paper-based lesson materials. Results of this study show that the content, support, and interface of the program combine to form a tool that is

  8. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

    Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

    2013-03-01

    Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  9. Embedded Library Guides in Learning Management Systems Help Students Get Started on Research Assignments

    Dominique Daniel

    2016-01-01

    Objective – To determine whether library guides embedded in learning management systems (LMS) get used by students, and to identify best practices for the creation and promotion of these guides by librarians. Design – Mixed methods combining quantitative and qualitative data collection and analysis (survey, interviews, and statistical analysis). Setting – A large public university in the United States of America. Subjects – 100 undergraduate students and 14 librarians. Met...

  10. Identifying Key Features of Effective Active Learning: The Effects of Writing and Peer Discussion

    Pangle, Wiline M.; Wyatt, Kevin H.; Powell, Karli N.; Sherwood, Rachel E.

    2014-01-01

    We investigated some of the key features of effective active learning by comparing the outcomes of three different methods of implementing active-learning exercises in a majors introductory biology course. Students completed activities in one of three treatments: discussion, writing, and discussion + writing. Treatments were rotated weekly between three sections taught by three different instructors in a full factorial design. The data set was analyzed by generalized linear mixed-effect models with three independent variables: student aptitude, treatment, and instructor, and three dependent (assessment) variables: change in score on pre- and postactivity clicker questions, and coding scores on in-class writing and exam essays. All independent variables had significant effects on student performance for at least one of the dependent variables. Students with higher aptitude scored higher on all assessments. Student scores were higher on exam essay questions when the activity was implemented with a writing component compared with peer discussion only. There was a significant effect of instructor, with instructors showing different degrees of effectiveness with active-learning techniques. We suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning. PMID:25185230

  11. Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction

    Jie Zhang

    2017-04-01

    Full Text Available Device-free localization (DFL is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF DFL system, radio transmitters (RTs and radio receivers (RXs are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN, support vector machine (SVM, back propagation neural network (BPNN, as well as the well-known radio tomographic imaging (RTI DFL approach.

  12. Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning.

    Sung, Yao-Ting; Chen, Ju-Ling; Cha, Ji-Her; Tseng, Hou-Chiang; Chang, Tao-Hsing; Chang, Kuo-En

    2015-06-01

    Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.

  13. Distraction during learning with hypermedia: Difficult tasks help to keep task goals on track

    Katharina eScheiter

    2014-03-01

    Full Text Available In educational hypermedia environments, students are often confronted with potential sources of distraction arising from additional information that, albeit interesting, is unrelated to their current task goal. The paper investigates the conditions under which distraction occurs and hampers performance. Based on theories of volitional action control it was hypothesized that interesting information, especially if related to a pending goal, would interfere with task performance only when working on easy, but not on difficult tasks. In Experiment 1, 66 students learned about probability theory using worked examples and solved corresponding test problems, whose task difficulty was manipulated. As a second factor, the presence of interesting information unrelated to the primary task was varied. Results showed that students solved more easy than difficult probability problems correctly. However, the presence of interesting, but task-irrelevant information did not interfere with performance. In Experiment 2, 68 students again engaged in example-based learning and problem solving in the presence of task-irrelevant information. Problem-solving difficulty was varied as a first factor. Additionally, the presence of a pending goal related to the task-irrelevant information was manipulated. As expected, problem-solving performance declined when a pending goal was present during working on easy problems, whereas no interference was observed for difficult problems. Moreover, the presence of a pending goal reduced the time on task-relevant information and increased the time on task-irrelevant information while working on easy tasks. However, as revealed by mediation analyses these changes in overt information processing behavior did not explain the decline in problem-solving performance. As an alternative explanation it is suggested that goal conflicts resulting from pending goals claim cognitive resources, which are then no longer available for learning and

  14. CLASS-PAIR-GUIDED MULTIPLE KERNEL LEARNING OF INTEGRATING HETEROGENEOUS FEATURES FOR CLASSIFICATION

    Q. Wang

    2017-10-01

    Full Text Available In recent years, many studies on remote sensing image classification have shown that using multiple features from different data sources can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL can conveniently be embedded in a variety of characteristics. The conventional combined kernel learned by MKL can be regarded as the compromise of all basic kernels for all classes in classification. It is the best of the whole, but not optimal for each specific class. For this problem, this paper proposes a class-pair-guided MKL method to integrate the heterogeneous features (HFs from multispectral image (MSI and light detection and ranging (LiDAR data. In particular, the one-against-one strategy is adopted, which converts multiclass classification problem to a plurality of two-class classification problem. Then, we select the best kernel from pre-constructed basic kernels set for each class-pair by kernel alignment (KA in the process of classification. The advantage of the proposed method is that only the best kernel for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on two real data sets, and the experimental results show that the proposed method achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms.

  15. Recursiveness in learning processes: An analogy to help software development for ABA intervention for autistic kids

    Presti, Giovambattista; Premarini, Claudio; Leuzzi, Martina; Di Blasi, Melina; Squatrito, Valeria

    2017-11-01

    The operant was conceptualized by Skinner as a class of behaviors which have common effect on the environment and that, as a class can be shown to vary lawfully in their relations to the other environmental variables, namely antecedents and consequences. And Skinner himself underlined the fact that "operant field is the very field purpose of behavior". The operant offers interesting basic and applied characteristic to conceptualize complex behavior as a recursive process of learning. In this paper we will discuss how the operant concept can be applied in the implementation of software oriented to increase cognitive skills in autistic children and provide an example.

  16. Unforgettable French Memory Tricks to Help You Learn and Remember French Grammar

    Rice-Jones, Maria

    2010-01-01

    Unforgettable French uses memory tricks to teach and reinforce major points of rench grammar from the basics up to high school level, to learners of all ages. It may be used: by anyone who wishes to gain confidence in speaking French, as a evision aid, to consolidate the learner's grasp of grammatical points, to complement whatever French scheme you are using, and by French teachers at all levels, from elementary school through to adult. These tried-and-tested memory tricks help to explain "tri

  17. Educators' Interprofessional Collaborative Relationships: Helping Pharmacy Students Learn to Work with Other Professions.

    Croker, Anne; Smith, Tony; Fisher, Karin; Littlejohns, Sonja

    2016-03-30

    Similar to other professions, pharmacy educators use workplace learning opportunities to prepare students for collaborative practice. Thus, collaborative relationships between educators of different professions are important for planning, implementing and evaluating interprofessional learning strategies and role modelling interprofessional collaboration within and across university and workplace settings. However, there is a paucity of research exploring educators' interprofessional relationships. Using collaborative dialogical inquiry we explored the nature of educators' interprofessional relationships in a co-located setting. Data from interprofessional focus groups and semi-structured interviews were interpreted to identify themes that transcended the participants' professional affiliations. Educators' interprofessional collaborative relationships involved the development and interweaving of five interpersonal behaviours: being inclusive of other professions; developing interpersonal connections with colleagues from other professions; bringing a sense of own profession in relation to other professions; giving and receiving respect to other professions; and being learner-centred for students' collaborative practice . Pharmacy educators, like other educators, need to ensure that interprofessional relationships are founded on positive experiences rather than vested in professional interests.

  18. Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

    Steren Chabert

    2017-01-01

    to predict by themselves the risk of rupture. Therefore, our hypothesis is that the risk of rupture lies on the combination of multiple actors. These actors together would play different roles that could be: weakening of the artery wall, increasing biomechanical stresses on the wall induced by blood flow, in addition to personal sensitivity due to family history, or personal history of comorbidity, or even seasonal variations that could gate different inflammation mechanisms. The main goal of this project is to identify relevant variables that may help in the process of predicting the risk of intracranial aneurysm rupture using machine learning and image processing techniques based on structured and non-structured data from multiple sources. We believe that the identification and the combined use of relevant variables extracted from clinical, demographical, environmental and medical imaging data sources will improve the estimation of the aneurysm rupture risk, with respect to the actual practiced method based essentially on the aneurysm size. The methodology of this work consist of four phases: (1 Data collection and storage, (2 feature extraction from multiple sources in particular from angiographic images, (3 development of the model that could describe the risk of aneurysm rupture based on the fusion and combination of the features, and (4 Identification of relevant variables related to the aneurysm rupture process. This study corresponds to an analytic transversal study with prospective and retrospective characteristics. This work will be based on publicly available health statistics data, data of weather conditions, together with clinical and demographic data of patients diagnosed with intracranial aneurysm in the Hospital Carlos van Buren. As main results of this project we are expecting to identify relevant variables extracted from images and other sources that could play a role in the risk of aneurysm rupture. The proposed model will be presented to the

  19. A unified framework of image latent feature learning on Sina microblog

    Wei, Jinjin; Jin, Zhigang; Zhou, Yuan; Zhang, Rui

    2015-10-01

    Large-scale user-contributed images with texts are rapidly increasing on the social media websites, such as Sina microblog. However, the noise and incomplete correspondence between the images and the texts give rise to the difficulty in precise image retrieval and ranking. In this paper, a hypergraph-based learning framework is proposed for image ranking, which simultaneously utilizes visual feature, textual content and social link information to estimate the relevance between images. Representing each image as a vertex in the hypergraph, complex relationship between images can be reflected exactly. Then updating the weight of hyperedges throughout the hypergraph learning process, the effect of different edges can be adaptively modulated in the constructed hypergraph. Furthermore, the popularity degree of the image is employed to re-rank the retrieval results. Comparative experiments on a large-scale Sina microblog data-set demonstrate the effectiveness of the proposed approach.

  20. Multi-center MRI carotid plaque component segmentation using feature normalization and transfer learning

    van Engelen, Arna; van Dijk, Anouk C; Truijman, Martine T.B.

    2015-01-01

    implementation of supervised methods. In this paper we segment carotid plaque components of clinical interest (fibrous tissue, lipid tissue, calcification and intraplaque hemorrhage) in a multicenter MRI study. We perform voxelwise tissue classification by traditional same-center training, and compare results...... not yield significant differences from that reference. We conclude that both extensive feature normalization and transfer learning can be valuable for the development of supervised methods that perform well on different types of datasets.......Automated segmentation of plaque components in carotid artery MRI is important to enable large studies on plaque vulnerability, and for incorporating plaque composition as an imaging biomarker in clinical practice. Especially supervised classification techniques, which learn from labeled examples...

  1. Identifying Features of Bodily Expression As Indicators of Emotional Experience during Multimedia Learning

    Valentin Riemer

    2017-07-01

    Full Text Available The importance of emotions experienced by learners during their interaction with multimedia learning systems, such as serious games, underscores the need to identify sources of information that allow the recognition of learners’ emotional experience without interrupting the learning process. Bodily expression is gaining in attention as one of these sources of information. However, to date, the question of how bodily expression can convey different emotions has largely been addressed in research relying on acted emotion displays. Following a more contextualized approach, the present study aims to identify features of bodily expression (i.e., posture and activity of the upper body and the head that relate to genuine emotional experience during interaction with a serious game. In a multimethod approach, 70 undergraduates played a serious game relating to financial education while their bodily expression was captured using an off-the-shelf depth-image sensor (Microsoft Kinect. In addition, self-reports of experienced enjoyment, boredom, and frustration were collected repeatedly during gameplay, to address the dynamic changes in emotions occurring in educational tasks. Results showed that, firstly, the intensities of all emotions indeed changed significantly over the course of the game. Secondly, by using generalized estimating equations, distinct features of bodily expression could be identified as significant indicators for each emotion under investigation. A participant keeping their head more turned to the right was positively related to frustration being experienced, whereas keeping their head more turned to the left was positively related to enjoyment. Furthermore, having their upper body positioned more closely to the gaming screen was also positively related to frustration. Finally, increased activity of a participant’s head emerged as a significant indicator of boredom being experienced. These results confirm the value of bodily

  2. How Do Lessons Learned on the International Space Station (ISS) Help Plan Life Support for Mars?

    Jones, Harry W.; Hodgson, Edward W.; Gentry, Gregory J.; Kliss, Mark H.

    2016-01-01

    How can our experience in developing and operating the International Space Station (ISS) guide the design, development, and operation of life support for the journey to Mars? The Mars deep space Environmental Control and Life Support System (ECLSS) must incorporate the knowledge and experience gained in developing ECLSS for low Earth orbit, but it must also meet the challenging new requirements of operation in deep space where there is no possibility of emergency resupply or quick crew return. The understanding gained by developing ISS flight hardware and successfully supporting a crew in orbit for many years is uniquely instructive. Different requirements for Mars life support suggest that different decisions may be made in design, testing, and operations planning, but the lessons learned developing the ECLSS for ISS provide valuable guidance.

  3. Analysis of Secondary School Students’ Algebraic Thinking and Math-Talk Learning Community to Help Students Learn

    Nurhayati, D. M.; Herman, T.; Suhendra, S.

    2017-09-01

    This study aims to determine the difficulties of algebraic thinking ability of students in one of secondary school on quadrilateral subject and to describe Math-Talk Learning Community as the alternative way that can be done to overcome the difficulties of the students’ algebraic thinking ability. Research conducted by using quantitative approach with descriptive method. The population in this research was all students of that school and twenty three students as the sample that was chosen by purposive sampling technique. Data of algebraic thinking were collected through essay test. The results showed the percentage of achievement of students’ algebraic thinking’s indicators on three aspects: a) algebra as generalized arithmetic with the indicators (conceptually based computational strategies and estimation); b) algebra as the language of mathematics (meaning of variables, variable expressions and meaning of solution); c) algebra as a tool for functions and mathematical modelling (representing mathematical ideas using equations, tables, or words and generalizing patterns and rules in real-world contexts) is still low. It is predicted that because the secondary school students was not familiar with the abstract problem and they are still at a semi-concrete stage where the stage of cognitive development is between concrete and abstract. Based on the percentage achievement of each indicators, it can be concluded that the level of achievement of student’s mathematical communication using conventional learning is still low, so students’ algebraic thinking ability need to be improved.

  4. Learned Compact Local Feature Descriptor for Tls-Based Geodetic Monitoring of Natural Outdoor Scenes

    Gojcic, Z.; Zhou, C.; Wieser, A.

    2018-05-01

    The advantages of terrestrial laser scanning (TLS) for geodetic monitoring of man-made and natural objects are not yet fully exploited. Herein we address one of the open challenges by proposing feature-based methods for identification of corresponding points in point clouds of two or more epochs. We propose a learned compact feature descriptor tailored for point clouds of natural outdoor scenes obtained using TLS. We evaluate our method both on a benchmark data set and on a specially acquired outdoor dataset resembling a simplified monitoring scenario where we successfully estimate 3D displacement vectors of a rock that has been displaced between the scans. We show that the proposed descriptor has the capacity to generalize to unseen data and achieves state-of-the-art performance while being time efficient at the matching step due the low dimension.

  5. Discriminative kernel feature extraction and learning for object recognition and detection

    Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

    2015-01-01

    Feature extraction and learning is critical for object recognition and detection. By embedding context cue of image attributes into the kernel descriptors, we propose a set of novel kernel descriptors called context kernel descriptors (CKD). The motivation of CKD is to use the spatial consistency...... even in high-dimensional space. In addition, the latent connection between Rényi quadratic entropy and the mapping data in kernel feature space further facilitates us to capture the geometric structure as well as the information about the underlying labels of the CKD using CSQMI. Thus the resulting...... codebook and reduced CKD are discriminative. We report superior performance of our algorithm for object recognition on benchmark datasets like Caltech-101 and CIFAR-10, as well as for detection on a challenging chicken feet dataset....

  6. Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models

    Olivier Aycard

    2004-12-01

    Full Text Available In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

  7. A novel approach for fire recognition using hybrid features and manifold learning-based classifier

    Zhu, Rong; Hu, Xueying; Tang, Jiajun; Hu, Sheng

    2018-03-01

    Although image/video based fire recognition has received growing attention, an efficient and robust fire detection strategy is rarely explored. In this paper, we propose a novel approach to automatically identify the flame or smoke regions in an image. It is composed to three stages: (1) a block processing is applied to divide an image into several nonoverlapping image blocks, and these image blocks are identified as suspicious fire regions or not by using two color models and a color histogram-based similarity matching method in the HSV color space, (2) considering that compared to other information, the flame and smoke regions have significant visual characteristics, so that two kinds of image features are extracted for fire recognition, where local features are obtained based on the Scale Invariant Feature Transform (SIFT) descriptor and the Bags of Keypoints (BOK) technique, and texture features are extracted based on the Gray Level Co-occurrence Matrices (GLCM) and the Wavelet-based Analysis (WA) methods, and (3) a manifold learning-based classifier is constructed based on two image manifolds, which is designed via an improve Globular Neighborhood Locally Linear Embedding (GNLLE) algorithm, and the extracted hybrid features are used as input feature vectors to train the classifier, which is used to make decision for fire images or non fire images. Experiments and comparative analyses with four approaches are conducted on the collected image sets. The results show that the proposed approach is superior to the other ones in detecting fire and achieving a high recognition accuracy and a low error rate.

  8. Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery

    Lin Liang

    2015-01-01

    Full Text Available A new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness. Under the phase space reconstructed by single vibration signal, the self-organization mapping (SOM with expectation maximization iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment algorithm is adopted to compress the high-dimensional phase space into low-dimensional feature space. The proposed method takes advantages of the manifold learning in low-dimensional feature extraction and adaptive neighborhood construction of SOM and can extract intrinsic fault features of interest in two dimensional projection space. To evaluate the performance of the proposed method, the Lorenz system was simulated and rotation machinery with nonlinear faults was obtained for test purposes. Compared with the holospectrum approaches, the results reveal that the proposed method is superior in identifying faults and effective for rotating machinery condition monitoring.

  9. In Defense of the Sage on the Stage: Escaping from the "Sorcery" of Learning Styles and Helping Students Learn How to Learn

    Jennings, Marianne M.

    2012-01-01

    Beginning in the late 1980s and early 1990s, higher education was swept up in the theoretical phenomena of mastery learning, cooperative learning, and small-group learning. Professors, instructors, and teachers at the K-12 level became facilitators, guides, supervisors, counselors, and advocates for all things team and group. The thought of a…

  10. Virtual help to the learning of Geology in the Madrid School of Mines and Energy

    José EUGENIO ORTIZ

    2014-11-01

    Full Text Available Learning Geology requires a skill that is primarily achieved with practice in nature, being more effective when one tries to transmit knowledge to others. Here, we show the results of an educational innovation program in courses related to Geology using new technologies (ITC in order to increase the acquisition of geological knowledge. This program is designed mainly on the basis of individual work with video recordings in the field in which students explain geological concepts at various scales. These videos have been uploaded to the “moodle”, “facebook” and “youtube” channel, where people can view them. We also elaborated "Geological routes," which are accompanied by these videos indicating the most important geological aspects that can be observed, that were uploaded to “moodle” platform. The realization of these videos has been warmly welcomed by students, and they show increased motivation, accompanied by an improvement in grades. They also gained confidence in public speaking using technical language. Also, students can make itineraries of geological interest without having to be accompanied by a professor, deeping into the most interesting topics. 

  11. Selected chapters from general chemistry in physics teaching with the help of e - learning

    Feszterová, Melánia

    2017-01-01

    Education in the field of natural disciplines - Mathematics, Physics, Chemistry, Ecology and Biology takes part in general education at all schools on the territory of Slovakia. Its aim is to reach the state of balanced development of all personal characteristics of pupils, to teach them correctly identify and analyse problems, propose solutions and above all how to solve the problem itself. High quality education can be reached only through the pedagogues who have a good expertise knowledge, practical experience and high level of pedagogical abilities. The teacher as a disseminator of natural-scientific knowledge should be not only well-informed about modern tendencies in the field, but he/she also should actively participate in project tasks This is the reason why students of 1st year of study (bachelor degree) at the Department of Physics of Constantine the Philosopher University in Nitra attend lectures in the frame of subject General Chemistry. In this paper we present and describe an e - learning course called General Chemistry that is freely accessible to students. One of the aims of this course is to attract attention towards the importance of cross-curricular approach which seems to be fundamental in contemporary natural-scientific education (e.g. between Physics and Chemistry). This is why it is so important to implement a set of new topics and tasks that support development of abilities to realise cross-curricular goals into the process of preparation of future teachers of Physics.

