Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming

ERIC Educational Resources Information Center

Zafra, Amelia; Ventura, Sebastian

2009-01-01

The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a…

Learn ++.NC: combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes.

PubMed

Muhlbaier, Michael D; Topalis, Apostolos; Polikar, Robi

2009-01-01

We have previously introduced an incremental learning algorithm Learn(++), which learns novel information from consecutive data sets by generating an ensemble of classifiers with each data set, and combining them by weighted majority voting. However, Learn(++) suffers from an inherent "outvoting" problem when asked to learn a new class omega(new) introduced by a subsequent data set, as earlier classifiers not trained on this class are guaranteed to misclassify omega(new) instances. The collective votes of earlier classifiers, for an inevitably incorrect decision, then outweigh the votes of the new classifiers' correct decision on omega(new) instances--until there are enough new classifiers to counteract the unfair outvoting. This forces Learn(++) to generate an unnecessarily large number of classifiers. This paper describes Learn(++).NC, specifically designed for efficient incremental learning of multiple new classes using significantly fewer classifiers. To do so, Learn (++).NC introduces dynamically weighted consult and vote (DW-CAV), a novel voting mechanism for combining classifiers: individual classifiers consult with each other to determine which ones are most qualified to classify a given instance, and decide how much weight, if any, each classifier's decision should carry. Experiments on real-world problems indicate that the new algorithm performs remarkably well with substantially fewer classifiers, not only as compared to its predecessor Learn(++), but also as compared to several other algorithms recently proposed for similar problems.

A visual tracking method based on improved online multiple instance learning

NASA Astrophysics Data System (ADS)

He, Xianhui; Wei, Yuxing

2016-09-01

Visual tracking is an active research topic in the field of computer vision and has been well studied in the last decades. The method based on multiple instance learning (MIL) was recently introduced into the tracking task, which can solve the problem that template drift well. However, MIL method has relatively poor performance in running efficiency and accuracy, due to its strong classifiers updating strategy is complicated, and the speed of the classifiers update is not always same with the change of the targets' appearance. In this paper, we present a novel online effective MIL (EMIL) tracker. A new update strategy for strong classifier was proposed to improve the running efficiency of MIL method. In addition, to improve the t racking accuracy and stability of the MIL method, a new dynamic mechanism for learning rate renewal of the classifier and variable search window were proposed. Experimental results show that our method performs good performance under the complex scenes, with strong stability and high efficiency.

How to Schedule Multiple Graphical Representations? A Classroom Experiment with an Intelligent Tutoring System for Fractions

ERIC Educational Resources Information Center

Rau, M. A.; Aleven, V.; Rummel, N.

2011-01-01

Graphical representations (GRs) of the learning content are often used for instruction (Ainsworth, 2006). When used in learning technology, GRs can be especially useful since they allow for interactions across representations that are physically impossible, for instance by dragging and dropping symbolic statements into a chart that automatically…

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

Automated detection of age-related macular degeneration in OCT images using multiple instance learning

NASA Astrophysics Data System (ADS)

Sun, Weiwei; Liu, Xiaoming; Yang, Zhou

2017-07-01

Age-related Macular Degeneration (AMD) is a kind of macular disease which mostly occurs in old people，and it may cause decreased vision or even lead to permanent blindness. Drusen is an important clinical indicator for AMD which can help doctor diagnose disease and decide the strategy of treatment. Optical Coherence Tomography (OCT) is widely used in the diagnosis of ophthalmic diseases, include AMD. In this paper, we propose a classification method based on Multiple Instance Learning (MIL) to detect AMD. Drusen can exist in a few slices of OCT images, and MIL is utilized in our method. We divided the method into two phases: training phase and testing phase. We train the initial features and clustered to create a codebook, and employ the trained classifier in the test set. Experiment results show that our method achieved high accuracy and effectiveness.

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

PubMed

Yousefi, Mina; Krzyżak, Adam; Suen, Ching Y

2018-05-01

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

NASA Astrophysics Data System (ADS)

Fang, Leyuan; Yang, Liumao; Li, Shutao; Rabbani, Hossein; Liu, Zhimin; Peng, Qinghua; Chen, Xiangdong

2017-06-01

Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.

Constrained Deep Weak Supervision for Histopathology Image Segmentation.

PubMed

Jia, Zhipeng; Huang, Xingyi; Chang, Eric I-Chao; Xu, Yan

2017-11-01

In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive instances are introduced in our approach to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates the state-of-the-art results on large-scale histopathology image data sets and can be applied to various applications in medical imaging beyond histopathology images, such as MRI, CT, and ultrasound images.

Object instance recognition using motion cues and instance specific appearance models

NASA Astrophysics Data System (ADS)

Schumann, Arne

2014-03-01

In this paper we present an object instance retrieval approach. The baseline approach consists of a pool of image features which are computed on the bounding boxes of a query object track and compared to a database of tracks in order to find additional appearances of the same object instance. We improve over this simple baseline approach in multiple ways: 1) we include motion cues to achieve improved robustness to viewpoint and rotation changes, 2) we include operator feedback to iteratively re-rank the resulting retrieval lists and 3) we use operator feedback and location constraints to train classifiers and learn an instance specific appearance model. We use these classifiers to further improve the retrieval results. The approach is evaluated on two popular public datasets for two different applications. We evaluate person re-identification on the CAVIAR shopping mall surveillance dataset and vehicle instance recognition on the VIVID aerial dataset and achieve significant improvements over our baseline results.

Six to Ten Digits Multiplication Fun Learning Using Puppet Prototype

NASA Astrophysics Data System (ADS)

Islamiah Rosli, D.'oria; Ali, Azita; Peng, Lim Soo; Sujardi, Imam; Usodo, Budi; Adie Perdana, Fengky

2017-01-01

Logic and technical subjects require students to understand basic knowledge in mathematic. For instance, addition, minus, division and multiplication operations need to be mastered by students due to mathematic complexity as the learning mathematic grows higher. Weak foundation in mathematic also contribute to high failure rate in mathematic subjects in schools. In fact, students in primary schools are struggling to learn mathematic because they need to memorize formulas, multiplication or division operations. To date, this study will develop a puppet prototyping for learning mathematic for six to ten digits multiplication. Ten participants involved in the process of developing the prototype in this study. Students involved in the study were those from the intermediate class students whilst teachers were selected based on their vast knowledge and experiences and have more than five years of experience in teaching mathematic. Close participatory analysis will be used in the prototyping process as to fulfil the requirements of the students and teachers whom will use the puppet in learning six to ten digit multiplication in mathematic. Findings showed that, the students had a great time and fun learning experience in learning multiplication and they able to understand the concept of multiplication using puppet. Colour and materials of the puppet also help to attract student attention during learning. Additionally, students able to visualized and able to calculate accurate multiplication value and the puppet help them to recall in multiplying and adding the digits accordingly.

Video to Text (V2T) in Wide Area Motion Imagery

DTIC Science & Technology

2015-09-01

microtext) or a document (e.g., using Sphinx or Apache NLP ) as an automated approach [102]. Previous work in natural language full-text searching...language processing ( NLP ) based module. The heart of the structured text processing module includes the following seven key word banks...Features Tracker MHT Multiple Hypothesis Tracking MIL Multiple Instance Learning NLP Natural Language Processing OAB Online AdaBoost OF Optic Flow

The company objects keep: Linking referents together during cross-situational word learning.

PubMed

Zettersten, Martin; Wojcik, Erica; Benitez, Viridiana L; Saffran, Jenny

2018-04-01

Learning the meanings of words involves not only linking individual words to referents but also building a network of connections among entities in the world, concepts, and words. Previous studies reveal that infants and adults track the statistical co-occurrence of labels and objects across multiple ambiguous training instances to learn words. However, it is less clear whether, given distributional or attentional cues, learners also encode associations amongst the novel objects. We investigated the consequences of two types of cues that highlighted object-object links in a cross-situational word learning task: distributional structure - how frequently the referents of novel words occurred together - and visual context - whether the referents were seen on matching backgrounds. Across three experiments, we found that in addition to learning novel words, adults formed connections between frequently co-occurring objects. These findings indicate that learners exploit statistical regularities to form multiple types of associations during word learning.

Identity from Variation: Representations of Faces Derived from Multiple Instances

ERIC Educational Resources Information Center

Burton, A. Mike; Kramer, Robin S. S.; Ritchie, Kay L.; Jenkins, Rob

2016-01-01

Research in face recognition has tended to focus on discriminating between individuals, or "telling people apart." It has recently become clear that it is also necessary to understand how images of the same person can vary, or "telling people together." Learning a new face, and tracking its representation as it changes from…

Learning of Alignment Rules between Concept Hierarchies

NASA Astrophysics Data System (ADS)

Ichise, Ryutaro; Takeda, Hideaki; Honiden, Shinichi

With the rapid advances of information technology, we are acquiring much information than ever before. As a result, we need tools for organizing this data. Concept hierarchies such as ontologies and information categorizations are powerful and convenient methods for accomplishing this goal, which have gained wide spread acceptance. Although each concept hierarchy is useful, it is difficult to employ multiple concept hierarchies at the same time because it is hard to align their conceptual structures. This paper proposes a rule learning method that inputs information from a source concept hierarchy and finds suitable location for them in a target hierarchy. The key idea is to find the most similar categories in each hierarchy, where similarity is measured by the κ(kappa) statistic that counts instances belonging to both categories. In order to evaluate our method, we conducted experiments using two internet directories: Yahoo! and LYCOS. We map information instances from the source directory into the target directory, and show that our learned rules agree with a human-generated assignment 76% of the time.

Simultaneous acquisition of multiple auditory-motor transformations in speech

PubMed Central

Rochet-Capellan, Amelie; Ostry, David J.

2011-01-01

The brain easily generates the movement that is needed in a given situation. Yet surprisingly, the results of experimental studies suggest that it is difficult to acquire more than one skill at a time. To do so, it has generally been necessary to link the required movement to arbitrary cues. In the present study, we show that speech motor learning provides an informative model for the acquisition of multiple sensorimotor skills. During training, subjects are required to repeat aloud individual words in random order while auditory feedback is altered in real-time in different ways for the different words. We find that subjects can quite readily and simultaneously modify their speech movements to correct for these different auditory transformations. This multiple learning occurs effortlessly without explicit cues and without any apparent awareness of the perturbation. The ability to simultaneously learn several different auditory-motor transformations is consistent with the idea that in speech motor learning, the brain acquires instance specific memories. The results support the hypothesis that speech motor learning is fundamentally local. PMID:21325534

The Case for Multiple Measures. Info Brief. Number 52

ERIC Educational Resources Information Center

Fuller, Dan; Fitzgerald, Kevin; Lee, Ji Sun

2008-01-01

What is the best use for tests? Testing should provide insight and information to educators and students. The primary purpose of testing is to inform teaching and learning. Yet, for too many schools, testing has been perverted to accommodate only measurement. Lesson plans are built around helping students pass the tests. In many instances, schools…

A Multiple Case Study of Ethical Preparedness of Guam K12 Principals in High Poverty Public Schools

ERIC Educational Resources Information Center

Quitano, Elwin Champaco

2016-01-01

The answer to effective formal principal preparation reform across the nation has been to adopt and implement the "Interstate School Leaders Licensure Consortium" (ISLLC) standards, which in most instances begins with graduate program courses and field training within higher learning institutions. Close adherence to Standard 5, a…

Early comprehension of the Spanish plural*

PubMed Central

Arias-Trejo, Natalia; Cantrell, Lisa M.; Smith, Linda B.; Alva Canto, Elda A.

2015-01-01

Understanding how linguistic cues map to the environment is crucial for early language comprehension and may provide a way for bootstrapping and learning words. Research has suggested that learning how plural syntax maps to the perceptual environment may show a trajectory in which children first learn surrounding cues (verbs, modifiers) before a full mastery of the noun morpheme alone. The Spanish plural system of simple codas, dominated by one allomorph -s, and with redundant agreement markers, may facilitate early understanding of how plural linguistic cues map to novel referents. Two-year-old Mexican children correctly identified multiple novel object referents when multiple verbal cues in a phrase indicated plurality as well as in instances when the noun morphology in novel nouns was the ONLY indicator of plurality. These results demonstrate Spanish-speaking children’s ability to use plural noun inflectional morphology to infer novel word referents which may have implications for their word learning. PMID:24560441

A preclustering-based ensemble learning technique for acute appendicitis diagnoses.

PubMed

Lee, Yen-Hsien; Hu, Paul Jen-Hwa; Cheng, Tsang-Hsiang; Huang, Te-Chia; Chuang, Wei-Yao

2013-06-01

Acute appendicitis is a common medical condition, whose effective, timely diagnosis can be difficult. A missed diagnosis not only puts the patient in danger but also requires additional resources for corrective treatments. An acute appendicitis diagnosis constitutes a classification problem, for which a further fundamental challenge pertains to the skewed outcome class distribution of instances in the training sample. A preclustering-based ensemble learning (PEL) technique aims to address the associated imbalanced sample learning problems and thereby support the timely, accurate diagnosis of acute appendicitis. The proposed PEL technique employs undersampling to reduce the number of majority-class instances in a training sample, uses preclustering to group similar majority-class instances into multiple groups, and selects from each group representative instances to create more balanced samples. The PEL technique thereby reduces potential information loss from random undersampling. It also takes advantage of ensemble learning to improve performance. We empirically evaluate this proposed technique with 574 clinical cases obtained from a comprehensive tertiary hospital in southern Taiwan, using several prevalent techniques and a salient scoring system as benchmarks. The comparative results show that PEL is more effective and less biased than any benchmarks. The proposed PEL technique seems more sensitive to identifying positive acute appendicitis than the commonly used Alvarado scoring system and exhibits higher specificity in identifying negative acute appendicitis. In addition, the sensitivity and specificity values of PEL appear higher than those of the investigated benchmarks that follow the resampling approach. Our analysis suggests PEL benefits from the more representative majority-class instances in the training sample. According to our overall evaluation results, PEL records the best overall performance, and its area under the curve measure reaches 0.619. The PEL technique is capable of addressing imbalanced sample learning associated with acute appendicitis diagnosis. Our evaluation results suggest PEL is less biased toward a positive or negative class than the investigated benchmark techniques. In addition, our results indicate the overall effectiveness of the proposed technique, compared with prevalent scoring systems or salient classification techniques that follow the resampling approach. Copyright © 2013 Elsevier B.V. All rights reserved.

Generalized multiple kernel learning with data-dependent priors.

PubMed

Mao, Qi; Tsang, Ivor W; Gao, Shenghua; Wang, Li

2015-06-01

Multiple kernel learning (MKL) and classifier ensemble are two mainstream methods for solving learning problems in which some sets of features/views are more informative than others, or the features/views within a given set are inconsistent. In this paper, we first present a novel probabilistic interpretation of MKL such that maximum entropy discrimination with a noninformative prior over multiple views is equivalent to the formulation of MKL. Instead of using the noninformative prior, we introduce a novel data-dependent prior based on an ensemble of kernel predictors, which enhances the prediction performance of MKL by leveraging the merits of the classifier ensemble. With the proposed probabilistic framework of MKL, we propose a hierarchical Bayesian model to learn the proposed data-dependent prior and classification model simultaneously. The resultant problem is convex and other information (e.g., instances with either missing views or missing labels) can be seamlessly incorporated into the data-dependent priors. Furthermore, a variety of existing MKL models can be recovered under the proposed MKL framework and can be readily extended to incorporate these priors. Extensive experiments demonstrate the benefits of our proposed framework in supervised and semisupervised settings, as well as in tasks with partial correspondence among multiple views.

Supporting inquiry learning by promoting normative understanding of multivariable causality

NASA Astrophysics Data System (ADS)

Keselman, Alla

2003-11-01

Early adolescents may lack the cognitive and metacognitive skills necessary for effective inquiry learning. In particular, they are likely to have a nonnormative mental model of multivariable causality in which effects of individual variables are neither additive nor consistent. Described here is a software-based intervention designed to facilitate students' metalevel and performance-level inquiry skills by enhancing their understanding of multivariable causality. Relative to an exploration-only group, sixth graders who practiced predicting an outcome (earthquake risk) based on multiple factors demonstrated increased attention to evidence, improved metalevel appreciation of effective strategies, and a trend toward consistent use of a controlled comparison strategy. Sixth graders who also received explicit instruction in making predictions based on multiple factors showed additional improvement in their ability to compare multiple instances as a basis for inferences and constructed the most accurate knowledge of the system. Gains were maintained in transfer tasks. The cognitive skills and metalevel understanding examined here are essential to inquiry learning.

Managing and learning with multiple models: Objectives and optimization algorithms

USGS Publications Warehouse

Probert, William J. M.; Hauser, C.E.; McDonald-Madden, E.; Runge, M.C.; Baxter, P.W.J.; Possingham, H.P.

2011-01-01

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.

Collaborative mining of graph patterns from multiple sources

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Colonna-Romanoa, John

2016-05-01

Intelligence analysts require automated tools to mine multi-source data, including answering queries, learning patterns of life, and discovering malicious or anomalous activities. Graph mining algorithms have recently attracted significant attention in intelligence community, because the text-derived knowledge can be efficiently represented as graphs of entities and relationships. However, graph mining models are limited to use-cases involving collocated data, and often make restrictive assumptions about the types of patterns that need to be discovered, the relationships between individual sources, and availability of accurate data segmentation. In this paper we present a model to learn the graph patterns from multiple relational data sources, when each source might have only a fragment (or subgraph) of the knowledge that needs to be discovered, and segmentation of data into training or testing instances is not available. Our model is based on distributed collaborative graph learning, and is effective in situations when the data is kept locally and cannot be moved to a centralized location. Our experiments show that proposed collaborative learning achieves learning quality better than aggregated centralized graph learning, and has learning time comparable to traditional distributed learning in which a knowledge of data segmentation is needed.

Active Learning by Querying Informative and Representative Examples.

PubMed

Huang, Sheng-Jun; Jin, Rong; Zhou, Zhi-Hua

2014-10-01

Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select either informative or representative unlabeled instances to query their labels, which could significantly limit their performance. Although several active learning algorithms were proposed to combine the two query selection criteria, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this limitation by developing a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance. Further, by incorporating the correlation among labels, we extend the QUIRE approach to multi-label learning by actively querying instance-label pairs. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches in both single-label and multi-label learning.

Reducing Annotation Effort Using Generalized Expectation Criteria

DTIC Science & Technology

2007-11-30

constraints additionally consider input variables. Active learning is a related problem in which the learner can choose the particular instances to be...labeled. In pool-based active learning [Cohn et al., 1994], the learner has access to a set of unlabeled instances, and can choose the instance that...has the highest expected utility according to some metric. A standard pool- based active learning method is uncertainty sampling [Lewis and Catlett

Observational learning of fly casting using traditional and virtual modeling with and without authority figure.

PubMed

Kernodle, Michael W; McKethan, Robert N; Rabinowitz, Erik

2008-10-01

Traditional and virtual modeling were compared during learning of a multiple degree-of-freedom skill (fly casting) to assess the effect of the presence or absence of an authority figure on observational learning via virtual modeling. Participants were randomly assigned to one of four groups: Virtual Modeling with an authority figure present (VM-A) (n = 16), Virtual Modeling without an authority figure (VM-NA) (n = 16), Traditional Instruction (n = 17), and Control (n = 19). Results showed significant between-group differences on Form and Skill Acquisition scores. Except for one instance, all three learning procedures resulted in significant learning of fly casting. Virtual modeling with or without an authority figure present was as effective as traditional instruction; however, learning without an authority figure was less effective with regard to Accuracy scores.

Learned Helplessness: Theory and Evidence

ERIC Educational Resources Information Center

Maier, Steven F.; Seligman, Martin E. P.

1976-01-01

Authors believes that three phenomena are all instances of "learned helplessness," instances in which an organism has learned that outcomes are uncontrollable by his responses and is seriously debilitated by this knowledge. This article explores the evidence for the phenomena of learned helplessness, and discussed a variety of theoretical…

Learning to remember by learning to speak.

PubMed

Ettlinger, Marc; Lanter, Jennifer; Van Pay, Craig K

2014-02-01

Does the language we speak affect the way we think, and if so, how? Previous researchers have considered this question by exploring the cognitive abilities of speakers of different languages. In the present study, we looked for evidence of linguistic relativity within a language and within participants by looking at memory recall for monolingual children ages 3-5 years old. At this age, children use grammatical markers with variable fluency depending on ease of articulation: Children produce the correct plural more often for vowel-final words (e.g., shoes) than plosive-final words (e.g., socks) and for plosive-final words more often than sibilant-final words (e.g., dresses). We examined whether these phonological principles governing plural production also influence children's recall of the plurality of seen objects. Fifty children were shown pictures of familiar objects presented as either singular or multiple instances. After a break, they were required to indicate whether they saw the singular- or multiple-instance version of each picture. Results show that children's memory for object plurality does depend on the phonology of the word. Subsequent tests of each child's production ability showed a correlation between a child's memory and his or her ability to articulate novel plurals with the same phonological properties. That is, what children can say impacts what they can remember.

Group-Based Active Learning of Classification Models.

PubMed

Luo, Zhipeng; Hauskrecht, Milos

2017-05-01

Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labeling groups of examples. Briefly, unlike traditional active learning methods that seek feedback on individual examples, we develop a new group-based active learning framework that solicits label information on groups of multiple examples. In order to describe groups in a user-friendly way, conjunctive patterns are used to compactly represent groups. Our empirical study on 12 UCI data sets demonstrates the advantages and superiority of our approach over both classic instance-based active learning work, as well as existing group-based active-learning methods.

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human andmore » machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.« less

Why Are There Developmental Stages in Language Learning? A Developmental Robotics Model of Language Development.

