WorldWideScience

Sample records for supervised learning task

  1. Extendable supervised dictionary learning for exploring diverse and concurrent brain activities in task-based fMRI.

    Science.gov (United States)

    Zhao, Shijie; Han, Junwei; Hu, Xintao; Jiang, Xi; Lv, Jinglei; Zhang, Tuo; Zhang, Shu; Guo, Lei; Liu, Tianming

    2017-06-09

    Recently, a growing body of studies have demonstrated the simultaneous existence of diverse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those diverse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing. To deal with this problem, in this paper, we propose a novel hybrid framework, called extendable supervised dictionary learning (E-SDL), to explore diverse and concurrent brain activities under task conditions. A critical difference between E-SDL framework and previous methods is that we systematically extend the basic task paradigm regressor into meaningful regressor groups to account for possible regressor variation during the information processing procedure in the brain. Applications of the proposed framework on five independent and publicly available tfMRI datasets from human connectome project (HCP) simultaneously revealed more meaningful group-wise consistent task-evoked networks and common intrinsic connectivity networks (ICNs). These results demonstrate the advantage of the proposed framework in identifying the diversity of concurrent brain activities in tfMRI datasets.

  2. Exploration of joint redundancy but not task space variability facilitates supervised motor learning.

    Science.gov (United States)

    Singh, Puneet; Jana, Sumitash; Ghosal, Ashitava; Murthy, Aditya

    2016-12-13

    The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.

  3. Inductive Supervised Quantum Learning

    Science.gov (United States)

    Monràs, Alex; Sentís, Gael; Wittek, Peter

    2017-05-01

    In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interpretation of being nonsignaling. Thus, two seemingly different definitions of inductive learning happen to coincide. This follows from the properties of classical information that break down in the quantum setup. We prove a quantum de Finetti theorem for quantum channels, which shows that in the quantum case, the equivalence holds in the asymptotic setting, that is, for large numbers of test instances. This reveals a natural analogy between classical learning protocols and their quantum counterparts, justifying a similar treatment, and allowing us to inquire about standard elements in computational learning theory, such as structural risk minimization and sample complexity.

  4. Supervised Dictionary Learning

    CERN Document Server

    Mairal, Julien; Ponce, Jean; Sapiro, Guillermo; Zisserman, Andrew

    2008-01-01

    It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data. Recent research has been aimed at learning discriminative sparse models instead of purely reconstructive ones. This paper proposes a new step in that direction, with a novel sparse representation for signals belonging to different classes in terms of a shared dictionary and multiple class-decision functions. The linear variant of the proposed model admits a simple probabilistic interpretation, while its most general variant admits an interpretation in terms of kernels. An optimization framework for learning all the components of the proposed model is presented, along with experimental results on standard handwritten digit and texture classification tasks.

  5. Multi-Instance Learning from Supervised View

    Institute of Scientific and Technical Information of China (English)

    Zhi-Hua Zhou

    2006-01-01

    In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances,and the task is to predict the labels of unseen bags. This paper studies multi-instance learning from the view of supervised learning. First, by analyzing some representative learning algorithms, this paper shows that multi-instance learners can be derived from supervised learners by shifting their focuses from the discrimination on the instances to the discrimination on the bags. Second, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build multi-instance ensembles to solve multi-instance problems. Experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners.

  6. Supervised Dictionary Learning

    Science.gov (United States)

    2008-11-01

    recently led to state-of-the-art results for numerous low-level image processing tasks such as denoising [2], show- ing that sparse models are well... denoising via sparse and redundant representations over learned dictio- naries. IEEE Trans. IP, 54(12), 2006. [3] K. Huang and S. Aviyente. Sparse...2006. [19] M. Aharon, M. Elad, and A. M. Bruckstein. The K- SVD : An algorithm for designing of overcomplete dictionaries for sparse representations

  7. Considering Alternate Futures to Classify Off-Task Behavior as Emotion Self-Regulation: A Supervised Learning Approach

    Science.gov (United States)

    Sabourin, Jennifer L.; Rowe, Jonathan P.; Mott, Bradford W.; Lester, James C.

    2013-01-01

    Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as off-task behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we…

  8. Learning Dynamics in Doctoral Supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

    This doctoral research explores doctoral supervision within life science research in a Danish university. From one angle it investigates doctoral students’ experiences with strengthening the relationship with their supervisors through a structured meeting with the supervisor, prepared as part...... investigates learning opportunities in supervision with multiple supervisors. This was investigated through observations and recording of supervision, and subsequent analysis of transcripts. The analyses used different perspectives on learning; learning as participation, positioning theory and variation theory....... The research illuminates how learning opportunities are created in the interaction through the scientific discussions. It also shows how multiple supervisors can contribute to supervision by providing new perspectives and opinions that have a potential for creating new understandings. The combination...

  9. Missing Data Imputation for Supervised Learning

    OpenAIRE

    Poulos, Jason; Valle, Rafael

    2016-01-01

    This paper compares methods for imputing missing categorical data for supervised learning tasks. The ability of researchers to accurately fit a model and yield unbiased estimates may be compromised by missing data, which are prevalent in survey-based social science research. We experiment on two machine learning benchmark datasets with missing categorical data, comparing classifiers trained on non-imputed (i.e., one-hot encoded) or imputed data with different degrees of missing-data perturbat...

  10. Web Interfacing for Task Supervision and Specification

    OpenAIRE

    Tomatis, N.; Moreau, B.

    2001-01-01

    The Autonomous Systems Lab at the Swiss Federal Institute of Technology Lausanne (EPFL) is engaged in mobile robotics research. The lab’s research focuses mainly on indoor localization and map building, outdoor locomotion and navigation, and micro mobile robotics. In the framework of a research project on mobile robot localization, a graphical web interface for our indoor robots has been developed. The purpose of this interface is twofold: it serves as a tool for task supervision for the rese...

  11. Supervision Learning as Conceptual Threshold Crossing: When Supervision Gets "Medieval"

    Science.gov (United States)

    Carter, Susan

    2016-01-01

    This article presumes that supervision is a category of teaching, and that we all "learn" how to teach better. So it enquires into what novice supervisors need to learn. An anonymised digital questionnaire sought data from supervisors [n226] on their experiences of supervision to find out what was difficult, and supervisor interviews…

  12. Supervision Learning as Conceptual Threshold Crossing: When Supervision Gets "Medieval"

    Science.gov (United States)

    Carter, Susan

    2016-01-01

    This article presumes that supervision is a category of teaching, and that we all "learn" how to teach better. So it enquires into what novice supervisors need to learn. An anonymised digital questionnaire sought data from supervisors [n226] on their experiences of supervision to find out what was difficult, and supervisor interviews…

  13. Supervised Speech Separation Based on Deep Learning: An Overview

    OpenAIRE

    Wang, DeLiang; Chen, Jitong

    2017-01-01

    Speech separation is the task of separating target speech from background interference. Traditionally, speech separation is studied as a signal processing problem. A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data. Over the past decade, many supervised separation algorithms have been put forward. In particular, the recent introduction of deep learning ...

  14. Weakly supervised visual dictionary learning by harnessing image attributes.

    Science.gov (United States)

    Gao, Yue; Ji, Rongrong; Liu, Wei; Dai, Qionghai; Hua, Gang

    2014-12-01

    Bag-of-features (BoFs) representation has been extensively applied to deal with various computer vision applications. To extract discriminative and descriptive BoF, one important step is to learn a good dictionary to minimize the quantization loss between local features and codewords. While most existing visual dictionary learning approaches are engaged with unsupervised feature quantization, the latest trend has turned to supervised learning by harnessing the semantic labels of images or regions. However, such labels are typically too expensive to acquire, which restricts the scalability of supervised dictionary learning approaches. In this paper, we propose to leverage image attributes to weakly supervise the dictionary learning procedure without requiring any actual labels. As a key contribution, our approach establishes a generative hidden Markov random field (HMRF), which models the quantized codewords as the observed states and the image attributes as the hidden states, respectively. Dictionary learning is then performed by supervised grouping the observed states, where the supervised information is stemmed from the hidden states of the HMRF. In such a way, the proposed dictionary learning approach incorporates the image attributes to learn a semantic-preserving BoF representation without any genuine supervision. Experiments in large-scale image retrieval and classification tasks corroborate that our approach significantly outperforms the state-of-the-art unsupervised dictionary learning approaches.

  15. Disciplinary supervision following ethics complaints: goals, tasks, and ethical dimensions.

    Science.gov (United States)

    Thomas, Janet T

    2014-11-01

    Clinical supervision is considered an integral component of the training of psychologists, and most of the professional literature is focused on this type of supervision. But psychologists also may supervise fully credentialed colleagues in other circumstances. One such context occurs when licensing boards mandate supervision as part of a disciplinary order. When supervision is provided in disciplinary cases, there are significant implications for the ethical dimensions of the supervisory relationship and concomitant ethical challenges for supervisors. Not only are the goals, objectives, and supervisory tasks of disciplinary supervision distinct from other types of supervision, but the supervisor's ethical responsibilities also encompass unique dimensions. Competence, informed consent, boundaries, confidentiality, and documentation are examined. Recommendations for reports to licensing boards include a statement of the clinical or ethical problems instigating discipline, description of how these problems have been addressed, and an assessment of the supervisee's current practices and ability to perform competently.

  16. Semi-supervised Eigenvectors for Locally-biased Learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2012-01-01

    of this sort are particularly challenging for popular eigenvector-based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities. In this paper, we address this issue by providing a methodology to construct semi-supervised eigenvectors of a graph......In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks "nearby" that pre-specified target region. Locally-biased problems...... Laplacian, and we illustrate how these locally-biased eigenvectors can be used to perform locally-biased machine learning. These semi-supervised eigenvectors capture successively-orthogonalized directions of maximum variance, conditioned on being well-correlated with an input seed set of nodes...

  17. Incremental Supervised Subspace Learning for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Subspace learning algorithms have been well studied in face recognition. Among them, linear discriminant analysis (LDA) is one of the most widely used supervised subspace learning method. Due to the difficulty of designing an incremental solution of the eigen decomposition on the product of matrices, there is little work for computing LDA incrementally. To avoid this limitation, an incremental supervised subspace learning (ISSL) algorithm was proposed, which incrementally learns an adaptive subspace by optimizing the maximum margin criterion (MMC). With the dynamically added face images, ISSL can effectively constrain the computational cost. Feasibility of the new algorithm has been successfully tested on different face data sets.

  18. Supervised Learning in Multilayer Spiking Neural Networks

    CERN Document Server

    Sporea, Ioana

    2012-01-01

    The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.

  19. Learning Dynamics in Doctoral Supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

    This doctoral research explores doctoral supervision within life science research in a Danish university. From one angle it investigates doctoral students’ experiences with strengthening the relationship with their supervisors through a structured meeting with the supervisor, prepared as part...... of an introduction course for new doctoral students. This study showed how the course provides an effective way build supervisee agency and strengthening supervisory relationships through clarification and alignment of expectations and sharing goals about doctoral studies. From the other angle the research...

  20. Action learning in undergraduate engineering thesis supervision

    Directory of Open Access Journals (Sweden)

    Brad Stappenbelt

    2017-03-01

    Full Text Available In the present action learning implementation, twelve action learning sets were conducted over eight years. The action learning sets consisted of students involved in undergraduate engineering research thesis work. The concurrent study accompanying this initiative, investigated the influence of the action learning environment on student approaches to learning and any accompanying academic, learning and personal benefits realised. The influence of preferred learning styles on set function and student adoption of the action learning process were also examined. The action learning environment implemented had a measurable significant positive effect on student academic performance, their ability to cope with the stresses associated with conducting a research thesis, the depth of learning, the development of autonomous learners and student perception of the research thesis experience. The present study acts as an addendum to a smaller scale implementation of this action learning approach, applied to supervision of third and fourth year research projects and theses, published in 2010.

  1. Balancing Design Project Supervision and Learning Facilitation

    DEFF Research Database (Denmark)

    Nielsen, Louise Møller

    2012-01-01

    set of demands to the design lecturer. On one hand she is the facilitator of the learning process, where the students are in charge of their own projects, and where learning happens through the students’ own experiences, successes and mistakes and on the other hand she is a supervisor, who uses her...... experiences and expertise to guide the students’ decisions in relation to the design project. This paper focuses on project supervision in the context of design education – and more specifically on how this supervision is unfolded in a Problem Based Learning culture. The paper explores the supervisor......In design there is a long tradition for apprenticeship, as well as tradition for learning through design projects. Today many design educations are positioned within the University context, and have to be aligned with the learning culture and structure, which they represent. This raises a specific...

  2. Balancing Design Project Supervision and Learning Facilitation

    DEFF Research Database (Denmark)

    Nielsen, Louise Møller

    2012-01-01

    experiences and expertise to guide the students’ decisions in relation to the design project. This paper focuses on project supervision in the context of design education – and more specifically on how this supervision is unfolded in a Problem Based Learning culture. The paper explores the supervisor......’s balance between the roles: 1) Design Project Supervisor – and 2) Learning Facilitator – with the aim to understand when to apply the different roles, and what to be aware of when doing so. This paper represents the first pilot-study of a larger research effort. It is based on a Lego Serious Play workshop......In design there is a long tradition for apprenticeship, as well as tradition for learning through design projects. Today many design educations are positioned within the University context, and have to be aligned with the learning culture and structure, which they represent. This raises a specific...

  3. Equality of Opportunity in Supervised Learning

    OpenAIRE

    Hardt, Moritz; Price, Eric; Srebro, Nathan

    2016-01-01

    We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to...

  4. The Supervised Learning Gaussian Mixture Model

    Institute of Scientific and Technical Information of China (English)

    马继涌; 高文

    1998-01-01

    The traditional Gaussian Mixture Model(GMM)for pattern recognition is an unsupervised learning method.The parameters in the model are derived only by the training samples in one class without taking into account the effect of sample distributions of other classes,hence,its recognition accuracy is not ideal sometimes.This paper introduces an approach for estimating the parameters in GMM in a supervising way.The Supervised Learning Gaussian Mixture Model(SLGMM)improves the recognition accuracy of the GMM.An experimental example has shown its effectiveness.The experimental results have shown that the recognition accuracy derived by the approach is higher than those obtained by the Vector Quantization(VQ)approach,the Radial Basis Function (RBF) network model,the Learning Vector Quantization (LVQ) approach and the GMM.In addition,the training time of the approach is less than that of Multilayer Perceptrom(MLP).

  5. A review of supervised machine learning applied to ageing research.

    Science.gov (United States)

    Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

    2017-04-01

    Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

  6. Opportunities to Learn Scientific Thinking in Joint Doctoral Supervision

    Science.gov (United States)

    Kobayashi, Sofie; Grout, Brian W.; Rump, Camilla Østerberg

    2015-01-01

    Research into doctoral supervision has increased rapidly over the last decades, yet our understanding of how doctoral students learn scientific thinking from supervision is limited. Most studies are based on interviews with little work being reported that is based on observation of actual supervision. While joint supervision has become widely…

  7. Using Supervised Deep Learning for Human Age Estimation Problem

    Science.gov (United States)

    Drobnyh, K. A.; Polovinkin, A. N.

    2017-05-01

    Automatic facial age estimation is a challenging task upcoming in recent years. In this paper, we propose using the supervised deep learning features to improve an accuracy of the existing age estimation algorithms. There are many approaches solving the problem, an active appearance model and the bio-inspired features are two of them which showed the best accuracy. For experiments we chose popular publicly available FG-NET database, which contains 1002 images with a broad variety of light, pose, and expression. LOPO (leave-one-person-out) method was used to estimate the accuracy. Experiments demonstrated that adding supervised deep learning features has improved accuracy for some basic models. For example, adding the features to an active appearance model gave the 4% gain (the error decreased from 4.59 to 4.41).

  8. Graph-based semi-supervised learning

    CERN Document Server

    Subramanya, Amarnag

    2014-01-01

    While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer visi

  9. Supervised dictionary learning for inferring concurrent brain networks.

    Science.gov (United States)

    Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

    2015-10-01

    Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

  10. Function approximation using combined unsupervised and supervised learning.

    Science.gov (United States)

    Andras, Peter

    2014-03-01

    Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

  11. Semi-Supervised Learning Based on Manifold in BCI

    Institute of Scientific and Technical Information of China (English)

    Ji-Ying Zhong; Xu Lei; De-Zhong Yao

    2009-01-01

    A Laplacian support vector machine (LapSVM) algorithm,a semi-supervised learning based on manifold,is introduced to brain-computer interface (BCI) to raise the classification precision and reduce the subjects' training complexity.The data are collected from three subjects in a three-task mental imagery experiment.LapSVM and transductive SVM (TSVM) are trained with a few labeled samples and a large number of unlabeled samples.The results confirm that LapSVM has a much better classification than TSVM.

  12. How Supervisor Experience Influences Trust, Supervision, and Trainee Learning: A Qualitative Study.

    Science.gov (United States)

    Sheu, Leslie; Kogan, Jennifer R; Hauer, Karen E

    2017-09-01

    Appropriate trust and supervision facilitate trainees' growth toward unsupervised practice. The authors investigated how supervisor experience influences trust, supervision, and subsequently trainee learning. In a two-phase qualitative inductive content analysis, phase one entailed reviewing 44 internal medicine resident and attending supervisor interviews from two institutions (July 2013 to September 2014) for themes on how supervisor experience influences trust and supervision. Three supervisor exemplars (early, developing, experienced) were developed and shared in phase two focus groups at a single institution, wherein 23 trainees validated the exemplars and discussed how each impacted learning (November 2015). Phase one: Four domains of trust and supervision varying with experience emerged: data, approach, perspective, clinical. Early supervisors were detail oriented and determined trust depending on task completion (data), were rule based (approach), drew on their experiences as trainees to guide supervision (perspective), and felt less confident clinically compared with more experienced supervisors (clinical). Experienced supervisors determined trust holistically (data), checked key aspects of patient care selectively and covertly (approach), reflected on individual experiences supervising (perspective), and felt comfortable managing clinical problems and gauging trainee abilities (clinical). Phase two: Trainees felt the exemplars reflected their experiences, described their preferences and learning needs shifting over time, and emphasized the importance of supervisor flexibility to match their learning needs. With experience, supervisors differ in their approach to trust and supervision. Supervisors need to trust themselves before being able to trust others. Trainees perceive these differences and seek supervision approaches that align with their learning needs.

  13. Predicting incomplete gene microarray data with the use of supervised learning algorithms

    CSIR Research Space (South Africa)

    Twala, B

    2010-10-01

    Full Text Available of many well-established supervised learning (SL) algorithms in an attempt to provide more accurate and automatic diagnosis class (cancer/non cancer) prediction. Virtually all research on SL addresses the task of learning to classify complete domain...

  14. Genetic classification of populations using supervised learning.

    LENUS (Irish Health Repository)

    Bridges, Michael

    2011-01-01

    There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case-control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.

  15. Semi-supervised Learning with Deep Generative Models

    NARCIS (Netherlands)

    Kingma, D.P.; Rezende, D.J.; Mohamed, S.; Welling, M.

    2014-01-01

    The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and

  16. SUPERVISED LEARNING METHODS FOR BANGLA WEB DOCUMENT CATEGORIZATION

    Directory of Open Access Journals (Sweden)

    Ashis Kumar Mandal

    2014-09-01

    Full Text Available This paper explores the use of machine learning approaches, or more specifically, four supervised learning Methods, namely Decision Tree(C 4.5, K-Nearest Neighbour (KNN, Naïve Bays (NB, and Support Vector Machine (SVM for categorization of Bangla web documents. This is a task of automatically sorting a set of documents into categories from a predefined set. Whereas a wide range of methods have been applied to English text categorization, relatively few studies have been conducted on Bangla language text categorization. Hence, we attempt to analyze the efficiency of those four methods for categorization of Bangla documents. In order to validate, Bangla corpus from various websites has been developed and used as examples for the experiment. For Bangla, empirical results support that all four methods produce satisfactory performance with SVM attaining good result in terms of high dimensional and relatively noisy document feature vectors.

  17. Mining visual collocation patterns via self-supervised subspace learning.

    Science.gov (United States)

    Yuan, Junsong; Wu, Ying

    2012-04-01

    Traditional text data mining techniques are not directly applicable to image data which contain spatial information and are characterized by high-dimensional visual features. It is not a trivial task to discover meaningful visual patterns from images because the content variations and spatial dependence in visual data greatly challenge most existing data mining methods. This paper presents a novel approach to coping with these difficulties for mining visual collocation patterns. Specifically, the novelty of this work lies in the following new contributions: 1) a principled solution to the discovery of visual collocation patterns based on frequent itemset mining and 2) a self-supervised subspace learning method to refine the visual codebook by feeding back discovered patterns via subspace learning. The experimental results show that our method can discover semantically meaningful patterns efficiently and effectively.

  18. The Learning Alliance: Ethics in Doctoral Supervision

    Science.gov (United States)

    Halse, Christine; Bansel, Peter

    2012-01-01

    This paper is concerned with the ethics of relationships in doctoral supervision. We give an overview of four paradigms of doctoral supervision that have endured over the past 25 years and elucidate some of their strengths and limitations, contextualise them historically and consider their implications for doctoral supervision in the contemporary…

  19. A new supervised learning algorithm for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

  20. Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression.

    Science.gov (United States)

    Zhen, Xiantong; Yu, Mengyang; Islam, Ali; Bhaduri, Mousumi; Chan, Ian; Li, Shuo

    2016-06-08

    Multioutput regression has recently shown great ability to solve challenging problems in both computer vision and medical image analysis. However, due to the huge image variability and ambiguity, it is fundamentally challenging to handle the highly complex input-target relationship of multioutput regression, especially with indiscriminate high-dimensional representations. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm for multioutput regression, which can establish discriminative and compact feature representations to improve the multivariate estimation performance. The SDL is formulated as generalized low-rank approximations of matrices with a supervised manifold regularization. The SDL is able to simultaneously extract discriminative features closely related to multivariate targets and remove irrelevant and redundant information by transforming raw features into a new low-dimensional space aligned to targets. The achieved discriminative while compact descriptor largely reduces the variability and ambiguity for multioutput regression, which enables more accurate and efficient multivariate estimation. We conduct extensive evaluation of the proposed SDL on both synthetic data and real-world multioutput regression tasks for both computer vision and medical image analysis. Experimental results have shown that the proposed SDL can achieve high multivariate estimation accuracy on all tasks and largely outperforms the algorithms in the state of the arts. Our method establishes a novel SDL framework for multioutput regression, which can be widely used to boost the performance in different applications.

  1. Integrating the Supervised Information into Unsupervised Learning

    Directory of Open Access Journals (Sweden)

    Ping Ling

    2013-01-01

    Full Text Available This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs’ extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.

  2. Opportunities to learn scientific thinking in joint doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Grout, Brian William Wilson; Rump, Camilla Østerberg

    2015-01-01

    Research into doctoral supervision has increased rapidly over the last decades, yet our understanding of how doctoral students learn scientific thinking from supervision is limited. Most studies are based on interviews with little work being reported that is based on observation of actual supervi...

  3. Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

    OpenAIRE

    Kurth, Thorsten; Zhang, Jian; Satish, Nadathur; Mitliagkas, Ioannis; Racah, Evan; Patwary, Mostofa Ali; Malas, Tareq; Sundaram, Narayanan; Bhimji, Wahid; Smorkalov, Mikhail; Deslippe, Jack; Shiryaev, Mikhail; Sridharan, Srinivas; Prabhat; Dubey, Pradeep

    2017-01-01

    This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures. We develop supervised convolutional architectures for discriminating signals in high-energy physics data as well as semi-supervised architectures for localizing and classifying extreme weather in climate data. Our Intelcaffe-based implementation obtains $\\sim$2TFLOP/s on a single Cori Phase-II Xeon-Phi node. We use a hybrid strategy employin...

  4. Towards designing an email classification system using multi-view based semi-supervised learning

    NARCIS (Netherlands)

    Li, Wenjuan; Meng, Weizhi; Tan, Zhiyuan; Xiang, Yang

    2014-01-01

    The goal of email classification is to classify user emails into spam and legitimate ones. Many supervised learning algorithms have been invented in this domain to accomplish the task, and these algorithms require a large number of labeled training data. However, data labeling is a labor intensive t

  5. Subsampled Hessian Newton Methods for Supervised Learning.

    Science.gov (United States)

    Wang, Chien-Chih; Huang, Chun-Heng; Lin, Chih-Jen

    2015-08-01

    Newton methods can be applied in many supervised learning approaches. However, for large-scale data, the use of the whole Hessian matrix can be time-consuming. Recently, subsampled Newton methods have been proposed to reduce the computational time by using only a subset of data for calculating an approximation of the Hessian matrix. Unfortunately, we find that in some situations, the running speed is worse than the standard Newton method because cheaper but less accurate search directions are used. In this work, we propose some novel techniques to improve the existing subsampled Hessian Newton method. The main idea is to solve a two-dimensional subproblem per iteration to adjust the search direction to better minimize the second-order approximation of the function value. We prove the theoretical convergence of the proposed method. Experiments on logistic regression, linear SVM, maximum entropy, and deep networks indicate that our techniques significantly reduce the running time of the subsampled Hessian Newton method. The resulting algorithm becomes a compelling alternative to the standard Newton method for large-scale data classification.

  6. Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification

    Directory of Open Access Journals (Sweden)

    R. Sathya

    2013-02-01

    Full Text Available This paper presents a comparative account of unsupervised and supervised learning models and their pattern classification evaluations as applied to the higher education scenario. Classification plays a vital role in machine based learning algorithms and in the present study, we found that, though the error back-propagation learning algorithm as provided by supervised learning model is very efficient for a number of non-linear real-time problems, KSOM of unsupervised learning model, offers efficient solution and classification in the present study.

  7. Semi-supervised prediction of gene regulatory networks using machine learning algorithms

    Indian Academy of Sciences (India)

    Nihir Patel; T L Wang

    2015-10-01

    Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

  8. Semi-supervised eigenvectors for large-scale locally-biased learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2014-01-01

    -based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing......In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks nearby that prespecified target region. For example, one might...... a methodology to construct semi-supervised eigenvectors of a graph Laplacian, and we illustrate how these locally-biased eigenvectors can be used to perform locally-biased machine learning. These semi-supervised eigenvectors capture successively-orthogonalized directions of maximum variance, conditioned...

  9. QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms

    Directory of Open Access Journals (Sweden)

    Ardjan Zwartjes

    2016-10-01

    Full Text Available In this work, we introduce QUEST (QUantile Estimation after Supervised Training, an adaptive classification algorithm for Wireless Sensor Networks (WSNs that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

  10. Document Classification Using Expectation Maximization with Semi Supervised Learning

    CERN Document Server

    Nigam, Bhawna; Salve, Sonal; Vamney, Swati

    2011-01-01

    As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is "Dynamically Generation of New Class". The algorithm first trains a classifier using the labeled document and probabilistically classifies the unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.

  11. A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation

    Science.gov (United States)

    Cruz-Roa, Angel; Arevalo, John; Basavanhally, Ajay; Madabhushi, Anant; González, Fabio

    2015-01-01

    Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.

  12. Enhancing Adult Learning in Clinical Supervision

    Science.gov (United States)

    Goldman, Stuart

    2011-01-01

    Objective/Background: For decades, across almost every training site, clinical supervision has been considered "central to the development of skills" in psychiatry. The crucial supervisor/supervisee relationship has been described extensively in the literature, most often framed as a clinical apprenticeship of the novice to the master craftsman.…

  13. 监督学习的发展动态%Current Directions in Supervised Learning Research

    Institute of Scientific and Technical Information of China (English)

    蒋艳凰; 周海芳; 杨学军

    2003-01-01

    Supervised learning is very important in machine learning area. It has been making great progress in manydirections. This article summarizes three of these directions ,which are the hot problems in supervised learning field.These three directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods forscaling up supervised learning algorithm, (c) extracting understandable rules from classifiers.

  14. Action Learning in Undergraduate Engineering Thesis Supervision

    Science.gov (United States)

    Stappenbelt, Brad

    2017-01-01

    In the present action learning implementation, twelve action learning sets were conducted over eight years. The action learning sets consisted of students involved in undergraduate engineering research thesis work. The concurrent study accompanying this initiative investigated the influence of the action learning environment on student approaches…

  15. Improving Semi-Supervised Learning with Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    Deep generative models based upon continuous variational distributions parameterized by deep networks give state-of-the-art performance. In this paper we propose a framework for extending the latent representation with extra auxiliary variables in order to make the variational distribution more...... expressive for semi-supervised learning. By utilizing the stochasticity of the auxiliary variable we demonstrate how to train discriminative classifiers resulting in state-of-the-art performance within semi-supervised learning exemplified by an 0.96% error on MNIST using 100 labeled data points. Furthermore...

  16. Pulsar Search Using Supervised Machine Learning

    Science.gov (United States)

    Ford, John M.

    2017-05-01

    Pulsars are rapidly rotating neutron stars which emit a strong beam of energy through mechanisms that are not entirely clear to physicists. These very dense stars are used by astrophysicists to study many basic physical phenomena, such as the behavior of plasmas in extremely dense environments, behavior of pulsar-black hole pairs, and tests of general relativity. Many of these tasks require a large ensemble of pulsars to provide enough statistical information to answer the scientific questions posed by physicists. In order to provide more pulsars to study, there are several large-scale pulsar surveys underway, which are generating a huge backlog of unprocessed data. Searching for pulsars is a very labor-intensive process, currently requiring skilled people to examine and interpret plots of data output by analysis programs. An automated system for screening the plots will speed up the search for pulsars by a very large factor. Research to date on using machine learning and pattern recognition has not yielded a completely satisfactory system, as systems with the desired near 100% recall have false positive rates that are higher than desired, causing more manual labor in the classification of pulsars. This work proposed to research, identify, propose and develop methods to overcome the barriers to building an improved classification system with a false positive rate of less than 1% and a recall of near 100% that will be useful for the current and next generation of large pulsar surveys. The results show that it is possible to generate classifiers that perform as needed from the available training data. While a false positive rate of 1% was not reached, recall of over 99% was achieved with a false positive rate of less than 2%. Methods of mitigating the imbalanced training and test data were explored and found to be highly effective in enhancing classification accuracy.

  17. Semi-supervised Eigenvectors for Locally-biased Learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2012-01-01

    of this sort are particularly challenging for popular eigenvector-based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities. In this paper, we address this issue by providing a methodology to construct semi-supervised eigenvectors of a graph...

  18. SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS

    Data.gov (United States)

    National Aeronautics and Space Administration — SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS GOO JUN * AND JOYDEEP GHOSH* Abstract. A semi-supervised learning...

  19. SLEAS: Supervised Learning using Entropy as Attribute Selection Measure

    Directory of Open Access Journals (Sweden)

    Kishor Kumar Reddy C

    2014-10-01

    Full Text Available There is embryonic importance in scaling up the broadly used decision tree learning algorithms to huge datasets. Even though abundant diverse methodologies have been proposed, a fast tree growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential to a greater extent. This paper aims at improving the performance of the SLIQ (Supervised Learning in Quest decision tree algorithm for classification in data mining. In the present research, we adopted entropy as attribute selection measure, which overcomes the problems facing with Gini Index. Classification accuracy of the proposed supervised learning using entropy as attribute selection measure (SLEAS algorithm is compared with the existing SLIQ algorithm using twelve datasets taken from UCI Machine Learning Repository, and the results yields that the SLEAS outperforms when compared with SLIQ decision tree. Further, error rate is also computed and the results clearly show that the SLEAS algorithm is giving less error rate when compared with SLIQ decision tree.

  20. Transfer learning improves supervised image segmentation across imaging protocols

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.;

    2015-01-01

    well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data...

  1. Task-Based Learning: The Interaction between Tasks and Learners.

    Science.gov (United States)

    Murphy, Jacky

    2003-01-01

    Investigates the relationship between tasks and learners in task-based learning. Findings suggest that manipulation of task characteristics and conditions may not achieve the intended pedagogic outcomes, and that new ways are needed to focus learners' attention of form without sacrificing the meaning-driven principles of task-based learning.…

  2. Combining Unsupervised and Supervised Learning for Discovering Disease Subclasses

    OpenAIRE

    Tucker, A; Bosoni, P; Bellazzi, R.; Nihtyanova, S; Denton, C.

    2016-01-01

    Diseases are often umbrella terms for many subcategories of disease. The identification of these subcategories is vital if we are to develop personalised treatments that are better focussed on individual patients. In this short paper, we explore the use of a combination of unsupervised learning to identify potential subclasses, and supervised learning to build models for better predicting a number of different health outcomes for patients that suffer from systemic sclerosis, a rare chronic co...

  3. Effects of coaching supervision, mentoring supervision and abusive supervision on talent development among trainee doctors in public hospitals: moderating role of clinical learning environment.

    Science.gov (United States)

    Subramaniam, Anusuiya; Silong, Abu Daud; Uli, Jegak; Ismail, Ismi Arif

    2015-08-13

    Effective talent development requires robust supervision. However, the effects of supervisory styles (coaching, mentoring and abusive supervision) on talent development and the moderating effects of clinical learning environment in the relationship between supervisory styles and talent development among public hospital trainee doctors have not been thoroughly researched. In this study, we aim to achieve the following, (1) identify the extent to which supervisory styles (coaching, mentoring and abusive supervision) can facilitate talent development among trainee doctors in public hospital and (2) examine whether coaching, mentoring and abusive supervision are moderated by clinical learning environment in predicting talent development among trainee doctors in public hospital. A questionnaire-based critical survey was conducted among trainee doctors undergoing housemanship at six public hospitals in the Klang Valley, Malaysia. Prior permission was obtained from the Ministry of Health Malaysia to conduct the research in the identified public hospitals. The survey yielded 355 responses. The results were analysed using SPSS 20.0 and SEM with AMOS 20.0. The findings of this research indicate that coaching and mentoring supervision are positively associated with talent development, and that there is no significant relationship between abusive supervision and talent development. The findings also support the moderating role of clinical learning environment on the relationships between coaching supervision-talent development, mentoring supervision-talent development and abusive supervision-talent development among public hospital trainee doctors. Overall, the proposed model indicates a 26 % variance in talent development. This study provides an improved understanding on the role of the supervisory styles (coaching and mentoring supervision) on facilitating talent development among public hospital trainee doctors. Furthermore, this study extends the literature to better

  4. Enhancing fieldwork learning using blended learning, GIS and remote supervision

    Science.gov (United States)

    Marra, Wouter A.; Alberti, Koko; Karssenberg, Derek

    2015-04-01

    Fieldwork is an important part of education in geosciences and essential to put theoretical knowledge into an authentic context. Fieldwork as teaching tool can take place in various forms, such as field-tutorial, excursion, or supervised research. Current challenges with fieldwork in education are to incorporate state-of-the art methods for digital data collection, on-site GIS-analysis and providing high-quality feedback to large groups of students in the field. We present a case on first-year earth-sciences fieldwork with approximately 80 students in the French Alps focused on geological and geomorphological mapping. Here, students work in couples and each couple maps their own fieldwork area to reconstruct the formative history. We present several major improvements for this fieldwork using a blended-learning approach, relying on open source software only. An important enhancement to the French Alps fieldwork is improving students' preparation. In a GIS environment, students explore their fieldwork areas using existing remote sensing data, a digital elevation model and derivatives to formulate testable hypotheses before the actual fieldwork. The advantage of this is that the students already know their area when arriving in the field, have started to apply the empirical cycle prior to their field visit, and are therefore eager to investigate their own research questions. During the fieldwork, students store and analyze their field observations in the same GIS environment. This enables them to get a better overview of their own collected data, and to integrate existing data sources also used in the preparation phase. This results in a quicker and enhanced understanding by the students. To enable remote access to observational data collected by students, the students synchronize their data daily with a webserver running a web map application. Supervisors can review students' progress remotely, examine and evaluate their observations in a GIS, and provide

  5. Collaborative Supervised Learning for Sensor Networks

    Science.gov (United States)

    Wagstaff, Kiri L.; Rebbapragada, Umaa; Lane, Terran

    2011-01-01

    Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

  6. Facial nerve image enhancement from CBCT using supervised learning technique.

    Science.gov (United States)

    Ping Lu; Barazzetti, Livia; Chandran, Vimal; Gavaghan, Kate; Weber, Stefan; Gerber, Nicolas; Reyes, Mauricio

    2015-08-01

    Facial nerve segmentation plays an important role in surgical planning of cochlear implantation. Clinically available CBCT images are used for surgical planning. However, its relatively low resolution renders the identification of the facial nerve difficult. In this work, we present a supervised learning approach to enhance facial nerve image information from CBCT. A supervised learning approach based on multi-output random forest was employed to learn the mapping between CBCT and micro-CT images. Evaluation was performed qualitatively and quantitatively by using the predicted image as input for a previously published dedicated facial nerve segmentation, and cochlear implantation surgical planning software, OtoPlan. Results show the potential of the proposed approach to improve facial nerve image quality as imaged by CBCT and to leverage its segmentation using OtoPlan.

  7. Modeling Time Series Data for Supervised Learning

    Science.gov (United States)

    Baydogan, Mustafa Gokce

    2012-01-01

    Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

  8. Semi-supervised learning for ordinal Kernel Discriminant Analysis.

    Science.gov (United States)

    Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

    2016-12-01

    Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function.

  9. Biomedical data analysis by supervised manifold learning.

    Science.gov (United States)

    Alvarez-Meza, A M; Daza-Santacoloma, G; Castellanos-Dominguez, G

    2012-01-01

    Biomedical data analysis is usually carried out by assuming that the information structure embedded into the biomedical recordings is linear, but that statement actually does not corresponds to the real behavior of the extracted features. In order to improve the accuracy of an automatic system to diagnostic support, and to reduce the computational complexity of the employed classifiers, we propose a nonlinear dimensionality reduction methodology based on manifold learning with multiple kernel representations, which learns the underlying data structure of biomedical information. Moreover, our approach can be used as a tool that allows the specialist to do a visual analysis and interpretation about the studied variables describing the health condition. Obtained results show how our approach maps the original high dimensional features into an embedding space where simple and straightforward classification strategies achieve a suitable system performance.

  10. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Nan Zhao

    2014-05-01

    Full Text Available Single nucleotide polymorphisms (SNPs are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs have been found near or inside the protein-protein interaction (PPI interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor. Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1 a 2-class problem (strengthening/weakening PPI mutations, (2 another 2-class problem (mutations that disrupt/preserve a PPI, and (3 a 3-class classification (detrimental/neutral/beneficial mutation effects. In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the

  11. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

    Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

  12. Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.

    Science.gov (United States)

    Peng, Yong; Lu, Bao-Liang; Wang, Suhang

    2015-05-01

    Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labeled and unlabeled samples, where the edge weights are calculated based on the LRR coefficients. However, most of existing LRR related approaches fail to consider the geometrical structure of data, which has been shown beneficial for discriminative tasks. In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation. MLRR can explicitly take the data local manifold structure into consideration, which can be identified by the geometric sparsity idea; specifically, the local tangent space of each data point was sought by solving a sparse representation objective. Therefore, the graph to depict the relationship of data points can be built once the manifold information is obtained. We incorporate a regularizer into LRR to make the learned coefficients preserve the geometric constraints revealed in the data space. As a result, MLRR combines both the global information emphasized by low-rank property and the local information emphasized by the identified manifold structure. Extensive experimental results on semi-supervised classification tasks demonstrate that MLRR is an excellent method in comparison with several state-of-the-art graph construction approaches.

  13. Semi-supervised Learning with Density Based Distances

    CERN Document Server

    Bijral, Avleen S; Srebro, Nathan

    2012-01-01

    We present a simple, yet effective, approach to Semi-Supervised Learning. Our approach is based on estimating density-based distances (DBD) using a shortest path calculation on a graph. These Graph-DBD estimates can then be used in any distance-based supervised learning method, such as Nearest Neighbor methods and SVMs with RBF kernels. In order to apply the method to very large data sets, we also present a novel algorithm which integrates nearest neighbor computations into the shortest path search and can find exact shortest paths even in extremely large dense graphs. Significant runtime improvement over the commonly used Laplacian regularization method is then shown on a large scale dataset.

  14. Very Short Literature Survey From Supervised Learning To Surrogate Modeling

    CERN Document Server

    Brusan, Altay

    2012-01-01

    The past century was era of linear systems. Either systems (especially industrial ones) were simple (quasi)linear or linear approximations were accurate enough. In addition, just at the ending decades of the century profusion of computing devices were available, before then due to lack of computational resources it was not easy to evaluate available nonlinear system studies. At the moment both these two conditions changed, systems are highly complex and also pervasive amount of computation strength is cheap and easy to achieve. For recent era, a new branch of supervised learning well known as surrogate modeling (meta-modeling, surface modeling) has been devised which aimed at answering new needs of modeling realm. This short literature survey is on to introduce surrogate modeling to whom is familiar with the concepts of supervised learning. Necessity, challenges and visions of the topic are considered.

  15. Leadership for Learning: Tasks of Learning Culture

    Science.gov (United States)

    Corrigan, Joe

    2012-01-01

    This is a comparative analysis of leadership related to organizational culture and change that occurred at a large Canadian university during a twenty year period 1983-2003. From an institutional development perspective, leadership is characterized as a culture creation and development responsibility. By centering on the tasks of learning culture,…

  16. Semi-supervised Learning for Photometric Supernova Classification

    CERN Document Server

    Richards, Joseph W; Freeman, Peter E; Schafer, Chad M; Poznanski, Dovi

    2011-01-01

    We present a semi-supervised method for photometric supernova typing. Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template based methods. Applied to supernova data simulated by Kessler et al. (2010b) to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 96% Type Ia purity and 86% Type Ia efficiency on the spectroscopic sample, but only 56% Type Ia purity and 48% efficiency on the photometric sample due to their spectroscopic followup strategy. To improve the performance on the photometric sample...

  17. Baccalaureate nursing students' perceptions of learning and supervision in the clinical environment.

    Science.gov (United States)

    Dimitriadou, Maria; Papastavrou, Evridiki; Efstathiou, Georgios; Theodorou, Mamas

    2015-06-01

    This study is an exploration of nursing students' experiences within the clinical learning environment (CLE) and supervision provided in hospital settings. A total of 357 second-year nurse students from all universities in Cyprus participated in the study. Data were collected using the Clinical Learning Environment, Supervision and Nurse Teacher instrument. The dimension "supervisory relationship (mentor)", as well as the frequency of individualized supervision meetings, were found to be important variables in the students' clinical learning. However, no statistically-significant connection was established between successful mentor relationship and team supervision. The majority of students valued their mentor's supervision more highly than a nurse teacher's supervision toward the fulfillment of learning outcomes. The dimensions "premises of nursing care" and "premises of learning" were highly correlated, indicating that a key component of a quality clinical learning environment is the quality of care delivered. The results suggest the need to modify educational strategies that foster desirable learning for students in response to workplace demands.

  18. Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates

    CERN Document Server

    Jamal, Wasifa; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

    2014-01-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave ...

  19. Robust head pose estimation via supervised manifold learning.

    Science.gov (United States)

    Wang, Chao; Song, Xubo

    2014-05-01

    Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with the pose being the only variable, the face images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background clutter, facial expression, and illumination. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. The process has three stages: neighborhood construction, graph weight computation and projection learning. For the first two stages, we redefine inter-point distance for neighborhood construction as well as graph weight by constraining them with the pose angle information. For Stage 3, we present a supervised neighborhood-based linear feature transformation algorithm to keep the data points with similar pose angles close together but the data points with dissimilar pose angles far apart. The experimental results show that our method has higher estimation accuracy than the other state-of-art algorithms and is robust to identity and illumination variations.

  20. Generalization of Supervised Learning for Binary Mask Estimation

    DEFF Research Database (Denmark)

    May, Tobias; Gerkmann, Timo

    2014-01-01

    This paper addresses the problem of speech segregation by es- timating the ideal binary mask (IBM) from noisy speech. Two methods will be compared, one supervised learning approach that incorporates a priori knowledge about the feature distri- bution observed during training. The second method...... solely relies on a frame-based speech presence probability (SPP) es- timation, and therefore, does not depend on the acoustic con- dition seen during training. We investigate the influence of mismatches between the acoustic conditions used for training and testing on the IBM estimation performance...

