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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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

Learning slow features for behavior analysis

NARCIS (Netherlands)

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

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Unsupervised Learning of Spatiotemporal Features by Video Completion

OpenAIRE

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...

Novel Automatic Filter-Class Feature Selection for Machine Learning Regression

DEFF Research Database (Denmark)

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...

Mathematic anxiety, help seeking behavior and cooperative learning

OpenAIRE

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...

Cross-Domain Semi-Supervised Learning Using Feature Formulation.

Science.gov (United States)

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.

Machine learning spatial geometry from entanglement features

Science.gov (United States)

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).

Breast image feature learning with adaptive deconvolutional networks

Science.gov (United States)

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).

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

Science.gov (United States)

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.

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

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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.

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

Directory of Open Access Journals (Sweden)

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.

Visual attention to features by associative learning.

Science.gov (United States)

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.

Helping While Learning: A Skilled Group Helper Training Program.

Science.gov (United States)

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)

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Learning features for tissue classification with the classification restricted Boltzmann machine

DEFF Research Database (Denmark)

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....

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

Science.gov (United States)

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…

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

Directory of Open Access Journals (Sweden)

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

Help Your Child Learn To Write Well.

Science.gov (United States)

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…

Learning Transferable Features with Deep Adaptation Networks

OpenAIRE

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...

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

OpenAIRE

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.

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

Science.gov (United States)

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.

Enhancing the Pronunciation of English Suprasegmental Features through Reflective Learning Method

Science.gov (United States)

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…

Feature selection for domain knowledge representation through multitask learning

CSIR Research Space (South Africa)

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...

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

Science.gov (United States)

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…

Multimodal Feature Learning for Video Captioning

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.…

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

Science.gov (United States)

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

Neighbors Based Discriminative Feature Difference Learning for Kinship Verification

DEFF Research Database (Denmark)

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...

Pairwise Constraint-Guided Sparse Learning for Feature Selection.

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

Science.gov (United States)

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.

ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

Directory of Open Access Journals (Sweden)

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.

Features and characteristics of problem based learning

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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.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

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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.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

Multi-task feature learning by using trace norm regularization

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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)

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Clustering-based Feature Learning on Variable Stars

Science.gov (United States)

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.

CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

International Nuclear Information System (INIS)

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

CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

Energy Technology Data Exchange (ETDEWEB)

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.

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

Directory of Open Access Journals (Sweden)

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.

Towards Stable Adversarial Feature Learning for LiDAR based Loop Closure Detection

OpenAIRE

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...

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

Unsupervised Feature Learning for Heart Sounds Classification Using Autoencoder

Science.gov (United States)

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.

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

Science.gov (United States)

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…

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

Science.gov (United States)

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).

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

Science.gov (United States)

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)

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

Science.gov (United States)

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

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

Science.gov (United States)

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

Zero-Shot Learning by Generating Pseudo Feature Representations

OpenAIRE

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...

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

DEFF Research Database (Denmark)

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....

Enhanced HMAX model with feedforward feature learning for multiclass categorization

Directory of Open Access Journals (Sweden)

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.

Enhanced HMAX model with feedforward feature learning for multiclass categorization.

Science.gov (United States)

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.

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

Science.gov (United States)

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…

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

The Effectiveness of Three Serious Games Measuring Generic Learning Features

Science.gov (United States)

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…

Which assessment features shape students' learning? A review study

NARCIS (Netherlands)

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.

Systemverilog for verification a guide to learning the testbench language features

CERN Document Server

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...

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

DEFF Research Database (Denmark)

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....

Learning deep features with adaptive triplet loss for person reidentification

Science.gov (United States)

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.

Peculiarities of learning style in adolescents with the features of hyperactivity

OpenAIRE

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...

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

KAUST Repository

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.

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

Science.gov (United States)

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.

Context-Dependent Help for the DynaLearn Modelling and Simulation Workbench

NARCIS (Netherlands)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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…

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

Science.gov (United States)

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…

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

Science.gov (United States)

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)…

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

Science.gov (United States)

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.

Infrared image enhancement with learned features

Science.gov (United States)

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.

Local Feature Learning for Face Recognition under Varying Poses

DEFF Research Database (Denmark)

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....

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

Science.gov (United States)

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…

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

Science.gov (United States)

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

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

KAUST Repository

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.

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

Science.gov (United States)

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…

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

Science.gov (United States)

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…

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

Science.gov (United States)

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…

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

Science.gov (United States)

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…

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

Directory of Open Access Journals (Sweden)

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.

Can learning health systems help organisations deliver personalised care?

Science.gov (United States)

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.

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

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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.

Asking for Help: A Relational Perspective on Help Seeking in the Workplace

Science.gov (United States)

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…

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

Energy Technology Data Exchange (ETDEWEB)

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.

Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference

OpenAIRE

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...

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

Science.gov (United States)

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…

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

Science.gov (United States)

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

Feature weighting using particle swarm optimization for learning vector quantization classifier

Science.gov (United States)

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.

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

Indian Academy of Sciences (India)

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, ...

Feature economy vs. logical complexity in phonological pattern learning

NARCIS (Netherlands)

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

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

DEFF Research Database (Denmark)

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...

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

Science.gov (United States)

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.

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

Science.gov (United States)

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…

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

NARCIS (Netherlands)

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

Getting Help

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... 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 ...

LEARNING TECHNOLOGIES FOR STUDENTS IN THE CLOUD ORIENTED LEARNING ENVIRONMENT OF COMPREHENSIVE EDUCATIONAL INSTITUTIONS

Directory of Open Access Journals (Sweden)

Svitlana G. Lytvynova

2015-06-01

Full Text Available The paper analyzes the «flipped» learning and «Web Quest» technologies. The features of the «flipped» learning technology are generalized, as well as compared with traditional learning, clarified the benefits of the technology for teachers and students, described the features of the technology used by teacher and students, developed a teacher’s and student’s flow chart for preparation to the lesson, generalized control and motivation components for activating learning activities of students, found out that a component of cloud oriented learning environment (COLE – Lync (Skype Pro can be used to develop video clips and support «flipped» learning technology. The author defines the concept of «Web Quest» technology, generalizes the «Web Quest» structure components. In the article the functions, features of this technology, the types of problems that can be solved with the help of this technology, as well as «Web Quest» classification are presented. It has been found out that the cloud oriented learning environment gives all the possibilities for «Web Quest» technology implementation in teaching of different subjects of all branches of science. With the help of «flipped» technology training and «Web Quest» a number of important problems of education can be solved – providing the continuous communication intensive training beyond general educational establishment and activation of learning activities of students.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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…

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

Science.gov (United States)

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…

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

Science.gov (United States)

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].

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

DEFF Research Database (Denmark)

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...

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

Science.gov (United States)

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…

Advancing Affect Modeling via Preference Learning and Unsupervised Feature Extraction

DEFF Research Database (Denmark)

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...

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

Learning probabilistic features for robotic navigation using laser sensors.

Directory of Open Access Journals (Sweden)

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.

Learning probabilistic features for robotic navigation using laser sensors.

Science.gov (United States)

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.

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

NARCIS (Netherlands)

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

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

Science.gov (United States)

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.

Feature Selection Methods for Zero-Shot Learning of Neural Activity

Directory of Open Access Journals (Sweden)

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.

SPECIAL FEATURES OF VIRTUAL PRACTICE INTERACTIVE MEDIA DISCIPLINES FOR DISTANCE LEARNING

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

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.

HTML5 eLearning kit for dummies

CERN Document Server

Boumphrey, Frank

2012-01-01

Helping self-directed learners of all levels learn HTML5 If you want to develop and structure pages for the web, HTML5 is one of the tools you need. This invaluable eLearning kit steps you through learning HTML5, CSS3, and JavaScript. With this dynamic combination of a full-color printed book and a Dummies interactive eLearning course on CD, you'll find a wealth of information on HTML5. Featuring both written and animated step-by-step how-tos, practice labs, helpful videos, numerous examples, and a host of Dummies hints and tips, this package makes your learning process easier. Follow the mate

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

Learning effective color features for content based image retrieval in dermatology

NARCIS (Netherlands)

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

Deep Feature Learning and Cascaded Classifier for Large Scale Data

DEFF Research Database (Denmark)

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...

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

Science.gov (United States)

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.

Helping Your Child through Early Adolescence -- Helping Your Child Series

Science.gov (United States)

... 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 ...

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

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

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

Science.gov (United States)

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

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

Science.gov (United States)

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.

Online feature selection with streaming features.

Science.gov (United States)

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.

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

Science.gov (United States)

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…

Machine Learning for Medical Imaging.

Science.gov (United States)

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.

Step 1: Learn about Diabetes

Science.gov (United States)

... 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 ...

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

Science.gov (United States)

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.

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

DEFF Research Database (Denmark)

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...

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

OpenAIRE

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...

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

KAUST Repository

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.

“What and How do we learn from LinkedIn Forums?”

DEFF Research Database (Denmark)

Broillet, Alexandra; Kampf, Constance Elizabeth; Emad, Sabine

2014-01-01

new interfaces and features, and c) social networking. These three interactions offer a preliminary understanding of the potential for LinkedIn forums as a lifelong learning space, and an innovation space where weak ties and transactive memory systems have the potential to affect multidisciplinary......This study examines several academic and professional LinkedIn forums, and using a grounded theory perspective, observes three key lifelong learning interactions for participants—a) problem solving through shared learning and helping processes,” b) a technical features learning center for learning...

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.

Science.gov (United States)

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).

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

Science.gov (United States)

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.

An Evolutionary Machine Learning Framework for Big Data Sequence Mining

Science.gov (United States)

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…

Helping Your Child Learn Geography

Science.gov (United States)

,

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.

Of mice, birds, and men: the mouse ultrasonic song system has some features similar to humans and song-learning birds.

Directory of Open Access Journals (Sweden)

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.

Deep Learning in Medical Image Analysis.

Science.gov (United States)

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2017-06-21

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

WE-H-201-01: The Opportunities and Benefits of Helping LMICs: How Helping Them Can Help You

International Nuclear Information System (INIS)

Pollard, J.