PubMed

Morse, Anthony F; Cangelosi, Angelo

2017-02-01

Most theories of learning would predict a gradual acquisition and refinement of skills as learning progresses, and while some highlight exponential growth, this fails to explain why natural cognitive development typically progresses in stages. Models that do span multiple developmental stages typically have parameters to "switch" between stages. We argue that by taking an embodied view, the interaction between learning mechanisms, the resulting behavior of the agent, and the opportunities for learning that the environment provides can account for the stage-wise development of cognitive abilities. We summarize work relevant to this hypothesis and suggest two simple mechanisms that account for some developmental transitions: neural readiness focuses on changes in the neural substrate resulting from ongoing learning, and perceptual readiness focuses on the perceptual requirements for learning new tasks. Previous work has demonstrated these mechanisms in replications of a wide variety of infant language experiments, spanning multiple developmental stages. Here we piece this work together as a single model of ongoing learning with no parameter changes at all. The model, an instance of the Epigenetic Robotics Architecture (Morse et al 2010) embodied on the iCub humanoid robot, exhibits ongoing multi-stage development while learning pre-linguistic and then basic language skills. Copyright © 2016 Cognitive Science Society, Inc.

Signature-based store checking buffer

DOEpatents

Sridharan, Vilas; Gurumurthi, Sudhanva

2015-06-02

A system and method for optimizing redundant output verification, are provided. A hardware-based store fingerprint buffer receives multiple instances of output from multiple instances of computation. The store fingerprint buffer generates a signature from the content included in the multiple instances of output. When a barrier is reached, the store fingerprint buffer uses the signature to verify the content is error-free.

Diverse expected gradient active learning for relative attributes.

PubMed

You, Xinge; Wang, Ruxin; Tao, Dacheng

2014-07-01

The use of relative attributes for semantic understanding of images and videos is a promising way to improve communication between humans and machines. However, it is extremely labor- and time-consuming to define multiple attributes for each instance in large amount of data. One option is to incorporate active learning, so that the informative samples can be actively discovered and then labeled. However, most existing active-learning methods select samples one at a time (serial mode), and may therefore lose efficiency when learning multiple attributes. In this paper, we propose a batch-mode active-learning method, called diverse expected gradient active learning. This method integrates an informativeness analysis and a diversity analysis to form a diverse batch of queries. Specifically, the informativeness analysis employs the expected pairwise gradient length as a measure of informativeness, while the diversity analysis forces a constraint on the proposed diverse gradient angle. Since simultaneous optimization of these two parts is intractable, we utilize a two-step procedure to obtain the diverse batch of queries. A heuristic method is also introduced to suppress imbalanced multiclass distributions. Empirical evaluations of three different databases demonstrate the effectiveness and efficiency of the proposed approach.

Diverse Expected Gradient Active Learning for Relative Attributes.

PubMed

You, Xinge; Wang, Ruxin; Tao, Dacheng

2014-06-02

The use of relative attributes for semantic understanding of images and videos is a promising way to improve communication between humans and machines. However, it is extremely labor- and time-consuming to define multiple attributes for each instance in large amount of data. One option is to incorporate active learning, so that the informative samples can be actively discovered and then labeled. However, most existing active-learning methods select samples one at a time (serial mode), and may therefore lose efficiency when learning multiple attributes. In this paper, we propose a batch-mode active-learning method, called Diverse Expected Gradient Active Learning (DEGAL). This method integrates an informativeness analysis and a diversity analysis to form a diverse batch of queries. Specifically, the informativeness analysis employs the expected pairwise gradient length as a measure of informativeness, while the diversity analysis forces a constraint on the proposed diverse gradient angle. Since simultaneous optimization of these two parts is intractable, we utilize a two-step procedure to obtain the diverse batch of queries. A heuristic method is also introduced to suppress imbalanced multi-class distributions. Empirical evaluations of three different databases demonstrate the effectiveness and efficiency of the proposed approach.

Category Representation for Classification and Feature Inference

ERIC Educational Resources Information Center

Johansen, Mark K.; Kruschke, John K.

2005-01-01

This research's purpose was to contrast the representations resulting from learning of the same categories by either classifying instances or inferring instance features. Prior inference learning research, particularly T. Yamauchi and A. B. Markman (1998), has suggested that feature inference learning fosters prototype representation, whereas…

An instance theory of associative learning.

PubMed

Jamieson, Randall K; Crump, Matthew J C; Hannah, Samuel D

2012-03-01

We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.

Parsing learning in networks using brain-machine interfaces.

PubMed

Orsborn, Amy L; Pesaran, Bijan

2017-10-01

Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Learning to rank atlases for multiple-atlas segmentation.

PubMed

Sanroma, Gerard; Wu, Guorong; Gao, Yaozong; Shen, Dinggang

2014-10-01

Recently, multiple-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption is that multiple atlases have greater chances of correctly labeling a target image than a single atlas. However, the problem of atlas selection still remains unexplored. Traditionally, image similarity is used to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to the final segmentation performance. To solve this seemingly simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would lead to a more accurate segmentation. Our main idea is to learn the relationship between the pairwise appearance of observed instances (i.e., a pair of atlas and target images) and their final labeling performance (e.g., using the Dice ratio). In this way, we select the best atlases based on their expected labeling accuracy. Our atlas selection method is general enough to be integrated with any existing MAS method. We show the advantages of our atlas selection method in an extensive experimental evaluation in the ADNI, SATA, IXI, and LONI LPBA40 datasets. As shown in the experiments, our method can boost the performance of three widely used MAS methods, outperforming other learning-based and image-similarity-based atlas selection methods.

An Optimization-based Framework to Learn Conditional Random Fields for Multi-label Classification

PubMed Central

Naeini, Mahdi Pakdaman; Batal, Iyad; Liu, Zitao; Hong, CharmGil; Hauskrecht, Milos

2015-01-01

This paper studies multi-label classification problem in which data instances are associated with multiple, possibly high-dimensional, label vectors. This problem is especially challenging when labels are dependent and one cannot decompose the problem into a set of independent classification problems. To address the problem and properly represent label dependencies we propose and study a pairwise conditional random Field (CRF) model. We develop a new approach for learning the structure and parameters of the CRF from data. The approach maximizes the pseudo likelihood of observed labels and relies on the fast proximal gradient descend for learning the structure and limited memory BFGS for learning the parameters of the model. Empirical results on several datasets show that our approach outperforms several multi-label classification baselines, including recently published state-of-the-art methods. PMID:25927015

Prolonged training does not result in a greater extent of interlimb transfer following visuomotor adaptation.

PubMed

Lei, Yuming; Wang, Jinsung

2014-11-01

Learning a visumotor adaptation task with one arm typically facilitates subsequent performance with the other. The extent of transfer across the arms, however, is generally much smaller than that across different conditions within the same arm. This may be attributed to a possibility that intralimb transfer involves both algorithmic and instance-reliant learning, whereas interlimb transfer only involves algorithmic learning. Here, we investigated whether prolonged training with one arm could facilitate subsequent performance with the other arm to a greater extent, by examining the effect of varying lengths of practice trials on the extent of interlimb transfer. We had 18 subjects adapt to a 30° visuomotor rotation with the left arm first (training), then with the right arm (transfer). During the training session, the subjects reached toward multiple targets for 160, 320 or 400 trials; during the transfer session, all subjects performed the same task for 160 trials. Our results revealed substantial initial transfer from the left to the right arm in all three conditions. However, neither the amount of initial transfer nor the rate of adaptation during the transfer session was significantly different across the conditions, indicating that the extent of transfer was similar regardless of the length of initial training. Our findings suggest that interlimb transfer of visuomotor adaptation may only occur through algorithmic learning, which is effector independent, and that prolonged training may only have beneficial effects when instance-reliant learning, which is effector dependent, is also involved in the learning process. Copyright © 2014 Elsevier Inc. All rights reserved.

Automatic classification and detection of clinically relevant images for diabetic retinopathy

NASA Astrophysics Data System (ADS)

Xu, Xinyu; Li, Baoxin

2008-03-01

We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

PubMed

Han, Wenjing; Coutinho, Eduardo; Ruan, Huabin; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

2016-01-01

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances.

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments

PubMed Central

Han, Wenjing; Coutinho, Eduardo; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

2016-01-01

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances. PMID:27627768

A novel semi-transductive learning framework for efficient atypicality detection in chest radiographs

NASA Astrophysics Data System (ADS)

Alzubaidi, Mohammad; Balasubramanian, Vineeth; Patel, Ameet; Panchanathan, Sethuraman; Black, John A., Jr.

2012-03-01

Inductive learning refers to machine learning algorithms that learn a model from a set of training data instances. Any test instance is then classified by comparing it to the learned model. When the set of training instances lend themselves well to modeling, the use of a model substantially reduces the computation cost of classification. However, some training data sets are complex, and do not lend themselves well to modeling. Transductive learning refers to machine learning algorithms that classify test instances by comparing them to all of the training instances, without creating an explicit model. This can produce better classification performance, but at a much higher computational cost. Medical images vary greatly across human populations, constituting a data set that does not lend itself well to modeling. Our previous work showed that the wide variations seen across training sets of "normal" chest radiographs make it difficult to successfully classify test radiographs with an inductive (modeling) approach, and that a transductive approach leads to much better performance in detecting atypical regions. The problem with the transductive approach is its high computational cost. This paper develops and demonstrates a novel semi-transductive framework that can address the unique challenges of atypicality detection in chest radiographs. The proposed framework combines the superior performance of transductive methods with the reduced computational cost of inductive methods. Our results show that the proposed semitransductive approach provides both effective and efficient detection of atypical regions within a set of chest radiographs previously labeled by Mayo Clinic expert thoracic radiologists.

Solving Multiple Isolated, Interleaved, and Blended Tasks through Modular Neuroevolution.

PubMed

Schrum, Jacob; Miikkulainen, Risto

2016-01-01

Many challenging sequential decision-making problems require agents to master multiple tasks. For instance, game agents may need to gather resources, attack opponents, and defend against attacks. Learning algorithms can thus benefit from having separate policies for these tasks, and from knowing when each one is appropriate. How well this approach works depends on how tightly coupled the tasks are. Three cases are identified: Isolated tasks have distinct semantics and do not interact, interleaved tasks have distinct semantics but do interact, and blended tasks have regions where semantics from multiple tasks overlap. Learning across multiple tasks is studied in this article with Modular Multiobjective NEAT, a neuroevolution framework applied to three variants of the challenging Ms. Pac-Man video game. In the standard blended version of the game, a surprising, highly effective machine-discovered task division surpasses human-specified divisions, achieving the best scores to date in this game. In isolated and interleaved versions of the game, human-specified task divisions are also successful, though the best scores are surprisingly still achieved by machine discovery. Modular neuroevolution is thus shown to be capable of finding useful, unexpected task divisions better than those apparent to a human designer.

Reducing Labeling Effort for Structured Prediction Tasks

DTIC Science & Technology

2005-01-01

correctly annotated for the instance to be of use to the learner. Traditional active learning addresses this problem by optimizing the order in which the...than for others. We propose a new active learning paradigm which reduces not only how many instances the annotator must label, but also how difficult...We validate this active learning framework in an interactive information extraction system, reducing the total number of annotation actions by 22%.

Classification as clustering: a Pareto cooperative-competitive GP approach.

PubMed

McIntyre, Andrew R; Heywood, Malcolm I

2011-01-01

Intuitively population based algorithms such as genetic programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to prespecifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parameterization of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member representing an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from evolutionary multiobjective optimization (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet nonoverlaping behaviors; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced datasets. Benchmarking is performed against recent examples of nonlinear SVM classifiers over 12 UCI datasets with between 150 and 200,000 training instances. Solutions from the proposed coevolutionary multiobjective GP framework appear to provide a good balance between classification performance and model complexity, especially as the dataset instance count increases.

Maritime Analytics Prototype: Final Development Report

DTIC Science & Technology

2014-04-01

access management platform OpenAM , support for multiple instances of the same type of widget and support for installation specific configuration files to...et de la gestion de l’accès OpenAM , le support pour plusieurs instances du même type de widget et le support des fichiers d’installation de...open source authentication and access management platform OpenAM , support for multiple instances of the same type of widget and support for

Refactoring a CS0 Course for Engineering Students to Use Active Learning

ERIC Educational Resources Information Center

Lokkila, Erno; Kaila, Erkki; Lindén, Rolf; Laakso, Mikko-Jussi; Sutinen, Erkki

2017-01-01

Purpose: The purpose of this paper was to determine whether applying e-learning material to a course leads to consistently improved student performance. Design/methodology/approach: This paper analyzes grade data from seven instances of the course. The first three instances were performed traditionally. After an intervention, in the form of…

Instance, Cue, and Dimension Learning in Concept Shift Task.

ERIC Educational Resources Information Center

Prentice, Joan L.; Panda, Kailas C.

Experiment I was designed to demonstrate that young children fail to abstract the positive cue as the relevant stimulus event in a restricted concept-learning task. Sixteen kindergarten and 16 fourth grade subjects were trained to criterion on a Kendler-type task, whereupon each subject was presented a pair of new instances which contrasted only…

Proceedings of the NATO IST-128 Workshop: Assessing Mission Impact of Cyberattacks Held in Istanbul, Turkey on 15-17 June 2015

DTIC Science & Technology

2015-12-01

combine satisficing behaviour with learning and adaptation through environmental feedback. This a sequential decision making with one alternative...next action that an opponent will most likely take in a strategic interaction. Also, cognitive models derived from instance- based learning theory (IBL... through instance- based learning . In Y. Li (Ed.), Lecture Notes in Computer Science (Vol. 6818, pp. 281-293). Heidelberg: Springer Berlin. Gonzalez, C

Learning Instance-Specific Predictive Models

PubMed Central

Visweswaran, Shyam; Cooper, Gregory F.

2013-01-01

This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algorithm learns Markov blanket models, carries out Bayesian model averaging over a set of models to predict a target variable of the instance at hand, and employs an instance-specific heuristic to locate a set of suitable models to average over. We call this method the instance-specific Markov blanket (ISMB) algorithm. The ISMB algorithm was evaluated on 21 UCI data sets using five different performance measures and its performance was compared to that of several commonly used predictive algorithms, including nave Bayes, C4.5 decision tree, logistic regression, neural networks, k-Nearest Neighbor, Lazy Bayesian Rules, and AdaBoost. Over all the data sets, the ISMB algorithm performed better on average on all performance measures against all the comparison algorithms. PMID:25045325

Generalized query-based active learning to identify differentially methylated regions in DNA.

PubMed

Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

2013-01-01

Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

Kernel Methods for Mining Instance Data in Ontologies

NASA Astrophysics Data System (ADS)

Bloehdorn, Stephan; Sure, York

The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

ACTIVIS: Visual Exploration of Industry-Scale Deep Neural Network Models.

PubMed

Kahng, Minsuk; Andrews, Pierre Y; Kalro, Aditya; Polo Chau, Duen Horng

2017-08-30

While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ACTIVIS, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ACTIVIS has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ACTIVIS may work with different models.

Feature and Region Selection for Visual Learning.

PubMed

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

2016-03-01

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

Awarding Credit Where Credit Is Due: Effective Practices for the Implementation of Credit by Exam. Adopted Spring 2014

ERIC Educational Resources Information Center

Academic Senate for California Community Colleges, 2014

2014-01-01

Credit by Exam is a mechanism employed in the California community colleges as a means of granting credit for student learning outside of the traditional classroom. In some instances, credit by exam is the means used to award college credit for structured learning experiences in a secondary educational setting, while in other instances knowledge…

Refuting Data Aggregation Arguments and How the Instance-Based Learning Model Stands Criticism: A Reply to Hills and Hertwig (2012)

ERIC Educational Resources Information Center

Gonzalez, Cleotilde; Dutt, Varun

2012-01-01

Hills and Hertwig (2012) challenge the proposed similarity of the exploration-exploitation transitions found in Gonzalez and Dutt (2011) between the 2 experimental paradigms of decisions from experience (sampling and repeated-choice), which was predicted by an instance-based learning (IBL) model. The heart of their argument is that in the sampling…

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

NASA Astrophysics Data System (ADS)

Su, Lihong

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.

The boundaries of instance-based learning theory for explaining decisions from experience.

PubMed

Gonzalez, Cleotilde

2013-01-01

Most demonstrations of how people make decisions in risky situations rely on decisions from description, where outcomes and their probabilities are explicitly stated. But recently, more attention has been given to decisions from experience where people discover these outcomes and probabilities through exploration. More importantly, risky behavior depends on how decisions are made (from description or experience), and although prospect theory explains decisions from description, a comprehensive model of decisions from experience is yet to be found. Instance-based learning theory (IBLT) explains how decisions are made from experience through interactions with dynamic environments (Gonzalez et al., 2003). The theory has shown robust explanations of behavior across multiple tasks and contexts, but it is becoming unclear what the theory is able to explain and what it does not. The goal of this chapter is to start addressing this problem. I will introduce IBLT and a recent cognitive model based on this theory: the IBL model of repeated binary choice; then I will discuss the phenomena that the IBL model explains and those that the model does not. The argument is for the theory's robustness but also for clarity in terms of concrete effects that the theory can or cannot account for. Copyright © 2013 Elsevier B.V. All rights reserved.

Variety Preserved Instance Weighting and Prototype Selection for Probabilistic Multiple Scope Simulations

DTIC Science & Technology

2017-05-30

including analysis, control and management of the systems across their multiple scopes . These difficulties will become more significant in near future...behaviors of the systems , it tends to cover their many scopes . Accordingly, we may obtain better models for the simulations in a data-driven manner...to capture variety of the instance distribution in a given data set for covering multiple scopes of our objective system in a seamless manner. (2

Creation and Delphi-method refinement of pediatric disaster triage simulations.

PubMed

Cicero, Mark X; Brown, Linda; Overly, Frank; Yarzebski, Jorge; Meckler, Garth; Fuchs, Susan; Tomassoni, Anthony; Aghababian, Richard; Chung, Sarita; Garrett, Andrew; Fagbuyi, Daniel; Adelgais, Kathleen; Goldman, Ran; Parker, James; Auerbach, Marc; Riera, Antonio; Cone, David; Baum, Carl R

2014-01-01

There is a need for rigorously designed pediatric disaster triage (PDT) training simulations for paramedics. First, we sought to design three multiple patient incidents for EMS provider training simulations. Our second objective was to determine the appropriate interventions and triage level for each victim in each of the simulations and develop evaluation instruments for each simulation. The final objective was to ensure that each simulation and evaluation tool was free of bias toward any specific PDT strategy. We created mixed-methods disaster simulation scenarios with pediatric victims: a school shooting, a school bus crash, and a multiple-victim house fire. Standardized patients, high-fidelity manikins, and low-fidelity manikins were used to portray the victims. Each simulation had similar acuity of injuries and 10 victims. Examples include children with special health-care needs, gunshot wounds, and smoke inhalation. Checklist-based evaluation tools and behaviorally anchored global assessments of function were created for each simulation. Eight physicians and paramedics from areas with differing PDT strategies were recruited as Subject Matter Experts (SMEs) for a modified Delphi iterative critique of the simulations and evaluation tools. The modified Delphi was managed with an online survey tool. The SMEs provided an expected triage category for each patient. The target for modified Delphi consensus was ≥85%. Using Likert scales and free text, the SMEs assessed the validity of the simulations, including instances of bias toward a specific PDT strategy, clarity of learning objectives, and the correlation of the evaluation tools to the learning objectives and scenarios. After two rounds of the modified Delphi, consensus for expected triage level was >85% for 28 of 30 victims, with the remaining two achieving >85% consensus after three Delphi iterations. To achieve consensus, we amended 11 instances of bias toward a specific PDT strategy and corrected 10 instances of noncorrelation between evaluations and simulation. The modified Delphi process, used to derive novel PDT simulation and evaluation tools, yielded a high degree of consensus among the SMEs, and eliminated biases toward specific PDT strategies in the evaluations. The simulations and evaluation tools may now be tested for reliability and validity as part of a prehospital PDT curriculum.

Bidirectional Active Learning: A Two-Way Exploration Into Unlabeled and Labeled Data Set.

PubMed

Zhang, Xiao-Yu; Wang, Shupeng; Yun, Xiaochun

2015-12-01

In practical machine learning applications, human instruction is indispensable for model construction. To utilize the precious labeling effort effectively, active learning queries the user with selective sampling in an interactive way. Traditional active learning techniques merely focus on the unlabeled data set under a unidirectional exploration framework and suffer from model deterioration in the presence of noise. To address this problem, this paper proposes a novel bidirectional active learning algorithm that explores into both unlabeled and labeled data sets simultaneously in a two-way process. For the acquisition of new knowledge, forward learning queries the most informative instances from unlabeled data set. For the introspection of learned knowledge, backward learning detects the most suspiciously unreliable instances within the labeled data set. Under the two-way exploration framework, the generalization ability of the learning model can be greatly improved, which is demonstrated by the encouraging experimental results.

Building Diversified Multiple Trees for classification in high dimensional noisy biomedical data.

PubMed

Li, Jiuyong; Liu, Lin; Liu, Jixue; Green, Ryan

2017-12-01

It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper will test an ensemble method, Diversified Multiple Tree (DMT), on its capability for classifying instances in a new laboratory using the classifier built on the instances of another laboratory. DMT is tested on three real world biomedical data sets from different laboratories in comparison with four benchmark ensemble methods, AdaBoost, Bagging, Random Forests, and Random Trees. Experiments have also been conducted on studying the limitation of DMT and its possible variations. Experimental results show that DMT is significantly more accurate than other benchmark ensemble classifiers on classifying new instances of a different laboratory from the laboratory where instances are used to build the classifier. This paper demonstrates that an ensemble classifier, DMT, is more robust in classifying noisy data than other widely used ensemble methods. DMT works on the data set that supports multiple simple trees.