  1. Learning outcomes using video in supervision and peer feedback during clinical skills training

    DEFF Research Database (Denmark)

    Lauridsen, Henrik Hein; Toftgård, Rie Castella; Nørgaard, Cita

    supervision of clinical skills (formative assessment). Demonstrations of these principles will be presented as video podcasts during the session. The learning outcomes of video supervision and peer-feedback were assessed in an online questionnaire survey. Results Results of the supervision showed large self...

  2. Effects of automation and task load on task switching during human supervision of multiple semi-autonomous robots in a dynamic environment.

    Science.gov (United States)

    Squire, P N; Parasuraman, R

    2010-08-01

    The present study assessed the impact of task load and level of automation (LOA) on task switching in participants supervising a team of four or eight semi-autonomous robots in a simulated 'capture the flag' game. Participants were faster to perform the same task than when they chose to switch between different task actions. They also took longer to switch between different tasks when supervising the robots at a high compared to a low LOA. Task load, as manipulated by the number of robots to be supervised, did not influence switch costs. The results suggest that the design of future unmanned vehicle (UV) systems should take into account not simply how many UVs an operator can supervise, but also the impact of LOA and task operations on task switching during supervision of multiple UVs. The findings of this study are relevant for the ergonomics practice of UV systems. This research extends the cognitive theory of task switching to inform the design of UV systems and results show that switching between UVs is an important factor to consider.

  3. ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system

    National Research Council Canada - National Science Library

    Hughey, Jacob J; Hastie, Trevor; Butte, Atul J

    2016-01-01

    Numerous biological systems oscillate over time or space. Despite these oscillators' importance, data from an oscillatory system is problematic for existing methods of regularized supervised learning...

  4. Path Control Experiment of Mobile Robot Based on Supervised Learning

    Directory of Open Access Journals (Sweden)

    Gao Chi

    2013-07-01

    Full Text Available To solve the weak capacity and low control accuracy of the robots which adapt to the complex working conditions, proposed that a path control method based on the driving experience and supervised learning. According to the slope road geometry characteristics, established the modeling study due to ramp pavement path control method and the control structure based on monitoring and self-learning. Made use of the Global Navigation Satellite System did the experiment. The test data illustrates that when the running speed is not greater than 5 m / s, the straight-line trajectory path transverse vertical deviation within 士20cm ,which proved that the control method has a high feasibility. 

  5. Prototype Vector Machine for Large Scale Semi-Supervised Learning

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Kai; Kwok, James T.; Parvin, Bahram

    2009-04-29

    Practicaldataminingrarelyfalls exactlyinto the supervisedlearning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computationalintensivenessofgraph-based SSLarises largely from the manifold or graph regularization, which in turn lead to large models that are dificult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highlyscalable,graph-based algorithm for large-scale SSL. Our key innovation is the use of"prototypes vectors" for effcient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of the kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.

  6. Exploiting Attribute Correlations: A Novel Trace Lasso-Based Weakly Supervised Dictionary Learning Method.

    Science.gov (United States)

    Wu, Lin; Wang, Yang; Pan, Shirui

    2016-10-04

    It is now well established that sparse representation models are working effectively for many visual recognition tasks, and have pushed forward the success of dictionary learning therein. Recent studies over dictionary learning focus on learning discriminative atoms instead of purely reconstructive ones. However, the existence of intraclass diversities (i.e., data objects within the same category but exhibit large visual dissimilarities), and interclass similarities (i.e., data objects from distinct classes but share much visual similarities), makes it challenging to learn effective recognition models. To this end, a large number of labeled data objects are required to learn models which can effectively characterize these subtle differences. However, labeled data objects are always limited to access, committing it difficult to learn a monolithic dictionary that can be discriminative enough. To address the above limitations, in this paper, we propose a weakly-supervised dictionary learning method to automatically learn a discriminative dictionary by fully exploiting visual attribute correlations rather than label priors. In particular, the intrinsic attribute correlations are deployed as a critical cue to guide the process of object categorization, and then a set of subdictionaries are jointly learned with respect to each category. The resulting dictionary is highly discriminative and leads to intraclass diversity aware sparse representations. Extensive experiments on image classification and object recognition are conducted to show the effectiveness of our approach.

  7. Multicultural supervision: lessons learned about an ongoing struggle.

    Science.gov (United States)

    Christiansen, Abigail Tolhurst; Thomas, Volker; Kafescioglu, Nilufer; Karakurt, Gunnur; Lowe, Walter; Smith, William; Wittenborn, Andrea

    2011-01-01

    This article examines the experiences of seven diverse therapists in a supervision course as they wrestled with the real-world application of multicultural supervision. Existing literature on multicultural supervision does not address the difficulties that arise in addressing multicultural issues in the context of the supervision relationship. The experiences of six supervisory candidates and one mentoring supervisor in addressing multicultural issues in supervision are explored. Guidelines for conversations regarding multicultural issues are provided.

  8. Supervised learning for neural manifold using spatiotemporal brain activity

    Science.gov (United States)

    Kuo, Po-Chih; Chen, Yong-Sheng; Chen, Li-Fen

    2015-12-01

    Objective. Determining the means by which perceived stimuli are compactly represented in the human brain is a difficult task. This study aimed to develop techniques for the construction of the neural manifold as a representation of visual stimuli. Approach. We propose a supervised locally linear embedding method to construct the embedded manifold from brain activity, taking into account similarities between corresponding stimuli. In our experiments, photographic portraits were used as visual stimuli and brain activity was calculated from magnetoencephalographic data using a source localization method. Main results. The results of 10 × 10-fold cross-validation revealed a strong correlation between manifolds of brain activity and the orientation of faces in the presented images, suggesting that high-level information related to image content can be revealed in the brain responses represented in the manifold. Significance. Our experiments demonstrate that the proposed method is applicable to investigation into the inherent patterns of brain activity.

  9. Online semi-supervised learning: algorithm and application in metagenomics

    NARCIS (Netherlands)

    S. Imangaliyev; B. Keijser; W. Crielaard; E. Tsivtsivadze

    2013-01-01

    As the amount of metagenomic data grows rapidly, online statistical learning algorithms are poised to play key role in metagenome analysis tasks. Frequently, data are only partially labeled, namely dataset contains partial information about the problem of interest. This work presents an algorithm an

  10. Online Semi-Supervised Learning: Algorithm and Application in Metagenomics

    NARCIS (Netherlands)

    Imangaliyev, S.; Keijser, B.J.F.; Crielaard, W.; Tsivtsivadze, E.

    2013-01-01

    As the amount of metagenomic data grows rapidly, online statistical learning algorithms are poised to play key rolein metagenome analysis tasks. Frequently, data are only partially labeled, namely dataset contains partial information about the problem of interest. This work presents an algorithm and

  11. Generating a Spanish Affective Dictionary with Supervised Learning Techniques

    Science.gov (United States)

    Bermudez-Gonzalez, Daniel; Miranda-Jiménez, Sabino; García-Moreno, Raúl-Ulises; Calderón-Nepamuceno, Dora

    2016-01-01

    Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective…

  12. Supervised learning of semantic classes for image annotation and retrieval.

    Science.gov (United States)

    Carneiro, Gustavo; Chan, Antoni B; Moreno, Pedro J; Vasconcelos, Nuno

    2007-03-01

    A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning.

  13. An AdaBoost algorithm for multiclass semi-supervised learning

    NARCIS (Netherlands)

    Tanha, J.; van Someren, M.; Afsarmanesh, H.; Zaki, M.J.; Siebes, A.; Yu, J.X.; Goethals, B.; Webb, G.; Wu, X.

    2012-01-01

    We present an algorithm for multiclass Semi-Supervised learning which is learning from a limited amount of labeled data and plenty of unlabeled data. Existing semi-supervised algorithms use approaches such as one-versus-all to convert the multiclass problem to several binary classification problems

  14. Task-Driven Dictionary Learning for Hyperspectral Image Classification with Structured Sparsity Constraints

    Science.gov (United States)

    2015-02-03

    0188 3. DATES COVERED (From - To) - UU UU UU UU Approved for public release; distribution is unlimited. Task-Driven Dictionary Learning for... dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in order to ensure both a stable high sparsity level and a low...Research Triangle Park, NC 27709-2211 Sparse representation, supervised dictionarylearning, task-driven dictionary learning, joint sparsity

  15. Phenotype classification of zebrafish embryos by supervised learning.

    Directory of Open Access Journals (Sweden)

    Nathalie Jeanray

    Full Text Available Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  16. Detection of money laundering groups using supervised learning in networks

    CERN Document Server

    Savage, David; Chou, Pauline; Zhang, Xiuzhen; Yu, Xinghuo

    2016-01-01

    Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals, and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates th...

  17. Unsupervised/supervised learning concept for 24-hour load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M. (Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia)); Babic, B. (Electrical Power Industry of Serbia, Belgrade (Yugoslavia)); Sobajic, D.J.; Pao, Y.-H. (Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Computer Science)

    1993-07-01

    An application of artificial neural networks in short-term load forecasting is described. An algorithm using an unsupervised/supervised learning concept and historical relationship between the load and temperature for a given season, day type and hour of the day to forecast hourly electric load with a lead time of 24 hours is proposed. An additional approach using functional link net, temperature variables, average load and last one-hour load of previous day is introduced and compared with the ANN model with one hidden layer load forecast. In spite of limited available weather variables (maximum, minimum and average temperature for the day) quite acceptable results have been achieved. The 24-hour-ahead forecast errors (absolute average) ranged from 2.78% for Saturdays and 3.12% for working days to 3.54% for Sundays. (Author)

  18. Online Semi-Supervised Learning on Quantized Graphs

    CERN Document Server

    Valko, Michal; Huang, Ling; Ting, Daniel

    2012-01-01

    In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose a fast approximate online SSL algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local "representative points" that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We apply our algorithm to face recognition and optical character recognition applications to show that we can take advantage of the manifold structure to outperform the previous methods. Unlike previous heuristic approaches, we show that our method yields provable performance bounds.

  19. Using Supervised Learning to Improve Monte Carlo Integral Estimation

    CERN Document Server

    Tracey, Brendan; Alonso, Juan J

    2011-01-01

    Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function. In light of the increasing interest in uncertainty quantification and robust design applications in aerospace engineering, the calculation of expected values of such functions (e.g. performance measures) becomes important. However, MC techniques often suffer from high variance and slow convergence as the number of samples increases. In this paper we present Stacked Monte Carlo (StackMC), a new method for post-processing an existing set of MC samples to improve the associated integral estimate. StackMC is based on the supervised learning techniques of fitting functions and cross validation. It should reduce the variance of any type of Monte Carlo integral estimate (simple sampling, importance sampling, quasi-Monte Carlo, MCMC, etc.) without adding bias. We report on an extensive set of experiments confirming that the StackMC estimate of an integral is more accurate than ...

  20. Phenotype classification of zebrafish embryos by supervised learning.

    Science.gov (United States)

    Jeanray, Nathalie; Marée, Raphaël; Pruvot, Benoist; Stern, Olivier; Geurts, Pierre; Wehenkel, Louis; Muller, Marc

    2015-01-01

    Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  1. Semi-supervised eigenvectors for large-scale locally-biased learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2014-01-01

    In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks nearby that prespecified target region. For example, one might...... machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing...... be interested in the clustering structure of a data graph near a prespecified seed set of nodes, or one might be interested in finding partitions in an image that are near a prespecified ground truth set of pixels. Locally-biased problems of this sort are particularly challenging for popular eigenvector-based...

  2. Semi-supervised eigenvectors for large-scale locally-biased learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2014-01-01

    -based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing......In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks nearby that prespecified target region. For example, one might...... be interested in the clustering structure of a data graph near a prespecified seed set of nodes, or one might be interested in finding partitions in an image that are near a prespecified ground truth set of pixels. Locally-biased problems of this sort are particularly challenging for popular eigenvector...

  3. Understanding Trust as an Essential Element of Trainee Supervision and Learning in the Workplace

    Science.gov (United States)

    Hauer, Karen E.; ten Cate, Olle; Boscardin, Christy; Irby, David M.; Iobst, William; O'Sullivan, Patricia S.

    2014-01-01

    Clinical supervision requires that supervisors make decisions about how much independence to allow their trainees for patient care tasks. The simultaneous goals of ensuring quality patient care and affording trainees appropriate and progressively greater responsibility require that the supervising physician trusts the trainee. Trust allows the…

  4. I’m just thinking - How learning opportunities are created in doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Berge, Maria; Grout, Brian William Wilson;

    With this paper we aim to contribute towards an understanding of learning dynamics in doctoral supervision by analysing how learning opportunities are created in the interaction. We analyse interaction between supervisors and doctoral students using the notion of experiencing variation as a key...... for learning. Earlier research into doctoral supervision has been rather vague on how doctoral students learn to carry out research. Empirically, we have based the study on four cases each with one doctoral student and their supervisors. The supervision sessions were captured on video and audio to provide...

  5. Semi-supervised learning of causal relations in biomedical scientific discourse

    Science.gov (United States)

    2014-01-01

    Background The increasing number of daily published articles in the biomedical domain has become too large for humans to handle on their own. As a result, bio-text mining technologies have been developed to improve their workload by automatically analysing the text and extracting important knowledge. Specific bio-entities, bio-events between these and facts can now be recognised with sufficient accuracy and are widely used by biomedical researchers. However, understanding how the extracted facts are connected in text is an extremely difficult task, which cannot be easily tackled by machinery. Results In this article, we describe our method to recognise causal triggers and their arguments in biomedical scientific discourse. We introduce new features and show that a self-learning approach improves the performance obtained by supervised machine learners to 83.47% for causal triggers. Furthermore, the spans of causal arguments can be recognised to a slightly higher level that by using supervised or rule-based methods that have been employed before. Conclusion Exploiting the large amount of unlabelled data that is already available can help improve the performance of recognising causal discourse relations in the biomedical domain. This improvement will further benefit the development of multiple tasks, such as hypothesis generation for experimental laboratories, contradiction detection, and the creation of causal networks. PMID:25559746

  6. Learning Multiple Tasks with Deep Relationship Networks

    OpenAIRE

    Long, Mingsheng; Wang, Jianmin

    2015-01-01

    Deep neural networks trained on large-scale dataset can learn transferable features that promote learning multiple tasks for inductive transfer and labeling mitigation. As deep features eventually transition from general to specific along the network, a fundamental problem is how to exploit the relationship structure across different tasks while accounting for the feature transferability in the task-specific layers. In this work, we propose a novel Deep Relationship Network (DRN) architecture...

  7. Supervised Filter Learning for Representation Based Face Recognition.

    Directory of Open Access Journals (Sweden)

    Chao Bi

    Full Text Available Representation based classification methods, such as Sparse Representation Classification (SRC and Linear Regression Classification (LRC have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm.

  8. Towards harmonized seismic analysis across Europe using supervised machine learning approaches

    Science.gov (United States)

    Zaccarelli, Riccardo; Bindi, Dino; Cotton, Fabrice; Strollo, Angelo

    2017-04-01

    In the framework of the Thematic Core Services for Seismology of EPOS-IP (European Plate Observing System-Implementation Phase), a service for disseminating a regionalized logic-tree of ground motions models for Europe is under development. While for the Mediterranean area the large availability of strong motion data qualified and disseminated through the Engineering Strong Motion database (ESM-EPOS), supports the development of both selection criteria and ground motion models, for the low-to-moderate seismic regions of continental Europe the development of ad-hoc models using weak motion recordings of moderate earthquakes is unavoidable. Aim of this work is to present a platform for creating application-oriented earthquake databases by retrieving information from EIDA (European Integrated Data Archive) and applying supervised learning models for earthquake records selection and processing suitable for any specific application of interest. Supervised learning models, i.e. the task of inferring a function from labelled training data, have been extensively used in several fields such as spam detection, speech and image recognition and in general pattern recognition. Their suitability to detect anomalies and perform a semi- to fully- automated filtering on large waveform data set easing the effort of (or replacing) human expertise is therefore straightforward. Being supervised learning algorithms capable of learning from a relatively small training set to predict and categorize unseen data, its advantage when processing large amount of data is crucial. Moreover, their intrinsic ability to make data driven predictions makes them suitable (and preferable) in those cases where explicit algorithms for detection might be unfeasible or too heuristic. In this study, we consider relatively simple statistical classifiers (e.g., Naive Bayes, Logistic Regression, Random Forest, SVMs) where label are assigned to waveform data based on "recognized classes" needed for our use case

  9. Efficient supervised learning in networks with binary synapses

    CERN Document Server

    Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

    2007-01-01

    Recent experimental studies indicate that synaptic changes induced by neuronal activity are discrete jumps between a small number of stable states. Learning in systems with discrete synapses is known to be a computationally hard problem. Here, we study a neurobiologically plausible on-line learning algorithm that derives from Belief Propagation algorithms. We show that it performs remarkably well in a model neuron with binary synapses, and a finite number of `hidden' states per synapse, that has to learn a random classification task. Such system is able to learn a number of associations close to the theoretical limit, in time which is sublinear in system size. This is to our knowledge the first on-line algorithm that is able to achieve efficiently a finite number of patterns learned per binary synapse. Furthermore, we show that performance is optimal for a finite number of hidden states which becomes very small for sparse coding. The algorithm is similar to the standard `perceptron' learning algorithm, with a...

  10. SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    T. Hamsapriya

    2011-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.

  11. Active semi-supervised learning method with hybrid deep belief networks.

    Science.gov (United States)

    Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

    2014-01-01

    In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

  12. The Practice of Supervision for Professional Learning: The Example of Future Forensic Specialists

    Science.gov (United States)

    Köpsén, Susanne; Nyström, Sofia

    2015-01-01

    Supervision intended to support learning is of great interest in professional knowledge development. No single definition governs the implementation and enactment of supervision because of different conditions, intentions, and pedagogical approaches. Uncertainty exists at a time when knowledge and methods are undergoing constant development. This…

  13. The Practice of Supervision for Professional Learning: The Example of Future Forensic Specialists

    Science.gov (United States)

    Köpsén, Susanne; Nyström, Sofia

    2015-01-01

    Supervision intended to support learning is of great interest in professional knowledge development. No single definition governs the implementation and enactment of supervision because of different conditions, intentions, and pedagogical approaches. Uncertainty exists at a time when knowledge and methods are undergoing constant development. This…

  14. An approach to elemental task learning

    Energy Technology Data Exchange (ETDEWEB)

    Belmans, P

    1990-01-01

    In this article we deal with the automated learning of tasks by a robotic system through observation of a human operator. Particularly, we explain what is meant by a learning ability in autonomous robots and in teleoperation systems, where several operators and several machines may work in cooperation to perform tasks. We discuss different approaches to learning in these systems and outline the features of the models they are based upon. This leads us to choose an analytical model suited for tasks analysis. We then present the software architecture for our proposed approach and show the first results obtained on sample tests. 5 refs., 9 figs.

  15. Semi-Supervised Learning for Classification of Protein Sequence Data

    Directory of Open Access Journals (Sweden)

    Brian R. King

    2008-01-01

    Full Text Available Protein sequence data continue to become available at an exponential rate. Annotation of functional and structural attributes of these data lags far behind, with only a small fraction of the data understood and labeled by experimental methods. Classification methods that are based on semi-supervised learning can increase the overall accuracy of classifying partly labeled data in many domains, but very few methods exist that have shown their effect on protein sequence classification. We show how proven methods from text classification can be applied to protein sequence data, as we consider both existing and novel extensions to the basic methods, and demonstrate restrictions and differences that must be considered. We demonstrate comparative results against the transductive support vector machine, and show superior results on the most difficult classification problems. Our results show that large repositories of unlabeled protein sequence data can indeed be used to improve predictive performance, particularly in situations where there are fewer labeled protein sequences available, and/or the data are highly unbalanced in nature.

  16. Combining theories to reach multi-faceted insights into learning opportunities in doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Rump, Camilla Østerberg

    The aim of this paper is to illustrate how theories can be combined to explore opportunities for learning in doctoral supervision. While our earlier research into learning dynamics in doctoral supervision in life science research (Kobayashi, 2014) has focused on illustrating learning opportunities...... this paper focuses on the methodological advantages and potential criticism of combining theories. Learning in doctoral education, as in classroom learning, can be analysed from different perspectives. Zembylas (2005) suggests three perspectives with the aim of linking the cognitive and the emotional...

  17. Visual Recognition by Learning From Web Data via Weakly Supervised Domain Generalization.

    Science.gov (United States)

    Niu, Li; Li, Wen; Xu, Dong; Cai, Jianfei

    2016-06-01

    In this paper, a weakly supervised domain generalization (WSDG) method is proposed for real-world visual recognition tasks, in which we train classifiers by using Web data (\\eg, Web images and Web videos) with noisy labels. In particular, two challenging problems need to be solved when learning robust classifiers, in which the first issue is to cope with the label noise of training Web data from the source domain, while the second issue is to enhance the generalization capability of learned classifiers to an arbitrary target domain. In order to handle the first problem, the training samples within each category are partitioned into clusters, where we use one bag to denote each cluster and instances to denote the samples in each cluster. Then, we identify a proportion of good training samples in each bag and train robust classifiers by using the good training samples, which leads to a multi-instance learning (MIL) problem. In order to handle the second problem, we assume that the training samples possibly form a set of hidden domains, with each hidden domain associated with a distinctive data distribution. Then, for each category and each hidden latent domain, we propose to learn one classifier by extending our MIL formulation, which leads to our WSDG approach. In the testing stage, our approach can obtain better generalization capability by effectively integrating multiple classifiers from different latent domains in each category. Moreover, our WSDG approach is further extended to utilize additional textual descriptions associated with Web data as privileged information (PI), although testing data do not have such PI. Extensive experiments on three benchmark data sets indicate that our newly proposed methods are effective for real-world visual recognition tasks by learning from Web data.

  18. A new semi-supervised classification strategy combining active learning and spectral unmixing of hyperspectral data

    Science.gov (United States)

    Sun, Yanli; Zhang, Xia; Plaza, Antonio; Li, Jun; Dópido, Inmaculada; Liu, Yi

    2016-10-01

    Hyperspectral remote sensing allows for the detailed analysis of the surface of the Earth by providing high-dimensional images with hundreds of spectral bands. Hyperspectral image classification plays a significant role in hyperspectral image analysis and has been a very active research area in the last few years. In the context of hyperspectral image classification, supervised techniques (which have achieved wide acceptance) must address a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. Semi-supervised learning offers an effective solution that can take advantage of both unlabeled and a small amount of labeled samples. Spectral unmixing is another widely used technique in hyperspectral image analysis, developed to retrieve pure spectral components and determine their abundance fractions in mixed pixels. In this work, we propose a method to perform semi-supervised hyperspectral image classification by combining the information retrieved with spectral unmixing and classification. Two kinds of samples that are highly mixed in nature are automatically selected, aiming at finding the most informative unlabeled samples. One kind is given by the samples minimizing the distance between the first two most probable classes by calculating the difference between the two highest abundances. Another kind is given by the samples minimizing the distance between the most probable class and the least probable class, obtained by calculating the difference between the highest and lowest abundances. The effectiveness of the proposed method is evaluated using a real hyperspectral data set collected by the airborne visible infrared imaging spectrometer (AVIRIS) over the Indian Pines region in Northwestern Indiana. In the

  19. Literature mining of protein-residue associations with graph rules learned through distant supervision

    Directory of Open Access Journals (Sweden)

    Ravikumar KE

    2012-10-01

    Full Text Available Abstract Background We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model. Results The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods. Conclusions The primary contributions of this work are to (1 demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2 show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature.

  20. Semi-Supervised Learning Techniques in AO Applications: A Novel Approach To Drift Counteraction

    Science.gov (United States)

    De Vito, S.; Fattoruso, G.; Pardo, M.; Tortorella, F.; Di Francia, G.

    2011-11-01

    In this work we proposed and tested the use of SSL techniques in the AO domain. The SSL characteristics have been exploited to reduce the need for costly supervised samples and the effects of time dependant drift of state-of-the-art statistical learning approaches. For this purpose, an on-field recorded one year long atmospheric pollution dataset has been used. The semi-supervised approach benefitted from the use of updated unlabeled samples, adapting its knowledge to the slowly changing drift effects. We expect that semi-supervised learning can provide significant advantages to the performance of sensor fusion subsystems in artificial olfaction exhibiting an interesting drift counteraction effect.

  1. Pedagogical entrepreneurship in learning tasks

    Directory of Open Access Journals (Sweden)

    Marit Engum Hansen

    2016-11-01

    Full Text Available Background: The action plan "Entrepreneurship in Education – from primary to higher education "(2009-2014, proposed to establish a site for digital learning materials within entrepreneurship in basic education. PedEnt (Pedagogical Entrepreneurship was launched in autumn of 2014, and both the authors have contributed to the professional development of the site. Two of the learning assignments published on PedEnt constitute the research objects of this study. Methods: Based on pedagogical entrepreneurship we present a case study of learning work carried out by students at lower and upper secondary level. Using an analysis of assignment texts and as well as with video recordings we have identified the characteristics of entrepreneurial learning methods as they were expressed through each case. Results: The analysis showed that learning assignments can be characterized as entrepreneurial because they promoted the actor role and creativity of the students. We found that the relationship between the relevance of the assignments and the context in which they are given pose an important prerequisite for the students in order to experience the learning work as meaningful. Conclusions: Entrepreneurial learning methods challenge the traditional view that theory tends to take primacy over practice. To orient learning assignments within relevant contexts gives students opportunities to experience by themselves the need for increased knowledge.

  2. Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

    Science.gov (United States)

    Jamal, Wasifa; Das, Saptarshi; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

    2014-08-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

  3. Supervised learning of short and high-dimensional temporal sequences for life science measurements

    CERN Document Server

    Schleif, F -M; Hammer, B

    2011-01-01

    The analysis of physiological processes over time are often given by spectrometric or gene expression profiles over time with only few time points but a large number of measured variables. The analysis of such temporal sequences is challenging and only few methods have been proposed. The information can be encoded time independent, by means of classical expression differences for a single time point or in expression profiles over time. Available methods are limited to unsupervised and semi-supervised settings. The predictive variables can be identified only by means of wrapper or post-processing techniques. This is complicated due to the small number of samples for such studies. Here, we present a supervised learning approach, termed Supervised Topographic Mapping Through Time (SGTM-TT). It learns a supervised mapping of the temporal sequences onto a low dimensional grid. We utilize a hidden markov model (HMM) to account for the time domain and relevance learning to identify the relevant feature dimensions mo...

  4. Combining theories to reach multi-faceted insights into learning opportunities in doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Rump, Camilla Østerberg

    in science learning; conceptual change, socio-constructivism and post-structuralism. In the present study we employ variation theory (Marton & Tsui, 2004) to study the individual acquisition perspective, what Zembylas terms conceptual change. As for the post-structural perspective we employ positioning......The aim of this paper is to illustrate how theories can be combined to explore opportunities for learning in doctoral supervision. While our earlier research into learning dynamics in doctoral supervision in life science research (Kobayashi, 2014) has focused on illustrating learning opportunities......-another when intertwining the analyses to get a multi-faceted insight into the phenomenon of learning to be a life science researcher. The data was derived from four observations of supervision of doctoral students in life science, each with a doctoral student and two supervisors. The storylines hypothesized...

  5. Divergent Perceptions of Telecollaborative Language Learning Tasks: Task-as-Workplan vs. Task-as-Process

    National Research Council Canada - National Science Library

    Dooly, Melinda

    2011-01-01

    ... place. The task design and its implementation are key elements for efficient language learning to develop--a carefully designed task or activity that requires off- and online co-construction of knowledge not only provides opportunities for target language practice, it also helps integrate language use as the means for shared knowledge-build...

  6. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Chihyun Park

    Full Text Available BACKGROUND: The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. RESULTS: In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. CONCLUSIONS: The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

  7. Weakly supervised learning of a classifier for unusual event detection.

    Science.gov (United States)

    Jäger, Mark; Knoll, Christian; Hamprecht, Fred A

    2008-09-01

    In this paper, we present an automatic classification framework combining appearance based features and hidden Markov models (HMM) to detect unusual events in image sequences. One characteristic of the classification task is that anomalies are rare. This reflects the situation in the quality control of industrial processes, where error events are scarce by nature. As an additional restriction, class labels are only available for the complete image sequence, since frame-wise manual scanning of the recorded sequences for anomalies is too expensive and should, therefore, be avoided. The proposed framework reduces the feature space dimension of the image sequences by employing subspace methods and encodes characteristic temporal dynamics using continuous hidden Markov models (CHMMs). The applied learning procedure is as follows. 1) A generative model for the regular sequences is trained (one-class learning). 2) The regular sequence model (RSM) is used to locate potentially unusual segments within error sequences by means of a change detection algorithm (outlier detection). 3) Unusual segments are used to expand the RSM to an error sequence model (ESM). The complexity of the ESM is controlled by means of the Bayesian Information Criterion (BIC). The likelihood ratio of the data given the ESM and the RSM is used for the classification decision. This ratio is close to one for sequences without error events and increases for sequences containing error events. Experimental results are presented for image sequences recorded from industrial laser welding processes. We demonstrate that the learning procedure can significantly reduce the user interaction and that sequences with error events can be found with a small false positive rate. It has also been shown that a modeling of the temporal dynamics is necessary to reach these low error rates.

  8. An efficient flow-based botnet detection using supervised machine learning

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2014-01-01

    Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper...... introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs...... to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates...

  9. Modeling Multiple Annotator Expertise in the Semi-Supervised Learning Scenario

    CERN Document Server

    Yan, Yan; Fung, Glenn; Dy, Jennifer

    2012-01-01

    Learning algorithms normally assume that there is at most one annotation or label per data point. However, in some scenarios, such as medical diagnosis and on-line collaboration,multiple annotations may be available. In either case, obtaining labels for data points can be expensive and time-consuming (in some circumstances ground-truth may not exist). Semi-supervised learning approaches have shown that utilizing the unlabeled data is often beneficial in these cases. This paper presents a probabilistic semi-supervised model and algorithm that allows for learning from both unlabeled and labeled data in the presence of multiple annotators. We assume that it is known what annotator labeled which data points. The proposed approach produces annotator models that allow us to provide (1) estimates of the true label and (2) annotator variable expertise for both labeled and unlabeled data. We provide numerical comparisons under various scenarios and with respect to standard semi-supervised learning. Experiments showed ...

  10. Multiclass Semi-Supervised Boosting and Similarity Learning

    NARCIS (Netherlands)

    Tanha, J.; Saberian, M.J.; van Someren, M.; Xiong, H.; Karypis, G.; Thuraisingham, B.; Cook, D.; Wu, X.

    2013-01-01

    In this paper, we consider the multiclass semi-supervised classification problem. A boosting algorithm is proposed to solve the multiclass problem directly. The proposed multiclass approach uses a new multiclass loss function, which includes two terms. The first term is the cost of the multiclass ma

  11. Learning to Teach: Teaching Internships in Counselor Education and Supervision

    Science.gov (United States)

    Hunt, Brandon; Gilmore, Genevieve Weber

    2011-01-01

    In an effort to ensure the efficacy of preparing emerging counselors in the field, CACREP standards require that by 2013 all core faculty at accredited universities have a doctorate in Counselor Education and Supervision. However, literature suggests that a disparity may exist in the preparation of counselor educators and the actual…

  12. Learning to Model Task-Oriented Attention

    Directory of Open Access Journals (Sweden)

    Xiaochun Zou

    2016-01-01

    Full Text Available For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene with a particular task. Models of saliency can be used to predict fixation locations, but a large body of previous saliency models focused on free-viewing task. They are based on bottom-up computation that does not consider task-oriented image semantics and often does not match actual eye movements. To address this problem, we collected eye tracking data of 11 subjects when they performed some particular search task in 1307 images and annotation data of 2,511 segmented objects with fine contours and 8 semantic attributes. Using this database as training and testing examples, we learn a model of saliency based on bottom-up image features and target position feature. Experimental results demonstrate the importance of the target information in the prediction of task-oriented visual attention.

  13. On the Task-based Collaborative Learning

    Institute of Scientific and Technical Information of China (English)

    曲囡囡; 马卓

    2008-01-01

    <正>Task-based language teaching(TBLT) has been a prevalent teaching practice in the TEFL field in the recent years and its momentum for striving to be the legitimate one has never ceased. The present study tries to provide a theoretical foundation for its application in the communicative learning approach of English as the second language(ESL),namely the collaborative learning mode.

  14. Leveraging Qualitative Reasoning to Learning Manipulation Tasks

    Directory of Open Access Journals (Sweden)

    Diedrich Wolter

    2015-07-01

    Full Text Available Learning and planning are powerful AI methods that exhibit complementary strengths. While planning allows goal-directed actions to be computed when a reliable forward model is known, learning allows such models to be obtained autonomously. In this paper we describe how both methods can be combined using an expressive qualitative knowledge representation. We argue that the crucial step in this integration is to employ a representation based on a well-defined semantics. This article proposes the qualitative spatial logic QSL, a representation that combines qualitative abstraction with linear temporal logic, allowing us to represent relevant information about the learning task, possible actions, and their consequences. Doing so, we empower reasoning processes to enhance learning performance beyond the positive effects of learning in abstract state spaces. Proof-of-concept experiments in two simulation environments show that this approach can help to improve learning-based robotics by quicker convergence and leads to more reliable action planning.

  15. Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management

    CERN Document Server

    Emtiyaz, Siavash; 10.4156/AISS.vol3.issue9.31

    2012-01-01

    Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential cus...

  16. Learning in a unidimensional absolute identification task.

    Science.gov (United States)

    Rouder, Jeffrey N; Morey, Richard D; Cowan, Nelson; Pfaltz, Monique

    2004-10-01

    We tested whether there is long-term learning in the absolute identification of line lengths. Line lengths are unidimensional stimuli, and there is a common belief that learning of these stimuli quickly reaches a low-level asymptote of about seven items and progresses no more. We show that this is not the case. Our participants served in a 1.5-h session each day for over a week. Although they did not achieve perfect performance, they continued to improve day by day throughout the week and eventually learned to distinguish between 12 and 20 line lengths. These results are in contrast to common characterizations of learning in absolute identification tasks with unidimensional stimuli. We suggest that this learning reflects improvement in short-term processing.

  17. Pre-trained Convolutional Networks and generative statiscial models: a study in semi-supervised learning

    OpenAIRE

    John Michael Salgado Cebola

    2016-01-01

    Comparative study between the performance of Convolutional Networks using pretrained models and statistical generative models on tasks of image classification in semi-supervised enviroments.Study of multiple ensembles using these techniques and generated data from estimated pdfs.Pretrained Convents, LDA, pLSA, Fisher Vectors, Sparse-coded SPMs, TSVMs being the key models worked upon.

  18. Affordance Analysis--Matching Learning Tasks with Learning Technologies

    Science.gov (United States)

    Bower, Matt

    2008-01-01

    This article presents a design methodology for matching learning tasks with learning technologies. First a working definition of "affordances" is provided based on the need to describe the action potentials of the technologies (utility). Categories of affordances are then proposed to provide a framework for analysis. Following this, a…

  19. Contributions to unsupervised and supervised learning with applications in digital image processing

    OpenAIRE

    2012-01-01

    311 p. : il. [EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digita...

  20. Contributions to unsupervised and supervised learning with applications in digital image processing

    OpenAIRE

    González Acuña, Ana Isabel

    2014-01-01

    311 p. : il. [EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digita...

  1. Semi-supervised learning and domain adaptation in natural language processing

    CERN Document Server

    Søgaard, Anders

    2013-01-01

    This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias.This book is intended to be both

  2. Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

    CERN Document Server

    Grüning, André

    2011-01-01

    Few algorithms for supervised training of spiking neural networks exist that can deal with patterns of multiple spikes, and their computational properties are largely unexplored. We demonstrate in a set of simulations that the ReSuMe learning algorithm can be successfully applied to layered neural networks. Input and output patterns are encoded as spike trains of multiple precisely timed spikes, and the network learns to transform the input trains into target output trains. This is done by combining the ReSuMe learning algorithm with multiplicative scaling of the connections of downstream neurons. We show in particular that layered networks with one hidden layer can learn the basic logical operations, including Exclusive-Or, while networks without hidden layer cannot, mirroring an analogous result for layered networks of rate neurons. While supervised learning in spiking neural networks is not yet fit for technical purposes, exploring computational properties of spiking neural networks advances our understand...

  3. Out-of-Sample Generalizations for Supervised Manifold Learning for Classification

    Science.gov (United States)

    Vural, Elif; Guillemot, Christine

    2016-03-01

    Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving way, while enhancing the separation between different classes in the learned embedding. Most nonlinear supervised manifold learning methods compute the embedding of the manifolds only at the initially available training points, while the generalization of the embedding to novel points, known as the out-of-sample extension problem in manifold learning, becomes especially important in classification applications. In this work, we propose a semi-supervised method for building an interpolation function that provides an out-of-sample extension for general supervised manifold learning algorithms studied in the context of classification. The proposed algorithm computes a radial basis function (RBF) interpolator that minimizes an objective function consisting of the total embedding error of unlabeled test samples, defined as their distance to the embeddings of the manifolds of their own class, as well as a regularization term that controls the smoothness of the interpolation function in a direction-dependent way. The class labels of test data and the interpolation function parameters are estimated jointly with a progressive procedure. Experimental results on face and object images demonstrate the potential of the proposed out-of-sample extension algorithm for the classification of manifold-modeled data sets.

  4. Task shifting-perception of stake holders about adequacy of training and supervision for community mental health workers in Ghana.

    Science.gov (United States)

    Agyapong, Vincent I O; Osei, Akwasi; Mcloughlin, Declan M; McAuliffe, Eilish

    2016-06-01

    There is growing interest in the effectiveness of task shifting as a strategy for addressing expanding health care challenges in settings with shortages of qualified health personnel. The aim of this study is to examine the perception of stakeholders about the adequacy of training, supervision and support offered to community mental health workers (CMHWs) in Ghana. To address this aim we designed and administered self-completed, semi-structured questionnaires adapted to three specific stakeholder groups in Ghana. The questionnaires were administered to 11 psychiatrists, 29 health policy implementers/coordinators and 164 CMHWs, across Ghana, including 71 (43.3%) Community Psychiatric Nurses (CPNs), 19 (11.6%) Clinical Psychiatric Officers (CPOs) and 74 (45.1%) Community Mental Health Officers (CMHOs). Almost all the stakeholders believed CMHWs in Ghana receive adequate training for the role they are expected to play although many identify some gaps in the training of these mental health workers for the expanded roles they actually play. There were statistically significant differences between the different CMHW groups and the types of in-service training they said they had attended, the frequency with which their work was supervised, and the frequency with which they received feedback from supervisors. CPOs were more likely to attend all the different kinds of in-service training than CMHOs and CPNs, while CMHOs were more likely than CPOs and CPNs to report that their work is never supervised or that they rarely or never receive feedback from supervisors. There was disparity between what CMHWs said were their experiences and the perception of policy makers with respect to the types of in-service training that is available to CMHWs. There is a need to review the task shifting arrangements, perhaps with a view to expanding it to include more responsibilities, and therefore review the curriculum of the training institution for CMHWs and also to offer them regular in

  5. Programming Tasks in E-Learning

    Directory of Open Access Journals (Sweden)

    Krzysztof Barteczko

    2012-06-01

    Full Text Available The article discusses the goals of teaching programing languages, kinds of programming tasks, evaluation criteria and methods for solutions checking. Many aspects of the assessments need, especially within e-learning framework, dedicated tools for solutions checking. Considered are the possibilities and methods for their automatic application. Integration of automatic evaluation tools in a consistent system is proposed. Through the rich content of the interaction with students such a system would lead to increase of e-learning quality. Examples presented in this article apply to programs and tools for the Java platform.

  6. On Task-based English Learning Method

    Institute of Scientific and Technical Information of China (English)

    朱蕾

    2010-01-01

    @@ Task-Based learning(TBL)is becoming a catchword in English circles.The new national English Curricular Syllabus also recommends the use of the TBL approach in classroom teaching.The purpose of learning a foreign language is the most direct communicative in the target language,and speaking is the most direct communicative method.In recent years,with the publication of the New Curriculum Standard by the State Education Department,the teaching reform in middle and primary schools has been being implemented step by step.

  7. Supervised learning with decision tree-based methods in computational and systems biology.

    Science.gov (United States)

    Geurts, Pierre; Irrthum, Alexandre; Wehenkel, Louis

    2009-12-01

    At the intersection between artificial intelligence and statistics, supervised learning allows algorithms to automatically build predictive models from just observations of a system. During the last twenty years, supervised learning has been a tool of choice to analyze the always increasing and complexifying data generated in the context of molecular biology, with successful applications in genome annotation, function prediction, or biomarker discovery. Among supervised learning methods, decision tree-based methods stand out as non parametric methods that have the unique feature of combining interpretability, efficiency, and, when used in ensembles of trees, excellent accuracy. The goal of this paper is to provide an accessible and comprehensive introduction to this class of methods. The first part of the review is devoted to an intuitive but complete description of decision tree-based methods and a discussion of their strengths and limitations with respect to other supervised learning methods. The second part of the review provides a survey of their applications in the context of computational and systems biology.

  8. Re/Learning Student Teaching Supervision: A Co/Autoethnographic Self-Study

    Science.gov (United States)

    Butler, Brandon M.; Diacopoulos, Mark M.

    2016-01-01

    This article documents the critical friendship of an experienced teacher educator and a doctoral student through our joint exploration of student teaching supervision. By adopting a co/autoethnographic approach, we learned from biographical and contemporaneous critical incidents that informed short- and long-term practices. In particular, we…

  9. Undergraduate Internship Supervision in Psychology Departments: Use of Experiential Learning Best Practices

    Science.gov (United States)

    Bailey, Sarah F.; Barber, Larissa K.; Nelson, Videl L.

    2017-01-01

    This study examined trends in how psychology internships are supervised compared to current experiential learning best practices in the literature. We sent a brief online survey to relevant contact persons for colleges/universities with psychology departments throughout the United States (n = 149 responded). Overall, the majority of institutions…

  10. Multiclass semi-supervised learning for animal behavior recognition from accelerometer data

    NARCIS (Netherlands)

    Tanha, J.; van Someren, M.; de Bakker, M.; Bouten, W.; Shamoun-Baranes, J.; Afsarmanesh, H.

    2012-01-01

    In this paper we present a new Multiclass semi-supervised learning algorithm that uses a base classifier in combination with a similarity function applied to all data to find a classifier that maximizes the margin and consistency over all data. A novel multiclass loss function is presented and used

  11. Social media research: The application of supervised machine learning in organizational communication research

    NARCIS (Netherlands)

    van Zoonen, W.; van der Meer, T.G.L.A.

    2016-01-01

    Despite the online availability of data, analysis of this information in academic research is arduous. This article explores the application of supervised machine learning (SML) to overcome challenges associated with online data analysis. In SML classifiers are used to categorize and code binary dat

  12. Cost-conscious comparison of supervised learning algorithms over multiple data sets

    OpenAIRE

    Ulaş, Aydın; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem

    2012-01-01

    In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi(2)Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from "best" to "worst" where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi2Test generates orderings using pairwise...

  13. Developing a practice of supervision in university as a collective learning process

    DEFF Research Database (Denmark)

    Lund, Birthe; Jensen, Annie Aarup

    2009-01-01

    of the framework surrounding the supervision process, both as regards the students and the teachers; to de-privatize the problems encountered by the individual teacher during the supervision; to ensure that students would be able to graduate within the timeframe of the education (the institutional economic...... of creating a transformation in the sense that it may change from being a top-down project (instigated by the Faculty) and develop into being a bottom-up project. It may hold the potential for developing collective learning processes assuming that good structures and frameworks can be created, as well...