  12. Unlatching the Gate – Helping Adult Students Learn Mathematics by Katherine Safford-Ramus, (2008

    Armin Hollenstein

    2010-08-01

    Full Text Available Katherine Safford-Ramus is an associate professor of mathematics at Saint Peter’s College, a Jesuit College in New Jersey, USA. She has been teaching introductory mathematics courses at the tertiary level for 24 years at a community college. This book is based on her doctoral thesis. In Chapter 1, Unlatching the Gate deliberates a rich specra of conditions for, and peculiarities of, mathematics learning by adults in a formal environment. Influential theories and empirical findings in the fields of educational psychology, adult education and mathematics education are surveyed with a focus on adult learners and – of course –teachers and institutions. The text does not discuss empirical research undertaken by the author; it examines her broad personal teaching experience in the light of the above-mentioned body of knowledge and proposes directions for the development of adult mathematics education. In this sense, Unlatching the Gate is a theoretical book reflecting on practical issues. The target audience would be adult educators and students of post secondary mathematics education.

  13. Usage of a learning virtual environment with interactive virtual reality for helping in reactor engineering teaching

    Miguel, Lucas de Castro

    2017-01-01

    In the last few decades, several studies have been conducted regarding the effectiveness of the use of virtual reality as a teaching tool. New and complex IT tools (Information and Communication Technologies) have also been developed. One such tool, is the Virtual Learning Environment (VLE). VLEs are internet media that use cyberspace to convey didactic content and can complement the orthodox teaching method, allowing students a new way of understanding complex content through digital interaction. This work aims to teach the operation of the first and second cycles of a pressurized water nuclear reactor through the development and use of a VLE. The VLE will use interactive virtual reality to demonstrate to the student the 'anatomy' of a generating nuclear power plant. There are several possibilities for future work using this VLE. One is the use as a data repository and 'virtual exhibition room' of each component of the nuclear reactor that researchers are modelling and developing. With these virtual objects allocated in a category, teachers could use this VLE in the classroom as a teaching tool while researchers could use the platform as a quick and practical way of viewing their online work and sharing it with other researchers. Thus, this VLE will be an effective tool for spreading knowledge of nuclear power more easily within, as well as outside of the research community. (author)

  14. Identifying key features of effective active learning: the effects of writing and peer discussion.

    Linton, Debra L; Pangle, Wiline M; Wyatt, Kevin H; Powell, Karli N; Sherwood, Rachel E

    2014-01-01

    We investigated some of the key features of effective active learning by comparing the outcomes of three different methods of implementing active-learning exercises in a majors introductory biology course. Students completed activities in one of three treatments: discussion, writing, and discussion + writing. Treatments were rotated weekly between three sections taught by three different instructors in a full factorial design. The data set was analyzed by generalized linear mixed-effect models with three independent variables: student aptitude, treatment, and instructor, and three dependent (assessment) variables: change in score on pre- and postactivity clicker questions, and coding scores on in-class writing and exam essays. All independent variables had significant effects on student performance for at least one of the dependent variables. Students with higher aptitude scored higher on all assessments. Student scores were higher on exam essay questions when the activity was implemented with a writing component compared with peer discussion only. There was a significant effect of instructor, with instructors showing different degrees of effectiveness with active-learning techniques. We suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning. © 2014 D. L. Linton et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  15. Is it cheating or learning the craft of writing? Using Turnitin to help students avoid plagiarism

    Lynne Graham-Matheson

    2013-04-01

    Full Text Available Plagiarism is a growing problem for universities, many of which are turning to software detection for help in detecting and dealing with it. This paper explores issues around plagiarism and reports on a study of the use of Turnitin in a new university. The purpose of the study was to inform the senior management team about the plagiarism policy and the use of Turnitin. The study found that staff and students largely understood the university's policy and Turnitin's place within it, and were very supportive of the use of Turnitin in originality checking. Students who had not used Turnitin were generally keen to do so. The recommendation to the senior management team, which was implemented, was that the use of Turnitin for originality checking should be made compulsory where possible – at the time of the study the use of Turnitin was at the discretion of programme directors. A further aim of the study was to contribute to the sector's body of knowledge. Prevention of plagiarism through education is a theme identified by Badge and Scott (2009 who conclude an area lacking in research is “investigation of the impact of these tools on staff teaching practices”. Although a number of recent studies have considered educational use of Turnitin they focus on individual programmes or subject areas rather than institutions as a whole and the relationship with policy.

  16. Blended Learning Tools in Geosciences: A New Set of Online Tools to Help Students Master Skills

    Cull, S.; Spohrer, J.; Natarajan, S.; Chin, M.

    2013-12-01

    In most geoscience courses, students are expected to develop specific skills. To master these skills, students need to practice them repeatedly. Unfortunately, few geosciences courses have enough class time to allow students sufficient in-class practice, nor enough instructor attention and time to provide fast feedback. To address this, we have developed an online tool called an Instant Feedback Practice (IFP). IFPs are low-risk, high-frequency exercises that allow students to practice skills repeatedly throughout a semester, both in class and at home. After class, students log onto a course management system (like Moodle or Blackboard), and click on that day's IFP exercise. The exercise might be visually identifying a set of minerals that they're practicing. After answering each question, the IFP tells them if they got it right or wrong. If they got it wrong, they try again until they get it right. There is no penalty - students receive the full score for finishing. The goal is low-stakes practice. By completing dozens of these practices throughout the semester, students have many, many opportunities to practice mineral identification with quick feedback. Students can also complete IFPs during class in groups and teams, with in-lab hand samples or specimens. IFPs can also be used to gauge student skill levels as the semester progresses, as they can be set up to provide the instructor feedback on specific skills or students. When IFPs were developed for and implemented in a majors-level mineralogy class, students reported that in-class and online IFPs were by far the most useful technique they used to master mineral hand sample identification. Final grades in the course were significantly higher than historical norms, supporting students' anecdotal assessment of the impact of IFPs on their learning.

  17. Identifying predictive features in drug response using machine learning: opportunities and challenges.

    Vidyasagar, Mathukumalli

    2015-01-01

    This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

  18. The cognitive impact of interactive design features for learning complex materials in medical education.

    Song, Hyuksoon S; Pusic, Martin; Nick, Michael W; Sarpel, Umut; Plass, Jan L; Kalet, Adina L

    2014-02-01

    To identify the most effective way for medical students to interact with a browser-based learning module on the symptoms and neurological underpinnings of stroke syndromes, this study manipulated the way in which subjects interacted with a graphical model of the brain and examined the impact of functional changes on learning outcomes. It was hypothesized that behavioral interactions that were behaviorally more engaging and which required deeper consideration of the model would result in heightened cognitive interaction and better learning than those whose manipulation required less deliberate behavioral and cognitive processing. One hundred forty four students were randomly assigned to four conditions whose model controls incorporated features that required different levels of behavioral and cognitive interaction: Movie (low behavioral/low cognitive, n = 40), Slider (high behavioral/low cognitive, n = 36), Click (low behavioral/high cognitive, n = 30), and Drag (high behavioral/high cognitive, n = 38). Analysis of Covariates (ANCOVA) showed that students who received the treatments associated with lower cognitive interactivity (Movie and Slider) performed better on a transfer task than those receiving the module associated with high cognitive interactivity (Click and Drag, partial eta squared = .03). In addition, the students in the high cognitive interactivity conditions spent significantly more time on the stroke locator activity than other conditions (partial eta squared = .36). The results suggest that interaction with controls that were tightly coupled with the model and whose manipulation required deliberate consideration of the model's features may have overtaxed subjects' cognitive resources. Cognitive effort that facilitated manipulation of content, though directed at the model, may have resulted in extraneous cognitive load, impeding subjects in recognizing the deeper, global relationships in the materials. Instructional designers must, therefore, keep in

  19. An algorithm for finding biologically significant features in microarray data based on a priori manifold learning.

    Zena M Hira

    Full Text Available Microarray databases are a large source of genetic data, which, upon proper analysis, could enhance our understanding of biology and medicine. Many microarray experiments have been designed to investigate the genetic mechanisms of cancer, and analytical approaches have been applied in order to classify different types of cancer or distinguish between cancerous and non-cancerous tissue. However, microarrays are high-dimensional datasets with high levels of noise and this causes problems when using machine learning methods. A popular approach to this problem is to search for a set of features that will simplify the structure and to some degree remove the noise from the data. The most widely used approach to feature extraction is principal component analysis (PCA which assumes a multivariate Gaussian model of the data. More recently, non-linear methods have been investigated. Among these, manifold learning algorithms, for example Isomap, aim to project the data from a higher dimensional space onto a lower dimension one. We have proposed a priori manifold learning for finding a manifold in which a representative set of microarray data is fused with relevant data taken from the KEGG pathway database. Once the manifold has been constructed the raw microarray data is projected onto it and clustering and classification can take place. In contrast to earlier fusion based methods, the prior knowledge from the KEGG databases is not used in, and does not bias the classification process--it merely acts as an aid to find the best space in which to search the data. In our experiments we have found that using our new manifold method gives better classification results than using either PCA or conventional Isomap.

  20. Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data.

    Shah, M; Marchand, M; Corbeil, J

    2012-01-01

    One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance guarantees. There are few, if any, approaches that successfully address the two goals simultaneously. To the best of our knowledge, such algorithms that give theoretical bounds on the future performance have not been proposed so far in the context of the classification of gene expression data. In this work, we investigate the premise of learning a conjunction (or disjunction) of decision stumps in Occam's Razor, Sample Compression, and PAC-Bayes learning settings for identifying a small subset of attributes that can be used to perform reliable classification tasks. We apply the proposed approaches for gene identification from DNA microarray data and compare our results to those of the well-known successful approaches proposed for the task. We show that our algorithm not only finds hypotheses with a much smaller number of genes while giving competitive classification accuracy but also having tight risk guarantees on future performance, unlike other approaches. The proposed approaches are general and extensible in terms of both designing novel algorithms and application to other domains.

  1. Portal vein branching order helps in the recognition of anomalous right-sided round ligament: common features and variations in portal vein anatomy.

    Yamashita, Rikiya; Yamaoka, Toshihide; Nishitai, Ryuta; Isoda, Hiroyoshi; Taura, Kojiro; Arizono, Shigeki; Furuta, Akihiro; Ohno, Tsuyoshi; Ono, Ayako; Togashi, Kaori

    2017-07-01

    This study aimed to evaluate the common features and variations of portal vein anatomy in right-sided round ligament (RSRL), which can help propose a method to detect and diagnose this anomaly. In this retrospective study of 14 patients with RSRL, the branching order of the portal tree was analyzed, with special focus on the relationship between the dorsal branch of the right anterior segmental portal vein (P A-D ) and the lateral segmental portal vein (P LL ), to determine the common features. The configuration of the portal vein from the main portal trunk to the right umbilical portion (RUP), the inclination of the RUP, and the number and thickness of the ramifications branching from the right anterior segmental portal vein (P A ) were evaluated for variations. In all subjects, the diverging point of the P A-D was constantly distal to that of the P LL . The portal vein configuration was I- and Z-shaped in nine and five subjects, respectively. The RUP was tilted to the right in all subjects. In Z-shaped subjects, the portal trunk between the branching point of the right posterior segmental portal vein and that of the P LL was tilted to the left in one subject and was almost parallel to the vertical plane in four subjects. Multiple ramifications were radially distributed from the P A in eight subjects, whereas one predominant P A-D branched from the P A in six subjects. Based on the diverging points of the P A-D and P LL , we proposed a three-step method for the detection and diagnosis of RSRL.

  2. Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

    Younghak Shin; Balasingham, Ilangko

    2017-07-01

    Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

  3. Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

    Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio

    2018-02-01

    Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

    2018-03-01

    Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p breast cancer detected in the next subsequent mammography screening.

  5. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    Park, Sang Hyun [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong, E-mail: yzgao@cs.unc.edu [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Yinghuan, E-mail: syh@nju.edu.cn [State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-11-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  6. Online prediction of respiratory motion: multidimensional processing with low-dimensional feature learning

    Ruan, Dan; Keall, Paul

    2010-01-01

    Accurate real-time prediction of respiratory motion is desirable for effective motion management in radiotherapy for lung tumor targets. Recently, nonparametric methods have been developed and their efficacy in predicting one-dimensional respiratory-type motion has been demonstrated. To exploit the correlation among various coordinates of the moving target, it is natural to extend the 1D method to multidimensional processing. However, the amount of learning data required for such extension grows exponentially with the dimensionality of the problem, a phenomenon known as the 'curse of dimensionality'. In this study, we investigate a multidimensional prediction scheme based on kernel density estimation (KDE) in an augmented covariate-response space. To alleviate the 'curse of dimensionality', we explore the intrinsic lower dimensional manifold structure and utilize principal component analysis (PCA) to construct a proper low-dimensional feature space, where kernel density estimation is feasible with the limited training data. Interestingly, the construction of this lower dimensional representation reveals a useful decomposition of the variations in respiratory motion into the contribution from semiperiodic dynamics and that from the random noise, as it is only sensible to perform prediction with respect to the former. The dimension reduction idea proposed in this work is closely related to feature extraction used in machine learning, particularly support vector machines. This work points out a pathway in processing high-dimensional data with limited training instances, and this principle applies well beyond the problem of target-coordinate-based respiratory-based prediction. A natural extension is prediction based on image intensity directly, which we will investigate in the continuation of this work. We used 159 lung target motion traces obtained with a Synchrony respiratory tracking system. Prediction performance of the low-dimensional feature learning

  7. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-01-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  8. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection.

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-11-01

    Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency

  9. Development, Usability, and Efficacy of a Serious Game to Help Patients Learn About Pain Management After Surgery: An Evaluation Study.

    Ingadottir, Brynja; Blondal, Katrin; Thue, David; Zoega, Sigridur; Thylen, Ingela; Jaarsma, Tiny

    2017-05-10

    Postoperative pain is a persistent problem after surgery and can delay recovery and develop into chronic pain. Better patient education has been proposed to improve pain management of patients. Serious games have not been previously developed to help patients to learn how to manage their postoperative pain. The aim of this study was to describe the development of a computer-based game for surgical patients to learn about postoperative pain management and to evaluate the usability, user experience, and efficacy of the game. A computer game was developed by an interdisciplinary team following a structured approach. The usability, user experience, and efficacy of the game were evaluated using self-reported questionnaires (AttrakDiff2, Postoperative Pain Management Game Survey, Patient Knowledge About Postoperative Pain Management questionnaire), semi-structured interviews, and direct observation in one session with 20 participants recruited from the general public via Facebook (mean age 48 [SD 14]; 11 women). Adjusted Barriers Questionnaire II and 3 questions on health literacy were used to collect background information. Theories of self-care and adult learning, evidence for the educational needs of patients about pain management, and principles of gamification were used to develop the computer game. Ease of use and usefulness received a median score between 2.00 (IQR 1.00) and 5.00 (IQR 2.00) (possible scores 0-5; IQR, interquartile range), and ease of use was further confirmed by observation. Participants expressed satisfaction with this novel method of learning, despite some technological challenges. The attributes of the game, measured with AttrakDiff2, received a median score above 0 in all dimensions; highest for attraction (median 1.43, IQR 0.93) followed by pragmatic quality (median 1.31, IQR 1.04), hedonic quality interaction (median 1.00, IQR 1.04), and hedonic quality stimulation (median 0.57, IQR 0.68). Knowledge of pain medication and pain management

  10. Clickers don't always help: Classroom context and goals can mitigate clicker effects on student learning

    Shapiro, Amy; O'Rielly, Grant; Sims-Knight, Judith

    2014-03-01

    Clickers are commonly used in large-enrollment introductory courses in order to encourage attendance, increase student engagement and improve learning. We report the results from a highly controlled study of factual and conceptual clicker questions in calculus-based introductory physics courses, on students' performance on the factual and conceptual exam questions they targeted. We found that clicker questions did not enhance student performance on either type of exam question. The use of factual clicker questions actually decreased student performance on conceptual exam questions, however. Directing students' attention to surface features of the course content may distract them from the important underlying concepts. The conceptual clicker questions were likely ineffective because the practice students got on homework questions had a stronger effect than the single question posed in class. Interestingly, the same studies in general education biology and psychology courses show a strong, positive effect of clickers on student learning. This study suggest that the usefulness of clickers should be weighed in the context of other course activities and goals. Secondary analyses will explore the effect of students' GPA, motivation and study strategies on the results. This work was supported by the Institute of Education Sciences, US Dept. of Education, through Grant R305A100625 to UMass Dartmouth. The opinions expressed are those of the authors and do not represent views of the Institute or the US Dept. of Education.

  11. Learning discriminative features from RGB-D images for gender and ethnicity identification

    Azzakhnini, Safaa; Ballihi, Lahoucine; Aboutajdine, Driss

    2016-11-01

    The development of sophisticated sensor technologies gave rise to an interesting variety of data. With the appearance of affordable devices, such as the Microsoft Kinect, depth-maps and three-dimensional data became easily accessible. This attracted many computer vision researchers seeking to exploit this information in classification and recognition tasks. In this work, the problem of face classification in the context of RGB images and depth information (RGB-D images) is addressed. The purpose of this paper is to study and compare some popular techniques for gender recognition and ethnicity classification to understand how much depth data can improve the quality of recognition. Furthermore, we investigate which combination of face descriptors, feature selection methods, and learning techniques is best suited to better exploit RGB-D images. The experimental results show that depth data improve the recognition accuracy for gender and ethnicity classification applications in many use cases.

  12. Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

    Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney

    2016-01-01

    Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. PMID:27014079

  13. Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

    Hettige, Nuwan C; Nguyen, Thai Binh; Yuan, Chen; Rajakulendran, Thanara; Baddour, Jermeen; Bhagwat, Nikhil; Bani-Fatemi, Ali; Voineskos, Aristotle N; Mallar Chakravarty, M; De Luca, Vincenzo

    2017-07-01

    Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integration of many risk factors in order to build an algorithm that predicts which patients are likely to attempt suicide. Currently it is unclear how to integrate previously identified risk factors into a clinically relevant predictive tool to estimate the probability of a patient with schizophrenia for attempting suicide. We conducted a cross-sectional assessment on a sample of 345 participants diagnosed with schizophrenia spectrum disorders. Suicide attempters and non-attempters were clearly identified using the Columbia Suicide Severity Rating Scale (C-SSRS) and the Beck Suicide Ideation Scale (BSS). We developed four classification algorithms using a regularized regression, random forest, elastic net and support vector machine models with sociocultural and clinical variables as features to train the models. All classification models performed similarly in identifying suicide attempters and non-attempters. Our regularized logistic regression model demonstrated an accuracy of 67% and an area under the curve (AUC) of 0.71, while the random forest model demonstrated 66% accuracy and an AUC of 0.67. Support vector classifier (SVC) model demonstrated an accuracy of 67% and an AUC of 0.70, and the elastic net model demonstrated and accuracy of 65% and an AUC of 0.71. Machine learning algorithms offer a relatively successful method for incorporating many clinical features to predict individuals at risk for future suicide attempts. Increased performance of these models using clinically relevant variables offers the potential to facilitate early treatment and intervention to prevent future suicide attempts. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

    Szantoi, Zoltan; Escobedo, Francisco J; Abd-Elrahman, Amr; Pearlstine, Leonard; Dewitt, Bon; Smith, Scot

    2015-05-01

    Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (αtexture features.

  15. A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

    Muhammad Sohaib

    2017-12-01

    Full Text Available Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE-based deep neural networks (DNNs to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs and backpropagation neural networks (BPNNs.

  16. A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis.

    Sohaib, Muhammad; Kim, Cheol-Hong; Kim, Jong-Myon

    2017-12-11

    Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

  17. A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

    Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

    2017-06-09

    Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

    Muhammad Naveed Iqbal Qureshi

    2017-09-01

    Full Text Available Multimodal features of structural and functional magnetic resonance imaging (MRI of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001 accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

  19. 'Learn the signs. Act early': a campaign to help every child reach his or her full potential.

    Daniel, K L; Prue, C; Taylor, M K; Thomas, J; Scales, M

    2009-09-01

    To examine the application of a social marketing approach to increase the early identification and treatment of autism and other developmental disorders. The intervention used formative research, behaviour change theory and traditional social marketing techniques to develop a campaign targeting parents, healthcare professionals and early educators to increase awareness of autism and other developmental delays, and to prompt action if a developmental delay was suspected. Using social marketing principles, the Centers for Disease Control and Prevention applied baseline research with the target audiences to understand the barriers and motivators to behaviour change, which included a lack of knowledge and resources (barriers), along with a willingness to learn and do more (motivators). Focus group testing of potential campaign concepts led to one particular approach and accompanying images, which together increased perceived severity of the problem and encouraged taking action. The audience research also helped to shape the marketing mix (product, price, place and promotion). Three-year follow-up research in this case study indicates a significant change in parent target behaviours, particularly among parents aware of the campaign, and substantially more healthcare professionals believe that they have the resources to educate parents about monitoring their child's cognitive, social and physical development. Qualitative results from early educators and childcare professional associations have been positive about products developed for daycare settings. The application of social marketing principles, behavior change theory and audience research was an effective approach to changing behaviours in this case. Understanding what the target audiences want and need, looking beyond parents to engage healthcare professionals and early educators, and engaging many strategic partners to extend the reach of the message helped campaign planners to develop a campaign that resonated

  20. Help prevent hospital errors

    ... this page: //medlineplus.gov/ency/patientinstructions/000618.htm Help prevent hospital errors To use the sharing features ... in the hospital. If You Are Having Surgery, Help Keep Yourself Safe Go to a hospital you ...