Induced subgraph searching for geometric model fitting

NASA Astrophysics Data System (ADS)

Xiao, Fan; Xiao, Guobao; Yan, Yan; Wang, Xing; Wang, Hanzi

2017-11-01

In this paper, we propose a novel model fitting method based on graphs to fit and segment multiple-structure data. In the graph constructed on data, each model instance is represented as an induced subgraph. Following the idea of pursuing the maximum consensus, the multiple geometric model fitting problem is formulated as searching for a set of induced subgraphs including the maximum union set of vertices. After the generation and refinement of the induced subgraphs that represent the model hypotheses, the searching process is conducted on the "qualified" subgraphs. Multiple model instances can be simultaneously estimated by solving a converted problem. Then, we introduce the energy evaluation function to determine the number of model instances in data. The proposed method is able to effectively estimate the number and the parameters of model instances in data severely corrupted by outliers and noises. Experimental results on synthetic data and real images validate the favorable performance of the proposed method compared with several state-of-the-art fitting methods.

Representations as mediation between purposes as junior secondary science students learn about the human body

NASA Astrophysics Data System (ADS)

Olander, Clas; Wickman, Per-Olof; Tytler, Russell; Ingerman, Åke

2018-01-01

The aim of this article is to investigate students' meaning-making processes of multiple representations during a teaching sequence about the human body in lower secondary school. Two main influences are brought together to accomplish the analysis: on the one hand, theories on signs and representations as scaffoldings for learning and, on the other hand, pragmatist theories on how continuity between the purposes of different inquiry activities can be sustained. Data consist of 10 videotaped and transcribed lessons with 14-year-old students (N = 26) in Sweden. The analysis focused instances where meaning of representations was negotiated. Findings indicate that continuity is established in multiple ways, for example, as the use of metaphors articulated as an interlanguage expression that enables the students (and the teacher) to maintain the conversation and explain pressing issues in ways that support of the end-in-view of the immediate action. Continuity is also established between every day and scientific registers and between organisation levels as well as between the smaller parts and the whole system.

Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review.

PubMed

Pombo, Nuno; Garcia, Nuno; Bousson, Kouamana

2017-03-01

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios. This study aims to systematically review the literature on systems for the detection and/or prediction of apnea events using a classification model. Forty-five included studies revealed a combination of classification techniques for the diagnosis of apnea, such as threshold-based (14.75%) and machine learning (ML) models (85.25%). In addition, the ML models, were clustered in a mind map, include neural networks (44.26%), regression (4.91%), instance-based (11.47%), Bayesian algorithms (1.63%), reinforcement learning (4.91%), dimensionality reduction (8.19%), ensemble learning (6.55%), and decision trees (3.27%). A classification model should provide an auto-adaptive and no external-human action dependency. In addition, the accuracy of the classification models is related with the effective features selection. New high-quality studies based on randomized controlled trials and validation of models using a large and multiple sample of data are recommended. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

An overview of learning disabilities: psychoeducational perspectives.

PubMed

Johnson, D J

1995-01-01

In general, people with learning disabilities are a heterogeneous population that require a multidisciplinary evaluation and careful, well-planned intervention. Despite this heterogeneity, patterns of problems often co-occur. Therefore, diagnosticians and educators should look beyond single areas of achievement such as reading or arithmetic. In addition, problems in one area of learning typically have secondary impacts on higher levels of learning. That is, comprehension problems typically interfere with expression. Every effort should be made to examine patterns of problems and to avoid fragmentation of services so that each area of underachievement is not treated separately. Although learning disabilities usually interfere with school performance, they are not simply academic handicaps. They interfere with certain social activities as well as occupational pursuits. In many instances, they impact on mental health and self-esteem. Therefore, students need multiple services. And, as emphasized throughout this journal issue, learning disabled individuals may have comorbid conditions such as attention deficit disorder, depression, and neurologic problems. Furthermore, the problems may change over time. Children may first be identified because of language comprehension problems but later have reading or mathematics difficulty. With intervention, oral expressive problems may be alleviated but may be manifested later in written language.

Hybrid foraging search: Searching for multiple instances of multiple types of target.

PubMed

Wolfe, Jeremy M; Aizenman, Avigael M; Boettcher, Sage E P; Cain, Matthew S

2016-02-01

This paper introduces the "hybrid foraging" paradigm. In typical visual search tasks, observers search for one instance of one target among distractors. In hybrid search, observers search through visual displays for one instance of any of several types of target held in memory. In foraging search, observers collect multiple instances of a single target type from visual displays. Combining these paradigms, in hybrid foraging tasks observers search visual displays for multiple instances of any of several types of target (as might be the case in searching the kitchen for dinner ingredients or an X-ray for different pathologies). In the present experiment, observers held 8-64 target objects in memory. They viewed displays of 60-105 randomly moving photographs of objects and used the computer mouse to collect multiple targets before choosing to move to the next display. Rather than selecting at random among available targets, observers tended to collect items in runs of one target type. Reaction time (RT) data indicate searching again for the same item is more efficient than searching for any other targets, held in memory. Observers were trying to maximize collection rate. As a result, and consistent with optimal foraging theory, they tended to leave 25-33% of targets uncollected when moving to the next screen/patch. The pattern of RTs shows that while observers were collecting a target item, they had already begun searching memory and the visual display for additional targets, making the hybrid foraging task a useful way to investigate the interaction of visual and memory search. Copyright © 2015 Elsevier Ltd. All rights reserved.

Hybrid foraging search: Searching for multiple instances of multiple types of target

PubMed Central

Wolfe, Jeremy M.; Aizenman, Avigael M.; Boettcher, Sage E.P.; Cain, Matthew S.

2016-01-01

This paper introduces the “hybrid foraging” paradigm. In typical visual search tasks, observers search for one instance of one target among distractors. In hybrid search, observers search through visual displays for one instance of any of several types of target held in memory. In foraging search, observers collect multiple instances of a single target type from visual displays. Combining these paradigms, in hybrid foraging tasks observers search visual displays for multiple instances of any of several types of target (as might be the case in searching the kitchen for dinner ingredients or an X-ray for different pathologies). In the present experiment, observers held 8–64 targets objects in memory. They viewed displays of 60–105 randomly moving photographs of objects and used the computer mouse to collect multiple targets before choosing to move to the next display. Rather than selecting at random among available targets, observers tended to collect items in runs of one target type. Reaction time (RT) data indicate searching again for the same item is more efficient than searching for any other targets, held in memory. Observers were trying to maximize collection rate. As a result, and consistent with optimal foraging theory, they tended to leave 25–33% of targets uncollected when moving to the next screen/patch. The pattern of RTs shows that while observers were collecting a target item, they had already begun searching memory and the visual display for additional targets, making the hybrid foraging task a useful way to investigate the interaction of visual and memory search. PMID:26731644

Boosting instance prototypes to detect local dermoscopic features.

PubMed

Situ, Ning; Yuan, Xiaojing; Zouridakis, George

2010-01-01

Local dermoscopic features are useful in many dermoscopic criteria for skin cancer detection. We address the problem of detecting local dermoscopic features from epiluminescence (ELM) microscopy skin lesion images. We formulate the recognition of local dermoscopic features as a multi-instance learning (MIL) problem. We employ the method of diverse density (DD) and evidence confidence (EC) function to convert MIL to a single-instance learning (SIL) problem. We apply Adaboost to improve the classification performance with support vector machines (SVMs) as the base classifier. We also propose to boost the selection of instance prototypes through changing the data weights in the DD function. We validate the methods on detecting ten local dermoscopic features from a dataset with 360 images. We compare the performance of the MIL approach, its boosting version, and a baseline method without using MIL. Our results show that boosting can provide performance improvement compared to the other two methods.

Learning to segment mouse embryo cells

NASA Astrophysics Data System (ADS)

León, Juan; Pardo, Alejandro; Arbeláez, Pablo

2017-11-01

Recent advances in microscopy enable the capture of temporal sequences during cell development stages. However, the study of such sequences is a complex task and time consuming task. In this paper we propose an automatic strategy to adders the problem of semantic and instance segmentation of mouse embryos using NYU's Mouse Embryo Tracking Database. We obtain our instance proposals as refined predictions from the generalized hough transform, using prior knowledge of the embryo's locations and their current cell stage. We use two main approaches to learn the priors: Hand crafted features and automatic learned features. Our strategy increases the baseline jaccard index from 0.12 up to 0.24 using hand crafted features and 0.28 by using automatic learned ones.

Sociomaterial Perspectives on Work and Learning: Sites of Emergent Learning

ERIC Educational Resources Information Center

Reich, Ann; Rooney, Donna; Hopwood, Nick

2017-01-01

Purpose: This paper aims to introduce, explain and illustrate the concept of "sites of emergent learning" (SEL), which pinpoints particular instances of learning in everyday practice. This concept is located within contemporary practice-oriented and sociomaterial approaches to understanding workplace learning.…

Inter- and Intra-Dimensional Dependencies in Implicit Phonotactic Learning

ERIC Educational Resources Information Center

Moreton, Elliott

2012-01-01

Is phonological learning subject to the same inductive biases as learning in other domains? Previous studies of non-linguistic learning found that intra-dimensional dependencies (between two instances of the same feature) were learned more easily than inter-dimensional ones. This study compares implicit learning of intra- and inter-dimensional…

Detecting overlapping instances in microscopy images using extremal region trees.

PubMed

Arteta, Carlos; Lempitsky, Victor; Noble, J Alison; Zisserman, Andrew

2016-01-01

In many microscopy applications the images may contain both regions of low and high cell densities corresponding to different tissues or colonies at different stages of growth. This poses a challenge to most previously developed automated cell detection and counting methods, which are designed to handle either the low-density scenario (through cell detection) or the high-density scenario (through density estimation or texture analysis). The objective of this work is to detect all the instances of an object of interest in microscopy images. The instances may be partially overlapping and clustered. To this end we introduce a tree-structured discrete graphical model that is used to select and label a set of non-overlapping regions in the image by a global optimization of a classification score. Each region is labeled with the number of instances it contains - for example regions can be selected that contain two or three object instances, by defining separate classes for tuples of objects in the detection process. We show that this formulation can be learned within the structured output SVM framework and that the inference in such a model can be accomplished using dynamic programming on a tree structured region graph. Furthermore, the learning only requires weak annotations - a dot on each instance. The candidate regions for the selection are obtained as extremal region of a surface computed from the microscopy image, and we show that the performance of the model can be improved by considering a proxy problem for learning the surface that allows better selection of the extremal regions. Furthermore, we consider a number of variations for the loss function used in the structured output learning. The model is applied and evaluated over six quite disparate data sets of images covering: fluorescence microscopy, weak-fluorescence molecular images, phase contrast microscopy and histopathology images, and is shown to exceed the state of the art in performance. Copyright © 2015 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Langan, Roisin T.; Archibald, Richard K.; Lamberti, Vincent

We have applied a new imputation-based method for analyzing incomplete data, called Monte Carlo Bayesian Database Generation (MCBDG), to the Spent Fuel Isotopic Composition (SFCOMPO) database. About 60% of the entries are absent for SFCOMPO. The method estimates missing values of a property from a probability distribution created from the existing data for the property, and then generates multiple instances of the completed database for training a machine learning algorithm. Uncertainty in the data is represented by an empirical or an assumed error distribution. The method makes few assumptions about the underlying data, and compares favorably against results obtained bymore » replacing missing information with constant values.« less

Handwriting generates variable visual output to facilitate symbol learning.

PubMed

Li, Julia X; James, Karin H

2016-03-01

Recent research has demonstrated that handwriting practice facilitates letter categorization in young children. The present experiments investigated why handwriting practice facilitates visual categorization by comparing 2 hypotheses: that handwriting exerts its facilitative effect because of the visual-motor production of forms, resulting in a direct link between motor and perceptual systems, or because handwriting produces variable visual instances of a named category in the environment that then changes neural systems. We addressed these issues by measuring performance of 5-year-old children on a categorization task involving novel, Greek symbols across 6 different types of learning conditions: 3 involving visual-motor practice (copying typed symbols independently, tracing typed symbols, tracing handwritten symbols) and 3 involving visual-auditory practice (seeing and saying typed symbols of a single typed font, of variable typed fonts, and of handwritten examples). We could therefore compare visual-motor production with visual perception both of variable and similar forms. Comparisons across the 6 conditions (N = 72) demonstrated that all conditions that involved studying highly variable instances of a symbol facilitated symbol categorization relative to conditions where similar instances of a symbol were learned, regardless of visual-motor production. Therefore, learning perceptually variable instances of a category enhanced performance, suggesting that handwriting facilitates symbol understanding by virtue of its environmental output: supporting the notion of developmental change though brain-body-environment interactions. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Handwriting generates variable visual input to facilitate symbol learning

PubMed Central

Li, Julia X.; James, Karin H.

2015-01-01

Recent research has demonstrated that handwriting practice facilitates letter categorization in young children. The present experiments investigated why handwriting practice facilitates visual categorization by comparing two hypotheses: That handwriting exerts its facilitative effect because of the visual-motor production of forms, resulting in a direct link between motor and perceptual systems, or because handwriting produces variable visual instances of a named category in the environment that then changes neural systems. We addressed these issues by measuring performance of 5 year-old children on a categorization task involving novel, Greek symbols across 6 different types of learning conditions: three involving visual-motor practice (copying typed symbols independently, tracing typed symbols, tracing handwritten symbols) and three involving visual-auditory practice (seeing and saying typed symbols of a single typed font, of variable typed fonts, and of handwritten examples). We could therefore compare visual-motor production with visual perception both of variable and similar forms. Comparisons across the six conditions (N=72) demonstrated that all conditions that involved studying highly variable instances of a symbol facilitated symbol categorization relative to conditions where similar instances of a symbol were learned, regardless of visual-motor production. Therefore, learning perceptually variable instances of a category enhanced performance, suggesting that handwriting facilitates symbol understanding by virtue of its environmental output: supporting the notion of developmental change though brain-body-environment interactions. PMID:26726913

Hybrid value foraging: How the value of targets shapes human foraging behavior.

PubMed

Wolfe, Jeremy M; Cain, Matthew S; Alaoui-Soce, Abla

2018-04-01

In hybrid foraging, observers search visual displays for multiple instances of multiple target types. In previous hybrid foraging experiments, although there were multiple types of target, all instances of all targets had the same value. Under such conditions, behavior was well described by the marginal value theorem (MVT). Foragers left the current "patch" for the next patch when the instantaneous rate of collection dropped below their average rate of collection. An observer's specific target selections were shaped by previous target selections. Observers were biased toward picking another instance of the same target. In the present work, observers forage for instances of four target types whose value and prevalence can vary. If value is kept constant and prevalence manipulated, participants consistently show a preference for the most common targets. Patch-leaving behavior follows MVT. When value is manipulated, observers favor more valuable targets, though individual foraging strategies become more diverse, with some observers favoring the most valuable target types very strongly, sometimes moving to the next patch without collecting any of the less valuable targets.

Systems and methods to control multiple peripherals with a single-peripheral application code

DOEpatents

Ransom, Ray M.

2013-06-11

Methods and apparatus are provided for enhancing the BIOS of a hardware peripheral device to manage multiple peripheral devices simultaneously without modifying the application software of the peripheral device. The apparatus comprises a logic control unit and a memory in communication with the logic control unit. The memory is partitioned into a plurality of ranges, each range comprising one or more blocks of memory, one range being associated with each instance of the peripheral application and one range being reserved for storage of a data pointer related to each peripheral application of the plurality. The logic control unit is configured to operate multiple instances of the control application by duplicating one instance of the peripheral application for each peripheral device of the plurality and partitioning a memory device into partitions comprising one or more blocks of memory, one partition being associated with each instance of the peripheral application. The method then reserves a range of memory addresses for storage of a data pointer related to each peripheral device of the plurality, and initializes each of the plurality of peripheral devices.

Discriminative exemplar coding for sign language recognition with Kinect.

PubMed

Sun, Chao; Zhang, Tianzhu; Bao, Bing-Kun; Xu, Changsheng; Mei, Tao

2013-10-01

Sign language recognition is a growing research area in the field of computer vision. A challenge within it is to model various signs, varying with time resolution, visual manual appearance, and so on. In this paper, we propose a discriminative exemplar coding (DEC) approach, as well as utilizing Kinect sensor, to model various signs. The proposed DEC method can be summarized as three steps. First, a quantity of class-specific candidate exemplars are learned from sign language videos in each sign category by considering their discrimination. Then, every video of all signs is described as a set of similarities between frames within it and the candidate exemplars. Instead of simply using a heuristic distance measure, the similarities are decided by a set of exemplar-based classifiers through the multiple instance learning, in which a positive (or negative) video is treated as a positive (or negative) bag and those frames similar to the given exemplar in Euclidean space as instances. Finally, we formulate the selection of the most discriminative exemplars into a framework and simultaneously produce a sign video classifier to recognize sign. To evaluate our method, we collect an American sign language dataset, which includes approximately 2000 phrases, while each phrase is captured by Kinect sensor with color, depth, and skeleton information. Experimental results on our dataset demonstrate the feasibility and effectiveness of the proposed approach for sign language recognition.

Applications of Machine Learning and Rule Induction,

DTIC Science & Technology

1995-02-15

An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. In this paper...we review the major paradigms for machine learning , including neural networks, instance-based methods, genetic learning, rule induction, and analytic

Hierarchical Traces for Reduced NSM Memory Requirements

NASA Astrophysics Data System (ADS)

Dahl, Torbjørn S.

This paper presents work on using hierarchical long term memory to reduce the memory requirements of nearest sequence memory (NSM) learning, a previously published, instance-based reinforcement learning algorithm. A hierarchical memory representation reduces the memory requirements by allowing traces to share common sub-sequences. We present moderated mechanisms for estimating discounted future rewards and for dealing with hidden state using hierarchical memory. We also present an experimental analysis of how the sub-sequence length affects the memory compression achieved and show that the reduced memory requirements do not effect the speed of learning. Finally, we analyse and discuss the persistence of the sub-sequences independent of specific trace instances.

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function prediction in the context of PPI networks, the explicit consideration of network autocorrelation increases the predictive performance of the learned models. Overall, we found that this holds for different gene features/ descriptions, functional annotation schemes, and PPI networks: Best results are achieved when the PPI network is dense and contains a large proportion of function-relevant interactions.

Neurobiology of Schemas and Schema-Mediated Memory.

PubMed

Gilboa, Asaf; Marlatte, Hannah

2017-08-01

Schemas are superordinate knowledge structures that reflect abstracted commonalities across multiple experiences, exerting powerful influences over how events are perceived, interpreted, and remembered. Activated schema templates modulate early perceptual processing, as they get populated with specific informational instances (schema instantiation). Instantiated schemas, in turn, can enhance or distort mnemonic processing from the outset (at encoding), impact offline memory transformation and accelerate neocortical integration. Recent studies demonstrate distinctive neurobiological processes underlying schema-related learning. Interactions between the ventromedial prefrontal cortex (vmPFC), hippocampus, angular gyrus (AG), and unimodal associative cortices support context-relevant schema instantiation and schema mnemonic effects. The vmPFC and hippocampus may compete (as suggested by some models) or synchronize (as suggested by others) to optimize schema-related learning depending on the specific operationalization of schema memory. This highlights the need for more precise definitions of memory schemas. Copyright © 2017 Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beaver, Justin M; Borges, Raymond Charles; Buckner, Mark A

Critical infrastructure Supervisory Control and Data Acquisition (SCADA) systems were designed to operate on closed, proprietary networks where a malicious insider posed the greatest threat potential. The centralization of control and the movement towards open systems and standards has improved the efficiency of industrial control, but has also exposed legacy SCADA systems to security threats that they were not designed to mitigate. This work explores the viability of machine learning methods in detecting the new threat scenarios of command and data injection. Similar to network intrusion detection systems in the cyber security domain, the command and control communications in amore » critical infrastructure setting are monitored, and vetted against examples of benign and malicious command traffic, in order to identify potential attack events. Multiple learning methods are evaluated using a dataset of Remote Terminal Unit communications, which included both normal operations and instances of command and data injection attack scenarios.« less

Cognitive Model of Trust Dynamics Predicts Human Behavior within and between Two Games of Strategic Interaction with Computerized Confederate Agents

PubMed Central

Collins, Michael G.; Juvina, Ion; Gluck, Kevin A.

2016-01-01

When playing games of strategic interaction, such as iterated Prisoner's Dilemma and iterated Chicken Game, people exhibit specific within-game learning (e.g., learning a game's optimal outcome) as well as transfer of learning between games (e.g., a game's optimal outcome occurring at a higher proportion when played after another game). The reciprocal trust players develop during the first game is thought to mediate transfer of learning effects. Recently, a computational cognitive model using a novel trust mechanism has been shown to account for human behavior in both games, including the transfer between games. We present the results of a study in which we evaluate the model's a priori predictions of human learning and transfer in 16 different conditions. The model's predictive validity is compared against five model variants that lacked a trust mechanism. The results suggest that a trust mechanism is necessary to explain human behavior across multiple conditions, even when a human plays against a non-human agent. The addition of a trust mechanism to the other learning mechanisms within the cognitive architecture, such as sequence learning, instance-based learning, and utility learning, leads to better prediction of the empirical data. It is argued that computational cognitive modeling is a useful tool for studying trust development, calibration, and repair. PMID:26903892

Drug-related webpages classification based on multi-modal local decision fusion

NASA Astrophysics Data System (ADS)

Hu, Ruiguang; Su, Xiaojing; Liu, Yanxin

2018-03-01

In this paper, multi-modal local decision fusion is used for drug-related webpages classification. First, meaningful text are extracted through HTML parsing, and effective images are chosen by the FOCARSS algorithm. Second, six SVM classifiers are trained for six kinds of drug-taking instruments, which are represented by PHOG. One SVM classifier is trained for the cannabis, which is represented by the mid-feature of BOW model. For each instance in a webpage, seven SVMs give seven labels for its image, and other seven labels are given by searching the names of drug-taking instruments and cannabis in its related text. Concatenating seven labels of image and seven labels of text, the representation of those instances in webpages are generated. Last, Multi-Instance Learning is used to classify those drugrelated webpages. Experimental results demonstrate that the classification accuracy of multi-instance learning with multi-modal local decision fusion is much higher than those of single-modal classification.