  14. Distributed Multi-Level Supervision to Effectively Monitor the Operations of a Fleet of Autonomous Vehicles in Agricultural Tasks

    Science.gov (United States)

    Conesa-Muñoz, Jesús; Gonzalez-de-Soto, Mariano; Gonzalez-de-Santos, Pablo; Ribeiro, Angela

    2015-01-01

    This paper describes a supervisor system for monitoring the operation of automated agricultural vehicles. The system analyses all of the information provided by the sensors and subsystems on the vehicles in real time and notifies the user when a failure or potentially dangerous situation is detected. In some situations, it is even able to execute a neutralising protocol to remedy the failure. The system is based on a distributed and multi-level architecture that divides the supervision into different subsystems, allowing for better management of the detection and repair of failures. The proposed supervision system was developed to perform well in several scenarios, such as spraying canopy treatments against insects and diseases and selective weed treatments, by either spraying herbicide or burning pests with a mechanical-thermal actuator. Results are presented for selective weed treatment by the spraying of herbicide. The system successfully supervised the task; it detected failures such as service disruptions, incorrect working speeds, incorrect implement states, and potential collisions. Moreover, the system was able to prevent collisions between vehicles by taking action to avoid intersecting trajectories. The results show that the proposed system is a highly useful tool for managing fleets of autonomous vehicles. In particular, it can be used to manage agricultural vehicles during treatment operations. PMID:25751079

  15. Distributed Multi-Level Supervision to Effectively Monitor the Operations of a Fleet of Autonomous Vehicles in Agricultural Tasks

    Directory of Open Access Journals (Sweden)

    Jesús Conesa-Muñoz

    2015-03-01

    Full Text Available This paper describes a supervisor system for monitoring the operation of automated agricultural vehicles. The system analyses all of the information provided by the sensors and subsystems on the vehicles in real time and notifies the user when a failure or potentially dangerous situation is detected. In some situations, it is even able to execute a neutralising protocol to remedy the failure. The system is based on a distributed and multi-level architecture that divides the supervision into different subsystems, allowing for better management of the detection and repair of failures. The proposed supervision system was developed to perform well in several scenarios, such as spraying canopy treatments against insects and diseases and selective weed treatments, by either spraying herbicide or burning pests with a mechanical-thermal actuator. Results are presented for selective weed treatment by the spraying of herbicide. The system successfully supervised the task; it detected failures such as service disruptions, incorrect working speeds, incorrect implement states, and potential collisions. Moreover, the system was able to prevent collisions between vehicles by taking action to avoid intersecting trajectories. The results show that the proposed system is a highly useful tool for managing fleets of autonomous vehicles. In particular, it can be used to manage agricultural vehicles during treatment operations.

  16. Supervised orthogonal discriminant subspace projects learning for face recognition.

    Science.gov (United States)

    Chen, Yu; Xu, Xiao-Hong

    2014-02-01

    In this paper, a new linear dimension reduction method called supervised orthogonal discriminant subspace projection (SODSP) is proposed, which addresses high-dimensionality of data and the small sample size problem. More specifically, given a set of data points in the ambient space, a novel weight matrix that describes the relationship between the data points is first built. And in order to model the manifold structure, the class information is incorporated into the weight matrix. Based on the novel weight matrix, the local scatter matrix as well as non-local scatter matrix is defined such that the neighborhood structure can be preserved. In order to enhance the recognition ability, we impose an orthogonal constraint into a graph-based maximum margin analysis, seeking to find a projection that maximizes the difference, rather than the ratio between the non-local scatter and the local scatter. In this way, SODSP naturally avoids the singularity problem. Further, we develop an efficient and stable algorithm for implementing SODSP, especially, on high-dimensional data set. Moreover, the theoretical analysis shows that LPP is a special instance of SODSP by imposing some constraints. Experiments on the ORL, Yale, Extended Yale face database B and FERET face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of SODSP.

  17. Developing a practice of supervision in university as a collective learning process

    DEFF Research Database (Denmark)

    Lund, Birthe; Jensen, Annie Aarup

    2009-01-01

    of the framework surrounding the supervision process, both as regards the students and the teachers; to de-privatize the problems encountered by the individual teacher during the supervision; to ensure that students would be able to graduate within the timeframe of the education (the institutional economic......The point of departure of the paper is a university pedagogical course established with the purpose of strengthening the university teachers’ competence regarding the supervision of students working on their master’s thesis. The purpose of the course is furthermore to ensure the improvement...... of creating a transformation in the sense that it may change from being a top-down project (instigated by the Faculty) and develop into being a bottom-up project. It may hold the potential for developing collective learning processes assuming that good structures and frameworks can be created, as well...

  18. Gene classification using parameter-free semi-supervised manifold learning.

    Science.gov (United States)

    Huang, Hong; Feng, Hailiang

    2012-01-01

    A new manifold learning method, called parameter-free semi-supervised local Fisher discriminant analysis (pSELF), is proposed to map the gene expression data into a low-dimensional space for tumor classification. Motivated by the fact that semi-supervised and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The proposed method preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, which can be computed efficiently by eigen decomposition. Experimental results on synthetic data and SRBCT, DLBCL, and Brain Tumor gene expression data sets demonstrate the effectiveness of the proposed method.

  19. Electroencephalographic Coherence and Learning: Distinct Patterns of Change during Word Learning and Figure Learning Tasks

    Science.gov (United States)

    Collins, Peter; Hogan, Michael; Kilmartin, Liam; Keane, Michael; Kaiser, Jochen; Fischer, Kurt

    2010-01-01

    One likely mechanism in learning new skills is change in synchronous connections between distributed neural networks, which can be measured by coherence analysis of electroencephalographic patterns. This study examined coherence changes during the learning of two tasks, a word association task and a figure association task. Although learning…

  20. A neural network multi-task learning approach to biomedical named entity recognition.

    Science.gov (United States)

    Crichton, Gamal; Pyysalo, Sampo; Chiu, Billy; Korhonen, Anna

    2017-08-15

    Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use them together to improve NER performance. To investigate this, we develop supervised, multi-task, convolutional neural network models and apply them to a large number of varied existing biomedical named entity datasets. Additionally, we investigated the effect of dataset size on performance in both single- and multi-task settings. We present a single-task model for NER, a Multi-output multi-task model and a Dependent multi-task model. We apply the three models to 15 biomedical datasets containing multiple named entities including Anatomy, Chemical, Disease, Gene/Protein and Species. Each dataset represent a task. The results from the single-task model and the multi-task models are then compared for evidence of benefits from Multi-task Learning. With the Multi-output multi-task model we observed an average F-score improvement of 0.8% when compared to the single-task model from an average baseline of 78.4%. Although there was a significant drop in performance on one dataset, performance improves significantly for five datasets by up to 6.3%. For the Dependent multi-task model we observed an average improvement of 0.4% when compared to the single-task model. There were no significant drops in performance on any dataset, and performance improves significantly for six datasets by up to 1.1%. The dataset size experiments found that as dataset size decreased, the multi-output model's performance increased compared to the single-task model's. Using 50, 25 and 10% of the training data resulted in an average drop of approximately 3.4, 8 and 16.7% respectively for the single-task model but approximately 0.2, 3.0 and 9.8% for the multi-task model. Our

  1. Facilitating the Learning Process in Design-Based Learning Practices: An Investigation of Teachers' Actions in Supervising Students

    Science.gov (United States)

    Gómez Puente, S. M.; van Eijck, M.; Jochems, W.

    2013-01-01

    Background: In research on design-based learning (DBL), inadequate attention is paid to the role the teacher plays in supervising students in gathering and applying knowledge to design artifacts, systems, and innovative solutions in higher education. Purpose: In this study, we examine whether teacher actions we previously identified in the DBL…

  2. Emotional Literacy Support Assistants' Views on Supervision Provided by Educational Psychologists: What EPs Can Learn from Group Supervision

    Science.gov (United States)

    Osborne, Cara; Burton, Sheila

    2014-01-01

    The Educational Psychology Service in this study has responsibility for providing group supervision to Emotional Literacy Support Assistants (ELSAs) working in schools. To date, little research has examined this type of inter-professional supervision arrangement. The current study used a questionnaire to examine ELSAs' views on the supervision…

  3. Extended apprenticeship learning in doctoral training and supervision - moving beyond 'cookbook recipes'

    DEFF Research Database (Denmark)

    Tanggaard, Lene; Wegener, Charlotte

    An apprenticeship perspective on learning in academia sheds light on the potential for mutual learning and production, and also reveals the diverse range of learning resources beyond the formal novice-–expert relationship. Although apprenticeship is a well-known concept in educational research......, in this case apprenticeship offers an innovative perspective on future practice and research in academia allowing more students access to high high-quality research training and giving supervisors a chance to combine their own research with their supervision obligations....

  4. Recent advances on techniques and theories of feedforward networks with supervised learning

    Science.gov (United States)

    Xu, Lei; Klasa, Stan

    1992-07-01

    The rediscovery and popularization of the back propagation training technique for multilayer perceptrons as well as the invention of the Boltzmann Machine learning algorithm has given a new boost to the study of supervised learning networks. In recent years, besides the widely spread applications and the various further improvements of the classical back propagation technique, many new supervised learning models, techniques as well as theories, have also been proposed in a vast number of publications. This paper tries to give a rather systematical review on the recent advances on supervised learning techniques and theories for static feedforward networks. We summarize a great number of developments into four aspects: (1) Various improvements and variants made on the classical back propagation techniques for multilayer (static) perceptron nets, for speeding up training, avoiding local minima, increasing the generalization ability, as well as for many other interesting purposes. (2) A number of other learning methods for training multilayer (static) perceptron, such as derivative estimation by perturbation, direct weight update by perturbation, genetic algorithms, recursive least square estimate and extended Kalman filter, linear programming, the policy of fixing one layer while updating another, constructing networks by converting decision tree classifiers, and others. (3) Various other feedforward models which are also able to implement function approximation, probability density estimation and classification, including various models of basis function expansion (e.g., radial basis functions, restricted coulomb energy, multivariate adaptive regression splines, trigonometric and polynomial bases, projection pursuit, basis function tree, and may others), and several other supervised learning models. (4) Models with complex structures, e.g., modular architecture, hierarchy architecture, and others. (5) A number of theoretical issues involving the universal

  5. Assessing Miniaturized Sensor Performance using Supervised Learning, with Application to Drug and Explosive Detection

    DEFF Research Database (Denmark)

    Alstrøm, Tommy Sonne

    of sensors, as the sensors are designed to provide robust and reliable measurements. That means, the sensors are designed to have repeated measurement clusters. Sensor fusion is presented for the sensor based on chemoselective compounds. An array of color changing compounds are handled and in unity they make......This Ph.D. thesis titled “Assessing Miniaturized Sensor Performance using Supervised Learning, with Application to Drug and Explosive Detection” is a part of the strategic research project “Miniaturized sensors for explosives detection in air” funded by the Danish Agency for Science and Technology...... before the sensor responses can be applied to supervised learning algorithms. The technologies used for sensing consist of Calorimetry, Cantilevers, Chemoselective compounds, Quartz Crystal Microbalance and Surface Enhanced Raman Scattering. Each of the sensors have their own strength and weaknesses...

  6. Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.

    Science.gov (United States)

    Heck, Gabriela S; Pintro, Val O; Pereira, Richard R; de Ávila, Mauricio B; Levin, Nayara M B; de Azevedo, Walter F

    2017-01-01

    Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. Our goal here is to review recent developments in the application of machine learning methods to predict ligand-binding affinity. We focus our review on the application of computational methods to predict binding affinity for protein targets. In addition, we also describe the major available databases for experimental binding constants and protein structures. Furthermore, we explain the most successful methods to evaluate the predictive power of scoring functions. Association of structural information with ligand-binding affinity makes it possible to generate scoring functions targeted to a specific biological system. Through regression analysis, this data can be used as a base to generate mathematical models to predict ligandbinding affinities, such as inhibition constant, dissociation constant and binding energy. Experimental biophysical techniques were able to determine the structures of over 120,000 macromolecules. Considering also the evolution of binding affinity information, we may say that we have a promising scenario for development of scoring functions, making use of machine learning techniques. Recent developments in this area indicate that building scoring functions targeted to the biological systems of interest shows superior predictive performance, when compared with other approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  7. Integrating learning assessment and supervision in a competency framework for clinical workplace education.

    Science.gov (United States)

    Embo, M; Driessen, E; Valcke, M; van der Vleuten, C P M

    2015-02-01

    Although competency-based education is well established in health care education, research shows that the competencies do not always match the reality of clinical workplaces. Therefore, there is a need to design feasible and evidence-based competency frameworks that fit the workplace reality. This theoretical paper outlines a competency-based framework, designed to facilitate learning, assessment and supervision in clinical workplace education. Integration is the cornerstone of this holistic competency framework.

  8. Task Design in Videoconferencing-Supported Distance Language Learning

    Science.gov (United States)

    Wang, Yuping

    2007-01-01

    This article addresses a pervasive need in the area of videoconference-supported distance language learning: task design. On the basis of Chapelle's (2001) criteria for CALL task appropriateness, this article proposes a set of criteria for evaluating videoconferencing-based tasks which examine such aspects of a task as practicality,…

  9. Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid Semi-Supervised Classifier Learning

    Directory of Open Access Journals (Sweden)

    Liang Ding

    2007-11-01

    Full Text Available Wireless multimedia sensor networks (WMSN have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This paper proposes acollaborative semi-supervised classifier learning algorithm to achieve durative onlinelearning for support vector machine (SVM based robust target classification. The proposedalgorithm incrementally carries out the semi-supervised classifier learning process inhierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computingparadigm. For decreasing the energy consumption and improving the performance, somemetrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes,and a sensor node selection strategy is also proposed to reduce the impact of inevitablemissing detection and false detection. With the ant optimization routing, the learningprocess is implemented with the selected sensor nodes, which can decrease the energyconsumption. Experimental results demonstrate that the collaborative hybrid semi-supervised classifier learning algorithm can effectively implement target classification inhierarchical WMSN. It has outstanding performance in terms of energy efficiency and timecost, which verifies the effectiveness of the sensor nodes selection and ant optimizationrouting.

  10. Clinical learning environment, supervision and nurse teacher evaluation scale: psychometric evaluation of the Swedish version.

    Science.gov (United States)

    Johansson, Unn-Britt; Kaila, Päivi; Ahlner-Elmqvist, Marianne; Leksell, Janeth; Isoaho, Hannu; Saarikoski, Mikko

    2010-09-01

    This article is a report of the development and psychometric testing of the Swedish version of the Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale. To achieve quality assurance, collaboration between the healthcare and nursing systems is a pre-requisite. Therefore, it is important to develop a tool that can measure the quality of clinical education. The Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale is a previously validated instrument, currently used in several universities across Europe. The instrument has been suggested for use as part of quality assessment and evaluation of nursing education. The scale was translated into Swedish from the English version. Data were collected between March 2008 and May 2009 among nursing students from three university colleges, with 324 students completing the questionnaire. Exploratory factor analysis was performed on the 34-item scale to determine construct validity and Cronbach's alpha was used to measure the internal consistency. The five sub-dimensions identified in the original scale were replicated in the exploratory factor analysis. The five factors had explanation percentages of 60.2%, which is deemed sufficient. Cronbach's alpha coefficient for the total scale was 0.95, and varied between 0.96 and 0.75 within the five sub-dimensions. The Swedish version of Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale has satisfactory psychometric properties and could be a useful quality instrument in nursing education. However, further investigation is required to develop and evaluate the questionnaire.

  11. Fall detection using supervised machine learning algorithms: A comparative study

    KAUST Repository

    Zerrouki, Nabil

    2017-01-05

    Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.

  12. A Supervised Learning Approach to Search of Definitions

    Institute of Scientific and Technical Information of China (English)

    Jun Xu; Yun-Bo Cao; Hang Li; Min Zhao; Ya-Lou Huang

    2006-01-01

    This paper addresses the issue of search of definitions. Specifically, for a given term, we are to find out its definition candidates and rank the candidates according to their likelihood of being good definitions. This is in contrast to the traditional methods of either generating a single combined definition or outputting all retrieved definitions. Definition ranking is essential for tasks. A specification for judging the goodness of a definition is given. In the specification, a definition is categorized into one of the three levels: good definition, indifferent definition, or bad definition. Methods of performing definition ranking are also proposed in this paper, which formalize the problem as either classification or ordinal regression.We employ SVM (Support Vector Machines) as the classification model and Ranking SVM as the ordinal regression model respectively, and thus they rank definition candidates according to their likelihood of being good definitions. Features for constructing the SVM and Ranking SVM models are defined, which represent the characteristics of terms, definition candidate, and their relationship. Experimental results indicate that the use of SVM and Ranking SVM can significantly outperform the baseline methods such as heuristic rules, the conventional information retrieval-Okapi, or SVM regression.This is true when both the answers are paragraphs and they are sentences. Experimental results also show that SVM or Ranking SVM models trained in one domain can be adapted to another domain, indicating that generic models for definition ranking can be constructed.

  13. Efficient dynamic graph construction for inductive semi-supervised learning.

    Science.gov (United States)

    Dornaika, F; Dahbi, R; Bosaghzadeh, A; Ruichek, Y

    2017-10-01

    Most of graph construction techniques assume a transductive setting in which the whole data collection is available at construction time. Addressing graph construction for inductive setting, in which data are coming sequentially, has received much less attention. For inductive settings, constructing the graph from scratch can be very time consuming. This paper introduces a generic framework that is able to make any graph construction method incremental. This framework yields an efficient and dynamic graph construction method that adds new samples (labeled or unlabeled) to a previously constructed graph. As a case study, we use the recently proposed Two Phase Weighted Regularized Least Square (TPWRLS) graph construction method. The paper has two main contributions. First, we use the TPWRLS coding scheme to represent new sample(s) with respect to an existing database. The representative coefficients are then used to update the graph affinity matrix. The proposed method not only appends the new samples to the graph but also updates the whole graph structure by discovering which nodes are affected by the introduction of new samples and by updating their edge weights. The second contribution of the article is the application of the proposed framework to the problem of graph-based label propagation using multiple observations for vision-based recognition tasks. Experiments on several image databases show that, without any significant loss in the accuracy of the final classification, the proposed dynamic graph construction is more efficient than the batch graph construction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Clinical learning environment and supervision of international nursing students: A cross-sectional study.

    Science.gov (United States)

    Mikkonen, Kristina; Elo, Satu; Miettunen, Jouko; Saarikoski, Mikko; Kääriäinen, Maria

    2017-05-01

    Previously, it has been shown that the clinical learning environment causes challenges for international nursing students, but there is a lack of empirical evidence relating to the background factors explaining and influencing the outcomes. To describe international and national students' perceptions of their clinical learning environment and supervision, and explain the related background factors. An explorative cross-sectional design was used in a study conducted in eight universities of applied sciences in Finland during September 2015-May 2016. All nursing students studying English language degree programs were invited to answer a self-administered questionnaire based on both the clinical learning environment, supervision and nurse teacher scale and Cultural and Linguistic Diversity scale with additional background questions. Participants (n=329) included international (n=231) and Finnish (n=98) nursing students. Binary logistic regression was used to identify background factors relating to the clinical learning environment and supervision. International students at a beginner level in Finnish perceived the pedagogical atmosphere as worse than native speakers. In comparison to native speakers, these international students generally needed greater support from the nurse teacher at their university. Students at an intermediate level in Finnish reported two times fewer negative encounters in cultural diversity at their clinical placement than the beginners. To facilitate a successful learning experience, international nursing students require a sufficient level of competence in the native language when conducting clinical placements. Educational interventions in language education are required to test causal effects on students' success in the clinical learning environment. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. On the contingent nature of language‐learning tasks

    OpenAIRE

    Hellermann, John; Pekarek Doehler, Simona

    2013-01-01

    Using methods from conversation analysis, this paper explores ways that teacher‐designed language‐learning task interactions can vary in their performance due to the nature of face‐to‐face interaction. The analysis describes three task interactions from language‐learning classrooms, showing how the contingencies that are necessitated by learners working in small groups provide for different task performance as well as different potentials for language learning. The video‐recorded interactions...

  16. Learning a Markov Logic network for supervised gene regulatory network inference.

    Science.gov (United States)

    Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

    2013-09-12

    Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

  17. Automated labeling of cancer textures in larynx histopathology slides using quasi-supervised learning.

    Science.gov (United States)

    Onder, Devrim; Sarioglu, Sulen; Karacali, Bilge

    2014-12-01

    To evaluate the performance of a quasi-supervised statistical learning algorithm, operating on datasets having normal and neoplastic tissues, to identify larynx squamous cell carcinomas. Furthermore, cancer texture separability measures against normal tissues are to be developed and compared either for colorectal or larynx tissues. Light microscopic digital images from histopathological sections were obtained from laryngectomy materials including squamous cell carcinoma and nonneoplastic regions. The texture features were calculated by using co-occurrence matrices and local histograms. The texture features were input to the quasi-supervised learning algorithm. Larynx regions containing squamous cell carcinomas were accurately identified, having false and true positive rates up to 21% and 87%, respectively. Larynx squamous cell carcinoma versus normal tissue texture separability measures were higher than colorectal adenocarcinoma versus normal textures for the colorectal database. Furthermore, the resultant labeling performances for all larynx datasets are higher than or equal to that of colorectal datasets. The results in larynx datasets, in comparison with the former colorectal study, suggested that quasi-supervised texture classification is to be a helpful method in histopathological image classification and analysis.

  18. Musical Instrument Classification Based on Nonlinear Recurrence Analysis and Supervised Learning

    Directory of Open Access Journals (Sweden)

    R.Rui

    2013-04-01

    Full Text Available In this paper, the phase space reconstruction of time series produced by different instruments is discussed based on the nonlinear dynamic theory. The dense ratio, a novel quantitative recurrence parameter, is proposed to describe the difference of wind instruments, stringed instruments and keyboard instruments in the phase space by analyzing the recursive property of every instrument. Furthermore, a novel supervised learning algorithm for automatic classification of individual musical instrument signals is addressed deriving from the idea of supervised non-negative matrix factorization (NMF algorithm. In our approach, the orthogonal basis matrix could be obtained without updating the matrix iteratively, which NMF is unable to do. The experimental results indicate that the accuracy of the proposed method is improved by 3% comparing with the conventional features in the individual instrument classification.

  19. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

    Directory of Open Access Journals (Sweden)

    Hua Wen-qiang

    2015-02-01

    Full Text Available In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM. Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

  20. Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

    Directory of Open Access Journals (Sweden)

    Ivana Šemanjski

    2015-12-01

    Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

  1. New supervised learning theory applied to cerebellar modeling for suppression of variability of saccade end points.

    Science.gov (United States)

    Fujita, Masahiko

    2013-06-01

    A new supervised learning theory is proposed for a hierarchical neural network with a single hidden layer of threshold units, which can approximate any continuous transformation, and applied to a cerebellar function to suppress the end-point variability of saccades. In motor systems, feedback control can reduce noise effects if the noise is added in a pathway from a motor center to a peripheral effector; however, it cannot reduce noise effects if the noise is generated in the motor center itself: a new control scheme is necessary for such noise. The cerebellar cortex is well known as a supervised learning system, and a novel theory of cerebellar cortical function developed in this study can explain the capability of the cerebellum to feedforwardly reduce noise effects, such as end-point variability of saccades. This theory assumes that a Golgi-granule cell system can encode the strength of a mossy fiber input as the state of neuronal activity of parallel fibers. By combining these parallel fiber signals with appropriate connection weights to produce a Purkinje cell output, an arbitrary continuous input-output relationship can be obtained. By incorporating such flexible computation and learning ability in a process of saccadic gain adaptation, a new control scheme in which the cerebellar cortex feedforwardly suppresses the end-point variability when it detects a variation in saccadic commands can be devised. Computer simulation confirmed the efficiency of such learning and showed a reduction in the variability of saccadic end points, similar to results obtained from experimental data.

  2. [Connectionist models of social learning: a case of learning by observing a simple task].

    Science.gov (United States)

    Paignon, A; Desrichard, O; Bollon, T

    2004-03-01

    This article proposes a connectionist model of the social learning theory developed by Bandura (1977). The theory posits that an individual in an interactive situation is capable of learning new behaviours merely by observing them in others. Such learning is acquired through an initial phase in which the individual memorizes what he has observed (observation phase), followed by a second phase where he puts the recorded observations to use as a guide for adjusting his own behaviour (reproduction phase). We shall refer to the two above-mentioned phases to demonstrate that it is conceivable to simulate learning by observation otherwise than through the recording of perceived information using symbolic representation. To this end we shall rely on the formalism of ecological neuron networks (Parisi, Cecconi, & Nolfi, 1990) to implement an agent provided with the major processes identified as essential to learning through observation. The connectionist model so designed shall implement an agent capable of recording perceptive information and producing motor behaviours. The learning situation we selected associates an agent demonstrating goal-achievement behaviour and an observer agent learning the same behaviour by observation. Throughout the acquisition phase, the demonstrator supervises the observer's learning process based on association between spatial information (input) and behavioural information (output). Representation thus constructed then serves as an adjustment guide during the production phase, involving production by the observer of a sequence of actions which he compares to the representation stored in distributed form as constructed through observation. An initial simulation validates model architecture by confirming the requirement for both phases identified in the literature (Bandura, 1977) to simulate learning through observation. The representation constructed over the observation phase evidences acquisition of observed behaviours, although this phase

  3. Asymmetrical learning between a tactile and visual serial RT task

    NARCIS (Netherlands)

    Abrahamse, E.L.; van der Lubbe, Robert Henricus Johannes; Verwey, Willem B.

    2007-01-01

    According to many researchers, implicit learning in the serial reaction-time task is predominantly motor based and therefore should be independent of stimulus modality. Previous research on the task, however, has focused almost completely on the visual domain. Here we investigated sequence learning

  4. Applications of Task-Based Learning in TESOL

    Science.gov (United States)

    Shehadeh, Ali, Ed.; Coombe, Christine, Ed.

    2010-01-01

    Why are many teachers around the world moving toward task-based learning (TBL)? This shift is based on the strong belief that TBL facilitates second language acquisition and makes second language learning and teaching more principled and effective. Based on insights gained from using tasks as research tools, this volume shows how teachers can use…

  5. Context dependent learning in the serial RT task

    NARCIS (Netherlands)

    Abrahamse, Elger L.; Verwey, Willem B.

    2007-01-01

    This study investigated the development of contextual dependencies for sequential perceptual-motor learning on static features in the learning environment. In three experiments we assessed the eVect of manipulating task irrelevant static context features in a serial reaction-time task. Experiment 1

  6. DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

    Science.gov (United States)

    Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

    2015-12-01

    Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

  7. Exploring Open-Ended Tasks as Teacher Learning

    Science.gov (United States)

    Sullivan, Peter; Griffioen, Mel; Gray, Hayley; Powers, Chris

    2009-01-01

    The Task Types and Mathematics Learning project is investigating the opportunities and constraints that teachers experience when using particular types of mathematics tasks. Some assumptions underlying this aspect of the project are: (1) that teachers seeking a balanced curriculum choose to use a mix of types of tasks; (2) open-ended questions…

  8. Autonomous Inter-Task Transfer in Reinforcement Learning Domains

    Science.gov (United States)

    2008-08-01

    because it is only learning the source task(s) to assist learning in the target task.4 This scenario can be thought of as expressing an “ engineering ...available actions. Skills are extracted using the ILP engine Aleph [Srinivasan, 2001] by using the F1 score (the harmonic mean of precision and recall). These...Aamodt and Enric Plaza. Case-based reasoning: Foundational issues, methodological variations, and system approaches, 1994. Mazda Ahmadi, Matthew E

  9. Virtual Calibration of Cosmic Ray Sensor: Using Supervised Ensemble Machine Learning

    Directory of Open Access Journals (Sweden)

    Ritaban Dutta

    2013-09-01

    Full Text Available In this paper an ensemble of supervised machine learning methods has been investigated to virtually and dynamically calibrate the cosmic ray sensors measuring area wise bulk soil moisture. Main focus of this study was to find an alternative to the currently available field calibration method; based on expensive and time consuming soil sample collection methodology. Data from the Australian Water Availability Project (AWAP database was used as independent soil moisture ground truth and results were compared against the conventionally estimated soil moisture using a Hydroinnova CRS-1000 cosmic ray probe deployed in Tullochgorum, Australia. Prediction performance of a complementary ensemble of four supervised estimators, namely Sugano type Adaptive Neuro-Fuzzy Inference System (S-ANFIS, Cascade Forward Neural Network (CFNN, Elman Neural Network (ENN and Learning Vector Quantization Neural Network (LVQN was evaluated using training and testing paradigms. An AWAP trained ensemble of four estimators was able to predict bulk soil moisture directly from cosmic ray neutron counts with 94.4% as best accuracy. The ensemble approach outperformed the individual performances from these networks. This result proved that an ensemble machine learning based paradigm could be a valuable alternative data driven calibration method for cosmic ray sensors against the current expensive and hydrological assumption based field calibration method.

  10. Test-retest reliability of the Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T) scale.

    Science.gov (United States)

    Gustafsson, Margareta; Blomberg, Karin; Holmefur, Marie

    2015-07-01

    The Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T) scale evaluates the student nurses' perception of the learning environment and supervision within the clinical placement. It has never been tested in a replication study. The aim of the present study was to evaluate the test-retest reliability of the CLES + T scale. The CLES + T scale was administered twice to a group of 42 student nurses, with a one-week interval. Test-retest reliability was determined by calculations of Intraclass Correlation Coefficients (ICCs) and weighted Kappa coefficients. Standard Error of Measurements (SEM) and Smallest Detectable Difference (SDD) determined the precision of individual scores. Bland-Altman plots were created for analyses of systematic differences between the test occasions. The results of the study showed that the stability over time was good to excellent (ICC 0.88-0.96) in the sub-dimensions "Supervisory relationship", "Pedagogical atmosphere on the ward" and "Role of the nurse teacher". Measurements of "Premises of nursing on the ward" and "Leadership style of the manager" had lower but still acceptable stability (ICC 0.70-0.75). No systematic differences occurred between the test occasions. This study supports the usefulness of the CLES + T scale as a reliable measure of the student nurses' perception of the learning environment within the clinical placement at a hospital.

  11. Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Zhiling Guo

    2016-03-01

    Full Text Available In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost and convolutional neural networks (CNN. To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%.

  12. Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening.

    Science.gov (United States)

    Situ, Ning; Yuan, Xiaojing; Zouridakis, George

    2011-01-01

    In typical classification problems, high level concept features provided by a domain expert are usually available during classifier training but not during its deployment. We address this problem from a multitask learning (MTL) perspective by treating these features as auxiliary learning tasks. Previous efforts in MTL have mostly assumed that all tasks have the same input space. However, auxiliary tasks can have different input spaces, since their learning targets are different. Thus, to handle cases with heterogeneous input, in this paper we present a newly developed model using heterogeneous auxiliary tasks to help main task learning. First, we formulate a convex optimization problem for the proposed model, and then, we analyze its hypothesis class and derive true risk bounds. Finally, we compare the proposed model with other relevant methods when applied to the problem of skin cancer screening and public datasets. Our results show that the performance of the proposed method is highly competitive compared to other relevant methods.

  13. Algorithm of Supervised Learning on Outlier Manifold%有监督的噪音流形学习算法

    Institute of Scientific and Technical Information of China (English)

    黄添强; 李凯; 郑之

    2011-01-01

    流形学习算法是维度约简与数据可视化领域的重要工具,提高算法的效率与健壮性对其实际应用有积极意义.经典的流形学习算法普遍的对噪音点较为敏感,现有的改进算法尚存在不足.本文提出一种基于监督学习与核函数的健壮流形学习算法,把核方法与监督学习引入降维过程,利用已知标签数据信息与核函数特性,使得同类样本变得紧密,不同类样本变成分散,提高后续分类任务的效果,降低算法对流形上噪音的敏感性.在UCI数据与白血病拉曼光谱数据上的实验表明本文改进的算法具有更高的抗噪性.%Manifold learning algorithm is an important tool in the field of dimension reduction and data visualization. Improving the algorithm's efficiency and robustness is of positive significance to its practical application. Classical manifold learning algorithm is sensitive to noise points,and its improved algorithms have been imperfect. This paper presents a robust manifold learning algorithm based on supervised learning and kernel function. It introduces nuclear methods and supervised learning into the dimensionality reduction ,and takes full advantage of the label of some data and the property of kernel function. The proposed algorithm can make close and same types of samples and distribute different types of samples,thus to improves the effect of the classification task and reduce the noise sensitivity of outliers on manifold. The experiments on the UCI data and Raman data of leukemia reveal that the algorithm has better noise immunity.

  14. Performance of machine learning methods for classification tasks

    OpenAIRE

    B. Krithika; Dr. V. Ramalingam; Rajan, K

    2013-01-01

    In this paper, the performance of various machine learning methods on pattern classification and recognition tasks are proposed. The proposed method for evaluating performance will be based on the feature representation, feature selection and setting model parameters. The nature of the data, the methods of feature extraction and feature representation are discussed. The results of the Machine Learning algorithms on the classification task are analysed. The performance of Machine Learning meth...

  15. Collaborative Tasks in Wiki-Based Environment in EFL Learning

    Science.gov (United States)

    Zou, Bin; Wang, Dongshuo; Xing, Minjie

    2016-01-01

    Wikis provide users with opportunities to post and edit messages to collaborate in the language learning process. Many studies have offered findings to show positive impact of Wiki-based language learning for learners. This paper explores the effect of collaborative task in error correction for English as a Foreign Language learning in an online…

  16. Multi-Modal Curriculum Learning for Semi-Supervised Image Classification.

    Science.gov (United States)

    Gong, Chen; Tao, Dacheng; Maybank, Stephen J; Liu, Wei; Kang, Guoliang; Yang, Jie

    2016-07-01

    Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.

  17. A supervised machine learning estimator for the non-linear matter power spectrum - SEMPS

    CERN Document Server

    Mohammed, Irshad

    2015-01-01

    In this article, we argue that models based on machine learning (ML) can be very effective in estimating the non-linear matter power spectrum ($P(k)$). We employ the prediction ability of the supervised ML algorithms to build an estimator for the $P(k)$. The estimator is trained on a set of cosmological models, and redshifts for which the $P(k)$ is known, and it learns to predict $P(k)$ for any other set. We review three ML algorithms -- Random Forest, Gradient Boosting Machines, and K-Nearest Neighbours -- and investigate their prime parameters to optimize the prediction accuracy of the estimator. We also compute an optimal size of the training set, which is realistic enough, and still yields high accuracy. We find that, employing the optimal values of the internal parameters, a set of $50-100$ cosmological models is enough to train the estimator that can predict the $P(k)$ for a wide range of cosmological models, and redshifts. Using this configuration, we build a blackbox -- Supervised Estimator for Matter...

  18. AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements.

    Science.gov (United States)

    Resheff, Yehezkel S; Rotics, Shay; Harel, Roi; Spiegel, Orr; Nathan, Ran

    2014-01-01

    The study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Biologgers with Acceleration (ACC) recordings are becoming increasingly popular in the fields of animal behavior and movement ecology, for estimating energy expenditure and identifying behavior, with prospects for other potential uses as well. Supervised learning of behavioral modes from acceleration data has shown promising results in many species, and for a diverse range of behaviors. However, broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach. This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data. Here we present a free-access python-based web application called AcceleRater, for rapidly training, visualizing and using models for supervised learning of behavioral modes from ACC measurements. We introduce AcceleRater, and illustrate its successful application for classifying vulture behavioral modes from acceleration data obtained from free-ranging vultures. The seven models offered in the AcceleRater application achieved overall accuracy of between 77.68% (Decision Tree) and 84.84% (Artificial Neural Network), with a mean overall accuracy of 81.51% and standard deviation of 3.95%. Notably, variation in performance was larger between behavioral modes than between models. AcceleRater provides the means to identify animal behavior, offering a user-friendly tool for ACC-based behavioral annotation, which will be dynamically upgraded and maintained.

  19. A Multi-Task Learning Framework for Head Pose Estimation under Target Motion.

    Science.gov (United States)

    Yan, Yan; Ricci, Elisa; Subramanian, Ramanathan; Liu, Gaowen; Lanz, Oswald; Sebe, Nicu

    2016-06-01

    Recently, head pose estimation (HPE) from low-resolution surveillance data has gained in importance. However, monocular and multi-view HPE approaches still work poorly under target motion, as facial appearance distorts owing to camera perspective and scale changes when a person moves around. To this end, we propose FEGA-MTL, a novel framework based on Multi-Task Learning (MTL) for classifying the head pose of a person who moves freely in an environment monitored by multiple, large field-of-view surveillance cameras. Upon partitioning the monitored scene into a dense uniform spatial grid, FEGA-MTL simultaneously clusters grid partitions into regions with similar facial appearance, while learning region-specific head pose classifiers. In the learning phase, guided by two graphs which a-priori model the similarity among (1) grid partitions based on camera geometry and (2) head pose classes, FEGA-MTL derives the optimal scene partitioning and associated pose classifiers. Upon determining the target's position using a person tracker at test time, the corresponding region-specific classifier is invoked for HPE. The FEGA-MTL framework naturally extends to a weakly supervised setting where the target's walking direction is employed as a proxy in lieu of head orientation. Experiments confirm that FEGA-MTL significantly outperforms competing single-task and multi-task learning methods in multi-view settings.

  20. Semi-supervised analysis of human brain tumours from partially labeled MRS information, using manifold learning models.

    Science.gov (United States)

    Cruz-Barbosa, Raúl; Vellido, Alfredo

    2011-02-01

    Medical diagnosis can often be understood as a classification problem. In oncology, this typically involves differentiating between tumour types and grades, or some type of discrete outcome prediction. From the viewpoint of computer-based medical decision support, this classification requires the availability of accurate diagnoses of past cases as training target examples. The availability of such labeled databases is scarce in most areas of oncology, and especially so in neuro-oncology. In such context, semi-supervised learning oriented towards classification can be a sensible data modeling choice. In this study, semi-supervised variants of Generative Topographic Mapping, a model of the manifold learning family, are applied to two neuro-oncology problems: the diagnostic discrimination between different brain tumour pathologies, and the prediction of outcomes for a specific type of aggressive brain tumours. Their performance compared favorably with those of the alternative Laplacian Eigenmaps and Semi-Supervised SVM for Manifold Learning models in most of the experiments.

  1. Mapping Learning Outcomes and Assignment Tasks for SPIDER Activities

    Directory of Open Access Journals (Sweden)

    Lyn Brodie

    2011-05-01

    Full Text Available Modern engineering programs have to address rapidly changing technical content and have to enable students to develop transferable skills such as critical evaluation, communication skills and lifelong learning. This paper introduces a combined learning and assessment activity that provides students with opportunities to develop and practice their soft skills, but also extends their theoretical knowledge base. Key tasks included self directed inquiry, oral and written communication as well as peer assessment. To facilitate the SPIDER activities (Select, Prepare and Investigate, Discuss, Evaluate, Reflect, a software tool has been implemented in the learning management system Moodle. Evidence shows increased student engagement and better learning outcomes for both transferable as well as technical skills. The study focuses on generalising the relationship between learning outcomes and assignment tasks as well as activities that drive these tasks. Trail results inform the approach. Staff evaluations and their views of assignments and intended learning outcomes also supported this analysis.

  2. A Blended Learning Study on Implementing Video Recorded Speaking Tasks in Task-Based Classroom Instruction

    Science.gov (United States)

    Kirkgoz, Yasemin

    2011-01-01

    This study investigates designing and implementing a speaking course in which face-to-face instruction informed by the principles of Task-Based Learning is blended with the use of technology, the video, for the first-year student teachers of English in Turkish higher education. The study consisted of three hours of task-based classroom…

  3. Using Goal Setting and Task Analysis to Enhance Task-Based Language Learning and Teaching

    Science.gov (United States)

    Rubin, Joan

    2015-01-01

    Task-Based Language Learning and Teaching has received sustained attention from teachers and researchers for over thirty years. It is a well-established pedagogy that includes the following characteristics: major focus on authentic and real-world tasks, choice of linguistic resources by learners, and a clearly defined non-linguistic outcome. This…

  4. Hearing in a shoe-box : binaural source position and wall absorption estimation using virtually supervised learning

    OpenAIRE

    Kataria, Saurabh; Gaultier, Clément; Deleforge, Antoine

    2016-01-01

    This paper introduces a new framework for supervised sound source localization referred to as virtually-supervised learning. An acoustic shoe-box room simulator is used to generate a large number of binaural single-source audio scenes. These scenes are used to build a dataset of spatial binaural features annotated with acoustic properties such as the 3D source position and the walls' absorption coefficients. A probabilis-tic high-to low-dimensional regression framework is used to learn a mapp...

  5. Concrete and abstract visualizations in history learning tasks

    NARCIS (Netherlands)

    Prangsma, Maaike; Van Boxtel, Carla; Kanselaar, Gellof; Kirschner, Paul A.

    2010-01-01

    Prangsma, M. E., Van Boxtel, C. A. M., Kanselaar, G., & Kirschner, P. A. (2009). Concrete and abstract visualizations in history learning tasks. British Journal of Educational Psychology, 79, 371-387.

  6. Concrete and abstract visualizations in history learning tasks

    NARCIS (Netherlands)

    Prangsma, M.E.; van Boxtel, C.A.M.; Kanselaar, G.; Kirschner, P.A.

    2009-01-01

    Background: History learning requires that students understand historical phenomena, abstract concepts and the relations between them. Students have problems grasping, using and relating complex historical developments and structures. Aims: A study was conducted to determine the effects of tasks

  7. Supervised learning classification models for prediction of plant virus encoded RNA silencing suppressors.

    Directory of Open Access Journals (Sweden)

    Zeenia Jagga

    Full Text Available Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a

  8. Designing Digital Problem Based Learning Tasks that Motivate Students

    Science.gov (United States)

    van Loon, Anne-Marieke; Ros, Anje; Martens, Rob

    2013-01-01

    This study examines whether teachers are able to apply the principles of autonomy support and structure support in designing digital problem based learning (PBL) tasks. We examine whether these tasks are more autonomy- and structure-supportive and whether primary and secondary school students experience greater autonomy, competence, and motivation…

  9. Perceptual learning in the absence of task or stimulus specificity.

    Directory of Open Access Journals (Sweden)

    Ben S Webb

    Full Text Available Performance on most sensory tasks improves with practice. When making particularly challenging sensory judgments, perceptual improvements in performance are tightly coupled to the trained task and stimulus configuration. The form of this specificity is believed to provide a strong indication of which neurons are solving the task or encoding the learned stimulus. Here we systematically decouple task- and stimulus-mediated components of trained improvements in perceptual performance and show that neither provides an adequate description of the learning process. Twenty-four human subjects trained on a unique combination of task (three-element alignment or bisection and stimulus configuration (vertical or horizontal orientation. Before and after training, we measured subjects' performance on all four task-configuration combinations. What we demonstrate for the first time is that learning does actually transfer across both task and configuration provided there is a common spatial axis to the judgment. The critical factor underlying the transfer of learning effects is not the task or stimulus arrangements themselves, but rather the recruitment of commons sets of neurons most informative for making each perceptual judgment.

  10. Designing Digital Problem Based Learning Tasks that Motivate Students

    Science.gov (United States)

    van Loon, Anne-Marieke; Ros, Anje; Martens, Rob

    2013-01-01

    This study examines whether teachers are able to apply the principles of autonomy support and structure support in designing digital problem based learning (PBL) tasks. We examine whether these tasks are more autonomy- and structure-supportive and whether primary and secondary school students experience greater autonomy, competence, and motivation…

  11. Learning from Student Experiences for Online Assessment Tasks

    Science.gov (United States)

    Qayyum, M. Asim; Smith, David

    2015-01-01

    Introduction: Use of the Internet for open Web searches is common among university students in academic learning tasks. The tools used by students to find relevant information for online assessment tasks were investigated and their information seeking behaviour was documented to explore the impact on assessment design. Method: A mixed methods…

  12. Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors.