2016-01-01

The desperate need for radiotherapy in low and mid-income countries (LMICs) has been well documented. Roughly 60 % of the worldwide incidence of cancer occurs in these resource-limited settings and the international community alongside governmental and non-profit agencies have begun publishing reports and seeking help from qualified volunteers. However, the focus of several reports has been on how dire the situation is and the magnitude of the problem, leaving most to feel overwhelmed and unsure as to how to help and why to get involved. This session will help to explain the specific ways that Medical Physicists can uniquely assist in this grand effort to help bring radiotherapy to grossly-underserved areas. Not only can these experts fulfill an important purpose, they also can benefit professionally, academically, emotionally and socially from the endeavor. By assisting others worldwide with their skillset, Medical Physicists can end up helping themselves. Learning Objectives: Understand the need for radiotherapy in LMICs. Understand which agencies are seeking Medical Physicists for help in LMICs. Understand the potential research funding mechanisms are available to establish academic collaborations with LMIC researchers/physicians. Understand the potential social and emotional benefits for both the physicist and the LMIC partners when collaborations are made. Understand the potential for collaboration with other high-income scientists that can develop as the physicist partners with other large institutions to assist LMICs. Wil Ngwa - A recent United Nations Study reports that in developing countries more people have access to cell phones than toilets. In Africa, only 63% of the population has access to piped water, yet, 93% of Africans have cell phone service. Today, these cell phones, Skype, WhatsApp and other information and communication technologies (ICTs) connect us in unprecedented ways and are increasingly recognized as powerful, indispensable to global

WE-H-201-01: The Opportunities and Benefits of Helping LMICs: How Helping Them Can Help You

Energy Technology Data Exchange (ETDEWEB)

Pollard, J. [MD Anderson Cancer Center (United States)

2016-06-15

The desperate need for radiotherapy in low and mid-income countries (LMICs) has been well documented. Roughly 60 % of the worldwide incidence of cancer occurs in these resource-limited settings and the international community alongside governmental and non-profit agencies have begun publishing reports and seeking help from qualified volunteers. However, the focus of several reports has been on how dire the situation is and the magnitude of the problem, leaving most to feel overwhelmed and unsure as to how to help and why to get involved. This session will help to explain the specific ways that Medical Physicists can uniquely assist in this grand effort to help bring radiotherapy to grossly-underserved areas. Not only can these experts fulfill an important purpose, they also can benefit professionally, academically, emotionally and socially from the endeavor. By assisting others worldwide with their skillset, Medical Physicists can end up helping themselves. Learning Objectives: Understand the need for radiotherapy in LMICs. Understand which agencies are seeking Medical Physicists for help in LMICs. Understand the potential research funding mechanisms are available to establish academic collaborations with LMIC researchers/physicians. Understand the potential social and emotional benefits for both the physicist and the LMIC partners when collaborations are made. Understand the potential for collaboration with other high-income scientists that can develop as the physicist partners with other large institutions to assist LMICs. Wil Ngwa - A recent United Nations Study reports that in developing countries more people have access to cell phones than toilets. In Africa, only 63% of the population has access to piped water, yet, 93% of Africans have cell phone service. Today, these cell phones, Skype, WhatsApp and other information and communication technologies (ICTs) connect us in unprecedented ways and are increasingly recognized as powerful, indispensable to global

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

Science.gov (United States)

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.

Feature extraction for SAR target recognition based on supervised manifold learning

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

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.

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

Energy Technology Data Exchange (ETDEWEB)

Chen, Barry Y. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2016-11-29

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patterns are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.

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

Science.gov (United States)

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.

Abdominal tuberculosis: Imaging features

International Nuclear Information System (INIS)

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

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Classifying smoking urges via machine learning.

Science.gov (United States)

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-12-01

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

HELP OPTIONS AND MULTIMEDIA LISTENING: STUDENTS’ USE OF SUBTITLES AND THE TRANSCRIPT

Directory of Open Access Journals (Sweden)

Maja Grgurović

2007-02-01

Full Text Available As multimedia language learning materials become prevalent in foreign and second language classrooms, their design is an important avenue of research in Computer-Assisted Language Learning (CALL. Some argue that the design of the pedagogical materials should be informed by theory such as the interactionist SLA theory, which suggests that input modification can help comprehension, but does not provide specific guidance regarding choices designers should make when they attempt to implement theory-based features like modified input. This empirical study was designed to provide evidence about one such issue: whether subtitles or transcripts are more effective in providing modified input to learners. A multimedia listening activity containing a video of an academic lecture was designed to offer help in the form of target language subtitles (captions and lecture transcripts in cases of comprehension breakdowns. Eighteen intermediate ESL students enrolled in an academic listening class at a research university participated in the study. Two tests and questionnaires in addition to screen recordings were used to analyze students' performance on the activity and their use of help. The results indicate that participants interacted with the subtitles more frequently and for longer periods of time than with the transcript. Also, the study identified four patterns of learner interaction with the help options. Since, overall, the participants interacted with help less than half of the time they opened help pages, an important challenge in investigating help options lies in finding ways to promote the use of help.

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.

Science.gov (United States)

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

Learning Less.js

CERN Document Server

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

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.

Introduction to machine learning.

Science.gov (United States)

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.

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

Science.gov (United States)

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.…

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

Science.gov (United States)

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.

Unsupervised Feature Subset Selection

DEFF Research Database (Denmark)

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....

Abdominal tuberculosis: Imaging features

Energy Technology Data Exchange (ETDEWEB)

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.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

Simultenious binary hash and features learning for image retrieval

Science.gov (United States)

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.

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

Energy Technology Data Exchange (ETDEWEB)

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.)

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

International Nuclear Information System (INIS)

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.)

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

Directory of Open Access Journals (Sweden)

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.

Towards a Serious Game to Help Students Learn Computer Programming

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

KAUST Repository

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

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

Science.gov (United States)

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…

Identifying significant environmental features using feature recognition.

Science.gov (United States)

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...

The Self-Regulated Learning Model and Music Education

OpenAIRE

Maja Marijan

2017-01-01

Self-regulation and self-regulated learning (SRL) are important features in music education. In this research self-regulated learning model is presented as a complex, multidimensional structure. SRL starts with the self-regulation. Self-regulation is formed through interaction with the environment, thus self-learning, self-analysis, self-judgment, self-instruction, and self-monitoring are the main functions in self-regulatory structure. Co-regulation is needed, and helps self-regulation to be...

Help prevent hospital errors

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... 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 ...

Hierarchical feature selection for erythema severity estimation

Science.gov (United States)

Wang, Li; Shi, Chenbo; Shu, Chang

2014-10-01

At present PASI system of scoring is used for evaluating erythema severity, which can help doctors to diagnose psoriasis [1-3]. The system relies on the subjective judge of doctors, where the accuracy and stability cannot be guaranteed [4]. This paper proposes a stable and precise algorithm for erythema severity estimation. Our contributions are twofold. On one hand, in order to extract the multi-scale redness of erythema, we design the hierarchical feature. Different from traditional methods, we not only utilize the color statistical features, but also divide the detect window into small window and extract hierarchical features. Further, a feature re-ranking step is introduced, which can guarantee that extracted features are irrelevant to each other. On the other hand, an adaptive boosting classifier is applied for further feature selection. During the step of training, the classifier will seek out the most valuable feature for evaluating erythema severity, due to its strong learning ability. Experimental results demonstrate the high precision and robustness of our algorithm. The accuracy is 80.1% on the dataset which comprise 116 patients' images with various kinds of erythema. Now our system has been applied for erythema medical efficacy evaluation in Union Hosp, China.

An Effective Performance Analysis of Machine Learning Techniques for Cardiovascular Disease

Directory of Open Access Journals (Sweden)

Vinitha DOMINIC

2015-03-01

Full Text Available Machine learning techniques will help in deriving hidden knowledge from clinical data which can be of great benefit for society, such as reduce the number of clinical trials required for precise diagnosis of a disease of a person etc. Various areas of study are available in healthcare domain like cancer, diabetes, drugs etc. This paper focuses on heart disease dataset and how machine learning techniques can help in understanding the level of risk associated with heart diseases. Initially, data is preprocessed then analysis is done in two stages, in first stage feature selection techniques are applied on 13 commonly used attributes and in second stage feature selection techniques are applied on 75 attributes which are related to anatomic structure of the heart like blood vessels of the heart, arteries etc. Finally, validation of the reduced set of features using an exhaustive list of classifiers is done.In parallel study of the anatomy of the heart is done using the identified features and the characteristics of each class is understood. It is observed that these reduced set of features are anatomically relevant. Thus, it can be concluded that, applying machine learning techniques on clinical data is beneficial and necessary.

FCMPSO: An Imputation for Missing Data Features in Heart Disease Classification

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Salleh, Mohd Najib Mohd; Ashikin Samat, Nurul

2017-08-01

The application of data mining and machine learning in directing clinical research into possible hidden knowledge is becoming greatly influential in medical areas. Heart Disease is a killer disease around the world, and early prevention through efficient methods can help to reduce the mortality number. Medical data may contain many uncertainties, as they are fuzzy and vague in nature. Nonetheless, imprecise features data such as no values and missing values can affect quality of classification results. Nevertheless, the other complete features are still capable to give information in certain features. Therefore, an imputation approach based on Fuzzy C-Means and Particle Swarm Optimization (FCMPSO) is developed in preprocessing stage to help fill in the missing values. Then, the complete dataset is trained in classification algorithm, Decision Tree. The experiment is trained with Heart Disease dataset and the performance is analysed using accuracy, precision, and ROC values. Results show that the performance of Decision Tree is increased after the application of FCMSPO for imputation.

Linking Theory to Practice in Learning Technology Research

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Gunn, Cathy; Steel, Caroline

2012-01-01

We present a case to reposition theory so that it plays a pivotal role in learning technology research and helps to build an ecology of learning. To support the case, we present a critique of current practice based on a review of articles published in two leading international journals from 2005 to 2010. Our study reveals that theory features only…

Group Formation Based on Learning Styles: Can It Improve Students' Teamwork?

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Kyprianidou, Maria; Demetriadis, Stavros; Tsiatsos, Thrasyvoulos; Pombortsis, Andreas

2012-01-01

This work explores the impact of teacher-led heterogeneous group formation on students' teamwork, based on students' learning styles. Fifty senior university students participated in a project-based course with two key organizational features: first, a web system (PEGASUS) was developed to help students identify their learning styles and…

Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis.

Science.gov (United States)

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.

Effects of help-seeking in a blended high school Biology class

Science.gov (United States)

Deguzman, Paolo

Distance learning provides an opportunity for students to learn valuable information through technology and interactive media. Distance learning additionally offers educational institutions the flexibility of synchronous and asynchronous instruction while increasing enrollment and lowering cost. However, distance education has not been well documented within the context of urban high schools. Distance learning may allow high school students to understand material at an individualized pace for either enrichment or remediation. A successful high school student who participates in distance learning should exhibit high self regulatory skills. However, most urban high school students have not been exposed to distance learning and should be introduced to proper self regulatory strategies that should increase the likelihood of understanding the material. To help facilitate a move into distance learning, a blended distance learning model, the combination of distance learning and traditional learning, will be used. According to O'Neil's (in preparation) revised problem solving model, self regulation is a component of problem solving. Within the Blended Biology course, urban high school students will be trained in help-seeking strategies to further their understanding of genetics and Punnett Square problem solving. This study investigated the effects of help-seeking in a blended high school Biology course. The main study consisted of a help-seeking group (n=55) and a control group (n=53). Both the help-seeking group and the control group were taught by one teacher for two weeks. The help-seeking group had access to Blended Biology with Help-Seeking while the control group only had access to Blended Biology. The main study used a pretest and posttest to measure Genetics Content Understanding, Punnett Square Problem Solving, Adaptive Help-Seeking, Maladaptive Help-Seeking, and Self Regulation. The analysis showed no significant difference in any of the measures in terms of

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

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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.

Discriminative kernel feature extraction and learning for object recognition and detection

DEFF Research Database (Denmark)

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....

Finding faults: analogical comparison supports spatial concept learning in geoscience.