  1. Help Helps, but Only so Much: Research on Help Seeking with Intelligent Tutoring Systems

    Aleven, Vincent; Roll, Ido; McLaren, Bruce M.; Koedinger, Kenneth R.

    2016-01-01

    Help seeking is an important process in self-regulated learning (SRL). It may influence learning with intelligent tutoring systems (ITSs), because many ITSs provide help, often at the student's request. The Help Tutor was a tutor agent that gave in-context, real-time feedback on students' help-seeking behavior, as they were learning with an ITS.…

  2. Rasgos Pedagógicos de los Objetos de Aprendizaje Pedagogical Features of Learning Objects

    Chiappe Laverde Andres

    2008-07-01

    Full Text Available Los objetos de aprendizaje (OA son considerados hoy como una alternativa viable e interesante para el desarrollo de contenidos para programas educativos mediados por las Tecnologías de la Información y la Comunicación (TIC. No obstante la pertinencia del tema para el momento actual de la educación superior en toda Latinoamérica, es preciso reflexionar más en profundidad acerca de lo que significan los OA y su impacto en el proceso educativo. El estudio de los rasgos pedagógicos de los objetos de aprendizaje coloca a la comunidad académica frente a un escenario que requiere la pronta identificación de sus retos y limitaciones de cara a la inminente ejecución de procesos de incorporación de los OA en las dinámicas de las instituciones educativas. Learning objects (LO are regarded today as a viable and interesting alternative for development of content for educational programs mediated by information and communication technologies (ICT. Attending the relevance of this topic just for this historic moment of higher education throughout Latin America, we need to think more deeply about what LO means and its impact on the educational process. The study of the pedagogical features of learning objects placed to the academic community in front of a scenario that requires prompt identification of their challenges and constraints facing the imminent execution of incorporation processes of LO in educational institutions dynamics.

  3. Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

    Jamal, Salma; Goyal, Sukriti; Shanker, Asheesh; Grover, Abhinav

    2016-10-18

    Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics. In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings. To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

  4. Optical beam classification using deep learning: a comparison with rule- and feature-based classification

    Alom, Md. Zahangir; Awwal, Abdul A. S.; Lowe-Webb, Roger; Taha, Tarek M.

    2017-08-01

    Deep-learning methods are gaining popularity because of their state-of-the-art performance in image classification tasks. In this paper, we explore classification of laser-beam images from the National Ignition Facility (NIF) using a novel deeplearning approach. NIF is the world's largest, most energetic laser. It has nearly 40,000 optics that precisely guide, reflect, amplify, and focus 192 laser beams onto a fusion target. NIF utilizes four petawatt lasers called the Advanced Radiographic Capability (ARC) to produce backlighting X-ray illumination to capture implosion dynamics of NIF experiments with picosecond temporal resolution. In the current operational configuration, four independent short-pulse ARC beams are created and combined in a split-beam configuration in each of two NIF apertures at the entry of the pre-amplifier. The subaperture beams then propagate through the NIF beampath up to the ARC compressor. Each ARC beamlet is separately compressed with a dedicated set of four gratings and recombined as sub-apertures for transport to the parabola vessel, where the beams are focused using parabolic mirrors and pointed to the target. Small angular errors in the compressor gratings can cause the sub-aperture beams to diverge from one another and prevent accurate alignment through the transport section between the compressor and parabolic mirrors. This is an off-normal condition that must be detected and corrected. The goal of the off-normal check is to determine whether the ARC beamlets are sufficiently overlapped into a merged single spot or diverged into two distinct spots. Thus, the objective of the current work is three-fold: developing a simple algorithm to perform off-normal classification, exploring the use of Convolutional Neural Network (CNN) for the same task, and understanding the inter-relationship of the two approaches. The CNN recognition results are compared with other machine-learning approaches, such as Deep Neural Network (DNN) and Support

  5. Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning

    2013-01-01

    Background Plastids are an important component of plant cells, being the site of manufacture and storage of chemical compounds used by the cell, and contain pigments such as those used in photosynthesis, starch synthesis/storage, cell color etc. They are essential organelles of the plant cell, also present in algae. Recent advances in genomic technology and sequencing efforts is generating a huge amount of DNA sequence data every day. The predicted proteome of these genomes needs annotation at a faster pace. In view of this, one such annotation need is to develop an automated system that can distinguish between plastid and non-plastid proteins accurately, and further classify plastid-types based on their functionality. We compared the amino acid compositions of plastid proteins with those of non-plastid ones and found significant differences, which were used as a basis to develop various feature-based prediction models using similarity-search and machine learning. Results In this study, we developed separate Support Vector Machine (SVM) trained classifiers for characterizing the plastids in two steps: first distinguishing the plastid vs. non-plastid proteins, and then classifying the identified plastids into their various types based on their function (chloroplast, chromoplast, etioplast, and amyloplast). Five diverse protein features: amino acid composition, dipeptide composition, the pseudo amino acid composition, Nterminal-Center-Cterminal composition and the protein physicochemical properties are used to develop SVM models. Overall, the dipeptide composition-based module shows the best performance with an accuracy of 86.80% and Matthews Correlation Coefficient (MCC) of 0.74 in phase-I and 78.60% with a MCC of 0.44 in phase-II. On independent test data, this model also performs better with an overall accuracy of 76.58% and 74.97% in phase-I and phase-II, respectively. The similarity-based PSI-BLAST module shows very low performance with about 50% prediction

  6. Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors.

    Yang, Wei; Zhong, Liming; Chen, Yang; Lin, Liyan; Lu, Zhentai; Liu, Shupeng; Wu, Yao; Feng, Qianjin; Chen, Wufan

    2018-04-01

    Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through k-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 ± 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 ± 1.15 dB, a relative MAE of 1.56 ± 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 ± 0.107% in , as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.

  7. Learning a Language with Web 2.0: Exploring the Use of Social Networking Features of Foreign Language Learning Websites

    Stevenson, Megan P.; Liu, Min

    2010-01-01

    This paper presents the results of an online survey and a usability test performed on three foreign language learning websites that use Web 2.0 technology. The online survey was conducted to gain an understanding of how current users of language learning websites use them for learning and social purposes. The usability test was conducted to gain…

  8. Scaling up spike-and-slab models for unsupervised feature learning.

    Goodfellow, Ian J; Courville, Aaron; Bengio, Yoshua

    2013-08-01

    We describe the use of two spike-and-slab models for modeling real-valued data, with an emphasis on their applications to object recognition. The first model, which we call spike-and-slab sparse coding (S3C), is a preexisting model for which we introduce a faster approximate inference algorithm. We introduce a deep variant of S3C, which we call the partially directed deep Boltzmann machine (PD-DBM) and extend our S3C inference algorithm for use on this model. We describe learning procedures for each. We demonstrate that our inference procedure for S3C enables scaling the model to unprecedented large problem sizes, and demonstrate that using S3C as a feature extractor results in very good object recognition performance, particularly when the number of labeled examples is low. We show that the PD-DBM generates better samples than its shallow counterpart, and that unlike DBMs or DBNs, the PD-DBM may be trained successfully without greedy layerwise training.

  9. Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning

    Arif Mehmood

    2017-01-01

    Full Text Available This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. Bibliometric research uses the evaluation criteria to find relevancy of articles instead. Researchers mostly face relevancy issues while searching articles. Therefore, they classify the articles manually. However, manual classification is burdensome due to time needed for evaluation. The proposed model performs automatic essay evaluation using multi-text features and ensemble machine learning. The proposed method is implemented in two data sets: a Kaggle short answer data set for essay scoring that includes four ranges of disciplines (Science, Biology, English, and English language Arts, and a bibliometric data set having IoT (Internet of Things and non-IoT classes. The efficacy of the model is measured against the Tandalla and AutoP approach using Cohen’s kappa. The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has better performance than those of earlier approaches.

  10. Automated age-related macular degeneration classification in OCT using unsupervised feature learning

    Venhuizen, Freerk G.; van Ginneken, Bram; Bloemen, Bart; van Grinsven, Mark J. J. P.; Philipsen, Rick; Hoyng, Carel; Theelen, Thomas; Sánchez, Clara I.

    2015-03-01

    Age-related Macular Degeneration (AMD) is a common eye disorder with high prevalence in elderly people. The disease mainly affects the central part of the retina, and could ultimately lead to permanent vision loss. Optical Coherence Tomography (OCT) is becoming the standard imaging modality in diagnosis of AMD and the assessment of its progression. However, the evaluation of the obtained volumetric scan is time consuming, expensive and the signs of early AMD are easy to miss. In this paper we propose a classification method to automatically distinguish AMD patients from healthy subjects with high accuracy. The method is based on an unsupervised feature learning approach, and processes the complete image without the need for an accurate pre-segmentation of the retina. The method can be divided in two steps: an unsupervised clustering stage that extracts a set of small descriptive image patches from the training data, and a supervised training stage that uses these patches to create a patch occurrence histogram for every image on which a random forest classifier is trained. Experiments using 384 volume scans show that the proposed method is capable of identifying AMD patients with high accuracy, obtaining an area under the Receiver Operating Curve of 0:984. Our method allows for a quick and reliable assessment of the presence of AMD pathology in OCT volume scans without the need for accurate layer segmentation algorithms.

  11. Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

    Yeom, Ha-Neul

    2014-09-01

    Full Text Available In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

  12. How do postgraduate GP trainees regulate their learning and what helps and hinders them? A qualitative study

    Sagasser, M.H.; Kramer, A.W.M.; Vleuten, C.P.M. van der

    2012-01-01

    ABSTRACT: BACKGROUND: Self-regulation is essential for professional development. It involves monitoring of performance, identifying domains for improvement, undertaking learning activities, applying newly learned knowledge and skills and self-assessing performance. Since self-assessment alone is

  13. Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning

    Vetrivel, Anand; Gerke, Markus; Kerle, Norman; Nex, Francesco; Vosselman, George

    2018-06-01

    Oblique aerial images offer views of both building roofs and façades, and thus have been recognized as a potential source to detect severe building damages caused by destructive disaster events such as earthquakes. Therefore, they represent an important source of information for first responders or other stakeholders involved in the post-disaster response process. Several automated methods based on supervised learning have already been demonstrated for damage detection using oblique airborne images. However, they often do not generalize well when data from new unseen sites need to be processed, hampering their practical use. Reasons for this limitation include image and scene characteristics, though the most prominent one relates to the image features being used for training the classifier. Recently features based on deep learning approaches, such as convolutional neural networks (CNNs), have been shown to be more effective than conventional hand-crafted features, and have become the state-of-the-art in many domains, including remote sensing. Moreover, often oblique images are captured with high block overlap, facilitating the generation of dense 3D point clouds - an ideal source to derive geometric characteristics. We hypothesized that the use of CNN features, either independently or in combination with 3D point cloud features, would yield improved performance in damage detection. To this end we used CNN and 3D features, both independently and in combination, using images from manned and unmanned aerial platforms over several geographic locations that vary significantly in terms of image and scene characteristics. A multiple-kernel-learning framework, an effective way for integrating features from different modalities, was used for combining the two sets of features for classification. The results are encouraging: while CNN features produced an average classification accuracy of about 91%, the integration of 3D point cloud features led to an additional

  14. Como los padres ocupados pueden ayudar a sus hijos a aprender y desarrollarse (How Busy Parents Can Help Their Children Learn and Develop). Early Childhood Digest.

    Mayer, Ellen; Kreider, Holly; Vaughan, Peggy

    Although parents are often very busy with work and family responsibilities, there are many things they can do to help their school-age children learn and develop. This Spanish-language early childhood digest for parents provides tips obtained from parents of first and second graders in the School Transition Study on creative ways to stay involved…

  15. Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution

    Kinnebrew, John S.; Biswas, Gautam

    2012-01-01

    Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…

  16. Creation of an Integrated Environment to Supply e-Learning Platforms with Office Automation Features

    Palumbo, Emilio; Verga, Francesca

    2015-01-01

    Over the last years great efforts have been made within the University environment to implement e-learning technologies in the standard educational practice. These learning technologies distribute online educational multimedia contents through technological platforms. Even though specific e-learning tools for technical disciplines were already…

  17. Improve Outcomes Study subjects Chemistry Teaching and Learning Strategies through independent study with the help of computer-based media

    Sugiharti, Gulmah

    2018-03-01

    This study aims to see the improvement of student learning outcomes by independent learning using computer-based learning media in the course of STBM (Teaching and Learning Strategy) Chemistry. Population in this research all student of class of 2014 which take subject STBM Chemistry as many as 4 class. While the sample is taken by purposive as many as 2 classes, each 32 students, as control class and expriment class. The instrument used is the test of learning outcomes in the form of multiple choice with the number of questions as many as 20 questions that have been declared valid, and reliable. Data analysis techniques used one-sided t test and improved learning outcomes using a normalized gain test. Based on the learning result data, the average of normalized gain values for the experimental class is 0,530 and for the control class is 0,224. The result of the experimental student learning result is 53% and the control class is 22,4%. Hypothesis testing results obtained t count> ttable is 9.02> 1.6723 at the level of significance α = 0.05 and db = 58. This means that the acceptance of Ha is the use of computer-based learning media (CAI Computer) can improve student learning outcomes in the course Learning Teaching Strategy (STBM) Chemistry academic year 2017/2018.

  18. Features of an effective operative dentistry learning environment: students' perceptions and relationship with performance.

    Suksudaj, N; Lekkas, D; Kaidonis, J; Townsend, G C; Winning, T A

    2015-02-01

    Students' perceptions of their learning environment influence the quality of outcomes they achieve. Learning dental operative techniques in a simulated clinic environment is characterised by reciprocal interactions between skills training, staff- and student-related factors. However, few studies have examined how students perceive their operative learning environments and whether there is a relationship between their perceptions and subsequent performance. Therefore, this study aimed to clarify which learning activities and interactions students perceived as supporting their operative skills learning and to examine relationships with their outcomes. Longitudinal data about examples of operative laboratory sessions that were perceived as effective or ineffective for learning were collected twice a semester, using written critical incidents and interviews. Emergent themes from these data were identified using thematic analysis. Associations between perceptions of learning effectiveness and performance were analysed using chi-square tests. Students indicated that an effective learning environment involved interactions with tutors and peers. This included tutors arranging group discussions to clarify processes and outcomes, providing demonstrations and constructive feedback. Feedback focused on mistakes, and not improvement, was reported as being ineffective for learning. However, there was no significant association between students' perceptions of the effectiveness of their learning experiences and subsequent performance. It was clear that learning in an operative technique setting involved various factors related not only to social interactions and observational aspects of learning but also to cognitive, motivational and affective processes. Consistent with studies that have demonstrated complex interactions between students, their learning environment and outcomes, other factors need investigation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  19. A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

    Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

    2014-01-01

    Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

  20. BLENDED LEARNING AND FEATURES OF THE USE OF THE ROTATION MODEL IN THE EDUCATIONAL PROCESS

    Tkachuk H.

    2017-12-01

    Full Text Available The article analyzes of the problem of blended learning in higher education institutions. In particular, the article analyzes the legislative documents about the implementation of information technologies in the educational process, strategies for higher education, the introduction of distance learning, that determine importance of blended learning. The author also analyzes the concept of blended learning based on the definitions that are considered in the scientific and pedagogical literature. That analysis determines the ambiguity and incorrectness of the different definitions. It was proposed author's definition for this term. For order to identify the benefits of blended learning, it was analyzed of the positive and negative aspects of all technologies that are combined in the system of blended learning. Based on the analysis of different learning models, it was determined that the most optimal models is the station rotation model and the flipped classroom. The article provides an example of the use of a combination of these models for learning the topic "Computer Structure" by the students of the direction of training "Informatics". The education session was taking place in several stages and involves changing the five stations. Based on the conducted research was identified the general didactic and methodical principles of organization of blended learning.

  1. Learning about Probability from Text and Tables: Do Color Coding and Labeling through an Interactive-User Interface Help?

    Clinton, Virginia; Morsanyi, Kinga; Alibali, Martha W.; Nathan, Mitchell J.

    2016-01-01

    Learning from visual representations is enhanced when learners appropriately integrate corresponding visual and verbal information. This study examined the effects of two methods of promoting integration, color coding and labeling, on learning about probabilistic reasoning from a table and text. Undergraduate students (N = 98) were randomly…

  2. Evaluating Computer-Based Simulations, Multimedia and Animations that Help Integrate Blended Learning with Lectures in First Year Statistics

    Neumann, David L.; Neumann, Michelle M.; Hood, Michelle

    2011-01-01

    The discipline of statistics seems well suited to the integration of technology in a lecture as a means to enhance student learning and engagement. Technology can be used to simulate statistical concepts, create interactive learning exercises, and illustrate real world applications of statistics. The present study aimed to better understand the…

  3. SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features

    Lafata, K; Ren, L; Wu, Q; Kelsey, C; Hong, J; Cai, J; Yin, F [Duke University Medical Center, Durham, NC (United States)

    2016-06-15

    Purpose: To develop a data-mining methodology based on quantum clustering and machine learning to predict expected dosimetric endpoints for lung SBRT applications based on patient-specific anatomic features. Methods: Ninety-three patients who received lung SBRT at our clinic from 2011–2013 were retrospectively identified. Planning information was acquired for each patient, from which various features were extracted using in-house semi-automatic software. Anatomic features included tumor-to-OAR distances, tumor location, total-lung-volume, GTV and ITV. Dosimetric endpoints were adopted from RTOG-0195 recommendations, and consisted of various OAR-specific partial-volume doses and maximum point-doses. First, PCA analysis and unsupervised quantum-clustering was used to explore the feature-space to identify potentially strong classifiers. Secondly, a multi-class logistic regression algorithm was developed and trained to predict dose-volume endpoints based on patient-specific anatomic features. Classes were defined by discretizing the dose-volume data, and the feature-space was zero-mean normalized. Fitting parameters were determined by minimizing a regularized cost function, and optimization was performed via gradient descent. As a pilot study, the model was tested on two esophageal dosimetric planning endpoints (maximum point-dose, dose-to-5cc), and its generalizability was evaluated with leave-one-out cross-validation. Results: Quantum-Clustering demonstrated a strong separation of feature-space at 15Gy across the first-and-second Principle Components of the data when the dosimetric endpoints were retrospectively identified. Maximum point dose prediction to the esophagus demonstrated a cross-validation accuracy of 87%, and the maximum dose to 5cc demonstrated a respective value of 79%. The largest optimized weighting factor was placed on GTV-to-esophagus distance (a factor of 10 greater than the second largest weighting factor), indicating an intuitively strong

  4. Do “trainee-centered ward rounds” help overcome barriers to learning and improve the learning satisfaction of junior doctors in the workplace?

    Acharya V

    2015-10-01

    Full Text Available Vikas Acharya,1Amir Reyahi,2 Samuel M Amis,3 Sami Mansour2 1Department of Neurosurgery, University Hospitals Coventry and Warwickshire, Coventry, 2Luton and Dunstable University Hospital, Luton, 3Warwick Medical School, University of Warwick, Coventry, UK Abstract: Ward rounds are widely considered an underutilized resource with regard to medical education, and therefore, a project was undertaken to assess if the initiation of “trainee-centered ward rounds” would help improve the confidence, knowledge acquisition, and workplace satisfaction of junior doctors in the clinical environment. Data were collated from junior doctors, registrar grade doctors, and consultants working in the delivery suite at Luton and Dunstable University Hospital in Luton over a 4-week period in March–April 2013. A review of the relevant literature was also undertaken. This pilot study found that despite the reservations around time constraints held by both junior and senior clinicians alike, feedback following the intervention was largely positive. The junior doctors enjoyed having a defined role and responsibility during the ward round and felt they benefited from their senior colleagues’ feedback. Both seniors and junior colleagues agreed that discussing learning objectives prior to commencing the round was beneficial and made the round more learner-orientated; this enabled maximal learner-focused outcomes to be addressed and met. The juniors were generally encouraged to participate more during the round and the consultants endeavored to narrate their decision-making, both were measures that led to greater satisfaction of both parties. This was in keeping with the concept of “Legitimate peripheral participation” as described by Lave and Wenger. Overall, trainee-centered ward rounds did appear to be effective in overcoming some of the traditional barriers to teaching in the ward environment, although further work to formalize and quantify these findings

  5. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

    Hussain, Lal

    2018-06-01

    Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

  6. EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression

    Cukic, Milena; Pokrajac, David; Stokic, Miodrag; Simic, slobodan; Radivojevic, Vlada; Ljubisavljevic, Milos

    2018-01-01

    Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. In this study, we aimed to elucidate the effectiveness of two non-linear measures, Higuchi Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linea...