Multistrategy learning: A case study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Domingos, P.

1996-12-31

Two of the most popular approaches to induction are instance-based learning (IBL) and rule generation. Their strengths and weaknesses are largely complementary. IBL methods are able to identify small details in the instance space, but have trouble with attributes that are relevant in some parts of the space but not others. Conversely, rule induction methods may overlook small exception regions, but are able to select different attributes in different parts of the instance space. The two methods have been unified in the RISE algorithm. RISE views instances as maximally specific rules, forms more general rules by gradually clustering instances ofmore » the same class, and classifies a test example by letting the nearest rule win. This approach potentially combines the advantages of rule induction and IBL, and has indeed been observed to be more accurate than each on a large number of bench-mark datasets. However, it is important to determine if this performance is indeed due to the hypothesized advantages, and to define the situations in which RISE`s bias will and will not be preferable to those of the individual approaches. This abstract reports experiments to this end in artificial domains.« less

Integrated Authoring Tool for Mobile Augmented Reality-Based E-Learning Applications

ERIC Educational Resources Information Center

Lobo, Marcos Fermin; Álvarez García, Víctor Manuel; del Puerto Paule Ruiz, María

2013-01-01

Learning management systems are increasingly being used to complement classroom teaching and learning and in some instances even replace traditional classroom settings with online educational tools. Mobile augmented reality is an innovative trend in e-learning that is creating new opportunities for teaching and learning. This article proposes a…

Cognitive Anatomy of Tutor Learning: Lessons Learned with SimStudent

ERIC Educational Resources Information Center

Matsuda, Noboru; Yarzebinski, Evelyn; Keiser, Victoria; Raizada, Rohan; Cohen, William W.; Stylianides, Gabriel J.; Koedinger, Kenneth R.

2013-01-01

This article describes an advanced learning technology used to investigate hypotheses about learning by teaching. The proposed technology is an instance of a teachable agent, called SimStudent, that learns skills (e.g., for solving linear equations) from examples and from feedback on performance. SimStudent has been integrated into an online,…

Co-Labeling for Multi-View Weakly Labeled Learning.

PubMed

Xu, Xinxing; Li, Wen; Xu, Dong; Tsang, Ivor W

2016-06-01

It is often expensive and time consuming to collect labeled training samples in many real-world applications. To reduce human effort on annotating training samples, many machine learning techniques (e.g., semi-supervised learning (SSL), multi-instance learning (MIL), etc.) have been studied to exploit weakly labeled training samples. Meanwhile, when the training data is represented with multiple types of features, many multi-view learning methods have shown that classifiers trained on different views can help each other to better utilize the unlabeled training samples for the SSL task. In this paper, we study a new learning problem called multi-view weakly labeled learning, in which we aim to develop a unified approach to learn robust classifiers by effectively utilizing different types of weakly labeled multi-view data from a broad range of tasks including SSL, MIL and relative outlier detection (ROD). We propose an effective approach called co-labeling to solve the multi-view weakly labeled learning problem. Specifically, we model the learning problem on each view as a weakly labeled learning problem, which aims to learn an optimal classifier from a set of pseudo-label vectors generated by using the classifiers trained from other views. Unlike traditional co-training approaches using a single pseudo-label vector for training each classifier, our co-labeling approach explores different strategies to utilize the predictions from different views, biases and iterations for generating the pseudo-label vectors, making our approach more robust for real-world applications. Moreover, to further improve the weakly labeled learning on each view, we also exploit the inherent group structure in the pseudo-label vectors generated from different strategies, which leads to a new multi-layer multiple kernel learning problem. Promising results for text-based image retrieval on the NUS-WIDE dataset as well as news classification and text categorization on several real-world multi-view datasets clearly demonstrate that our proposed co-labeling approach achieves state-of-the-art performance for various multi-view weakly labeled learning problems including multi-view SSL, multi-view MIL and multi-view ROD.

Color Modulates Olfactory Learning in Honeybees by an Occasion-Setting Mechanism

ERIC Educational Resources Information Center

Mota, Theo; Giurfa, Martin; Sandoz, Jean-Christophe

2011-01-01

A sophisticated form of nonelemental learning is provided by occasion setting. In this paradigm, animals learn to disambiguate an uncertain conditioned stimulus using alternative stimuli that do not enter into direct association with the unconditioned stimulus. For instance, animals may learn to discriminate odor rewarded from odor nonrewarded…

InstanceCollage: A Tool for the Particularization of Collaborative IMS-LD Scripts

ERIC Educational Resources Information Center

Villasclaras-Fernandez, Eloy D.; Hernandez-Gonzalo, Julio A.; Hernandez-Leo, Davinia; Asensio-Perez, Juan I.; Dimitriadis, Yannis; Martinez-Mones, Alejandra

2009-01-01

Current research work in e-learning and more specifically in the field of CSCL (Computer Supported Collaborative Learning) deals with design of collaborative activities, according to computer-interpretable specifications, such as IMS Learning Design, and their posterior enactment using LMSs (Learning Management Systems). A script that describes…

Outdoor Natural Science Learning with an RFID-Supported Immersive Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Liu, Tsung-Yu; Tan, Tan-Hsu; Chu, Yu-Ling

2009-01-01

Despite their successful use in many conscientious studies involving outdoor learning applications, mobile learning systems still have certain limitations. For instance, because students cannot obtain real-time, context-aware content in outdoor locations such as historical sites, endangered animal habitats, and geological landscapes, they are…

SIPHER: Scalable Implementation of Primitives for Homomorphic Encryption

DTIC Science & Technology

2015-11-01

595–618. 2009. [Ajt96] M. Ajtai. Generating hard instances of lattice problems. Quaderni di Matematica , 13:1–32, 2004. Preliminary version in STOC...1), pages 403–415. 2011. [Ajt96] M. Ajtai. Generating hard instances of lattice problems. Quaderni di Matematica , 13:1–32, 2004. Preliminary version...learning with errors. In ASIACRYPT. 2011. To appear. [Ajt96] M. Ajtai. Generating hard instances of lattice problems. Quaderni di Matematica , 13:1–32

How Young Children Learn from Examples: Descriptive and Inferential Problems

ERIC Educational Resources Information Center

Kalish, Charles W.; Kim, Sunae; Young, Andrew G.

2012-01-01

Three experiments with preschool- and young school-aged children (N = 75 and 53) explored the kinds of relations children detect in samples of instances (descriptive problem) and how they generalize those relations to new instances (inferential problem). Each experiment initially presented a perfect biconditional relation between two features…

Nuclear Forensics Analysis with Missing and Uncertain Data

DOE PAGES

Langan, Roisin T.; Archibald, Richard K.; Lamberti, Vincent

2015-10-05

We have applied a new imputation-based method for analyzing incomplete data, called Monte Carlo Bayesian Database Generation (MCBDG), to the Spent Fuel Isotopic Composition (SFCOMPO) database. About 60% of the entries are absent for SFCOMPO. The method estimates missing values of a property from a probability distribution created from the existing data for the property, and then generates multiple instances of the completed database for training a machine learning algorithm. Uncertainty in the data is represented by an empirical or an assumed error distribution. The method makes few assumptions about the underlying data, and compares favorably against results obtained bymore » replacing missing information with constant values.« less

A Multi-Dimensional Functional Principal Components Analysis of EEG Data

PubMed Central

Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A.; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla

2017-01-01

Summary The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. PMID:28072468

A multi-dimensional functional principal components analysis of EEG data.

PubMed

Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla

2017-09-01

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. © 2017, The International Biometric Society.

Scanning sequences after Gibbs sampling to find multiple occurrences of functional elements

PubMed Central

Tharakaraman, Kannan; Mariño-Ramírez, Leonardo; Sheetlin, Sergey L; Landsman, David; Spouge, John L

2006-01-01

Background Many DNA regulatory elements occur as multiple instances within a target promoter. Gibbs sampling programs for finding DNA regulatory elements de novo can be prohibitively slow in locating all instances of such an element in a sequence set. Results We describe an improvement to the A-GLAM computer program, which predicts regulatory elements within DNA sequences with Gibbs sampling. The improvement adds an optional "scanning step" after Gibbs sampling. Gibbs sampling produces a position specific scoring matrix (PSSM). The new scanning step resembles an iterative PSI-BLAST search based on the PSSM. First, it assigns an "individual score" to each subsequence of appropriate length within the input sequences using the initial PSSM. Second, it computes an E-value from each individual score, to assess the agreement between the corresponding subsequence and the PSSM. Third, it permits subsequences with E-values falling below a threshold to contribute to the underlying PSSM, which is then updated using the Bayesian calculus. A-GLAM iterates its scanning step to convergence, at which point no new subsequences contribute to the PSSM. After convergence, A-GLAM reports predicted regulatory elements within each sequence in order of increasing E-values, so users have a statistical evaluation of the predicted elements in a convenient presentation. Thus, although the Gibbs sampling step in A-GLAM finds at most one regulatory element per input sequence, the scanning step can now rapidly locate further instances of the element in each sequence. Conclusion Datasets from experiments determining the binding sites of transcription factors were used to evaluate the improvement to A-GLAM. Typically, the datasets included several sequences containing multiple instances of a regulatory motif. The improvements to A-GLAM permitted it to predict the multiple instances. PMID:16961919

Criterion for correct recalls in associative-memory neural networks

NASA Astrophysics Data System (ADS)

Ji, Han-Bing

1992-12-01

A novel weighted outer-product learning (WOPL) scheme for associative memory neural networks (AMNNs) is presented. In the scheme, each fundamental memory is allocated a learning weight to direct its correct recall. Both the Hopfield and multiple training models are instances of the WOPL model with certain sets of learning weights. A necessary condition of choosing learning weights for the convergence property of the WOPL model is obtained through neural dynamics. A criterion for choosing learning weights for correct associative recalls of the fundamental memories is proposed. In this paper, an important parameter called signal to noise ratio gain (SNRG) is devised, and it is found out empirically that SNRGs have their own threshold values which means that any fundamental memory can be correctly recalled when its corresponding SNRG is greater than or equal to its threshold value. Furthermore, a theorem is given and some theoretical results on the conditions of SNRGs and learning weights for good associative recall performance of the WOPL model are accordingly obtained. In principle, when all SNRGs or learning weights chosen satisfy the theoretically obtained conditions, the asymptotic storage capacity of the WOPL model will grow at the greatest rate under certain known stochastic meaning for AMNNs, and thus the WOPL model can achieve correct recalls for all fundamental memories. The representative computer simulations confirm the criterion and theoretical analysis.

Multiple myeloma. Houses and spouses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kyle, R.A.; Greipp, P.R.

1983-02-15

Two families in which successive spouses who lived in the same house developed multiple myeloma are reported. In the first instance, a man whose first wife died of multiple myeloma remarried, and his second wife also developed myeloma. In the second family, a woman's first husband died of multiple myeloma and her second husband did too. Radiation studies of the houses and yards showed no increased radioactivity. No evidence was found for excessive exposure to chemicals or other environmental agents, for contact with other patients having similar malignancies, or for exposure to viruses or other transmissible factors. The significance ofmore » the occurrence of multiple myeloma in subsequent spouses is unknown. It is hoped that this report will stimulate research for other instances and lead to careful epidemiologic, radiologic, and virologic studies.« less

Distance learning on the Internet: web-based archived curriculum.

PubMed

Burgess, Lawrence P A; Garshnek, Victoria; Birkmire-Peters, Deborah; Seifried, Steven E

2004-10-01

Web-based education through archived educational modules offers a significant opportunity to provide didactic education. By archiving lectures and teaching materials, it reduces the educators' time of preparation, especially when many students will need to take the same curriculum over a long period of time. The site can package educational material in multiple formats including audio, video, and readable text, allowing the student to tailor the educational experience to his/her learning preferences. This can be a stand-alone program, or integrated into a program combining distance and in-person education. Assessment through on-line tests can also be conducted, but these must be considered open-book assessments where collaboration cannot be prevented. As such, this vehicle can be utilized effectively for continuing education programs in health care, where open book is permitted and credits are generally awarded on the honor system. However, tests for certificate courses should only be given with a proctor in attendance. In this instance, on-line tests can be used as pre-tests for the student, while being structured to enhance further learning.

Academic Staff's Perceptions of Characteristics of Learning Organization in a Higher Learning Institution

ERIC Educational Resources Information Center

Ali, Ali Khamis

2012-01-01

Purpose: The main objective of this study was to examine academic staff's perceptions of the characteristics of a learning organization within higher education: in this instance, the International Islamic University Malaysia (IIUM). The study also examined the relationship between the characteristics of a learning organization and satisfaction…

Using Ontologies to Interlink Linguistic Annotations and Improve Their Accuracy

ERIC Educational Resources Information Center

Pareja-Lora, Antonio

2016-01-01

For the new approaches to language e-learning (e.g. language blended learning, language autonomous learning or mobile-assisted language learning) to succeed, some automatic functions for error correction (for instance, in exercises) will have to be included in the long run in the corresponding environments and/or applications. A possible way to…

Cooperative Education Is a Superior Strategy for Using Basic Learning Processes.

ERIC Educational Resources Information Center

Reed, V. Gerald

Cooperative education is a learning strategy that fits very well with basic laws of learning. In fact, several basic important learning processes are far better adapted to the cooperative education strategy than to methods that lean entirely on classroom instruction. For instance, cooperative education affords more opportunities for reinforcement,…

When Collaborative Is Not Collaborative: Supporting Student Learning through Self-Surveillance

ERIC Educational Resources Information Center

Kotsopoulos, Donna

2010-01-01

Collaborative learning has been widely endorsed in education. This qualitative research examines instances of collaborative learning during mathematics that were seen to be predominantly non-collaborative despite the pedagogical efforts and intentions of the teacher and the task. In an effort to disrupt the non-collaborative learning, small groups…

Teachers' Experiences with Integrating Play-Based Learning into Standards-Driven Curriculum: A Phenomenological Study

ERIC Educational Resources Information Center

Nugent, Mary Beth Anderson

2017-01-01

The purpose of this phenomenological study was to describe kindergarten teachers' experiences with integrating play-based learning into standards-based academic curriculum in a school district in South Carolina. Play-based learning experiences were defined as instances which allow children to engage in active, social learning experiences in…

Supporting Information Linking and Discovery Across Organizations Using the VIVO Semantic Web Software Suite

NASA Astrophysics Data System (ADS)

Mayernik, M. S.; Daniels, M. D.; Maull, K. E.; Khan, H.; Krafft, D. B.; Gross, M. B.; Rowan, L. R.

2016-12-01

Geosciences research is often conducted using distributed networks of researchers and resources. To better enable the discovery of the research output from the scientists and resources used within these organizations, UCAR, Cornell University, and UNAVCO are collaborating on the EarthCollab (http://earthcube.org/group/earthcollab) project which seeks to leverage semantic technologies to manage and link scientific data. As part of this effort, we have been exploring how to leverage information distributed across multiple research organizations. EarthCollab is using the VIVO semantic software suite to lookup and display Semantic Web information across our project partners.Our presentation will include a demonstration of linking between VIVO instances, discussing how to create linkages between entities in different VIVO instances where both entities describe the same person or resource. This discussion will explore how we designate the equivalence of these entities using "same as" assertions between identifiers representing these entities including URIs and ORCID IDs and how we have extended the base VIVO architecture to support the lookup of which entities in separate VIVO instances may be equivalent and to then display information from external linked entities. We will also discuss how these extensions can support other linked data lookups and sources of information.This VIVO cross-linking mechanism helps bring information from multiple VIVO instances together and helps users in navigating information spread-out between multiple VIVO instances. Challenges and open questions for this approach relate to how to display the information obtained from an external VIVO instance, both in order to preserve the brands of the internal and external systems and to handle discrepancies between ontologies, content, and/or VIVO versions.

Fronto-temporal white matter connectivity predicts reversal learning errors

PubMed Central

Alm, Kylie H.; Rolheiser, Tyler; Mohamed, Feroze B.; Olson, Ingrid R.

2015-01-01

Each day, we make hundreds of decisions. In some instances, these decisions are guided by our innate needs; in other instances they are guided by memory. Probabilistic reversal learning tasks exemplify the close relationship between decision making and memory, as subjects are exposed to repeated pairings of a stimulus choice with a reward or punishment outcome. After stimulus–outcome associations have been learned, the associated reward contingencies are reversed, and participants are not immediately aware of this reversal. Individual differences in the tendency to choose the previously rewarded stimulus reveal differences in the tendency to make poorly considered, inflexible choices. Lesion studies have strongly linked reversal learning performance to the functioning of the orbitofrontal cortex, the hippocampus, and in some instances, the amygdala. Here, we asked whether individual differences in the microstructure of the uncinate fasciculus, a white matter tract that connects anterior and medial temporal lobe regions to the orbitofrontal cortex, predict reversal learning performance. Diffusion tensor imaging and behavioral paradigms were used to examine this relationship in 33 healthy young adults. The results of tractography revealed a significant negative relationship between reversal learning performance and uncinate axial diffusivity, but no such relationship was demonstrated in a control tract, the inferior longitudinal fasciculus. Our findings suggest that the uncinate might serve to integrate associations stored in the anterior and medial temporal lobes with expectations about expected value based on feedback history, computed in the orbitofrontal cortex. PMID:26150776

Learning Sequences of Actions in Collectives of Autonomous Agents

NASA Technical Reports Server (NTRS)

Turner, Kagan; Agogino, Adrian K.; Wolpert, David H.; Clancy, Daniel (Technical Monitor)

2001-01-01

In this paper we focus on the problem of designing a collective of autonomous agents that individually learn sequences of actions such that the resultant sequence of joint actions achieves a predetermined global objective. We are particularly interested in instances of this problem where centralized control is either impossible or impractical. For single agent systems in similar domains, machine learning methods (e.g., reinforcement learners) have been successfully used. However, applying such solutions directly to multi-agent systems often proves problematic, as agents may work at cross-purposes, or have difficulty in evaluating their contribution to achievement of the global objective, or both. Accordingly, the crucial design step in multiagent systems centers on determining the private objectives of each agent so that as the agents strive for those objectives, the system reaches a good global solution. In this work we consider a version of this problem involving multiple autonomous agents in a grid world. We use concepts from collective intelligence to design goals for the agents that are 'aligned' with the global goal, and are 'learnable' in that agents can readily see how their behavior affects their utility. We show that reinforcement learning agents using those goals outperform both 'natural' extensions of single agent algorithms and global reinforcement, learning solutions based on 'team games'.

Beyond Physics: A Case for Far Transfer

ERIC Educational Resources Information Center

Forsyth, Benjamin Robert

2012-01-01

This is a case study of a physics undergraduate who claimed that he "uses physics to understand other subjects." This statement suggested that this student could describe issues concerning the transfer of learning and especially instances of far transfer. Detailed instances of far transfer have been difficult to replicate in lab settings.…

Problem-Based Learning: Instructor Characteristics, Competencies, and Professional Development

DTIC Science & Technology

2011-01-01

cognitive learning objectives addressed by student -centered instruction . For instance, experiential learning , a variation of which is used at the...based learning in grade school science or mathematics . However, the measures could be modified to focus on adult PBL (or student -centered learning ... student -centered learning methods, the findings should generalize across instructional methods of interest to the Army. Further research is required

Semi-supervised protein subcellular localization.

PubMed

Xu, Qian; Hu, Derek Hao; Xue, Hong; Yu, Weichuan; Yang, Qiang

2009-01-30

Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data. In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions. Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.

Kernel learning at the first level of inference.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automatic analysis of online image data for law enforcement agencies by concept detection and instance search

NASA Astrophysics Data System (ADS)

de Boer, Maaike H. T.; Bouma, Henri; Kruithof, Maarten C.; ter Haar, Frank B.; Fischer, Noëlle M.; Hagendoorn, Laurens K.; Joosten, Bart; Raaijmakers, Stephan

2017-10-01

The information available on-line and off-line, from open as well as from private sources, is growing at an exponential rate and places an increasing demand on the limited resources of Law Enforcement Agencies (LEAs). The absence of appropriate tools and techniques to collect, process, and analyze the volumes of complex and heterogeneous data has created a severe information overload. If a solution is not found, the impact on law enforcement will be dramatic, e.g. because important evidence is missed or the investigation time is too long. Furthermore, there is an uneven level of capabilities to deal with the large volumes of complex and heterogeneous data that come from multiple open and private sources at national level across the EU, which hinders cooperation and information sharing. Consequently, there is a pertinent need to develop tools, systems and processes which expedite online investigations. In this paper, we describe a suite of analysis tools to identify and localize generic concepts, instances of objects and logos in images, which constitutes a significant portion of everyday law enforcement data. We describe how incremental learning based on only a few examples and large-scale indexing are addressed in both concept detection and instance search. Our search technology allows querying of the database by visual examples and by keywords. Our tools are packaged in a Docker container to guarantee easy deployment on a system and our tools exploit possibilities provided by open source toolboxes, contributing to the technical autonomy of LEAs.

Comparative Learning in Partnerships: Control, Competition or Collaboration?