Science.gov (United States)

Jee, Benjamin D; Uttal, David H; Gentner, Dedre; Manduca, Cathy; Shipley, Thomas F; Sageman, Bradley

2013-05-01

A central issue in education is how to support the spatial thinking involved in learning science, technology, engineering, and mathematics (STEM). We investigated whether and how the cognitive process of analogical comparison supports learning of a basic spatial concept in geoscience, fault. Because of the high variability in the appearance of faults, it may be difficult for students to learn the category-relevant spatial structure. There is abundant evidence that comparing analogous examples can help students gain insight into important category-defining features (Gentner in Cogn Sci 34(5):752-775, 2010). Further, comparing high-similarity pairs can be especially effective at revealing key differences (Sagi et al. 2012). Across three experiments, we tested whether comparison of visually similar contrasting examples would help students learn the fault concept. Our main findings were that participants performed better at identifying faults when they (1) compared contrasting (fault/no fault) cases versus viewing each case separately (Experiment 1), (2) compared similar as opposed to dissimilar contrasting cases early in learning (Experiment 2), and (3) viewed a contrasting pair of schematic block diagrams as opposed to a single block diagram of a fault as part of an instructional text (Experiment 3). These results suggest that comparison of visually similar contrasting cases helped distinguish category-relevant from category-irrelevant features for participants. When such comparisons occurred early in learning, participants were more likely to form an accurate conceptual representation. Thus, analogical comparison of images may provide one powerful way to enhance spatial learning in geoscience and other STEM disciplines.

Gender Differences in the Use and Benefit of Advanced Learning Technologies for Mathematics

Science.gov (United States)

Arroyo, Ivon; Burleson, Winslow; Tai, Minghui; Muldner, Kasia; Woolf, Beverly Park

2013-01-01

We provide evidence of persistent gender effects for students using advanced adaptive technology while learning mathematics. This technology improves each gender's learning and affective predispositions toward mathematics, but specific features in the software help either female or male students. Gender differences were seen in the students' style…

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

Science.gov (United States)

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.

Effective collaborative learning in biomedical education using a web-based infrastructure.

Science.gov (United States)

Wu, Yunfeng; Zheng, Fang; Cai, Suxian; Xiang, Ning; Zhong, Zhangting; He, Jia; Xu, Fang

2012-01-01

This paper presents a feature-rich web-based system used for biomedical education at the undergraduate level. With the powerful groupware features provided by the wiki system, the instructors are able to establish a community-centered mentoring environment that capitalizes on local expertise to create a sense of online collaborative learning among students. The web-based infrastructure can help the instructors effectively organize and coordinate student research projects, and the groupware features may support the interactive activities, such as interpersonal communications and data sharing. The groupware features also provide the web-based system with a wide range of additional ways of organizing collaboratively developed materials, which makes it become an effective tool for online active learning. Students are able to learn the ability to work effectively in teams, with an improvement of project management, design collaboration, and technical writing skills. With the fruitful outcomes in recent years, it is positively thought that the web-based collaborative learning environment can perform an excellent shift away from the conventional instructor-centered teaching to community- centered collaborative learning in the undergraduate education.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

Science.gov (United States)

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

Learning and the cooperative computational universe

NARCIS (Netherlands)

Adriaans, P.; Adriaans, P.; van Benthem, J.

2008-01-01

In the summer of 1956, a number of scientists gathered at the Dartmouth College in Hanover, New Hampshire. Their goal was to study human intelligence with the help of computers. Their central hypothesis was: "that every aspect of learning or any other feature of intelligence can in principle be so

Learning Disorders: Know the Signs, How to Help

Science.gov (United States)

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 ...

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

Directory of Open Access Journals (Sweden)

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:

Helping Education Students Understand Learning through Designing

Science.gov (United States)

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…

A unified framework of image latent feature learning on Sina microblog

Science.gov (United States)

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.

Aura: A Multi-Featured Programming Framework in Python

Directory of Open Access Journals (Sweden)

2010-09-01

Full Text Available This paper puts forward the design, programming and application of innovative educational software, ‘Aura’ made using Python and PyQt Python bindings. The research paper presents a new concept of using a single tool to relate between syntaxes of various programming languages and algorithms. It radically increases their understanding and retaining capacity, since they can correlate between many programming languages. The software is a totally unorthodox attempt towards helping students who have their first tryst with programming languages. The application is designed to help students understand how algorithms work and thus, help them in learning multiple programming languages on a single platform using an interactive graphical user interface. This paper elucidates how using Python and PyQt bindings, a comprehensive feature rich application, that implements an interactive algorithm building technique, a web browser, multiple programming language framework, a code generator and a real time code sharing hub be embedded into a single interface. And also explains, that using Python as building tool, it requires much less coding than conventional feature rich applications coded in other programming languages, and at the same time does not compromise on stability, inter-operability and robustness of the application.

Helping students learn effective problem solving strategies by reflecting with peers

Science.gov (United States)

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.

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

DEFF Research Database (Denmark)

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...

Linking theory to practice in learning technology research

OpenAIRE

Cathy Gunn; Caroline Steel

2012-01-01

We present a case to reposition theory so that it plays a pivotal role in learning technology research and helps to build an ecology of learning. To support the case, we present a critique of current practice based on a review of articles published in two leading international journals from 2005 to 2010. Our study reveals that theory features only incidentally or not at all in many cases. We propose theory development as a unifying theme for learning technology research study design and repor...

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

Science.gov (United States)

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…

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

DEFF Research Database (Denmark)

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...

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

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.

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

OpenAIRE

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 ...

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

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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

Skype me! Socially Contingent Interactions Help Toddlers Learn Language

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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

Skype me! Socially contingent interactions help toddlers learn language.

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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.

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

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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.

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

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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.

An Evaluation of Online Help for the NOTIS OPAC.

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White, Frank

1994-01-01

Discussion of online help systems in online public access catalogs (OPACs) focuses on a study that evaluated the online help system for the NOTIS (Northwestern Online Total Integrated System) OPAC. Features of the system reviewed include online functions; training features; general interface features; access points; and message content and display…

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

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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

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

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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…

Exploring College Students' Online Help-Seeking Behavior in a Flipped Classroom with a Web-Based Help-Seeking Tool

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Er, Erkan; Kopcha, Theodore J.; Orey, Michael

2015-01-01

Today's generation often seeks help from each other in online environments; however, only a few investigated the role of Internet technologies and the nature of online help-seeking behavior in collaborative learning environments. This paper presents an educational design research project that examines college students' online help-seeking…

SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature

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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.

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

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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.

Theory-based Support for Mobile Language Learning: Noticing and Recording

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Agnes Kukulska-Hulme

2009-04-01

Full Text Available This paper considers the issue of 'noticing' in second language acquisition, and argues for the potential of handheld devices to: (i support language learners in noticing and recording noticed features 'on the spot', to help them develop their second language system; (ii help language teachers better understand the specific difficulties of individuals or those from a particular language background; and (iii facilitate data collection by applied linguistics researchers, which can be fed back into educational applications for language learning. We consider: theoretical perspectives drawn from the second language acquisition literature, relating these to the practice of writing language learning diaries; and the potential for learner modelling to facilitate recording and prompting noticing in mobile assisted language learning contexts. We then offer guidelines for developers of mobile language learning solutions to support the development of language awareness in learners.

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

OpenAIRE

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...

Nature as a treasure map! Teaching geoscience with the help of earth caches?!

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Zecha, Stefanie; Schiller, Thomas

2015-04-01

This presentation looks at how earth caches are influence the learning process in the field of geo science in non-formal education. The development of mobile technologies using Global Positioning System (GPS) data to point geographical location together with the evolving Web 2.0 supporting the creation and consumption of content, suggest a potential for collaborative informal learning linked to location. With the help of the GIS in smartphones you can go directly in nature, search for information by your smartphone, and learn something about nature. Earth caches are a very good opportunity, which are organized and supervised geocaches with special information about physical geography high lights. Interested people can inform themselves about aspects in geoscience area by earth caches. The main question of this presentation is how these caches are created in relation to learning processes. As is not possible, to analyze all existing earth caches, there was focus on Bavaria and a certain feature of earth caches. At the end the authors show limits and potentials for the use of earth caches and give some remark for the future.

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

International Nuclear Information System (INIS)

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

Effects of Concept-Mapping-Based Interactive E-Books on Active and Reflective-Style Students' Learning Performances in Junior High School Law Courses

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Hwang, Gwo-Jen; Sung, Han-Yu; Chang, Hsuan

2017-01-01

Researchers have pointed out that interactive e-books have rich content and interactive features which can promote students' learning interest. However, researchers have also indicated the need to integrate effective learning supports or tools to help students organize what they have learned so as to increase their learning performance, in…

Peer-Led Team Learning Helps Minority Students Succeed.

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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.

Remote health monitoring: predicting outcome success based on contextual features for cardiovascular disease.

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Alshurafa, Nabil; Eastwood, Jo-Ann; Pourhomayoun, Mohammad; Liu, Jason J; Sarrafzadeh, Majid

2014-01-01

Current studies have produced a plethora of remote health monitoring (RHM) systems designed to enhance the care of patients with chronic diseases. Many RHM systems are designed to improve patient risk factors for cardiovascular disease, including physiological parameters such as body mass index (BMI) and waist circumference, and lipid profiles such as low density lipoprotein (LDL) and high density lipoprotein (HDL). There are several patient characteristics that could be determining factors for a patient's RHM outcome success, but these characteristics have been largely unidentified. In this paper, we analyze results from an RHM system deployed in a six month Women's Heart Health study of 90 patients, and apply advanced feature selection and machine learning algorithms to identify patients' key baseline contextual features and build effective prediction models that help determine RHM outcome success. We introduce Wanda-CVD, a smartphone-based RHM system designed to help participants with cardiovascular disease risk factors by motivating participants through wireless coaching using feedback and prompts as social support. We analyze key contextual features that secure positive patient outcomes in both physiological parameters and lipid profiles. Results from the Women's Heart Health study show that health threat of heart disease, quality of life, family history, stress factors, social support, and anxiety at baseline all help predict patient RHM outcome success.

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

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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

The Attitude of Math Teachers toward Cooperative Learning and Institutional Elements that May Help or Hinder its Use as a Teaching Methodology

Directory of Open Access Journals (Sweden)

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.

Visual texture perception via graph-based semi-supervised learning

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Zhang, Qin; Dong, Junyu; Zhong, Guoqiang

2018-04-01

Perceptual features, for example direction, contrast and repetitiveness, are important visual factors for human to perceive a texture. However, it needs to perform psychophysical experiment to quantify these perceptual features' scale, which requires a large amount of human labor and time. This paper focuses on the task of obtaining perceptual features' scale of textures by small number of textures with perceptual scales through a rating psychophysical experiment (what we call labeled textures) and a mass of unlabeled textures. This is the scenario that the semi-supervised learning is naturally suitable for. This is meaningful for texture perception research, and really helpful for the perceptual texture database expansion. A graph-based semi-supervised learning method called random multi-graphs, RMG for short, is proposed to deal with this task. We evaluate different kinds of features including LBP, Gabor, and a kind of unsupervised deep features extracted by a PCA-based deep network. The experimental results show that our method can achieve satisfactory effects no matter what kind of texture features are used.

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

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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.