    Science.gov (United States)

    Khoshgoftaar, Taghi M; Van Hulse, Jason; Napolitano, Amri

    2010-05-01

    Neural network algorithms such as multilayer perceptrons (MLPs) and radial basis function networks (RBFNets) have been used to construct learners which exhibit strong predictive performance. Two data related issues that can have a detrimental impact on supervised learning initiatives are class imbalance and labeling errors (or class noise). Imbalanced data can make it more difficult for the neural network learning algorithms to distinguish between examples of the various classes, and class noise can lead to the formulation of incorrect hypotheses. Both class imbalance and labeling errors are pervasive problems encountered in a wide variety of application domains. Many studies have been performed to investigate these problems in isolation, but few have focused on their combined effects. This study presents a comprehensive empirical investigation using neural network algorithms to learn from imbalanced data with labeling errors. In particular, the first component of our study investigates the impact of class noise and class imbalance on two common neural network learning algorithms, while the second component considers the ability of data sampling (which is commonly used to address the issue of class imbalance) to improve their performances. Our results, for which over two million models were trained and evaluated, show that conclusions drawn using the more commonly studied C4.5 classifier may not apply when using neural networks.

  13. SimNest: Social Media Nested Epidemic Simulation via Online Semi-supervised Deep Learning.

    Science.gov (United States)

    Zhao, Liang; Chen, Jiangzhuo; Chen, Feng; Wang, Wei; Lu, Chang-Tien; Ramakrishnan, Naren

    2015-11-01

    Infectious disease epidemics such as influenza and Ebola pose a serious threat to global public health. It is crucial to characterize the disease and the evolution of the ongoing epidemic efficiently and accurately. Computational epidemiology can model the disease progress and underlying contact network, but suffers from the lack of real-time and fine-grained surveillance data. Social media, on the other hand, provides timely and detailed disease surveillance, but is insensible to the underlying contact network and disease model. This paper proposes a novel semi-supervised deep learning framework that integrates the strengths of computational epidemiology and social media mining techniques. Specifically, this framework learns the social media users' health states and intervention actions in real time, which are regularized by the underlying disease model and contact network. Conversely, the learned knowledge from social media can be fed into computational epidemic model to improve the efficiency and accuracy of disease diffusion modeling. We propose an online optimization algorithm to substantialize the above interactive learning process iteratively to achieve a consistent stage of the integration. The extensive experimental results demonstrated that our approach can effectively characterize the spatio-temporal disease diffusion, outperforming competing methods by a substantial margin on multiple metrics.

  14. A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

    Directory of Open Access Journals (Sweden)

    Pengfei Jia

    2016-03-01

    Full Text Available When an electronic nose (E-nose is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde. Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.

  15. Using distant supervised learning to identify protein subcellular localizations from full-text scientific articles.

    Science.gov (United States)

    Zheng, Wu; Blake, Catherine

    2015-10-01

    Databases of curated biomedical knowledge, such as the protein-locations reflected in the UniProtKB database, provide an accurate and useful resource to researchers and decision makers. Our goal is to augment the manual efforts currently used to curate knowledge bases with automated approaches that leverage the increased availability of full-text scientific articles. This paper describes experiments that use distant supervised learning to identify protein subcellular localizations, which are important to understand protein function and to identify candidate drug targets. Experiments consider Swiss-Prot, the manually annotated subset of the UniProtKB protein knowledge base, and 43,000 full-text articles from the Journal of Biological Chemistry that contain just under 11.5 million sentences. The system achieves 0.81 precision and 0.49 recall at sentence level and an accuracy of 57% on held-out instances in a test set. Moreover, the approach identifies 8210 instances that are not in the UniProtKB knowledge base. Manual inspection of the 50 most likely relations showed that 41 (82%) were valid. These results have immediate benefit to researchers interested in protein function, and suggest that distant supervision should be explored to complement other manual data curation efforts.

  16. Task-Space Iterative Learning for Redundant Robotic Systems: Existence of a Task-Space Control and Convergence of Learning

    Science.gov (United States)

    Arimoto, Suguru; Sekimoto, Masahiro; Kawamura, Sadao

    This paper presents a feasibility study of iterative learning control for a class of redundant multi-joint robotic systems when a desired motion trajectory is specified in task-space with less dimension than that of joint space. First, it is shown that if the desired trajectory described in task-space for a time interval t ∈ [0,T] is twice continuously differentiable then a unique control signal describable in task-space exists despite of the system joint-redundancy. Second, a learning control update law is constructed through transpose of the Jacobian matrix of task-space coordinates with respect to joint coordinates by using measured data of motion trajectories in task-space. Third, the convergence of trajectory trackings through iterative learning is proved theoretically on the basis of original nonlinear robot dynamics in joint space.

  17. Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning

    Science.gov (United States)

    Remmele, Steffen; Ritzerfeld, Julia; Nickel, Walter; Hesser, Jürgen

    2011-03-01

    RNAi-based high-throughput microscopy screens have become an important tool in biological sciences in order to decrypt mostly unknown biological functions of human genes. However, manual analysis is impossible for such screens since the amount of image data sets can often be in the hundred thousands. Reliable automated tools are thus required to analyse the fluorescence microscopy image data sets usually containing two or more reaction channels. The herein presented image analysis tool is designed to analyse an RNAi screen investigating the intracellular trafficking and targeting of acylated Src kinases. In this specific screen, a data set consists of three reaction channels and the investigated cells can appear in different phenotypes. The main issue of the image processing task is an automatic cell segmentation which has to be robust and accurate for all different phenotypes and a successive phenotype classification. The cell segmentation is done in two steps by segmenting the cell nuclei first and then using a classifier-enhanced region growing on basis of the cell nuclei to segment the cells. The classification of the cells is realized by a support vector machine which has to be trained manually using supervised learning. Furthermore, the tool is brightness invariant allowing different staining quality and it provides a quality control that copes with typical defects during preparation and acquisition. A first version of the tool has already been successfully applied for an RNAi-screen containing three hundred thousand image data sets and the SVM extended version is designed for additional screens.

  18. Task-based Teaching and Learning in English Listening Class

    Institute of Scientific and Technical Information of China (English)

    鲍蓉芳

    2008-01-01

    In technical college English listening class, task-based teaching and learning method can not only create harmonious environment for students' learning, but also motivate students' enthusiasm in listening class, thus students can benefit a great deal in listening class and the listening can be carried out successfully.

  19. Principles of the Experiments on Task-based Learning Approach

    Institute of Scientific and Technical Information of China (English)

    赵晖

    2012-01-01

    this research reminds teachers to keep in mind that English teachers are not just teaching a language, but also a culture appropriate for the English learning classroom. The learning situation of the task activities can increase students’ English culture awareness, which is one of the factors that contribute towards integrative motivation.

  20. Task-Based Language Teaching and Expansive Learning Theory

    Science.gov (United States)

    Robertson, Margaret

    2014-01-01

    Task-Based Language Teaching (TBLT) has become increasingly recognized as an effective pedagogy, but its location in generalized sociocultural theories of learning has led to misunderstandings and criticism. The purpose of this article is to explain the congruence between TBLT and Expansive Learning Theory and the benefits of doing so. The merit…

  1. Non-Supervised Learning for Spread Spectrum Signal Pseudo-Noise Sequence Acquisition

    Institute of Scientific and Technical Information of China (English)

    Hao Cheng; Na Yu,; Tai-Jun Wang

    2015-01-01

    Abstract¾An idea of estimating the direct sequence spread spectrum (DSSS) signal pseudo-noise (PN) sequence is presented. Without the apriority knowledge about the DSSS signal in the non-cooperation condition, we propose a self-organizing feature map (SOFM) neural network algorithm to detect and identify the PN sequence. A non-supervised learning algorithm is proposed according the Kohonen rule in SOFM. The blind algorithm can also estimate the PN sequence in a low signal-to-noise (SNR) and computer simulation demonstrates that the algorithm is effective. Compared with the traditional correlation algorithm based on slip-correlation, the proposed algorithm’s bit error rate (BER) and complexity are lower.

  2. Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

    CERN Document Server

    Lu, Zhiwu; Peng, Yuxin

    2011-01-01

    This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal mul...

  3. Anxiety, supervision and a space for thinking: some narcissistic perils for clinical psychologists in learning psychotherapy.

    Science.gov (United States)

    Mollon, P

    1989-06-01

    The process of learning psychotherapy involves narcissistic dangers--there may be injuries to self-esteem and self-image, especially when working with certain kinds of disturbed and hostile patients. Some patients will unconsciously recreate, in the transference, representations of early damaging experiences with parents, but now reversed with the therapist as the victim. It is vital for the trainee to be helped to understand these powerful interactional pressures. There are aspects of the professional culture and ideals of clinical psychologists (and possibly of some psychiatrists and social workers as well) which may make them particularly vulnerable in work with the hostile patient. It is argued that the function of supervision is not to teach a technique directly, but to create a 'space for thinking'--a kind of thinking which is more akin to maternal reverie, as described by Bion, than problem solving.

  4. Lessons Learned from Crowdsourcing Complex Engineering Tasks.

    Science.gov (United States)

    Staffelbach, Matthew; Sempolinski, Peter; Kijewski-Correa, Tracy; Thain, Douglas; Wei, Daniel; Kareem, Ahsan; Madey, Gregory

    2015-01-01

    Crowdsourcing is the practice of obtaining needed ideas, services, or content by requesting contributions from a large group of people. Amazon Mechanical Turk is a web marketplace for crowdsourcing microtasks, such as answering surveys and image tagging. We explored the limits of crowdsourcing by using Mechanical Turk for a more complicated task: analysis and creation of wind simulations. Our investigation examined the feasibility of using crowdsourcing for complex, highly technical tasks. This was done to determine if the benefits of crowdsourcing could be harnessed to accurately and effectively contribute to solving complex real world engineering problems. Of course, untrained crowds cannot be used as a mere substitute for trained expertise. Rather, we sought to understand how crowd workers can be used as a large pool of labor for a preliminary analysis of complex data. We compared the skill of the anonymous crowd workers from Amazon Mechanical Turk with that of civil engineering graduate students, making a first pass at analyzing wind simulation data. For the first phase, we posted analysis questions to Amazon crowd workers and to two groups of civil engineering graduate students. A second phase of our experiment instructed crowd workers and students to create simulations on our Virtual Wind Tunnel website to solve a more complex task. With a sufficiently comprehensive tutorial and compensation similar to typical crowd-sourcing wages, we were able to enlist crowd workers to effectively complete longer, more complex tasks with competence comparable to that of graduate students with more comprehensive, expert-level knowledge. Furthermore, more complex tasks require increased communication with the workers. As tasks become more complex, the employment relationship begins to become more akin to outsourcing than crowdsourcing. Through this investigation, we were able to stretch and explore the limits of crowdsourcing as a tool for solving complex problems.

  5. Lessons Learned from Crowdsourcing Complex Engineering Tasks.

    Directory of Open Access Journals (Sweden)

    Matthew Staffelbach

    Full Text Available Crowdsourcing is the practice of obtaining needed ideas, services, or content by requesting contributions from a large group of people. Amazon Mechanical Turk is a web marketplace for crowdsourcing microtasks, such as answering surveys and image tagging. We explored the limits of crowdsourcing by using Mechanical Turk for a more complicated task: analysis and creation of wind simulations.Our investigation examined the feasibility of using crowdsourcing for complex, highly technical tasks. This was done to determine if the benefits of crowdsourcing could be harnessed to accurately and effectively contribute to solving complex real world engineering problems. Of course, untrained crowds cannot be used as a mere substitute for trained expertise. Rather, we sought to understand how crowd workers can be used as a large pool of labor for a preliminary analysis of complex data.We compared the skill of the anonymous crowd workers from Amazon Mechanical Turk with that of civil engineering graduate students, making a first pass at analyzing wind simulation data. For the first phase, we posted analysis questions to Amazon crowd workers and to two groups of civil engineering graduate students. A second phase of our experiment instructed crowd workers and students to create simulations on our Virtual Wind Tunnel website to solve a more complex task.With a sufficiently comprehensive tutorial and compensation similar to typical crowd-sourcing wages, we were able to enlist crowd workers to effectively complete longer, more complex tasks with competence comparable to that of graduate students with more comprehensive, expert-level knowledge. Furthermore, more complex tasks require increased communication with the workers. As tasks become more complex, the employment relationship begins to become more akin to outsourcing than crowdsourcing. Through this investigation, we were able to stretch and explore the limits of crowdsourcing as a tool for solving complex

  6. Reward-based learning of a redundant task.

    Science.gov (United States)

    Tamagnone, Irene; Casadio, Maura; Sanguineti, Vittorio

    2013-06-01

    Motor skill learning has different components. When we acquire a new motor skill we have both to learn a reliable action-value map to select a highly rewarded action (task model) and to develop an internal representation of the novel dynamics of the task environment, in order to execute properly the action previously selected (internal model). Here we focus on a 'pure' motor skill learning task, in which adaptation to a novel dynamical environment is negligible and the problem is reduced to the acquisition of an action-value map, only based on knowledge of results. Subjects performed point-to-point movement, in which start and target positions were fixed and visible, but the score provided at the end of the movement depended on the distance of the trajectory from a hidden viapoint. Subjects did not have clues on the correct movement other than the score value. The task is highly redundant, as infinite trajectories are compatible with the maximum score. Our aim was to capture the strategies subjects use in the exploration of the task space and in the exploitation of the task redundancy during learning. The main findings were that (i) subjects did not converge to a unique solution; rather, their final trajectories are determined by subject-specific history of exploration. (ii) with learning, subjects reduced the trajectory's overall variability, but the point of minimum variability gradually shifted toward the portion of the trajectory closer to the hidden via-point.

  7. Heuristic for Task-Worker Assignment with Varying Learning Slopes

    Directory of Open Access Journals (Sweden)

    Wipawee Tharmmaphornphilas

    2010-04-01

    Full Text Available Fashion industry has variety products, so the multi-skilled workers are required to improve flexibility in production and assignment. Generally the supervisor will assign task to the workers based on skill and skill levels of worker. Since in fashion industry new product styles are launched more frequently and the order size tends to be smaller, the workers always learn when the raw material and the production process changes. Consequently they require less time to produce the succeeding units of a task based on their learning ability. Since the workers have both experience and inexperience workers, so each worker has different skill level and learning ability. Consequently, the assignment which assumed constant skill level is not proper to use. This paper proposes a task-worker assignment considering worker skill levels and learning abilities. Processing time of each worker changes along production period due to a worker learning ability. We focus on a task-worker assignment in a fashion industry where tasks are ordered in series; the number of tasks is greater than the number of workers. Therefore, workers can perform multiple assignments followed the precedence restriction as an assembly line balancing problem. The problem is formulated in an integer linear programming model with objective to minimize makespan. A heuristic is proposed to determine the lower bound (LB and the upper bound (UB of the problem and the best assignment is determined. The performance of the heuristic method is tested by comparing quality of solution and computational time to optimal solutions.

  8. Response monitoring using quantitative ultrasound methods and supervised dictionary learning in locally advanced breast cancer

    Science.gov (United States)

    Gangeh, Mehrdad J.; Fung, Brandon; Tadayyon, Hadi; Tran, William T.; Czarnota, Gregory J.

    2016-03-01

    A non-invasive computer-aided-theragnosis (CAT) system was developed for the early assessment of responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer. The CAT system was based on quantitative ultrasound spectroscopy methods comprising several modules including feature extraction, a metric to measure the dissimilarity between "pre-" and "mid-treatment" scans, and a supervised learning algorithm for the classification of patients to responders/non-responders. One major requirement for the successful design of a high-performance CAT system is to accurately measure the changes in parametric maps before treatment onset and during the course of treatment. To this end, a unified framework based on Hilbert-Schmidt independence criterion (HSIC) was used for the design of feature extraction from parametric maps and the dissimilarity measure between the "pre-" and "mid-treatment" scans. For the feature extraction, HSIC was used to design a supervised dictionary learning (SDL) method by maximizing the dependency between the scans taken from "pre-" and "mid-treatment" with "dummy labels" given to the scans. For the dissimilarity measure, an HSIC-based metric was employed to effectively measure the changes in parametric maps as an indication of treatment effectiveness. The HSIC-based feature extraction and dissimilarity measure used a kernel function to nonlinearly transform input vectors into a higher dimensional feature space and computed the population means in the new space, where enhanced group separability was ideally obtained. The results of the classification using the developed CAT system indicated an improvement of performance compared to a CAT system with basic features using histogram of intensity.

  9. A semi-supervised learning framework for biomedical event extraction based on hidden topics.

    Science.gov (United States)

    Zhou, Deyu; Zhong, Dayou

    2015-05-01

    Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing. Therefore, biomedical event extraction, automatically acquiring knowledge of molecular events in research articles, has attracted community-wide efforts recently. Most approaches are based on statistical models, requiring large-scale annotated corpora to precisely estimate models' parameters. However, it is usually difficult to obtain in practice. Therefore, employing un-annotated data based on semi-supervised learning for biomedical event extraction is a feasible solution and attracts more interests. In this paper, a semi-supervised learning framework based on hidden topics for biomedical event extraction is presented. In this framework, sentences in the un-annotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. More specifically, not only the structures of the sentences, but also the hidden topics embedded in the sentences are used for describing the distance. The sentences and newly assigned event annotations, together with the annotated corpus, are employed for training. Experiments were conducted on the multi-level event extraction corpus, a golden standard corpus. Experimental results show that more than 2.2% improvement on F-score on biomedical event extraction is achieved by the proposed framework when compared to the state-of-the-art approach. The results suggest that by incorporating un-annotated data, the proposed framework indeed improves the performance of the state-of-the-art event extraction system and the similarity between sentences might be precisely

  10. Entry-Level Technical Skills That Teachers Expected Students to Learn through Supervised Agricultural Experiences (SAEs): A Modified Delphi Study

    Science.gov (United States)

    Ramsey, Jon W.; Edwards, M. Craig

    2012-01-01

    Supervised experiences are designed to provide opportunities for the hands-on learning of skills and practices that lead to successful personal growth and future employment in an agricultural career (Talbert, Vaughn, Croom, & Lee, 2007). In the Annual Report for Agricultural Education (2005-2006), it was stated that 91% of the respondents…

  11. Just How Much Can School Pupils Learn from School Gardening? A Study of Two Supervised Agricultural Experience Approaches in Uganda

    Science.gov (United States)

    Okiror, John James; Matsiko, Biryabaho Frank; Oonyu, Joseph

    2011-01-01

    School systems in Africa are short of skills that link well with rural communities, yet arguments to vocationalize curricula remain mixed and school agriculture lacks the supervised practical component. This study, conducted in eight primary (elementary) schools in Uganda, sought to compare the learning achievement of pupils taught using…

  12. Teaching the computer to code frames in news: comparing two supervised machine learning approaches to frame analysis

    NARCIS (Netherlands)

    Burscher, B.; Odijk, D.; Vliegenthart, R.; de Rijke, M.; de Vreese, C.H.

    2014-01-01

    We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation

  13. Teaching the computer to code frames in news: comparing two supervised machine learning approaches to frame analysis

    NARCIS (Netherlands)

    Burscher, B.; Odijk, D.; Vliegenthart, R.; de Rijke, M.; de Vreese, C.H.

    2014-01-01

    We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation

  14. Entry-Level Technical Skills That Teachers Expected Students to Learn through Supervised Agricultural Experiences (SAEs): A Modified Delphi Study

    Science.gov (United States)

    Ramsey, Jon W.; Edwards, M. Craig

    2012-01-01

    Supervised experiences are designed to provide opportunities for the hands-on learning of skills and practices that lead to successful personal growth and future employment in an agricultural career (Talbert, Vaughn, Croom, & Lee, 2007). In the Annual Report for Agricultural Education (2005-2006), it was stated that 91% of the respondents (i.e.,…

  15. A Kernel Approach to Multi-Task Learning with Task-Specific Kernels

    Institute of Scientific and Technical Information of China (English)

    Wei Wu; Hang Li; Yun-Hua Hu; Rong Jin

    2012-01-01

    Several kernel-based methods for multi-task learning have been proposed,which leverage relations among tasks as regularization to enhance the overall learning accuracies.These methods assume that the tasks share the same kernel,which could limit their applications because in practice different tasks may need different kernels.The main challenge of introducing multiple kernels into multiple tasks is that models from different reproducing kernel Hilbert spaces (RKHSs) are not comparable,making it difficult to exploit relations among tasks.This paper addresses the challenge by formalizing the problem in the square integrable space (SIS).Specially,it proposes a kernel-based method which makes use of a regularization term defined in SIS to represent task relations.We prove a new representer theorem for the proposed approach in SIS.We further derive a practical method for solving the learning problem and conduct consistency analysis of the method.We discuss the relationship between our method and an existing method.We also give an SVM (support vector machine)-based implementation of our method for multi-label classification.Experiments on an artificial example and two real-world datasets show that the proposed method performs better than the existing method.

  16. Using Tasks to Assess Spanish Language Learning

    Science.gov (United States)

    Herrera Mosquera, Leonardo

    2012-01-01

    The methodology of Task-based teaching (TBT) has been positively regarded by many researchers and language teachers around the world. Yet, this language teaching methodology has been mainly implemented in English as a second language (ESL) classrooms and in English for specific purpose (ESP) courses; and more specifically with advanced-level…

  17. The Predictive Evaluation of Language Learning Tasks

    Science.gov (United States)

    Vasiljevic, Zorana

    2011-01-01

    Teachers are often faced with difficulty in choosing appropriate teaching activities for use in their classroom. In selecting suitable materials for their learners, teachers need to be able to analyze any tasks (i.e., their objectives, procedures and intended outcomes) before they are applied in the classroom. This paper will attempt to outline a…

  18. 基于半监督的SVM迁移学习文本分类算法%Semi-Supervised Transfer Learning Text Classiifcation Algorithms Based on SVM

    Institute of Scientific and Technical Information of China (English)

    谭建平; 刘波; 肖燕珊

    2016-01-01

    随着互联网的快速发展,文本信息量巨大,大规模的文本处理已经成为一个挑战。文本处理的一个重要技术便是分类,基于SVM的传统文本分类算法已经无法满足快速的文本增长分类。于是如何利用过时的历史文本数据(源任务数据)进行迁移来帮助新产生文本数据进行分类显得异常重要。文章提出了基于半监督的SVM迁移学习算法(Semi-supervised TL_SVM)来对文本进行分类。首先,在半监督SVM的模型中引入迁移学习,构建分类模型。其次,采用交互迭代的方法对目标方程求解,最终得到面向目标领域的分类器。实验验证了基于半监督的SVM迁移学习分类器具有比传统分类器更高的精确度。%With the rapid development of the Internet, texts contain a huge amount of information and the large-scale text processing has become a challenge. An important technical of the text processing is classiifcation, the traditional text categorization algorithm based on SVM has been unable to meet the rapid growth of text classiifcation. So how to utilize the source tasks data to help build a transfer learning classiifer for the target task is especially important. Semi-supervised TL_SVM algorithms is proposed to text classiifcation. First, semi-supervised SVM model combines transfer learning to build the model of classiifcation. Second, we utilize the iterative algorithm to solve the optimization function and obtain the transfer classiifer for the target task. Experiments have shown that our Semi-supervised-based transfer SVM can obtain higher accuracy compared with the traditional method.

  19. Collective Academic Supervision: A Model for Participation and Learning in Higher Education

    Science.gov (United States)

    Nordentoft, Helle Merete; Thomsen, Rie; Wichmann-Hansen, Gitte

    2013-01-01

    Supervision of graduate students is a core activity in higher education. Previous research on graduate supervision focuses on individual and relational aspects of the supervisory relationship rather than collective, pedagogical and methodological aspects of the supervision process. In presenting a collective model we have developed for academic…

  20. Learning redundant motor tasks with and without overlapping dimensions: facilitation and interference effects.

    Science.gov (United States)

    Ranganathan, Rajiv; Wieser, Jon; Mosier, Kristine M; Mussa-Ivaldi, Ferdinando A; Scheidt, Robert A

    2014-06-11

    Prior learning of a motor skill creates motor memories that can facilitate or interfere with learning of new, but related, motor skills. One hypothesis of motor learning posits that for a sensorimotor task with redundant degrees of freedom, the nervous system learns the geometric structure of the task and improves performance by selectively operating within that task space. We tested this hypothesis by examining if transfer of learning between two tasks depends on shared dimensionality between their respective task spaces. Human participants wore a data glove and learned to manipulate a computer cursor by moving their fingers. Separate groups of participants learned two tasks: a prior task that was unique to each group and a criterion task that was common to all groups. We manipulated the mapping between finger motions and cursor positions in the prior task to define task spaces that either shared or did not share the task space dimensions (x-y axes) of the criterion task. We found that if the prior task shared task dimensions with the criterion task, there was an initial facilitation in criterion task performance. However, if the prior task did not share task dimensions with the criterion task, there was prolonged interference in learning the criterion task due to participants finding inefficient task solutions. These results show that the nervous system learns the task space through practice, and that the degree of shared task space dimensionality influences the extent to which prior experience transfers to subsequent learning of related motor skills.

  1. Whither Supervision?

    Directory of Open Access Journals (Sweden)

    Duncan Waite

    2006-11-01

    Full Text Available This paper inquires if the school supervision is in decadence. Dr. Waite responds that the answer will depend on which perspective you look at it. Dr. Waite suggests taking in consideration three elements that are related: the field itself, the expert in the field (the professor, the theorist, the student and the administrator, and the context. When these three elements are revised, it emphasizes that there is not a consensus about the field of supervision, but there are coincidences related to its importance and that it is related to the improvement of the practice of the students in the school for their benefit. Dr. Waite suggests that the practice on this field is not always in harmony with what the theorists affirm. When referring to the supervisor or the skilled person, the author indicates that his or her perspective depends on his or her epistemological believes or in the way he or she conceives the learning; that is why supervision can be understood in different ways. About the context, Waite suggests that there have to be taken in consideration the social or external forces that influent the people and the society, because through them the education is affected. Dr. Waite concludes that the way to understand the supervision depends on the performer’s perspective. He responds to the initial question saying that the supervision authorities, the knowledge on this field, the performers, and its practice, are maybe spread but not extinct because the supervision will always be part of the great enterprise that we called education.

  2. Prefrontal Dynamics Underlying Rapid Instructed Task Learning Reverse with Practice

    Science.gov (United States)

    Cole, Michael W.; Bagic, Anto; Kass, Robert; Schneider, Walter

    2011-01-01

    The ability to rapidly reconfigure our minds to perform novel tasks is important for adapting to an ever-changing world, yet little is understood about its basis in the brain. Furthermore, it is unclear how this kind of task preparation changes with practice. Previous research suggests that prefrontal cortex (PFC) is essential when preparing to perform either novel or practiced tasks. Building upon recent evidence that PFC is organized in an anterior-to-posterior hierarchy, we postulated that novel and practiced task preparation would differentiate hierarchically distinct regions within PFC across time. Specifically, we hypothesized and confirmed using functional magnetic resonance imaging and magnetoencephalography with humans that novel task preparation is a bottom-up process that involves lower-level rule representations in dorsolateral PFC (DLPFC) before a higher-level rule-integrating task representation in anterior PFC (aPFC). In contrast, we identified a complete reversal of this activity pattern during practiced task preparation. Specifically, we found that practiced task preparation is a top-down process that involves a higher-level rule-integrating task representation (recalled from long-term memory) in aPFC before lower-level rule representations in DLPFC. These findings reveal two distinct yet highly inter-related mechanisms for task preparation, one involving task set formation from instructions during rapid instructed task learning and the other involving task set retrieval from long-term memory to facilitate familiar task performance. These two mechanisms demonstrate the exceptional flexibility of human PFC as it rapidly reconfigures cognitive brain networks to implement a wide variety of possible tasks. PMID:20962245

  3. Learning Probabilistic Hierarchical Task Networks to Capture User Preferences

    CERN Document Server

    Li, Nan; Kambhampati, Subbarao; Yoon, Sungwook

    2010-01-01

    We propose automatically learning probabilistic Hierarchical Task Networks (pHTNs) in order to capture a user's preferences on plans, by observing only the user's behavior. HTNs are a common choice of representation for a variety of purposes in planning, including work on learning in planning. Our contributions are (a) learning structure and (b) representing preferences. In contrast, prior work employing HTNs considers learning method preconditions (instead of structure) and representing domain physics or search control knowledge (rather than preferences). Initially we will assume that the observed distribution of plans is an accurate representation of user preference, and then generalize to the situation where feasibility constraints frequently prevent the execution of preferred plans. In order to learn a distribution on plans we adapt an Expectation-Maximization (EM) technique from the discipline of (probabilistic) grammar induction, taking the perspective of task reductions as productions in a context-free...

  4. Multi-tasking to Address Diversity in Language Learning

    Institute of Scientific and Technical Information of China (English)

    LEI Kun

    2014-01-01

    With focus now placed on the learner, more attention is given to his learning style, multiple intelligence and develop⁃ing learning strategies to enable him to make sense of and use of the target language appropriately in varied contexts and with dif⁃ferent uses of the language. To attain this, the teacher is tasked with designing, monitoring and processing language learning activi⁃ties for students to carry out and in the process learn by doing and reflecting on the learning process they went through as they in⁃teracted socially with each other. This paper describes a task named“The Fishbowl Technique”and found to be effective in large ESL classes in the secondary level in the Philippines.

  5. Manifold regularized multitask learning for semi-supervised multilabel image classification.

    Science.gov (United States)

    Luo, Yong; Tao, Dacheng; Geng, Bo; Xu, Chao; Maybank, Stephen J

    2013-02-01

    It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.

  6. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Ricardo Andres Pizarro

    2016-12-01

    Full Text Available High-resolution three-dimensional magnetic resonance imaging (3D-MRI is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM algorithm in the quality assessment of structural brain images, using global and region of interest (ROI automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

  7. Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

    Science.gov (United States)

    Adal, Kedir M; Sidibé, Désiré; Ali, Sharib; Chaum, Edward; Karnowski, Thomas P; Mériaudeau, Fabrice

    2014-04-01

    Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

  8. The effects of supervised learning on event-related potential correlates of music-syntactic processing.

    Science.gov (United States)

    Guo, Shuang; Koelsch, Stefan

    2015-11-11

    Humans process music even without conscious effort according to implicit knowledge about syntactic regularities. Whether such automatic and implicit processing is modulated by veridical knowledge has remained unknown in previous neurophysiological studies. This study investigates this issue by testing whether the acquisition of veridical knowledge of a music-syntactic irregularity (acquired through supervised learning) modulates early, partly automatic, music-syntactic processes (as reflected in the early right anterior negativity, ERAN), and/or late controlled processes (as reflected in the late positive component, LPC). Excerpts of piano sonatas with syntactically regular and less regular chords were presented repeatedly (10 times) to non-musicians and amateur musicians. Participants were informed by a cue as to whether the following excerpt contained a regular or less regular chord. Results showed that the repeated exposure to several presentations of regular and less regular excerpts did not influence the ERAN elicited by less regular chords. By contrast, amplitudes of the LPC (as well as of the P3a evoked by less regular chords) decreased systematically across learning trials. These results reveal that late controlled, but not early (partly automatic), neural mechanisms of music-syntactic processing are modulated by repeated exposure to a musical piece. This article is part of a Special Issue entitled SI: Prediction and Attention. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

    Science.gov (United States)

    Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

    2015-01-01

    Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

  10. Effects of Variations in Task Design on Mathematics Teachers' Learning Experiences: A Case of a Sorting Task

    Science.gov (United States)

    Koichu, Boris; Zaslavsky, Orit; Dolev, Lea

    2016-01-01

    The goal of the study presented in this article was to examine how variations in task design may affect mathematics teachers' learning experiences. The study focuses on sorting tasks, i.e., learning tasks that require grouping a given set of mathematical items, in as many ways as possible, according to different criteria suggested by the learners.…

  11. Adding the goal to learn strengthens learning in an unintentional learning task.

    Science.gov (United States)

    Schmidt, James R; De Houwer, Jan

    2012-08-01

    Previous research has demonstrated that contingency learning can take place in the absence of the intention to learn. For instance, in the color-word contingency learning task, each distracting word is presented most often in a given target color (e.g., "month" in red and "plate" in green), and less often in the other colors. Participants respond more quickly and accurately when the word is presented in the expected rather than an unexpected color, even though there is no reason why they would have the intention to learn the contingencies between the words and the colors. It remains to be determined, however, whether learning in such situations would benefit or suffer from adding the goal to learn contingencies. In the reported experiment, half of the participants were informed that each word was presented most often in a certain color, and they were instructed to try to learn these contingencies. The other half of the participants were not informed that contingencies would be present. The participants given the learning goal produced a larger response time contingency effect than did the control participants. In contrast to some results from other learning paradigms, these results suggest that intentional learning adds to, rather than interferes with, unintentional learning, and we propose an explanation for some of the conflicting results.

  12. Multimodal Task-Driven Dictionary Learning for Image Classification.

    Science.gov (United States)

    Bahrampour, Soheil; Nasrabadi, Nasser M; Ray, Asok; Jenkins, William Kenneth

    2016-01-01

    Dictionary learning algorithms have been successfully used for both reconstructive and discriminative tasks, where an input signal is represented with a sparse linear combination of dictionary atoms. While these methods are mostly developed for single-modality scenarios, recent studies have demonstrated the advantages of feature-level fusion based on the joint sparse representation of the multimodal inputs. In this paper, we propose a multimodal task-driven dictionary learning algorithm under the joint sparsity constraint (prior) to enforce collaborations among multiple homogeneous/heterogeneous sources of information. In this task-driven formulation, the multimodal dictionaries are learned simultaneously with their corresponding classifiers. The resulting multimodal dictionaries can generate discriminative latent features (sparse codes) from the data that are optimized for a given task such as binary or multiclass classification. Moreover, we present an extension of the proposed formulation using a mixed joint and independent sparsity prior, which facilitates more flexible fusion of the modalities at feature level. The efficacy of the proposed algorithms for multimodal classification is illustrated on four different applications--multimodal face recognition, multi-view face recognition, multi-view action recognition, and multimodal biometric recognition. It is also shown that, compared with the counterpart reconstructive-based dictionary learning algorithms, the task-driven formulations are more computationally efficient in the sense that they can be equipped with more compact dictionaries and still achieve superior performance.

  13. Identifying beneficial task relations for multi-task learning in deep neural networks

    DEFF Research Database (Denmark)

    Bingel, Joachim; Søgaard, Anders

    2017-01-01

    Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP...

  14. Performance of machine learning methods for classification tasks

    Directory of Open Access Journals (Sweden)

    B. Krithika

    2013-06-01

    Full Text Available In this paper, the performance of various machine learning methods on pattern classification and recognition tasks are proposed. The proposed method for evaluating performance will be based on the feature representation, feature selection and setting model parameters. The nature of the data, the methods of feature extraction and feature representation are discussed. The results of the Machine Learning algorithms on the classification task are analysed. The performance of Machine Learning methods on classifying Tamil word patterns, i.e., classification of noun and verbs are analysed.The software WEKA (data mining tool is used for evaluating the performance. WEKA has several machine learning algorithms like Bayes, Trees, Lazy, Rule based classifiers.

  15. Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation.

    Directory of Open Access Journals (Sweden)

    Chao Wei

    Full Text Available Topic models and neural networks can discover meaningful low-dimensional latent representations of text corpora; as such, they have become a key technology of document representation. However, such models presume all documents are non-discriminatory, resulting in latent representation dependent upon all other documents and an inability to provide discriminative document representation. To address this problem, we propose a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative. We first determine the discriminative neighbors set with Euclidean distance in observation spaces. Then, the autoencoder is trained by joint minimization of the Bernoulli cross-entropy error between input and output and the sum of the square error between neighbors of input and output. The results of two widely used corpora show that our method yields at least a 15% improvement in document clustering and a nearly 7% improvement in classification tasks compared to comparative methods. The evidence demonstrates that our method can readily capture more discriminative latent representation of new documents. Moreover, some meaningful combinations of words can be efficiently discovered by activating features that promote the comprehensibility of latent representation.

  16. Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation.

    Science.gov (United States)

    Wei, Chao; Luo, Senlin; Ma, Xincheng; Ren, Hao; Zhang, Ji; Pan, Limin

    2016-01-01

    Topic models and neural networks can discover meaningful low-dimensional latent representations of text corpora; as such, they have become a key technology of document representation. However, such models presume all documents are non-discriminatory, resulting in latent representation dependent upon all other documents and an inability to provide discriminative document representation. To address this problem, we propose a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative. We first determine the discriminative neighbors set with Euclidean distance in observation spaces. Then, the autoencoder is trained by joint minimization of the Bernoulli cross-entropy error between input and output and the sum of the square error between neighbors of input and output. The results of two widely used corpora show that our method yields at least a 15% improvement in document clustering and a nearly 7% improvement in classification tasks compared to comparative methods. The evidence demonstrates that our method can readily capture more discriminative latent representation of new documents. Moreover, some meaningful combinations of words can be efficiently discovered by activating features that promote the comprehensibility of latent representation.

  17. L2 Learner Perceptions of Creative Communicative Learning Tasks

    OpenAIRE

    Mutahar, AL-MURTADHA

    2015-01-01

    This study investigates the effectiveness of creative communicative learning tasks, created to supplement a reading textbook, on students’ reading skills, communicative ability, and motivation. Science and Engineering students find it difficult to understand academic English readings. Therefore, the researcher developed content-based conversations to help students understand the content, improve their reading skills, and raise their motivation to learn English. After finishing the course, stu...

  18. Multi-task Gaussian Process Learning of Robot Inverse Dynamics

    OpenAIRE

    Chai, Kian Ming; Williams, Christopher K. I.; Klanke, Stefan; Vijayakumar, Sethu

    2008-01-01

    The inverse dynamics problem for a robotic manipulator is to compute the torques needed at the joints to drive it along a given trajectory; it is beneficial to be able to learn this function for adaptive control. A robotic manipulator will often need to be controlled while holding different loads in its end effector, giving rise to a multi-task learning problem. By placing independent Gaussian process priors over the latent functions of the inverse dynamics, we obtain a multi-t...

  19. Lack of supervision? The building manager`s task of supervising facility management systems; Ein Stiefkind der Planung? Objektueberwachung (Fachbauleitung) der technischen Gebaeudeausruestung

    Energy Technology Data Exchange (ETDEWEB)

    Bernhard, M. [IMB-Ingenieurteam Manfred Bernhard GmbH, Muehlheim am Main (Germany). Fachbauleitung Technische Gebaeudeausruestung

    1998-12-31

    The technical comfort requirements of new buildings and the resulting density of techncal facilities makes the task of builder-owners, architects and building managers increasingly difficult. Reasons for this are the lack of space available for the technical systems and the increasingly short construction times. (orig.) [Deutsch] Die Aufgabe des Fachbauleiters ist es, Fachkompetenz mit Auftraggeberfunktion zu praktizieren und dafuer zu sorgen, dass im Sinne des Auftraggebers der gesetzte Termin- und Finanzrahmen bei gleichzeitig hohem Qualitaetsanspruch eingehalten wird. Die technische Komfortausstattung von Grossobjekten und die daraus resultierende hohe Installationsdichte stellt immer groessere Anforderungen bei der Auftragsabwicklung an Auftraggeber, Architekten und planende Fachingenieure. Die Gruende hierfuer liegen u.a. in dem meist sehr begrenzten Platzangebot fuer die Technik und vor allem in den immer kuerzeren Bauzeiten der Objekte. (orig.)

  20. Multisensory perceptual learning is dependent upon task difficulty.

    Science.gov (United States)

    De Niear, Matthew A; Koo, Bonhwang; Wallace, Mark T

    2016-11-01

    There has been a growing interest in developing behavioral tasks to enhance temporal acuity as recent findings have demonstrated changes in temporal processing in a number of clinical conditions. Prior research has demonstrated that perceptual training can enhance temporal acuity both within and across different sensory modalities. Although certain forms of unisensory perceptual learning have been shown to be dependent upon task difficulty, this relationship has not been explored for multisensory learning. The present study sought to determine the effects of task difficulty on multisensory perceptual learning. Prior to and following a single training session, participants completed a simultaneity judgment (SJ) task, which required them to judge whether a visual stimulus (flash) and auditory stimulus (beep) presented in synchrony or at various stimulus onset asynchronies (SOAs) occurred synchronously or asynchronously. During the training session, participants completed the same SJ task but received feedback regarding the accuracy of their responses. Participants were randomly assigned to one of three levels of difficulty during training: easy, moderate, and hard, which were distinguished based on the SOAs used during training. We report that only the most difficult (i.e., hard) training protocol enhanced temporal acuity. We conclude that perceptual training protocols for enhancing multisensory temporal acuity may be optimized by employing audiovisual stimuli for which it is difficult to discriminate temporal synchrony from asynchrony.

  1. A framework to facilitate self-directed learning, assessment and supervision in midwifery practice: a qualitative study of supervisors' perceptions.

    Science.gov (United States)

    Embo, M; Driessen, E; Valcke, M; van der Vleuten, C P M

    2014-08-01

    Self-directed learning is an educational concept that has received increasing attention. The recent workplace literature, however, reports problems with the facilitation of self-directed learning in clinical practice. We developed the Midwifery Assessment and Feedback Instrument (MAFI) as a framework to facilitate self-directed learning. In the present study, we sought clinical supervisors' perceptions of the usefulness of MAFI. Interviews with fifteen clinical supervisors were audio taped, transcribed verbatim and analysed thematically using Atlas-Ti software for qualitative data analysis. Four themes emerged from the analysis. (1) The competency-based educational structure promotes the setting of realistic learning outcomes and a focus on competency development, (2) instructing students to write reflections facilitates student-centred supervision, (3) creating a feedback culture is necessary to achieve continuity in supervision and (4) integrating feedback and assessment might facilitate competency development under the condition that evidence is discussed during assessment meetings. Supervisors stressed the need for direct observation, and instruction how to facilitate a self-directed learning process. The MAFI appears to be a useful framework to promote self-directed learning in clinical practice. The effect can be advanced by creating a feedback and assessment culture where learners and supervisors share the responsibility for developing self-directed learning. Copyright © 2014 Elsevier Ltd. All rights reserved.

  2. Enhancing Automaticity through Task-Based Language Learning

    Science.gov (United States)

    De Ridder, Isabelle; Vangehuchten, Lieve; Gomez, Marta Sesena

    2007-01-01

    In general terms automaticity could be defined as the subconscious condition wherein "we perform a complex series of tasks very quickly and efficiently, without having to think about the various components and subcomponents of action involved" (DeKeyser 2001: 125). For language learning, Segalowitz (2003) characterised automaticity as a…

  3. Positive versus Negative Communication Strategies in Task-Based Learning

    Science.gov (United States)

    Rohani, Siti

    2013-01-01

    This study aimed at describing how the implementation of Task-Based Learning (TBL) would shape or change students' use of oral communication strategies. Students' problems and strategies to solve the problems during the implementation of TBL were also explored. The study was a mixed method, employing both quantitative and qualitative analysis…

  4. Trayectorias: A New Model for Online Task-Based Learning

    Science.gov (United States)

    Ros i Sole, Cristina; Mardomingo, Raquel

    2004-01-01

    This paper discusses a framework for designing online tasks that capitalizes on the possibilities that the Internet and the Web offer for language learning. To present such a framework, we draw from constructivist theories (Brooks and Brooks, 1993) and their application to educational technology (Newby, Stepich, Lehman and Russell, 1996; Jonassen,…

  5. 3Ps, Task-Based Learning, and the Japanese Learner.