  7. The Attitude of Math Teachers toward Cooperative Learning and Institutional Elements that May Help or Hinder its Use as a Teaching Methodology

    Luis Gerardo Meza-Cascante

    2015-01-01

    Full Text Available This paper presents the results of research conducted in high schools in the central region of the Cartago province, Costa Rica. The goal of the research was to determine the attitude of high school math teachers toward cooperative learning in math and identify factors in secondary education institutions that can help or hinder the implementation of cooperative learning as a strategy for teaching mathematics. The research was conducted with 39 secondary education math teachers, who participated in a workshop on cooperative learning in mathematics. The attitude toward this methodology was measured by using semantic differential. This information was triangulated with data obtained from non-participant observation. A combination of in-depth interviews and non-participant observation was used to access data that identifies institutional factors helping or hindering the implementation of math cooperative learning. Findings suggest a positive attitude from teachers toward integrating cooperative work as a teaching strategy to promote math learning and toward the role played by school principals in the adoption of educational innovations. It also reveals that high schools have adequate material conditions to implement the methodology, although the need for training is considered. This finding should be taken into account by the proponents of this methodological option.

  8. Do "trainee-centered ward rounds" help overcome barriers to learning and improve the learning satisfaction of junior doctors in the workplace?

    Acharya, Vikas; Reyahi, Amir; Amis, Samuel M; Mansour, Sami

    2015-01-01

    Ward rounds are widely considered an underutilized resource with regard to medical education, and therefore, a project was undertaken to assess if the initiation of "trainee-centered ward rounds" would help improve the confidence, knowledge acquisition, and workplace satisfaction of junior doctors in the clinical environment. Data were collated from junior doctors, registrar grade doctors, and consultants working in the delivery suite at Luton and Dunstable University Hospital in Luton over a 4-week period in March-April 2013. A review of the relevant literature was also undertaken. This pilot study found that despite the reservations around time constraints held by both junior and senior clinicians alike, feedback following the intervention was largely positive. The junior doctors enjoyed having a defined role and responsibility during the ward round and felt they benefited from their senior colleagues' feedback. Both seniors and junior colleagues agreed that discussing learning objectives prior to commencing the round was beneficial and made the round more learner-orientated; this enabled maximal learner-focused outcomes to be addressed and met. The juniors were generally encouraged to participate more during the round and the consultants endeavored to narrate their decision-making, both were measures that led to greater satisfaction of both parties. This was in keeping with the concept of "Legitimate peripheral participation" as described by Lave and Wenger. Overall, trainee-centered ward rounds did appear to be effective in overcoming some of the traditional barriers to teaching in the ward environment, although further work to formalize and quantify these findings, as well as using greater sample sizes from different hospital departments and the inclusion of a control group, is needed.

  9. Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach

    Salcedo-Sanz, S.; Pastor-Sánchez, A.; Prieto, L.; Blanco-Aguilera, A.; García-Herrera, R.

    2014-01-01

    Highlights: • A novel approach for short-term wind speed prediction is presented. • The system is formed by a coral reefs optimization algorithm and an extreme learning machine. • Feature selection is carried out with the CRO to improve the ELM performance. • The method is tested in real wind farm data in USA, for the period 2007–2008. - Abstract: This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks’ training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO–ELM approach in this FSP wind speed prediction problem

  10. SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature

    Shengli Song

    2016-08-01

    Full Text Available Automatic target recognition (ATR in synthetic aperture radar (SAR images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG-like feature for SAR ATR (called SAR-HOG. Then, we propose a supervised discriminative dictionary learning (SDDL method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR, in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC scenario “Training 17 ∘ —Testing 45 ∘ ”, the proposed method improves remarkably with respect to the previous works.

  11. Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis.

    Burlina, Philippe; Pacheco, Katia D; Joshi, Neil; Freund, David E; Bressler, Neil M

    2017-03-01

    When left untreated, age-related macular degeneration (AMD) is the leading cause of vision loss in people over fifty in the US. Currently it is estimated that about eight million US individuals have the intermediate stage of AMD that is often asymptomatic with regard to visual deficit. These individuals are at high risk for progressing to the advanced stage where the often treatable choroidal neovascular form of AMD can occur. Careful monitoring to detect the onset and prompt treatment of the neovascular form as well as dietary supplementation can reduce the risk of vision loss from AMD, therefore, preferred practice patterns recommend identifying individuals with the intermediate stage in a timely manner. Past automated retinal image analysis (ARIA) methods applied on fundus imagery have relied on engineered and hand-designed visual features. We instead detail the novel application of a machine learning approach using deep learning for the problem of ARIA and AMD analysis. We use transfer learning and universal features derived from deep convolutional neural networks (DCNN). We address clinically relevant 4-class, 3-class, and 2-class AMD severity classification problems. Using 5664 color fundus images from the NIH AREDS dataset and DCNN universal features, we obtain values for accuracy for the (4-, 3-, 2-) class classification problem of (79.4%, 81.5%, 93.4%) for machine vs. (75.8%, 85.0%, 95.2%) for physician grading. This study demonstrates the efficacy of machine grading based on deep universal features/transfer learning when applied to ARIA and is a promising step in providing a pre-screener to identify individuals with intermediate AMD and also as a tool that can facilitate identifying such individuals for clinical studies aimed at developing improved therapies. It also demonstrates comparable performance between computer and physician grading. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. FEATURES OF ORGANIZATION OF BLENDED LEARNING IN PREPARATION OF FUTURE TEACHERS OF INFORMATICS

    Inna Stoliarenko

    2015-10-01

    Full Text Available The term "blended learning" described by domestic and foreign scientists is considered in the article. A number of advantages of blended learning have been marked out in comparison with traditional one: flexibility, learning personification, increase of motivation of students to training, variety of forms of arrangement of educational process and forms of presentation of teaching material and increase of efficiency of activity of the teacher. A set of key competencies a teacher should possess to support effective activity in the mixed educational environment has been analyzed. The scientists of the Learning Accelerator organization engaged in support of introduction of blended learning in American schools presented it. It is determined that its main difference from a teacher who uses traditional methods and training forms – desire to experiment, introducing various innovative pedagogical technologies in educational process to achieve maximum result. There is also a desire to create favorable conditions for successful learning of each student considering strong and weak sides. The scientists of Clayton Christensen Institute designed the models of organization of blended learning. These models were analyzed. Two expedient models for implementation in higher school, in particular, in preparation of future teachers of informatics have been defined: station rotation and "flipped classroom".

  13. A Machine Learning Approach to Measurement of Text Readability for EFL Learners Using Various Linguistic Features

    Kotani, Katsunori; Yoshimi, Takehiko; Isahara, Hitoshi

    2011-01-01

    The present paper introduces and evaluates a readability measurement method designed for learners of EFL (English as a foreign language). The proposed readability measurement method (a regression model) estimates the text readability based on linguistic features, such as lexical, syntactic and discourse features. Text readability refers to the…

  14. Discovery and analysis of topographic features using learning algorithms: A seamount case study

    Valentine, A.P.; Kalnins, L.M.; Trampert, J.

    2013-01-01

    Identifying and cataloging occurrences of particular topographic features are important but time-consuming tasks. Typically, automation is challenging, as simple models do not fully describe the complexities of natural features. We propose a new approach, where a particular class of neural network

  15. Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

    2013-01-01

    Background Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers. Results In the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3-input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81%; AUC = 0.90) for the oral cancer prognosis. Conclusions The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies. PMID:23725313

  16. A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

    Torija, Antonio J; Ruiz, Diego P

    2015-02-01

    The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Learning From Our Past: How a Vietnam-Era Pacification Program Can Help Us Win in Afghanistan

    2009-09-01

    it as an operational and management problem—it was everybody’s business and nobody’s. It fell between the cracks . The reason I began zeroing in on...kind of help from us.”264 Farming programs can also help revive the economy by growing what other nations want to import: pomegranates , almonds...pistachios, raisins, and fruits such as apricots that can be dried or turned into juice.265 The Agribusiness Development Teams manned by state- based

  18. Eight-Legged Encounters—Arachnids, Volunteers, and Art help to Bridge the Gap between Informal and Formal Science Learning

    Hebets, Eileen A.; Welch-Lazoritz, Melissa; Tisdale, Pawl; Wonch Hill, Trish

    2018-01-01

    Increased integration and synergy between formal and informal learning environments is proposed to provide multiple benefits to science learners. In an effort to better bridge these two learning contexts, we developed an educational model that employs the charismatic nature of arachnids to engage the public of all ages in science learning; learning that aligns with the Next Generation Science Standards (NGSS Disciplinary Core Ideas associated with Biodiversity and Evolution). We created, implemented, and evaluated a family-focused, interactive science event—Eight-Legged Encounters (ELE)—which encompasses more than twenty modular activities. Volunteers facilitated participant involvement at each activity station and original artwork scattered throughout the event was intended to attract visitors. Initial ELE goals were to increase interest in arachnids and science more generally, among ELE participants. In this study, we tested the efficacy of ELE in terms of (i) activity-specific visitation rates and self-reported interest levels, (ii) the self-reported efficacy of our use of volunteers and original artwork on visitor engagement, and (iii) self-reported increases in interest in both spiders and science more generally. We collected survey data across five ELE events at four museum and zoo sites throughout the Midwest. We found that all activities were successful at attracting visitors and capturing their interest. Both volunteers and artwork were reported to be effective at engaging visitors, though likely in different ways. Additionally, most participants reported increased interest in learning about arachnids and science. In summary, ELE appears effective at engaging the public and piquing their interest. Future work is now required to assess learning outcomes directly, as well as the ability for participants to transfer knowledge gain across learning environments. PMID:29495395

  19. Eight-Legged Encounters-Arachnids, Volunteers, and Art help to Bridge the Gap between Informal and Formal Science Learning.

    Hebets, Eileen A; Welch-Lazoritz, Melissa; Tisdale, Pawl; Wonch Hill, Trish

    2018-02-26

    Increased integration and synergy between formal and informal learning environments is proposed to provide multiple benefits to science learners. In an effort to better bridge these two learning contexts, we developed an educational model that employs the charismatic nature of arachnids to engage the public of all ages in science learning; learning that aligns with the Next Generation Science Standards (NGSS Disciplinary Core Ideas associated with Biodiversity and Evolution). We created, implemented, and evaluated a family-focused, interactive science event- Eight-Legged Encounters (ELE )-which encompasses more than twenty modular activities. Volunteers facilitated participant involvement at each activity station and original artwork scattered throughout the event was intended to attract visitors. Initial ELE goals were to increase interest in arachnids and science more generally, among ELE participants. In this study, we tested the efficacy of ELE in terms of (i) activity-specific visitation rates and self-reported interest levels, (ii) the self-reported efficacy of our use of volunteers and original artwork on visitor engagement, and (iii) self-reported increases in interest in both spiders and science more generally. We collected survey data across five ELE events at four museum and zoo sites throughout the Midwest. We found that all activities were successful at attracting visitors and capturing their interest. Both volunteers and artwork were reported to be effective at engaging visitors, though likely in different ways. Additionally, most participants reported increased interest in learning about arachnids and science. In summary, ELE appears effective at engaging the public and piquing their interest. Future work is now required to assess learning outcomes directly, as well as the ability for participants to transfer knowledge gain across learning environments.

  20. Eight-Legged Encounters—Arachnids, Volunteers, and Art help to Bridge the Gap between Informal and Formal Science Learning

    Eileen A. Hebets

    2018-02-01

    Full Text Available Increased integration and synergy between formal and informal learning environments is proposed to provide multiple benefits to science learners. In an effort to better bridge these two learning contexts, we developed an educational model that employs the charismatic nature of arachnids to engage the public of all ages in science learning; learning that aligns with the Next Generation Science Standards (NGSS Disciplinary Core Ideas associated with Biodiversity and Evolution. We created, implemented, and evaluated a family-focused, interactive science event—Eight-Legged Encounters (ELE—which encompasses more than twenty modular activities. Volunteers facilitated participant involvement at each activity station and original artwork scattered throughout the event was intended to attract visitors. Initial ELE goals were to increase interest in arachnids and science more generally, among ELE participants. In this study, we tested the efficacy of ELE in terms of (i activity-specific visitation rates and self-reported interest levels, (ii the self-reported efficacy of our use of volunteers and original artwork on visitor engagement, and (iii self-reported increases in interest in both spiders and science more generally. We collected survey data across five ELE events at four museum and zoo sites throughout the Midwest. We found that all activities were successful at attracting visitors and capturing their interest. Both volunteers and artwork were reported to be effective at engaging visitors, though likely in different ways. Additionally, most participants reported increased interest in learning about arachnids and science. In summary, ELE appears effective at engaging the public and piquing their interest. Future work is now required to assess learning outcomes directly, as well as the ability for participants to transfer knowledge gain across learning environments.

  1. Feature selection and multi-kernel learning for sparse representation on a manifold

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2014-01-01

    combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity

  2. Work-based learning experiences help students with disabilities transition to careers: a case study of University of Washington projects.

    Bellman, Scott; Burgstahler, Sheryl; Ladner, Richard

    2014-01-01

    This case study describes evidence-based practices employed by a collection of University of Washington projects that engage high school and postsecondary students with disabilities in work-based learning experiences such as industry and research internships, career development activities, job shadows, field trips, and mock interviews. The purpose of the article is two-fold. First, authors share best practices with others who wish to increase the participation of students with disabilities in work-based learning and thereby contribute to their academic and career success. The article discusses methods used to recruit students, employers and mentors, match students with specific opportunities, and prepare students for success. Second, authors share outcomes from studies regarding participation in these work-based learning opportunities, which include increased employment success, motivation to work toward a career, knowledge about careers and the workplace, job-related skills, ability to work with supervisors and coworkers, skills in self-advocating for accommodations, and perceived career options.

  3. The Helping Horse: How Equine Assisted Learning Contributes to the Wellbeing of First Nations Youth in Treatment for Volatile Substance Misuse

    Adams, Cindy; Arratoon, Cheryl; Boucher, Janice; Cartier, Gail; Chalmers, Darlene; Dell, Colleen Anne; Dell, Debra; Dryka, Dominique; Duncan, Randy; Dunn, Kathryn; Hopkins, Carol; Longclaws, Loni; MacKinnon, Tamara; Sauve, Ernie; Spence, Serene; Wuttunee, Mallory

    2015-01-01

    There has been recent interest in Canada exploring the benefits of equine assisted interventions in the treatment of First Nations youth who misuse volatile substances. Using the richness of an exploratory case study involving the White Buffalo Youth Inhalant Treatment Centre and the Cartier Equine Learning Center, our community-based study examined the question of how an Equine Assisted Learning (EAL) program contributes to the wellbeing of First Nations female youth who misuse volatile substances. Both programs are grounded in a holistic bio-psycho-social-spiritual framework of healing. Our study shares how the EAL horses, facilitators and program content contributed to youths’ wellbeing in each area of the healing framework (bio-psycho-social-spiritual), with emphasis on the cultural significance of the horse and its helping role. The horse is a helper in the girls’ journeys toward improved wellbeing—the horse helps through its very nature as a highly instinctive animal, it helps the facilitators do their jobs, and it also helps put the treatment program activities into practice. In addition, the role of First Nations culture in the girls’ lives was enhanced through their encounters with the horses. The findings support the limited literature on equine assisted interventions and add important insights to the youth addictions treatment literature. Key implications to consider for EAL and volatile substance misuse policy, practice and research are identified. PMID:26793794

  4. Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

    Shi, Bibo; Grimm, Lars J; Mazurowski, Maciej A; Baker, Jay A; Marks, Jeffrey R; King, Lorraine M; Maley, Carlo C; Hwang, E Shelley; Lo, Joseph Y

    2018-03-01

    The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. In this retrospective study, digital mammographic magnification views were collected for 99 subjects with DCIS at biopsy, 25 of which were subsequently upstaged to invasive cancer. A deep convolutional neural network model that was pretrained on nonmedical images (eg, animals, plants, instruments) was used as the feature extractor. Through a statistical pooling strategy, deep features were extracted at different levels of convolutional layers from the lesion areas, without sacrificing the original resolution or distorting the underlying topology. A multivariate classifier was then trained to predict which tumors contain occult invasive disease. This was compared with the performance of traditional "handcrafted" computer vision (CV) features previously developed specifically to assess mammographic calcifications. The generalization performance was assessed using Monte Carlo cross-validation and receiver operating characteristic curve analysis. Deep features were able to distinguish DCIS with occult invasion from pure DCIS, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval, 0.68-0.73). This performance was comparable with the handcrafted CV features (area under the curve = 0.68; 95% confidence interval, 0.66-0.71) that were designed with prior domain knowledge. Despite being pretrained on only nonmedical images, the deep features extracted from digital mammograms demonstrated comparable performance with handcrafted CV features for the challenging task of predicting DCIS upstaging. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  5. How Iconicity Helps People Learn New Words: Neural Correlates and Individual Differences in Sound-Symbolic Bootstrapping

    Gwilym Lockwood

    2016-07-01

    Full Text Available Sound symbolism is increasingly understood as involving iconicity, or perceptual analogies and cross-modal correspondences between form and meaning, but the search for its functional and neural correlates is ongoing. Here we study how people learn sound-symbolic words, using behavioural, electrophysiological and individual difference measures. Dutch participants learned Japanese ideophones —lexical sound- symbolic words— with a translation of either the real meaning (in which form and meaning show cross-modal correspondences or the opposite meaning (in which form and meaning show cross-modal clashes. Participants were significantly better at identifying the words they learned in the real condition, correctly remembering the real word pairing 86.7% of the time, but the opposite word pairing only 71.3% of the time. Analysing event-related potentials (ERPs during the test round showed that ideophones in the real condition elicited a greater P3 component and late positive complex than ideophones in the opposite condition. In a subsequent forced choice task, participants were asked to guess the real translation from two alternatives. They did this with 73.0% accuracy, well above chance level even for words they had encountered in the opposite condition, showing that people are generally sensitive to the sound-symbolic cues in ideophones. Individual difference measures showed that the ERP effect in the test round of the learning task was greater for participants who were more sensitive to sound symbolism in the forced choice task. The main driver of the difference was a lower amplitude of the P3 component in response to ideophones in the opposite condition, suggesting that people who are more sensitive to sound symbolism may have more difficulty to suppress conflicting cross-modal information. The findings provide new evidence that cross-modal correspondences between sound and meaning facilitate word learning, while cross-modal clashes make word

  6. A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning.

    Lu, Wei; Li, Zhe; Chu, Jinghui

    2017-04-01

    Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques have an increasingly important role in the early detection and diagnosis of breast cancer. In this paper, we propose an automated computer-aided diagnosis (CADx) framework for magnetic resonance imaging (MRI). The scheme consists of an ensemble of several machine learning-based techniques, including ensemble under-sampling (EUS) for imbalanced data processing, the Relief algorithm for feature selection, the subspace method for providing data diversity, and Adaboost for improving the performance of base classifiers. We extracted morphological, various texture, and Gabor features. To clarify the feature subsets' physical meaning, subspaces are built by combining morphological features with each kind of texture or Gabor feature. We tested our proposal using a manually segmented Region of Interest (ROI) data set, which contains 438 images of malignant tumors and 1898 images of normal tissues or benign tumors. Our proposal achieves an area under the ROC curve (AUC) value of 0.9617, which outperforms most other state-of-the-art breast MRI CADx systems. Compared with other methods, our proposal significantly reduces the false-positive classification rate. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

    Hor, Soheil; Moradi, Mehdi

    2016-12-01

    Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

  8. A Guide for Reading: How Parents Can Help Their Children Be Ready To Read and Ready To Learn = Guia Para Leer: Como los padres pueden preparar a sus hijos a leer y aprender desde la infancia.