Engaging Focus Group Methodology: The 4-H Middle School-Aged Youth Learning and Leading Study

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Scott, Siri; Grant, Samantha; Nippolt, Pamela Larson

2015-01-01

With young people, discussing complex issues such as learning and leading in a focus group can be a challenge. To help prime youth for the discussion, we created a focus group approach that featured a fun, interactive activity. This article includes a description of the focus group activity, lessons learned, and suggestions for additional…

Sharing and helping: predictors of adolescents' willingness to share diabetes personal health information with peers.

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Vaala, Sarah E; Lee, Joyce M; Hood, Korey K; Mulvaney, Shelagh A

2018-02-01

Sharing personal information about type 1 diabetes (T1D) can help adolescents obtain social support, enhance social learning, and improve self-care. Diabetes technologies, online communities, and health interventions increasingly feature data-sharing components. This study examines factors underlying adolescents' willingness to share personal T1D information with peers. Participants were 134 adolescents (12-17 years of age; 56% female) who completed an online survey regarding experiences helping others with T1D, perceived social resources, beliefs about the value of sharing information and helping others, and willingness to share T1D information. Hemoglobin A1c values were obtained from medical records. Adolescents were more willing to share how they accomplished T1D tasks than how often they completed them, and least willing to share glucose control status. In multivariate analyses, sharing/helping beliefs (β = 0.26, P value; β = -0.26, P personal health information. Glucose control moderated relationships such that adolescents with worse A1c values had stronger relationships between sharing/helping beliefs and willingness to share (β = 0.18, P personal health information, particularly if they have better diabetes health status and a stronger belief in the benefits of sharing. Social learning and social media components may improve intervention participation, engagement, and outcomes by boosting adolescents' beliefs about the benefits of sharing information and helping others. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Help Options in CALL: A Systematic Review

Science.gov (United States)

Cardenas-Claros, Monica S.; Gruba, Paul A.

2009-01-01

This paper is a systematic review of research investigating help options in the different language skills in computer-assisted language learning (CALL). In this review, emerging themes along with is-sues affecting help option research are identified and discussed. We argue that help options in CALL are application resources that do not only seem…

Features and Characteristics of Problem Based Learning

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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…

Haptic exploration of fingertip-sized geometric features using a multimodal tactile sensor

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Ponce Wong, Ruben D.; Hellman, Randall B.; Santos, Veronica J.

2014-06-01

Haptic perception remains a grand challenge for artificial hands. Dexterous manipulators could be enhanced by "haptic intelligence" that enables identification of objects and their features via touch alone. Haptic perception of local shape would be useful when vision is obstructed or when proprioceptive feedback is inadequate, as observed in this study. In this work, a robot hand outfitted with a deformable, bladder-type, multimodal tactile sensor was used to replay four human-inspired haptic "exploratory procedures" on fingertip-sized geometric features. The geometric features varied by type (bump, pit), curvature (planar, conical, spherical), and footprint dimension (1.25 - 20 mm). Tactile signals generated by active fingertip motions were used to extract key parameters for use as inputs to supervised learning models. A support vector classifier estimated order of curvature while support vector regression models estimated footprint dimension once curvature had been estimated. A distal-proximal stroke (along the long axis of the finger) enabled estimation of order of curvature with an accuracy of 97%. Best-performing, curvature-specific, support vector regression models yielded R2 values of at least 0.95. While a radial-ulnar stroke (along the short axis of the finger) was most helpful for estimating feature type and size for planar features, a rolling motion was most helpful for conical and spherical features. The ability to haptically perceive local shape could be used to advance robot autonomy and provide haptic feedback to human teleoperators of devices ranging from bomb defusal robots to neuroprostheses.

Teachers' Perceptions of the Learning Environment and Their Knowledge Base in a Training Program for Novice University Teachers

Science.gov (United States)

Johannes, Christine; Fendler, Jan; Seidel, Tina

2013-01-01

Despite the complexity of teaching, learning to teach in universities is often "learning by doing". To provide novice university teachers with pedagogic teaching knowledge and to help them develop specific teaching objectives, we created a structured, video-based, one-year training program. In focusing on the core features of…

Block Study: Learning About Your Local Community.

Science.gov (United States)

Eckbreth, Catherine

Designed for 7th- and 8th-grade students, five lessons using a block of houses in an urban neighborhood help students learn about the history of a neighborhood, the owners of the houses, and the style and architectural features of the homes. Although this unit has been developed for a specific neighborhood, a similar block study could be conducted…

Predicting help-seeking behavior: The impact of knowing someone close who has sought help.

Science.gov (United States)

Disabato, David J; Short, Jerome L; Lameira, Diane M; Bagley, Karen D; Wong, Stephanie J

2018-02-15

This study sought to replicate and extend research on social facilitators of college student's help seeking for psychological problems. We collected data on 420 ethnically diverse college students at a large public university (September 2008-May 2010). Students completed a cross-sectional online survey. We found that students who were aware of close others' (eg, family, friends) help seeking were two times more likely to have sought formal (eg, psychologist) and informal (eg, clergy) help themselves. Tests of moderation revealed the incremental effect (ie, controlling for help-seeking attitudes, internalizing symptoms, cultural demographics) of close others' formal help seeking was strong and significant for men (R 2 = 0.112), while it was negligible and nonsignificant for women (R 2 = .002). We discuss the importance for students-particularly men-to learn about close others' help seeking for facilitating their own help seeking during times of distress.

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

Science.gov (United States)

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.

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

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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.

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

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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…

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

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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.

An online app platform enhances collaborative medical student group learning and classroom management.

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Peacock, Justin G; Grande, Joseph P

2016-01-01

The authors presented their results in effectively using a free and widely-accessible online app platform to manage and teach a first-year pathology course at Mayo Medical School. The authors utilized the Google "Blogger", "Forms", "Flubaroo", "Sheets", "Docs", and "Slides" apps to effectively build a collaborative classroom teaching and management system. Students were surveyed on the use of the app platform in the classroom, and 44 (94%) students responded. Thirty-two (73%) of the students reported that "Blogger" was an effective place for online discussion of pathology topics and questions. 43 (98%) of the students reported that the "Forms/Flubaroo" grade-reporting system was helpful. 40 (91%) of the students used the remote, collaborative features of "Slides" to create team-based learning presentations, and 39 (89%) of the students found those collaborative features helpful. "Docs" helped teaching assistants to collaboratively create study guides or grading rubrics. Overall, 41 (93%) of the students found that the app platform was helpful in establishing a collaborative, online classroom environment. The online app platform allowed faculty to build an efficient and effective classroom teaching and management system. The ease of accessibility and opportunity for collaboration allowed for collaborative learning, grading, and teaching.

Dissociation between Features and Feature Relations in Infant Memory: Effects of Memory Load.

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Bhatt, Ramesh S.; Rovee-Collier, Carolyn

1997-01-01

Four experiments examined effects of the number of features and feature relations on learning and long-term memory in 3-month olds. Findings suggested that memory load size selectively constrained infants' long-term memory for relational information, suggesting that in infants, features and relations are psychologically distinct and that memory…

Skype me! Socially Contingent Interactions Help Toddlers Learn Language

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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...

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

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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…

Rats demonstrate helping behavior toward a soaked conspecific.

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Sato, Nobuya; Tan, Ling; Tate, Kazushi; Okada, Maya

2015-09-01

Helping behavior is a prosocial behavior whereby an individual helps another irrespective of disadvantages to him or herself. In the present study, we examined whether rats would help distressed, conspecific rats that had been soaked with water. In Experiment 1, rats quickly learned to liberate a soaked cagemate from the water area by opening the door to allow the trapped rat into a safe area. Additional tests showed that the presentation of a distressed cagemate was necessary to induce rapid door-opening behavior. In addition, it was shown that rats dislike soaking and that rats that had previously experienced a soaking were quicker to learn how to help a cagemate than those that had never been soaked. In Experiment 2, the results indicated that rats did not open the door to a cagemate that was not distressed. In Experiment 3, we tested behavior when rats were forced to choose between opening the door to help a distressed cagemate and opening a different door to obtain a food reward. Irrespective of how they learned to open the door, in most test trials, rats chose to help the cagemate before obtaining a food reward, suggesting that the relative value of helping others is greater than the value of a food reward. These results suggest that rats can behave prosocially and that helper rats may be motivated by empathy-like feelings toward their distressed cagemate.

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

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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

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

Directory of Open Access Journals (Sweden)

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

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

Science.gov (United States)

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

Spatiotemporal Features for Asynchronous Event-based Data

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Xavier eLagorce

2015-02-01

Full Text Available Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the realiable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.

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

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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.

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

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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.

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

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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

How Computer Games Help Children Learn

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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…

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?

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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

The Helping Horse: How Equine Assisted Learning Contributes to the Wellbeing of First Nations Youth in Treatment for Volatile Substance Misuse

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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

An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

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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.

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

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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…

Attentional Bias in Human Category Learning: The Case of Deep Learning.

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Hanson, Catherine; Caglar, Leyla Roskan; Hanson, Stephen José

2018-01-01

Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This "failure" to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures

Attentional Bias in Human Category Learning: The Case of Deep Learning

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Catherine Hanson

2018-04-01

Full Text Available Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987 showed that stimuli can have structures with features that are statistically uncorrelated (separable or statistically correlated (integral within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974. In contrast to humans, a single hidden layer backpropagation (BP neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993. This “failure” to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1 by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2 by investigating whether a Deep Learning (DL network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc., would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993. Second, we show that using the same low dimensional stimuli, Deep Learning (DL, unlike BP but similar to humans, learns separable category structures more quickly than integral category

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

Science.gov (United States)

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.

Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review.

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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

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

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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.

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

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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.

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

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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

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

Science.gov (United States)

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.

FUNCTIONING FEATURES OF COMPUTER TECHNOLOGY WHILE FORMING PRIMARY SCHOOLCHILDREN’S COMMUNICATIVE COMPETENCE

Directory of Open Access Journals (Sweden)

Olena Beskorsa

2017-04-01

Full Text Available The article reveals the problem of functioning features of computer technology while forming primary schoolchildren’s communicative competence whose relevance is proved by the increasing role of a foreign language as a means of communication and modernization of foreign language education. There is a great deal of publications devoted to the issue of foreign language learning at primary school by N. Biriukevych, O. Kolominova, O. Metolkina, O. Petrenko, V. Redko, S. Roman. Implementing of innovative technology as well as computer one is to intensify the language learning process and to improve young learners’ communicative skills. The aim of the article is to identify computer technology functioning features while forming primary schoolchildren communicative competence. In this study we follow the definition of the computer technology as an information technology whose implementation may be accompanied with a computer as one of the tools, excluding the use of audio and video equipment, projectors and other technical tools. Using computer technologies is realized due to a number of tools which are divided into two main groups: electronic learning materials; computer testing software. The analysis of current textbooks and learning and methodological complexes shows that teachers prefer authentic electronic materials to the national ones. The most available English learning materials are on the Internet and they are free. The author of the article discloses several on-line English learning tools and depict the opportunities to use them while forming primary schoolchildren’s communicative competence. Special attention is also paid to multimedia technology, its functioning features and multimedia lesson structure. Computer testing software provides tools for current and control assessing results of mastering language material, communicative skills, and self-assessing in an interactive way. For making tests for assessing English skill

A Logical Approach to Supporting Professional Learning Communities

Directory of Open Access Journals (Sweden)

Ralph Seward

2011-12-01

Full Text Available Collaborative knowledge sharing requires that dialogues successfully cross organizational barriers and information silos. Successful communication in person or in a virtual community involves a willingness to share ideas and consider diverse viewpoints. This research examines a science, technology, engineering, and mathematics (STEM content management system called NASATalk, which offers public and private blog posts, file sharing, asynchronous discussion, and live chat services. The service is designed to provide a virtual environment where educators can share ideas, suggestions, successes, and innovations in STEM teaching and learning activities. This study features qualitative data from STEM education groups that helped extend the design of the NASATalk Web 2.0 collaborative tools and features. The analysis shows that the context, e-collaborative tools, integration strategies, and outcomes varied, but also contributed additional space, time, tools, integration strategies, and outcomes through the virtual collaborative learning environment. This study is designed to inform the STEM education community as well as those offering virtual community resources and tools of the added value of using virtual communities to help STEM educators work together in collaborative, virtual environments to discuss ways they can improve their instruction and student performance.