    Science.gov (United States)

    Tanasarnsanee, Mika

    2002-01-01

    Summarizes the findings of a work in progress that attempted to investigate to what extent task-based learning was more effective than the 3Ps approach in the teaching of Japanese as a foreign language in Thailand. (Author/VWL)

  6. Implementing Task-Based Learning with Young Learners.

    Science.gov (United States)

    Carless, David

    2002-01-01

    Draws on qualitative classroom observation data from case studies of three English-as-a-Foreign-Language classes in Hong Kong primary schools. Analyzes four themes relevant to the classroom implementation of task-based learning with young learners: noise/in discipline, use of the mother tongue, extent of pupil involvement, and the role of drawing…

  7. Enhancing Automaticity through Task-Based Language Learning

    Science.gov (United States)

    De Ridder, Isabelle; Vangehuchten, Lieve; Gomez, Marta Sesena

    2007-01-01

    In general terms automaticity could be defined as the subconscious condition wherein "we perform a complex series of tasks very quickly and efficiently, without having to think about the various components and subcomponents of action involved" (DeKeyser 2001: 125). For language learning, Segalowitz (2003) characterised automaticity as a…

  8. Positive versus Negative Communication Strategies in Task-Based Learning

    Science.gov (United States)

    Rohani, Siti

    2013-01-01

    This study aimed at describing how the implementation of Task-Based Learning (TBL) would shape or change students' use of oral communication strategies. Students' problems and strategies to solve the problems during the implementation of TBL were also explored. The study was a mixed method, employing both quantitative and qualitative analysis…

  9. Normative Feedback Effects on Learning a Timing Task

    Science.gov (United States)

    Wulf, Gabriele; Chiviacowsky, Suzete; Lewthwaite, Rebecca

    2010-01-01

    This study investigated the influence of normative feedback on learning a sequential timing task. In addition to feedback about their performance per trial, two groups of participants received bogus normative feedback about a peer group's average block-to-block improvement after each block of 10 trials. Scores indicated either greater (better…

  10. Sucrose Responsiveness, Learning Success, and Task Specialization in Ants

    Science.gov (United States)

    Perez, Margot; Rolland, Uther; Giurfa,, Martin; d'Ettorre, Patrizia

    2013-01-01

    Social insects possess remarkable learning capabilities, which are crucial for their ecological success. They also exhibit interindividual differences in responsiveness to environmental stimuli, which underlie task specialization and division of labor. Here we investigated for the first time the relationships between sucrose responsiveness,…

  11. Preservice Teachers' Learning to Plan Intellectually Challenging Tasks

    Science.gov (United States)

    Kang, Hosun

    2017-01-01

    This study explores how and under which conditions preservice secondary science teachers (PSTs) engage in effective planning practices that incorporate intellectually challenging tasks into lessons. Drawing upon a situative perspective on learning, eight PSTs' trajectories of participation in communities of practice are examined with a focus on…

  12. Selecting Texts and Tasks for Content Area Reading and Learning

    Science.gov (United States)

    Fisher, Douglas; Frey, Nancy

    2015-01-01

    For students to learn science, social studies, and technical subjects, their teachers have to engage them in meaningful lessons. As part of those lessons, students read informational texts. The selection of those texts is critical. Teachers can select texts worthy of attention and then align instruction and the post-reading tasks such that…

  13. Sucrose Responsiveness, Learning Success, and Task Specialization in Ants

    Science.gov (United States)

    Perez, Margot; Rolland, Uther; Giurfa,, Martin; d'Ettorre, Patrizia

    2013-01-01

    Social insects possess remarkable learning capabilities, which are crucial for their ecological success. They also exhibit interindividual differences in responsiveness to environmental stimuli, which underlie task specialization and division of labor. Here we investigated for the first time the relationships between sucrose responsiveness,…

  14. Task-specific transfer of perceptual learning across sensory modalities.

    Science.gov (United States)

    McGovern, David P; Astle, Andrew T; Clavin, Sarah L; Newell, Fiona N

    2016-01-11

    It is now widely accepted that primary cortical areas of the brain that were once thought to be sensory-specific undergo significant functional reorganisation following sensory deprivation. For instance, loss of vision or audition leads to the brain areas normally associated with these senses being recruited by the remaining sensory modalities [1]. Despite this, little is known about the rules governing crossmodal plasticity in people who experience typical sensory development, or the potential behavioural consequences. Here, we used a novel perceptual learning paradigm to assess whether the benefits associated with training on a task in one sense transfer to another sense. Participants were randomly assigned to a spatial or temporal task that could be performed visually or aurally, which they practiced for five days; before and after training, we measured discrimination thresholds on all four conditions and calculated the extent of transfer between them. Our results show a clear transfer of learning between sensory modalities; however, generalisation was limited to particular conditions. Specifically, learned improvements on the spatial task transferred from the visual domain to the auditory domain, but not vice versa. Conversely, benefits derived from training on the temporal task transferred from the auditory domain to visual domain, but not vice versa. These results suggest a unidirectional transfer of perceptual learning from dominant to non-dominant sensory modalities and place important constraints on models of multisensory processing and plasticity.

  15. Covert Operant Reinforcement of Remedial Reading Learning Tasks.

    Science.gov (United States)

    Schmickley, Verne G.

    The effects of covert operant reinforcement upon remedial reading learning tasks were investigated. Forty junior high school students were taught to imagine either neutral scenes (control) or positive scenes (treatment) upon cue while reading. It was hypothesized that positive covert reinforcement would enhance performance on several measures of…

  16. Concrete and Abstract Visualizations in History Learning Tasks

    Science.gov (United States)

    Prangsma, Maaike E.; van Boxtel, Carla A. M.; Kanselaar, Gellof; Kirschner, Paul A.

    2009-01-01

    Background: History learning requires that students understand historical phenomena, abstract concepts and the relations between them. Students have problems grasping, using and relating complex historical developments and structures. Aims: A study was conducted to determine the effects of tasks with abstract and/or concrete visualizations on the…

  17. A Model for Detecting Tor Encrypted Traffic using Supervised Machine Learning

    Directory of Open Access Journals (Sweden)

    Alaeddin Almubayed

    2015-06-01

    Full Text Available Tor is the low-latency anonymity tool and one of the prevalent used open source anonymity tools for anonymizing TCP traffic on the Internet used by around 500,000 people every day. Tor protects user's privacy against surveillance and censorship by making it extremely difficult for an observer to correlate visited websites in the Internet with the real physical-world identity. Tor accomplished that by ensuring adequate protection of Tor traffic against traffic analysis and feature extraction techniques. Further, Tor ensures anti-website fingerprinting by implementing different defences like TLS encryption, padding, and packet relaying. However, in this paper, an analysis has been performed against Tor from a local observer in order to bypass Tor protections; the method consists of a feature extraction from a local network dataset. Analysis shows that it's still possible for a local observer to fingerprint top monitored sites on Alexa and Tor traffic can be classified amongst other HTTPS traffic in the network despite the use of Tor's protections. In the experiment, several supervised machine-learning algorithms have been employed. The attack assumes a local observer sitting on a local network fingerprinting top 100 sites on Alexa; results gave an improvement amongst previous results by achieving an accuracy of 99.64% and 0.01% false positive.

  18. An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning

    Directory of Open Access Journals (Sweden)

    Jingsha He

    2017-03-01

    Full Text Available In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people’s daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users’ privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL, to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.

  19. How to measure metallicity from five-band photometry with supervised machine learning algorithms

    CERN Document Server

    Acquaviva, Viviana

    2015-01-01

    We demonstrate that it is possible to measure metallicity from the SDSS five-band photometry to better than 0.1 dex using supervised machine learning algorithms. Using spectroscopic estimates of metallicity as ground truth, we build, optimize and train several estimators to predict metallicity. We use the observed photometry, as well as derived quantities such as stellar mass and photometric redshift, as features, and we build two sample data sets at median redshifts of 0.103 and 0.218 and median r-band magnitude of 17.5 and 18.3 respectively. We find that ensemble methods, such as Random Forests of Trees and Extremely Randomized Trees, and Support Vector Machines all perform comparably well and can measure metallicity with a Root Mean Square Error (RMSE) of 0.081 and 0.090 for the two data sets when all objects are included. The fraction of outliers (objects for which the difference between true and predicted metallicity is larger than 0.2 dex) is only 2.2 and 3.9% respectively, and the RMSE decreases to 0.0...

  20. Supervised Learning Detection of Sixty Non-transiting Hot Jupiter Candidates

    Science.gov (United States)

    Millholland, Sarah; Laughlin, Gregory

    2017-09-01

    The optical full-phase photometric variations of a short-period planet provide a unique view of the planet’s atmospheric composition and dynamics. The number of planets with optical phase curve detections, however, is currently too small to study them as an aggregate population, motivating an extension of the search to non-transiting planets. Here we present an algorithm for the detection of non-transiting short-period giant planets in the Kepler field. The procedure uses the phase curves themselves as evidence for the planets’ existence. We employ a supervised learning algorithm to recognize the salient time-dependent properties of synthetic phase curves; we then search for detections of signals that match these properties. After demonstrating the algorithm’s capabilities, we classify 142,630 FGK Kepler stars without confirmed planets or Kepler Objects of Interest, and for each one, we assign a probability of a phase curve of a non-transiting planet being present. We identify 60 high-probability non-transiting hot Jupiter candidates. We also derive constraints on the candidates’ albedos and offsets of the phase curve maxima. These targets are strong candidates for follow-up radial velocity confirmation and characterization. Once confirmed, the atmospheric information content in the phase curves may be studied in yet greater detail.

  1. Distributed multisensory integration in a recurrent network model through supervised learning

    Science.gov (United States)

    Wang, He; Wong, K. Y. Michael

    Sensory integration between different modalities has been extensively studied. It is suggested that the brain integrates signals from different modalities in a Bayesian optimal way. However, how the Bayesian rule is implemented in a neural network remains under debate. In this work we propose a biologically plausible recurrent network model, which can perform Bayesian multisensory integration after trained by supervised learning. Our model is composed of two modules, each for one modality. We assume that each module is a recurrent network, whose activity represents the posterior distribution of each stimulus. The feedforward input on each module is the likelihood of each modality. Two modules are integrated through cross-links, which are feedforward connections from the other modality, and reciprocal connections, which are recurrent connections between different modules. By stochastic gradient descent, we successfully trained the feedforward and recurrent coupling matrices simultaneously, both of which resembles the Mexican-hat. We also find that there are more than one set of coupling matrices that can approximate the Bayesian theorem well. Specifically, reciprocal connections and cross-links will compensate each other if one of them is removed. Even though trained with two inputs, the network's performance with only one input is in good accordance with what is predicted by the Bayesian theorem.

  2. Semi-supervised learning for detecting text-lines in noisy document images

    Science.gov (United States)

    Liu, Zongyi; Zhou, Hanning

    2010-01-01

    Document layout analysis is a key step in document image understanding with wide applications in document digitization and reformatting. Identifying correct layout from noisy scanned images is especially challenging. In this paper, we introduce a semi-supervised learning framework to detect text-lines from noisy document images. Our framework consists of three steps. The first step is the initial segmentation that extracts text-lines and images using simple morphological operations. The second step is a grouping-based layout analysis that identifies text-lines, image zones, column separator and vertical border noise. It is able to efficiently remove the vertical border noises from multi-column pages. The third step is an online classifier that is trained with the high confidence line detection results from Step Two, and filters out noise from low confidence lines. The classifier effectively removes speckle noises embedded inside the content zones. We compare the performance of our algorithm to the state-of-the-art work in the field on the UW-III database. We choose the results reported by the Image Understanding Pattern Recognition Research (IUPR) and Scansoft Omnipage SDK 15.5. We evaluate the performances at both the page frame level and the text-line level. The result shows that our system has much lower false-alarm rate, while maintains similar content detection rate. In addition, we also show that our online training model generalizes better than algorithms depending on offline training.

  3. Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

    Science.gov (United States)

    Cao, Peng; Liu, Xiaoli; Bao, Hang; Yang, Jinzhu; Zhao, Dazhe

    2015-01-01

    The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.

  4. Supporting and Supervising Teachers Working With Adults Learning English. CAELA Network Brief

    Science.gov (United States)

    Young, Sarah

    2009-01-01

    This brief provides an overview of the knowledge and skills that administrators need in order to support and supervise teachers of adult English language learners. It begins with a review of resources and literature related to teacher supervision in general and to adult ESL education. It continues with information on the background and…

  5. Enhancing the Doctoral Journey: The Role of Group Supervision in Supporting Collaborative Learning and Creativity

    Science.gov (United States)

    Fenge, Lee-Ann

    2012-01-01

    This article explores the role of group supervision within doctoral education, offering an exploration of the experience of group supervision processes through a small-scale study evaluating both student and staff experience across three cohorts of one professional doctorate programme. There has been very little research to date exploring…

  6. Is Direct Supervision in Clinical Education for Athletic Training Students Always Necessary to Enhance Student Learning?

    Science.gov (United States)

    Scriber, Kent; Trowbridge, Cindy

    2009-01-01

    Objective: To present an alternative model of supervision within clinical education experiences. Background: Several years ago direct supervision was defined more clearly in the accreditation standards for athletic training education programs (ATEPs). Currently, athletic training students may not gain any clinical experience without their clinical…

  7. Clinical group supervision in yoga therapy: model effects, and lessons learned.

    Science.gov (United States)

    Forbes, Bo; Volpe Horii, Cassandra; Earls, Bethany; Mashek, Stephanie; Akhtar, Fiona

    2012-01-01

    Clinical supervision is an integral component of therapist training and professional development because of its capacity for fostering knowledge, self-awareness, and clinical acumen. Individual supervision is part of many yoga therapy training programs and is referenced in the IAYT Standards as "mentoring." Group supervision is not typically used in the training of yoga therapists. We propose that group supervision effectively supports the growth and development of yoga therapists-in-training. We present a model of group supervision for yoga therapist trainees developed by the New England School of Integrative Yoga Therapeutics™ (The NESIYT Model) that includes the background, structure, format, and development of our inaugural 18-month supervision group. Pre-and post-supervision surveys and analyzed case notes, which captured key didactic and process themes, are discussed. Clinical issues, such as boundaries, performance anxiety, sense of self efficacy, the therapeutic alliance, transference and counter transference, pacing of yoga therapy sessions, evaluation of client progress, and adjunct therapist interaction are reviewed. The timing and sequence of didactic and process themes and benefits for yoga therapist trainees' professional development, are discussed. The NESIYT group supervision model is offered as an effective blueprint for yoga therapy training programs.

  8. Knowledge Work Supervision: Transforming School Systems into High Performing Learning Organizations.

    Science.gov (United States)

    Duffy, Francis M.

    1997-01-01

    This article describes a new supervision model conceived to help a school system redesign its anatomy (structures), physiology (flow of information and webs of relationships), and psychology (beliefs and values). The new paradigm (Knowledge Work Supervision) was constructed by reviewing the practices of several interrelated areas: sociotechnical…

  9. Classification and Diagnostic Output Prediction of Cancer Using Gene Expression Profiling and Supervised Machine Learning Algorithms

    DEFF Research Database (Denmark)

    Yoo, C.; Gernaey, Krist

    2008-01-01

    In this paper, a new supervised clustering and classification method is proposed. First, the application of discriminant partial least squares (DPLS) for the selection of a minimum number of key genes is applied on a gene expression microarray data set. Second, supervised hierarchical clustering ...

  10. Robust visual tracking via structured multi-task sparse learning

    KAUST Repository

    Zhang, Tianzhu

    2012-11-09

    In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing lp,q mixed norms (specifically p∈2,∞ and q=1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L1 tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259-2272, 2011) is a special case of our MTT formulation (denoted as the L11 tracker) when p=q=1. Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers. © 2012 Springer Science+Business Media New York.

  11. Material classification and automatic content enrichment of images using supervised learning and knowledge bases

    Science.gov (United States)

    Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

    2011-02-01

    In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

  12. Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

    Science.gov (United States)

    Lin, Yu-Pu; Bennett, Christopher H.; Cabaret, Théo; Vodenicarevic, Damir; Chabi, Djaafar; Querlioz, Damien; Jousselme, Bruno; Derycke, Vincent; Klein, Jacques-Olivier

    2016-09-01

    Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

  13. Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

    Science.gov (United States)

    Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian; Augustson, Erik

    2015-08-25

    Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Social media outlets like Twitter can uncover real-time snapshots of

  14. Social learning of an associative foraging task in zebrafish

    Science.gov (United States)

    Zala, Sarah M.; Määttänen, Ilmari

    2013-05-01

    The zebrafish ( Danio rerio) is increasingly becoming an important model species for studies on the genetic and neural mechanisms controlling behaviour and cognition. Here, we utilized a conditioned place preference (CPP) paradigm to study social learning in zebrafish. We tested whether social interactions with conditioned demonstrators enhance the ability of focal naïve individuals to learn an associative foraging task. We found that the presence of conditioned demonstrators improved focal fish foraging behaviour through the process of social transmission, whereas the presence of inexperienced demonstrators interfered with the learning of the control focal fish. Our results indicate that zebrafish use social learning for finding food and that this CPP paradigm is an efficient assay to study social learning and memory in zebrafish.

  15. Social learning of an associative foraging task in zebrafish.

    Science.gov (United States)

    Zala, Sarah M; Määttänen, Ilmari

    2013-05-01

    The zebrafish (Danio rerio) is increasingly becoming an important model species for studies on the genetic and neural mechanisms controlling behaviour and cognition. Here, we utilized a conditioned place preference (CPP) paradigm to study social learning in zebrafish. We tested whether social interactions with conditioned demonstrators enhance the ability of focal naïve individuals to learn an associative foraging task. We found that the presence of conditioned demonstrators improved focal fish foraging behaviour through the process of social transmission, whereas the presence of inexperienced demonstrators interfered with the learning of the control focal fish. Our results indicate that zebrafish use social learning for finding food and that this CPP paradigm is an efficient assay to study social learning and memory in zebrafish.

  16. Plagiarism in solutions of programming tasks in distance learning

    Directory of Open Access Journals (Sweden)

    Krzysztof Barteczko

    2012-12-01

    Full Text Available Source code plagiarism in students solutions of programming tasks is a serious problem, especially important in distance learning. Naturally, it should be prevented, but publicly available code plagiarism detection tools are not fully adjusted to this purpose. This paper proposes the specific approach to detecting code duplicates. This approach is based on adapting of detection process to characteristics of programming tasks and comprise of freshly developed detecting tools, which could be configured and tuned to fit individual features of the programming task. Particular attention is paid to the possibility of an automatic elimination of duplicate codes from the set of all solutions. As a minimum, this requires the rejection of false-positive duplicates, even for simple, schematic tasks. The case in the use of tools is presented in this context. The discussion is illustrated by applying of proposed tools to duplicates detection in the set of actual, real-life, codes written in Java programming language.

  17. Supervised Transfer Sparse Coding

    KAUST Repository

    Al-Shedivat, Maruan

    2014-07-27

    A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

  18. Attend in groups: a weakly-supervised deep learning framework for learning from web data

    OpenAIRE

    Zhuang, Bohan; Liu, Lingqiao; Li, Yao; Shen, Chunhua; Reid, Ian

    2016-01-01

    Large-scale datasets have driven the rapid development of deep neural networks for visual recognition. However, annotating a massive dataset is expensive and time-consuming. Web images and their labels are, in comparison, much easier to obtain, but direct training on such automatically harvested images can lead to unsatisfactory performance, because the noisy labels of Web images adversely affect the learned recognition models. To address this drawback we propose an end-to-end weakly-supervis...

  19. Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

    Directory of Open Access Journals (Sweden)

    Jacquomo Monk

    2012-11-01

    Full Text Available An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC, Quick, Unbiased, Efficient Statistical Tree (QUEST, Random Forest (RF and Support Vector Machine (SVM were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30° and 50°. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats.

  20. Exposing sequence learning in a double-step task.

    Science.gov (United States)

    Oostwoud Wijdenes, Leonie; Brenner, Eli; Smeets, Jeroen B J

    2016-06-01

    Is it possible to learn to perform a motor sequence without awareness of the sequence? In two experiments, we presented participants with the most elementary sequence: an alternation between two options. We used a double-step pointing task in which the final position of the target alternated between two quite similar values. The task forced participants to start moving before the final target was visible, allowing us to determine participants' expectations about the final target position without explicitly asking them. We tracked participants' expectations (and thus motor sequence learning) by measuring the direction of the initial part of the movement, before any response to the final step. We found that participants learnt to anticipate the average size of the final step, but that they did not learn the sequence. In a second experiment, we extended the duration of the learning period and increased the difference in size between the target position changes. Some participants started anticipating the step size in accordance with the sequence at some time during the experiment. These participants reported having noticed the simple sequence. The participants who had not noticed the sequence did not move in anticipation of the sequence. This suggests that participants who did not learn this very simple sequence explicitly also did not learn it implicitly.

  1. Optimizing learning of a locomotor task: amplifying errors as needed.

    Science.gov (United States)

    Marchal-Crespo, Laura; López-Olóriz, Jorge; Jaeger, Lukas; Riener, Robert

    2014-01-01

    Research on motor learning has emphasized that errors drive motor adaptation. Thereby, several researchers have proposed robotic training strategies that amplify movement errors rather than decrease them. In this study, the effect of different robotic training strategies that amplify errors on learning a complex locomotor task was investigated. The experiment was conducted with a one degree-of freedom robotic stepper (MARCOS). Subjects were requested to actively coordinate their legs in a desired gait-like pattern in order to track a Lissajous figure presented on a visual display. Learning with three different training strategies was evaluated: (i) No perturbation: the robot follows the subjects' movement without applying any perturbation, (ii) Error amplification: existing errors were amplified with repulsive forces proportional to errors, (iii) Noise disturbance: errors were evoked with a randomly-varying force disturbance. Results showed that training without perturbations was especially suitable for a subset of initially less-skilled subjects, while error amplification seemed to benefit more skilled subjects. Training with error amplification, however, limited transfer of learning. Random disturbing forces benefited learning and promoted transfer in all subjects, probably because it increased attention. These results suggest that learning a locomotor task can be optimized when errors are randomly evoked or amplified based on subjects' initial skill level.

  2. Incidental orthographic learning during a color detection task.

    Science.gov (United States)

    Protopapas, Athanassios; Mitsi, Anna; Koustoumbardis, Miltiadis; Tsitsopoulou, Sofia M; Leventi, Marianna; Seitz, Aaron R

    2017-09-01

    Orthographic learning refers to the acquisition of knowledge about specific spelling patterns forming words and about general biases and constraints on letter sequences. It is thought to occur by strengthening simultaneously activated visual and phonological representations during reading. Here we demonstrate that a visual perceptual learning procedure that leaves no time for articulation can result in orthographic learning evidenced in improved reading and spelling performance. We employed task-irrelevant perceptual learning (TIPL), in which the stimuli to be learned are paired with an easy task target. Assorted line drawings and difficult-to-spell words were presented in red color among sequences of other black-colored words and images presented in rapid succession, constituting a fast-TIPL procedure with color detection being the explicit task. In five experiments, Greek children in Grades 4-5 showed increased recognition of words and images that had appeared in red, both during and after the training procedure, regardless of within-training testing, and also when targets appeared in blue instead of red. Significant transfer to reading and spelling emerged only after increased training intensity. In a sixth experiment, children in Grades 2-3 showed generalization to words not presented during training that carried the same derivational affixes as in the training set. We suggest that reinforcement signals related to detection of the target stimuli contribute to the strengthening of orthography-phonology connections beyond earlier levels of visually-based orthographic representation learning. These results highlight the potential of perceptual learning procedures for the reinforcement of higher-level orthographic representations. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  3. Kollegial supervision

    DEFF Research Database (Denmark)

    Andersen, Ole Dibbern; Petersson, Erling

    Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution......Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution...

  4. Robust visual tracking via multi-task sparse learning

    KAUST Repository

    Zhang, Tianzhu

    2012-06-01

    In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in MTT. By employing popular sparsity-inducing p, q mixed norms (p D; 1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L 1 tracker [15] is a special case of our MTT formulation (denoted as the L 11 tracker) when p q 1. The learning problem can be efficiently solved using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, MTT is computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that MTT methods consistently outperform state-of-the-art trackers. © 2012 IEEE.

  5. Task-based learning (TBL) in undergraduate medical education.

    Science.gov (United States)

    Virjo, Irma; Holmberg-Marttila, Doris; Mattila, Kari

    2001-01-01

    Problem-based learning (PBL) is a proven method to learn medicine during the first years of studies. In the clinical phase the active, self-directive student may experience difficulties in adapting to the life of professionals in health care units, where students usually have to attend and work according to preplanned timetables. Task-based learning (TBL) can serve as an intermediary in the meeting of these two cultures. Here we describe a TBL study module for fourth-year medical students and experiences of implementing it at the University of Tampere in Finland. Eighty-five students participated in this study in 1998 and 1999. Our results show that this method works and that it leads to learning. Students evaluate their skills connected with the general practitioner's work in a health centre hospital as better after the study module than at the onset.

  6. Semi-supervised Machine Learning for Analysis of Hydrogeochemical Data and Models

    Science.gov (United States)

    Vesselinov, Velimir; O'Malley, Daniel; Alexandrov, Boian; Moore, Bryan

    2017-04-01

    Data- and model-based analyses such as uncertainty quantification, sensitivity analysis, and decision support using complex physics models with numerous model parameters and typically require a huge number of model evaluations (on order of 10^6). Furthermore, model simulations of complex physics may require substantial computational time. For example, accounting for simultaneously occurring physical processes such as fluid flow and biogeochemical reactions in heterogeneous porous medium may require several hours of wall-clock computational time. To address these issues, we have developed a novel methodology for semi-supervised machine learning based on Non-negative Matrix Factorization (NMF) coupled with customized k-means clustering. The algorithm allows for automated, robust Blind Source Separation (BSS) of groundwater types (contamination sources) based on model-free analyses of observed hydrogeochemical data. We have also developed reduced order modeling tools, which coupling support vector regression (SVR), genetic algorithms (GA) and artificial and convolutional neural network (ANN/CNN). SVR is applied to predict the model behavior within prior uncertainty ranges associated with the model parameters. ANN and CNN procedures are applied to upscale heterogeneity of the porous medium. In the upscaling process, fine-scale high-resolution models of heterogeneity are applied to inform coarse-resolution models which have improved computational efficiency while capturing the impact of fine-scale effects at the course scale of interest. These techniques are tested independently on a series of synthetic problems. We also present a decision analysis related to contaminant remediation where the developed reduced order models are applied to reproduce groundwater flow and contaminant transport in a synthetic heterogeneous aquifer. The tools are coded in Julia and are a part of the MADS high-performance computational framework (https://github.com/madsjulia/Mads.jl).

  7. SU-E-J-107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis

    Energy Technology Data Exchange (ETDEWEB)

    Bredfeldt, J; Liu, Y; Conklin, M; Keely, P; Eliceiri, K; Mackie, T [University of Wisconsin, Madison, WI (United States)

    2014-06-01

    Purpose: Our goal is to develop and apply a set of optical and computational tools to enable large-scale investigations of the interaction between collagen and tumor cells. Methods: We have built a novel imaging system for automating the capture of whole-slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi-automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). This group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups. Results: We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells. Conclusion: We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.

  8. Semantics boosts syntax in artificial grammar learning tasks with recursion.

    Science.gov (United States)

    Fedor, Anna; Varga, Máté; Szathmáry, Eörs

    2012-05-01

    Center-embedded recursion (CER) in natural language is exemplified by sentences such as "The malt that the rat ate lay in the house." Parsing center-embedded structures is in the focus of attention because this could be one of the cognitive capacities that make humans distinct from all other animals. The ability to parse CER is usually tested by means of artificial grammar learning (AGL) tasks, during which participants have to infer the rule from a set of artificial sentences. One of the surprising results of previous AGL experiments is that learning CER is not as easy as had been thought. We hypothesized that because artificial sentences lack semantic content, semantics could help humans learn the syntax of center-embedded sentences. To test this, we composed sentences from 4 vocabularies of different degrees of semantic content due to 3 factors (familiarity, meaning of words, and semantic relationship between words). According to our results, these factors have no effect one by one but they make learning significantly faster when combined. This leads to the assumption that there were different mechanisms at work when CER was parsed in natural and in artificial languages. This finding questions the suitability of AGL tasks with artificial vocabularies for studying the learning and processing of linguistic CER.

  9. Investigating the control of climatic oscillations over global terrestrial evaporation using a simple supervised learning method

    Science.gov (United States)

    Martens, Brecht; Miralles, Diego; Waegeman, Willem; Dorigo, Wouter; Verhoest, Niko

    2017-04-01

    Intra-annual and multi-decadal variations in the Earth's climate are to a large extent driven by periodic oscillations in the coupled state of atmosphere and ocean. These oscillations alter not only the climate in nearby regions, but also have an important impact on the local climate in remote areas, a phenomenon that is often referred to as 'teleconnection'. Because changes in local climate immediately impact terrestrial ecosystems through a series of complex processes and feedbacks, ocean-atmospheric teleconnections are expected to influence land evaporation - i.e. the return flux of water from land to atmosphere. In this presentation, the effects of these intra-annual and multi-decadal climate oscillations on global terrestrial evaporation are analysed. To this end, we use satellite observations of different essential climate variables in combination with a simple supervised learning method, the lasso regression. A total of sixteen Climate Oscillation Indices (COIs) - which are routinely used to diagnose the major ocean-atmospheric oscillations - are selected. Multi-decadal data of terrestrial evaporation are retrieved from the Global Land Evaporation Amsterdam Model (GLEAM, www.gleam.eu). Using the lasso regression, it is shown that more than 30% of the inter-annual variations in terrestrial evaporation can be explained by ocean-atmospheric oscillations. In addition, the impact in different regions across the globe can typically be attributed to a small subset of the sixteen COIs. For instance, the dynamics in terrestrial evaporation over Australia are substantially impacted by both the El Niño Southern Oscillation (here diagnosed using the Southern Oscillation Index, SOI) and the Indian Ocean Dipole Oscillation (here diagnosed using the Indian Dipole Mode Index, DMI). Subsequently, using the same learning method but regressing terrestrial evaporation to its local climatic drivers (air temperature, precipitation, radiation), allows us to discern through which

  10. Taking advantage of sparsity in multi-task learning

    OpenAIRE

    Lounici, K.; Pontil, M.; Tsybakov, A. B.; van de Geer, S. A.

    2009-01-01

    We study the problem of estimating multiple linear regression equations for the purpose of both prediction and variable selection. Following recent work on multi-task learning Argyriou et al. [2008], we assume that the regression vectors share the same sparsity pattern. This means that the set of relevant predictor variables is the same across the different equations. This assumption leads us to consider the Group Lasso as a candidate estimation method. We show that this estimator enjoys nice...

  11. A Developmental Perspective in Learning the Mirror-Drawing Task

    Directory of Open Access Journals (Sweden)

    Mona Sharon Julius

    2016-03-01

    Full Text Available Is there late maturation of skill learning? This notion has been raised to explain an adult advantage in learning a variety of tasks, such as auditory temporal-interval discrimination, locomotion adaptation, and drawing visually-distorted spatial patterns (mirror-drawing. Here, we test this assertion by following the practice of the mirror-drawing task in two 5 min daily sessions separated by a 10 min break, over the course of two days, in 5–6-year-old kindergarten children, 7–8-year-old second-graders, and young adults. In the mirror-drawing task, participants were required to trace a square while looking at their hand only as a reflection in a mirror. Kindergarteners did not show learning of the visual-motor mapping, and on average, did not produce even one full side of a square correctly. Second-graders showed increased online movement control with longer strokes, and robust learning of the visual-motor mapping, resulting in a between-day increase in the number of correctly drawn sides with no loss in accuracy. Overall, kindergarteners and second-graders producing at least one correct polygon-side on Day 1 were more likely to improve their performance between days. Adults showed better performance with greater improvements in the number of correctly drawn sides between- and within-days, and in accuracy between days. It has been suggested that 5-year-olds cannot learn the task due to their inability to detect and encapsulate previously produced accurate movements. Our findings suggest, instead, that these children did not have initial, accurate performance that could be enhanced through training. Recently, it has been shown that in a simple grapho-motor task the three age-groups improved their speed of performance within a session and between-days, while maintaining accuracy scores. Taken together, these data suggest that children's motor skill learning depends on the task’s characteristics and their adopting an efficient performance

  12. Learning a locomotor task: with or without errors?

    Science.gov (United States)

    Marchal-Crespo, Laura; Schneider, Jasmin; Jaeger, Lukas; Riener, Robert

    2014-03-04

    Robotic haptic guidance is the most commonly used robotic training strategy to reduce performance errors while training. However, research on motor learning has emphasized that errors are a fundamental neural signal that drive motor adaptation. Thus, researchers have proposed robotic therapy algorithms that amplify movement errors rather than decrease them. However, to date, no study has analyzed with precision which training strategy is the most appropriate to learn an especially simple task. In this study, the impact of robotic training strategies that amplify or reduce errors on muscle activation and motor learning of a simple locomotor task was investigated in twenty two healthy subjects. The experiment was conducted with the MAgnetic Resonance COmpatible Stepper (MARCOS) a special robotic device developed for investigations in the MR scanner. The robot moved the dominant leg passively and the subject was requested to actively synchronize the non-dominant leg to achieve an alternating stepping-like movement. Learning with four different training strategies that reduce or amplify errors was evaluated: (i) Haptic guidance: errors were eliminated by passively moving the limbs, (ii) No guidance: no robot disturbances were presented, (iii) Error amplification: existing errors were amplified with repulsive forces, (iv) Noise disturbance: errors were evoked intentionally with a randomly-varying force disturbance on top of the no guidance strategy. Additionally, the activation of four lower limb muscles was measured by the means of surface electromyography (EMG). Strategies that reduce or do not amplify errors limit muscle activation during training and result in poor learning gains. Adding random disturbing forces during training seems to increase attention, and therefore improve motor learning. Error amplification seems to be the most suitable strategy for initially less skilled subjects, perhaps because subjects could better detect their errors and correct them

  13. Observation learning of a motor task: who and when?

    Science.gov (United States)

    Andrieux, Mathieu; Proteau, Luc

    2013-08-01

    Observation contributes to motor learning. It was recently demonstrated that the observation of both a novice and an expert model (mixed observation) resulted in better learning of a complex spatiotemporal task than the observation of either a novice or an expert model. In experiment 1, we aimed to determine whether mixed observation better promotes learning due to the information that can be gained from two models who exhibit different skill levels or simply because multiple models, regardless of their level of expertise, better promote learning than would a single model. The results revealed that the observation of both an expert and a novice model resulted in better short-term retention than the observation of either two novice or two expert models. In experiment 2, we wanted to determine whether these benefits would last longer if physical practice trials were interspersed with observation. Mixed and (to some extent) expert observations resulted in better long-term retention than observation of a novice model. We suggest that alternating mixed/expert observation with physical practice trials makes one's error more salient than when all observation trials are completed before one first starts performing the experimental task, which increases activation of the action observation network.

  14. Learning New Grammatical Structures in Task-Based Language Learning: The Effects of Recasts and Prompts

    Science.gov (United States)

    Van de Guchte, Marrit; Braaksma, Martine; Rijlaarsdam, Gert; Bimmel, Peter

    2015-01-01

    In the present study, we examine the effects of prompts and recasts on the acquisition of two new and different grammar structures in a task-based learning environment. Sixty-four 14-year-old 9th grade students (low intermediate) learning German as a foreign language were randomly assigned to three conditions: two experimental groups (one…

  15. Learning New Grammatical Structures in Task-Based Language Learning: The Effects of Recasts and Prompts

    NARCIS (Netherlands)

    van de Guchte, M.; Braaksma, M.; Rijlaarsdam, G.; Bimmel, P.

    2015-01-01

    In the present study, we examine the effects of prompts and recasts on the acquisition of two new and different grammar structures in a task-based learning environment. Sixty-four 14-year-old 9th grade students (low intermediate) learning German as a foreign language were randomly assigned to three

  16. Learning New Grammatical Structures in Task-Based Language Learning: The Effects of Recasts and Prompts

    Science.gov (United States)

    Van de Guchte, Marrit; Braaksma, Martine; Rijlaarsdam, Gert; Bimmel, Peter

    2015-01-01

    In the present study, we examine the effects of prompts and recasts on the acquisition of two new and different grammar structures in a task-based learning environment. Sixty-four 14-year-old 9th grade students (low intermediate) learning German as a foreign language were randomly assigned to three conditions: two experimental groups (one…

  17. Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction

    Directory of Open Access Journals (Sweden)

    Che Ngufor

    2013-01-01

    Full Text Available The task identifying changes and irregularities in medical insurance claim pay-ments is a difficult process of which the traditional practice involves queryinghistorical claims databases and flagging potential claims as normal or abnor-mal. Because what is considered as normal payment is usually unknown andmay change over time, abnormal payments often pass undetected; only to bediscovered when the payment period has passed.This paper presents the problem of on-line unsupervised learning from datastreams when the distribution that generates the data changes or drifts overtime. Automated algorithms for detecting drifting concepts in a probabilitydistribution of the data are presented. The idea behind the presented driftdetection methods is to transform the distribution of the data within a slidingwindow into a more convenient distribution. Then, a test statistics p-value ata given significance level can be used to infer the drift rate, adjust the windowsize and decide on the status of the drift. The detected concepts drifts areused to label the data, for subsequent learning of classification models by asupervised learner. The algorithms were tested on several synthetic and realmedical claims data sets.

  18. Learning Task Knowledge from Dialog and Web Access

    Directory of Open Access Journals (Sweden)

    Vittorio Perera

    2015-06-01

    Full Text Available We present KnoWDiaL, an approach for Learning and using task-relevant Knowledge from human-robot Dialog and access to the Web. KnoWDiaL assumes that there is an autonomous agent that performs tasks, as requested by humans through speech. The agent needs to “understand” the request, (i.e., to fully ground the task until it can proceed to plan for and execute it. KnoWDiaL contributes such understanding by using and updating a Knowledge Base, by dialoguing with the user, and by accessing the web. We believe that KnoWDiaL, as we present it, can be applied to general autonomous agents. However, we focus on our work with our autonomous collaborative robot, CoBot, which executes service tasks in a building, moving around and transporting objects between locations. Hence, the knowledge acquired and accessed consists of groundings of language to robot actions, and building locations, persons, and objects. KnoWDiaL handles the interpretation of voice commands, is robust regarding speech recognition errors, and is able to learn commands involving referring expressions in an open domain, (i.e., without requiring a lexicon. We present in detail the multiple components of KnoWDiaL, namely a frame-semantic parser, a probabilistic grounding model, a web-based predicate evaluator, a dialog manager, and the weighted predicate-based Knowledge Base. We illustrate the knowledge access and updates from the dialog and Web access, through detailed and complete examples. We further evaluate the correctness of the predicate instances learned into the Knowledge Base, and show the increase in dialog efficiency as a function of the number of interactions. We have extensively and successfully used KnoWDiaL in CoBot dialoguing and accessing the Web, and extract a few corresponding example sequences from captured videos.

  19. Self-regulated learning processes of medical students during an academic learning task.

    Science.gov (United States)

    Gandomkar, Roghayeh; Mirzazadeh, Azim; Jalili, Mohammad; Yazdani, Kamran; Fata, Ladan; Sandars, John

    2016-10-01

    This study was designed to identify the self-regulated learning (SRL) processes of medical students during a biomedical science learning task and to examine the associations of the SRL processes with previous performance in biomedical science examinations and subsequent performance on a learning task. A sample of 76 Year 1 medical students were recruited based on their performance in biomedical science examinations and stratified into previous high and low performers. Participants were asked to complete a biomedical science learning task. Participants' SRL processes were assessed before (self-efficacy, goal setting and strategic planning), during (metacognitive monitoring) and after (causal attributions and adaptive inferences) their completion of the task using an SRL microanalytic interview. Descriptive statistics were used to analyse the means and frequencies of SRL processes. Univariate and multiple logistic regression analyses were conducted to examine the associations of SRL processes with previous examination performance and the learning task performance. Most participants (from 88.2% to 43.4%) reported task-specific processes for SRL measures. Students who exhibited higher self-efficacy (odds ratio [OR] 1.44, 95% confidence interval [CI] 1.09-1.90) and reported task-specific processes for metacognitive monitoring (OR 6.61, 95% CI 1.68-25.93) and causal attributions (OR 6.75, 95% CI 2.05-22.25) measures were more likely to be high previous performers. Multiple analysis revealed that similar SRL measures were associated with previous performance. The use of task-specific processes for causal attributions (OR 23.00, 95% CI 4.57-115.76) and adaptive inferences (OR 27.00, 95% CI 3.39-214.95) measures were associated with being a high learning task performer. In multiple analysis, only the causal attributions measure was associated with high learning task performance. Self-efficacy, metacognitive monitoring and causal attributions measures were associated

  20. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    Science.gov (United States)

    Ceylan Koydemir, Hatice; Feng, Steve; Liang, Kyle; Nadkarni, Rohan; Benien, Parul; Ozcan, Aydogan

    2017-06-01

    Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of 0.8 cm2 and weighs only 180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved a

  1. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    Directory of Open Access Journals (Sweden)

    Ceylan Koydemir Hatice

    2017-06-01

    Full Text Available Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of ~0.8 cm2 and weighs only ~180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond

  2. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    KAUST Repository

    Ceylan Koydemir, Hatice

    2017-06-14

    Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of ~0.8 cm2 and weighs only ~180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved

  3. 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

  4. Presentation, practice and production versus task based learning using form focused tasks

    OpenAIRE

    Zavala Carrión, Belinda

    2012-01-01

    The present research has the intention to experience a different teaching model called Task Based Learning and compared to the Presentation-Practice-Production model measure somehow the students’ response towards the model and at the end see how homogeneous the language skills are developed plus their level of achievement. Tesis (Maestría en Educación. Mención en Enseñanza de inglés como Lengua Extranjera)--Universidad de Piura. Facultad de Ciencias de la Educación, 2012.

  5. Support of the collaborative inquiry learning process: influence of support on task and team regulation

    NARCIS (Netherlands)

    Saab, N.; van Joolingen, W.; van Hout-Wolters, B.

    2012-01-01

    Regulation of the learning process is an important condition for efficient and effective learning. In collaborative learning, students have to regulate their collaborative activities (team regulation) next to the regulation of their own learning process focused on the task at hand (task regulation).

  6. Support of the collaborative inquiry learning process: influence of support on task and team regulation

    NARCIS (Netherlands)

    N. Saab; W. van Joolingen; B. van Hout-Wolters

    2012-01-01

    Regulation of the learning process is an important condition for efficient and effective learning. In collaborative learning, students have to regulate their collaborative activities (team regulation) next to the regulation of their own learning process focused on the task at hand (task regulation).

  7. Emotion-based learning: Insights from the Iowa Gambling Task

    Directory of Open Access Journals (Sweden)

    Oliver Hugh Turnbull

    2014-03-01

    Full Text Available Interest in the cognitive and/or emotional basis of complex decision-making, and the related phenomenon of emotion-based learning, has been heavily influenced by the Iowa Gambling Task. A number of psychological variables have been investigated as potentially important in understanding emotion-based learning. This paper reviews the extent to which humans are explicitly aware of how we make such decisions; the biasing influence of pre-existing emotional labels; and the extent to which emotion-based systems are anatomically and functionally independent of episodic memory. Systematic review suggests that (i an aspect of conscious awareness does appear to be readily achieved during the IGT, but as a relatively unfocused emotion-based ‘gut-feeling’, akin to intuition; (ii Several studies have manipulated the affective pre-loading of IGT tasks, and make it clear that such labelling has a substantial influence on performance, an experimental manipulation similar to the phenomenon of prejudice. (iii Finally, it appears that complex emotion-based learning can remain intact despite profound amnesia, at least in some neurological patients, a finding with a range of potentially important clinical implications: in the management of dementia; in explaining infantile amnesia; and in understanding of the possible mechanisms of psychotherapy.

  8. Emotion-based learning: insights from the Iowa Gambling Task.

    Science.gov (United States)

    Turnbull, Oliver H; Bowman, Caroline H; Shanker, Shanti; Davies, Julie L

    2014-01-01

    Interest in the cognitive and/or emotional basis of complex decision-making, and the related phenomenon of emotion-based learning, has been heavily influenced by the Iowa Gambling Task. A number of psychological variables have been investigated as potentially important in understanding emotion-based learning. This paper reviews the extent to which humans are explicitly aware of how we make such decisions; the biasing influence of pre-existing emotional labels; and the extent to which emotion-based systems are anatomically and functionally independent of episodic memory. Review of literature suggests that (i) an aspect of conscious awareness does appear to be readily achieved during the IGT, but as a relatively unfocused emotion-based "gut-feeling," akin to intuition; (ii) Several studies have manipulated the affective pre-loading of IGT tasks, and make it clear that such labeling has a substantial influence on performance, an experimental manipulation similar to the phenomenon of prejudice. (iii) Finally, it appears that complex emotion-based learning can remain intact despite profound amnesia, at least in some neurological patients, a finding with a range of potentially important clinical implications: in the management of dementia; in explaining infantile amnesia; and in understanding of the possible mechanisms of psychotherapy.