    White House Initiative on Educational Excellence for Hispanic Americans, Washington, DC.

    As part of the White House Initiative on Educational Excellence for Hispanic Americans, this brochure (in English and Spanish) provides a guide to assist parents in helping their children become ready to read and to learn. The suggestions include: (1) talking to infants/toddlers to help them learn to speak and understand the meaning of words; (2)…

  9. Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

    Xin Wang

    2018-02-01

    Full Text Available To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

  10. E-Learning, Resilience and Change in Higher Education: Helping a University Cope after a Natural Disaster

    Ayebi-Arthur, Kofi

    2017-01-01

    This paper presents a case study of one College of Business (College of Business and Law from 2013) impacted in 2011 by earthquakes in New Zealand. Analyses from interviews of nine staff and documents were used to describe processes of increasing resilience with e-learning over the worst seismic events. Increasing deployment of the University's…

  11. The Light Bulb Clicks on: Consultants Help Teachers, Administrators, and Coaches See the Value of Learning Teams

    Tobia, Ed; Chauvin, Ramona; Lewis, Dale; Hammel, Patti

    2011-01-01

    Sometimes partners find one another when they're not looking. In South Carolina, education leaders at Georgetown County School District were seeking only information when they attended a workshop sponsored by the South Carolina Department of Education. The two-day learning experience, provided by SEDL, a nonprofit organization based in Austin,…

  12. Applied Explanatory Style, Self-Esteem, and Early-Adolescents with Learning Disabilities: An Informational Website for Helping Professionals

    Saks, Brian C.

    2010-01-01

    Approximately 2.6 million students are diagnosed with a learning disability (LD) in the United States. There are many negative psychological and psychosocial consequences that can be attributed to having a LD, including a decrease in self- esteem. Low self-esteem has been shown to be liked to depression, suicidal ideation, and anxiety. Early…

  13. Constructing Videocases to Help Novices Learn to Facilitate Discussions in Science and English: How Does Subject Matter Matter?

    Rosaen, Cheryl L.; Lundeberg, Mary; Terpstra, Marjorie; Cooper, Marjorie; Niu, Rui; Fu, Jing

    2010-01-01

    Learning to conduct interactive classroom discussions is a high priority for becoming an effective teacher, and most teachers view conducting productive classroom discussions as a complex undertaking. Because the dynamics of facilitating classroom discussions are multifaceted and hard to analyze in real time, there is a growing interest in how…

  14. The Use of Linking Adverbials in Academic Essays by Non-Native Writers: How Data-Driven Learning Can Help

    Garner, James Robert

    2013-01-01

    Over the past several decades, the TESOL community has seen an increased interest in the use of data-driven learning (DDL) approaches. Most studies of DDL have focused on the acquisition of vocabulary items, including a wide range of information necessary for their correct usage. One type of vocabulary that has yet to be properly investigated has…

  15. Helping reasoners succeed in the Wason selection task: when executive learning discourages heuristic response but does not necessarily encourage logic.

    Sandrine Rossi

    Full Text Available Reasoners make systematic logical errors by giving heuristic responses that reflect deviations from the logical norm. Influential studies have suggested first that our reasoning is often biased because we minimize cognitive effort to surpass a cognitive conflict between heuristic response from system 1 and analytic response from system 2 thinking. Additionally, cognitive control processes might be necessary to inhibit system 1 responses to activate a system 2 response. Previous studies have shown a significant effect of executive learning (EL on adults who have transferred knowledge acquired on the Wason selection task (WST to another isomorphic task, the rule falsification task (RFT. The original paradigm consisted of teaching participants to inhibit a classical matching heuristic that sufficed the first problem and led to significant EL transfer on the second problem. Interestingly, the reasoning tasks differed in inhibiting-heuristic metacognitive cost. Success on the WST requires half-suppression of the matching elements. In contrast, the RFT necessitates a global rejection of the matching elements for a correct answer. Therefore, metacognitive learning difficulty most likely differs depending on whether one uses the first or second task during the learning phase. We aimed to investigate this difficulty and various matching-bias inhibition effects in a new (reversed paradigm. In this case, the transfer effect from the RFT to the WST could be more difficult because the reasoner learns to reject all matching elements in the first task. We observed that the EL leads to a significant reduction in matching selections on the WST without increasing logical performances. Interestingly, the acquired metacognitive knowledge was too "strictly" transferred and discouraged matching rather than encouraging logic. This finding underlines the complexity of learning transfer and adds new evidence to the pedagogy of reasoning.

  16. Helping reasoners succeed in the Wason selection task: when executive learning discourages heuristic response but does not necessarily encourage logic.

    Rossi, Sandrine; Cassotti, Mathieu; Moutier, Sylvain; Delcroix, Nicolas; Houdé, Olivier

    2015-01-01

    Reasoners make systematic logical errors by giving heuristic responses that reflect deviations from the logical norm. Influential studies have suggested first that our reasoning is often biased because we minimize cognitive effort to surpass a cognitive conflict between heuristic response from system 1 and analytic response from system 2 thinking. Additionally, cognitive control processes might be necessary to inhibit system 1 responses to activate a system 2 response. Previous studies have shown a significant effect of executive learning (EL) on adults who have transferred knowledge acquired on the Wason selection task (WST) to another isomorphic task, the rule falsification task (RFT). The original paradigm consisted of teaching participants to inhibit a classical matching heuristic that sufficed the first problem and led to significant EL transfer on the second problem. Interestingly, the reasoning tasks differed in inhibiting-heuristic metacognitive cost. Success on the WST requires half-suppression of the matching elements. In contrast, the RFT necessitates a global rejection of the matching elements for a correct answer. Therefore, metacognitive learning difficulty most likely differs depending on whether one uses the first or second task during the learning phase. We aimed to investigate this difficulty and various matching-bias inhibition effects in a new (reversed) paradigm. In this case, the transfer effect from the RFT to the WST could be more difficult because the reasoner learns to reject all matching elements in the first task. We observed that the EL leads to a significant reduction in matching selections on the WST without increasing logical performances. Interestingly, the acquired metacognitive knowledge was too "strictly" transferred and discouraged matching rather than encouraging logic. This finding underlines the complexity of learning transfer and adds new evidence to the pedagogy of reasoning.

  17. Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

    Tangaro, Sabina; Amoroso, Nicola; Brescia, Massimo; Cavuoti, Stefano; Chincarini, Andrea; Errico, Rosangela; Paolo, Inglese; Longo, Giuseppe; Maglietta, Rosalia; Tateo, Andrea; Riccio, Giuseppe; Bellotti, Roberto

    2015-01-01

    Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.

  18. Educating for Active Citizenship: Service-Learning, School-Based Service and Youth Civic Engagement. Youth Helping America Series

    Spring, Kimberly; Dietz, Nathan; Grimm, Robert, Jr.

    2006-01-01

    This brief is the second in the Youth Helping America Series, a series of reports based on data from the Youth Volunteering and Civic Engagement Survey, a national survey of 3,178 American youth between the ages of 12 and 18 that was conducted by the Corporation for National and Community Service in 2005 in collaboration with the U.S. Census…

  19. Benefits Access for College Completion: Lessons Learned from a Community College Initiative to Help Low-Income Students

    Duke-Benfield, Amy Ellen; Saunders, Katherine

    2016-01-01

    This report analyzes how students were served by Benefits Access for College Completion (BACC), a 2.5-year initiative designed to increase access to public benefits (such as SNAP or Medicaid) for eligible low-income students. These crucial supports reduce students' unmet financial needs and help them finish school. Launched in 2011, BACC funded…

  20. "Never Again"? Helping Year 9 Think about What Happened after the Holocaust and Learning Lessons from Genocides

    Kelleway, Elisabeth; Spillane, Thomas; Haydn, Terry

    2013-01-01

    "Never again" is the clarion call of much Holocaust and genocide education. There is a danger, however, that it can become an empty, if pious, wish. How can we help pupils reflect seriously on genocide prevention? Elisabeth Kellaway, Thomas Spillane and Terry Haydn report teaching strategies that focused students' attention on what came…

  1. Attending to Structural Programming Features Predicts Differences in Learning and Motivation

    Witherspoon, Eben B.; Schunn, Christian D.; Higashi, Ross M.; Shoop, Robin

    2018-01-01

    Educational robotics programs offer an engaging opportunity to potentially teach core computer science concepts and practices in K-12 classrooms. Here, we test the effects of units with different programming content within a virtual robotics context on both learning gains and motivational changes in middle school (6th-8th grade) robotics…

  2. Essential Features of Serious Games Design in Higher Education: Linking Learning Attributes to Game Mechanics

    Lameras, Petros; Arnab, Sylvester; Dunwell, Ian; Stewart, Craig; Clarke, Samantha; Petridis, Panagiotis

    2017-01-01

    This paper consolidates evidence and material from a range of specialist and disciplinary fields to provide an evidence-based review and synthesis on the design and use of serious games in higher education. Search terms identified 165 papers reporting conceptual and empirical evidence on how learning attributes and game mechanics may be planned,…

  3. Using machine learning to classify image features from canine pelvic radiographs

    McEvoy, Fintan; Amigo Rubio, Jose Manuel

    2013-01-01

    As the number of images per study increases in the field of veterinary radiology, there is a growing need for computer-assisted diagnosis techniques. The purpose of this study was to evaluate two machine learning statistical models for automatically identifying image regions that contain the canine...

  4. Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

    Harley, Jason M.; Lajoie, Susanne P.; Frasson, Claude; Hall, Nathan C.

    2017-01-01

    A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven approaches to enhance learners' experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to…

  5. Adding Instructional Features that Promote Learning in a Game-Like Environment

    Mayer, Richard E.; Johnson, Cheryl I.

    2010-01-01

    Students learned about electrical circuits in an arcade-type game consisting of 10 levels. For example, in one level students saw two circuits consisting of various batteries and resistors connected in series or parallel, and had to indicate which one had a higher rate of moving current. On levels 1-9, all students received a correct tone and had…

  6. Bayesian feature weighting for unsupervised learning, with application to object recognition

    Carbonetto , Peter; De Freitas , Nando; Gustafson , Paul; Thompson , Natalie

    2003-01-01

    International audience; We present a method for variable selection/weighting in an unsupervised learning context using Bayesian shrinkage. The basis for the model parameters and cluster assignments can be computed simultaneous using an efficient EM algorithm. Applying our Bayesian shrinkage model to a complex problem in object recognition (Duygulu, Barnard, de Freitas and Forsyth 2002), our experiments yied good results.

  7. Semantic Features, Perceptual Expectations, and Frequency as Factors in the Learning of Polar Spatial Adjective Concepts.

    Dunckley, Candida J. Lutes; Radtke, Robert C.

    Two semantic theories of word learning, a perceptual complexity hypothesis (H. Clark, 1970) and a quantitative complexity hypothesis (E. Clark, 1972) were tested by teaching 24 preschoolers and 16 college students CVC labels for five polar spatial adjective concepts having single word representations in English, and for three having no direct…

  8. A Comparison of the Linguistic and Interactional Features of Language Learning Websites and Textbooks

    Kong, Kenneth

    2009-01-01

    Self-study is playing an increasingly important role in the learning and instruction of many subjects, including second and foreign languages. With the rapid development of the internet, language websites for self-study are flourishing. While the language of print-based teaching materials has received some attention, the linguistic and…

  9. Multi-script handwritten character recognition : Using feature descriptors and machine learning

    Surinta, Olarik

    2016-01-01

    Handwritten character recognition plays an important role in transforming raw visual image data obtained from handwritten documents using for example scanners to a format which is understandable by a computer. It is an important application in the field of pattern recognition, machine learning and

  10. Implementing Motivational Features in Reactive Blended Learning: Application to an Introductory Control Engineering Course

    Mendez, J. A.; Gonzalez, E. J.

    2011-01-01

    This paper presents a significant advance in a reactive blended learning methodology applied to an introductory control engineering course. This proposal was based on the inclusion of a reactive element (a fuzzy-logic-based controller) designed to regulate the workload for each student according to his/her activity and performance. The…

  11. In vivo classification of human skin burns using machine learning and quantitative features captured by optical coherence tomography

    Singla, Neeru; Srivastava, Vishal; Singh Mehta, Dalip

    2018-02-01

    We report the first fully automated detection of human skin burn injuries in vivo, with the goal of automatic surgical margin assessment based on optical coherence tomography (OCT) images. Our proposed automated procedure entails building a machine-learning-based classifier by extracting quantitative features from normal and burn tissue images recorded by OCT. In this study, 56 samples (28 normal, 28 burned) were imaged by OCT and eight features were extracted. A linear model classifier was trained using 34 samples and 22 samples were used to test the model. Sensitivity of 91.6% and specificity of 90% were obtained. Our results demonstrate the capability of a computer-aided technique for accurately and automatically identifying burn tissue resection margins during surgical treatment.

  12. Acceptability of an e-learning program to help nursing assistants manage relationship conflict in nursing homes.

    Marziali, Elsa; Mackenzie, Corey Scott; Tchernikov, Illia

    2015-02-01

    Management of nursing assistants' (NAs) emotional stress from relationship conflicts with residents, families, and coworkers is rarely the focus of educational programs. Our objective was to gather feedback from NAs and their nursing supervisors (NSs) about the utility of our e-learning program for managing relationship stress. A total of 147 NAs and their NSs from 17 long-term care homes viewed the educational modules (DVD slides with voice-over), either individually or in small groups, and provided feedback using conference call focus groups. Qualitative analysis of NA feedback showed that workplace relationship conflict stress was associated with workload and the absence of a forum for discussing relationship conflicts that was not acknowledged by NSs. This accessible e-learning program provides NAs with strategies for managing stressful emotions arising from workplace relationship conflict situations and underscores the importance of supervisory support and team collaboration in coping with emotionally evoked workplace stress. © The Author(s) 2014.

  13. Developing an evidence-based curriculum designed to help psychiatric nurses learn to use computers and the Internet.

    Koivunen, Marita; Välimäki, Maritta; Jakobsson, Tiina; Pitkänen, Anneli

    2008-01-01

    This article describes the systematic process in which an evidence-based approach was used to develop a curriculum designed to support the computer and Internet skills of nurses in psychiatric hospitals in Finland. The pressure on organizations to have skilled and motivated nurses who use modern information and communication technology in health care organizations has increased due to rapid technology development at the international and national levels. However, less frequently has the development of those computer education curricula been based on evidence-based knowledge. First, we identified psychiatric nurses' learning experiences and barriers to computer use by examining written essays. Second, nurses' computer skills were surveyed. Last, evidence from the literature was scrutinized to find effective methods that can be used to teach and learn computer use in health care. This information was integrated and used for the development process of an education curriculum designed to support nurses' computer and Internet skills.

  14. Energy-Smart Building Choices: How Parents and Teachers Are Helping to Create Better Environments for Learning

    Energy Smart Schools Team

    2001-01-01

    Most K-12 schools could save 25% of their energy costs by being smart about energy. Nationwide, the savings potential is$6 billion. While improving energy use in buildings and busses, schools are likely to create better places for teaching and learning, with better lighting, temperature control, acoustics, and air quality. This brochure, targeted to parents and teachers, describes how schools can become more energy efficient

  15. YouTube as a potential learning tool to help distinguish tonic-clonic seizures from nonepileptic attacks.

    Muhammed, Louwai; Adcock, Jane E; Sen, Arjune

    2014-08-01

    Medical students are increasingly turning to the website YouTube as a learning resource. This study set out to determine whether the videos on YouTube accurately depict the type of seizures that a medical student may search for. Two consultant epileptologists independently assessed the top YouTube videos returned following searches for eight terms relating to different categories of seizures. The videos were rated for their technical quality, concordance of diagnosis with an epileptologist-assigned diagnosis, and efficacy as a learning tool for medical education. Of the 200 videos assessed, 106 (63%) met the inclusion criteria for further analysis. Technical quality was generally good and only interfered with the diagnostic process in 8.5% of the videos. Of the included videos, 40.6-46.2% were judged to depict the purported diagnosis with moderate agreement between raters (75% agreement, κ=0.50). Of the videos returned after searching "tonic-clonic seizure", 28.6-35.7% were judged to show nonepileptic seizures with almost perfect interrater agreement (92.9% agreement, κ=0.84). Of the videos returned following the search "pseudoseizure", 77.8-88.9% of videos were judged to show nonepileptic seizures with substantial agreement (88.9% agreement, κ=0.61). Across all search terms, 19.8-33% of videos were judged as potentially useful as a learning resource, with fair agreement between raters (75.5% agreement, κ=0.38). These findings suggest that the majority of videos on YouTube claiming to show specific seizure subtypes are inaccurate, and YouTube should not be recommended as a learning tool for students. However, a small group of videos provides excellent demonstrations of tonic-clonic and nonepileptic seizures, which could be used by an expert teacher to demonstrate the difference between epileptic and nonepileptic seizures. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. Do questions help? The impact of audience response systems on medical student learning: a randomised controlled trial.

    Mains, Tyler E; Cofrancesco, Joseph; Milner, Stephen M; Shah, Nina G; Goldberg, Harry

    2015-07-01

    Audience response systems (ARSs) are electronic devices that allow educators to pose questions during lectures and receive immediate feedback on student knowledge. The current literature on the effectiveness of ARSs is contradictory, and their impact on student learning remains unclear. This randomised controlled trial was designed to isolate the impact of ARSs on student learning and students' perception of ARSs during a lecture. First-year medical student volunteers at Johns Hopkins were randomly assigned to either (i) watch a recorded lecture on an unfamiliar topic in which three ARS questions were embedded or (ii) watch the same lecture without the ARS questions. Immediately after the lecture on 5 June 2012, and again 2 weeks later, both groups were asked to complete a questionnaire to assess their knowledge of the lecture content and satisfaction with the learning experience. 92 students participated. The mean (95% CI) initial knowledge assessment score was 7.63 (7.17 to 8.09) for the ARS group (N=45) and 6.39 (5.81 to 6.97) for the control group (N=47), p=0.001. Similarly, the second knowledge assessment mean score was 6.95 (6.38 to 7.52) for the ARS group and 5.88 (5.29 to 6.47) for the control group, p=0.001. The ARS group also reported higher levels of engagement and enjoyment. Embedding three ARS questions within a 30 min lecture increased students' knowledge immediately after the lecture and 2 weeks later. We hypothesise that this increase was due to forced information retrieval by students during the learning process, a form of the testing effect. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  17. Comparison of the Features of EPUB E-Book and SCORM E-Learning Content Model

    Chang, Hsuan-Pu; Hung, Jason C.

    2018-01-01

    E-books nowadays have greatly evolved in its presentation and functions, however its features for education need to be investigated and inspired because people who are accustomed to using printed books may consider and approach it in the same way as they do printed ones. Therefore, the authors compared the EPUB e-book content model with the SCORM…

  18. Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

    Wang, Jim Jing-Yan; Huang, Jianhua Z.; Sun, Yijun; Gao, Xin

    2014-01-01

    by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant

  19. Cell Phone Video Recording Feature as a Language Learning Tool: A Case Study

    Gromik, Nicolas A.

    2012-01-01

    This paper reports on a case study conducted at a Japanese national university. Nine participants used the video recording feature on their cell phones to produce weekly video productions. The task required that participants produce one 30-second video on a teacher-selected topic. Observations revealed the process of video creation with a cell…

  20. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

    McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

    2018-04-01

    Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. Learning to Help Through Humble Inquiry and Implications for Management Research, Practice, and Education: An Interview With Edgar H. Schein

    LAMBRECHTS, Frank; Bouwen, Rene; Grieten, Styn; HUYBRECHTS, Jolien; Schein, Edgar H.

    2011-01-01

    For more than 50 years, Edgar H. Schein, the Sloan Fellows Professor of Management Emeritus at the Massachusetts Institute of Technology's Sloan School of Management, has creatively shaped management and organizational scholarship and practice. He is the author of 15 books, including Process Consultation Revisited, Organizational Culture and Leadership, Career Anchors, Organizational Psychology, Career Dynamics, and Helping, as well as numerous articles in academic and professional journals. ...

  2. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review.