ERIC Educational Resources Information Center

Takahashi, Chie

2008-01-01

This paper examines the quality and development of relations between organisations and the ways in which these are informed by incidental learning experiences in two projects. The paper conceptualizes instances of inter-organisational learning (IOL) applying theories such as principal-agent, prisoners' dilemma and women's place in community…

Learning through Work: Exploring Instances of Relational Interdependencies

ERIC Educational Resources Information Center

Billett, Stephen

2008-01-01

This paper provides an account of the inter-psychological processes that constitute learning through work. It does this by drawing on deliberations about the relative contributions of the immediate social world (i.e., workplace setting) that individuals encounter and the personal premises for individuals' learning. This account is realised through…

The Radical Challenge of Family Learning

ERIC Educational Resources Information Center

West, Linden

2005-01-01

This article focuses on the nature of "family learning" programmes in marginalised communities. Such programmes present a series of radical challenges (in the sense of getting to the root of things) to policy makers and professionals alike: about, for instance, the kinds of "learning" on offer and the neglect, perhaps…

Overcoming the Low-Learning Scar Effect: Narratives of Learning and Resilience of Italian Low-Skilled

ERIC Educational Resources Information Center

Tomassini, Massimo

2016-01-01

The idea of the "low-learning scar" is borrowed from recent labour economics literature in which concepts such as "unemployment scarring", "wage scarring" and "scarred generation" are increasingly used for the interpretation of problems (the NEETs problem, for instance) which presently plague all Western…

Learning the Association between a Context and a Target Location in Infancy

ERIC Educational Resources Information Center

Bertels, Julie; San Anton, Estibaliz; Gebuis, Titia; Destrebecqz, Arnaud

2017-01-01

Extracting the statistical regularities present in the environment is a central learning mechanism in infancy. For instance, infants are able to learn the associations between simultaneously or successively presented visual objects (Fiser & Aslin, 2002; Kirkham, Slemmer & Johnson, 2002). The present study extends these results by…

An Examination of the Disruptive Innovation Paradox: The Application of the Innovators Dilemma to SME's

NASA Astrophysics Data System (ADS)

Nagle, Tadhg; Golden, William

Managing strategic contradiction and paradoxical situations has been gaining importance in technological, innovation and management domains. As a result, more and more paradoxical instances and types have been documented in literature. The innovators dilemma is such an instance that gives a detailed description of how disruptive innovations affect firms. However, the innovators dilemma has only been applied to large organisations and more specifically industry incumbents. Through a multiple case study of six eLearning SME’s, this paper investigates the applicability of the innovators dilemma as well as the disruptive effects of Web 2.0 on the organisations. Analysing the data collected over 18 months, it was found that the innovators dilemma did indeed apply to SME’s. However, inline with the original thesis the dilemma only applied to the SME’s established (pre-2002) before the development of Web 2.0 technologies began. Furthermore, the study highlights that the post-2002 firms were also partly vulnerable to the dilemma but were able to avoid any negative effects though technological visionary leadership. In contrast, the pre-2002 firms were lacking this visionary ability and were also constrained by low risk profiles.

The influence of negative training set size on machine learning-based virtual screening.

PubMed

Kurczab, Rafał; Smusz, Sabina; Bojarski, Andrzej J

2014-01-01

The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening.

The influence of negative training set size on machine learning-based virtual screening

PubMed Central

2014-01-01

Background The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. Results The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. Conclusions In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening. PMID:24976867

Image annotation based on positive-negative instances learning

NASA Astrophysics Data System (ADS)

Zhang, Kai; Hu, Jiwei; Liu, Quan; Lou, Ping

2017-07-01

Automatic image annotation is now a tough task in computer vision, the main sense of this tech is to deal with managing the massive image on the Internet and assisting intelligent retrieval. This paper designs a new image annotation model based on visual bag of words, using the low level features like color and texture information as well as mid-level feature as SIFT, and mixture the pic2pic, label2pic and label2label correlation to measure the correlation degree of labels and images. We aim to prune the specific features for each single label and formalize the annotation task as a learning process base on Positive-Negative Instances Learning. Experiments are performed using the Corel5K Dataset, and provide a quite promising result when comparing with other existing methods.

Transfer of Learning between 2D and 3D Sources during Infancy: Informing Theory and Practice

ERIC Educational Resources Information Center

Barr, Rachel

2010-01-01

The ability to transfer learning across contexts is an adaptive skill that develops rapidly during early childhood. Learning from television is a specific instance of transfer of learning between a two-dimensional (2D) representation and a three-dimensional (3D) object. Understanding the conditions under which young children might accomplish this…

Earthquake: Game-based learning for 21st century STEM education

NASA Astrophysics Data System (ADS)

Perkins, Abigail Christine

To play is to learn. A lack of empirical research within game-based learning literature, however, has hindered educational stakeholders to make informed decisions about game-based learning for 21st century STEM education. In this study, I modified a research and development (R&D) process to create a collaborative-competitive educational board game illuminating elements of earthquake engineering. I oriented instruction- and game-design principles around 21st century science education to adapt the R&D process to develop the educational game, Earthquake. As part of the R&D, I evaluated Earthquake for empirical evidence to support the claim that game-play results in student gains in critical thinking, scientific argumentation, metacognitive abilities, and earthquake engineering content knowledge. I developed Earthquake with the aid of eight focus groups with varying levels of expertise in science education research, teaching, administration, and game-design. After developing a functional prototype, I pilot-tested Earthquake with teacher-participants (n=14) who engaged in semi-structured interviews after their game-play. I analyzed teacher interviews with constant comparison methodology. I used teachers' comments and feedback from content knowledge experts to integrate game modifications, implementing results to improve Earthquake. I added player roles, simplified phrasing on cards, and produced an introductory video. I then administered the modified Earthquake game to two groups of high school student-participants (n = 6), who played twice. To seek evidence documenting support for my knowledge claim, I analyzed videotapes of students' game-play using a game-based learning checklist. My assessment of learning gains revealed increases in all categories of students' performance: critical thinking, metacognition, scientific argumentation, and earthquake engineering content knowledge acquisition. Players in both student-groups improved mostly in critical thinking, having doubled the number of exhibited instances of critical thinking between games. Players in the first group exhibited about a third more instances of metacognition between games, while players in the second group doubled such instances. Between games, players in both groups more than doubled the number of exhibited instances of using earthquake engineering content knowledge. The student-players expanded use of scientific argumentation for all game-based learning checklist categories. With empirical evidence, I conclude play and learning can connect for successful 21 st century STEM education.

A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets.

PubMed

Fernández, Alberto; Carmona, Cristobal José; José Del Jesus, María; Herrera, Francisco

2017-09-01

Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.

Teacher Learning during Transition from Pre-Service to Novice EFL Teacher: A Longitudinal Case Study

ERIC Educational Resources Information Center

Bulut Albaba, Melike

2017-01-01

In language teacher education literature, attention is predominantly centered on the content of teacher learning and cognition. The process of teacher learning and cognitive change over time remains relatively underexplored. For instance, despite its importance in career trajectory, there is limited empirical evidence on the transition process…

Instance-Based Ontology Matching for Open and Distance Learning Materials

ERIC Educational Resources Information Center

Cerón-Figueroa, Sergio; López-Yáñez, Itzamá; Villuendas-Rey, Yenny; Camacho-Nieto, Oscar; Aldape-Pérez, Mario; Yáñez-Márquez, Cornelio

2017-01-01

The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is…

Picture-Word Differences in Discrimination Learning: II. Effects of Conceptual Categories.

ERIC Educational Resources Information Center

Bourne, Lyle E., Jr.; And Others

A well established finding in the discrimination learning literature is that pictures are learned more rapidly than their associated verbal labels. It was hypothesized in this study that the usual superiority of pictures over words in a discrimination list containing same-instance repetitions would disappear in a discrimination list containing…

What We Muggles Can Learn about Teaching from Hogwarts

ERIC Educational Resources Information Center

Bixler, Andrea

2011-01-01

The Harry Potter series furnishes many instances of both good and bad teaching. From them, we can learn more about three principles outlined in "How People Learn" (National Research Council 2000a). (1) Teachers should question students about their prior knowledge, as Professor Lupin does before his lessons; (2) we should encourage students to…

Adaptive 3D Virtual Learning Environments--A Review of the Literature

ERIC Educational Resources Information Center

Scott, Ezequiel; Soria, Alvaro; Campo, Marcelo

2017-01-01

New ways of learning have emerged in the last years by using computers in education. For instance, many Virtual Learning Environments have been widely adopted by educators, obtaining promising outcomes. Recently, these environments have evolved into more advanced ones using 3D technologies and taking into account the individual learner needs and…

Poverty and Brain Development in Children: Implications for Learning

ERIC Educational Resources Information Center

Dike, Victor E.

2017-01-01

Debates on the effect of poverty on brain development in children and its implications for learning have been raging for decades. Research suggests that poverty affects brain development in children and that the implications for learning are more compelling today given the attention the issue has attracted. For instance, studies in the fields of…

Accounting for Taste: Learning by Doing in the College Classroom

ERIC Educational Resources Information Center

Bradshaw, Kathlyn E.; Harvey, Robert W.

2017-01-01

This article presents Edelson and Reiser's (2006) strategies as a framework for analyzing an instance of authentic practice in a managerial accounting course. Specifically, this article presents an analysis of a managerial accounting project design created to provide learning-by-doing via authentic practice. Students need more than to learn about…

Consciousness and the Consolidation of Motor Learning

PubMed Central

Song, Sunbin

2009-01-01

It is no secret that motor learning benefits from repetition. For example, pianists devote countless hours to performing complicated sequences of key presses, and golfers practice their swings thousands of times to reach a level of proficiency. Interestingly, the subsequent waking and sleeping hours after practice also play important roles in motor learning. During this time, a motor skill can consolidate into a more stable form that can lead to improved future performance without intervening practice. Though it is widely believed that sleep is crucial for this consolidation of motor learning, this is not generally true. In many instances only day-time consolidates motor learning, while in other instances neither day-time nor sleep consolidates learning. Recent studies have suggested that conscious awareness during motor training can determine whether sleep or day-time plays a role in consolidation. However, ongoing studies suggest that this explanation is also incomplete. In addition to conscious awareness, attention is an important factor to consider. This review discusses how attention and conscious awareness interact with day and night processes to consolidate a motor memory. PMID:18951924

Ask-the-expert: Active Learning Based Knowledge Discovery Using the Expert

NASA Technical Reports Server (NTRS)

Das, Kamalika; Avrekh, Ilya; Matthews, Bryan; Sharma, Manali; Oza, Nikunj

2017-01-01

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the backend. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

Ask-the-Expert: Active Learning Based Knowledge Discovery Using the Expert

NASA Technical Reports Server (NTRS)

Das, Kamalika

2017-01-01

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the back end. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

Inferential Learning of Serial Order of Perceptual Categories by Rhesus Monkeys (Macaca mulatta)

PubMed Central

2017-01-01

Category learning in animals is typically trained explicitly, in most instances by varying the exemplars of a single category in a matching-to-sample task. Here, we show that male rhesus macaques can learn categories by a transitive inference paradigm in which novel exemplars of five categories were presented throughout training. Instead of requiring decisions about a constant set of repetitively presented stimuli, we studied the macaque's ability to determine the relative order of multiple exemplars of particular stimuli that were rarely repeated. Ordinal decisions generalized both to novel stimuli and, as a consequence, to novel pairings. Thus, we showed that rhesus monkeys could learn to categorize on the basis of implied ordinal position, without prior matching-to-sample training, and that they could then make inferences about category order. Our results challenge the plausibility of association models of category learning and broaden the scope of the transitive inference paradigm. SIGNIFICANCE STATEMENT The cognitive abilities of nonhuman animals are of enduring interest to scientists and the general public because they blur the dividing line between human and nonhuman intelligence. Categorization and sequence learning are highly abstract cognitive abilities each in their own right. This study is the first to provide evidence that visual categories can be ordered serially by macaque monkeys using a behavioral paradigm that provides no explicit feedback about category or serial order. These results strongly challenge accounts of learning based on stimulus–response associations. PMID:28546309

Understanding Positive and Negative Communication Instances between Special Educators and Parents of High School Students with EBD

ERIC Educational Resources Information Center

Mires, Carolyn B.

2015-01-01

Using a multiple case study methodology, interviews were conducted to examine current practices and perceptions of the communication practices of teachers working with high school students with emotional and behavioral disorders (EBD). These interviews involved questions about general communication instances which occurred each week, communication…

Instances selection algorithm by ensemble margin

NASA Astrophysics Data System (ADS)

Saidi, Meryem; Bechar, Mohammed El Amine; Settouti, Nesma; Chikh, Mohamed Amine

2018-05-01

The main limit of data mining algorithms is their inability to deal with the huge amount of available data in a reasonable processing time. A solution of producing fast and accurate results is instances and features selection. This process eliminates noisy or redundant data in order to reduce the storage and computational cost without performances degradation. In this paper, a new instance selection approach called Ensemble Margin Instance Selection (EMIS) algorithm is proposed. This approach is based on the ensemble margin. To evaluate our approach, we have conducted several experiments on different real-world classification problems from UCI Machine learning repository. The pixel-based image segmentation is a field where the storage requirement and computational cost of applied model become higher. To solve these limitations we conduct a study based on the application of EMIS and other instance selection techniques for the segmentation and automatic recognition of white blood cells WBC (nucleus and cytoplasm) in cytological images.

A Generic Authentication LoA Derivation Model

NASA Astrophysics Data System (ADS)

Yao, Li; Zhang, Ning

One way of achieving a more fine-grained access control is to link an authentication level of assurance (LoA) derived from a requester’s authentication instance to the authorisation decision made to the requester. To realise this vision, there is a need for designing a LoA derivation model that supports the use and quantification of multiple LoA-effecting attributes, and analyse their composite effect on a given authentication instance. This paper reports the design of such a model, namely a generic LoA derivation model (GEA- LoADM). GEA-LoADM takes into account of multiple authentication attributes along with their relationships, abstracts the composite effect by the multiple attributes into a generic value, authentication LoA, and provides algorithms for the run-time derivation of LoA. The algorithms are tailored to reflect the relationships among the attributes involved in an authentication instance. The model has a number of valuable properties, including flexibility and extensibility; it can be applied to different application contexts and support easy addition of new attributes and removal of obsolete ones.

A Road to Understanding: A Qualitative Study into Why Learners Drop out of a Blended Language Learning (BLL) Environment

ERIC Educational Resources Information Center

Stracke, Elke

2007-01-01

This paper addresses the views of students of blended language learning (BLL)--a particular learning and teaching environment, that combines face-to-face (f2f) and computer-assisted language learning (CALL). In this instance, the "blend" consisted of learners' independent self-study phases at a computer, with a CD-ROM, and traditional f2f…

ClimateNet: A Machine Learning dataset for Climate Science Research

NASA Astrophysics Data System (ADS)

Prabhat, M.; Biard, J.; Ganguly, S.; Ames, S.; Kashinath, K.; Kim, S. K.; Kahou, S.; Maharaj, T.; Beckham, C.; O'Brien, T. A.; Wehner, M. F.; Williams, D. N.; Kunkel, K.; Collins, W. D.

2017-12-01

Deep Learning techniques have revolutionized commercial applications in Computer vision, speech recognition and control systems. The key for all of these developments was the creation of a curated, labeled dataset ImageNet, for enabling multiple research groups around the world to develop methods, benchmark performance and compete with each other. The success of Deep Learning can be largely attributed to the broad availability of this dataset. Our empirical investigations have revealed that Deep Learning is similarly poised to benefit the task of pattern detection in climate science. Unfortunately, labeled datasets, a key pre-requisite for training, are hard to find. Individual research groups are typically interested in specialized weather patterns, making it hard to unify, and share datasets across groups and institutions. In this work, we are proposing ClimateNet: a labeled dataset that provides labeled instances of extreme weather patterns, as well as associated raw fields in model and observational output. We develop a schema in NetCDF to enumerate weather pattern classes/types, store bounding boxes, and pixel-masks. We are also working on a TensorFlow implementation to natively import such NetCDF datasets, and are providing a reference convolutional architecture for binary classification tasks. Our hope is that researchers in Climate Science, as well as ML/DL, will be able to use (and extend) ClimateNet to make rapid progress in the application of Deep Learning for Climate Science research.

Computerized Hammer Sounding Interpretation for Concrete Assessment with Online Machine Learning.

PubMed

Ye, Jiaxing; Kobayashi, Takumi; Iwata, Masaya; Tsuda, Hiroshi; Murakawa, Masahiro

2018-03-09

Developing efficient Artificial Intelligence (AI)-enabled systems to substitute the human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel hammering response analysis system using online machine learning, which aims at achieving near-human performance in assessment of concrete structures. Current computerized hammer sounding systems commonly employ lab-scale data to validate the models. In practice, however, the response signal patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for response characterization. More specifically, the proposed system can adaptively update itself to approach human performance in hammering sounding data interpretation. To this end, a two-stage framework has been introduced, including feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 response instances from multiple inspection sites; each sample was annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable assessment accuracy with high efficiency and low computation load.

Research Says / Tap ELLs' Strengths to Spur Success

ERIC Educational Resources Information Center

Goodwin, Bryan; Hein, Heather

2016-01-01

On the surface, learning a second language may seem to be a simple one- to two-year undertaking. Research shows, however, that it's a far more complex endeavor. This article considers the depth of learning required to become academically proficient in a second language. For instance, language learners learn the basics of reading in a second…

Malaysian Perspective: Designing Interactive Multimedia Learning Environment for Moral Values Education

ERIC Educational Resources Information Center

Mukti, Norhayati Abd; Hwa, Siew Pei

2004-01-01

The field of education is faced with various new challenges in meeting the demands of teaching and learning for the 21st century. One of the new challenges is the call for the integration of ICT (Information and communication technologies) in teaching and learning as an alternative mode of instruction delivery. Multimedia technology for instance,…

Transferring Road Maps for Learning and Assessment Procedures to Marketing

ERIC Educational Resources Information Center

Salzberger, Thomas

2011-01-01

Learning is a lifelong process. It is therefore worthwhile looking at instances where learning takes place outside educational institutions and see how educational principles can be applied there. In a market economy companies have to quest for profit to ensure their long-term survival. In the end, their educational goals have to serve themselves.…

Surprise and Awe: Learning from Indigenous Managers and Implications for Management Education

ERIC Educational Resources Information Center

Schwabenland, Christina

2011-01-01

This article describes a self-reflexive exploration of five instances of encounters with indigenous managers that challenged my preconceptions about management. My focus is on the praxis of the moments in which these challenges occurred. I analyze these experiences to answer four questions: How did learning occur? What was that learning? How did…

Transformative Learning as a Metatheory: Definition, Criteria, and Typology

ERIC Educational Resources Information Center

Hoggan, Chad D.

2016-01-01

This article addresses a significant problem with transformative learning theory; namely, that it is increasingly being used to refer to almost any instance of learning. This article offers several points of clarity to resolve this problem. First, it portrays a subtle but important evolution in the way the theory has been used in the literature…

An efficient abnormal cervical cell detection system based on multi-instance extreme learning machine

NASA Astrophysics Data System (ADS)

Zhao, Lili; Yin, Jianping; Yuan, Lihuan; Liu, Qiang; Li, Kuan; Qiu, Minghui

2017-07-01

Automatic detection of abnormal cells from cervical smear images is extremely demanded in annual diagnosis of women's cervical cancer. For this medical cell recognition problem, there are three different feature sections, namely cytology morphology, nuclear chromatin pathology and region intensity. The challenges of this problem come from feature combination s and classification accurately and efficiently. Thus, we propose an efficient abnormal cervical cell detection system based on multi-instance extreme learning machine (MI-ELM) to deal with above two questions in one unified framework. MI-ELM is one of the most promising supervised learning classifiers which can deal with several feature sections and realistic classification problems analytically. Experiment results over Herlev dataset demonstrate that the proposed method outperforms three traditional methods for two-class classification in terms of well accuracy and less time.

Synesthesia and learning: a critical review and novel theory

PubMed Central

Watson, Marcus R.; Akins, Kathleen A.; Spiker, Chris; Crawford, Lyle; Enns, James T.

2014-01-01

Learning and synesthesia are profoundly interconnected. On the one hand, the development of synesthesia is clearly influenced by learning. Synesthetic inducers – the stimuli that evoke these unusual experiences – often involve the perception of complex properties learned in early childhood, e.g., letters, musical notes, numbers, months of the year, and even swimming strokes. Further, recent research has shown that the associations individual synesthetes make with these learned inducers are not arbitrary, but are strongly influenced by the structure of the learned domain. For instance, the synesthetic colors of letters are partially determined by letter frequency and the relative positions of letters in the alphabet. On the other hand, there is also a small, but growing, body of literature which shows that synesthesia can influence or be helpful in learning. For instance, synesthetes appear to be able to use their unusual experiences as mnemonic devices and can even exploit them while learning novel abstract categories. Here we review these two directions of influence and argue that they are interconnected. We propose that synesthesia arises, at least in part, because of the cognitive demands of learning in childhood, and that it is used to aid perception and understanding of a variety of learned categories. Our thesis is that the structural similarities between synesthetic triggering stimuli and synesthetic experiences are the remnants, the fossilized traces, of past learning challenges for which synsethesia was helpful. PMID:24592232

The role of partial knowledge in statistical word learning

PubMed Central

Fricker, Damian C.; Yu, Chen; Smith, Linda B.

2013-01-01

A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word's meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: Partial knowledge of one word–object mapping can speed up the acquisition of other word–object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word–object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word's meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data. PMID:23702980

Learner Personas in CALL

ERIC Educational Resources Information Center

Heift, Trude

2007-01-01

In examining the titles of this year's conference presentations, the author noticed quite a few papers that focus on learner-specific issues, for instance, papers that address learning styles, learner needs, personality and learning, learner modeling and, more generally, pedagogical issues that deal with individual learner differences in…

Learning What to Learn: The Effects of Task Experience on Strategy Shifts in the Allocation of Study Time

ERIC Educational Resources Information Center

Ariel, Robert

2013-01-01

Learners typically allocate more resources to learning items that are higher in value than they do to items lower in value. For instance, when items vary in point value for learning, participants allocate more study time to the higher point items than they do to the lower point items. The current experiments extend this research to a context where…

Preparing for the Worst: Psychological Excellence of First Responders - A Katrina Lessons Learned Study

DTIC Science & Technology

2008-01-01

cases on human cognition and performance. For instance, when you learn to fly an airplane, you will be instructed to use a simple rule to avoid...Existing Training Technologies; First Responders; Katrina; Lesson Learned 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER... student . Based in Maryland, the training institute prepares first responders using online learning courses or training exercises. Such topics

Interactive Machine Learning at Scale with CHISSL

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arendt, Dustin L.; Grace, Emily A.; Volkova, Svitlana

We demonstrate CHISSL, a scalable client-server system for real-time interactive machine learning. Our system is capa- ble of incorporating user feedback incrementally and imme- diately without a structured or pre-defined prediction task. Computation is partitioned between a lightweight web-client and a heavyweight server. The server relies on representation learning and agglomerative clustering to learn a dendrogram, a hierarchical approximation of a representation space. The client uses only this dendrogram to incorporate user feedback into the model via transduction. Distances and predictions for each unlabeled instance are updated incrementally and deter- ministically, with O(n) space and time complexity. Our al- gorithmmore » is implemented in a functional prototype, designed to be easy to use by non-experts. The prototype organizes the large amounts of data into recommendations. This allows the user to interact with actual instances by dragging and drop- ping to provide feedback in an intuitive manner. We applied CHISSL to several domains including cyber, social media, and geo-temporal analysis.« less

Inferring Ice Thickness from a Glacier Dynamics Model and Multiple Surface Datasets.