The ethnography of help - Supporting families with children with intellectual disabilities

OpenAIRE

Summers, N.

2010-01-01

This thesis explored parents’ of children with learning disabilities perceptions of family support workers’ helping strategies. A qualitative approach drawing on the principles of ethnography was used to explore the experiences of six families of the helping strategies adopted by family workers and posed three research questions:\\ud (1) What are the perceptions of parents, of children with learning disabilities, of the helping strategies of family support workers?\\ud (2) How do parents unders...

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

Science.gov (United States)

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…

Transferring managerial learning back to the workplace : the influence of personality and the workplace environment

OpenAIRE

Belling, Ruth

2000-01-01

This thesis identifies the influences of individual characteristics, particularly psychological type preferences, and workplace environment features, on managers’ perceptions of the barriers and facilitators to transferring their learning from management development programmes. In doing so, it provides information and insights to help increase understanding of the transfer of learning process through the building of a model of transfer. Guided by a Realist perspective, this ...

Features of Fragile X Syndrome

Science.gov (United States)

... Disabilities in FXS include a range from moderate learning disabilities to more severe intellectual disabilities. Physical features may ... intellectual disability. Others may have moderate or mild learning disabilities, emotional/mental health issues, general anxiety and/or ...

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

Science.gov (United States)

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.

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

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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.

Grammar-based feature generation for time-series prediction

CERN Document Server

De Silva, Anthony Mihirana

2015-01-01

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

Learning To Be Vélomobile

DEFF Research Database (Denmark)

McIlvenny, Paul

to use a bike-friendly infrastructure, and how in such an environment they learn to ride with the help of others. Video and audio recordings of family bike rides as well as school bike training sessions and school bike tours were made to capture from multi-angles the aural and visual features......There are few qualitative studies to date of how children learn to become vélomobile, and of how parents and caregivers talk and interact with children to instruct them how to ride safely. This paper reports on a study of how children learn to sit in a carrier bike as a passenger and to ride...... their own bike. In my data from Denmark, these activities take place in urban areas with good biking infrastructure, including separate bike lanes and dedicated cycle paths, as well as bike-only traffic lights at intersections. Therefore, this study provides insight into how riders actually use and learn...

The Identification, Implementation, and Evaluation of Critical User Interface Design Features of Computer-Assisted Instruction Programs in Mathematics for Students with Learning Disabilities

Science.gov (United States)

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…

A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

Science.gov (United States)

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.

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

African Journals Online (AJOL)

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-.

Person Re-Identification by Camera Correlation Aware Feature Augmentation.

Science.gov (United States)

Chen, Ying-Cong; Zhu, Xiatian; Zheng, Wei-Shi; Lai, Jian-Huang

2018-02-01

The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coR relation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state

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

Science.gov (United States)

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).

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

Energy Technology Data Exchange (ETDEWEB)

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

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

International Nuclear Information System (INIS)

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

Using Social Learning Methodologies in Higher Education

Directory of Open Access Journals (Sweden)

María-Estrella Sousa-Vieira

2015-05-01

Full Text Available It is commonly accepted that contemporary cohorts of students witness and experience the benefits of information technologies in their learning processes. The so-called ``digital natives'' acquire, as a consequence of their early exposure to these technologies, different patterns of work, distinct attention conducts, new learning preferences and, generally, better skills for learning and working within rich online social contexts. So, it seems reasonable that the traditional education systems evolve and shape their practice to leverage those new patterns. Despite the fact that online social networks (OSNs are widely recognized as a powerful tool for adding a new social dimension to the learning management systems (LMSs, OSNs do not fully integrate the specific features of the learning process yet and LMSs do not exploit the advantages of an active social environment for reinforcing the learning experience. We report in this paper the design, development and use of a software platform which enlarges and adapts the basic features of an OSN in order to be useful for very general learning environments. The software allows the creation, assessment and reporting of a range of collaborative activities based on social interactions among the students, and offers a reward mechanism by means of ranking and reputation. We argue that this approach is helpful in increasing the students' motivation, besides improving the learning experience and performance. The software has been tested in an undergraduate course about computer networks. Different tests confirm that the impact on learning success is statistically significant and positive.

The Self-Regulated Learning Model and Music Education

Directory of Open Access Journals (Sweden)

Maja Marijan

2017-02-01

Full Text Available Self-regulation and self-regulated learning (SRL are important features in music education. In this research self-regulated learning model is presented as a complex, multidimensional structure. SRL starts with the self-regulation. Self-regulation is formed through interaction with the environment, thus self-learning, self-analysis, self-judgment, self-instruction, and self-monitoring are the main functions in self-regulatory structure. Co-regulation is needed, and helps self-regulation to be activated and monitored. In music education, co-regulation refers to the instructions that teacher introduces in the lessons. These instructions have to enhance learning and develop regulation over emotions, cognitive, auditor, and motor skills in students. Learning techniques and learning strategies are core components in music education. Adapting those, students become aware of their learning processes, actions, thoughts, feelings and behaviors that are involved in learning. It is suggested that every teaching methodology has to develop learning techniques, as well as metamemory and metacognition in students, in order to gain expertise. The author has emphasized her attention to every aspect that is believed to belong to SRL. There are not many articles on the SRL in music education, written by musicians, in compare with those written by psychologists and neurologists,. Therefore, the author has suggested that this paper would encourage music teachers and performers to take an advantage in the research of SRL. These researches would help music educational systems and teachers to develop and promote learning techniques and strategies. The results would show improvement in student’s learning and self-regulation.

E-Learning in postsecondary education.

Science.gov (United States)

Bell, Bradford S; Federman, Jessica E

2013-01-01

Over the past decade postsecondary education has been moving increasingly from the classroom to online. During the fall 2010 term 31 percent of U.S. college students took at least one online course. The primary reasons for the growth of e-learning in the nation's colleges and universities include the desire of those institutions to generate new revenue streams, improve access, and offer students greater scheduling flexibility. Yet the growth of e-learning has been accompanied by a continuing debate about its effectiveness and by the recognition that a number of barriers impede its widespread adoption in higher education. Through an extensive research review, Bradford Bell and Jessica Federman examine three key issues in the growing use of e-learning in postsecondary education. The first is whether e-learning is as effective as other delivery methods. The debate about the effectiveness of e-learning, the authors say, has been framed in terms of how it compares with other means of delivering instruction, most often traditional instructor-led classroom instruction. Bell and Federman review a number of meta-analyses and other studies that, taken together, show that e-learning produces outcomes equivalent to other delivery media when instructional conditions are held constant. The second issue is what particular features of e-learning influence its effectiveness. Here the authors move beyond the "does it work" question to examine how different instructional features and supports, such as immersion and interactivity, influence the effectiveness of e-learning programs. They review research that shows how these features can be configured to create e-learning programs that help different types of learners acquire different types of knowledge. In addressing the third issue--the barriers to the adoption of e-learning in postsecondary education--Bell and Federman discuss how concerns about fraud and cheating, uncertainties about the cost of e-learning, and the unique

Using tactile features to help functionally blind individuals denominate banknotes.

Science.gov (United States)

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.

Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

Directory of Open Access Journals (Sweden)

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.

Learning Bing maps API

CERN Document Server

Sinani, Artan

2013-01-01

This is a practical, hands-on guide with illustrative examples, which will help you explore the vast universe of Bing maps.If you are a developer who wants to learn how to exploit the numerous features of Bing Maps then this book is ideal for you. It can also be useful for more experienced developers who wish to explore other areas of the APIs. It is assumed that you have some knowledge of JavaScript, HTML, and CSS. For some chapters a working knowledge of .Net and Visual Studio is also needed.

Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

Science.gov (United States)

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

Algorithm-Dependent Generalization Bounds for Multi-Task Learning.

Science.gov (United States)

Liu, Tongliang; Tao, Dacheng; Song, Mingli; Maybank, Stephen J

2017-02-01

Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present novel algorithm-dependent generalization bounds for MTL by exploiting the notion of algorithmic stability. We focus on the performance of one particular task and the average performance over multiple tasks by analyzing the generalization ability of a common parameter that is shared in MTL. When focusing on one particular task, with the help of a mild assumption on the feature structures, we interpret the function of the other tasks as a regularizer that produces a specific inductive bias. The algorithm for learning the common parameter, as well as the predictor, is thereby uniformly stable with respect to the domain of the particular task and has a generalization bound with a fast convergence rate of order O(1/n), where n is the sample size of the particular task. When focusing on the average performance over multiple tasks, we prove that a similar inductive bias exists under certain conditions on the feature structures. Thus, the corresponding algorithm for learning the common parameter is also uniformly stable with respect to the domains of the multiple tasks, and its generalization bound is of the order O(1/T), where T is the number of tasks. These theoretical analyses naturally show that the similarity of feature structures in MTL will lead to specific regularizations for predicting, which enables the learning algorithms to generalize fast and correctly from a few examples.

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

Science.gov (United States)

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,…

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

Science.gov (United States)

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.

Watching Subtitled Films Can Help Learning Foreign Languages.

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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).

Helping Students on the Margin Succeed in Schools.

Science.gov (United States)

Langenfeld, Michelle Schoen; Cumming, Brenda

1996-01-01

Addresses how Apple Valley High School (Minnesota) has been able to help marginal students succeed in school. The fundamental actions that contributed to the effectiveness of study-team efforts to help marginal students are discussed, and what has been learned through these efforts is considered. (GR)

Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.

Science.gov (United States)

Li, Zhongyu; Butler, Erik; Li, Kang; Lu, Aidong; Ji, Shuiwang; Zhang, Shaoting

2018-02-12

Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.

The New School-Based Learning (SBL) to Work-Based Learning (WBL) Transition Module: A Practical Implementation in the Technical and Vocational Education (TVE) System in Bahrain

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Alseddiqi, M.; Mishra, R.; Pislaru, C.