  9. Learning a novel myoelectric-controlled interface task.

    Science.gov (United States)

    Radhakrishnan, Saritha M; Baker, Stuart N; Jackson, Andrew

    2008-10-01

    Control of myoelectric prostheses and brain-machine interfaces requires learning abstract neuromotor transformations. To investigate the mechanisms underlying this ability, we trained subjects to move a two-dimensional cursor using a myoelectric-controlled interface. With the upper limb immobilized, an electromyogram from multiple hand and arm muscles moved the cursor in directions that were either intuitive or nonintuitive and with high or low variability. We found that subjects could learn even nonintuitive arrangements to a high level of performance. Muscle-tuning functions were cosine shaped and modulated so as to reduce cursor variability. Subjects exhibited an additional preference for using hand muscles over arm muscles, which resulted from a greater capacity of these to form novel, task-specific synergies. In a second experiment, nonvisual feedback from the hand was degraded with amplitude- and frequency-modulated vibration. Although vibration impaired task performance, it did not affect the rate at which learning occurred. We therefore conclude that the motor system can acquire internal models of novel, abstract neuromotor mappings even in the absence of overt movements or accurate proprioceptive signals, but that the distal motor system may be better suited to provide flexible control signals for neuromotor prostheses than structures related to the arm.

  10. Analytical reasoning task reveals limits of social learning in networks.

    Science.gov (United States)

    Rahwan, Iyad; Krasnoshtan, Dmytro; Shariff, Azim; Bonnefon, Jean-François

    2014-04-06

    Social learning-by observing and copying others-is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias', which limits their social learning to the output, rather than the process, of their peers' reasoning-even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

  11. Multi-task learning for pKa prediction.

    Science.gov (United States)

    Skolidis, Grigorios; Hansen, Katja; Sanguinetti, Guido; Rupp, Matthias

    2012-07-01

    Many compound properties depend directly on the dissociation constants of its acidic and basic groups. Significant effort has been invested in computational models to predict these constants. For linear regression models, compounds are often divided into chemically motivated classes, with a separate model for each class. However, sometimes too few measurements are available for a class to build a reasonable model, e.g., when investigating a new compound series. If data for related classes are available, we show that multi-task learning can be used to improve predictions by utilizing data from these other classes. We investigate performance of linear Gaussian process regression models (single task, pooling, and multi-task models) in the low sample size regime, using a published data set (n = 698, mostly monoprotic, in aqueous solution) divided beforehand into 15 classes. A multi-task regression model using the intrinsic model of co-regionalization and incomplete Cholesky decomposition performed best in 85% of all experiments. The presented approach can be applied to estimate other molecular properties where few measurements are available.

  12. A Neurophysiological examination of quality of learning in a feedback-based learning task.

    Science.gov (United States)

    Arbel, Yael; Wu, Hao

    2016-12-01

    The efficiency with which one processes external feedback contributes to the speed and quality of one's learning. Previous findings that the feedback related negativity (FRN) event related potential (ERP) is modulated by learning outcomes suggested that this ERP reflects the extent to which feedback is used by the learner to improve performance. To further test this suggestion, we measured whether the FRN and the fronto-central positivity (FCP) that follows it are modulated by learning slopes, and as a function of individual differences in learning outcomes. Participants were tasked with learning names (non-words) of 42 novel objects in a two-choice feedback-based visual learning task. The items were divided into three sets of 14 items, each presented in five learning blocks and a sixth test block. Individual learning slopes based on performance on the task, as well as FRN and FCP slopes based on positive and negative feedback related activation in each block were created for 53 participants. Our data pointed to an interaction between slopes of the FRN elicited by negative feedback and learning slopes, such that a sharper decrease in the amplitude of the FRN to negative feedback was associated with sharper learning slopes. We further examined the predictive power of the FRN and FCP elicited in the training blocks on the learning outcomes as measured by performance on the test blocks. We found that small FRN to negative feedback, large FRN to positive feedback, and large FCP to negative feedback in the first training block predicted better learning outcomes. These results add to the growing evidence that the processes giving rise to the FRN and FCP are sensitive to individual differences in the extent to which feedback is used for learning. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Conducting Supervised Experiential Learning/Field Experiences for Students' Development and Career Reinforcement.

    Science.gov (United States)

    Leventhal, Jerome I.

    A major problem in the educational system of the United States is that a great number of students and graduates lack a career objective, and, therefore, many workers are unhappy. Offering a variety of supervised field experiences, paid or unpaid, in which students see workers in their occupations will help students identify career choices.…

  14. Don't Leave Teaching to Chance: Learning Objectives for Psychodynamic Psychotherapy Supervision

    Science.gov (United States)

    Rojas, Alicia; Arbuckle, Melissa; Cabaniss, Deborah

    2010-01-01

    Objective: The way in which the competencies for psychodynamic psychotherapy specified by the Psychiatry Residency Review Committee of the Accreditation Council for Graduate Medical Education translate into the day-to-day work of individual supervision remains unstudied and unspecified. The authors hypothesized that despite the existence of…

  15. Fieldwork online: a GIS-based electronic learning environment for supervising fieldwork

    NARCIS (Netherlands)

    Alberti, K.; Marra, W.A.; Baarsma, R.J.; Karssenberg, D.J.

    2016-01-01

    Fieldwork comes in many forms: individual research projects in unique places, large groups of students on organized fieldtrips, and everything in between those extremes. Supervising students in often distant places can be a logistical challenge and requires a significant time investment of their

  16. Enabling Connections in Postgraduate Supervision for an Applied eLearning Professional Development Programme

    Science.gov (United States)

    Donnelly, Roisin

    2013-01-01

    This article describes the practice of postgraduate supervision on a blended professional development programme for academics, and discusses how connectivism has been a useful lens to explore a complex form of instruction. By examining the processes by which supervisors and their students on a two-year part-time masters in Applied eLearning…

  17. An Early Historical Examination of the Educational Intent of Supervised Agricultural Experiences (SAEs) and Project-Based Learning in Agricultural Education

    Science.gov (United States)

    Smith, Kasee L.; Rayfield, John

    2016-01-01

    Project-based learning has been a component of agricultural education since its inception. In light of the current call for additional emphasis of the Supervised Agricultural Experience (SAE) component of agricultural education, there is a need to revisit the roots of project-based learning. This early historical research study was conducted to…

  18. Semi-supervised Phonetic Category Learning: Does Word-level Information Enhance the Efficacy of Distributional Learning?

    Directory of Open Access Journals (Sweden)

    Till Poppels

    2014-08-01

    Full Text Available To test whether word-level information facilitates the learning of phonetic categories, 40 adult native English speakers were exposed to a bimodal distribution of vowels embedded in non-words. Half of the subjects received phonetic categories aligned with lexical categories, while the other half received no such cue. It was hypothesized that the subjects exposed to lexically-informative training stimuli that were aligned with the target categories would outperform the control subjects on a perceptual categorization task after training. While the results revealed no such group differences, the data indicated that many subjects used the relevant dimension for categorization before having received any training. Implications regarding experimental design and suggestions for future research based on the results are discussed.

  19. Clinical supervision in a community setting.

    Science.gov (United States)

    Evans, Carol; Marcroft, Emma

    Clinical supervision is a formal process of professional support, reflection and learning that contributes to individual development. First Community Health and Care is committed to providing clinical supervision to nurses and allied healthcare professionals to support the provision and maintenance of high-quality care. In 2012, we developed new guidelines for nurses and AHPs on supervision, incorporating a clinical supervision framework. This offers a range of options to staff so supervision accommodates variations in work settings and individual learning needs and styles.

  20. POSITIVE VERSUS NEGATIVE COMMUNICATION STRATEGIES IN TASK-BASED LEARNING

    Directory of Open Access Journals (Sweden)

    Siti Rohani

    2013-07-01

    Full Text Available This study aimed at describing how the implementation of Task Based Learning (TBL would shape or change students’ use of oral communication strategies. Students’ problems and strategies to solve the problems during the implementation of TBL were also explored. The study was a mixed method, employing both quantitative and qualitative analysis throughmulti-methods of questionnaire, interviews, focus group discussion, learning journals, and classroom observation. Participants were 26 second year students of the State Polytechnic of Malang. Data collection was conducted for one semester. Findingsshow linguistic and non-linguistic problems encountered by students during one-semester implementation of TBL. Students also performedincreased use of positive strategies but reduced use of negative strategies after the implementation of TBL.

  1. Teaching and Learning Through a Foreign Language - A Challenging Task

    DEFF Research Database (Denmark)

    Lauridsen, Karen M.

    2011-01-01

    learn and teachers teach through the medium of a foreign language, that is, English. While there is obviously a linguistic dimension to it, it turns out that there is also a cultural dimension that should not be underestimated whether we teach in our first or a foreign language. Have you also noticed......Karen M. Lauridsen, Aarhus School of Business and Social Sciences, Aarhus University, Denmark Teaching and Learning through a Foreign Language – A Challenging Task With a higher percentage of international faculty and students than ever before at many universities, more often than not students...... the challenges of teaching a heterogeneous group of students with different first languages and cultures? At this presentation you will be introduced to the results of recent Danish, Norwegian and Swedish studies of these challenges as seen from the perspective of both teachers and students...

  2. Lessons Learned from Deploying an Analytical Task Management Database

    Science.gov (United States)

    O'Neil, Daniel A.; Welch, Clara; Arceneaux, Joshua; Bulgatz, Dennis; Hunt, Mitch; Young, Stephen

    2007-01-01

    Defining requirements, missions, technologies, and concepts for space exploration involves multiple levels of organizations, teams of people with complementary skills, and analytical models and simulations. Analytical activities range from filling a To-Be-Determined (TBD) in a requirement to creating animations and simulations of exploration missions. In a program as large as returning to the Moon, there are hundreds of simultaneous analysis activities. A way to manage and integrate efforts of this magnitude is to deploy a centralized database that provides the capability to define tasks, identify resources, describe products, schedule deliveries, and generate a variety of reports. This paper describes a web-accessible task management system and explains the lessons learned during the development and deployment of the database. Through the database, managers and team leaders can define tasks, establish review schedules, assign teams, link tasks to specific requirements, identify products, and link the task data records to external repositories that contain the products. Data filters and spreadsheet export utilities provide a powerful capability to create custom reports. Import utilities provide a means to populate the database from previously filled form files. Within a four month period, a small team analyzed requirements, developed a prototype, conducted multiple system demonstrations, and deployed a working system supporting hundreds of users across the aeros pace community. Open-source technologies and agile software development techniques, applied by a skilled team enabled this impressive achievement. Topics in the paper cover the web application technologies, agile software development, an overview of the system's functions and features, dealing with increasing scope, and deploying new versions of the system.

  3. Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study

    Science.gov (United States)

    Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

    2016-01-01

    Background and objective: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. Methods: The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). Results: AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. Conclusion: The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions. PMID:27802228

  4. Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.

    Science.gov (United States)

    Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

    2017-01-01

    This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.

  5. Effects of Task-Based Instruction on Motivation to Learn

    Directory of Open Access Journals (Sweden)

    Efstathia Oekonomou

    2010-02-01

    Full Text Available The present research study examines the effectiveness of the Task-Based Learning framework, as this was proposed by J. Willis (1996 on the motivation to learn determinants, on a sample population consisting of two groups of elementary learners in the first grade of Secondary Education. The research was structured in the subsequent steps.The aspects of the motivation construct were decided upon and a Pre-TBL questionnaire was administered to the sample, providing, thus, baseline data concerning learners’ motivational profile. Based on learners’ revealed negative disposition towards the speaking and writing skills, two TBLT lessons were developed and implemented. On that account, the aforementioned lessons were actually an additional instrument in measuring possible changes in learners’ “motivation to learn” after the implementation. Following the implementation, a retrospection questionnaire was administered so that students would evaluate the accomplished outcome of their learning via TBLT and the researcher could draw attainable inferences about the effectiveness of the designed lessons in reshaping learners’ motivational intensity. The study proved that there is, indeed, a potent interrelation between this innovative teaching approach of TBLT and learners’ motivation-to-learn determinants as it was evidenced to contribute most effectively to the improvement of their motivational intensity. Moreover, it provided evidence that, with appropriate adaptations to conform to specific teaching contexts, this proposal can have a wider application to Junior High schools in Greece.

  6. Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

    Science.gov (United States)

    Cheng, Hsu-Yung; Lin, Chih-Lung

    2017-01-01

    Cloud detection is important for providing necessary information such as cloud cover in many applications. Existing cloud detection methods include red-to-blue ratio thresholding and other classification-based techniques. In this paper, we propose to perform cloud detection using supervised learning techniques with multi-resolution features. One of the major contributions of this work is that the features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We consider classifiers including random forest, support vector machine, and Bayesian classifier. To take advantage of the clues provided by multiple classifiers and various levels of patch sizes, we employ a voting scheme to combine the results to further increase the detection accuracy. In the experiments, we have shown that the proposed method can distinguish cloud and non-cloud pixels more accurately compared with existing works.

  7. Poster abstract: Water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learning

    KAUST Repository

    Mousa, Mustafa

    2014-04-01

    This article describes a machine learning approach to water level estimation in a dual ultrasonic/passive infrared urban flood sensor system. We first show that an ultrasonic rangefinder alone is unable to accurately measure the level of water on a road due to thermal effects. Using additional passive infrared sensors, we show that ground temperature and local sensor temperature measurements are sufficient to correct the rangefinder readings and improve the flood detection performance. Since floods occur very rarely, we use a supervised learning approach to estimate the correction to the ultrasonic rangefinder caused by temperature fluctuations. Preliminary data shows that water level can be estimated with an absolute error of less than 2 cm. © 2014 IEEE.

  8. Self-Control of Task Difficulty during Training Enhances Motor Learning of a Complex Coincidence-Anticipation Task

    Science.gov (United States)

    Andrieux, Mathieu; Danna, Jeremy; Thon, Bernard

    2012-01-01

    The aim of the present work was to analyze the influence of self-controlled task difficulty on motor learning. Participants had to intercept three targets falling at different velocities by displacing a stylus above a digitizer. Task difficulty corresponded to racquet width. Half the participants (self-control condition) could choose the racquet…

  9. Examining the Impact of Off-Task Multi-Tasking with Technology on Real-Time Classroom Learning

    Science.gov (United States)

    Wood, Eileen; Zivcakova, Lucia; Gentile, Petrice; Archer, Karin; De Pasquale, Domenica; Nosko, Amanda

    2012-01-01

    The purpose of the present study was to examine the impact of multi-tasking with digital technologies while attempting to learn from real-time classroom lectures in a university setting. Four digitally-based multi-tasking activities (texting using a cell-phone, emailing, MSN messaging and Facebook[TM]) were compared to 3 control groups…

  10. Self-Control of Task Difficulty during Training Enhances Motor Learning of a Complex Coincidence-Anticipation Task

    Science.gov (United States)

    Andrieux, Mathieu; Danna, Jeremy; Thon, Bernard

    2012-01-01

    The aim of the present work was to analyze the influence of self-controlled task difficulty on motor learning. Participants had to intercept three targets falling at different velocities by displacing a stylus above a digitizer. Task difficulty corresponded to racquet width. Half the participants (self-control condition) could choose the racquet…

  11. Determinants of Learning and Performance in an Associative Memory/Substitution Task: Task Constraints, Individual Differences, Volition, and Motivation.

    Science.gov (United States)

    Ackerman, Philip L.; Woltz, Dan J.

    1994-01-01

    Five experiments with 586 college students investigated how ability differences, learning task characteristics, and motivational and volitional processes combine to explain performance differences in an associative memory or substitution task. Results are discussed in terms of developing a more comprehensive understanding of learner differences.…

  12. Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

    Energy Technology Data Exchange (ETDEWEB)

    Maduskar, Pragnya, E-mail: pragnya.maduskar@radboudumc.nl; Hogeweg, Laurens; Sánchez, Clara I.; Ginneken, Bram van [Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Jong, Pim A. de [Department of Radiology, University Medical Center Utrecht, 3584 CX (Netherlands); Peters-Bax, Liesbeth [Department of Radiology, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Dawson, Rodney [University of Cape Town Lung Institute, Cape Town 7700 (South Africa); Ayles, Helen [Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London WC1E 7HT (United Kingdom)

    2014-07-15

    Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were

  13. Learning a nonmediated route for response selection in task switching.

    Science.gov (United States)

    Schneider, Darryl W; Logan, Gordon D

    2015-08-01

    Two modes of response selection--a mediated route involving categorization and a nonmediated route involving instance-based memory retrieval--have been proposed to explain response congruency effects in task-switching situations. In the present study, we sought a better understanding of the development and characteristics of the nonmediated route. In two experiments involving training and transfer phases, we investigated practice effects at the level of individual target presentations, transfer effects associated with changing category-response mappings, target-specific effects from comparisons of old and new targets during transfer, and the percentages of early responses associated with task-nonspecific response selection (the target preceded the task cue on every trial). The training results suggested that the nonmediated route is quickly learned in the context of target-cue order and becomes increasingly involved in response selection with practice. The transfer results suggested that the target-response instances underlying the nonmediated route involve abstract response labels coding response congruency that can be rapidly remapped to alternative responses, but not rewritten when category-response mappings change after practice. Implications for understanding the nonmediated route and its relationship with the mediated route are discussed.

  14. Secondary-Task Effects on Learning with Multimedia: An Investigation through Eye-Movement Analysis

    Science.gov (United States)

    Acarturk, Cengiz; Ozcelik, Erol

    2017-01-01

    This study investigates secondary-task interference on eye movements through learning with multimedia. We focus on the relationship between the influence of the secondary task on the eye movements of learners, and the learning outcomes as measured by retention, matching, and transfer. Half of the participants performed a spatial tapping task while…

  15. Implicit sequence learning based on instructed task set.

    Science.gov (United States)

    Gaschler, Robert; Frensch, Peter A; Cohen, Asher; Wenke, Dorit

    2012-09-01

    How does the way we code and control actions influence automatic skill acquisition processes? Wenke and Frensch (2005) showed that instructions can lead participants to code spatial responses based on color. Here, we tested in 3 experiments to what extent response labeling and instruction-based response coding can determine what is learned in implicit sequence learning. Instructions mapped 4 gray shape stimuli to 1 of the 4 keys each in a serial reaction task, referring to the keys in terms of either their color or their spatial location. In Experiments 1 and 2 we found that people in the color instruction conditions used color for action control and acquired sequence knowledge containing color: They were susceptible to irrelevant stimulus colors at transfer and could transfer color sequence knowledge to a new arrangement of response positions and fingers, whereas participants who had received spatial instructions could not. Implicit sequence learning was thus surprisingly flexible. Depending on whether an arbitrary nonspatial response feature was used or not used to explain the stimulus-response mappings, we either found or did not find evidence that this feature became part of action control and sequence learning. Furthermore, Experiment 3 suggested that response position might become part of the sequence knowledge even if instructions do not emphasize this response feature. Together, the findings suggest that implicit sequence learning is based on action control, which in turn strongly, but not entirely, depends on which response features are used to explain the stimulus-response mappings in the instructions. PsycINFO Database Record (c) 2012 APA, all rights reserved.

  16. Informal learning of secondary-school students and learning tasks of the family

    Directory of Open Access Journals (Sweden)

    Sanja Berčnik

    2006-12-01

    Full Text Available The' author speaks about the role of informal learning for young people and their family, differences about spending free-time and possibilities of using free-time for informal learning. The presupposition is that while learning scope is constantly expanding, also learning tasks of the family are increasing. Because of different social environments of young people, there is a question, what are actual possibilities for informal learning in their domestic environment and how this affects their development. The most important question, which must be asked according to the author is, whether parents are ware of their influence, of the influence of their actions on development and learning of their children.

  17. Adaptation and validation of the instrument Clinical Learning Environment and Supervision for medical students in primary health care.

    Science.gov (United States)

    Öhman, Eva; Alinaghizadeh, Hassan; Kaila, Päivi; Hult, Håkan; Nilsson, Gunnar H; Salminen, Helena

    2016-12-01

    Clinical learning takes place in complex socio-cultural environments that are workplaces for the staff and learning places for the students. In the clinical context, the students learn by active participation and in interaction with the rest of the community at the workplace. Clinical learning occurs outside the university, therefore is it important for both the university and the student that the student is given opportunities to evaluate the clinical placements with an instrument that allows evaluation from many perspectives. The instrument Clinical Learning Environment and Supervision (CLES) was originally developed for evaluation of nursing students' clinical learning environment. The aim of this study was to adapt and validate the CLES instrument to measure medical students' perceptions of their learning environment in primary health care. In the adaptation process the face validity was tested by an expert panel of primary care physicians, who were also active clinical supervisors. The adapted CLES instrument with 25 items and six background questions was sent electronically to 1,256 medical students from one university. Answers from 394 students were eligible for inclusion. Exploratory factor analysis based on principal component methods followed by oblique rotation was used to confirm the adequate number of factors in the data. Construct validity was assessed by factor analysis. Confirmatory factor analysis was used to confirm the dimensions of CLES instrument. The construct validity showed a clearly indicated four-factor model. The cumulative variance explanation was 0.65, and the overall Cronbach's alpha was 0.95. All items loaded similarly with the dimensions in the non-adapted CLES except for one item that loaded to another dimension. The CLES instrument in its adapted form had high construct validity and high reliability and internal consistency. CLES, in its adapted form, appears to be a valid instrument to evaluate medical students' perceptions of

  18. Adaptation and validation of the instrument Clinical Learning Environment and Supervision for medical students in primary health care

    Directory of Open Access Journals (Sweden)

    Eva Öhman

    2016-12-01

    Full Text Available Abstract Background Clinical learning takes place in complex socio-cultural environments that are workplaces for the staff and learning places for the students. In the clinical context, the students learn by active participation and in interaction with the rest of the community at the workplace. Clinical learning occurs outside the university, therefore is it important for both the university and the student that the student is given opportunities to evaluate the clinical placements with an instrument that allows evaluation from many perspectives. The instrument Clinical Learning Environment and Supervision (CLES was originally developed for evaluation of nursing students’ clinical learning environment. The aim of this study was to adapt and validate the CLES instrument to measure medical students’ perceptions of their learning environment in primary health care. Methods In the adaptation process the face validity was tested by an expert panel of primary care physicians, who were also active clinical supervisors. The adapted CLES instrument with 25 items and six background questions was sent electronically to 1,256 medical students from one university. Answers from 394 students were eligible for inclusion. Exploratory factor analysis based on principal component methods followed by oblique rotation was used to confirm the adequate number of factors in the data. Construct validity was assessed by factor analysis. Confirmatory factor analysis was used to confirm the dimensions of CLES instrument. Results The construct validity showed a clearly indicated four-factor model. The cumulative variance explanation was 0.65, and the overall Cronbach’s alpha was 0.95. All items loaded similarly with the dimensions in the non-adapted CLES except for one item that loaded to another dimension. The CLES instrument in its adapted form had high construct validity and high reliability and internal consistency. Conclusion CLES, in its adapted form, appears

  19. Industrial Electrical Maintenance Learning Guides and Task Listing by Occupational Titles.

    Science.gov (United States)

    Whitmer, Melvin

    Seven student learning guides are provided for an industrial electrical maintenance program at the secondary, postsecondary, or adult level. Each learning guide is composed of these component parts: a title page that states the task, purpose, program and task numbers, estimated time, and prerequisites; an optional learning contract that includes…

  20. Task-based incidental vocabulary learning in L2 Arabic: The role of proficiency and task performance

    Directory of Open Access Journals (Sweden)

    Ayman A. Mohamed

    2016-03-01

    Full Text Available This study tests the claim that word learning in a second language are contingent upon a task’s involvement load (i.e. the amount of need, search, and evaluation it imposes, as proposed by Laufer and Hulstijn (2001. Fifty-three English-speaking learners of Arabic were assigned to one of three vocabulary learning tasks that varied in the degree of involvement: reading comprehension with glosses (low, fill-in-the-gap task (medium, and sentence writing (high. Ten words, selected based on a pretest, were targeted in the tasks. Results showed a main effect of task, with the sentence writing task yielding the highest rates of vocabulary learning, followed by the gap-fill task, and finally the reading comprehension task. A significant correlation was found between accuracy of performance across participants and their subsequent vocabulary acquisition in the immediate posttest. Within groups, only the performance of the writing group correlated significantly with their posttest scores. Results of the present study validate the hypothesis and point to multiple factors at play in incidental vocabulary acquisition. The study provides further arguments to refine the hypothesis and implement pedagogical practices that accommodate incidental learning in foreign language settings.

  1. The effect of cognitive aging on implicit sequence learning and dual tasking

    Directory of Open Access Journals (Sweden)

    Jochen eVandenbossche

    2014-02-01

    Full Text Available We investigated the influence of attentional demands on sequence-specific learning by means of the serial reaction time (SRT task (Nissen & Bullemer, 1987 in young (age 18-25 and aged (age 55-75 adults. Participants had to respond as fast as possible to a stimulus presented in one of four horizontal locations by pressing a key corresponding to the spatial position of the stimulus. During the training phase sequential blocks were accompanied by (1 no secondary task (single, (2 a secondary tone counting task (dual tone, or (3 a secondary shape counting task (dual shape. Both secondary tasks were administered to investigate whether low and high interference tasks interact with implicit learning and age. The testing phase, under baseline single condition, was implemented to assess differences in sequence-specific learning between young and aged adults. Results indicate that (1 aged subjects show less sequence learning compared to young adults, (2 young participants show similar implicit learning effects under both single and dual task conditions when we account for explicit awareness, and (3 aged adults demonstrate reduced learning when the primary task is accompanied with a secondary task, even when explicit awareness is included as a covariate in the analysis. These findings point to implicit learning deficits under dual task conditions that can be related to cognitive aging, demonstrating the need for sufficient cognitive resources while performing a sequence learning task.

  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

    in the language learning in this course. The result indicates that course participants were generally positive about their experiences and learning processes during the course, not only the task-based method but also integrating culture into language learning. Learners of diverse backgrounds in terms of gender......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...... 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...

  3. 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

    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...... in the language learning in this course. The result indicates that course participants were generally positive about their experiences and learning processes during the course, not only the task-based method but also integrating culture into language learning. Learners of diverse backgrounds in terms of gender......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...

  4. Task-Based Learning and Content and Language Integrated Learning Materials Design: Process and Product

    Science.gov (United States)

    Moore, Pat; Lorenzo, Francisco

    2015-01-01

    Content and language integrated learning (CLIL) represents an increasingly popular approach to bilingual education in Europe. In this article, we describe and discuss a project which, in response to teachers' pleas for materials, led to the production of a significant bank of task-based primary and secondary CLIL units for three L2s (English,…

  5. Task-Based Learning and Content and Language Integrated Learning Materials Design: Process and Product

    Science.gov (United States)

    Moore, Pat; Lorenzo, Francisco

    2015-01-01

    Content and language integrated learning (CLIL) represents an increasingly popular approach to bilingual education in Europe. In this article, we describe and discuss a project which, in response to teachers' pleas for materials, led to the production of a significant bank of task-based primary and secondary CLIL units for three L2s (English,…

  6. Fieldwork online: a GIS-based electronic learning environment for supervising fieldwork

    Science.gov (United States)

    Alberti, Koko; Marra, Wouter; Baarsma, Rein; Karssenberg, Derek

    2016-04-01

    Fieldwork comes in many forms: individual research projects in unique places, large groups of students on organized fieldtrips, and everything in between those extremes. Supervising students in often distant places can be a logistical challenge and requires a significant time investment of their supervisors. We developed an online application for remote supervision of students on fieldwork. In our fieldworkonline webapp, which is accessible through a web browser, students can upload their field data in the form of a spreadsheet with coordinates (in a system of choice) and data-fields. Field data can be any combination of quantitative or qualitative data, and can contain references to photos or other documents uploaded to the app. The student's data is converted to a map with data-points that contain all the data-fields and links to photos and documents associated with that location. Supervisors can review the data of their students and provide feedback on observations, or geo-referenced feedback on the map. Similarly, students can ask geo-referenced questions to their supervisors. Furthermore, supervisors can choose different basemaps or upload their own. Fieldwork online is a useful tool for supervising students at a distant location in the field and is most suitable for first-order feedback on students' observations, can be used to guide students to interesting locations, and allows for short discussions on phenomena observed in the field. We seek user that like to use this system, we are able to provide support and add new features if needed. The website is built and controlled using Flask, an open-source Python Framework. The maps are generated and controlled using MapServer and OpenLayers, and the database is built in PostgreSQL with PostGIS support. Fieldworkonline and all tools used to create it are open-source. Experience fieldworkonline at our demo during this session, or online at fieldworkonline.geo.uu.nl (username: EGU2016, password: Vienna).

  7. Development and psychometric testing of the Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale (CLES+T): the Spanish version.

    Science.gov (United States)

    Vizcaya-Moreno, M Flores; Pérez-Cañaveras, Rosa M; De Juan, Joaquín; Saarikoski, Mikko

    2015-01-01

    The Clinical Learning Environment, Supervision and Nurse Teacher scale is a reliable and valid instrument to evaluate the quality of the clinical learning process in international nursing education contexts. This paper reports the development and psychometric testing of the Spanish version of the Clinical Learning Environment, Supervision and Nurse Teacher scale. Cross-sectional validation study of the scale. 10 public and private hospitals in the Alicante area, and the Faculty of Health Sciences (University of Alicante, Spain). 370 student nurses on clinical placement (January 2011-March 2012). The Clinical Learning Environment, Supervision and Nurse Teacher scale was translated using the modified direct translation method. Statistical analyses were performed using PASW Statistics 18 and AMOS 18.0.0 software. A multivariate analysis was conducted in order to assess construct validity. Cronbach's alpha coefficient was used to evaluate instrument reliability. An exploratory factorial analysis identified the five dimensions from the original version, and explained 66.4% of the variance. Confirmatory factor analysis supported the factor structure of the Spanish version of the instrument. Cronbach's alpha coefficient for the scale was .95, ranging from .80 to .97 for the subscales. This version of the Clinical Learning Environment, Supervision and Nurse Teacher scale instrument showed acceptable psychometric properties for use as an assessment scale in Spanish-speaking countries. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Learning in the Absence of Direct Supervision: Person-Dependent Scaffolding

    Science.gov (United States)

    Palesy, Debra

    2017-01-01

    Contemporary accounts of learning emphasise the importance of immediate social partners such as teachers and co-workers. Yet, much of our learning for work occurs without such experts. This paper provides an understanding of how and why new home care workers use scaffolding to learn and enact safe manual handling techniques in their workplaces,…

  9. Motor learning in children with spina bifida: intact learning and performance on a ballistic task.

    Science.gov (United States)

    Dennis, Maureen; Jewell, Derryn; Edelstein, Kim; Brandt, Michael E; Hetherington, Ross; Blaser, Susan E; Fletcher, Jack M

    2006-09-01

    Learning and performance on a ballistic task were investigated in children with spina bifida meningomyelocele (SBM), with either upper level spinal lesions (n = 21) or lower level spinal lesions (n = 81), and in typically developing controls (n = 35). Participants completed three phases (20 trials each) of an elbow goniometer task that required a ballistic arm movement to move a cursor to one of two target positions on a screen, including (1) an initial learning phase, (2) an adaptation phase with a gain change such that recalibration of the ballistic arm movement was required, and (3) a learning reactivation phase under the original gain condition. Initial error rate, asymptotic error rate, and learning rate did not differ significantly between the SBM and control groups. Relative to controls, the SBM group had reduced volumes in the cerebellar hemispheres and pericallosal gray matter (the region including the basal ganglia), although only the pericallosal gray matter was significantly correlated with motor adaptation. Congenital cerebellar dysmorphology is associated with preserved motor skill learning on voluntary, nonreflexive tasks in children with SBM, in whom the relative roles of the cerebellum and basal ganglia may differ from those in the adult brain.

  10. Learning the Task Management Space of an Aircraft Approach Model

    Science.gov (United States)

    Krall, Joseph; Menzies, Tim; Davies, Misty

    2014-01-01

    Validating models of airspace operations is a particular challenge. These models are often aimed at finding and exploring safety violations, and aim to be accurate representations of real-world behavior. However, the rules governing the behavior are quite complex: nonlinear physics, operational modes, human behavior, and stochastic environmental concerns all determine the responses of the system. In this paper, we present a study on aircraft runway approaches as modeled in Georgia Tech's Work Models that Compute (WMC) simulation. We use a new learner, Genetic-Active Learning for Search-Based Software Engineering (GALE) to discover the Pareto frontiers defined by cognitive structures. These cognitive structures organize the prioritization and assignment of tasks of each pilot during approaches. We discuss the benefits of our approach, and also discuss future work necessary to enable uncertainty quantification.

  11. Performance Monitoring Applied to System Supervision

    Directory of Open Access Journals (Sweden)

    Bertille Somon

    2017-07-01

    Full Text Available Nowadays, automation is present in every aspect of our daily life and has some benefits. Nonetheless, empirical data suggest that traditional automation has many negative performance and safety consequences as it changed task performers into task supervisors. In this context, we propose to use recent insights into the anatomical and neurophysiological substrates of action monitoring in humans, to help further characterize performance monitoring during system supervision. Error monitoring is critical for humans to learn from the consequences of their actions. A wide variety of studies have shown that the error monitoring system is involved not only in our own errors, but also in the errors of others. We hypothesize that the neurobiological correlates of the self-performance monitoring activity can be applied to system supervision. At a larger scale, a better understanding of system supervision may allow its negative effects to be anticipated or even countered. This review is divided into three main parts. First, we assess the neurophysiological correlates of self-performance monitoring and their characteristics during error execution. Then, we extend these results to include performance monitoring and error observation of others or of systems. Finally, we provide further directions in the study of system supervision and assess the limits preventing us from studying a well-known phenomenon: the Out-Of-the-Loop (OOL performance problem.

  12. Studying different tasks of implicit learning across multiple test sessions conducted on the web

    Directory of Open Access Journals (Sweden)

    Werner eSævland

    2016-06-01

    Full Text Available Implicit learning is usually studied through individual performance on a single task, with the most common tasks being Serial Reaction Time task (SRT; Nissen and Bullemer, 1987, Dynamic System Control task (DSC; (Berry and Broadbent, 1984 and artificial Grammar Learning task (AGL; (Reber, 1967. Few attempts have been made to compare performance across different implicit learning tasks within the same experiment. The current experiment was designed study the relationship between performance on the DSC Sugar factory task (Berry and Broadbent, 1984 and the Alternating Serial Reaction Time task (ASRT; (Howard and Howard, 1997. We also addressed another limitation to traditional implicit learning experiments, namely that implicit learning is usually studied in laboratory settings over a restricted time span lasting for less than an hour (Berry and Broadbent, 1984; Nissen and Bullemer, 1987; Reber, 1967. In everyday situations, implicit learning is assumed to involve a gradual accumulation of knowledge across several learning episodes over a larger time span (Norman and Price, 2012. One way to increase the ecological validity of implicit learning experiments could be to present the learning material repeatedly across shorter experimental sessions (Howard and Howard, 1997; Cleeremans and McClelland, 1991. This can most easily be done by using a web-based setup that participants can access from home. We therefore created an online web-based system for measuring implicit learning that could be administered in either single or multiple sessions. Participants (n = 66 were assigned to either a single-session or a multi-session condition. Learning and the degree of conscious awareness of the learned regularities was compared across condition (single vs. multiple sessions and tasks (DSC vs. ASRT. Results showed that learning on the two tasks was not related. However, participants in the multiple sessions condition did show greater improvements in reaction

  13. Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning

    Institute of Scientific and Technical Information of China (English)

    周竞; 朱山风; 黄晓地; 张彦春

    2015-01-01

    Time series clustering is widely applied in various areas. Existing researches focus mainly on distance measures between two time series, such as dynamic time warping (DTW) based methods, edit-distance based methods, and shapelets-based methods. In this work, we experimentally demonstrate, for the first time, that no single distance measure performs significantly better than others on clustering datasets of time series where spectral clustering is used. As such, a question arises as to how to choose an appropriate measure for a given dataset of time series. To answer this question, we propose an integration scheme that incorporates multiple distance measures using semi-supervised clustering. Our approach is able to integrate all the measures by extracting valuable underlying information for the clustering. To the best of our knowledge, this work demonstrates for the first time that the semi-supervised clustering method based on constraints is able to enhance time series clustering by combining multiple distance measures. Having tested on clustering various time series datasets, we show that our method outperforms individual measures, as well as typical integration approaches.

  14. Embedded interruptions and task complexity influence schema-related cognitive load progression in an abstract learning task.

    Science.gov (United States)

    Wirzberger, Maria; Esmaeili Bijarsari, Shirin; Rey, Günter Daniel

    2017-09-01

    Cognitive processes related to schema acquisition comprise an essential source of demands in learning situations. Since the related amount of cognitive load is supposed to change over time, plausible temporal models of load progression based on different theoretical backgrounds are inspected in this study. A total of 116 student participants completed a basal symbol sequence learning task, which provided insights into underlying cognitive dynamics. Two levels of task complexity were determined by the amount of elements within the symbol sequence. In addition, interruptions due to an embedded secondary task occurred at five predefined stages over the task. Within the resulting 2x5-factorial mixed between-within design, the continuous monitoring of efficiency in learning performance enabled assumptions on relevant resource investment. From the obtained results, a nonlinear change of learning efficiency over time seems most plausible in terms of cognitive load progression. Moreover, different effects of the induced interruptions show up in conditions of task complexity, which indicate the activation of distinct cognitive mechanisms related to structural aspects of the task. Findings are discussed in the light of evidence from research on memory and information processing. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Dynamic Sensor Tasking for Space Situational Awareness via Reinforcement Learning

    Science.gov (United States)

    Linares, R.; Furfaro, R.

    2016-09-01

    This paper studies the Sensor Management (SM) problem for optical Space Object (SO) tracking. The tasking problem is formulated as a Markov Decision Process (MDP) and solved using Reinforcement Learning (RL). The RL problem is solved using the actor-critic policy gradient approach. The actor provides a policy which is random over actions and given by a parametric probability density function (pdf). The critic evaluates the policy by calculating the estimated total reward or the value function for the problem. The parameters of the policy action pdf are optimized using gradients with respect to the reward function. Both the critic and the actor are modeled using deep neural networks (multi-layer neural networks). The policy neural network takes the current state as input and outputs probabilities for each possible action. This policy is random, and can be evaluated by sampling random actions using the probabilities determined by the policy neural network's outputs. The critic approximates the total reward using a neural network. The estimated total reward is used to approximate the gradient of the policy network with respect to the network parameters. This approach is used to find the non-myopic optimal policy for tasking optical sensors to estimate SO orbits. The reward function is based on reducing the uncertainty for the overall catalog to below a user specified uncertainty threshold. This work uses a 30 km total position error for the uncertainty threshold. This work provides the RL method with a negative reward as long as any SO has a total position error above the uncertainty threshold. This penalizes policies that take longer to achieve the desired accuracy. A positive reward is provided when all SOs are below the catalog uncertainty threshold. An optimal policy is sought that takes actions to achieve the desired catalog uncertainty in minimum time. This work trains the policy in simulation by letting it task a single sensor to "learn" from its performance

  16. Effect of Color and Monochrome Versions of a Film on Incidental and Task Relevant Learning.

    Science.gov (United States)

    Chute, Alan G.

    1980-01-01

    This study found that color in a film helped fourth- and fifth-grade students of all ability levels learn incidental information, but affected learning of task-relevant information differently depending on ability level. (Author/JEG)

  17. Enhancing the Standard of Teaching and Learning in the 21st Century via Qualitative School-Based Supervision in Secondary Schools in Abuja Municipal Area Council (AMAC)

    Science.gov (United States)

    Ebele, Uju F.; Olofu, Paul A.

    2017-01-01

    The study focused on enhancing the standard of teaching and learning in the 21st century via qualitative school-based supervision in secondary schools in Abuja municipal area council. To guide the study, two null hypotheses were formulated. A descriptive survey research design was adopted. The sample of the study constituted of 270 secondary…

  18. Reflections on Doctoral Supervision: Drawing from the Experiences of Students with Additional Learning Needs in Two Universities

    Science.gov (United States)

    Collins, Bethan

    2015-01-01

    Supervision is an essential part of doctoral study, consisting of relationship and process aspects, underpinned by a range of values. To date there has been limited research specifically about disabled doctoral students' experiences of supervision. This paper draws on qualitative, narrative interviews about doctoral supervision with disabled…

  19. Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.

    Science.gov (United States)

    Awaysheh, Abdullah; Wilcke, Jeffrey; Elvinger, François; Rees, Loren; Fan, Weiguo; Zimmerman, Kurt L

    2016-11-01

    Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p machine learning provided a method for distinguishing between ALA-IBD, ALA-normal, and IBD-normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats. © 2016 The Author(s).

  20. Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization

    CERN Document Server

    Garcia-Cardona, Cristina; Percus, Allon G

    2013-01-01

    We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semi-supervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with state-of-the-art graph-based multiclass segmentation methods.

  1. Dialog-based Language Learning

    OpenAIRE

    2016-01-01

    A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (tagging, parsing tasks) or sentence level (question answering, machine translation). This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. In this work, we study dialog-based langu...

  2. The effect of haptic guidance and visual feedback on learning a complex tennis task.

    Science.gov (United States)

    Marchal-Crespo, Laura; van Raai, Mark; Rauter, Georg; Wolf, Peter; Riener, Robert

    2013-11-01

    While haptic guidance can improve ongoing performance of a motor task, several studies have found that it ultimately impairs motor learning. However, some recent studies suggest that the haptic demonstration of optimal timing, rather than movement magnitude, enhances learning in subjects trained with haptic guidance. Timing of an action plays a crucial role in the proper accomplishment of many motor skills, such as hitting a moving object (discrete timing task) or learning a velocity profile (time-critical tracking task). The aim of the present study is to evaluate which feedback conditions-visual or haptic guidance-optimize learning of the discrete and continuous elements of a timing task. The experiment consisted in performing a fast tennis forehand stroke in a virtual environment. A tendon-based parallel robot connected to the end of a racket was used to apply haptic guidance during training. In two different experiments, we evaluated which feedback condition was more adequate for learning: (1) a time-dependent discrete task-learning to start a tennis stroke and (2) a tracking task-learning to follow a velocity profile. The effect that the task difficulty and subject's initial skill level have on the selection of the optimal training condition was further evaluated. Results showed that the training condition that maximizes learning of the discrete time-dependent motor task depends on the subjects' initial skill level. Haptic guidance was especially suitable for less-skilled subjects and in especially difficult discrete tasks, while visual feedback seems to benefit more skilled subjects. Additionally, haptic guidance seemed to promote learning in a time-critical tracking task, while visual feedback tended to deteriorate the performance independently of the task difficulty and subjects' initial skill level. Haptic guidance outperformed visual feedback, although additional studies are needed to further analyze the effect of other types of feedback visualization on

  3. Type of learning task impacts performance and strategy selection in decision making.

    Science.gov (United States)

    Pachur, Thorsten; Olsson, Henrik

    2012-09-01

    In order to be adaptive, cognition requires knowledge about the statistical structure of the environment. We show that decision performance and the selection between cue-based and exemplar-based inference mechanisms can depend critically on how this knowledge is acquired. Two types of learning tasks are distinguished: learning by comparison, by which the decision maker learns which of two objects has a higher criterion value, and direct criterion learning, by which the decision maker learns an object's criterion value directly. In three experiments, participants were trained either with learning by comparison or with direct criterion learning and subsequently tested with paired-comparison, classification, and estimation tasks. Experiments 1 and 2 showed that although providing less information, learning by comparison led to better generalization (at test), both when generalizing to new objects and when the task format at test differed from the task format during training. Moreover, learning by comparison enabled participants to provide rather accurate continuous estimates. Computational modeling suggests that the advantage of learning by comparison is due to differences in strategy selection: whereas direct criterion learning fosters the reliance on exemplar processing, learning by comparison fosters cue-based mechanisms. The pattern in decision performance reversed when the task environment was changed from a linear (Experiments 1 and 2) to a nonlinear structure (Experiment 3), where direct criterion learning led to better decisions. Our results demonstrate the critical impact of learning conditions for the subsequent selection of decision strategies and highlight the key role of comparison processes in cognition.