NASA Astrophysics Data System (ADS)

Guan, Y.; Haran, M.; Pollard, D.

2017-12-01

The future behavior of the West Antarctic Ice Sheet (WAIS) may have a major impact on future climate. For instance, ice sheet melt may contribute significantly to global sea level rise. Understanding the current state of WAIS is therefore of great interest. WAIS is drained by fast-flowing glaciers which are major contributors to ice loss. Hence, understanding the stability and dynamics of glaciers is critical for predicting the future of the ice sheet. Glacier dynamics are driven by the interplay between the topography, temperature and basal conditions beneath the ice. A glacier dynamics model describes the interactions between these processes. We develop a hierarchical Bayesian model that integrates multiple ice sheet surface data sets with a glacier dynamics model. Our approach allows us to (1) infer important parameters describing the glacier dynamics, (2) learn about ice sheet thickness, and (3) account for errors in the observations and the model. Because we have relatively dense and accurate ice thickness data from the Thwaites Glacier in West Antarctica, we use these data to validate the proposed approach. The long-term goal of this work is to have a general model that may be used to study multiple glaciers in the Antarctic.

Dynamic Encoding of Acoustic Features in Neural Responses to Continuous Speech.

PubMed

Khalighinejad, Bahar; Cruzatto da Silva, Guilherme; Mesgarani, Nima

2017-02-22

Humans are unique in their ability to communicate using spoken language. However, it remains unclear how the speech signal is transformed and represented in the brain at different stages of the auditory pathway. In this study, we characterized electroencephalography responses to continuous speech by obtaining the time-locked responses to phoneme instances (phoneme-related potential). We showed that responses to different phoneme categories are organized by phonetic features. We found that each instance of a phoneme in continuous speech produces multiple distinguishable neural responses occurring as early as 50 ms and as late as 400 ms after the phoneme onset. Comparing the patterns of phoneme similarity in the neural responses and the acoustic signals confirms a repetitive appearance of acoustic distinctions of phonemes in the neural data. Analysis of the phonetic and speaker information in neural activations revealed that different time intervals jointly encode the acoustic similarity of both phonetic and speaker categories. These findings provide evidence for a dynamic neural transformation of low-level speech features as they propagate along the auditory pathway, and form an empirical framework to study the representational changes in learning, attention, and speech disorders. SIGNIFICANCE STATEMENT We characterized the properties of evoked neural responses to phoneme instances in continuous speech. We show that each instance of a phoneme in continuous speech produces several observable neural responses at different times occurring as early as 50 ms and as late as 400 ms after the phoneme onset. Each temporal event explicitly encodes the acoustic similarity of phonemes, and linguistic and nonlinguistic information are best represented at different time intervals. Finally, we show a joint encoding of phonetic and speaker information, where the neural representation of speakers is dependent on phoneme category. These findings provide compelling new evidence for dynamic processing of speech sounds in the auditory pathway. Copyright © 2017 Khalighinejad et al.

Teaching Reluctant Students: Using the Principles and Techniques of Motivational Interviewing to Foster Better Student-Teacher Interactions

ERIC Educational Resources Information Center

Wells, Harvey; Jones, Anna; Jones, Sue C.

2014-01-01

In formal learning settings, there will always be instances of resistance to learning from students, resulting in either open conflict or withdrawal and consequent disillusionment on the part of both students and teachers. This paper presents a set of principles and associated practices for responding to disengagement from learning in constructive…

Learning as a shared responsibility: Insights from a series of dialogic workshops with practitioners, leaders, and researchers (Abstract)

Treesearch

Anne Black; Dave Thomas; Jennifer Ziegler; Jim Saveland

2012-01-01

For some time now, the wildland fire community has been interested in 'organizational learning' as a way to improve safety and overall performance. For instance, in the US, federal agencies have established and continue to support the Wildland Fire Lessons Learned Center, sponsored several national conferences and are currently considering how incident...

Learning as a shared responsibility: Insights from a series of dialogic workshops with practitioners, leaders, and researchers

Treesearch

Anne E. Black; Dave Thomas; Jennifer Ziegler; J. Saveland

2012-01-01

For some time now, the wildland fire community has been interested in 'organizational learning' as a way to improve safety and overall performance. For instance, in the US, federal agencies have established the Wildland Fire Lessons Learned Center, sponsored several national conferences, and are currently considering how incident reviews might be used to...

Dopaminergic and Prefrontal Contributions to Reward-Based Learning and Outcome Monitoring during Child Development and Aging

ERIC Educational Resources Information Center

Hammerer, Dorothea; Eppinger, Ben

2012-01-01

In many instances, children and older adults show similar difficulties in reward-based learning and outcome monitoring. These impairments are most pronounced in situations in which reward is uncertain (e.g., probabilistic reward schedules) and if outcome information is ambiguous (e.g., the relative value of outcomes has to be learned).…

Hill-Climbing Theories of Learning

DTIC Science & Technology

1987-12-01

process continues as long as new instances are encountered. In some cases, a constrained state generator replaces the evaluation function, producing the...instance, our model represents a particular animal (say a cat) as a set of eight cylinders - representing the head, neck , torso, tail, and four legs. The...variety of conditions. Figure 1 summarizes an experiment in which we ’defined’ four classes - cats, dogs, horse, and giraffes - with different amounts of

OntoPop: An Ontology Population System for the Semantic Web

NASA Astrophysics Data System (ADS)

Thongkrau, Theerayut; Lalitrojwong, Pattarachai

The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance.

Learner Control in Hypermedia Environments

ERIC Educational Resources Information Center

Scheiter, Katharina; Gerjets, Peter

2007-01-01

Contrary to system-controlled multimedia learning environments, hypermedia systems are characterized by a high level of interactivity. This interactivity is referred to as learner control in the respective literature. For several reasons this learner control is seen as a major advantage of hypermedia for learning and instruction. For instance,…

Fostering Civic Engagement in the Communication Research Methods Course

ERIC Educational Resources Information Center

Liu, Min

2011-01-01

Civic engagement has become an essential learning goal for institutions throughout higher education. Communication scholars employ various pedagogical tools to foster civic engagement. For instance, service learning has been shown to increase political and community engagement in courses such as family communication and public relations. Teachers…

Multi-Instance Learning Models for Automated Support of Analysts in Simulated Surveillance Environments

NASA Technical Reports Server (NTRS)

Birisan, Mihnea; Beling, Peter

2011-01-01

New generations of surveillance drones are being outfitted with numerous high definition cameras. The rapid proliferation of fielded sensors and supporting capacity for processing and displaying data will translate into ever more capable platforms, but with increased capability comes increased complexity and scale that may diminish the usefulness of such platforms to human operators. We investigate methods for alleviating strain on analysts by automatically retrieving content specific to their current task using a machine learning technique known as Multi-Instance Learning (MIL). We use MIL to create a real time model of the analysts' task and subsequently use the model to dynamically retrieve relevant content. This paper presents results from a pilot experiment in which a computer agent is assigned analyst tasks such as identifying caravanning vehicles in a simulated vehicle traffic environment. We compare agent performance between MIL aided trials and unaided trials.

A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies.

PubMed

Khondoker, Mizanur; Dobson, Richard; Skirrow, Caroline; Simmons, Andrew; Stahl, Daniel

2016-10-01

Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms. We compare the classification performance of a number of important and widely used machine learning algorithms, namely the Random Forests (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbour (kNN). Using massively parallel processing on high-performance supercomputers, we compare the generalisation errors at various combinations of levels of several factors: number of features, training sample size, biological variation, experimental variation, effect size, replication and correlation between features. For smaller number of correlated features, number of features not exceeding approximately half the sample size, LDA was found to be the method of choice in terms of average generalisation errors as well as stability (precision) of error estimates. SVM (with RBF kernel) outperforms LDA as well as RF and kNN by a clear margin as the feature set gets larger provided the sample size is not too small (at least 20). The performance of kNN also improves as the number of features grows and outplays that of LDA and RF unless the data variability is too high and/or effect sizes are too small. RF was found to outperform only kNN in some instances where the data are more variable and have smaller effect sizes, in which cases it also provide more stable error estimates than kNN and LDA. Applications to a number of real datasets supported the findings from the simulation study. © The Author(s) 2013.

"Shakespeare Reloaded": Teacher Professional Development within a Collaborative Learning Community

ERIC Educational Resources Information Center

Brady, Linzy

2009-01-01

This paper describes an instance of continuing professional development and explores the contribution it might make to the ongoing international dialogue of professional development. It reviews the way features of the current debate on effective teaching, teacher learning and continuing professional development overlap and feed into each other and…

Translating Neuroscience, Psychology and Education: An Abstracted Conceptual Framework for the Learning Sciences

ERIC Educational Resources Information Center

Donoghue, Gregory M.; Horvath, Jared C.

2016-01-01

Educators strive to understand and apply knowledge gained through scientific endeavours. Yet, within the various sciences of learning, particularly within educational neuroscience, there have been instances of seemingly contradictory or incompatible research findings and theories. We argue that this situation arises through confusion between…

Concept Mapping to Assess Learning and Understanding of Complexity in Courses on Global Climate Change

NASA Astrophysics Data System (ADS)

Rebich-Hespanha, S.; Gautier, C.

2010-12-01

The complex nature of climate change science poses special challenges for educators wishing to broaden and deepen student understanding of the climate system and its sensitivity to and impacts upon human activity. Learners have prior knowledge that may limit their perception and processing of the multiple relationships between processes (e.g., feedbacks) that arise in global change science, and these existing mental models serve as the scaffold for all future learning. Because adoption of complex scientific concepts is not likely if instruction includes presentation of information or concepts that are not compatible with the learners’ prior knowledge, providing effective instruction on this complex topic requires learning opportunities that are anchored upon an evaluation of the limitations and inaccuracies of the learners’ existing understandings of the climate system. The formative evaluation that serves as the basis for planning such instruction can also be useful as a baseline against which to evaluate subsequent learning. We will present concept-mapping activities that we have used to assess students’ knowledge and understanding about global climate change in courses that utilized multiple assessment methods including presentations, writings, discussions, and concept maps. The courses in which these activities were completed use a variety of instructional approaches (including standard lectures and lab assignments and a mock summit) to help students understand the inherently interdisciplinary topic of global climate change, its interwoven human and natural causes, and the connections it has with society through a complex range of political, social, technological and economic factors. Two instances of concept map assessment will be presented: one focused on evaluating student understanding of the major components of the climate system and their interconnections, and the other focused on student understanding of the connections between climate change and the global food system. We will discuss how concept mapping can be used to demonstrate evidence of learning and conceptual change, and also how it can be used to provide information about gaps in knowledge and misconceptions students have about the topic.

Not Only Size Matters: Early-Talker and Late-Talker Vocabularies Support Different Word-Learning Biases in Babies and Networks

ERIC Educational Resources Information Center

Colunga, Eliana; Sims, Clare E.

2017-01-01

In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2-year-olds seem to intuit the whole range of things in a category from hearing a single instance named--they have word-learning biases. This is not the case for children with relatively small vocabularies ("late…

The Benefits of a Challenge: Student Motivation and Flow Experience in Tablet-PC-Game-Based Learning

ERIC Educational Resources Information Center

Hung, Cheng-Yu; Sun, Jerry Chih-Yuan; Yu, Pao-Ta

2015-01-01

Advances in technology have led to continuous innovation in teaching and learning methods. For instance, the use of tablet PCs (TPCs) in classroom instruction has been shown to be effective in attracting and motivating students' interest and increasing their desire to participate in learning activities. In this paper, we used a TPCs game--an iPad…

KOJAK: Scalable Semantic Link Discovery Via Integrated Knowledge-Based and Statistical Reasoning

DTIC Science & Technology

2006-11-01

program can find interesting connections in a network without having to learn the patterns of interestingness beforehand. The key advantage of our...Interesting Instances in Semantic Graphs Below we describe how the UNICORN framework can discover interesting instances in a multi-relational dataset...We can now describe how UNICORN solves the first problem of finding the top interesting nodes in a semantic net by ranking them according to

A hybrid heuristic for the multiple choice multidimensional knapsack problem

NASA Astrophysics Data System (ADS)

Mansi, Raïd; Alves, Cláudio; Valério de Carvalho, J. M.; Hanafi, Saïd

2013-08-01

In this article, a new solution approach for the multiple choice multidimensional knapsack problem is described. The problem is a variant of the multidimensional knapsack problem where items are divided into classes, and exactly one item per class has to be chosen. Both problems are NP-hard. However, the multiple choice multidimensional knapsack problem appears to be more difficult to solve in part because of its choice constraints. Many real applications lead to very large scale multiple choice multidimensional knapsack problems that can hardly be addressed using exact algorithms. A new hybrid heuristic is proposed that embeds several new procedures for this problem. The approach is based on the resolution of linear programming relaxations of the problem and reduced problems that are obtained by fixing some variables of the problem. The solutions of these problems are used to update the global lower and upper bounds for the optimal solution value. A new strategy for defining the reduced problems is explored, together with a new family of cuts and a reformulation procedure that is used at each iteration to improve the performance of the heuristic. An extensive set of computational experiments is reported for benchmark instances from the literature and for a large set of hard instances generated randomly. The results show that the approach outperforms other state-of-the-art methods described so far, providing the best known solution for a significant number of benchmark instances.

Five-Year-Olds' Systematic Errors in Second-Order False Belief Tasks Are Due to First-Order Theory of Mind Strategy Selection: A Computational Modeling Study.

PubMed

Arslan, Burcu; Taatgen, Niels A; Verbrugge, Rineke

2017-01-01

The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback "Wrong," they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children's failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy.

Five-Year-Olds’ Systematic Errors in Second-Order False Belief Tasks Are Due to First-Order Theory of Mind Strategy Selection: A Computational Modeling Study

PubMed Central

Arslan, Burcu; Taatgen, Niels A.; Verbrugge, Rineke

2017-01-01

The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback “Wrong,” they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children’s failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy. PMID:28293206

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

PubMed Central

Ikeda, Mitsuru

2017-01-01

Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively. PMID:29090077

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

NASA Astrophysics Data System (ADS)

Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

2017-10-01

Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

Interprofessional Education and Practice Guide No. 6: Developing practice-based interprofessional learning using a short placement model.

PubMed

Anderson, Elizabeth Susan; Ford, Jenny; Kinnair, Daniel James

2016-07-01

Offering undergraduate and post-qualified learners opportunities to take part in, and reflect on, the nature of interprofessional working when in practice remains an important goal for interprofessional educators. There are a plethora of opportunities within hospital and community care for learners to actively participate in health and social care delivery where collaborative practice prevails. However, it remains challenging to know how to establish and sustain meaningful interprofessional practice-based learning. This is because profession-specific teaching is prioritised and many teams are under-resourced, leaving little time for additional teaching activities. In some instances, practitioners lack the knowledge concerning how to design meaningful interprofessional learning and often feel unprepared for this teaching because of limited interprofessional faculty development. Others are simply unaware of the presence of the different students within their practice area. This guide offers key lessons developed over many years for setting up practice-based interprofessional education. The learning model has been adapted and adopted in different settings and countries and offers a method for engaging clinical front-line practitioners in learning with, and from learners who can help support and in some instances advance care delivery.

Avoiding Communication Barriers in the Classroom: The APEINTA Project

ERIC Educational Resources Information Center

Iglesias, Ana; Jiménez, Javier; Revuelta, Pablo; Moreno, Lourdes

2016-01-01

Education is a fundamental human right, however unfortunately not everybody has the same learning opportunities. For instance, if a student has hearing impairments, s/he could face communications barriers in the classroom, which could affect his/her learning process. APEINTA is a Spanish educational project that aims for inclusive education for…

"Destroying" the Pedagogical Imaginary: Some Implications of Sexual Difference for Educational Philosophy

ERIC Educational Resources Information Center

Peers, Chris

2004-01-01

This article investigates some of the antecedent conditions underlying the imputation of autonomy within conceptions of "teaching" and "learning". It links the history of those concepts with the separate roles and functions assigned to males and females in specific instances of educational practice. "Teaching" and "learning" are psychoanalysed as…

Daddy, I Know What the Story Means--Now, I Just Need Help with the Words.

ERIC Educational Resources Information Center

Bintz, William

1998-01-01

Describes an instance of literacy learning involving the author and his two daughters at a local bookstore. Discusses how this literacy event challenged the author to consider alternative assumptions about reading, learning to read, and the relationship between reading and literacy. Offers lingering questions about what theoretical assumptions…

On Qualitative Differences in Learning: III--Study Skill and Learning

ERIC Educational Resources Information Center

Svensson, L.

1977-01-01

The intention in this research was to collect instances of study skill in different situations, and to relate study activity to levels of understanding and academic performance. Also reanalyzes data described by Marton and Saljo (1976a) which led to the concepts of deep-level processing and surface processing as explanations of qualitative…

Storytelling for Ordinary, Practical Purposes (Walter Benjamin's "The Storyteller")

ERIC Educational Resources Information Center

Pereira, Íris Susana Pires; Doecke, Brenton

2016-01-01

This essay explores the role that storytelling can play in teachers' learning. Walter Benjamin's "The Storyteller" provides a theoretical framework that enables us to highlight the complexity of the professional learning of teachers when they share stories about their everyday lives. We develop our argument by presenting two instances of…

"Posterlet": A Game-Based Assessment of Children's Choices to Seek Feedback and to Revise

ERIC Educational Resources Information Center

Cutumisu, Maria; Blair, Kristen P.; Chin, Doris B.; Schwartz, Daniel L.

2015-01-01

We introduce one instance of a game-based assessment designed to measure students' self-regulated learning choices. We describe our overarching measurement strategy and we present "Posterlet", an assessment game in which students design posters and learn graphic design principles from feedback. We designed "Posterlet" to assess…

Finding Connections: Using Accounting Concepts in a Career Planning Class

ERIC Educational Resources Information Center

Wang, Michelle

2013-01-01

In higher education, the most common challenge for students is the ability to find a connection between one subject that they have learned and another subject. Thus, students' learning becomes compartmentalized and piecemeal. For instance, accounting students may find attending a drawing class boring and a waste of time. Science students may…

From Reciprocity to Interdependence: Mass Incarceration and Service-Learning

ERIC Educational Resources Information Center

Ryder, Phyllis Mentzell

2016-01-01

This essay considers the difficulty of seeing systems of oppression--a challenging first step of writing for social change. I argue that service-learning faculty and public writing scholars have relied on outdated ways of thinking about racism and oppression, treating social issues as isolated instances of discrimination. Instead, by drawing from…

Processing Fluency in Education: How Metacognitive Feelings Shape Learning, Belief Formation, and Affect

ERIC Educational Resources Information Center

Reber, Rolf; Greifeneder, Rainer

2017-01-01

Processing fluency--the experienced ease with which a mental operation is performed--has attracted little attention in educational psychology, despite its relevance. The present article reviews and integrates empirical evidence on processing fluency that is relevant to school education. Fluency is important, for instance, in learning,…

Never-Ending Learning

DTIC Science & Technology

2010-08-01

they begin or end an instance. The learned model uses part-of-speech features to identify typical music group names (e.g., The Beatles , The Ramones...APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. STINFO COPY The views and...conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either

Action or Reaction, Learning or Display: Interactional Development and Usage-Based Data

ERIC Educational Resources Information Center

Huth, Thorsten

2013-01-01

This paper investigates how instances of language use can serve as analytic anchors for insight into interactional development over time. I present a usage-based, longitudinal study of multi-turn sequences underlying telephone openings in order to specify if and to whom "language learning" may be relevantly ascribed. Two successive…

Conceptual Complexity and the Bias/Variance Tradeoff

ERIC Educational Resources Information Center

Briscoe, Erica; Feldman, Jacob

2011-01-01

In this paper we propose that the conventional dichotomy between exemplar-based and prototype-based models of concept learning is helpfully viewed as an instance of what is known in the statistical learning literature as the "bias/variance tradeoff". The bias/variance tradeoff can be thought of as a sliding scale that modulates how closely any…

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran

2010-01-01

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

National Intelligence University’s Role in Interagency Research: Recommendations from the Intelligence Community

DTIC Science & Technology

2013-01-01

outreach, and (4) social science and historical research/lessons learned . In some instances, the research entity fit into more than one category. We...Bureau of Intelligence and Research (INR) and the Analytic Outreach Initiative (AOI) at ODNI. Social science and historical research/lessons learned ...its coordination efforts, CSIR was interested in learning more about potential interagency research partners and how collaboration could be improved

Extracting Dynamic Evidence Networks

DTIC Science & Technology

2004-12-01

on the performance of the three models as a function of training set size, and on experiments showing the viability of using active learning techniques...potential relation instances, which include 28K actual relations. 2.3.2 Active Learning We also ran a set of experiments designed to explore the...viability of using active learning techniques to maximize the usefulness of the training data annotated for use by the system. The idea is to

Cross-Modal Correspondence Among Vision, Audition, and Touch in Natural Objects: An Investigation of the Perceptual Properties of Wood.