2012-05-01

This paper diagnoses the implementation of a new engineering course entitled 'school-based learning (SBL) to work-based learning (WBL) transition module' in the Bahrain Technical and Vocational Education (TVE) learning environment. The module was designed to incorporate an innovative education and training approach with a variety of learning activities that are included in various learning case studies. Each case study was based on with learning objectives coupled with desired learning outcomes. The TVE students should meet the desired outcomes after the completion of the learning activities and assessments. To help with the implementation phase of the new module, the authors developed guidelines for each case study. The guidelines incorporated learning activities to be delivered in an integrated learning environment. The skills to be transferred were related to cognitive, affective, and technical proficiencies. The guidelines included structured instructions to help students during the learning process. In addition, technology was introduced to improve learning effectiveness and flexibility. The guidelines include learning indicators for each learning activity and were based on their interrelation with competencies to be achieved with respect to modern industrial requirements. Each learning indicator was then correlated against the type of learning environment, teaching and learning styles, examples of mode of delivery, and assessment strategy. Also, the learning activities were supported by technological features such as discussion forums for social perception and engagement and immediate feedback exercises for self-motivation. Through the developed module, TVE teachers can effectively manage the teaching and learning process as well as the assessment strategy to satisfy students' individual requirements and enable them to meet workplace requirements.

Enhanced Resource Descriptions Help Learning Matrix Users.

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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)

Do Interactive Globes and Games Help Students Learn Planetary Science?

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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.

SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features

Energy Technology Data Exchange (ETDEWEB)

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

Learning to recommend helpful hotel reviews

OpenAIRE

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...

Implementing and Assessing Inquiry-Based Learning through the CAREER Award

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Brudzinski, M. R.

2011-12-01

In order to fully attain the benefits of inquiry-based learning, instructors who typically employ the traditional lecture format need to make many adjustments to their approach. This change in styles can be intimidating and logistically difficult to overcome, both for instructors and students, such that a stepwise approach to this transformation is likely to be more manageable. In this session, I will describe a series of tools to promote inquiry-based learning that I am helping to implement and assess in classroom courses and student research projects. I will demonstrate the importance of integrating with existing institutional initiatives as well as recognizing how student development plays a key role in student engagement. Some of the features I will highlight include: defining both student learning outcomes and student development outcomes, converting content training to be self-directed and asynchronous, utilizing conceptests to help students practice thinking like scientists, and employing both objective pre/post assessment and student self-reflective assessment. Lastly, I will reflect on how the well-defined goal of teaching and research integration in the CAREER award solicitation resonated with me even as an undergraduate and helped inspire my early career.

A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning.

Science.gov (United States)

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.

Feature Scaling via Second-Order Cone Programming

Directory of Open Access Journals (Sweden)

Zhizheng Liang

2016-01-01

Full Text Available Feature scaling has attracted considerable attention during the past several decades because of its important role in feature selection. In this paper, a novel algorithm for learning scaling factors of features is proposed. It first assigns a nonnegative scaling factor to each feature of data and then adopts a generalized performance measure to learn the optimal scaling factors. It is of interest to note that the proposed model can be transformed into a convex optimization problem: second-order cone programming (SOCP. Thus the scaling factors of features in our method are globally optimal in some sense. Several experiments on simulated data, UCI data sets, and the gene data set are conducted to demonstrate that the proposed method is more effective than previous methods.

Pizza and Pasta Help Students Learn Metabolism

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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…

Learning with hierarchical-deep models.

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Salakhutdinov, Ruslan; Tenenbaum, Joshua B; Torralba, Antonio

2013-08-01

We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.

Math Is Like a Scary Movie? Helping Young People Overcome Math Anxiety

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Kulkin, Margaret

2016-01-01

Afterschool teachers who tutor students or provide homework help have a unique opportunity to help students overcome the social or emotional barriers that so often block learning. They can embrace a creative and investigative approach to math learning. Margaret Kulkin's interest in being a math attitude "myth-buster" led her to apply to…

Perceptions of Helpfulness of Teachers in Didactic Courses

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Moate, Randall M.; Cox, Jane A.; Brown, Steven R.; West, Erin M.

2017-01-01

Thirty-five novice counselors completed a Q sort that assessed their perceptions of what was most helpful about teachers of didactic classes in their master's degree program. Participants perceived teachers who used a contextual teaching pedagogy and had an authentic, empathic, and compassionate way of being as helpful to their learning.

Classification of health webpages as expert and non expert with a reduced set of cross-language features.

Science.gov (United States)

Grabar, Natalia; Krivine, Sonia; Jaulent, Marie-Christine

2007-10-11

Making the distinction between expert and non expert health documents can help users to select the information which is more suitable for them, according to whether they are familiar or not with medical terminology. This issue is particularly important for the information retrieval area. In our work we address this purpose through stylistic corpus analysis and the application of machine learning algorithms. Our hypothesis is that this distinction can be performed on the basis of a small number of features and that such features can be language and domain independent. The used features were acquired in source corpus (Russian language, diabetes topic) and then tested on target (French language, pneumology topic) and source corpora. These cross-language features show 90% precision and 93% recall with non expert documents in source language; and 85% precision and 74% recall with expert documents in target language.

High-quality and small-capacity e-learning video featuring lecturer-superimposing PC screen images

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Teaching medicine with the help of "Dr. House".

Science.gov (United States)

Jerrentrup, Andreas; Mueller, Tobias; Glowalla, Ulrich; Herder, Meike; Henrichs, Nadine; Neubauer, Andreas; Schaefer, Juergen R

2018-01-01

TV series such as "House MD", "Grey´s Anatomy" or "Emergency Room" are well perceived by medical students. Seminars featuring medical TV series such as "House MD" might serve as door-opener to attract medical students to learn more about rare diseases. The TV series "House MD" is troublesome for the main character Dr. House is an excellent diagnostician but at the same time a rather misanthropic person. Therefore, lecturing medicine with the help of "House MD" requires constant evaluation. From 2008 to 2016 we are using the well-known TV series "House MD" continuously to attract medical students and teach them about rare diseases as well as diagnostic strategies. We collected from 213 students a detailed questionnaire assessing their learning experience. 76.6% of our students (n = 157) reported to watching medical dramas on a regular basis. The Dr. House seminar was compared to traditional seminars and our students reported an improved learning effect (69.9%), better concentration (89.7%), higher motivation to participate (88.7%), and more fun (86.7%) (all pHouse's behavior quite critically. Likert assessment on a 5-point scale identified strong disagreement with Dr. House´s interpersonal skills in dealing with his colleagues (median = 1) and patients (median = 1). At the same time, the students strongly agreed with his outstanding diagnostic (median = 5) and therapeutic capabilities (median = 4). Medical students visiting a Dr. House teaching seminar are highly motivated to learn more about rare diseases. They were positively influenced by TV series such as Dr. House to improve their diagnostic and clinical skills. At the same time, they are critical enough not to see Dr. House as a role model for their own personality. Well performed medical TV shows such as Dr. House can successfully be used in an educational setting to motivate medical students to come into seminars to learn more about rare diseases.

Reusable Reinforcement Learning via Shallow Trails.

Science.gov (United States)

Yu, Yang; Chen, Shi-Yong; Da, Qing; Zhou, Zhi-Hua

2018-06-01

Reinforcement learning has shown great success in helping learning agents accomplish tasks autonomously from environment interactions. Meanwhile in many real-world applications, an agent needs to accomplish not only a fixed task but also a range of tasks. For this goal, an agent can learn a metapolicy over a set of training tasks that are drawn from an underlying distribution. By maximizing the total reward summed over all the training tasks, the metapolicy can then be reused in accomplishing test tasks from the same distribution. However, in practice, we face two major obstacles to train and reuse metapolicies well. First, how to identify tasks that are unrelated or even opposite with each other, in order to avoid their mutual interference in the training. Second, how to characterize task features, according to which a metapolicy can be reused. In this paper, we propose the MetA-Policy LEarning (MAPLE) approach that overcomes the two difficulties by introducing the shallow trail. It probes a task by running a roughly trained policy. Using the rewards of the shallow trail, MAPLE automatically groups similar tasks. Moreover, when the task parameters are unknown, the rewards of the shallow trail also serve as task features. Empirical studies on several controlling tasks verify that MAPLE can train metapolicies well and receives high reward on test tasks.

CLASSIFICATION OF LEARNING MANAGEMENT SYSTEMS

Directory of Open Access Journals (Sweden)

Yu. B. Popova

2016-01-01

Full Text Available Using of information technologies and, in particular, learning management systems, increases opportunities of teachers and students in reaching their goals in education. Such systems provide learning content, help organize and monitor training, collect progress statistics and take into account the individual characteristics of each user. Currently, there is a huge inventory of both paid and free systems are physically located both on college servers and in the cloud, offering different features sets of different licensing scheme and the cost. This creates the problem of choosing the best system. This problem is partly due to the lack of comprehensive classification of such systems. Analysis of more than 30 of the most common now automated learning management systems has shown that a classification of such systems should be carried out according to certain criteria, under which the same type of system can be considered. As classification features offered by the author are: cost, functionality, modularity, keeping the customer’s requirements, the integration of content, the physical location of a system, adaptability training. Considering the learning management system within these classifications and taking into account the current trends of their development, it is possible to identify the main requirements to them: functionality, reliability, ease of use, low cost, support for SCORM standard or Tin Can API, modularity and adaptability. According to the requirements at the Software Department of FITR BNTU under the guidance of the author since 2009 take place the development, the use and continuous improvement of their own learning management system.

Disseminating Innovations in Teaching Value-Based Care Through an Online Learning Network.

Science.gov (United States)

Gupta, Reshma; Shah, Neel T; Moriates, Christopher; Wallingford, September; Arora, Vineet M

2017-08-01

A national imperative to provide value-based care requires new strategies to teach clinicians about high-value care. We developed a virtual online learning network aimed at disseminating emerging strategies in teaching value-based care. The online Teaching Value in Health Care Learning Network includes monthly webinars that feature selected innovators, online discussion forums, and a repository for sharing tools. The learning network comprises clinician-educators and health system leaders across North America. We conducted a cross-sectional online survey of all webinar presenters and the active members of the network, and we assessed program feasibility. Six months after the program launched, there were 277 learning community members in 22 US states. Of the 74 active members, 50 (68%) completed the evaluation. Active members represented independently practicing physicians and trainees in 7 specialties, nurses, educators, and health system leaders. Nearly all speakers reported that the learning network provided them with a unique opportunity to connect with a different audience and achieve greater recognition for their work. Of the members who were active in the learning network, most reported that strategies gleaned from the network were helpful, and some adopted or adapted these innovations at their home institutions. One year after the program launched, the learning network had grown to 364 total members. The learning network helped participants share and implement innovations to promote high-value care. The model can help disseminate innovations in emerging areas of health care transformation, and is sustainable without ongoing support after a period of start-up funding.

Help Stop the Flu | NIH MedlinePlus the Magazine

Science.gov (United States)

... page please turn Javascript on. Feature: Flu Shot Help Stop the Flu Past Issues / Winter 2011 Table ... CDC recommends that Americans do the following to help stop the flu: Cover nose and mouth with ...

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

Directory of Open Access Journals (Sweden)

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.