  4. Effects of Dispositional Mindfulness on the Self-Controlled Learning of a Novel Motor Task

    Science.gov (United States)

    Kee, Ying Hwa; Liu, Yeou-Teh

    2011-01-01

    Current literature suggests that mindful learning is beneficial to learning but its links with motor learning is seldom examined. In the present study, we examine the effects of learners' mindfulness disposition on the self-controlled learning of a novel motor task. Thirty-two participants undertook five practice sessions, in addition to a pre-,…

  5. Student experiences in learning person-centred care of patients with Alzheimer's disease as perceived by nursing students and supervising nurses.

    Science.gov (United States)

    Skaalvik, Mari W; Normann, Hans Ketil; Henriksen, Nils

    2010-09-01

    The aims and objectives of this paper are to illuminate and discuss the experiences and perceptions of nursing students and supervising nurses regarding the students' learning of person- centred care of patients with Alzheimer's disease in a teaching nursing home. This information is then used to develop recommendations as to how student learning could be improved. The clinical experiences of nursing students are an important part of learning person-centred care. Caring for patients with Alzheimer's disease may cause frustration, sadness, fear and empathy. Person-centred care can be learned in clinical practice. A qualitative study. The study was performed in 2006 using field work with field notes and qualitative interviews with seven-fifth-semester nursing students and six supervising nurses. This study determined the variation in the perceptions of nursing students and supervising nurses with regards to the students' expertise in caring for patients with Alzheimer's disease. The nursing students experienced limited learning regarding person-centred approaches in caring for patients with Alzheimer's disease. However, the supervising nurses perceived the teaching nursing home as a site representing multiple learning opportunities in this area. Nursing students perceived limited learning outcomes because they did not observe or experience systematic person-centred approaches in caring for patients with Alzheimer's disease. It is important that measures of quality improvements in the care of patients with Alzheimer's disease are communicated and demonstrated for nursing students working in clinical practices in a teaching nursing home. Introduction of person-centred approaches is vital regarding learning outcomes for nursing students caring for patients with Alzheimer's disease. © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd.

  6. Automatic learning rate adjustment for self-supervising autonomous robot control

    Science.gov (United States)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

  7. Dolanan Dance Learning on Supervising Pre-Service Teachers during Teaching Practicum Program

    Directory of Open Access Journals (Sweden)

    Nilam Cahyaningrum

    2015-01-01

    Full Text Available Taman Kanak- kanak Mekarsari (Mekarsari Kindergarten is a school that choses dolanan anak dance lesson which is taught using demonstration methods. This study aims to find, understand, and describe the process and learning outcomes of dolanan anak dance in Mekarsari Kindergarten, Kandeman District of Batang. This study uses qualitative research methods with a phenomenological approach to research sites in Mekarsari Kindergarten, Kandeman District of Batang. Data collection techniques used were observation, interview techniques, and technical documentation. Data analysis were using data reduction, data presentation, drawing conclusions, and verification. The validity test were using triangulation of data sources, techniques, and time. Dolanan anak dance learning in Mekarsari Kindergarten consists of several components, namely teaching and learning activities, goals, teachers, students, materials, methods, media, tools and learning resources, and evaluation. Dolanan dance learning was using demonstration method implemented through three stages: pre-development activities, core activities, and closing activities. The learning outcomes of dolanan anak dance learning in Mekarsari kindergarten were categorized into three aspects, namely cognitive, affective, and psychomotor. Cognitive aspects can be seen from the students’ ability to remember, memorize and understand the dance. Affective aspects include familiar levels, namely learning to know friends and dance movements, respond the movements amomg friends, and appreciate the teacher’s explanation given to each student. Psychomotor aspects can be seen from the students’ ability to imitate the dance movements, use the concept of doing the movements and precision of movements, weave movement and exercise appropriately.

  8. Integrating the Use of Interdisciplinary Learning Activity Task in Creating Students' Mathematical Knowledge

    Science.gov (United States)

    Mahanin, Hajah Umisuzimah Haji; Shahrill, Masitah; Tan, Abby; Mahadi, Mar Aswandi

    2017-01-01

    This study investigated the use of interdisciplinary learning activity task to construct students' knowledge in Mathematics, specifically on the topic of scale drawing application. The learning activity task involved more than one academic discipline, which is Mathematics, English Language, Art, Geography and integrating the Brunei Darussalam…

  9. Autonomous Learning through Task-Based Instruction in Fully Online Language Courses

    Science.gov (United States)

    Lee, Lina

    2016-01-01

    This study investigated the affordances for autonomous learning in a fully online learning environment involving the implementation of task-based instruction in conjunction with Web 2.0 technologies. To that end, four-skill-integrated tasks and digital tools were incorporated into the coursework. Data were collected using midterm reflections,…

  10. Writing to Learn via Text Chat: Task Implementation and Focus on Form

    Science.gov (United States)

    Alwi, Nik Aloesnita Nik Mohd; Adams, Rebecca; Newton, Jonathan

    2012-01-01

    Research has shown that task-based computer-mediated communication (CMC) can foster attention to linguistic form in ways that may promote language learning (c.f., Blake, 2000; Smith, 2003, 2005). However, relatively little research has investigated how differences in the way that tasks are used in CMC settings influence learning opportunities…

  11. Task-Based Language Learning and Teaching: An Action-Research Study

    Science.gov (United States)

    Calvert, Megan; Sheen, Younghee

    2015-01-01

    The creation, implementation, and evaluation of language learning tasks remain a challenge for many teachers, especially those with limited experience with using tasks in their teaching. This action-research study reports on one teacher's experience of developing, implementing, critically reflecting on, and modifying a language learning task…

  12. Transfer in Rule-Based Category Learning Depends on the Training Task.

    Science.gov (United States)

    Kattner, Florian; Cox, Christopher R; Green, C Shawn

    2016-01-01

    While learning is often highly specific to the exact stimuli and tasks used during training, there are cases where training results in learning that generalizes more broadly. It has been previously argued that the degree of specificity can be predicted based upon the learning solution(s) dictated by the particular demands of the training task. Here we applied this logic in the domain of rule-based categorization learning. Participants were presented with stimuli corresponding to four different categories and were asked to perform either a category discrimination task (which permits learning specific rule to discriminate two categories) or a category identification task (which does not permit learning a specific discrimination rule). In a subsequent transfer stage, all participants were asked to discriminate stimuli belonging to two of the categories which they had seen, but had never directly discriminated before (i.e., this particular discrimination was omitted from training). As predicted, learning in the category-discrimination tasks tended to be specific, while the category-identification task produced learning that transferred to the transfer discrimination task. These results suggest that the discrimination and identification tasks fostered the acquisition of different category representations which were more or less generalizable.

  13. Task-Based Learning and Language Proficiency in a Business University

    Science.gov (United States)

    Newsom-Ray, Amelia Chloe Caroline; Rutter, Sarah Jane

    2016-01-01

    This project adds to the growing body of empirical research focusing on the effects of task-based learning (TBL) on second language acquisition. Through the design and implementation of two business English case studies, in which learning was scaffolded through a sequence of tasks, the authors argue that a TBL approach to language teaching more…

  14. Task-Based Language Learning and Teaching: An Action-Research Study

    Science.gov (United States)

    Calvert, Megan; Sheen, Younghee

    2015-01-01

    The creation, implementation, and evaluation of language learning tasks remain a challenge for many teachers, especially those with limited experience with using tasks in their teaching. This action-research study reports on one teacher's experience of developing, implementing, critically reflecting on, and modifying a language learning task…

  15. Task-Oriented Internet Assisted English Teaching and Learning in Colleges

    Science.gov (United States)

    Zhang, Juwu

    2014-01-01

    Task-Oriented Internet Assisted English Teaching and Learning (TIAETL) is a new English teaching and learning model which integrates the Internet-assisted and task-oriented teaching. This article analyzed the worldwide tendency of English teaching and prerequisites for TIAETL in colleges. The TIAETL has the following advantages:…

  16. High variability impairs motor learning regardless of whether it affects task performance.

    Science.gov (United States)

    Cardis, Marco; Casadio, Maura; Ranganathan, Rajiv

    2017-09-27

    Motor variability plays an important role in motor learning, although the exact mechanisms of how variability affects learning is not well understood. Recent evidence suggests that motor variability may have different effects on learning in redundant tasks, depending on whether it is present in the task space (where it affects task performance), or in the null space (where it has no effect on task performance). Here we examined the effect of directly introducing null and task space variability using a manipulandum during the learning of a motor task. Participants learned a bimanual shuffleboard task for 2 days, where their goal was to slide a virtual puck as close as possible towards a target. Critically, the distance traveled by the puck was determined by the sum of the left and right hand velocities, which meant that there was redundancy in the task. Participants were divided into five groups - based on both the dimension in which the variability was introduced and the amount of variability that was introduced during training. Results showed that although all groups were able to reduce error with practice, learning was affected more by the amount of variability introduced rather than the dimension in which variability was introduced. Specifically, groups with higher movement variability during practice showed larger errors at the end of practice compared to groups that had low variability during learning. These results suggest that although introducing variability can increase exploration of new solutions, this may adversely affect the ability to retain the learned solution. Copyright © 2017, Journal of Neurophysiology.

  17. Task-Oriented Internet Assisted English Teaching and Learning in Colleges

    Science.gov (United States)

    Zhang, Juwu

    2014-01-01

    Task-Oriented Internet Assisted English Teaching and Learning (TIAETL) is a new English teaching and learning model which integrates the Internet-assisted and task-oriented teaching. This article analyzed the worldwide tendency of English teaching and prerequisites for TIAETL in colleges. The TIAETL has the following advantages:…

  18. Evidence for effects of task difficulty but not learning on neurophysiological variables associated with effort

    NARCIS (Netherlands)

    Brouwer, A.M.; Hogervorst, M.A.; Holewijn, M.; Erp, J.B.F. van

    2014-01-01

    Learning to master a task is expected to be accompanied by a decrease in effort during task execution. We examine the possibility to monitor learning using physiological measures that have been reported to reflect effort or workload. Thirty-five participants performed different difficulty levels of

  19. Accuracy Feedback Improves Word Learning from Context: Evidence from a Meaning-Generation Task

    Science.gov (United States)

    Frishkoff, Gwen A.; Collins-Thompson, Kevyn; Hodges, Leslie; Crossley, Scott

    2016-01-01

    The present study asked whether accuracy feedback on a meaning generation task would lead to improved contextual word learning (CWL). Active generation can facilitate learning by increasing task engagement and memory retrieval, which strengthens new word representations. However, forced generation results in increased errors, which can be…

  20. Using Dual-Task Methodology to Dissociate Automatic from Nonautomatic Processes Involved in Artificial Grammar Learning

    Science.gov (United States)

    Hendricks, Michelle A.; Conway, Christopher M.; Kellogg, Ronald T.

    2013-01-01

    Previous studies have suggested that both automatic and intentional processes contribute to the learning of grammar and fragment knowledge in artificial grammar learning (AGL) tasks. To explore the relative contribution of automatic and intentional processes to knowledge gained in AGL, we utilized dual-task methodology to dissociate automatic and…

  1. Impact of Static Graphics, Animated Graphics and Mental Imagery on a Complex Learning Task

    Science.gov (United States)

    Lai, Feng-Qi; Newby, Timothy J.

    2012-01-01

    The present study compared the impact of different categories of graphics used within a complex learning task. One hundred eighty five native English speaking undergraduates participated in a task that required learning 18 Chinese radicals and their English equivalent translations. A post-test only control group design compared performance…

  2. Showing a model's eye movements in examples does not improve learning of problem-solving tasks

    NARCIS (Netherlands)

    van Marlen, Tim; van Wermeskerken, Margot; Jarodzka, Halszka; van Gog, Tamara

    2016-01-01

    Eye movement modeling examples (EMME) are demonstrations of a computer-based task by a human model (e.g., a teacher), with the model's eye movements superimposed on the task to guide learners' attention. EMME have been shown to enhance learning of perceptual classification tasks; however, it is an

  3. Showing a model's eye movements in examples does not improve learning of problem-solving tasks

    NARCIS (Netherlands)

    van Marlen, Tim; van Wermeskerken, Margot; Jarodzka, Halszka; van Gog, Tamara

    2016-01-01

    Eye movement modeling examples (EMME) are demonstrations of a computer-based task by a human model (e.g., a teacher), with the model's eye movements superimposed on the task to guide learners' attention. EMME have been shown to enhance learning of perceptual classification tasks; however, it is an o

  4. Second Language Learning with the Story Maze Task: Examining the Training Effect of Weaving through Stories

    Science.gov (United States)

    Enkin, Elizabeth

    2016-01-01

    The maze task is a psycholinguistic experimental procedure that measures real-time incremental sentence processing. The task has recently been tested as a language learning tool with promising results. Therefore, the present study examines the merits of a contextualized version of this task: the story maze. The findings are consistent with…

  5. Studying Mathematics Teacher Education: Analysing the Process of Task Variation on Learning

    Science.gov (United States)

    Bragg, Leicha A.

    2015-01-01

    Self-study of variations to task design offers a way of analysing how learning takes place. Over several years, variations were made to improve an assessment task completed by final-year teacher candidates in a primary mathematics teacher education subject. This article describes how alterations to a task informed on-going developments in…

  6. A Semi-Supervised Learning Approach to Enhance Health Care Community–Based Question Answering: A Case Study in Alcoholism

    Science.gov (United States)

    Klabjan, Diego; Jonnalagadda, Siddhartha Reddy

    2016-01-01

    Background Community-based question answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for Web-based health communities. Objective In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA). We also aimed to understand information embedded within Web-based health content that are good features in identifying valid answers. Methods Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA. To rank these candidates, we implemented a semi-supervised leaning algorithm that extracts the best answer to a question. We assessed this approach on a curated corpus from Yahoo! Answers and compared against a rule-based string similarity baseline. Results On our dataset, the semi-supervised learning algorithm has an accuracy of 86.2%. Unified medical language system–based (health related) features used in the model enhance the algorithm’s performance by proximately 8%. A reasonably high rate of accuracy is obtained given that the data are considerably noisy. Important features distinguishing a valid answer from an invalid answer include text length, number of stop words contained in a test question, a distance between the test question and other questions in the corpus, and a number of overlapping health-related terms between questions. Conclusions Overall, our automated QA system based on historical QA pairs is shown to be effective according to the dataset in this case study. It is developed for general use in the health care domain, which can also be applied to other CQA sites. PMID:27485666

  7. Assessment of work-integrated learning: comparison of the usage of a grading rubric by supervising radiographers and teachers

    Energy Technology Data Exchange (ETDEWEB)

    Kilgour, Andrew J, E-mail: akilgour@csu.edu.au [Charles Sturt University, Wagga Wagga, NSW (Australia); Kilgour, Peter W [Avondale College of Higher Education, Cooranbong, NSW (Australia); Gerzina, Tania [Dental Educational Research, Faculty of Dentistry, Jaw Function and Orofacial Pain Research Unit, Westmead Centre for Oral Health, C24- Westmead Hospital, The University of Sydney, Sydney, NSW, 2006 (Australia); Christian, Beverly [Avondale College of Higher Education, Cooranbong, NSW (Australia); Charles Sturt University, Wagga Wagga, NSW (Australia)

    2014-02-15

    Introduction: Professional work-integrated learning (WIL) that integrates the academic experience with off-campus professional experience placements is an integral part of many tertiary courses. Issues with the reliability and validity of assessment grades in these placements suggest that there is a need to strengthen the level of academic rigour of placements in these programmes. This study aims to compare the attitudes to the usage of assessment rubrics of radiographers supervising medical imaging students and teachers supervising pre-service teachers. Methods: WIL placement assessment practices in two programmes, pre-service teacher training (Avondale College of Higher Education, NSW) and medical diagnostic radiography (Faculty of Health Sciences, University of Sydney, NSW), were compared with a view to comparing assessment strategies across these two different educational domains. Educators (course coordinators) responsible for teaching professional development placements of teacher trainees and diagnostic radiography students developed a standards-based grading rubric designed to guide assessment of students’ work during WIL placement by assessors. After ∼12 months of implementation of the rubrics, assessors’ reaction to the effectiveness and usefulness of the grading rubric was determined using a specially created survey form. Data were collected over the period from March to June 2011. Quantitative and qualitative data found that assessors in both programmes considered the grading rubric to be a vital tool in the assessment process, though teacher supervisors were more positive about the benefits of its use than the radiographer supervisors. Results: Benefits of the grading rubric included accuracy and consistency of grading, ability to identify specific areas of desired development and facilitation of the provision of supervisor feedback. The use of assessment grading rubrics is of benefit to assessors in WIL placements from two very different

  8. Assessment of work-integrated learning: comparison of the usage of a grading rubric by supervising radiographers and teachers.

    Science.gov (United States)

    Kilgour, Andrew J; Kilgour, Peter W; Gerzina, Tania; Christian, Beverly

    2014-02-01

    IntroductionProfessional work-integrated learning (WIL) that integrates the academic experience with off-campus professional experience placements is an integral part of many tertiary courses. Issues with the reliability and validity of assessment grades in these placements suggest that there is a need to strengthen the level of academic rigour of placements in these programmes. This study aims to compare the attitudes to the usage of assessment rubrics of radiographers supervising medical imaging students and teachers supervising pre-service teachers. MethodsWIL placement assessment practices in two programmes, pre-service teacher training (Avondale College of Higher Education, NSW) and medical diagnostic radiography (Faculty of Health Sciences, University of Sydney, NSW), were compared with a view to comparing assessment strategies across these two different educational domains. Educators (course coordinators) responsible for teaching professional development placements of teacher trainees and diagnostic radiography students developed a standards-based grading rubric designed to guide assessment of students' work during WIL placement by assessors. After ∼12 months of implementation of the rubrics, assessors' reaction to the effectiveness and usefulness of the grading rubric was determined using a specially created survey form. Data were collected over the period from March to June 2011. Quantitative and qualitative data found that assessors in both programmes considered the grading rubric to be a vital tool in the assessment process, though teacher supervisors were more positive about the benefits of its use than the radiographer supervisors. ResultsBenefits of the grading rubric included accuracy and consistency of grading, ability to identify specific areas of desired development and facilitation of the provision of supervisor feedback. The use of assessment grading rubrics is of benefit to assessors in WIL placements from two very different teaching

  9. Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

    Science.gov (United States)

    Miconi, Thomas

    2017-02-23

    Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

  10. The relationship between explicit learning and consciousness-raising tasks within a communicative language context

    Directory of Open Access Journals (Sweden)

    Roscioli, Deise Carldart

    2015-01-01

    Full Text Available This study aims at investigating whether consciousness-raising tasks, used in a communicative learning environment of EFL, can be considered a valid instrument for eliciting explicit learning in that context. Five participants enrolled in the second level of a language course answered a cycle of tasks that intended to teach the use of comparatives. The materials used in this study consisted of a pre-task, consciousness-raising tasks, an untimed grammaticality judgment test, and a self-report questionnaire. Results showed that the instruments used in this research were of a valid nature for eliciting explicit learning. The findings also provide empirical support regarding the importance of consciousness-raising tasks to assist students’ second language learning in a communicative classroom environment. Despite being a small scale research, this study may contribute to a greater understanding of the SLA processes within a communicative context and highlight the importance of explicit knowledge learning within a meaning focused approach

  11. Classification models for clear cell renal carcinoma stage progression, based on tumor RNAseq expression trained supervised machine learning algorithms.

    Science.gov (United States)

    Jagga, Zeenia; Gupta, Dinesh

    2014-01-01

    Clear-cell Renal Cell Carcinoma (ccRCC) is the most- prevalent, chemotherapy resistant and lethal adult kidney cancer. There is a need for novel diagnostic and prognostic biomarkers for ccRCC, due to its heterogeneous molecular profiles and asymptomatic early stage. This study aims to develop classification models to distinguish early stage and late stage of ccRCC based on gene expression profiles. We employed supervised learning algorithms- J48, Random Forest, SMO and Naïve Bayes; with enriched model learning by fast correlation based feature selection to develop classification models trained on sequencing based gene expression data of RNAseq experiments, obtained from The Cancer Genome Atlas. Different models developed in the study were evaluated on the basis of 10 fold cross validations and independent dataset testing. Random Forest based prediction model performed best amongst the models developed in the study, with a sensitivity of 89%, accuracy of 77% and area under Receivers Operating Curve of 0.8. We anticipate that the prioritized subset of 62 genes and prediction models developed in this study will aid experimental oncologists to expedite understanding of the molecular mechanisms of stage progression and discovery of prognostic factors for ccRCC tumors.

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

    Directory of Open Access Journals (Sweden)

    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.

  13. Supervised practice in occupational therapy in a psychosocial care center: Challenges for the assistance and the teaching and learning process

    Directory of Open Access Journals (Sweden)

    Milton Carlos Mariotti

    2014-09-01

    Full Text Available The psychiatric reform in Brazil has replaced the hospital-centered model by the reintegration of users to their respective communities. The Center of Psychosocial Care (CAPS has been the main equipment in that scope. Objectives: To report the development of Supervised Practice in Occupational Therapy in a CAPS II unit in Curitiba, Parana state, Brazil. Methods: This is an experience report. It features the training field and describes the stages of the teaching and learning process which involved institutional observation, reporting and intervention proposal, collecting data about the users’ profile and attendances. The work focused the non-intensive users because they are close to hospital discharge. Results: We found that users of the non-intensive system, rather than crave the discharge, would like to return to the semi-intensive or intensive systems, aiming to regain sickness and transportation benefits, which are lost as users make progress. This fact denotes great contradictions in the system. We also attended intensive and semi-intensive systems users. Conclusions: The students’ learning included aspects such as direct contact with the institutional reality; knowledge about the health system, its limitations and contradictions; approach to users, their families, realities, socioeconomic conditions, desires, aspirations, or lack thereof; difficulties in engaging in meaningful occupations in their territories, limitations, and social stigma; working with frustrations, reflecting about ways to change the reality; in addition to expanded clinical practice, participating in the discussions and formulation of public policies on mental healthcare and social control.

  14. Promoting oral interaction in large groups through task-based learning

    OpenAIRE

    Forero Rocha, Yolima

    2009-01-01

    This research project attempts to show the way a group of five teachers used task-based learning with a group of 50 seventh graders to improve oral interaction. The students belonged to Isabel II School. They took an active part in the implementation of tasks and were asked to answer two questionnaires. Some English classes were observed and recorded; finally, an evaluation was taken by students to test their improvement. Key words: Task-based learning, oral interaction, large groups, hig...

  15. Trial-to-trial dynamics and learning in a generalized, redundant reaching task

    Science.gov (United States)

    Smallwood, Rachel F.; Cusumano, Joseph P.

    2013-01-01

    If humans exploit task redundancies as a general strategy, they should do so even if the redundancy is decoupled from the physical implementation of the task itself. Here, we derived a family of goal functions that explicitly defined infinite possible redundancies between distance (D) and time (T) for unidirectional reaching. All [T, D] combinations satisfying any specific goal function defined a goal-equivalent manifold (GEM). We tested how humans learned two such functions, D/T = c (constant speed) and D·T = c, that were very different but could both be achieved by neurophysiologically and biomechanically similar reaching movements. Subjects were never explicitly shown either relationship, but only instructed to minimize their errors. Subjects exhibited significant learning and consolidation of learning for both tasks. Initial error magnitudes were higher, but learning rates were faster, for the D·T task than for the D/T task. Learning the D/T task first facilitated subsequent learning of the D·T task. Conversely, learning the D·T task first interfered with subsequent learning of the D/T task. Analyses of trial-to-trial dynamics demonstrated that subjects actively corrected deviations perpendicular to each GEM faster than deviations along each GEM to the same degree for both tasks, despite exhibiting significantly greater variance ratios for the D/T task. Variance measures alone failed to capture critical features of trial-to-trial control. Humans actively exploited these abstract task redundancies, even though they did not have to. They did not use readily available alternative strategies that could have achieved the same performance. PMID:23054607

  16. On Training Targets for Supervised Speech Separation

    OpenAIRE

    Wang, Yuxuan; Narayanan, Arun; Wang, DeLiang

    2014-01-01

    Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the...

  17. Category learning in older adulthood: A study of the Shepard, Hovland, and Jenkins (1961) tasks.

    Science.gov (United States)

    Rabi, Rahel; Minda, John Paul

    2016-03-01

    Shepard, Hovland, and Jenkins (1961) examined the categorization abilities of younger adults using tasks involving single-dimensional rule learning, disjunctive rule learning, and family resemblance learning. The current study examined category learning in older adults using this well-known category set. Older adults, like younger adults, found category tasks with a single relevant dimension the easiest to learn. In contrast to younger adults, older adults found complex disjunctive rule-based categories harder to learn than family resemblance based categories. Disjunctive rule-based category learning appeared to be the most difficult for older adults to learn because this category set placed the heaviest demands on working memory, which is known to be a cognitive function that declines with normal aging. The authors discuss why complex rule-based category learning is considered more difficult for older adults to learn relative to younger adults, drawing parallels to developmental research.

  18. Částečně řízené učení algoritmů strojového učení (semi-supervised learning)

    OpenAIRE

    Burda, Karel

    2014-01-01

    The final thesis summarizes in its theoretical part basic knowledge of machine learning algorithms that involves supervised, semi-supervised, and unsupervised learning. Experiments with textual data in natural spoken language involving different machine learning methods and parameterization are carried out in its practical part. Conclusions made in the thesis may be of use to individuals that are at least slightly interested in this domain.

  19. ENGLISH LEARNING TASKS MEDIATED BY THE INTERACTIVE WHITEBOARD: THE MOTIVATIONAL EFFECTS ON PROBLEM-SOLVING AND SHARING PERSONAL EXPERIENCES TASKS

    Directory of Open Access Journals (Sweden)

    Samara Freitas OLIVEIRA

    2014-06-01

    Full Text Available Some authors have already suggested that different learning tasks influence L2 learners motivation in distinct ways. In this article, we seek (a to understand how English learners motivation varies along two Problem-solving and Sharing Personal Experiences tasks mediated by the Interactive Whiteboard (IWB and (b to analyze what may have caused the variability of these learners motivation. We consider the diachronic, situational and complex dimension of motivation based on the concepts of the Process-oriented model of L2 motivation by Dörnyei and Ottó (1998, the motivational basis of tasks (JULKÜNEN, 2001; DÖRNYEI; TSENG, 2009 and the IWB as a motivating element in L2 classes (BEELAND JR., 2002; MERCER; HENNESSY; WARWICK, 2010. This cross-sectional mixed methods study was carried out in a private language school with 29 English learners. We used observation notes during the learning tasks and Situational Scales as instruments of data collection. Results indicate, for instance, that tasks have different situational motivation patterns in consequence of the choice of the task topic, the teacher support, (insufficient self-regulatory strategies to sustain motivation, among others.

  20. Learning Data Driven Representations from Large Collections of Multidimensional Patterns with Minimal Supervision

    Science.gov (United States)

    2008-08-04

    knowledge is provided about the pattern that one is searching for, the task becomes that of aligning the prior with the observed patterns, and choos ...high-throughput, parallel fashion, we used the mass-isolation procedure developed by Eugene et al. [ Eugene et al., 1979] to gather hundreds of thousands...International Conference on Computer Vision, Nice, France, October 2003. [ Eugene et al., 1979] O. Eugene , A. Yund, and J. W. Fristrom. Tissue Culture

  1. Collective academic supervision

    DEFF Research Database (Denmark)

    Nordentoft, Helle Merete; Thomsen, Rie; Wichmann-Hansen, Gitte

    2013-01-01

    are interconnected. Collective Academic Supervision provides possibilities for systematic interaction between individual master students in their writing process. In this process they learn core academic competencies, such as the ability to assess theoretical and practical problems in their practice and present them...

  2. E-learning, dual-task, and cognitive load: The anatomy of a failed experiment.

    Science.gov (United States)

    Van Nuland, Sonya E; Rogers, Kem A

    2016-01-01

    The rising popularity of commercial anatomy e-learning tools has been sustained, in part, due to increased annual enrollment and a reduction in laboratory hours across educational institutions. While e-learning tools continue to gain popularity, the research methodologies used to investigate their impact on learning remain imprecise. As new user interfaces are introduced, it is critical to understand how functionality can influence the load placed on a student's memory resources, also known as cognitive load. To study cognitive load, a dual-task paradigm wherein a learner performs two tasks simultaneously is often used, however, its application within educational research remains uncommon. Using previous paradigms as a guide, a dual-task methodology was developed to assess the cognitive load imposed by two commercial anatomical e-learning tools. Results indicate that the standard dual-task paradigm, as described in the literature, is insensitive to the cognitive load disparities across e-learning tool interfaces. Confounding variables included automation of responses, task performance tradeoff, and poor understanding of primary task cognitive load requirements, leading to unreliable quantitative results. By modifying the secondary task from a basic visual response to a more cognitively demanding task, such as a modified Stroop test, the automation of secondary task responses can be reduced. Furthermore, by recording baseline measures for the primary task as well as the secondary task, it is possible for task performance tradeoff to be detected. Lastly, it is imperative that the cognitive load of the primary task be designed such that it does not overwhelm the individual's ability to learn new material.

  3. Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

    Directory of Open Access Journals (Sweden)

    Md. Abdullah-al-mamun

    2015-08-01

    Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

  4. Supervision of Teachers Based on Adjusted Arithmetic Learning in Special Education

    Science.gov (United States)

    Eriksson, Gota

    2008-01-01

    This article reports on 20 children's learning in arithmetic after teaching was adjusted to their conceptual development. The report covers periods from three months up to three terms in an ongoing intervention study of teachers and children in schools for the intellectually disabled and of remedial teaching in regular schools. The researcher…

  5. Probabilistic Category Learning in Developmental Dyslexia: Evidence from Feedback and Paired-Associate Weather Prediction Tasks

    Science.gov (United States)

    Gabay, Yafit; Vakil, Eli; Schiff, Rachel; Holt, Lori L.

    2015-01-01

    Objective Developmental dyslexia is presumed to arise from specific phonological impairments. However, an emerging theoretical framework suggests that phonological impairments may be symptoms stemming from an underlying dysfunction of procedural learning. Method We tested procedural learning in adults with dyslexia (n=15) and matched-controls (n=15) using two versions of the Weather Prediction Task: Feedback (FB) and Paired-associate (PA). In the FB-based task, participants learned associations between cues and outcomes initially by guessing and subsequently through feedback indicating the correctness of response. In the PA-based learning task, participants viewed the cue and its associated outcome simultaneously without overt response or feedback. In both versions, participants trained across 150 trials. Learning was assessed in a subsequent test without presentation of the outcome, or corrective feedback. Results The Dyslexia group exhibited impaired learning compared with the Control group on both the FB and PA versions of the weather prediction task. Conclusions The results indicate that the ability to learn by feedback is not selectively impaired in dyslexia. Rather it seems that the probabilistic nature of the task, shared by the FB and PA versions of the weather prediction task, hampers learning in those with dyslexia. Results are discussed in light of procedural learning impairments among participants with dyslexia. PMID:25730732

  6. Flexible explicit but rigid implicit learning in a visuomotor adaptation task.

    Science.gov (United States)

    Bond, Krista M; Taylor, Jordan A

    2015-06-01

    There is mounting evidence for the idea that performance in a visuomotor rotation task can be supported by both implicit and explicit forms of learning. The implicit component of learning has been well characterized in previous experiments and is thought to arise from the adaptation of an internal model driven by sensorimotor prediction errors. However, the role of explicit learning is less clear, and previous investigations aimed at characterizing the explicit component have relied on indirect measures such as dual-task manipulations, posttests, and descriptive computational models. To address this problem, we developed a new method for directly assaying explicit learning by having participants verbally report their intended aiming direction on each trial. While our previous research employing this method has demonstrated the possibility of measuring explicit learning over the course of training, it was only tested over a limited scope of manipulations common to visuomotor rotation tasks. In the present study, we sought to better characterize explicit and implicit learning over a wider range of task conditions. We tested how explicit and implicit learning change as a function of the specific visual landmarks used to probe explicit learning, the number of training targets, and the size of the rotation. We found that explicit learning was remarkably flexible, responding appropriately to task demands. In contrast, implicit learning was strikingly rigid, with each task condition producing a similar degree of implicit learning. These results suggest that explicit learning is a fundamental component of motor learning and has been overlooked or conflated in previous visuomotor tasks. Copyright © 2015 the American Physiological Society.

  7. Clinical supervision.

    Science.gov (United States)

    Goorapah, D

    1997-05-01

    The introduction of clinical supervision to a wider sphere of nursing is being considered from a professional and organizational point of view. Positive views are being expressed about adopting this concept, although there are indications to suggest that there are also strong reservations. This paper examines the potential for its success amidst the scepticism that exists. One important question raised is whether clinical supervision will replace or run alongside other support systems.

  8. Stress reduces use of negative feedback in a feedback-based learning task.

    Science.gov (United States)

    Petzold, Antje; Plessow, Franziska; Goschke, Thomas; Kirschbaum, Clemens

    2010-04-01

    In contrast to the well-established effects of stress on learning of declarative material, much less is known about stress effects on reward- or feedback-based learning. Differential effects on positive and negative feedback especially have received little attention. The objective of this study, thus, was to investigate effects of psychosocial stress on feedback-based learning with a particular focus on the use of negative and positive feedback during learning. Participants completed a probabilistic selection task in both a stress and a control condition. The task allowed quantification of how much participants relied on positive and negative feedback during learning. Although stress had no effect on general acquisition of the task, results indicate that participants used negative feedback significantly less during learning after stress compared with the control condition. An enhancing effect of stress on use of positive feedback failed to reach significance. These findings suggest that stress acts differentially on the use of positive and negative feedback during learning.

  9. Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision.

    Science.gov (United States)

    Wallace, Byron C; Kuiper, Joël; Sharma, Aakash; Zhu, Mingxi Brian; Marshall, Iain J

    2016-01-01

    Systematic reviews underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a PICO criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full-texts of trial reports is thus a critical yet time-consuming step in the systematic review process. We seek to expedite evidence synthesis by developing machine learning models to automatically extract sentences from articles relevant to PICO elements. Collecting a large corpus of training data for this task would be prohibitively expensive. Therefore, we derive distant supervision (DS) with which to train models using previously conducted reviews. DS entails heuristically deriving 'soft' labels from an available structured resource. However, we have access only to unstructured, free-text summaries of PICO elements for corresponding articles; we must derive from these the desired sentence-level annotations. To this end, we propose a novel method - supervised distant supervision (SDS) - that uses a small amount of direct supervision to better exploit a large corpus of distantly labeled instances by learning to pseudo-annotate articles using the available DS. We show that this approach tends to outperform existing methods with respect to automated PICO extraction.

  10. Tasks and learner motivation in learning Chinese as a foreign language

    DEFF Research Database (Denmark)

    Ruan, Youjin; Duan, Xiaoju; Du, Xiangyun

    2015-01-01

    factors, which can boost learners’ intrinsic motivation, when designing a task, especially at a beginning stage of foreign language learning, and to integrate cultural elements into tasks as an added value to motivate learners. Finally, this study identifies challenges and barriers related to TBTL......This study focuses on how beginner learners in a task-based teaching and learning (TBTL) environment perceive what is motivating to them in the process of learning Chinese as a foreign language (CFL) at Aalborg University (AAU), Denmark. Drawing upon empirical data from surveys, group interviews...... and participant observation, this study explores which kinds of tasks are perceived as motivating from the students’ perspective and which characteristics the learners associate with motivating tasks. The study indicates that it is important to consider the learners’ affective factors and learning situation...

  11. Task-based learning programme for clinical years of medical education.

    Science.gov (United States)

    Ozkan, Hasan; Degirmenci, Berna; Musal, Berna; Itil, Oya; Akalin, Elif; Kilinc, Oguz; Ozkan, Sebnem; Alici, Emin

    2006-03-01

    Task-based learning (TBL) is an educational strategy recommended for the later years of the medical education programme. The TBL programme was adopted for clinical years in the 2000-2001 academic year in Dokuz Eylul University School of Medicine (DEUSM). The aim of this paper is to describe the TBL programme of DEUSM. DEUSM outlined 50 clinical tasks for fourth-year students and 37 for fifth-year students. The tasks were grouped into four and five blocks. Interdisciplinary practicals, lectures and patient visits were organised in each task's schedule. The tasks were the focus of learning and each discipline contributed its own learning opportunities to the attached tasks. Formative and summative methods were used to evaluate the programme. Based on the experience and feedback provided by the students and trainers, the authors considered TBL an applicable and advisable approach for the clinical years of medical education.

  12. The influence of task demand and learning on the psychophysiological response.

    Science.gov (United States)

    Fairclough, Stephen H; Venables, Louise; Tattersall, Andrew

    2005-05-01

    The level of expertise of an operator may significantly influence his/her psychophysiological response to high task demand. A naive individual may invest considerable mental effort during performance of a difficult task and psychophysiological reactivity will be high compared to the psychophysiological response of a highly skilled operator. A study on multitasking performance was conducted to investigate the interaction between learning and task demand on psychophysiological reactivity. Thirty naive participants performed high and low demand versions of the Multi-attribute Task Battery (MATB) over a learning period of 64 min. High and low task demand setting were preset via a pilot study. Psychophysiological variables were collected from four channels of EEG (Cz, P3, P4, Pz), ECG, EOG and respiration rate to measure the impact of task demand and learning. Several variables were sensitive to the task demand manipulation but not time-on-task, e.g., heart rate, Theta activity at parietal sites. The sensitivity of certain variables to high demand was compromised by skill acquisition, e.g., respiration rate, suppression of alpha activity. A sustained learning effect was observed during the high demand condition only; multiple regression analyses revealed that specific psychophysiological variables predicted learning at different stages on the learning curve. The implications for the sensitivity of psychophysiological variables are discussed.

  13. 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.

  14. Task-Based Language Learning: Old Approach, New Style. A New Lesson to Learn

    Directory of Open Access Journals (Sweden)

    Rodríguez-Bonces Mónica

    2010-11-01

    Full Text Available This paper provides an overview of Task-Based Language Learning (TBL and its use in the teaching and learning of foreign languages. It begins by defining the concept of TBL, followed by a presentation of its framework and implications, and finally, a lesson plan based on TBL. The article presents an additional stage to be considered when planning a task-based lesson: the one of formal and informal assessment. The rubrics and a self-evaluation format appear as an additional constituent of any task cycle. Este artículo presenta una visión general del aprendizaje basado en tareas y su uso en la enseñanza y el aprendizaje de las lenguas extranjeras. Comenzamos por definir el concepto de aprendizaje basado en tareas, seguido por una presentación de sus fundamentos e implicaciones. Finalmente, presentamos una lección fundamentada en el aprendizaje basado en tareas. El artículo presenta una fase adicional cuando se planea una lección basada en tareas: la relacionada con la evaluación formal e informal. Así mismo, se explica que una parte importante del enfoque por tareas es un componente de evaluación, el cual debe contener rúbricas y un formato de autoevaluación.

  15. Task-specific effect of transcranial direct current stimulation on motor learning

    Directory of Open Access Journals (Sweden)

    Cinthia Maria Saucedo Marquez

    2013-07-01

    Full Text Available Transcranial direct current stimulation (tDCS is a relatively new non-invasive brain stimulation technique that modulates neural processes. When applied to the human primary motor cortex (M1, tDCS has beneficial effects on motor skill learning and consolidation in healthy controls and in patients. However, it remains unclear whether tDCS improves motor learning in a general manner or whether these effects depend on which motor task is acquired. Here we compare whether the effect of tDCS differs when the same individual acquires (1 a Sequential Finger Tapping Task (SEQTAP and (2 a Visual Isometric Pinch Force Task (FORCE. Both tasks have been shown to be sensitive to tDCS applied over M1, however, the underlying processes mediating learning and memory formation might benefit differently from anodal-tDCS. Thirty healthy subjects were randomly assigned to an anodal-tDCS group or sham-group. Using a double-blind, sham-controlled cross-over design, tDCS was applied over M1 while subjects acquired each of the motor tasks over 3 consecutive days, with the order being randomized across subjects. We found that anodal-tDCS affected each task differently: The SEQTAP task benefited from anodal-tDCS during learning, whereas the FORCE task showed improvements only at retention. These findings suggest that anodal tDCS applied over M1 appears to have a task-dependent effect on learning and memory formation.

  16. HTML5 in Development of Assessment Tasks for e-Learning

    Directory of Open Access Journals (Sweden)

    Synytsya Kateryna

    2014-12-01

    Full Text Available The paper describes various types of assessment tasks that are used in e-learning environments and studies the use of HTML5 in the development of user interface elements for e-learning systems. Popular existing practices of HTML5 user interface design are examined, and some examples relevant to e-learning environments are provided.

  17. Does Time-on-Task Estimation Matter? Implications for the Validity of Learning Analytics Findings

    Science.gov (United States)

    Kovanovic, Vitomir; Gaševic, Dragan; Dawson, Shane; Joksimovic, Srecko; Baker, Ryan S.; Hatala, Marek

    2015-01-01

    With\twidespread adoption of Learning Management Systems (LMS) and other learning technology, large amounts of data--commonly known as trace data--are readily accessible to researchers. Trace data has been extensively used to calculate time that students spend on different learning activities--typically referred to as time-on-task. These measures…

  18. A Time To Sow: Report from the Task Force on Learning Technologies.

    Science.gov (United States)

    Council of Ontario Universities, Toronto.

    Information technology and telecommunications advances affect universities in addition to business. Ontario universities need to address the importance of incorporating learning technologies (LTs) into their teaching. The Task Force on Learning Technologies was established to address Ontario universities' need to utilize learning technologies and…

  19. Interindividual Differences in Learning Performance: The Effects of Age, Intelligence, and Strategic Task Approach

    Science.gov (United States)

    Kliegel, Matthias; Altgassen, Mareike

    2006-01-01

    The present study investigated fluid and crystallized intelligence as well as strategic task approaches as potential sources of age-related differences in adult learning performance. Therefore, 45 young and 45 old adults were asked to learn pictured objects. Overall, young participants outperformed old participants in this learning test. However,…

  20. Task Complexity, Student Perceptions of Vocabulary Learning in EFL, and Task Performance

    Science.gov (United States)

    Wu, Xiaoli; Lowyck, Joost; Sercu, Lies; Elen, Jan

    2013-01-01

    Background: The study deepened our understanding of how students' self-ef?cacy beliefs contribute to the context of teaching English as a foreign language in the framework of cognitive mediational paradigm at a ?ne-tuned task-speci?c level. Aim: The aim was to examine the relationship among task complexity, self-ef?cacy beliefs, domain-related…

  1. A sequence learning impairment in dyslexia? It depends on the task.

    Science.gov (United States)

    Henderson, Lisa M; Warmington, Meesha

    2017-01-01

    Language acquisition is argued to be dependent upon an individuals' sensitivity to serial-order regularities in the environment (sequential learning), and impairments in reading and spelling in dyslexia have recently been attributed to a deficit in sequential learning. The present study examined the learning and consolidation of sequential knowledge in 30 adults with dyslexia and 29 typical adults matched on age and nonverbal ability using two tasks previously reported to be sensitive to a sequence learning deficit. Both groups showed evidence of sequential learning and consolidation on a serial response time (SRT) task (i.e., faster and more accurate responses to sequenced spatial locations than randomly ordered spatial locations during training that persisted one week later). Whilst typical adults showed evidence of sequential learning on a Hebb repetition task (i.e., more accurate serial recall of repetitive sequences of nonwords versus randomly ordered sequences), adults with dyslexia showed initial advantages for repetitive versus randomly ordered sequences in the first half of training trials, but this effect disappeared in the second half of trials. This Hebb repetition effect was positively correlated with spelling in the dyslexic group; however, there was no correlation between sequential learning on the two tasks, placing doubt over whether sequential learning in different modalities represents a single capacity. These data suggest that sequential learning difficulties in adults with dyslexia are not ubiquitous, and when present may be a consequence of task demands rather than sequence learning per se.