PubMed

Kanaya, Shoko; Kariya, Kenji; Fujisaki, Waka

2016-10-01

Certain systematic relationships are often assumed between information conveyed from multiple sensory modalities; for instance, a small figure and a high pitch may be perceived as more harmonious. This phenomenon, termed cross-modal correspondence, may result from correlations between multi-sensory signals learned in daily experience of the natural environment. If so, we would observe cross-modal correspondences not only in the perception of artificial stimuli but also in perception of natural objects. To test this hypothesis, we reanalyzed data collected previously in our laboratory examining perceptions of the material properties of wood using vision, audition, and touch. We compared participant evaluations of three perceptual properties (surface brightness, sharpness of sound, and smoothness) of the wood blocks obtained separately via vision, audition, and touch. Significant positive correlations were identified for all properties in the audition-touch comparison, and for two of the three properties regarding in the vision-touch comparison. By contrast, no properties exhibited significant positive correlations in the vision-audition comparison. These results suggest that we learn correlations between multi-sensory signals through experience; however, the strength of this statistical learning is apparently dependent on the particular combination of sensory modalities involved. © The Author(s) 2016.

Integrating instance selection, instance weighting, and feature weighting for nearest neighbor classifiers by coevolutionary algorithms.

PubMed

Derrac, Joaquín; Triguero, Isaac; Garcia, Salvador; Herrera, Francisco

2012-10-01

Cooperative coevolution is a successful trend of evolutionary computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by the use of evolutionary algorithms. It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal. A novel approach integrating instance selection, instance weighting, and feature weighting into the framework of a coevolutionary model is presented in this paper. We compare it with a wide range of evolutionary and nonevolutionary related methods, in order to show the benefits of the employment of coevolution to apply the techniques considered simultaneously. The results obtained, contrasted through nonparametric statistical tests, show that our proposal outperforms other methods in the comparison, thus becoming a suitable tool in the task of enhancing the nearest neighbor classifier.

A cross-cultural comparison of biology lessons between China and Germany: a video study

NASA Astrophysics Data System (ADS)

Liu, Ning; Neuhaus, Birgit Jana

2017-08-01

Given the globalization of science education and the different cultures between China and Germany, we tried to compare and explain the differences on teacher questions and real life instances in biology lessons between the two countries from a culture-related perspective. 22 biology teachers from China and 21 biology teachers from Germany participated in this study. Each teacher was videotaped for one lesson on the unit blood and circulatory system. Before the teaching unit, students' prior knowledge was tested with a pretest. After the teaching unit, students' content knowledge was tested with a posttest. The aim of the knowledge tests here was for the better selection of the four samples for qualitative comparison in the two countries. The quantitative analysis showed that more lower-order teacher questions and more real life instances that were introduced after learning relevant concepts were in Chinese lessons than in German lessons. There were no significant differences in the frequency of higher-order questions or real life instances that were introduced before learning concepts. Qualitative analysis showed that both German teachers guided students to analyze the reasoning process of Landsteiner experiment, but nor Chinese teachers did that. The findings reflected the subtle influence of culture on classroom teaching. Relatively, Chinese biology teachers focused more on learning content and the application of the content in real life; German biology teachers emphasized more on invoking students' reasoning and divergent thinking.

Learning fuzzy information in a hybrid connectionist, symbolic model

NASA Technical Reports Server (NTRS)

Romaniuk, Steve G.; Hall, Lawrence O.

1993-01-01

An instance-based learning system is presented. SC-net is a fuzzy hybrid connectionist, symbolic learning system. It remembers some examples and makes groups of examples into exemplars. All real-valued attributes are represented as fuzzy sets. The network representation and learning method is described. To illustrate this approach to learning in fuzzy domains, an example of segmenting magnetic resonance images of the brain is discussed. Clearly, the boundaries between human tissues are ill-defined or fuzzy. Example fuzzy rules for recognition are generated. Segmentations are presented that provide results that radiologists find useful.

Uninformative contexts support word learning for high-skill spellers.

PubMed

Eskenazi, Michael A; Swischuk, Natascha K; Folk, Jocelyn R; Abraham, Ashley N

2018-04-30

The current study investigated how high-skill spellers and low-skill spellers incidentally learn words during reading. The purpose of the study was to determine whether readers can use uninformative contexts to support word learning after forming a lexical representation for a novel word, consistent with instance-based resonance processes. Previous research has found that uninformative contexts damage word learning; however, there may have been insufficient exposure to informative contexts (only one) prior to exposure to uninformative contexts (Webb, 2007; Webb, 2008). In Experiment 1, participants read sentences with one novel word (i.e., blaph, clurge) embedded in them in three different conditions: Informative (six informative contexts to support word learning), Mixed (three informative contexts followed by three uninformative contexts), and Uninformative (six uninformative contexts). Experiment 2 added a new condition with only three informative contexts to further clarify the conclusions of Experiment 1. Results indicated that uninformative contexts can support word learning, but only for high-skill spellers. Further, when participants learned the spelling of the novel word, they were more likely to learn the meaning of that word. This effect was much larger for high-skill spellers than for low-skill spellers. Results are consistent with the Lexical Quality Hypothesis (LQH) in that high-skill spellers form stronger orthographic representations which support word learning (Perfetti, 2007). Results also support an instance-based resonance process of word learning in that prior informative contexts can be reactivated to support word learning in future contexts (Bolger, Balass, Landen, & Perfetti, 2008; Balass, Nelson, & Perfetti, 2010; Reichle & Perfetti, 2003). (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Imbalanced class learning in epigenetics.

PubMed

Haque, M Muksitul; Skinner, Michael K; Holder, Lawrence B

2014-07-01

In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class distribution. Such datasets can range from biological data, sensor data, medical diagnostics, or any other domain where labeling any instances of the minority class can be time-consuming or costly or the data may not be easily available. The current study investigates a number of imbalanced class algorithms for solving the imbalanced class distribution present in epigenetic datasets. Epigenetic (DNA methylation) datasets inherently come with few differentially DNA methylated regions (DMR) and with a higher number of non-DMR sites. For this class imbalance problem, a number of algorithms are compared, including the TAN+AdaBoost algorithm. Experiments performed on four epigenetic datasets and several known datasets show that an imbalanced dataset can have similar accuracy as a regular learner on a balanced dataset.

Efficient dynamic optimization of logic programs

NASA Technical Reports Server (NTRS)

Laird, Phil

1992-01-01

A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.

Excessive Stress Disrupts the Architecture of the Developing Brain. Working Paper #3

ERIC Educational Resources Information Center

National Scientific Council on the Developing Child, 2005

2005-01-01

New research suggests that exceptionally stressful experiences early in life may have long-term consequences for a child's learning, behavior, and both physical and mental health. Some types of "positive stress" in a child's life--overcoming the challenges and frustrations of learning a new, difficult task, for instance--can be beneficial. Severe,…

Pupils' Ideas about Flowering Plants. Learning in Science Project (Primary). Working Paper No. 125.

ERIC Educational Resources Information Center

Biddulph, Fred

The Learning in Science Project (Primary)--LISP(P)--investigated the ideas and interests children have about flowering plants (in particular whether these plants have a life cycle). Data were obtained from: individual interviews with children, ages 7- to 14-year-old (10 students for each age level), using the "interview-about-instances"…

Autonomous Inter-Task Transfer in Reinforcement Learning Domains

DTIC Science & Technology

2008-08-01

Twentieth International Joint Conference on Artificial Intelli - gence, 2007. 304 Fumihide Tanaka and Masayuki Yamamura. Multitask reinforcement learning...Functions . . . . . . . . . . . . . . . . . . . . . . 17 2.2.3 Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . 18 2.2.4 Instance-based...tures [Laird et al., 1986, Choi et al., 2007]. However, TL for RL tasks has only recently been gaining attention in the artificial intelligence

Opportunities to Learn: Inverse Relations in U.S. and Chinese Textbooks

ERIC Educational Resources Information Center

Ding, Meixia

2016-01-01

This study, focusing on inverse relations, examines how representative U.S. and Chinese elementary textbooks may provide opportunities to learn fundamental mathematical ideas. Findings from this study indicate that both of the U.S. textbook series (grades K-6) in comparison to the Chinese textbook samples (grades 1-6), presented more instances of…

Emotional Design Tutoring System Based on Multimodal Affective Computing Techniques

ERIC Educational Resources Information Center

Wang, Cheng-Hung; Lin, Hao-Chiang Koong

2018-01-01

In a traditional class, the role of the teacher is to teach and that of the students is to learn. However, the constant and rapid technological advancements have transformed education in numerous ways. For instance, in addition to traditional, face to face teaching, E-learning is now possible. Nevertheless, face to face teaching is unavailable in…

Software Agents to Assist in Distance Learning Environments

ERIC Educational Resources Information Center

Choy, Sheung-On; Ng, Sin-Chun; Tsang, Yiu-Chung

2005-01-01

The Open University of Hong Kong (OUHK) is a distance education university with about 22,500 students. In fulfilling its mission, the university has adopted various Web-based and electronic means to support distance learning. For instance, OUHK uses a Web-based course management system (CMS) to provide students with a flexible way to obtain course…

Learning about Allergies

MedlinePlus

... other things from some animals grass, flower, and tree pollen (the fine dust from plants) mold and ... instance, you might be allergic to pollen from trees, which is present in the air only in ...

Comprehensive Optimal Manpower and Personnel Analytic Simulation System (COMPASS)

DTIC Science & Technology

2009-10-01

4 The EDB consists of 4 major components (some of which are re-usable): 1. Metadata Editor ( MDE ): Also considered a leaf node, the metadata...end-user queries via the QB. The EDB supports multiple instances of the MDE , although currently, only a single instance is recommended. 2 Query...the MSB is a central collection of web services, responsible for the authentication and authorization of users, maintenance of the EDB metadata

Application of life cycle thinking in multidisciplinary multistakeholder contexts for cross-sectoral planning and implementation of sustainable development projects.

PubMed

Thabrew, Lanka; Ries, Robert

2009-07-01

Development planning and implementation is a multifaceted and multiscale task mainly because of the involvement of multiple stakeholders across sectors and disciplines. Even though top-down sectoral planning is commonly practiced, bottom-up cross-sectoral planning involving all relevant stakeholders in a transdisciplinary learning environment has been recognized as a better option, especially if the goal is to drive development projects toward sustainable implementation (Rowe and Fudge 2003; Müller et al. 2005; Global Development Research Center 2008). Even though many planning approaches have this goal, there are limited decision frameworks that are suitable for achieving consensus among stakeholders from multiple disciplines with sectoral objectives and priorities. In most instances, the upstream and downstream effects of development decisions are not thoroughly investigated or communicated with the relevant stakeholders, strongly affecting cross-sectoral integration in the real world (Wiek, Brundiers, et al. 2006). This article presents methodological aspects of developing a stakeholder based life cycle assessment framework (SBLCA) for upstream-downstream decision analysis in a multistakeholder development planning context. The applicability of the framework is demonstrated using simple examples extracted from a pilot case study conducted in Sri Lanka for sustainable posttsunami reconstruction at a village scale. The applicability of SBLCA in specific planning stages, how it promotes transdisciplinary learning and cross-sectoral stakeholder integration in phases of project cycles, and how local stakeholders can practice life cycle thinking in their village development planning and implementation are discussed.

Adaptive Batch Mode Active Learning.

PubMed

Chakraborty, Shayok; Balasubramanian, Vineeth; Panchanathan, Sethuraman

2015-08-01

Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts toward a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set. Real-world applications require adaptive approaches for batch selection in active learning, depending on the complexity of the data stream in question. However, the existing work in this field has primarily focused on static or heuristic batch size selection. In this paper, we propose two novel optimization-based frameworks for adaptive batch mode active learning (BMAL), where the batch size as well as the selection criteria are combined in a single formulation. We exploit gradient-descent-based optimization strategies as well as properties of submodular functions to derive the adaptive BMAL algorithms. The solution procedures have the same computational complexity as existing state-of-the-art static BMAL techniques. Our empirical results on the widely used VidTIMIT and the mobile biometric (MOBIO) data sets portray the efficacy of the proposed frameworks and also certify the potential of these approaches in being used for real-world biometric recognition applications.

When Tests Make You Nervous

MedlinePlus

... you figure out some solutions. For instance, learning study skills can boost your test-day confidence. Be prepared. Pay attention in class. Do your homework. Study for the test. On test day, you're ...

Perceptual learning and human expertise

NASA Astrophysics Data System (ADS)

Kellman, Philip J.; Garrigan, Patrick

2009-06-01

We consider perceptual learning: experience-induced changes in the way perceivers extract information. Often neglected in scientific accounts of learning and in instruction, perceptual learning is a fundamental contributor to human expertise and is crucial in domains where humans show remarkable levels of attainment, such as language, chess, music, and mathematics. In Section 2, we give a brief history and discuss the relation of perceptual learning to other forms of learning. We consider in Section 3 several specific phenomena, illustrating the scope and characteristics of perceptual learning, including both discovery and fluency effects. We describe abstract perceptual learning, in which structural relationships are discovered and recognized in novel instances that do not share constituent elements or basic features. In Section 4, we consider primary concepts that have been used to explain and model perceptual learning, including receptive field change, selection, and relational recoding. In Section 5, we consider the scope of perceptual learning, contrasting recent research, focused on simple sensory discriminations, with earlier work that emphasized extraction of invariance from varied instances in more complex tasks. Contrary to some recent views, we argue that perceptual learning should not be confined to changes in early sensory analyzers. Phenomena at various levels, we suggest, can be unified by models that emphasize discovery and selection of relevant information. In a final section, we consider the potential role of perceptual learning in educational settings. Most instruction emphasizes facts and procedures that can be verbalized, whereas expertise depends heavily on implicit pattern recognition and selective extraction skills acquired through perceptual learning. We consider reasons why perceptual learning has not been systematically addressed in traditional instruction, and we describe recent successful efforts to create a technology of perceptual learning in areas such as aviation, mathematics, and medicine. Research in perceptual learning promises to advance scientific accounts of learning, and perceptual learning technology may offer similar promise in improving education.

Discovering Motifs in Biological Sequences Using the Micron Automata Processor.

PubMed

Roy, Indranil; Aluru, Srinivas

2016-01-01

Finding approximately conserved sequences, called motifs, across multiple DNA or protein sequences is an important problem in computational biology. In this paper, we consider the (l, d) motif search problem of identifying one or more motifs of length l present in at least q of the n given sequences, with each occurrence differing from the motif in at most d substitutions. The problem is known to be NP-complete, and the largest solved instance reported to date is (26,11). We propose a novel algorithm for the (l,d) motif search problem using streaming execution over a large set of non-deterministic finite automata (NFA). This solution is designed to take advantage of the micron automata processor, a new technology close to deployment that can simultaneously execute multiple NFA in parallel. We demonstrate the capability for solving much larger instances of the (l, d) motif search problem using the resources available within a single automata processor board, by estimating run-times for problem instances (39,18) and (40,17). The paper serves as a useful guide to solving problems using this new accelerator technology.

Hierarchical acquisition of visual specificity in spatial contextual cueing.

PubMed

Lie, Kin-Pou

2015-01-01

Spatial contextual cueing refers to visual search performance's being improved when invariant associations between target locations and distractor spatial configurations are learned incidentally. Using the instance theory of automatization and the reverse hierarchy theory of visual perceptual learning, this study explores the acquisition of visual specificity in spatial contextual cueing. Two experiments in which detailed visual features were irrelevant for distinguishing between spatial contexts found that spatial contextual cueing was visually generic in difficult trials when the trials were not preceded by easy trials (Experiment 1) but that spatial contextual cueing progressed to visual specificity when difficult trials were preceded by easy trials (Experiment 2). These findings support reverse hierarchy theory, which predicts that even when detailed visual features are irrelevant for distinguishing between spatial contexts, spatial contextual cueing can progress to visual specificity if the stimuli remain constant, the task is difficult, and difficult trials are preceded by easy trials. However, these findings are inconsistent with instance theory, which predicts that when detailed visual features are irrelevant for distinguishing between spatial contexts, spatial contextual cueing will not progress to visual specificity. This study concludes that the acquisition of visual specificity in spatial contextual cueing is more plausibly hierarchical, rather than instance-based.

Accurate object tracking system by integrating texture and depth cues

NASA Astrophysics Data System (ADS)

Chen, Ju-Chin; Lin, Yu-Hang

2016-03-01

A robust object tracking system that is invariant to object appearance variations and background clutter is proposed. Multiple instance learning with a boosting algorithm is applied to select discriminant texture information between the object and background data. Additionally, depth information, which is important to distinguish the object from a complicated background, is integrated. We propose two depth-based models that can compensate texture information to cope with both appearance variants and background clutter. Moreover, in order to reduce the risk of drifting problem increased for the textureless depth templates, an update mechanism is proposed to select more precise tracking results to avoid incorrect model updates. In the experiments, the robustness of the proposed system is evaluated and quantitative results are provided for performance analysis. Experimental results show that the proposed system can provide the best success rate and has more accurate tracking results than other well-known algorithms.

A Generalized Mixture Framework for Multi-label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, …, Yd|X) using a product of posterior distributions over components of the output space. Our approach captures different input–output and output–output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods. PMID:26613069

Sexual Communication in the Family: Handling a Delicate Topic in the Family Communication Course

ERIC Educational Resources Information Center

Pawlowski, Donna R.

2006-01-01

Many times, families, and in particular parents, do not discuss important issues of sex with their children. Children learn about sex-related topics from their friends, other family members, or media sources. In such instances, while children at least may be learning about sex, the accuracy of the sources from which they are obtaining information…

Tensions and Transitions: Effecting Change towards Sustainability at a Mainstream University through Staff Living and Learning at an Alternative, Civil Society College

ERIC Educational Resources Information Center

Blake, Joanna; Sterling, Stephen

2011-01-01

This paper explores the impact of short immersive residentials at a radical institution for staff currently working in a mainstream one; in this instance, at Schumacher College for those at the nearby University of Plymouth (UK). Schumacher is an independent, alternative college offering residential courses in "transformative learning for…

Measuring Load on Working Memory: The Use of Heart Rate as a Means of Measuring Chemistry Students' Cognitive Load

ERIC Educational Resources Information Center

Cranford, Kristen N.; Tiettmeyer, Jessica M.; Chuprinko, Bryan C.; Jordan, Sophia; Grove, Nathaniel P.

2014-01-01

Information processing provides a powerful model for understanding how learning occurs and highlights the important role that cognitive load plays in this process. In instances in which the cognitive load of a problem exceeds the available working memory, learning can be seriously hindered. Previously reported methods for measuring cognitive load…

Cramming Facts and Thinking Concepts: Instance of Preparation of Student Geography Teachers in Liberec

ERIC Educational Resources Information Center

Vávra, Jaroslav

2014-01-01

In general, geographical education is closed into two strands. First, in geographical content (knowledge), there are geographical facts on the one hand and on the other hand there is geographical thinking. Second, in geographical cognition there is rote learning in behavioral strategy on the one hand and on the other hand meaningful learning in…

Using a Cognitive-Scientific Inflected Anthropological Approach to Researching the Teaching and Learning of Elementary School Mathematics: An Instance of the Use of Aggregates

ERIC Educational Resources Information Center

Davis, Zain

2016-01-01

Anthropological approaches to studying the contextual specificity of mathematical thought and practice in schools can productively inform descriptions and analyses of mathematical practices within and across different teaching and learning contexts. In this paper I argue for an anthropological methodological orientation that takes into…

Developing Children's Self Initiated Music Making through the Creation of a Shared Ethos in an Early Years Music Project

ERIC Educational Resources Information Center

Naughton, Christopher; Lines, David

2013-01-01

The three-month "Changing Places" project involved early childhood student teachers working with music students in developing children's music in centres in Auckland, New Zealand. The project set out to challenge the calculative aspect in music learning (Heidegger, 1993). The term calculative in this instance describes learning seen as…

Coaching the exploration and exploitation in active learning for interactive video retrieval.

PubMed

Wei, Xiao-Yong; Yang, Zhen-Qun

2013-03-01

Conventional active learning approaches for interactive video/image retrieval usually assume the query distribution is unknown, as it is difficult to estimate with only a limited number of labeled instances available. Thus, it is easy to put the system in a dilemma whether to explore the feature space in uncertain areas for a better understanding of the query distribution or to harvest in certain areas for more relevant instances. In this paper, we propose a novel approach called coached active learning that makes the query distribution predictable through training and, therefore, avoids the risk of searching on a completely unknown space. The estimated distribution, which provides a more global view of the feature space, can be used to schedule not only the timing but also the step sizes of the exploration and the exploitation in a principled way. The results of the experiments on a large-scale data set from TRECVID 2005-2009 validate the efficiency and effectiveness of our approach, which demonstrates an encouraging performance when facing domain-shift, outperforms eight conventional active learning methods, and shows superiority to six state-of-the-art interactive video retrieval systems.

Picture-Word Differences in Discrimination Learning: 11. Effects of Conceptual Categories

ERIC Educational Resources Information Center

Bourne, Lyle E.; And Others

1976-01-01

Investigates the prediction that the usual superiority of pictures over words for repetitions of the same items would disappear for items that were different instances of repeated categories. (Author/RK)

Beyond where to how: a machine learning approach for sensing mobility contexts using smartphone sensors.