    Issenberg, S Barry; McGaghie, William C; Petrusa, Emil R; Lee Gordon, David; Scalese, Ross J

    2005-01-01

    1969 to 2003, 34 years. Simulations are now in widespread use in medical education and medical personnel evaluation. Outcomes research on the use and effectiveness of simulation technology in medical education is scattered, inconsistent and varies widely in methodological rigor and substantive focus. Review and synthesize existing evidence in educational science that addresses the question, 'What are the features and uses of high-fidelity medical simulations that lead to most effective learning?'. The search covered five literature databases (ERIC, MEDLINE, PsycINFO, Web of Science and Timelit) and employed 91 single search terms and concepts and their Boolean combinations. Hand searching, Internet searches and attention to the 'grey literature' were also used. The aim was to perform the most thorough literature search possible of peer-reviewed publications and reports in the unpublished literature that have been judged for academic quality. Four screening criteria were used to reduce the initial pool of 670 journal articles to a focused set of 109 studies: (a) elimination of review articles in favor of empirical studies; (b) use of a simulator as an educational assessment or intervention with learner outcomes measured quantitatively; (c) comparative research, either experimental or quasi-experimental; and (d) research that involves simulation as an educational intervention. Data were extracted systematically from the 109 eligible journal articles by independent coders. Each coder used a standardized data extraction protocol. Qualitative data synthesis and tabular presentation of research methods and outcomes were used. Heterogeneity of research designs, educational interventions, outcome measures and timeframe precluded data synthesis using meta-analysis. Coding accuracy for features of the journal articles is high. The extant quality of the published research is generally weak. The weight of the best available evidence suggests that high-fidelity medical

  3. Learned helplessness: unique features and translational value of a cognitive depression model.

    Vollmayr, Barbara; Gass, Peter

    2013-10-01

    The concept of learned helplessness defines an escape or avoidance deficit after uncontrollable stress and is regarded as a depression-like coping deficit in aversive but avoidable situations. Based on a psychological construct, it ideally complements other stress-induced or genetic animal models for major depression. Because of excellent face, construct, and predictive validity, it has contributed to the elaboration of several pathophysiological concepts and has brought forward new treatment targets. Whereas learned helplessness can be modeled not only in a broad variety of mammals, but also in fish and Drosophila, we will focus here on the use of this model in rats and mice, which are today the most common species for preclinical in vivo research in psychiatry.

  4. Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets.

    Boareto, Marcelo; Cesar, Jonatas; Leite, Vitor B P; Caticha, Nestor

    2015-01-01

    We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.

  5. Helping Your Child through Early Adolescence -- Helping Your Child Series

    ... Bibliography Acknowledgements Tips to Help Your Child through Early Adolescence No Child Left Behind Printable ... Information About... Transforming Teaching Family and Community Engagement Early Learning Helping Your Child Our mission is to promote student achievement and ...

  6. Of mice, birds, and men: the mouse ultrasonic song system has some features similar to humans and song-learning birds.

    Gustavo Arriaga

    Full Text Available Humans and song-learning birds communicate acoustically using learned vocalizations. The characteristic features of this social communication behavior include vocal control by forebrain motor areas, a direct cortical projection to brainstem vocal motor neurons, and dependence on auditory feedback to develop and maintain learned vocalizations. These features have so far not been found in closely related primate and avian species that do not learn vocalizations. Male mice produce courtship ultrasonic vocalizations with acoustic features similar to songs of song-learning birds. However, it is assumed that mice lack a forebrain system for vocal modification and that their ultrasonic vocalizations are innate. Here we investigated the mouse song system and discovered that it includes a motor cortex region active during singing, that projects directly to brainstem vocal motor neurons and is necessary for keeping song more stereotyped and on pitch. We also discovered that male mice depend on auditory feedback to maintain some ultrasonic song features, and that sub-strains with differences in their songs can match each other's pitch when cross-housed under competitive social conditions. We conclude that male mice have some limited vocal modification abilities with at least some neuroanatomical features thought to be unique to humans and song-learning birds. To explain our findings, we propose a continuum hypothesis of vocal learning.

  7. Struggling readers learning with graphic-rich digital science text: Effects of a Highlight & Animate Feature and Manipulable Graphics

    Defrance, Nancy L.

    Technology offers promise of 'leveling the playing field' for struggling readers. That is, instructional support features within digital texts may enable all readers to learn. This quasi-experimental study examined the effects on learning of two support features, which offered unique opportunities to interact with text. The Highlight & Animate Feature highlighted an important idea in prose, while simultaneously animating its representation in an adjacent graphic. It invited readers to integrate ideas depicted in graphics and prose, using each one to interpret the other. The Manipulable Graphics had parts that the reader could operate to discover relationships among phenomena. It invited readers to test or refine the ideas that they brought to, or gleaned from, the text. Use of these support features was compulsory. Twenty fifth grade struggling readers read a graphic-rich digital science text in a clinical interview setting, under one of two conditions: using either the Highlight & Animate Feature or the Manipulable Graphics. Participants in both conditions made statistically significant gains on a multiple choice measure of knowledge of the topic of the text. While there were no significant differences by condition in the amount of knowledge gained; there were significant differences in the quality of knowledge expressed. Transcripts revealed that understandings about light and vision, expressed by those who used the Highlight & Animate Feature, were more often conceptually and linguistically 'complete.' That is, their understandings included both a description of phenomena as well as an explanation of underlying scientific principles, which participants articulated using the vocabulary of the text. This finding may be attributed to the multiple opportunities to integrate graphics (depicting the behavior of phenomena) and prose (providing the scientific explanation of that phenomena), which characterized the Highlight & Animate Condition. Those who used the

  8. Vocal Connections: How Voicework in Music Therapy Helped a Young Girl with Severe Learning Disabilities and Autism to Engage in her Learning

    Tina Warnock

    2012-01-01

    Full Text Available This article examines the use of the non-verbal voice in music therapy with children with severe learning disabilities, complex needs and autism. Recent literature on the use of the voice in music therapy is summarised and links are made between the aims of music therapy and those of special educational establishments. Theories regarding the voice and the self, and the important connection between body awareness and emotion as precursors to learning are referred to, particularly in relation to learning disability. Through a case study, I demonstrate how a young girl used voicework to build connections with herself and the music therapist, whereby consequently she became more motivated to interact with her surroundings. I argue hence that the use of the non-verbal voice in music therapy, through its intrinsic connection to identity and internal emotional states can contribute significantly towards the healthy developments necessary for a person to be able to learn. Therefore, by increasing our knowledge about the actual process of learning, and the significance of our work within that process, we can move towards demonstrating clearer outcomes of music therapy in the educational context and have a stronger ‘voice’ within the multi-disciplinary teams that serve this population.

  9. Which bundles of features in a Web-based personally controlled health management system are associated with consumer help-seeking behaviors for physical and emotional well-being?

    Lau, Annie Y S; Proudfoot, Judith; Andrews, Annie; Liaw, Siaw-Teng; Crimmins, Jacinta; Arguel, Amaël; Coiera, Enrico

    2013-05-06

    Personally controlled health management systems (PCHMS), which include a personal health record (PHR), health management tools, and consumer resources, represent the next stage in consumer eHealth systems. It is still unclear, however, what features contribute to an engaging and efficacious PCHMS. To identify features in a Web-based PCHMS that are associated with consumer utilization of primary care and counselling services, and help-seeking rates for physical and emotional well-being concerns. A one-group pre/posttest online prospective study was conducted on a university campus to measure use of a PCHMS for physical and emotional well-being needs during a university academic semester (July to November 2011). The PCHMS integrated an untethered personal health record (PHR) with well-being journeys, social forums, polls, diaries, and online messaging links with a health service provider, where journeys provide information for consumer participants to engage with clinicians and health services in an actionable way. 1985 students and staff aged 18 and above with access to the Internet were recruited online. Logistic regression, the Pearson product-moment correlation coefficient, and chi-square analyses were used to associate participants' help-seeking behaviors and health service utilization with PCHMS usage among the 709 participants eligible for analysis. A dose-response association was detected between the number of times a user logged into the PCHMS and the number of visits to a health care professional (P=.01), to the university counselling service (P=.03), and help-seeking rates (formal or informal) for emotional well-being matters (P=.03). No significant association was detected between participant pre-study characteristics or well-being ratings at different PCHMS login frequencies. Health service utilization was strongly correlated with use of a bundle of features including: online appointment booking (primary care: OR 1.74, 95% CI 1.01-3.00; counselling: OR 6

  10. Cocaine impairs serial-feature negative learning and blood-brain barrier integrity.

    Davidson, Terry L; Hargrave, Sara L; Kearns, David N; Clasen, Matthew M; Jones, Sabrina; Wakeford, Alison G P; Sample, Camille H; Riley, Anthony L

    2018-05-10

    Previous research has shown that diets high in fat and sugar [a.k.a., Western diets (WD)] can impair performance of rats on hippocampal-dependent learning and memory problems, an effect that is accompanied by selective increases in hippocampal blood brain barrier (BBB) permeability. Based on these types of findings, it has been proposed that overeating of a WD (and its resulting obesity) may be, in part, a consequence of impairments in these anatomical substrates and cognitive processes. Given that drug use (and addiction) represents another behavioral excess, the present experiments assessed if similar outcomes might occur with drug exposure by evaluating the effects of cocaine administration on hippocampal-dependent memory and on the integrity of the BBB. Experiment 1 of the present series of studies found that systemic cocaine administration in rats also appears to have disruptive effects on the same hippocampal-dependent learning and memory mechanism that has been proposed to underlie the inhibition of food intake. Experiment 2 demonstrated that the same regimen of cocaine exposure that produced disruptions in learning and memory in Experiment 1 also produced increased BBB permeability in the hippocampus, but not in the striatum. Although the predominant focus of previous research investigating the etiologies of substance use and abuse has been on the brain circuits that underlie the motivational properties of drugs, the current investigation implicates the possible involvement of hippocampal memory systems in such behaviors. It is important to note that these positions are not mutually exclusive and that neuroadaptations in these two circuits might occur in parallel that generate dysregulated drug use in a manner similar to that of excessive eating. Copyright © 2018 Elsevier Inc. All rights reserved.

  11. The Identification, Implementation, and Evaluation of Critical User Interface Design Features of Computer-Assisted Instruction Programs in Mathematics for Students with Learning Disabilities

    Seo, You-Jin; Woo, Honguk

    2010-01-01

    Critical user interface design features of computer-assisted instruction programs in mathematics for students with learning disabilities and corresponding implementation guidelines were identified in this study. Based on the identified features and guidelines, a multimedia computer-assisted instruction program, "Math Explorer", which delivers…

  12. Concept mapping to promote meaningful learning, help relate theory to practice and improve learning self-efficacy in Asian mental health nursing students: A mixed-methods pilot study.

    Bressington, Daniel T; Wong, Wai-Kit; Lam, Kar Kei Claire; Chien, Wai Tong

    2018-01-01

    Student nurses are provided with a great deal of knowledge within university, but they can find it difficult to relate theory to nursing practice. This study aimed to test the appropriateness and feasibility of assessing Novak's concept mapping as an educational strategy to strengthen the theory-practice link, encourage meaningful learning and enhance learning self-efficacy in nursing students. This pilot study utilised a mixed-methods quasi-experimental design. The study was conducted in a University school of Nursing in Hong Kong. A total of 40 third-year pre-registration Asian mental health nursing students completed the study; 12 in the concept mapping (CM) group and 28 in the usual teaching methods (UTM) group. The impact of concept mapping was evaluated thorough analysis of quantitative changes in students' learning self-efficacy, analysis of the structure and contents of the concept maps (CM group), a quantitative measure of students' opinions about their reflective learning activities and content analysis of qualitative data from reflective written accounts (CM group). There were no significant differences in self-reported learning self-efficacy between the two groups (p=0.38). The concept mapping helped students identify their current level of understanding, but the increased awareness may cause an initial drop in learning self-efficacy. The results highlight that most CM students were able to demonstrate meaningful learning and perceived that concept mapping was a useful reflective learning strategy to help them to link theory and practice. The results provide preliminary evidence that the concept mapping approach can be useful to help mental health nursing students visualise their learning progress and encourage the integration of theoretical knowledge with clinical knowledge. Combining concept mapping data with quantitative measures and qualitative reflective journal data appears to be a useful way of assessing and understanding the effectiveness of

  13. Handheld Devices and Video Modeling to Enhance the Learning of Self-Help Skills in Adolescents With Autism Spectrum Disorder.

    Campbell, Joseph E; Morgan, Michele; Barnett, Veronica; Spreat, Scott

    2015-04-01

    The viewing of videos is a much-studied intervention to teach self-help, social, and vocational skills. Many of the studies to date looked at video modeling using televisions, computers, and other large screens. This study looked at the use of video modeling on portable handheld devices to teach hand washing to three adolescent students with an autism spectrum disorder. Three students participated in this 4-week study conducted by occupational therapists. Baseline data were obtained for the first student for 1 week, the second for 2 weeks, and the third for 3 weeks; videos were introduced when the participants each finished the baseline phase. Given the cognitive and motor needs of the participants, the occupational therapist set the player so that the participants only had to press the play button to start the video playing. The participants were able to hold the players and view at distances that were most appropriate for their individual needs and preferences. The results suggest that video modeling on a handheld device improves the acquisition of self-help skills.

  14. Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.

    Oakden-Rayner, Luke; Carneiro, Gustavo; Bessen, Taryn; Nascimento, Jacinto C; Bradley, Andrew P; Palmer, Lyle J

    2017-05-10

    Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous 'manual' clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research - mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives.

  15. Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

    Faust, Kevin; Xie, Quin; Han, Dominick; Goyle, Kartikay; Volynskaya, Zoya; Djuric, Ugljesa; Diamandis, Phedias

    2018-05-16

    There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.

  16. An analysis of feature relevance in the classification of astronomical transients with machine learning methods

    D'Isanto, A.; Cavuoti, S.; Brescia, M.; Donalek, C.; Longo, G.; Riccio, G.; Djorgovski, S. G.

    2016-04-01

    The exploitation of present and future synoptic (multiband and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MultiLayer Perceptron with Quasi Newton Algorithm and K-Nearest Neighbours, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extragalactic objects and identification of supernovae.

  17. A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets.

    Fernández, Alberto; Carmona, Cristobal José; José Del Jesus, María; Herrera, Francisco

    2017-09-01

    Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.

  18. Help Yourself, Help Your Students

    Luft, Julie A.; Bang, EunJin; Hewson, Peter W.

    2016-01-01

    Science teachers often participate in professional development programs (PDPs) to improve their students' learning. They sign up for workshops, institutes, university classes, or professional learning communities to gain knowledge and new instructional practices and to find colleagues with whom to discuss their teaching. But with so many options…

  19. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

    Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

    2017-07-18

    Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

  20. Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity.

    Webb, Samuel J; Hanser, Thierry; Howlin, Brendan; Krause, Paul; Vessey, Jonathan D

    2014-03-25

    A new algorithm has been developed to enable the interpretation of black box models. The developed algorithm is agnostic to learning algorithm and open to all structural based descriptors such as fragments, keys and hashed fingerprints. The algorithm has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints.A fragmentation algorithm is utilised to investigate the model's behaviour on specific substructures present in the query. An output is formulated summarising causes of activation and deactivation. The algorithm is able to identify multiple causes of activation or deactivation in addition to identifying localised deactivations where the prediction for the query is active overall. No loss in performance is seen as there is no change in the prediction; the interpretation is produced directly on the model's behaviour for the specific query. Models have been built using multiple learning algorithms including support vector machine and random forest. The models were built on public Ames mutagenicity data and a variety of fingerprint descriptors were used. These models produced a good performance in both internal and external validation with accuracies around 82%. The models were used to evaluate the interpretation algorithm. Interpretation was revealed that links closely with understood mechanisms for Ames mutagenicity. This methodology allows for a greater utilisation of the predictions made by black box models and can expedite further study based on the output for a (quantitative) structure activity model. Additionally the algorithm could be utilised for chemical dataset investigation and knowledge extraction/human SAR development.

  1. Search Help

    Guidance and search help resource listing examples of common queries that can be used in the Google Search Appliance search request, including examples of special characters, or query term seperators that Google Search Appliance recognizes.

  2. Constructing engineers through practice: Gendered features of learning and identity development

    Tonso, Karen L.

    How do women and men student engineers develop an engineering identity (a sense of belonging, or not), while practicing "actual" engineering? What are the influences of gender, learning and knowledge, relations of power, and conceptions of equality on cultural identity development? I studied these issues in reform-minded engineering design classes, courses organized around teaching students communications, teamwork, and practical engineering. Engineering-student cultural identity categories revealed a status hierarchy, predicated on meeting "academic" criteria for excellence, and the almost total exclusion of women. While working as an engineering colleague on five student teams (three first-year and two senior) and attending their design classes, I documented how cultural identities were made evident and constructed in students' practical engineering. Design projects promoted linking academic knowledge with real-world situations, sharing responsibilities and trusting colleagues, communicating engineering knowledge to technical and non-technical members of business communities, and addressing gaps in students' knowledge. With a curriculum analysis and survey of students' perceptions of the differences between design and conventional courses, I embedded the design classes in the wider campus and found that: (1) Engineering education conferred prestige, power, and well-paying jobs on students who performed "academic" engineering, while failing to adequately encourage "actual" engineering practices. High-status student engineers were the least likely to perform "actual" engineering in design teams. (2) Engineering education advanced an ideology that encouraged its practitioners to consider men's privilege and women's invisibility normal. By making "acting like men act" the standards to which engineering students must conform, women learned to put up with oppressive treatment. Women's accepting their own mistreatment and hiding their womanhood became a condition of

  3. Simulation as a Central Feature of an Elective Course: Does Simulated Bedside Care Impact Learning?

    Michael C. Thomas

    2018-05-01

    Full Text Available A three-credit, simulation-based, emergency medicine elective course was designed and offered to doctor of pharmacy students for two years. The primary objective was to determine if there was a difference in exam performance stratified by student simulation experience, namely either as an active observer or as part of bedside clinical care. The secondary objective was to report student satisfaction. Examination performance for simulation-based questions was compared based on the student role (evaluator versus clinical using the Student’s t-test. Summary responses from Likert scale-based student satisfaction responses were collected. A total of 24 students took the course: 12 in each offering. Performance was similar whether the student was assigned to the evaluation team or the clinical team for all of the comparisons (mid-term and final 2015 and 2016, all p-values > 0.05. Students were very satisfied with the course. Of the 19 questions assessing the qualitative aspects of the course, all of the students agreed or strongly agreed to 17 statements, and all of the students were neutral, agreed, or strongly agreed to the remaining two statements. Direct participation and active observation in simulation-based experiences appear to be equally valuable in the learning process, as evidenced by examination performance.

  4. Just-in-time learning is effective in helping first responders manage weapons of mass destruction events.

    Motola, Ivette; Burns, William A; Brotons, Angel A; Withum, Kelly F; Rodriguez, Richard D; Hernandez, Salma; Rivera, Hector F; Issenberg, Saul Barry; Schulman, Carl I

    2015-10-01

    Chemical, biologic, radiologic, nuclear, and explosive (CBRNE) incidents require specialized training. The low frequency of these events leads to significant skill decay among first responders. To address skill decay and lack of experience with these high-impact events, educational modules were developed for mobile devices to provide just-in-time training to first responders en route to a CBRNE event. This study assessed the efficacy and usability of the mobile training. Ninety first responders were randomized to a control or an intervention group. All participants completed a pretest to measure knowledge of CBRNE topics. The intervention group then viewed personal protective equipment and weapons of mass destruction field management videos as an overview. Both groups were briefed on a disaster scenario (chemical nerve agent, radiologic, or explosives) requiring them to triage, assess, and manage a patient. Intervention group participants watched a mobile training video corresponding to the scenario. The control group did not receive prescenario video training. Observers rated participant performance in each scenario. After completing the scenarios, all participants answered a cognitive posttest. Those in the intervention group also answered a questionnaire on their impressions of the training. The intervention group outperformed the control group in the explosives and chemical nerve agent scenarios; the differences were statistically significant (explosives, mean of 26.32 for intervention and 22.85 for control, p just-in-time training improved first-responder knowledge of CBRNE events and is an effective tool in helping first responders manage simulated explosive and chemical agent scenarios. Therapeutic/care management study, level II.

  5. Dimensional feature weighting utilizing multiple kernel learning for single-channel talker location discrimination using the acoustic transfer function.

    Takashima, Ryoichi; Takiguchi, Tetsuya; Ariki, Yasuo

    2013-02-01

    This paper presents a method for discriminating the location of the sound source (talker) using only a single microphone. In a previous work, the single-channel approach for discriminating the location of the sound source was discussed, where the acoustic transfer function from a user's position is estimated by using a hidden Markov model of clean speech in the cepstral domain. In this paper, each cepstral dimension of the acoustic transfer function is newly weighted, in order to obtain the cepstral dimensions having information that is useful for classifying the user's position. Then, this paper proposes a feature-weighting method for the cepstral parameter using multiple kernel learning, defining the base kernels for each cepstral dimension of the acoustic transfer function. The user's position is trained and classified by support vector machine. The effectiveness of this method has been confirmed by sound source (talker) localization experiments performed in different room environments.