How Dispositional Learning Analytics helps understanding the worked-example principle

NARCIS (Netherlands)

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

Using Education Diplomacy to Help 15 Learning Champions Rethink Educational Assessment

Science.gov (United States)

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…

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

Science.gov (United States)

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…

Doubly sparse factor models for unifying feature transformation and feature selection

International Nuclear Information System (INIS)

Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato; Matsumoto, Narihisa; Sugase-Miyamoto, Yasuko

2010-01-01

A number of unsupervised learning methods for high-dimensional data are largely divided into two groups based on their procedures, i.e., (1) feature selection, which discards irrelevant dimensions of the data, and (2) feature transformation, which constructs new variables by transforming and mixing over all dimensions. We propose a method that both selects and transforms features in a common Bayesian inference procedure. Our method imposes a doubly automatic relevance determination (ARD) prior on the factor loading matrix. We propose a variational Bayesian inference for our model and demonstrate the performance of our method on both synthetic and real data.

Doubly sparse factor models for unifying feature transformation and feature selection

Energy Technology Data Exchange (ETDEWEB)

Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato [ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency, Saitama (Japan); Matsumoto, Narihisa; Sugase-Miyamoto, Yasuko, E-mail: okada@k.u-tokyo.ac.j [Human Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki (Japan)

2010-06-01

A number of unsupervised learning methods for high-dimensional data are largely divided into two groups based on their procedures, i.e., (1) feature selection, which discards irrelevant dimensions of the data, and (2) feature transformation, which constructs new variables by transforming and mixing over all dimensions. We propose a method that both selects and transforms features in a common Bayesian inference procedure. Our method imposes a doubly automatic relevance determination (ARD) prior on the factor loading matrix. We propose a variational Bayesian inference for our model and demonstrate the performance of our method on both synthetic and real data.

Convolutional neural network features based change detection in satellite images

Science.gov (United States)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Linking theory to practice in learning technology research

Directory of Open Access Journals (Sweden)

Cathy Gunn

2012-03-01

Full Text Available We present a case to reposition theory so that it plays a pivotal role in learning technology research and helps to build an ecology of learning. To support the case, we present a critique of current practice based on a review of articles published in two leading international journals from 2005 to 2010. Our study reveals that theory features only incidentally or not at all in many cases. We propose theory development as a unifying theme for learning technology research study design and reporting. The use of learning design as a strategy to develop and test theories in practice is integral to our argument. We conclude by supporting other researchers who recommend educational design research as a theory focused methodology to move the field forward in productive and consistent ways. The challenge of changing common practice will be involved. However, the potential to raise the profile of learning technology research and improve educational outcomes justifies the effort required.

Learning and nuclear safety: New reactors and US regulation

International Nuclear Information System (INIS)

Nichols, E.; Wildavsky, A.

1992-01-01

Gathering and analyzing data from operating reactors has become part of government and industry programs to improve performance in plants already on line and to inform development of future reactors. In the United States, however, early development and certain other factors combined to encourage a bias in learning. Regulation and learning from operational data intersect in ways that limit participation, data collection, and positive response to findings. Past learning has shown the advantage of simpler more standard designs with passive or inherent safety features. However, even designs incorporating these past lessons are apt to face tough regulatory tests and much criticism as operating experience is gathered. Only the operational success of new standardized reactors is apt to help rationalize regulation. (orig.)

Image fusion using sparse overcomplete feature dictionaries

Science.gov (United States)

Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

2015-10-06

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

How can we help students appreciate physics education?

Science.gov (United States)

Lin, Jia-Ling; Zaki, Eman; Schmidt, Jason; Woolston, Don

2004-03-01

Helping students appreciate physics education is a formidable task, considering that many students struggle to pass introductory physics courses. Numerous efforts have been made for this undertaking because it is an important step leading to successful learning. In an out-of-classroom academic program, the Supplemental Instruction (SI) Program, we have used the approach, INSPIRE (inquiry, network, skillfulness, perseverance, intuition, reasoning, and effort), to help more students value their experiences in these courses. The method basically includes key elements outlined by experts in physics education [1]. Student responses have been encouraging. Having undergraduates as facilitators in the program is advantageous in promoting principles of physics education. Their training emphasizes tenacity, resourcefulness, understanding, support, and teamwork, i.e. TRUST. We present the organization and focus of the SI Program, and discuss how these improve learning atmosphere and facilitate learning. [1] Edward F. Redish et al, Am J. Phys. 66(3), March 1998.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

Science.gov (United States)

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Science.gov (United States)

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

Science.gov (United States)

Jegadeeshwaran, R.; Sugumaran, V.

2015-02-01

Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images

Directory of Open Access Journals (Sweden)

Chen Xing

2016-01-01

Full Text Available Deep learning methods have been successfully applied to learn feature representations for high-dimensional data, where the learned features are able to reveal the nonlinear properties exhibited in the data. In this paper, deep learning method is exploited for feature extraction of hyperspectral data, and the extracted features can provide good discriminability for classification task. Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning. We utilized stacked denoise autoencoder (SDAE method to pretrain the network, which is robust to noise. In the top layer of the network, logistic regression (LR approach is utilized to perform supervised fine-tuning and classification. Since sparsity of features might improve the separation capability, we utilized rectified linear unit (ReLU as activation function in SDAE to extract high level and sparse features. Experimental results using Hyperion, AVIRIS, and ROSIS hyperspectral data demonstrated that the SDAE pretraining in conjunction with the LR fine-tuning and classification (SDAE_LR can achieve higher accuracies than the popular support vector machine (SVM classifier.

Mastering machine learning with scikit-learn

CERN Document Server

Hackeling, Gavin

2014-01-01

If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential.

Motivational interviewing: helping patients move toward change.

Science.gov (United States)

Richardson, Luann

2012-01-01

Motivational Interviewing (MI) is a valuable tool for nurses to help patients address behavior change. MI has been found effective for helping patients with multiple chronic conditions, adherence issues, and lifestyle issues change their health behaviors. For Christian nurses, MI is consistent with biblical principles and can be seen as a form of ministry. This article overviews the process of MI, stages of change, and offers direction for further learning.

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

Science.gov (United States)

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…

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

NARCIS (Netherlands)

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

Metrics Feedback Cycle: measuring and improving user engagement in gamified eLearning systems

Directory of Open Access Journals (Sweden)

Adam Atkins

2017-12-01

Full Text Available This paper presents the identification, design and implementation of a set of metrics of user engagement in a gamified eLearning application. The 'Metrics Feedback Cycle' (MFC is introduced as a formal process prescribing the iterative evaluation and improvement of application-wide engagement, using data collected from metrics as input to improve related engagement features. This framework was showcased using a gamified eLearning application as a case study. In this paper, we designed a prototype and tested it with thirty-six (N=36 students to validate the effectiveness of the MFC. The analysis and interpretation of metrics data shows that the gamification features had a positive effect on user engagement, and helped identify areas in which this could be improved. We conclude that the MFC has applications in gamified systems that seek to maximise engagement by iteratively evaluating implemented features against a set of evolving metrics.

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

Science.gov (United States)

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.

Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach

International Nuclear Information System (INIS)

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

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

Science.gov (United States)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Inferring feature relevances from metric learning

DEFF Research Database (Denmark)

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...

Beyond blended learning! Undiscovered potentials for e-learning in organizational learning

DEFF Research Database (Denmark)

Bang, Jørgen; Dalsgaard, Christian; Kjær, Arne

2007-01-01

The basic question raised in this article is: Is pure e-learning able to support learning in organizations better today than 4-5 years ago? Based on two case studies on blended learning courses for company training, the article discusses whether use of new Web 2.0 and social software tools may help...... overcome previous limitations of e-learning....

Probabilistic Slow Features for Behavior Analysis

NARCIS (Netherlands)

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

A recently introduced latent feature learning technique for time-varying dynamic phenomena analysis is the so-called slow feature analysis (SFA). SFA is a deterministic component analysis technique for multidimensional sequences that, by minimizing the variance of the first-order time derivative

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

Directory of Open Access Journals (Sweden)

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.

Personalized summarization using user preference for m-learning

Science.gov (United States)

Lee, Sihyoung; Yang, Seungji; Ro, Yong Man; Kim, Hyoung Joong

2008-02-01

As the Internet and multimedia technology is becoming advanced, the number of digital multimedia contents is also becoming abundant in learning area. In order to facilitate the access of digital knowledge and to meet the need of a lifelong learning, e-learning could be the helpful alternative way to the conventional learning paradigms. E-learning is known as a unifying term to express online, web-based and technology-delivered learning. Mobile-learning (m-learning) is defined as e-learning through mobile devices using wireless transmission. In a survey, more than half of the people remarked that the re-consumption was one of the convenient features in e-learning. However, it is not easy to find user's preferred segmentation from a full version of lengthy e-learning content. Especially in m-learning, a content-summarization method is strongly required because mobile devices are limited to low processing power and battery capacity. In this paper, we propose a new user preference model for re-consumption to construct personalized summarization for re-consumption. The user preference for re-consumption is modeled based on user actions with statistical model. Based on the user preference model for re-consumption with personalized user actions, our method discriminates preferred parts over the entire content. Experimental results demonstrated successful personalized summarization.

Seafloor Eruptions Offer a Teachable Moment to Help SEAS Students Understand Important Geological and Ecological Processes

Science.gov (United States)

Goehring, L.; Williams, C. S.

2006-12-01

In education parlance, a teachable moment is an opportunity that arises when students are engaged and primed to learn, typically in response to some memorable event. Earthquakes, volcanic eruptions, even natural disasters, if meaningful to the student, often serve to catalyze intense learning. Recent eruptions at the East Pacific Rise offer a potential teachable moment for students and teachers involved with SEAS, a Ridge 2000 education outreach program. SEAS uses a combination of web-facilitated and teacher-directed activities to make the remote deep-sea environment and the process of science relevant and meaningful. SEAS is a web-based, inquiry-oriented education program for middle and high school students. It features the science associated with Ridge 2000 research. Since 2003, SEAS has focused on the integrated study site at the East Pacific Rise (EPR) to help students understand geological and ecological processes at mid-ocean ridges and hydrothermal vents. SEAS students study EPR bathymetry maps, images of lava formations, photomosaics of diffuse flow communities, succession in the Bio-Geo Transect, as well as current research conducted during spring cruises. In the Classroom to Sea Lab, students make direct comparisons between shallow-water mussels and vent mussels (from the EPR) to understand differences in feeding strategies. The recent eruptions and loss of seafloor fauna at this site offer the Ridge 2000 program the opportunity to help students better understand the ephemeral and episodic nature of ridge environments, as well as the realities and processes of science (particularly field science). In January 2007, the SEAS program will again sail with a Ridge 2000 research team, and will work with scientists to report findings through the SEAS website. The eruptions at the EPR covered much of the study site, and scientists' instruments and experiments, in fresh lava. We intend to highlight the recency and effect of the eruptions, using the students

Bladder cancer treatment response assessment with radiomic, clinical, and radiologist semantic features

Science.gov (United States)

Gordon, Marshall N.; Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2018-02-01

We are developing a decision support system for assisting clinicians in assessment of response to neoadjuvant chemotherapy for bladder cancer. Accurate treatment response assessment is crucial for identifying responders and improving quality of life for non-responders. An objective machine learning decision support system may help reduce variability and inaccuracy in treatment response assessment. We developed a predictive model to assess the likelihood that a patient will respond based on image and clinical features. With IRB approval, we retrospectively collected a data set of pre- and post- treatment CT scans along with clinical information from surgical pathology from 98 patients. A linear discriminant analysis (LDA) classifier was used to predict the likelihood that a patient would respond to treatment based on radiomic features extracted from CT urography (CTU), a radiologist's semantic feature, and a clinical feature extracted from surgical and pathology reports. The classification accuracy was evaluated using the area under the ROC curve (AUC) with a leave-one-case-out cross validation. The classification accuracy was compared for the systems based on radiomic features, clinical feature, and radiologist's semantic feature. For the system based on only radiomic features the AUC was 0.75. With the addition of clinical information from examination under anesthesia (EUA) the AUC was improved to 0.78. Our study demonstrated the potential of designing a decision support system to assist in treatment response assessment. The combination of clinical features, radiologist semantic features and CTU radiomic features improved the performance of the classifier and the accuracy of treatment response assessment.

EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression

OpenAIRE

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...

Struggling readers learning with graphic-rich digital science text: Effects of a Highlight & Animate Feature and Manipulable Graphics

Science.gov (United States)

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

Enhancing facial features by using clear facial features

Science.gov (United States)

Rofoo, Fanar Fareed Hanna

2017-09-01

The similarity of features between individuals of same ethnicity motivated the idea of this project. The idea of this project is to extract features of clear facial image and impose them on blurred facial image of same ethnic origin as an approach to enhance a blurred facial image. A database of clear images containing 30 individuals equally divided to five different ethnicities which were Arab, African, Chines, European and Indian. Software was built to perform pre-processing on images in order to align the features of clear and blurred images. And the idea was to extract features of clear facial image or template built from clear facial images using wavelet transformation to impose them on blurred image by using reverse wavelet. The results of this approach did not come well as all the features did not align together as in most cases the eyes were aligned but the nose or mouth were not aligned. Then we decided in the next approach to deal with features separately but in the result in some cases a blocky effect was present on features due to not having close matching features. In general the available small database did not help to achieve the goal results, because of the number of available individuals. The color information and features similarity could be more investigated to achieve better results by having larger database as well as improving the process of enhancement by the availability of closer matches in each ethnicity.

Helping Families Succeed in Two Worlds.

Science.gov (United States)

Murray, Vivian

Kamehameha Schools' Prekindergarten Educational Program (PREP) was started in 1978 to prepare at-risk Hawaiian families and their children for success in school. PREP's direct services include: (1) parent-infant educational services, including home visits to help parents prepare for a new baby and later learn appropriate child development…

Workplace Learning by Action Learning: A Practical Example.

Science.gov (United States)

Miller, Peter

2003-01-01

An action learning approach to help managers enhance learning capacity involved a performance management seminar, work by action learning sets, implementation of a new performance management instrument with mentoring by action learning facilitators, and evaluation. Survey responses from 392 participants revealed satisfaction with managerial…

Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants.

Science.gov (United States)

Mustaqeem, Anam; Anwar, Syed Muhammad; Majid, Muahammad

2018-01-01

Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.

Cascaded K-means convolutional feature learner and its application to face recognition

Science.gov (United States)

Zhou, Daoxiang; Yang, Dan; Zhang, Xiaohong; Huang, Sheng; Feng, Shu

2017-09-01

Currently, considerable efforts have been devoted to devise image representation. However, handcrafted methods need strong domain knowledge and show low generalization ability, and conventional feature learning methods require enormous training data and rich parameters tuning experience. A lightened feature learner is presented to solve these problems with application to face recognition, which shares similar topology architecture as a convolutional neural network. Our model is divided into three components: cascaded convolution filters bank learning layer, nonlinear processing layer, and feature pooling layer. Specifically, in the filters learning layer, we use K-means to learn convolution filters. Features are extracted via convoluting images with the learned filters. Afterward, in the nonlinear processing layer, hyperbolic tangent is employed to capture the nonlinear feature. In the feature pooling layer, to remove the redundancy information and incorporate the spatial layout, we exploit multilevel spatial pyramid second-order pooling technique to pool the features in subregions and concatenate them together as the final representation. Extensive experiments on four representative datasets demonstrate the effectiveness and robustness of our model to various variations, yielding competitive recognition results on extended Yale B and FERET. In addition, our method achieves the best identification performance on AR and labeled faces in the wild datasets among the comparative methods.

Comparison of feature selection and classification for MALDI-MS data

Directory of Open Access Journals (Sweden)

Yang Mary

2009-07-01

Full Text Available Abstract Introduction In the classification of Mass Spectrometry (MS proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. Results We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE, and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC, Naïve Bayes Classifier (NBC, Nearest Mean Scaled Classifier (NMSC, uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data. Conclusion Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing

An Expert System Helps Students Learn Database Design

Science.gov (United States)

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…

Application of Kinect Technology in Blind Aerobics Learning

Directory of Open Access Journals (Sweden)

Hui Qu

2017-12-01

Full Text Available In order for blind people to learn aerobics more conveniently, we combined Kinect skeletal tracking technology with aerobics-assisted training to design a Kinect-based aerobics-assisted training system. Through the Kinect somatosensory camera, the feature extraction method and recognition algorithm of sign language are improved, and the sign language recognition system is realized. Sign language is translated through the sign language recognition system and expressed in understandable terms, providing a sound way of learning. The experimental results show that the system can automatically collect and recognize the aerobics movements. By comparing with the standard movements in the database, the system evaluates the posture of trainers from the perspectives of joint coordinates and joint angles, followed by the provision of movements contrast graphics and corresponding advice. Therefore, the system can effectively help the blind to learn aerobics.

Determining the Effects of LMS Learning Behaviors on Academic Achievement in a Learning Analytic Perspective

Directory of Open Access Journals (Sweden)

Mehmet FIRAT

2016-02-01

Full Text Available Two of the most important outcomes of learning analytics are predicting students’ learning and providing effective feedback. Learning Management Systems (LMS, which are widely used to support online and face-to-face learning, provide extensive research opportunities with detailed records of background data regarding users’ behaviors. The purpose of this study was to investigate the effects of undergraduate students’ LMS learning behaviors on their academic achievements. In line with this purpose, the participating students’ online learning behaviors in LMS were examined by using learning analytics for 14 weeks, and the relationship between students’ behaviors and their academic achievements was analyzed, followed by an analysis of their views about the influence of LMS on their academic achievement. The present study, in which quantitative and qualitative data were collected, was carried out with the explanatory mixed method. A total of 71 undergraduate students participated in the study. The results revealed that the students used LMSs as a support to face-to-face education more intensively on course days (at the beginning of the related lessons and at nights on course days and that they activated the content elements the most. Lastly, almost all the students agreed that LMSs helped increase their academic achievement only when LMSs included such features as effectiveness, interaction, reinforcement, attractive design, social media support, and accessibility.

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.

Science.gov (United States)

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)…

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

DEFF Research Database (Denmark)

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...

Place-based Learning About Climate with Elementary GLOBE

Science.gov (United States)

Hatheway, B.; Gardiner, L. S.; Harte, T.; Stanitski, D.; Taylor, J.

2017-12-01

Place-based education - helping students make connections between themselves, their community, and their local environment - is an important tool to help young learners understand their regional climate and start to learn about climate and environmental change. Elementary GLOBE storybooks and learning activities allow opportunities for place-based education instructional strategies about climate. In particular, two modules in the Elementary GLOBE unit - Seasons and Climate - provide opportunities for students to explore their local climate and environment. The storybooks and activities also make connections to other parts of elementary curriculum, such as arts, geography, and math. Over the long term, place-based education can also encourage students to be stewards of their local environment. A strong sense of place may help students to see themselves as stakeholders in their community and its resilience. In places that are particularly vulnerable to the impacts of climate and environmental change and the economic, social, and environmental tradeoffs of community decisions, helping young students developing a sense of place and to see the connection between Earth science, local community, and their lives can have a lasting impact on how a community evolves for decades to come. Elementary GLOBE was designed to help elementary teachers (i.e., grades K-4) integrate Earth system science topics into their curriculum as they teach literacy skills to students. This suite of instructional materials includes seven modules. Each module contains a science-based storybook and learning activities that support the science content addressed in the storybooks. Elementary GLOBE modules feature air quality, climate, clouds, Earth system, seasons, soil, and water. New eBooks allow students to read stories on computers or tablets, with the option of listening to each story with an audio recording. A new Elementary GLOBE Teacher Implementation Guide, published in 2017, provides

New learning : three ways to learn in a new balance

NARCIS (Netherlands)

Simons, P.R.J.

2000-01-01

Because people are learning all the time, we need criteria that can help us distinguish between better and worse kinds of learning. Organizations and societies as well as the psychology of learning ask for new learning outcomes, new learning processes and new forms of instruction. New learning

Permutation importance: a corrected feature importance measure.

Science.gov (United States)

Altmann, André; Toloşi, Laura; Sander, Oliver; Lengauer, Thomas

2010-05-15

In life sciences, interpretability of machine learning models is as important as their prediction accuracy. Linear models are probably the most frequently used methods for assessing feature relevance, despite their relative inflexibility. However, in the past years effective estimators of feature relevance have been derived for highly complex or non-parametric models such as support vector machines and RandomForest (RF) models. Recently, it has been observed that RF models are biased in such a way that categorical variables with a large number of categories are preferred. In this work, we introduce a heuristic for normalizing feature importance measures that can correct the feature importance bias. The method is based on repeated permutations of the outcome vector for estimating the distribution of measured importance for each variable in a non-informative setting. The P-value of the observed importance provides a corrected measure of feature importance. We apply our method to simulated data and demonstrate that (i) non-informative predictors do not receive significant P-values, (ii) informative variables can successfully be recovered among non-informative variables and (iii) P-values computed with permutation importance (PIMP) are very helpful for deciding the significance of variables, and therefore improve model interpretability. Furthermore, PIMP was used to correct RF-based importance measures for two real-world case studies. We propose an improved RF model that uses the significant variables with respect to the PIMP measure and show that its prediction accuracy is superior to that of other existing models. R code for the method presented in this article is available at http://www.mpi-inf.mpg.de/ approximately altmann/download/PIMP.R CONTACT: altmann@mpi-inf.mpg.de, laura.tolosi@mpi-inf.mpg.de Supplementary data are available at Bioinformatics online.

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

Science.gov (United States)

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.

Discriminating Induced-Microearthquakes Using New Seismic Features

Science.gov (United States)

Mousavi, S. M.; Horton, S.

2016-12-01

We studied characteristics of induced-microearthquakes on the basis of the waveforms recorded on a limited number of surface receivers using machine-learning techniques. Forty features in the time, frequency, and time-frequency domains were measured on each waveform, and several techniques such as correlation-based feature selection, Artificial Neural Networks (ANNs), Logistic Regression (LR) and X-mean were used as research tools to explore the relationship between these seismic features and source parameters. The results show that spectral features have the highest correlation to source depth. Two new measurements developed as seismic features for this study, spectral centroids and 2D cross-correlations in the time-frequency domain, performed better than the common seismic measurements. These features can be used by machine learning techniques for efficient automatic classification of low energy signals recorded at one or more seismic stations. We applied the technique to 440 microearthquakes-1.7Reference: Mousavi, S.M., S.P. Horton, C. A. Langston, B. Samei, (2016) Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression, Geophys. J. Int. doi: 10.1093/gji/ggw258.

Face Alignment via Regressing Local Binary Features.