  2. Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

    CERN Document Server

    Möller, A; Leloup, C; Neveu, J; Palanque-Delabrouille, N; Rich, J; Carlberg, R; Lidman, C; Pritchet, C

    2016-01-01

    In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts ($0.2learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia sa...

  3. Supervised Learning Approach for Spam Classification Analysis using Data Mining Tools

    Directory of Open Access Journals (Sweden)

    R.Deepa Lakshmi

    2010-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifierrelated issues. In recent days, Machine learning for spamclassification is an important research issue. This paper exploresand identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysisamong the algorithms has also been presented.

  4. Supervised Learning Approach for Spam Classification Analysis using Data Mining Tools

    Directory of Open Access Journals (Sweden)

    R.Deepa Lakshmi

    2010-11-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifierrelated issues. In recent days, Machine learning for spamclassification is an important research issue. This paper exploresand identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysisamong the algorithms has also been presented.

  5. Supervised Ensemble Classification of Kepler Variable Stars

    CERN Document Server

    Bass, Gideon

    2016-01-01

    Variable star analysis and classification is an important task in the understanding of stellar features and processes. While historically classifications have been done manually by highly skilled experts, the recent and rapid expansion in the quantity and quality of data has demanded new techniques, most notably automatic classification through supervised machine learning. We present an expansion of existing work on the field by analyzing variable stars in the {\\em Kepler} field using an ensemble approach, combining multiple characterization and classification techniques to produce improved classification rates. Classifications for each of the roughly 150,000 stars observed by {\\em Kepler} are produced separating the stars into one of 14 variable star classes.

  6. Problem-based learning and task-based learning: a practical synthesis.

    Science.gov (United States)

    Takahashi, Yuzo

    2008-03-01

    The author of this article attended the International PBL Workshop in Kaohsiung Medical University in 2007 as an international tutor. Based on his personal experiences in the workshop and at his own medical school, he finds there are frequent problems in PBL programs related to the difficulty in providing expert tutors. Students in PBL tutorials may fear they are unable to get sufficient guidance from tutors in terms of learning the issues they should research; moreover, PBL case writers fear their cases are less effective, because non-expert tutors may misdirect students in the step 1 tutorial discussion. The author proposes that combining standard problem-based learning (PBL) methods with elements of task-based learning (TBL) can be effective at addressing both of these problems. The TBL method he proposes involves providing students with an additional sheet at the end of PBL tutorials. This sheet is written by the case writer and details key learning issues, questions and perspectives the students should investigate during their research process. This reduces the need to have expert tutors who know the full range of facts about the case, and leaves students feeling supported and less concerned they will miss important learning issues.

  7. Problem-based Learning and Task-based Learning: A Practical Synthesis

    Directory of Open Access Journals (Sweden)

    Yuzo Takahashi

    2008-03-01

    Full Text Available The author of this article attended the International PBL Workshop in Kaohsiung Medical University in 2007 as an international tutor. Based on his personal experiences in the workshop and at his own medical school, he finds there are frequent problems in PBL programs related to the difficulty in providing expert tutors. Students in PBL tutorials may fear they are unable to get sufficient guidance from tutors in terms of learning the issues they should research; moreover, PBL case writers fear their cases are less effective, because non-expert tutors may misdirect students in the step 1 tutorial discussion. The author proposes that combining standard problem-based learning (PBL methods with elements of task-based learning (TBL can be effective at addressing both of these problems. The TBL method he proposes involves providing students with an additional sheet at the end of PBL tutorials. This sheet is written by the case writer and details key learning issues, questions and perspectives the students should investigate during their research process. This reduces the need to have expert tutors who know the full range of facts about the case, and leaves students feeling supported and less concerned they will miss important learning issues.

  8. [The effect of reading tasks on learning from multiple texts].

    Science.gov (United States)

    Kobayashi, Keiichi

    2014-06-01

    This study examined the effect of reading tasks on the integration of content and source information from multiple texts. Undergraduate students (N = 102) read five newspaper articles about a fictitious incident in either a summarization task condition or an evaluation task condition. Then, they performed an integration test and a source choice test, which assessed their understanding of a situation described in the texts and memory for the sources of text information. The results indicated that the summarization and evaluation task groups were not significantly different in situational understanding. However, the summarization task group significantly surpassed the evaluation task group for source memory. No significant correlation between the situational understanding and the source memory was found for the summarization group, whereas a significant positive correlation was found for the evaluation group. The results are discussed in terms of the documents model framework.

  9. Online kernel-based learning for task-space tracking robot control.

    Science.gov (United States)

    Nguyen-Tuong, Duy; Peters, Jan

    2012-09-01

    Task-space control of redundant robot systems based on analytical models is known to be susceptive to modeling errors. Data-driven model learning methods may present an interesting alternative approach. However, learning models for task-space tracking control from sampled data is an ill-posed problem. In particular, the same input data point can yield many different output values, which can form a nonconvex solution space. Because the problem is ill-posed, models cannot be learned from such data using common regression methods. While learning of task-space control mappings is globally ill-posed, it has been shown in recent work that it is locally a well-defined problem. In this paper, we use this insight to formulate a local kernel-based learning approach for online model learning for task-space tracking control. We propose a parametrization for the local model, which makes an application in task-space tracking control of redundant robots possible. The model parametrization further allows us to apply the kernel-trick and, therefore, enables a formulation within the kernel learning framework. In our evaluations, we show the ability of the method for online model learning for task-space tracking control of redundant robots.

  10. 基于流形正则化半监督学习的污水处理操作工况识别方法%Identification of wastewater operational conditions based on manifold regularization semi-supervised learning

    Institute of Scientific and Technical Information of China (English)

    赵立杰; 王海龙; 陈斌

    2016-01-01

    The wastewater treatment process is vulnerable to the impact of external shocks to cause sludge floating, aging, poisoning, expansion and other failure conditions, resulting in effluent deterioration and high energy consumption. It is urgent to quickly and accurately identify the operating conditions of wastewater treatment process. In the existing supervised learning methods all the data are labeled which are time consuming and expensive. A multitude of unlabeled data to collect easily and cheaply have rich and useful information about the operating condition. To overcome the disadvantage of supervised learning algorithms that they cannot make use of unlabeled data, a semi-supervised extreme learning machine algorithm based on manifold regularization is adopted to monitor the operation states of biochemical wastewater treatment process. The graph Laplacian matrix is constructed from both the labeled patterns and the unlabeled patterns. Extreme learning machine algorithm is adopted to handle the semi-supervised learning task under the framework of the manifold regularization. It constructs the hidden layer using random feature mapping and solves the weights between the hidden layer and the output layer, which exhibit the computational efficiency and generalization performance of the random neural network. The results of simulation experiments show that the fault identification method based on semi supervised learning machine has superiority to the basic extreme learning machine in improving the accuracy and reliability.%污水处理过程容易受外界冲激扰动影响,引发污泥上浮、老化、中毒、膨胀等故障工况,导致出水水质质量差,能源消耗高等问题,如何快速准确识别污水操作工况故障至关重要。针对污水工况识别过程中现有监督学习方法未利用大量未标记数据蕴含的丰富操作工况信息,采用基于流形正则化极限学习机的半监督学习方法,监视生化污水处

  11. Using Tasks in Teaching Practice: Helping Pre-service Teachers Learn to Promote Students' Language Use

    OpenAIRE

    2011-01-01

    This study investigates how tasks in pre-service teacher education canhelp aspiring teachers learn to promote students' language use in the Englishclass. A task is an activity where learners interact in the target language toachieve a non-linguistic outcome. Based on Willis's framework for Task BasedLearning(1996),three pre-service teachers conducted a jumble task in 4classes at a junior high school. Self evaluation sheets were given to studentsafter each class to determine how effective the ...

  12. VDES J2325-5229 a z=2.7 gravitationally lensed quasar discovered using morphology independent supervised machine learning

    CERN Document Server

    Ostrovski, Fernanda; Connolly, Andrew J; Lemon, Cameron A; Auger, Matthew W; Banerji, Manda; Hung, Johnathan M; Koposov, Sergey E; Lidman, Christopher E; Reed, Sophie L; Allam, Sahar; Benoit-Lévy, Aurélien; Bertin, Emmanuel; Brooks, David; Buckley-Geer, Elizabeth; Rosell, Aurelio Carnero; Kind, Matias Carrasco; Carretero, Jorge; Cunha, Carlos E; da Costa, Luiz N; Desai, Shantanu; Diehl, H Thomas; Dietrich, Jörg P; Evrard, August E; Finley, David A; Flaugher, Brenna; Fosalba, Pablo; Frieman, Josh; Gerdes, David W; Goldstein, Daniel A; Gruen, Daniel; Gruendl, Robert A; Gutierrez, Gaston; Honscheid, Klaus; James, David J; Kuehn, Kyler; Kuropatkin, Nikolay; Lima, Marcos; Lin, Huan; Maia, Marcio A G; Marshall, Jennifer L; Martini, Paul; Melchior, Peter; Miquel, Ramon; Ogando, Ricardo; Malagón, Andrés Plazas; Reil, Kevin; Romer, Kathy; Sanchez, Eusebio; Santiago, Basilio; Scarpine, Vic; Sevilla-Noarbe, Ignacio; Soares-Santos, Marcelle; Sobreira, Flavia; Suchyta, Eric; Tarle, Gregory; Thomas, Daniel; Tucker, Douglas L; Walker, Alistair R

    2016-01-01

    We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift $z_{s}=2.74$ and image separation of $2.9"$ lensed by a foreground $z_{l}=0.40$ elliptical galaxy. Since the images of gravitationally lensed quasars are the superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and $gi$ multicolour photometric observations from the Dark Energy Survey (DES), near IR $JK$ photometry from the VISTA Hemisphere Survey (VHS) and WISE mid IR photometry, we have identified a candidate system with two catalogue components with $i_{AB}=18.61$ and $i_{AB}=20.44$ comprised of an elliptical galaxy and two blue point sources. Spectroscopic follow-up with NTT and the use of an archival AAT spectrum show that the point sources can be identified as...

  13. A spatio-temporal latent atlas for semi-supervised learning of fetal brain segmentations and morphological age estimation.

    Science.gov (United States)

    Dittrich, Eva; Riklin Raviv, Tammy; Kasprian, Gregor; Donner, René; Brugger, Peter C; Prayer, Daniela; Langs, Georg

    2014-01-01

    Prenatal neuroimaging requires reference models that reflect the normal spectrum of fetal brain development, and summarize observations from a representative sample of individuals. Collecting a sufficiently large data set of manually annotated data to construct a comprehensive in vivo atlas of rapidly developing structures is challenging but necessary for large population studies and clinical application. We propose a method for the semi-supervised learning of a spatio-temporal latent atlas of fetal brain development, and corresponding segmentations of emerging cerebral structures, such as the ventricles or cortex. The atlas is based on the annotation of a few examples, and a large number of imaging data without annotation. It models the morphological and developmental variability across the population. Furthermore, it serves as basis for the estimation of a structures' morphological age, and its deviation from the nominal gestational age during the assessment of pathologies. Experimental results covering the gestational period of 20-30 gestational weeks demonstrate segmentation accuracy achievable with minimal annotation, and precision of morphological age estimation. Age estimation results on fetuses suffering from lissencephaly demonstrate that they detect significant differences in the age offset compared to a control group. Copyright © 2013. Published by Elsevier B.V.

  14. Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning.

    Science.gov (United States)

    Cordier, Tristan; Esling, Philippe; Lejzerowicz, Franck; Visco, Joana; Ouadahi, Amine; Martins, Catarina; Cedhagen, Tomas; Pawlowski, Jan

    2017-08-15

    Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise demanding. High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) represents a promising alternative for benthic monitoring. However, an important fraction of eDNA sequences remains unassigned or belong to taxa of unknown ecology, which prevent their use for assessing the ecological quality status. Here, we show that supervised machine learning (SML) can be used to build robust predictive models for benthic monitoring, regardless of the taxonomic assignment of eDNA sequences. We tested three SML approaches to assess the environmental impact of marine aquaculture using benthic foraminifera eDNA, a group of unicellular eukaryotes known to be good bioindicators, as features to infer macro-invertebrates based biotic indices. We found similar ecological status as obtained from macro-invertebrates inventories. We argue that SML approaches could overcome and even bypass the cost and time-demanding morpho-taxonomic approaches in future biomonitoring.

  15. Translation and validation of the clinical learning environment, supervision and nurse teacher scale (CLES + T) in Croatian language.

    Science.gov (United States)

    Lovrić, Robert; Piškorjanac, Silvija; Pekić, Vlasta; Vujanić, Jasenka; Ratković, Karolina Kramarić; Luketić, Suzana; Plužarić, Jadranka; Matijašić-Bodalec, Dubravka; Barać, Ivana; Žvanut, Boštjan

    2016-07-01

    Clinical practice is essential to nursing education as it provides experience with patients and work environments that prepare students for future work as nurses. The aim of this study was to translate the "Clinical Learning Environment, Supervision and Nurse Teacher" questionnaire in Croatian language and test its validity and reliability in practice. The study was performed at the Faculty of medicine, Josip Juraj Strossmayer University of Osijek, Croatia in April 2014. The translated questionnaire was submitted to 136 nursing students: 20 males and 116 females. Our results reflected a slightly different factor structure, consisting of four factors. All translated items of the original constructs "Supervisory relationship", "Role of nurse teacher" and "Leadership style of the ward manager" loaded on factor 1. Items of "Pedagogical atmosphere on the ward" are distributed on two factors (3 and 4). The items of "Premises of nursing on the ward" loaded on factor 2. Three items were identified as problematic and iteratively removed from the analysis. The translated version of the aforementioned questionnaire has properties suitable for the evaluation of clinical practice for nursing students within a Croatian context and reflects the specifics of the nursing clinical education in this country.

  16. Application of graph-based semi-supervised learning for development of cyber COP and network intrusion detection

    Science.gov (United States)

    Levchuk, Georgiy; Colonna-Romano, John; Eslami, Mohammed

    2017-05-01

    The United States increasingly relies on cyber-physical systems to conduct military and commercial operations. Attacks on these systems have increased dramatically around the globe. The attackers constantly change their methods, making state-of-the-art commercial and military intrusion detection systems ineffective. In this paper, we present a model to identify functional behavior of network devices from netflow traces. Our model includes two innovations. First, we define novel features for a host IP using detection of application graph patterns in IP's host graph constructed from 5-min aggregated packet flows. Second, we present the first application, to the best of our knowledge, of Graph Semi-Supervised Learning (GSSL) to the space of IP behavior classification. Using a cyber-attack dataset collected from NetFlow packet traces, we show that GSSL trained with only 20% of the data achieves higher attack detection rates than Support Vector Machines (SVM) and Naïve Bayes (NB) classifiers trained with 80% of data points. We also show how to improve detection quality by filtering out web browsing data, and conclude with discussion of future research directions.

  17. A neuron model with trainable activation function (TAF) and its MFNN supervised learning

    Institute of Scientific and Technical Information of China (English)

    吴佑寿; 赵明生

    2001-01-01

    This paper addresses a new kind of neuron model, which has trainable activation function (TAF) in addition to only trainable weights in the conventional M-P model. The final neuron activation function can be derived from a primitive neuron activation function by training. The BP like learning algorithm has been presented for MFNN constructed by neurons of TAF model. Several simulation examples are given to show the network capacity and performance advantages of the new MFNN in comparison with that of conventional sigmoid MFNN.

  18. Anticipatory Driving for a Robot-Car Based on Supervised Learning

    DEFF Research Database (Denmark)

    Markelic, I.; Kulvicius, Tomas; Tamosiunaite, M.

    2009-01-01

    Using look ahead information and plan making improves hu- man driving. We therefore propose that also autonomously driving systems should dispose over such abilities. We adapt a machine learning approach, where the system, a car-like robot, is trained by an experienced driver by correlating visual...... adapt a two-level ap- proach, where the result of the database is combined with an additional reactive controller for robust behavior. Concerning velocity control this paper makes a novel contribution which is the ability of the system to react adequatly to upcoming curves...

  19. Anticipatory Driving for a Robot-Car Based on Supervised Learning

    DEFF Research Database (Denmark)

    Markelic, I.; Kulvicius, Tomas; Tamosiunaite, M.

    2009-01-01

    Using look ahead information and plan making improves hu- man driving. We therefore propose that also autonomously driving systems should dispose over such abilities. We adapt a machine learning approach, where the system, a car-like robot, is trained by an experienced driver by correlating visual...... adapt a two-level ap- proach, where the result of the database is combined with an additional reactive controller for robust behavior. Concerning velocity control this paper makes a novel contribution which is the ability of the system to react adequatly to upcoming curves...

  20. Rate of learning and asymptotic performance in an automatization task and the relation to reading.

    Science.gov (United States)

    Hecht, Rozalia; Crewther, David; Crewther, Sheila

    2004-12-01

    In the present study, direct evidence was sought linking cognitive automatic processing with reading in the general adult population. Reading speed on single-task performance and dual-task performance were compared. A total of 18 adults without dyslexia participated (7 men and 11 women, age M=25.3 yr., SD=2.7). Participants initially were trained in single-task mode on two types of tasks. The first was a central alphanumeric equation task (true or false), which comprised 3 subtests of increasing difficulty, ranging from an easily automated task to a varied and unpredictable mathematical operation. The second task was a peripheral pattern subitization task for which stimulus exposure time was related to performance. Finally, participants received dual-task training, which required simultaneous processing of both tasks. Slower reading speed was significantly related to rate of learning and speed of performance on predictable alphanumeric operations in dual-task conditions. There was no effect of reading speed on performance in the varied alphanumeric task. Faster readers were no better than slower readers on the pattern-subitization task. These findings suggest that faster readers automatized the predictable alphanumeric task more rapidly than slower readers and hence were better able to cope with the dual-task condition.

  1. The Role of Subjective Task Value in Service-Learning Engagement among Chinese College Students.

    Science.gov (United States)

    Li, Yulan; Guo, Fangfang; Yao, Meilin; Wang, Cong; Yan, Wenfan

    2016-01-01

    Most service-learning studies in higher education focused on its effects on students' development. The dynamic processes and mechanisms of students' development during service-learning, however, have not been explored thoroughly. Student engagement in service-learning may affect service-learning outcomes and be affected by subjective task value at the same time. The present study aimed to explore the effect of subjective task value on Chinese college student engagement during service-learning. Fifty-four Chinese college students participated in a 9-weeks service-learning program of interacting with children with special needs. Students' engagement and subjective task value were assessed via self-report questionnaires and 433 weekly reflective journals. The results indicated that the cognitive, emotional and behavioral engagement of Chinese college students demonstrated different developmental trends during service-learning process. Subjective task value played an essential role in student engagement in service-learning activities. However, the role of subjective task value varied with different stages. Finally, the implications for implementing service-learning in Chinese education were discussed.

  2. Developmental Differences in Effects of Task Pacing on Implicit Sequence Learning

    Directory of Open Access Journals (Sweden)

    Amanda Sue Hodel

    2014-02-01

    Full Text Available Although there is now substantial evidence that developmental change occurs in implicit learning abilities over the lifespan, disparate results exist regarding the specific developmental trajectory of implicit learning skills. One possible reason for discrepancies across implicit learning studies may be that younger children show an increased sensitivity to variations in implicit learning task procedures and demands relative to adults. Studies using serial-reaction time (SRT tasks have suggested that in adults, measurements of implicit learning are robust across variations in task procedures. Most classic SRT tasks have used response-contingent pacing in which the participant’s own reaction time determines the duration of each trial. However, recent paradigms with adults and children have used fixed trial pacing, which leads to alterations in both response and attention demands, accuracy feedback, perceived agency, and task motivation for participants. In the current study, we compared learning on fixed-paced and self-paced versions of a spatial sequence learning paradigm in 4-year-old children and adults. Results indicated that preschool-aged children showed reduced evidence of implicit sequence learning in comparison to adults, regardless of the SRT paradigm used. In addition, we found the preschoolers showed significantly greater learning when stimulus presentation was self-paced. These data provide evidence for developmental differences in implicit sequence learning that are dependent on specific task demands such as stimulus pacing, which may be related to developmental changes in the impact of broader constructs such as attention and task motivation on implicit learning.

  3. Training self-assessment and task-selection skills : A cognitive approach to improving self-regulated learning

    NARCIS (Netherlands)

    Kostons, Danny; van Gog, Tamara; Paas, Fred

    For self-regulated learning to be effective, students need to be able to accurately assess their own performance on a learning task and use this assessment for the selection of a new learning task. Evidence suggests, however, that students have difficulties with accurate self-assessment and task

  4. Training Self-Assessment and Task-Selection Skills: A Cognitive Approach to Improving Self-Regulated Learning

    Science.gov (United States)

    Kostons, Danny; van Gog, Tamara; Paas, Fred

    2012-01-01

    For self-regulated learning to be effective, students need to be able to accurately assess their own performance on a learning task and use this assessment for the selection of a new learning task. Evidence suggests, however, that students have difficulties with accurate self-assessment and task selection, which may explain the poor learning…

  5. Training self-assessment and task-selection skills : A cognitive approach to improving self-regulated learning

    NARCIS (Netherlands)

    Kostons, Danny; van Gog, Tamara; Paas, Fred

    2012-01-01

    For self-regulated learning to be effective, students need to be able to accurately assess their own performance on a learning task and use this assessment for the selection of a new learning task. Evidence suggests, however, that students have difficulties with accurate self-assessment and task sel

  6. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.

    Science.gov (United States)

    Premaladha, J; Ravichandran, K S

    2016-04-01

    Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.

  7. Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

    Science.gov (United States)

    Möller, A.; Ruhlmann-Kleider, V.; Leloup, C.; Neveu, J.; Palanque-Delabrouille, N.; Rich, J.; Carlberg, R.; Lidman, C.; Pritchet, C.

    2016-12-01

    In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts (0.2 Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of 0.98.We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than 5%. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high-z SN survey with application to real SN data.

  8. Supervised machine learning on a network scale: application to seismic event classification and detection

    Science.gov (United States)

    Reynen, Andrew; Audet, Pascal

    2017-09-01

    A new method using a machine learning technique is applied to event classification and detection at seismic networks. This method is applicable to a variety of network sizes and settings. The algorithm makes use of a small catalogue of known observations across the entire network. Two attributes, the polarization and frequency content, are used as input to regression. These attributes are extracted at predicted arrival times for P and S waves using only an approximate velocity model, as attributes are calculated over large time spans. This method of waveform characterization is shown to be able to distinguish between blasts and earthquakes with 99 per cent accuracy using a network of 13 stations located in Southern California. The combination of machine learning with generalized waveform features is further applied to event detection in Oklahoma, United States. The event detection algorithm makes use of a pair of unique seismic phases to locate events, with a precision directly related to the sampling rate of the generalized waveform features. Over a week of data from 30 stations in Oklahoma, United States are used to automatically detect 25 times more events than the catalogue of the local geological survey, with a false detection rate of less than 2 per cent. This method provides a highly confident way of detecting and locating events. Furthermore, a large number of seismic events can be automatically detected with low false alarm, allowing for a larger automatic event catalogue with a high degree of trust.

  9. Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

    Science.gov (United States)

    Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

    2017-07-01

    Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

  10. Declarative and nondeclarative sequence learning tasks: closed-head injured patients versus control participants.

    Science.gov (United States)

    Vakil, E; Gordon, Y; Birnstok, S; Aberbuch, S; Groswasser, Z

    2001-04-01

    Patients who sustained closed-head injury (CHI) have been shown to have impaired memory for temporal order when measured under intentional, but not incidental, retrieval conditions. A group of 26 patients who sustained CHI and a matched control group of 26 individuals were tested on a declarative sequence learning task--"Chain Making" (CM), and a nondeclarative sequence learning task--Tower of Hanoi puzzle (TOHP). The TOHP is a problem solving task that requires planning and a strategic approach. The latter are cognitive processes known to be impaired following frontal lobe damage, as has been frequently documented in CHI patients. The goal of the present study was to test whether CHI patients' nondeclarative learning as measured by the TOHP task is preserved, as seen in amnesic patients, or impaired, as would be predicted following frontal lobe damage. Half of the participants in each group underwent active training, and the other half went through passive training of the tasks. The results demonstrate that the control group outperformed the CHI group (in most measures) in both declarative and nondeclarative sequence learning tasks. The effect of type of training differed for the two tasks: while performance of the control group on the TOHP was better under passive training (CHI patients did not improve on either one of the training modes), performance on the CM task was better under active training for both groups. The results are discussed in light of the role of the frontal lobes in memory generally, and in sequence learning particularly.

  11. Learning and inference using complex generative models in a spatial localization task.

    Science.gov (United States)

    Bejjanki, Vikranth R; Knill, David C; Aslin, Richard N

    2016-01-01

    A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here we ask if the Bayes-optimal integration seen with simple tasks also applies to such natural tasks when the generative model is more complex, or whether observers rely instead on a less efficient set of heuristics that approximate ideal performance. Participants localized a "hidden" target whose position on a touch screen was sampled from a location-contingent bimodal generative model with different variances around each mode. Over repeated exposure to this task, participants learned the a priori locations of the target (i.e., the bimodal generative model), and integrated this learned knowledge with uncertain sensory information on a trial-by-trial basis in a manner consistent with the predictions of Bayes-optimal behavior. In particular, participants rapidly learned the locations of the two modes of the generative model, but the relative variances of the modes were learned much more slowly. Taken together, our results suggest that human performance in a more complex localization task, which requires the integration of sensory information with learned knowledge of a bimodal generative model, is consistent with the predictions of Bayes-optimal behavior, but involves a much longer time-course than in simpler tasks.

  12. PNNL: A Supervised Maximum Entropy Approach to Word Sense Disambiguation

    Energy Technology Data Exchange (ETDEWEB)

    Tratz, Stephen C.; Sanfilippo, Antonio P.; Gregory, Michelle L.; Chappell, Alan R.; Posse, Christian; Whitney, Paul D.

    2007-06-23

    In this paper, we described the PNNL Word Sense Disambiguation system as applied to the English All-Word task in Se-mEval 2007. We use a supervised learning approach, employing a large number of features and using Information Gain for dimension reduction. Our Maximum Entropy approach combined with a rich set of features produced results that are significantly better than baseline and are the highest F-score for the fined-grained English All-Words subtask.

  13. CSCL in Teacher Training: What Learning Tasks Lead to Collaboration?

    Science.gov (United States)

    Lockhorst, Ditte; Admiraal, Wilfried; Pilot, Albert

    2010-01-01

    Professional teacher communities appear to be positively related to student learning, teacher learning, teacher practice and school culture. Teacher collaboration is a significant element of these communities. In initial teacher training as well as in-service training and other initiatives for teacher learning, collaborative skills should be…

  14. Task-based learning: the answer to integration and problem-based learning in the clinical years.

    Science.gov (United States)

    Harden, R; Crosby, J; Davis, M H; Howie, P W; Struthers, A D

    2000-05-01

    Integrated teaching and problem-based learning (PBL) are powerful educational strategies. Difficulties arise, however, in their application in the later years of the undergraduate medical curriculum, particularly in clinical attachments. Two solutions have been proposed - the use of integrated clinical teaching teams and time allocated during the week for PBL separate from the clinical work. Both approaches have significant disadvantages. Task-based learning (TBL) is a preferred strategy. In TBL, a range of tasks undertaken by a doctor are identified, e.g. management of a patient with abdominal pain, and these are used as the focus for learning. Students have responsibility for integrating their learning round the tasks as they move through a range of clinical attachments in different disciplines. They are assisted in this process by study guides. The implementation of TBL is described in one medical school. One hundred and thirteen tasks, arranged in 16 groups, serve to integrate the student learning as they rotate through 10 clinical attachments. This trans-disciplinary approach to integration, which incorporates the principles of PBL offers advantages to both teachers and students. It recognizes that clinical attachments in individual disciplines can offer rich learning opportunities and that such attachments can play a role in an integrated, as well as in a traditional, curriculum. In TBL, the contributions of the clinical attachments to the curriculum learning outcomes must be clearly defined and tasks selected which will serve as a focus for the integration of the students' learning over the range of attachments.

  15. 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.

  16. 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

  17. Worms under cover: relationships between performance in learning tasks and personality in great tits (Parus major).

    Science.gov (United States)

    Amy, Mathieu; van Oers, Kees; Naguib, Marc

    2012-09-01

    In animals, individual differences in learning ability are common and are in part explained by genetic differences, developmental conditions and by general experience. Yet, not all variations in learning are well understood. Individual differences in learning may be associated with elementary individual characteristics that are consistent across situations and over time, commonly referred to as personality or temperament. Here, we tested whether or not male great tits (Parus major) from two selection lines for fast or slow exploratory behaviour, an operational measure for avian personality, vary in their learning performance in two related consecutive tasks. In the first task, birds had to associate a colour with a reward whereas in the second task, they had to associate a new colour with a reward ignoring the previously rewarded colour. Slow explorers had shorter latencies to approach the experimental device compared with fast explorers in both tasks, but birds from the two selection lines did not differ in accomplishing the first task, that is, to associate a colour with a reward. However, in the second task, fast explorers had longer latencies to solve the trials than slow explorers. Moreover, relative to the number of trials needed to reach the learning criteria in the first task, birds from the slow selection line took more trials to associate a new colour with a reward while ignoring the previously learned association compared with birds from the fast selection line. Overall, the experiments suggest that personality in great tits is not strongly related to learning per se in such an association task, but that birds from different selection lines might express different learning strategies as birds from the different selection lines were differently affected by their previous learning performance.

  18. Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect

    NARCIS (Netherlands)

    Kirschner, Femke; Paas, Fred; Kirschner, Paul A.

    2010-01-01

    Kirschner, F., Paas, F., & Kirschner, P. A. (2011). Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect. Applied Cognitive Psychology, 25, 615–624. doi: 10.1002/acp.1730.

  19. Individual and group-based learning from complex cognitive tasks: Effects on retention and transfer efficiency

    NARCIS (Netherlands)

    Kirschner, Femke; Paas, Fred; Kirschner, Paul A.

    2009-01-01

    Kirschner, F., Paas, F., & Kirschner, P. (2009). Individual and group-based learning from complex cognitive tasks: Effects on retention and transfer efficiency. Computers in Human Behavior, 25, 306-314.

  20. Task-based teaching and learning of Igbo as second language: A ...

    African Journals Online (AJOL)

    Task-based teaching and learning of Igbo as second language: A musical approach. ... for vocabulary development for the Igbo second and foreign language (Igbo ... musical instruments and costumes; and other supplementary materials.

  1. Fostering complex learning-task performance through scripting student use of computer supported representational tools

    NARCIS (Netherlands)

    Slof, Bert; Erkens, Gijs; Kirschner, Paul A.; Janssen, Jeroen; Phielix, Chris

    2010-01-01

    Slof, B., Erkens, G., Kirschner, P. A., Janssen, J., & Phielix, C. (2010). Fostering complex learning-task performance through scripting student use of computer supported representational tools. Computers & Education, 55(4), 1707-1720.

  2. 高校学生管理工作的辩证思考%Dialectical thought about the supervision of students in institutions of higher learning

    Institute of Scientific and Technical Information of China (English)

    李宜祥; 邢大伟; 沈广元

    2001-01-01

    针对强化素质教育问题,研究了高校学生管理工作,论述了学生管理与自身建设、行为管理与思想疏导、理性说服与人情感化、群体教育与个体工作的辩证关系,提出加强自我修养、强化思想疏导、加大感情投入、做好个体工作,是新形势下做好学生管理工作的重要手段.%In accordance with the development of quality education thispaper deals with the supervision of students in institutions of higher learning and discusses the dialectical relations between the supervision of students and colleges and universities′ self reconstruction,the supervision of students′ behaviour and ideological mediation,rational persuasion and human feeling change by persuasion ,groups education and individual education,expounds important measures to improve the supervision of students such as raise teachers′ self quality,strengthening thought mediation,giving more affection to the work and neglecting no student.

  3. Task discrimination from myoelectric activity: a learning scheme for EMG-based interfaces.

    Science.gov (United States)

    Liarokapis, Minas V; Artemiadis, Panagiotis K; Kyriakopoulos, Kostas J

    2013-06-01

    A learning scheme based on Random Forests is used to discriminate the task to be executed using only myoelectric activity from the upper limb. Three different task features can be discriminated: subspace to move towards, object to be grasped and task to be executed (with the object). The discrimination between the different reach to grasp movements is accomplished with a random forests classifier, which is able to perform efficient features selection, helping us to reduce the number of EMG channels required for task discrimination. The proposed scheme can take advantage of both a classifier and a regressor that cooperate advantageously to split the task space, providing better estimation accuracy with task-specific EMG-based motion decoding models, as reported in [1] and [2]. The whole learning scheme can be used by a series of EMG-based interfaces, that can be found in rehabilitation cases and neural prostheses.

  4. Task-independent effects are potential confounders in longitudinal imaging studies of learning in schizophrenia.

    Science.gov (United States)

    Korostil, Michele; Fatima, Zainab; Kovacevic, Natasha; Menon, Mahesh; McIntosh, Anthony Randal

    2016-01-01

    Learning impairment is a core deficit in schizophrenia that impacts on real-world functioning and yet, elucidating its underlying neural basis remains a challenge. A key issue when interpreting learning-task experiments is that task-independent changes may confound interpretation of task-related signal changes in neuroimaging studies. The nature of these task-independent changes in schizophrenia is unknown. Therefore, we examined task-independent "time effects" in a group of participants with schizophrenia contrasted with healthy participants in a longitudinal fMRI learning-experiment designed to allow for examination of non-specific effects of time. Flanking the learning portions of the experiment with a task-of-no-interest allowed us to extract task-independent BOLD changes. Task-independent effects occurred in both groups, but were more robust in the schizophrenia group. There was a significant interaction effect between group and time in a distributed activity pattern that included inferior and superior temporal regions, frontal areas (left anterior insula and superior medial gyri), and parietal areas (posterior cingulate cortices and precuneus). This pattern showed task-independent linear decrease in BOLD amplitude over the two scanning sessions for the schizophrenia group, but showed either opposite effect or no activity changes for the control group. There was a trend towards a correlation between task-independent effects and the presence of more negative symptoms in the schizophrenia group. The strong interaction between group and time suggests that both the scanning experience as a whole and the transition between task-types evokes a different response in persons with schizophrenia and may confound interpretation of learning-related longitudinal imaging experiments if not explicitly considered.

  5. Task-independent effects are potential confounders in longitudinal imaging studies of learning in schizophrenia

    Science.gov (United States)

    Korostil, Michele; Fatima, Zainab; Kovacevic, Natasha; Menon, Mahesh; McIntosh, Anthony Randal

    2015-01-01

    Learning impairment is a core deficit in schizophrenia that impacts on real-world functioning and yet, elucidating its underlying neural basis remains a challenge. A key issue when interpreting learning-task experiments is that task-independent changes may confound interpretation of task-related signal changes in neuroimaging studies. The nature of these task-independent changes in schizophrenia is unknown. Therefore, we examined task-independent “time effects” in a group of participants with schizophrenia contrasted with healthy participants in a longitudinal fMRI learning-experiment designed to allow for examination of non-specific effects of time. Flanking the learning portions of the experiment with a task-of-no-interest allowed us to extract task-independent BOLD changes. Task-independent effects occurred in both groups, but were more robust in the schizophrenia group. There was a significant interaction effect between group and time in a distributed activity pattern that included inferior and superior temporal regions, frontal areas (left anterior insula and superior medial gyri), and parietal areas (posterior cingulate cortices and precuneus). This pattern showed task-independent linear decrease in BOLD amplitude over the two scanning sessions for the schizophrenia group, but showed either opposite effect or no activity changes for the control group. There was a trend towards a correlation between task-independent effects and the presence of more negative symptoms in the schizophrenia group. The strong interaction between group and time suggests that both the scanning experience as a whole and the transition between task-types evokes a different response in persons with schizophrenia and may confound interpretation of learning-related longitudinal imaging experiments if not explicitly considered. PMID:26759790

  6. Application of supervised machine learning algorithms for the classification of regulatory RNA riboswitches.

    Science.gov (United States)

    Singh, Swadha; Singh, Raghvendra

    2016-04-03

    Riboswitches, the small structured RNA elements, were discovered about a decade ago. It has been the subject of intense interest to identify riboswitches, understand their mechanisms of action and use them in genetic engineering. The accumulation of genome and transcriptome sequence data and comparative genomics provide unprecedented opportunities to identify riboswitches in the genome. In the present study, we have evaluated the following six machine learning algorithms for their efficiency to classify riboswitches: J48, BayesNet, Naïve Bayes, Multilayer Perceptron, sequential minimal optimization, hidden Markov model (HMM). For determining effective classifier, the algorithms were compared on the statistical measures of specificity, sensitivity, accuracy, F-measure and receiver operating characteristic (ROC) plot analysis. The classifier Multilayer Perceptron achieved the best performance, with the highest specificity, sensitivity, F-score and accuracy, and with the largest area under the ROC curve, whereas HMM was the poorest performer. At present, the available tools for the prediction and classification of riboswitches are based on covariance model, support vector machine and HMM. The present study determines Multilayer Perceptron as a better classifier for the genome-wide riboswitch searches.

  7. Aerobic fitness relates to learning on a virtual morris water task and hippocampal volume in adolescents

    OpenAIRE

    Herting, Megan M.; Nagel, Bonnie J

    2012-01-01

    In rodents, exercise increases hippocampal neurogenesis and allows for better learning and memory performance on water maze tasks. While exercise has also been shown to be beneficial for the brain and behavior in humans, no study has examined how exercise impacts spatial learning using a directly translational water maze task, or if these relationships exist during adolescence – a developmental period which the animal literature has shown to be especially vulnerable to exercise effects. In th...

  8. How to guide group to create learning-type project supervision department%如何带领团队创建学习型项目部

    Institute of Scientific and Technical Information of China (English)

    高春玉

    2011-01-01

    阐述了在工作中学习的重要性,介绍了如何创建学习型项目部的方法,并从三个方面加以分析,以建立和完善学习体制,有效地提高监理人员自身素质。%This paper expounds the significance of study in work, introduces methods of how to creating learning-type project supervision department, and makes an analysis from three aspects, with a view to establish and improve learning system and to effectively improve supervisors' quality.

  9. The effect of the external regulator's absence on children's speech use, manifested self-regulation, and task performance during learning tasks

    NARCIS (Netherlands)

    Agina, Adel M.; Agina, Adel Masaud; Kommers, Petrus A.M.; Steehouder, M.F.

    2011-01-01

    The present study was conducted to explore the effect of the absence of the external regulators on children’s use of speech (private/social), task performance, and self-regulation during learning tasks. A novel methodology was employed through a computer-based learning environment that proposed

  10. The effect of the external regulator’s absence on children’s speech use, manifested self-regulation, and task performance during learning tasks

    NARCIS (Netherlands)

    Agina, Adel M.; Kommers, Piet A.M.; Steehouder, Michael F.

    2011-01-01

    The present study was conducted to explore the effect of the absence of the external regulators on children’s use of speech (private/social), task performance, and self-regulation during learning tasks. A novel methodology was employed through a computer-based learning environment that proposed thre

  11. Attitudes toward Task-Based Language Learning: A Study of College Korean Language Learners

    Science.gov (United States)

    Pyun, Danielle Ooyoung

    2013-01-01

    This study explores second/foreign language (L2) learners' attitudes toward task-based language learning (TBLL) and how these attitudes relate to selected learner variables, namely anxiety, integrated motivation, instrumental motivation, and self-efficacy. Ninety-one college students of Korean as a foreign language, who received task-based…

  12. Attitudes toward Task-Based Language Learning: A Study of College Korean Language Learners

    Science.gov (United States)

    Pyun, Danielle Ooyoung

    2013-01-01

    This study explores second/foreign language (L2) learners' attitudes toward task-based language learning (TBLL) and how these attitudes relate to selected learner variables, namely anxiety, integrated motivation, instrumental motivation, and self-efficacy. Ninety-one college students of Korean as a foreign language, who received task-based…

  13. Researching Pedagogic Tasks: Second Language Learning, Teaching, and Testing. Applied Linguistics and Language Study.

    Science.gov (United States)

    Bygate, Martin; Skehan, Peter; Swain, Merrill

    This book brings together a series of empirical studies into the use of pedagogical tasks for second language learning, with a view to better understanding the structure of tasks, their impact on students, and their use by teachers. This edited volume starts with an introduction to the background and key issues in the topic area. Each section…

  14. Balancing Classroom Management with Mathematical Learning: Using Practice-Based Task Design in Mathematics Teacher Education

    Science.gov (United States)

    Biza, Irene; Nardi, Elena; Joel, Gareth

    2015-01-01

    In this paper we present the results from a study in which 21 mathematics trainee teachers engage with two practice-based tasks in which classroom management interferes with mathematical learning. We investigate the trainees' considerations when they make decisions in classroom situations and how these tasks can trigger their reflections on the…

  15. Optimizing the number of steps in learning tasks for complex skills.

    NARCIS (Netherlands)

    Nadolski, Rob; Kirschner, Paul A.; Van Merriënboer, Jeroen

    2007-01-01

    Background. Carrying out whole tasks is often too difficult for novice learners attempting to acquire complex skills. The common solution is to split up the tasks into a number of smaller steps. The number of steps must be optimised for efficient and effective learning. Aim. The aim of the study is

  16. A Comparison of Reinforcement Learning Models for the Iowa Gambling Task Using Parameter Space Partitioning

    Science.gov (United States)

    Steingroever, Helen; Wetzels, Ruud; Wagenmakers, Eric-Jan

    2013-01-01

    The Iowa gambling task (IGT) is one of the most popular tasks used to study decision-making deficits in clinical populations. In order to decompose performance on the IGT in its constituent psychological processes, several cognitive models have been proposed (e.g., the Expectancy Valence (EV) and Prospect Valence Learning (PVL) models). Here we…

  17. Task Card Instruction: The Effect of Different Cue Sequences on Students' Learning in Tennis

    Science.gov (United States)

    Iserbyt, Peter; Madou, Bob; Elen, Jan; Behets, Daniel

    2010-01-01

    In physical education, task cards are often used in student-centred learning models. Consequently, a better understanding of how to deliver effective instructions by means of task cards would make a contribution to the literature. In this study, 80 right-handed university students in kinesiology were randomized across three experimental conditions…

  18. The Effectiveness of the Continuation Task on Second Language Learning of English Articles

    Science.gov (United States)

    Jiang, Lin

    2015-01-01

    This article aims to uncover how alignment in the continuation task affects second language (L2) learning of English articles. Two classes of 47 Chinese students participated in the study which employed a pretest-treatment-posttest research design and lasted for a period of 20 weeks. One class received the continuation task treatment, during which…

  19. Task-Oriented Spoken Dialog System for Second-Language Learning

    Science.gov (United States)

    Kwon, Oh-Woog; Kim, Young-Kil; Lee, Yunkeun

    2016-01-01

    This paper introduces a Dialog-Based Computer Assisted second-Language Learning (DB-CALL) system using task-oriented dialogue processing technology. The system promotes dialogue with a second-language learner for a specific task, such as purchasing tour tickets, ordering food, passing through immigration, etc. The dialog system plays a role of a…

  20. A Six-Tier Cake: An Experiment with Self-Selected Learning Tasks.

    Science.gov (United States)

    Kraus-Srebric, Eva; And Others

    1981-01-01

    Describes experiment in self-directed learning using Bloom's Taxonomy of Educational Objectives to establish six levels of cognitive ability. Children in four classes in a Belgrade school selected the task they felt most appropriate and completed it with others who had chosen the same task. (Author/BK)