  6. How to successfully publish interdisciplinary research: learning from an Ecology and Society Special Feature

    Christian Pohl

    2015-06-01

    Full Text Available What are the factors that hinder or support publishing interdisciplinary research? What does a successful interdisciplinary publishing process look like? We address these questions by analyzing the publishing process of the interdisciplinary research project titled "Mountland." Project researchers published most of their main results as a Special Feature of Ecology and Society. Using the story wall method and qualitative content analysis, we identified ten factors contributing to the success or failure of publishing interdisciplinary research. They can be assigned to four groups of resources: scientific resources, i.e., previous joint research, simultaneously written manuscripts; human resources, i.e., coordination, flexibility, composition of the team; integrative resources, i.e., vision of integration, chronology of results; and feedback resources, i.e., internal reviews, subject editors, external reviewers. According to this analysis, an ideal-typical publishing process necessitates, among other things, (1 a strong, interdisciplinary coordinator, (2 a clear shared vision of integration and a common framework, (3 flexibility in terms of money and time, (4 a certain sense of timing regarding when and how to exchange results and knowledge, (5 subject editors who are familiar with the specific project and its interdisciplinary merits, and (6 reviewers who are open minded about interdisciplinary efforts.

  7. Identification of Protein Pupylation Sites Using Bi-Profile Bayes Feature Extraction and Ensemble Learning

    Xiaowei Zhao

    2013-01-01

    Full Text Available Pupylation, one of the most important posttranslational modifications of proteins, typically takes place when prokaryotic ubiquitin-like protein (Pup is attached to specific lysine residues on a target protein. Identification of pupylation substrates and their corresponding sites will facilitate the understanding of the molecular mechanism of pupylation. Comparing with the labor-intensive and time-consuming experiment approaches, computational prediction of pupylation sites is much desirable for their convenience and fast speed. In this study, a new bioinformatics tool named EnsemblePup was developed that used an ensemble of support vector machine classifiers to predict pupylation sites. The highlight of EnsemblePup was to utilize the Bi-profile Bayes feature extraction as the encoding scheme. The performance of EnsemblePup was measured with a sensitivity of 79.49%, a specificity of 82.35%, an accuracy of 85.43%, and a Matthews correlation coefficient of 0.617 using the 5-fold cross validation on the training dataset. When compared with other existing methods on a benchmark dataset, the EnsemblePup provided better predictive performance, with a sensitivity of 80.00%, a specificity of 83.33%, an accuracy of 82.00%, and a Matthews correlation coefficient of 0.629. The experimental results suggested that EnsemblePup presented here might be useful to identify and annotate potential pupylation sites in proteins of interest. A web server for predicting pupylation sites was developed.

  8. Step 1: Learn about Diabetes

    ... please turn JavaScript on. Feature: Type 2 Diabetes Step 1: Learn About Diabetes Past Issues / Fall 2014 ... the whole family healthy! Here are four key steps to help you control your diabetes and live ...

  9. Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping.

    Sadeghi-Tehran, Pouria; Virlet, Nicolas; Sabermanesh, Kasra; Hawkesford, Malcolm J

    2017-01-01

    Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy. The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.

  10. Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

    Pouria Sadeghi-Tehran

    2017-11-01

    Full Text Available Abstract Background Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. Results In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1 comparison with ground-truth images, (2 variation along a day with changes in ambient illumination, (3 comparison with manual measurements and (4 an estimation of performance along the full life cycle of a wheat canopy. Conclusion The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.

  11. Vascular dynamics aid a coupled neurovascular network learn sparse independent features: A computational model

    Ryan Thomas Philips

    2016-02-01

    Full Text Available Cerebral vascular dynamics are generally thought to be controlled by neural activity in a unidirectional fashion. However, both computational modeling and experimental evidence point to the feedback effects of vascular dynamics on neural activity. Vascular feedback in the form of glucose and oxygen controls neuronal ATP, either directly or via the agency of astrocytes, which in turn modulates neural firing. Recently, a detailed model of the neuron-astrocyte-vessel system has shown how vasomotion can modulate neural firing. Similarly, arguing from known cerebrovascular physiology, an approach known as `hemoneural hypothesis' postulates functional modulation of neural activity by vascular feedback. To instantiate this perspective, we present a computational model in which a network of `vascular units' supplies energy to a neural network. The complex dynamics of the vascular network, modeled by a network of oscillators, turns neurons ON and OFF randomly. The informational consequence of such dynamics is explored in the context of an auto-encoder network. In the proposed model, each vascular unit supplies energy to a subset of hidden neurons of an autoencoder network, which constitutes its `projective field'. Neurons that receive adequate energy in a given trial have reduced threshold, and thus are prone to fire. Dynamics of the vascular network are governed by changes in the reconstruction error of the auto-encoder network, interpreted as the neuronal demand. Vascular feedback causes random inactivation of a subset of hidden neurons in every trial. We observe that, under conditions of desynchronized vascular dynamics, the output reconstruction error is low and the feature vectors learnt are sparse and independent. Our earlier modeling study highlighted the link between desynchronized vascular dynamics and efficient energy delivery in skeletal muscle. We now show that desynchronized vascular dynamics leads to efficient training in an auto

  12. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework.

    Song, Jiangning; Li, Fuyi; Takemoto, Kazuhiro; Haffari, Gholamreza; Akutsu, Tatsuya; Chou, Kuo-Chen; Webb, Geoffrey I

    2018-04-14

    Determining the catalytic residues in an enzyme is critical to our understanding the relationship between protein sequence, structure, function, and enhancing our ability to design novel enzymes and their inhibitors. Although many enzymes have been sequenced, and their primary and tertiary structures determined, experimental methods for enzyme functional characterization lag behind. Because experimental methods used for identifying catalytic residues are resource- and labor-intensive, computational approaches have considerable value and are highly desirable for their ability to complement experimental studies in identifying catalytic residues and helping to bridge the sequence-structure-function gap. In this study, we describe a new computational method called PREvaIL for predicting enzyme catalytic residues. This method was developed by leveraging a comprehensive set of informative features extracted from multiple levels, including sequence, structure, and residue-contact network, in a random forest machine-learning framework. Extensive benchmarking experiments on eight different datasets based on 10-fold cross-validation and independent tests, as well as side-by-side performance comparisons with seven modern sequence- and structure-based methods, showed that PREvaIL achieved competitive predictive performance, with an area under the receiver operating characteristic curve and area under the precision-recall curve ranging from 0.896 to 0.973 and from 0.294 to 0.523, respectively. We demonstrated that this method was able to capture useful signals arising from different levels, leveraging such differential but useful types of features and allowing us to significantly improve the performance of catalytic residue prediction. We believe that this new method can be utilized as a valuable tool for both understanding the complex sequence-structure-function relationships of proteins and facilitating the characterization of novel enzymes lacking functional annotations

  13. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

    Lili Chen

    2017-01-01

    Full Text Available Preterm birth (PTB is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT and extreme learning machine (ELM. For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs using empirical mode decomposition (EMD. Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

  14. High-quality and small-capacity e-learning video featuring lecturer-superimposing PC screen images

    Nomura, Yoshihiko; Murakami, Michinobu; Sakamoto, Ryota; Sugiura, Tokuhiro; Matsui, Hirokazu; Kato, Norihiko

    2006-10-01

    Information processing and communication technology are progressing quickly, and are prevailing throughout various technological fields. Therefore, the development of such technology should respond to the needs for improvement of quality in the e-learning education system. The authors propose a new video-image compression processing system that ingeniously employs the features of the lecturing scene. While dynamic lecturing scene is shot by a digital video camera, screen images are electronically stored by a PC screen image capturing software in relatively long period at a practical class. Then, a lecturer and a lecture stick are extracted from the digital video images by pattern recognition techniques, and the extracted images are superimposed on the appropriate PC screen images by off-line processing. Thus, we have succeeded to create a high-quality and small-capacity (HQ/SC) video-on-demand educational content featuring the advantages: the high quality of image sharpness, the small electronic file capacity, and the realistic lecturer motion.

  15. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine.

    Chen, Lili; Hao, Yaru

    2017-01-01

    Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

  16. Identifying significant environmental features using feature recognition.

    2015-10-01

    The Department of Environmental Analysis at the Kentucky Transportation Cabinet has expressed an interest in feature-recognition capability because it may help analysts identify environmentally sensitive features in the landscape, : including those r...

  17. Developing and evaluating health education learning package (HELP) to control soil-transmitted helminth infections among Orang Asli children in Malaysia.

    Al-Delaimy, Ahmed K; Al-Mekhlafi, Hesham M; Lim, Yvonne A L; Nasr, Nabil A; Sady, Hany; Atroosh, Wahib M; Mahmud, Rohela

    2014-09-02

    This study was carried out to develop a health education learning package (HELP) about soil-transmitted helminth (STH) infections, and to evaluate what impact such a package could have in terms of reducing the incidence and intensity of STH infections among Orang Asli schoolchildren in Pahang, Malaysia. To identify the key risk factors of STH in Orang Asli communities, we applied an extensive mixed methods approach which involved an intensive literature review, as well as community-based discussions with children, their parents, teachers and health personnel, whilst also placing the children under direct observation. To evaluate the package, 317 children from two schools in Lipis, Pahang were screened for STH infections, treated by a 3-day course of albendazole and then followed up over the next 6 months. The knowledge of teachers, parents and children towards STH infections were assessed at baseline and after 3 months. The developed package consists of a half day workshop for teachers, a teacher's guide book to STH infections, posters, a comic book, a music video, a puppet show, drawing activities and an aid kit. The package was well-received with effective contributions being made by teachers, children and their parents. The incidence rates of hookworm infection at different assessment points were significantly lower among children in the intervention school compared to those in the control school. Similarly, the intensity of trichuriasis, ascariasis and hookworm infections were found to be significantly lower among children in the HELP group compared to those in the control group (P < 0.05). Moreover, the package significantly improved the knowledge, attitude and practices (KAP) of Orang Asli people and the knowledge of teachers towards STH infections. A school-based health education learning package (HELP) was developed which displayed a significant impact in terms of reducing the intensity of all three main STH infections, as well as in reducing the

  18. Harnessing Our Inner Angels and Demons: What We Have Learned About Want/Should Conflicts and How That Knowledge Can Help Us Reduce Short-Sighted Decision Making.

    Milkman, Katherine L; Rogers, Todd; Bazerman, Max H

    2008-07-01

    Although observers of human behavior have long been aware that people regularly struggle with internal conflict when deciding whether to behave responsibly or indulge in impulsivity, psychologists and economists did not begin to empirically investigate this type of want/should conflict until recently. In this article, we review and synthesize the latest research on want/should conflict, focusing our attention on the findings from an empirical literature on the topic that has blossomed over the last 15 years. We then turn to a discussion of how individuals and policy makers can use what has been learned about want/should conflict to help decision makers select far-sighted options. © 2008 Association for Psychological Science.

  19. A Data-Driven Voter Guide for U.S. Elections: Adapting Quantitative Measures of the Preferences and Priorities of Political Elites to Help Voters Learn About Candidates

    Adam Bonica

    2016-11-01

    Full Text Available Internet-based voter advice applications have experienced tremendous growth across Europe in recent years but have yet to be widely adopted in the United States. By comparison, the candidate-centered U.S. electoral system, which routinely requires voters to consider dozens of candidates across a dizzying array of local, state, and federal offices each time they cast a ballot, introduces challenges of scale to the systematic provision of information. Only recently have methodological advances combined with the rapid growth in publicly available data on candidates and their supporters to bring a comprehensive data-driven voter guide within reach. This paper introduces a set of newly developed software tools for collecting, disambiguating, and merging large amounts of data on candidates and other political elites. It then demonstrates how statistical methods developed by political scientists to measure the preferences and expressed priorities of politicians can be adapted to help voters learn about candidates.

  20. Abdominal tuberculosis: Imaging features

    Pereira, Jose M. [Department of Radiology, Hospital de S. Joao, Porto (Portugal)]. E-mail: jmpjesus@yahoo.com; Madureira, Antonio J. [Department of Radiology, Hospital de S. Joao, Porto (Portugal); Vieira, Alberto [Department of Radiology, Hospital de S. Joao, Porto (Portugal); Ramos, Isabel [Department of Radiology, Hospital de S. Joao, Porto (Portugal)

    2005-08-01

    Radiological findings of abdominal tuberculosis can mimic those of many different diseases. A high level of suspicion is required, especially in high-risk population. In this article, we will describe barium studies, ultrasound (US) and computed tomography (CT) findings of abdominal tuberculosis (TB), with emphasis in the latest. We will illustrate CT findings that can help in the diagnosis of abdominal tuberculosis and describe imaging features that differentiate it from other inflammatory and neoplastic diseases, particularly lymphoma and Crohn's disease. As tuberculosis can affect any organ in the abdomen, emphasis is placed to ileocecal involvement, lymphadenopathy, peritonitis and solid organ disease (liver, spleen and pancreas). A positive culture or hystologic analysis of biopsy is still required in many patients for definitive diagnosis. Learning objectives:1.To review the relevant pathophysiology of abdominal tuberculosis. 2.Illustrate CT findings that can help in the diagnosis.

  1. Abdominal tuberculosis: Imaging features

    Pereira, Jose M.; Madureira, Antonio J.; Vieira, Alberto; Ramos, Isabel

    2005-01-01

    Radiological findings of abdominal tuberculosis can mimic those of many different diseases. A high level of suspicion is required, especially in high-risk population. In this article, we will describe barium studies, ultrasound (US) and computed tomography (CT) findings of abdominal tuberculosis (TB), with emphasis in the latest. We will illustrate CT findings that can help in the diagnosis of abdominal tuberculosis and describe imaging features that differentiate it from other inflammatory and neoplastic diseases, particularly lymphoma and Crohn's disease. As tuberculosis can affect any organ in the abdomen, emphasis is placed to ileocecal involvement, lymphadenopathy, peritonitis and solid organ disease (liver, spleen and pancreas). A positive culture or hystologic analysis of biopsy is still required in many patients for definitive diagnosis. Learning objectives:1.To review the relevant pathophysiology of abdominal tuberculosis. 2.Illustrate CT findings that can help in the diagnosis

  2. Unsupervised Feature Subset Selection

    Søndberg-Madsen, Nicolaj; Thomsen, C.; Pena, Jose

    2003-01-01

    This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some ir...... irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily....

  3. Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

    Barbosa, Eduardo J Mortani; Lanclus, Maarten; Vos, Wim; Van Holsbeke, Cedric; De Backer, William; De Backer, Jan; Lee, James

    2018-02-19

    Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV 1 ) not explained by other causes. We assessed whether machine learning (ML) utilizing quantitative computed tomography (qCT) metrics can predict eventual development of BOS. Paired inspiratory-expiratory CT scans of 71 patients who underwent LTx were analyzed retrospectively (BOS [n = 41] versus non-BOS [n = 30]), using at least two different time points. The BOS cohort experienced a reduction in FEV 1 of >10% compared to baseline FEV 1 post LTx. Multifactor analysis correlated declining FEV 1 with qCT features linked to acute inflammation or BOS onset. Student t test and ML were applied on baseline qCT features to identify lung transplant patients at baseline that eventually developed BOS. The FEV 1 decline in the BOS cohort correlated with an increase in the lung volume (P = .027) and in the central airway volume at functional residual capacity (P = .018), not observed in non-BOS patients, whereas the non-BOS cohort experienced a decrease in the central airway volume at total lung capacity with declining FEV 1 (P = .039). Twenty-three baseline qCT parameters could significantly distinguish between non-BOS patients and eventual BOS developers (P machine), we could identify BOS developers at baseline with an accuracy of 85%, using only three qCT parameters. ML utilizing qCT could discern distinct mechanisms driving FEV 1 decline in BOS and non-BOS LTx patients and predict eventual onset of BOS. This approach may become useful to optimize management of LTx patients. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  4. A Meta-Analysis Method to Advance Design of Technology-Based Learning Tool: Combining Qualitative and Quantitative Research to Understand Learning in Relation to Different Technology Features

    Zhang, Lin

    2014-01-01

    Educators design and create various technology tools to scaffold students' learning. As more and more technology designs are incorporated into learning, growing attention has been paid to the study of technology-based learning tool. This paper discusses the emerging issues, such as how can learning effectiveness be understood in relation to…

  5. An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

    Muhammad Ali

    2017-11-01

    Full Text Available Current transformer (CT saturation is one of the significant problems for protection engineers. If CT saturation is not tackled properly, it can cause a disastrous effect on the stability of the power system, and may even create a complete blackout. To cope with CT saturation properly, an accurate detection or classification should be preceded. Recently, deep learning (DL methods have brought a subversive revolution in the field of artificial intelligence (AI. This paper presents a new DL classification method based on unsupervised feature extraction and supervised fine-tuning strategy to classify the saturated and unsaturated regions in case of CT saturation. In other words, if protection system is subjected to a CT saturation, proposed method will correctly classify the different levels of saturation with a high accuracy. Traditional AI methods are mostly based on supervised learning and rely heavily on human crafted features. This paper contributes to an unsupervised feature extraction, using autoencoders and deep neural networks (DNNs to extract features automatically without prior knowledge of optimal features. To validate the effectiveness of proposed method, a variety of simulation tests are conducted, and classification results are analyzed using standard classification metrics. Simulation results confirm that proposed method classifies the different levels of CT saturation with a remarkable accuracy and has unique feature extraction capabilities. Lastly, we provided a potential future research direction to conclude this paper.

  6. SU-F-R-08: Can Normalization of Brain MRI Texture Features Reduce Scanner-Dependent Effects in Unsupervised Machine Learning?

    Ogden, K; O’Dwyer, R [SUNY Upstate Medical University, Syracuse, NY (United States); Bradford, T [Syracuse University, Syracuse, NY (United States); Cussen, L [Rochester Institute of Technology, Rochester, NY (United States)

    2016-06-15

    Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysis was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.

  7. SU-F-R-08: Can Normalization of Brain MRI Texture Features Reduce Scanner-Dependent Effects in Unsupervised Machine Learning?

    Ogden, K; O’Dwyer, R; Bradford, T; Cussen, L

    2016-01-01

    Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysis was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.

  8. Bound feature combinations in visual short-term memory are fragile but influence long-term learning

    Logie, R.H.; Brockmole, J.R.; Vandenbroucke, A.R.E.

    2009-01-01

    We explored whether individual features and bindings between those features in VSTM tasks are completely lost from trial to trial or whether residual memory traces for these features and bindings are retained in long-term memory. Memory for arrays of coloured shapes was assessed using change

  9. Online feature selection with streaming features.

    Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

    2013-05-01

    We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

  10. Learning turning points--in life with long-term illness--visualized with the help of the life-world philosophy.

    Berglund, Mia M U

    2014-01-01

    A long-term illness is an occurrence that changes one's life and generates a need to learn how to live with it. This article is based on an empirical study of interviews on people living with different long-term illnesses. The results have shown that the learning process is a complex phenomenon interwoven with life as a whole. The essential meaning of learning to live with long-term illness concerns a movement toward a change of understanding of access to the world. In this movement, in which everyday lives as well as relationships with oneself and others are affected, a continual renegotiation is needed. Texts from existential/lifeworld philosopher, Heidegger and Gadamer, have been used to get a greater understanding of the empirical results. These texts have been analysed with particular focus on learning turning points and the importance of reflection. The results are highlighted under the following themes: Pursuit of balance-the aim of learning, The tense grip-the resistance to learning, To live more really-the possibilities of the learning, Distancing-the how of the learning, and The tense of the learning-the whole of the learning. In those learning turning points are present. Knowledge from this study has been used to make a didactic model designed to give caregivers a tool to support patients' learning. The didactic model is called: The challenge to take charge of life with a long-term illness.

  11. Learn & Go: Entwicklung einer Mobile Learning Applikation für Smartphones zur Nutzung von Learning Management Features und Funktionen via Webservice [Learn & Go: Development of a mobile learning application for smartphones to use learning management features and functions via web services

    Igel, Christoph

    2013-01-01

    Full Text Available [english] This paper indicates an attractive offer of the Saarland University to the economic study organisation in the mobile age. The realised solution allows students, without requiring any browser technologies, to use functions of the University’s learning management system avoiding to hold information and contents available more than once. Besides the methodical approch to gain the prototyp of the mobile application, the thereby achieved added value of accessing information for medical students regardless time or place is pointed out as well as the advantage perspectively gained as an innovative qualification option in the lifelong learning process for all different kinds of professional groups in the medical sector.[german] Dieser Beitrag zeigt im mobilen Zeitalter eine attraktive Offerte der Universität des Saarlandes zur ökonomischen Studienorganisation auf. Die realisierte Lösung ermöglicht Studierenden, fernab von Browsertechnologien, die Nutzung von Funktionen des Learning-Management-Systems der saarländischen Hochschulen ohne die Informationen und Inhalte mehrfach vorzuhalten. Das methodische Vorgehen zur Pilotierung der mobilen Applikation wird im Folgenden ebenso beleuchtet wie das hierdurch ermöglichte Potential des orts- und zeitunabhängigen Zugriffs auf Informationen, zum einen für Studierende der Human- und Zahnmedizin an der Universität des Saarlandes und zum anderen perspektivisch als innovative Qualifizierungsform im Lifelong Learning Prozess für alle Berufsgruppen im Gesundheitswesen.

  12. Identifying Effective Design Features of Technology-Infused Inquiry Learning Modules: A Two-Year Study of Students' Inquiry Abilities

    Hsu, Ying-Shao; Fang, Su-Chi; Zhang, Wen-Xin; Hsin-Kai, Wu; Wu, Pai-Hsing; Hwang, Fu-Kwun

    2016-01-01

    The two-year study aimed to explore how students' development of different inquiry abilities actually benefited from the design of technology-infused learning modules. Three learning modules on the topics of seasons, environmental issues and air pollution were developed to facilitate students' inquiry abilities: questioning, planning, analyzing,…

  13. Psychohygienic estimation of features of the formation of properties of character of pupils aged 14-17 years in the dynamics of learning at modern schools

    Serheta I.V.

    2013-10-01

    Full Text Available The aim of scientific work was psychohygienic estimation of features of the formation of properties of character of pupils aged 14-17 years old in the dynamics of learning at modern schools. The studies were conducted on the basis of secondary schools in the city of Vinnitsya. Evaluation of the formation of properties of character was carried out using a personality questionnaire Mini-mult. It was determined that in the structure of personal profile of properties of character in girls aged 14-17 years during the period of study at school predominance of characterologic features according to schizoid scales (Se and pscychasthenia (Pt (block 1 of characterologic features, hypochondria (Hs and depression (D (block 2 of characterologic features, hypomania (Ma and hysteria (Hy (block 3 of characterologic features is registered. In the structure of personal profile of properties of character of boys of 14-17 years during the period of study at school predominance of characterologic properties according to psychasthenia (Pt and schizoid (Se (block 1 of characterologic features, hypochondria (Hs and depression (D (block 2, paranoid (Pa and hypomania (Ma (block 3 is registered. The results suggest the need to consider the properties of character of schoolchildren in development of health-saving technologies, sanitation measures and psychohygienic effect on the pupils’ organism.

  14. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

    Yi-Hung Liu

    2014-01-01

    Full Text Available In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL algorithm and support vector machine (SVM. We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

  15. Drupal 7 first look learn the new features of Drupal 7, how they work and how they will impact you

    Noble, Mark

    2010-01-01

    This hands-on guide takes a look at the main functional areas of Drupal that have significant new features. It explains these new features and how to use them, drawing attention to significant differences from how things used to behave, and giving the rea

  16. Implementation of Online Peer Assessment in a Design for Learning and Portfolio (D4L+P) Program to Help Students Complete Science Projects

    Wuttisela, Karntarat; Wuttiprom, Sura; Phonchaiya, Sonthi; Saengsuwan, Sayant

    2016-01-01

    Peer assessment was one of the most effective strategies to improve students' understanding, metacognitive skills, and social interaction. An online tool, "Designing for Learning and Portfolio (D4L+P)", was developed solely to support the T5 (tasks, tools, tutorials, topicresources, and teamwork) method of teaching and learning. This…

  17. Content-Focused Teacher Meetings as Effective Teacher Learning Opportunities: Do They Really Help Improve Overall Reading Achievement and Reduce the Achievement Gap in First Grade Classrooms?

    Kang, Ho Soo

    2013-01-01

    Teacher professional development has long been of interest since it may affect teachers' learning, the practice of teaching, and student learning. Although empirical research has mainly explored the effect of specific professional development interventions on student achievement, these inventions have been initiated outside the school, and little…

  18. Learning

    Mohsen Laabidi

    2014-01-01

    Full Text Available Nowadays learning technologies transformed educational systems with impressive progress of Information and Communication Technologies (ICT. Furthermore, when these technologies are available, affordable and accessible, they represent more than a transformation for people with disabilities. They represent real opportunities with access to an inclusive education and help to overcome the obstacles they met in classical educational systems. In this paper, we will cover basic concepts of e-accessibility, universal design and assistive technologies, with a special focus on accessible e-learning systems. Then, we will present recent research works conducted in our research Laboratory LaTICE toward the development of an accessible online learning environment for persons with disabilities from the design and specification step to the implementation. We will present, in particular, the accessible version “MoodleAcc+” of the well known e-learning platform Moodle as well as new elaborated generic models and a range of tools for authoring and evaluating accessible educational content.

  19. Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

    Yansheng Li

    2016-08-01

    Full Text Available With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF. In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP, gray level co-occurrence (GLCM, maximal response 8 (MR8, and scale-invariant feature transform (SIFT. In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM dataset and the Wuhan University (WH dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.

  20. Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection.

    Prabusankarlal, Kadayanallur Mahadevan; Thirumoorthy, Palanisamy; Manavalan, Radhakrishnan

    2017-04-01

    A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a [Formula: see text]-fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew's correlation coefficient (MCC), and area ([Formula: see text]) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] compared to other classifiers.

  1. Helping Students Understand Gene Regulation with Online Tools: A Review of MEME and Melina II, Motif Discovery Tools for Active Learning in Biology

    David Treves

    2012-08-01

    Full Text Available Review of: MEME and Melina II, which are two free and easy-to-use online motif discovery tools that can be employed to actively engage students in learning about gene regulatory elements.

  2. The enigma of number: why children find the meanings of even small number words hard to learn and how we can help them do better.

    Michael Ramscar

    Full Text Available Although number words are common in everyday speech, learning their meanings is an arduous, drawn-out process for most children, and the source of this delay has long been the subject of inquiry. Children begin by identifying the few small numerosities that can be named without counting, and this has prompted further debate over whether there is a specific, capacity-limited system for representing these small sets, or whether smaller and larger sets are both represented by the same system. Here we present a formal, computational analysis of number learning that offers a possible solution to both puzzles. This analysis indicates that once the environment and the representational demands of the task of learning to identify sets are taken into consideration, a continuous system for learning, representing and discriminating set-sizes can give rise to effective discontinuities in processing. At the same time, our simulations illustrate how typical prenominal linguistic constructions ("there are three balls" structure information in a way that is largely unhelpful for discrimination learning, while suggesting that postnominal constructions ("balls, there are three" will facilitate such learning. A training-experiment with three-year olds confirms these predictions, demonstrating that rapid, significant gains in numerical understanding and competence are possible given appropriately structured postnominal input. Our simulations and results reveal how discrimination learning tunes children's systems for representing small sets, and how its capacity-limits result naturally out of a mixture of the learning environment and the increasingly complex task of discriminating and representing ever-larger number sets. They also explain why children benefit so little from the training that parents and educators usually provide. Given the efficacy of our intervention, the ease with which it can be implemented, and the large body of research showing how early

  3. Sound as Affective Design Feature in Multimedia Learning--Benefits and Drawbacks from a Cognitive Load Theory Perspective

    Königschulte, Anke

    2015-01-01

    The study presented in this paper investigates the potential effects of including non-speech audio such as sound effects into multimedia-based instruction taking into account Sweller's cognitive load theory (Sweller, 2005) and applied frameworks such as the cognitive theory of multimedia learning (Mayer, 2005) and the cognitive affective theory of…

  4. Mobile Augmented Reality as a Feature for Self-Oriented, Blended Learning in Medicine: Randomized Controlled Trial

    2017-01-01

    Background Advantages of mobile Augmented Reality (mAR) application-based learning versus textbook-based learning were already shown in a previous study. However, it was unclear whether the augmented reality (AR) component was responsible for the success of the self-developed app or whether this was attributable to the novelty of using mobile technology for learning. Objective The study’s aim was to test the hypothesis whether there is no difference in learning success between learners who employed the mobile AR component and those who learned without it to determine possible effects of mAR. Also, we were interested in potential emotional effects of using this technology. Methods Forty-four medical students (male: 25, female: 19, mean age: 22.25 years, standard deviation [SD]: 3.33 years) participated in this study. Baseline emotional status was evaluated using the Profile of Mood States (POMS) questionnaire. Dermatological knowledge was ascertained using a single choice (SC) test (10 questions). The students were randomly assigned to learn 45 min with either a mobile learning method with mAR (group A) or without AR (group B). Afterwards, both groups were again asked to complete the previous questionnaires. AttrakDiff 2 questionnaires were used to evaluate the perceived usability as well as pragmatic and hedonic qualities. For capturing longer term effects, after 14 days, all participants were again asked to complete the SC questionnaire. All evaluations were anonymous, and descriptive statistics were calculated. For hypothesis testing, an unpaired signed-rank test was applied. Results For the SC tests, there were only minor differences, with both groups gaining knowledge (average improvement group A: 3.59 [SD 1.48]; group B: 3.86 [SD 1.51]). Differences between both groups were statistically insignificant (exact Mann Whitney U, U=173.5; P=.10; r=.247). However, in the follow-up SC test after 14 days, group A had retained more knowledge (average decrease of the

  5. Mobile Augmented Reality as a Feature for Self-Oriented, Blended Learning in Medicine: Randomized Controlled Trial.

    Noll, Christoph; von Jan, Ute; Raap, Ulrike; Albrecht, Urs-Vito

    2017-09-14

    Advantages of mobile Augmented Reality (mAR) application-based learning versus textbook-based learning were already shown in a previous study. However, it was unclear whether the augmented reality (AR) component was responsible for the success of the self-developed app or whether this was attributable to the novelty of using mobile technology for learning. The study's aim was to test the hypothesis whether there is no difference in learning success between learners who employed the mobile AR component and those who learned without it to determine possible effects of mAR. Also, we were interested in potential emotional effects of using this technology. Forty-four medical students (male: 25, female: 19, mean age: 22.25 years, standard deviation [SD]: 3.33 years) participated in this study. Baseline emotional status was evaluated using the Profile of Mood States (POMS) questionnaire. Dermatological knowledge was ascertained using a single choice (SC) test (10 questions). The students were randomly assigned to learn 45 min with either a mobile learning method with mAR (group A) or without AR (group B). Afterwards, both groups were again asked to complete the previous questionnaires. AttrakDiff 2 questionnaires were used to evaluate the perceived usability as well as pragmatic and hedonic qualities. For capturing longer term effects, after 14 days, all participants were again asked to complete the SC questionnaire. All evaluations were anonymous, and descriptive statistics were calculated. For hypothesis testing, an unpaired signed-rank test was applied. For the SC tests, there were only minor differences, with both groups gaining knowledge (average improvement group A: 3.59 [SD 1.48]; group B: 3.86 [SD 1.51]). Differences between both groups were statistically insignificant (exact Mann Whitney U, U=173.5; P=.10; r=.247). However, in the follow-up SC test after 14 days, group A had retained more knowledge (average decrease of the number of correct answers group A: 0

  6. SU-F-R-05: Multidimensional Imaging Radiomics-Geodesics: A Novel Manifold Learning Based Automatic Feature Extraction Method for Diagnostic Prediction in Multiparametric Imaging

    Parekh, V [The Johns Hopkins University, Computer Science. Baltimore, MD (United States); Jacobs, MA [The Johns Hopkins University School of Medicine, Dept of Radiology and Oncology. Baltimore, MD (United States)

    2016-06-15

    Purpose: Multiparametric radiological imaging is used for diagnosis in patients. Potentially extracting useful features specific to a patient’s pathology would be crucial step towards personalized medicine and assessing treatment options. In order to automatically extract features directly from multiparametric radiological imaging datasets, we developed an advanced unsupervised machine learning algorithm called the multidimensional imaging radiomics-geodesics(MIRaGe). Methods: Seventy-six breast tumor patients underwent 3T MRI breast imaging were used for this study. We tested the MIRaGe algorithm to extract features for classification of breast tumors into benign or malignant. The MRI parameters used were T1-weighted, T2-weighted, dynamic contrast enhanced MR imaging (DCE-MRI) and diffusion weighted imaging(DWI). The MIRaGe algorithm extracted the radiomics-geodesics features (RGFs) from multiparametric MRI datasets. This enable our method to learn the intrinsic manifold representations corresponding to the patients. To determine the informative RGF, a modified Isomap algorithm(t-Isomap) was created for a radiomics-geodesics feature space(tRGFS) to avoid overfitting. Final classification was performed using SVM. The predictive power of the RGFs was tested and validated using k-fold cross validation. Results: The RGFs extracted by the MIRaGe algorithm successfully classified malignant lesions from benign lesions with a sensitivity of 93% and a specificity of 91%. The top 50 RGFs identified as the most predictive by the t-Isomap procedure were consistent with the radiological parameters known to be associated with breast cancer diagnosis and were categorized as kinetic curve characterizing RGFs, wash-in rate characterizing RGFs, wash-out rate characterizing RGFs and morphology characterizing RGFs. Conclusion: In this paper, we developed a novel feature extraction algorithm for multiparametric radiological imaging. The results demonstrated the power of the MIRa

  7. SU-F-R-05: Multidimensional Imaging Radiomics-Geodesics: A Novel Manifold Learning Based Automatic Feature Extraction Method for Diagnostic Prediction in Multiparametric Imaging

    Parekh, V; Jacobs, MA

    2016-01-01

    Purpose: Multiparametric radiological imaging is used for diagnosis in patients. Potentially extracting useful features specific to a patient’s pathology would be crucial step towards personalized medicine and assessing treatment options. In order to automatically extract features directly from multiparametric radiological imaging datasets, we developed an advanced unsupervised machine learning algorithm called the multidimensional imaging radiomics-geodesics(MIRaGe). Methods: Seventy-six breast tumor patients underwent 3T MRI breast imaging were used for this study. We tested the MIRaGe algorithm to extract features for classification of breast tumors into benign or malignant. The MRI parameters used were T1-weighted, T2-weighted, dynamic contrast enhanced MR imaging (DCE-MRI) and diffusion weighted imaging(DWI). The MIRaGe algorithm extracted the radiomics-geodesics features (RGFs) from multiparametric MRI datasets. This enable our method to learn the intrinsic manifold representations corresponding to the patients. To determine the informative RGF, a modified Isomap algorithm(t-Isomap) was created for a radiomics-geodesics feature space(tRGFS) to avoid overfitting. Final classification was performed using SVM. The predictive power of the RGFs was tested and validated using k-fold cross validation. Results: The RGFs extracted by the MIRaGe algorithm successfully classified malignant lesions from benign lesions with a sensitivity of 93% and a specificity of 91%. The top 50 RGFs identified as the most predictive by the t-Isomap procedure were consistent with the radiological parameters known to be associated with breast cancer diagnosis and were categorized as kinetic curve characterizing RGFs, wash-in rate characterizing RGFs, wash-out rate characterizing RGFs and morphology characterizing RGFs. Conclusion: In this paper, we developed a novel feature extraction algorithm for multiparametric radiological imaging. The results demonstrated the power of the MIRa

  8. Asking for Help: A Relational Perspective on Help Seeking in the Workplace

    van der Rijt, Janine; Van den Bossche, Piet; van de Wiel, Margje W. J.; De Maeyer, Sven; Gijselaers, Wim H.; Segers, Mien S. R.

    2013-01-01

    In the context of the complexity of today's organizations, help seeking behavior is considered as an important step to problem solving and learning in organizations. Yet, help seeking has received less attention in organizational literature. To increase the potential impact of help seeking on learning, it is essential to understand which…

  9. An Evolutionary Machine Learning Framework for Big Data Sequence Mining

    Kamath, Uday Krishna

    2014-01-01

    Sequence classification is an important problem in many real-world applications. Unlike other machine learning data, there are no "explicit" features or signals in sequence data that can help traditional machine learning algorithms learn and predict from the data. Sequence data exhibits inter-relationships in the elements that are…

  10. Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality

    Crossley, Scott A.; Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S.

    2016-01-01

    This study investigates a novel approach to automatically assessing essay quality that combines natural language processing approaches that assess text features with approaches that assess individual differences in writers such as demographic information, standardized test scores, and survey results. The results demonstrate that combining text…

  11. Feature selection and data sampling methods for learning reputation dimensions: The University of Amsterdam at RepLab 2014

    Gârbacea, C.; Tsagkias, M.; de Rijke, M.

    2014-01-01

    We report on our participation in the reputation dimension task of the CLEF RepLab 2014 evaluation initiative, i.e., to classify social media updates into eight predefined categories. We address the task by using corpus-based methods to extract textual features from the labeled training data to

  12. Learning Less.js

    Libby, Alex

    2014-01-01

    If you are a designer or developer who wants to quickly learn how to harness the power of Less.js to write more efficient CSS styles that can be applied to a website of any size, then this book is for you. This book will help you master both the basic functions and advanced features of Less.js. It would be helpful to have some familiarity of writing CSS styles, although no prior experience of using CSS preprocessors is required.

  13. Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network.

    Christoph Hartmann

    2015-12-01

    Full Text Available Even in the absence of sensory stimulation the brain is spontaneously active. This background "noise" seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN, which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network's spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network's behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural

  14. Introduction to machine learning.

    Baştanlar, Yalin; Ozuysal, Mustafa

    2014-01-01

    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.

  15. Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

    Jie Wang

    2017-03-01

    Full Text Available Deep convolutional neural networks (CNNs have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial “distance” of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral “distance” between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pretrained deep CNNs to other remote sensing tasks.

  16. Using Academic Journals to Help Students Learn Subject Matter Content, Develop and Practice Critical Reasoning Skills, and Reflect on Personal Values in Food Science and Human Nutrition Classes

    Iwaoka, Wayne T.; Crosetti, Lea M.

    2008-01-01

    It has been reported that students learn best when they use a wide variety of techniques to understand the information of the discipline, be it visual, auditory, discussion with others, metacognition, hands-on activities, or writing about the subject. We report in this article the use of academic journals not only as an aid for students to learn…

  17. Evaluating the Effects of Lesson Study as a Way to Help Student Teachers Learn How to Use Student Thinking when Planning and Revising Mathematics Lesson Plans

    Sisofo, Eric Joseph

    2010-01-01

    The use of student thinking in teaching has been linked to improved instruction and learning. It is reasonable to assume that the University of Delaware's undergraduate program might be interested in figuring out ways to develop this skill in its mathematics specialist pre-service teachers. Currently, the student teaching experience at the…

  18. GOSH! : an open and distance learning programme which helps in Gearing up to the Occupational Safety and Health Systems of the European Union

    Robertson, S.A.; Piek, W.S.M.; Kwantes, J.H.; Meeuwsen, J.M.; Man. M. de

    1999-01-01

    The GOSH! open and distance learning programme on the topic of occupational safety and health (OSH) was organized as part of the European Studies Programme. Prevention of damage to workers' health is an important issue in EU-policy. Apart from the individual human trauma, the socio-economic costs of

  19. Using Gloss to Help Fifth and Sixth Graders Comprehend Social Studies Text: An Informal Study of a Learning Aid. Working Paper No. 295.

    Witte, Pauline

    A two-part study examined the effectiveness of glossing (writing comments or questions in text to improve comprehension) when students use it in social studies texts in combination with discussions and other activities. Students were divided into two groups, one of which learned glossing while the other engaged in assigned workbook activities.…

  20. Optimizing the Noticing of Recasts via Computer-Delivered Feedback: Evidence That Oral Input Enhancement and Working Memory Help Second Language Learning

    Sagarra, Nuria; Abbuhl, Rebekha

    2013-01-01

    This study investigates whether practice with computer-administered feedback in the absence of meaning-focused interaction can help second language learners notice the corrective intent of recasts and develop linguistic accuracy. A group of 218 beginning Anglophone learners of Spanish received 1 of 4 types of automated feedback (no feedback,…