PubMed

Guinness, Robert E

2015-04-28

This paper presents the results of research on the use of smartphone sensors (namely, GPS and accelerometers), geospatial information (points of interest, such as bus stops and train stations) and machine learning (ML) to sense mobility contexts. Our goal is to develop techniques to continuously and automatically detect a smartphone user's mobility activities, including walking, running, driving and using a bus or train, in real-time or near-real-time (<5 s). We investigated a wide range of supervised learning techniques for classification, including decision trees (DT), support vector machines (SVM), naive Bayes classifiers (NB), Bayesian networks (BN), logistic regression (LR), artificial neural networks (ANN) and several instance-based classifiers (KStar, LWLand IBk). Applying ten-fold cross-validation, the best performers in terms of correct classification rate (i.e., recall) were DT (96.5%), BN (90.9%), LWL (95.5%) and KStar (95.6%). In particular, the DT-algorithm RandomForest exhibited the best overall performance. After a feature selection process for a subset of algorithms, the performance was improved slightly. Furthermore, after tuning the parameters of RandomForest, performance improved to above 97.5%. Lastly, we measured the computational complexity of the classifiers, in terms of central processing unit (CPU) time needed for classification, to provide a rough comparison between the algorithms in terms of battery usage requirements. As a result, the classifiers can be ranked from lowest to highest complexity (i.e., computational cost) as follows: SVM, ANN, LR, BN, DT, NB, IBk, LWL and KStar. The instance-based classifiers take considerably more computational time than the non-instance-based classifiers, whereas the slowest non-instance-based classifier (NB) required about five-times the amount of CPU time as the fastest classifier (SVM). The above results suggest that DT algorithms are excellent candidates for detecting mobility contexts in smartphones, both in terms of performance and computational complexity.

Beyond Where to How: A Machine Learning Approach for Sensing Mobility Contexts Using Smartphone Sensors †

PubMed Central

Guinness, Robert E.

2015-01-01

This paper presents the results of research on the use of smartphone sensors (namely, GPS and accelerometers), geospatial information (points of interest, such as bus stops and train stations) and machine learning (ML) to sense mobility contexts. Our goal is to develop techniques to continuously and automatically detect a smartphone user's mobility activities, including walking, running, driving and using a bus or train, in real-time or near-real-time (<5 s). We investigated a wide range of supervised learning techniques for classification, including decision trees (DT), support vector machines (SVM), naive Bayes classifiers (NB), Bayesian networks (BN), logistic regression (LR), artificial neural networks (ANN) and several instance-based classifiers (KStar, LWLand IBk). Applying ten-fold cross-validation, the best performers in terms of correct classification rate (i.e., recall) were DT (96.5%), BN (90.9%), LWL (95.5%) and KStar (95.6%). In particular, the DT-algorithm RandomForest exhibited the best overall performance. After a feature selection process for a subset of algorithms, the performance was improved slightly. Furthermore, after tuning the parameters of RandomForest, performance improved to above 97.5%. Lastly, we measured the computational complexity of the classifiers, in terms of central processing unit (CPU) time needed for classification, to provide a rough comparison between the algorithms in terms of battery usage requirements. As a result, the classifiers can be ranked from lowest to highest complexity (i.e., computational cost) as follows: SVM, ANN, LR, BN, DT, NB, IBk, LWL and KStar. The instance-based classifiers take considerably more computational time than the non-instance-based classifiers, whereas the slowest non-instance-based classifier (NB) required about five-times the amount of CPU time as the fastest classifier (SVM). The above results suggest that DT algorithms are excellent candidates for detecting mobility contexts in smartphones, both in terms of performance and computational complexity. PMID:25928060

Coordinating Multiple Representations in a Reform Calculus Textbook

ERIC Educational Resources Information Center

Chang, Briana L.; Cromley, Jennifer G.; Tran, Nhi

2015-01-01

Coordination of multiple representations (CMR) is widely recognized as a critical skill in mathematics and is frequently demanded in reform calculus textbooks. However, little is known about the prevalence of coordination tasks in such textbooks. We coded 707 instances of CMR in a widely used reform calculus textbook and analyzed the distributions…

Reversibility of Thought: An Instance in Multiplicative Tasks

ERIC Educational Resources Information Center

Ramful, Ajay; Olive, John

2008-01-01

In line with current efforts to understand the piece-by-piece structure and articulation of children's mathematical concepts, this case study compares the reversibility schemes of two eighth-grade students. The aim of the study was to identify the mechanism through which students reverse their thought processes in a multiplicative situation. Data…

Coordinating Multiple Representations in a Reform Calculus Textbook

ERIC Educational Resources Information Center

Chang, Briana L.; Cromley, Jennifer G.; Tran, Nhi

2016-01-01

Coordination of multiple representations (CMR) is widely recognized as a critical skill in mathematics and is frequently demanded in reform calculus textbooks. However, little is known about the prevalence of coordination tasks in such textbooks. We coded 707 instances of CMR in a widely used reform calculus textbook and analyzed the distributions…

S-CNN: Subcategory-aware convolutional networks for object detection.

PubMed

Chen, Tao; Lu, Shijian; Fan, Jiayuan

2017-09-26

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.

Strategies for generating multiple instances of common and ad hoc categories.

PubMed

Vallée-Tourangeau, F; Anthony, S H; Austin, N G

1998-09-01

In a free-emission procedure participants were asked to generate instances of a given category and to report, retrospectively, the strategies that they were aware of using in retrieving instances. In two studies reported here, participants generated instances for common categories (e.g. fruit) and for ad hoc categories (e.g., things people keep in their pockets) for 90 seconds and for each category described how they had proceeded in doing so. Analysis of the protocols identified three broad classes of strategy: (1) experiential, where memories of specific or generic personal experiences involving interactions with the category instances acted as cues; (2) semantic, where a consideration of abstract conceptual characteristics of a category were employed to retrieve category exemplars; (3) unmediated, where instances were effortlessly retrieved without mediating cognitions of which subjects were aware. Experiential strategies outnumbered semantic strategies (on average 4 to 1) not only for ad hoc categories but also for common categories. This pattern was noticeably reversed for ad hoc categories that subjects were unlikely to have experienced personally (e.g. things sold on the black market in Russia). Whereas more traditional accounts of semantic memory have favoured decontextualised abstract representations of category knowledge, to the extent that mode of access informs us of knowledge structures, our data suggest that category knowledge is significantly grounded in terms of everyday contexts where category instances are encountered.

Optimization of knowledge sharing through multi-forum using cloud computing architecture

NASA Astrophysics Data System (ADS)

Madapusi Vasudevan, Sriram; Sankaran, Srivatsan; Muthuswamy, Shanmugasundaram; Ram, N. Sankar

2011-12-01

Knowledge sharing is done through various knowledge sharing forums which requires multiple logins through multiple browser instances. Here a single Multi-Forum knowledge sharing concept is introduced which requires only one login session which makes user to connect multiple forums and display the data in a single browser window. Also few optimization techniques are introduced here to speed up the access time using cloud computing architecture.

Learning Science in the 21st century - a shared experience between schools

NASA Astrophysics Data System (ADS)

Pinto, Tânia; Soares, Rosa; Ruas, Fátima

2015-04-01

Problem Based Learning is considered an innovative teaching and learning inquiry methodology that is student centered, focused in the resolution of an authentic problem and in which the teacher acts like a facilitator of the work in small groups. In this process, it is expected that students develop attitudinal, procedural and communication skills, in addition to the cognitive typically valued. PBL implementation also allows the use of multiple educational strategies, like laboratorial experiments, analogue modeling or ICT (video animations, electronic presentations or software simulations, for instance), which can potentiate a more interactive environment in the classroom. In this study, taken in three schools in the north of Portugal, which resulted from the cooperation between three science teachers, with a 75 individuals sample, were examined students' opinions about the main difficulties and strengths concerning the PBL methodology, having as a common denominator the use of a laboratorial experiment followed by an adequate digital software as educational resource to interpret the obtained results and to make predictions (e.g. EarthQuake, Virtual Quake, Stellarium). The data collection methods were based on direct observation and questionnaires. The results globally show that this educational approach motivates students' towards science, helping them to solve problems from daily life and that the use of software was relevant, as well as the collaborative working. The cognitive strand continues to be the most valued by pupils.

Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

NASA Astrophysics Data System (ADS)

Chandakkar, Parag S.; Venkatesan, Ragav; Li, Baoxin

2013-02-01

Diabetic retinopathy (DR) is a vision-threatening complication from diabetes mellitus, a medical condition that is rising globally. Unfortunately, many patients are unaware of this complication because of absence of symptoms. Regular screening of DR is necessary to detect the condition for timely treatment. Content-based image retrieval, using archived and diagnosed fundus (retinal) camera DR images can improve screening efficiency of DR. This content-based image retrieval study focuses on two DR clinical findings, microaneurysm and neovascularization, which are clinical signs of non-proliferative and proliferative diabetic retinopathy. The authors propose a multi-class multiple-instance image retrieval framework which deploys a modified color correlogram and statistics of steerable Gaussian Filter responses, for retrieving clinically relevant images from a database of DR fundus image database.

Framework for Educational Robotics: A Multiphase Approach to Enhance User Learning in a Competitive Arena

ERIC Educational Resources Information Center

Lye, Ngit Chan; Wong, Kok Wai; Chiou, Andrew

2013-01-01

Educational robotics involves using robots as an educational tool to provide a long term, and progressive learning activity, to cater to different age group. The current concern is that, using robots in education should not be an instance of a one-off project for the sole purpose of participating in a competitive event. Instead, it should be a…

Understanding the Role of Teaching Materials in a Beginners' Level English as a Foreign Language Course: A Case Study

ERIC Educational Resources Information Center

Cruz Rondón, Elio Jesús; Velasco Vera, Leidy Fernanda

2016-01-01

Learning a foreign language may be a challenge for most people due to differences in the form and structure between one's mother tongue and a new one. However, there are some tools that facilitate the teaching and learning of a foreign language, for instance, new applications for digital devices, video blogs, educational platforms, and teaching…

Personalization, Personalized Learning and the Reform of Social Policy: The Prospect of Molecular Governance in the Digitized Society

ERIC Educational Resources Information Center

Peters, Michael A.

2009-01-01

This article argues that personalized learning has emerged in the last decade as a special instance of a more generalized response to the problem of the reorganization of the State in response to globalization and the end of the effectiveness of the industrial mass production model in the delivery of public services. The article examines…

ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery

PubMed Central

Li, Na; Xu, Zhaopeng; Zhao, Huijie; Huang, Xinchen; Drummond, Jane; Wang, Daming

2018-01-01

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively. PMID:29510547

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tamagnini, Paolo; Krause, Josua W.; Dasgupta, Aritra

2017-05-14

To realize the full potential of machine learning in diverse real- world domains, it is necessary for model predictions to be readily interpretable and actionable for the human in the loop. Analysts, who are the users but not the developers of machine learning models, often do not trust a model because of the lack of transparency in associating predictions with the underlying data space. To address this problem, we propose Rivelo, a visual analytic interface that enables analysts to understand the causes behind predictions of binary classifiers by interactively exploring a set of instance-level explanations. These explanations are model-agnostic, treatingmore » a model as a black box, and they help analysts in interactively probing the high-dimensional binary data space for detecting features relevant to predictions. We demonstrate the utility of the interface with a case study analyzing a random forest model on the sentiment of Yelp reviews about doctors.« less

DServO: A Peer-to-Peer-based Approach to Biomedical Ontology Repositories.

PubMed

Mambone, Zakaria; Savadogo, Mahamadi; Some, Borlli Michel Jonas; Diallo, Gayo

2015-01-01

We present in this poster an extension of the ServO ontology server system, which adopts a decentralized Peer-To-Peer approach for managing multiple heterogeneous knowledge organization systems. It relies on the use of the JXTA protocol coupled with information retrieval techniques to provide a decentralized infrastructure for managing multiples instances of Ontology Repositories.

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

NASA Astrophysics Data System (ADS)

Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro

2016-08-01

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact on deep learning and other machine-learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggest it will do so with an instance-dependent effective temperature, different from its physical temperature. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this work, we propose a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures. We also provide a comparison to k -step contrastive divergence (CD-k ) with k up to 100. Although assuming a suitable fixed effective temperature also allows us to outperform one-step contrastive divergence (CD-1), only when using an instance-dependent effective temperature do we find a performance close to that of CD-100 for the case studied here.

Performing a reaching task with one arm while adapting to a visuomotor rotation with the other can lead to complete transfer of motor learning across the arms

PubMed Central

Lei, Yuming; Binder, Jeffrey R.

2015-01-01

The extent to which motor learning is generalized across the limbs is typically very limited. Here, we investigated how two motor learning hypotheses could be used to enhance the extent of interlimb transfer. According to one hypothesis, we predicted that reinforcement of successful actions by providing binary error feedback regarding task success or failure, in addition to terminal error feedback, during initial training would increase the extent of interlimb transfer following visuomotor adaptation (experiment 1). According to the other hypothesis, we predicted that performing a reaching task repeatedly with one arm without providing performance feedback (which prevented learning the task with this arm), while concurrently adapting to a visuomotor rotation with the other arm, would increase the extent of transfer (experiment 2). Results indicate that providing binary error feedback, compared with continuous visual feedback that provided movement direction and amplitude information, had no influence on the extent of transfer. In contrast, repeatedly performing (but not learning) a specific task with one arm while visuomotor adaptation occurred with the other arm led to nearly complete transfer. This suggests that the absence of motor instances associated with specific effectors and task conditions is the major reason for limited interlimb transfer and that reinforcement of successful actions during initial training is not beneficial for interlimb transfer. These findings indicate crucial contributions of effector- and task-specific motor instances, which are thought to underlie (a type of) model-free learning, to optimal motor learning and interlimb transfer. PMID:25632082

A Dynamic Ensemble Framework for Mining Textual Streams with Class Imbalance

PubMed Central

Song, Ge

2014-01-01

Textual stream classification has become a realistic and challenging issue since large-scale, high-dimensional, and non-stationary streams with class imbalance have been widely used in various real-life applications. According to the characters of textual streams, it is technically difficult to deal with the classification of textual stream, especially in imbalanced environment. In this paper, we propose a new ensemble framework, clustering forest, for learning from the textual imbalanced stream with concept drift (CFIM). The CFIM is based on ensemble learning by integrating a set of clustering trees (CTs). An adaptive selection method, which flexibly chooses the useful CTs by the property of the stream, is presented in CFIM. In particular, to deal with the problem of class imbalance, we collect and reuse both rare-class instances and misclassified instances from the historical chunks. Compared to most existing approaches, it is worth pointing out that our approach assumes that both majority class and rareclass may suffer from concept drift. Thus the distribution of resampled instances is similar to the current concept. The effectiveness of CFIM is examined in five real-world textual streams under an imbalanced nonstationary environment. Experimental results demonstrate that CFIM achieves better performance than four state-of-the-art ensemble models. PMID:24982961

Automated gastric cancer diagnosis on H&E-stained sections; ltraining a classifier on a large scale with multiple instance machine learning

NASA Astrophysics Data System (ADS)

Cosatto, Eric; Laquerre, Pierre-Francois; Malon, Christopher; Graf, Hans-Peter; Saito, Akira; Kiyuna, Tomoharu; Marugame, Atsushi; Kamijo, Ken'ichi

2013-03-01

We present a system that detects cancer on slides of gastric tissue sections stained with hematoxylin and eosin (H&E). At its heart is a classi er trained using the semi-supervised multi-instance learning framework (MIL) where each tissue is represented by a set of regions-of-interest (ROI) and a single label. Such labels are readily obtained because pathologists diagnose each tissue independently as part of the normal clinical work ow. From a large dataset of over 26K gastric tissue sections from over 12K patients obtained from a clinical load spanning several months, we train a MIL classi er on a patient-level partition of the dataset (2/3 of the patients) and obtain a very high performance of 96% (AUC), tested on the remaining 1/3 never-seen before patients (over 8K tissues). We show this level of performance to match the more costly supervised approach where individual ROIs need to be labeled manually. The large amount of data used to train this system gives us con dence in its robustness and that it can be safely used in a clinical setting. We demonstrate how it can improve the clinical work ow when used for pre-screening or quality control. For pre-screening, the system can diagnose 47% of the tissues with a very low likelihood (< 1%) of missing cancers, thus halving the clinicians' caseload. For quality control, compared to random rechecking of 33% of the cases, the system achieves a three-fold increase in the likelihood of catching cancers missed by pathologists. The system is currently in regular use at independent pathology labs in Japan where it is used to double-check clinician's diagnoses. At the end of 2012 it will have analyzed over 80,000 slides of gastric and colorectal samples (200,000 tissues).

Incorporating Early Learning Strategies in the School Improvement Grants (SIG) Program: How Three Schools Integrated Early Childhood Strategies into School Turnaround Efforts to Improve Instruction for All Students

ERIC Educational Resources Information Center

Connors-Tadros, Lori; Dunn, Lenay; Martella, Jana; McCauley, Carlas

2015-01-01

A significant body of research shows that achievement gaps evident in persistently low-performing schools, in many instances, manifest prior to children entering kindergarten. High-quality early learning programs have proven to demonstrate positive effects on closing academic gaps both for individual children and in the aggregate for the school.…

Aerosol Concentration, Size, Hygroscopicity and MEE, Globally: What Do We Need to Know and How Can We Know It?

NASA Technical Reports Server (NTRS)

Kahn, Ralph

2017-01-01

Organizers of the Symposium Clouds, their Properties, and their Climate Feedbacks - What Have We Learned in the Satellite Era, held at Columbia University, NASAGISS June 6-8, 2017 plan to post the presented talks to an online website. http:www.gewex.orgeventclouds-their-properties-and-their-climate-feedbacks-what-have-we-learned-in-the-satellite-era?instance_id293534

Space-TimeScapes as Ecopedagogy

ERIC Educational Resources Information Center

Dunkley, Ria Ann

2018-01-01

This emergent field of ecopedagogy gives little conceptual, methodological, and empirical consideration to the significance of spatial and temporal elements of environmental learning. This article focuses on both spatial and temporal components of three ecopedagogic instances, examining experiences from participant's perspective. Specific…

The discovery of structural form

PubMed Central

Kemp, Charles; Tenenbaum, Joshua B.

2008-01-01

Algorithms for finding structure in data have become increasingly important both as tools for scientific data analysis and as models of human learning, yet they suffer from a critical limitation. Scientists discover qualitatively new forms of structure in observed data: For instance, Linnaeus recognized the hierarchical organization of biological species, and Mendeleev recognized the periodic structure of the chemical elements. Analogous insights play a pivotal role in cognitive development: Children discover that object category labels can be organized into hierarchies, friendship networks are organized into cliques, and comparative relations (e.g., “bigger than” or “better than”) respect a transitive order. Standard algorithms, however, can only learn structures of a single form that must be specified in advance: For instance, algorithms for hierarchical clustering create tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces. Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development. PMID:18669663

Landmark-based deep multi-instance learning for brain disease diagnosis.

PubMed

Liu, Mingxia; Zhang, Jun; Adeli, Ehsan; Shen, Dinggang

2018-01-01

In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. Copyright © 2017 Elsevier B.V. All rights reserved.

Scalable Iterative Classification for Sanitizing Large-Scale Datasets

PubMed Central

Li, Bo; Vorobeychik, Yevgeniy; Li, Muqun; Malin, Bradley

2017-01-01

Cheap ubiquitous computing enables the collection of massive amounts of personal data in a wide variety of domains. Many organizations aim to share such data while obscuring features that could disclose personally identifiable information. Much of this data exhibits weak structure (e.g., text), such that machine learning approaches have been developed to detect and remove identifiers from it. While learning is never perfect, and relying on such approaches to sanitize data can leak sensitive information, a small risk is often acceptable. Our goal is to balance the value of published data and the risk of an adversary discovering leaked identifiers. We model data sanitization as a game between 1) a publisher who chooses a set of classifiers to apply to data and publishes only instances predicted as non-sensitive and 2) an attacker who combines machine learning and manual inspection to uncover leaked identifying information. We introduce a fast iterative greedy algorithm for the publisher that ensures a low utility for a resource-limited adversary. Moreover, using five text data sets we illustrate that our algorithm leaves virtually no automatically identifiable sensitive instances for a state-of-the-art learning algorithm, while sharing over 93% of the original data, and completes after at most 5 iterations. PMID:28943741

[The consolidation of memory, one century on].

PubMed

Prado-Alcala, R A; Quirarte, G L

The theory of memory consolidation, based on the work published by Georg Elias Muller and Alfons Pilzecker over a century ago, continues to guide research into the neurobiology of memory, either directly or indirectly. In their classic monographic work, they concluded that fixing memory requires the passage of time (consolidation) and that memory is vulnerable during this period of consolidation, as symptoms of amnesia appear when brain functioning is interfered with before the consolidation process is completed. Most of the experimental data concerning this phenomenon strongly support the theory. In this article we present a review of experiments that have made it possible to put forward a model that explains the amnesia produced in conventional learning conditions, as well as another model related to the protection of memory when the same instances of learning are submitted to a situation involving intensive training. Findings from relatively recent studies have shown that treatments that typically produce amnesia when they are administered immediately after a learning experience (during the period in which the memory would be consolidating itself) no longer have any effect when the instances of learning involve a relatively large number of trials or training sessions, or relatively high intensity aversive events. These results are not congruent with the prevailing theories about consolidation.

Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.

PubMed

Liu, Zhenqiu; Hsiao, William; Cantarel, Brandi L; Drábek, Elliott Franco; Fraser-Liggett, Claire

2011-12-01

Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota. By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data. The MATLAB toolbox is freely available online at http://metadistance.igs.umaryland.edu/. zliu@umm.edu Supplementary data are available at Bioinformatics online.

A Machine Learning Approach to Student Modeling.

DTIC Science & Technology

1984-05-01

machine learning , and describe ACN, a student modeling system that incorporates this approach. This system begins with a set of overly general rules, which it uses to search a problem space until it arrives at the same answer as the student. The ACM computer program then uses the solution path it has discovered to determine positive and negative instances of its initial rules, and employs a discrimination learning mechanism to place additional conditions on these rules. The revised rules will reproduce the solution path without search, and constitute a cognitive model of

Machine learning with quantum relative entropy

NASA Astrophysics Data System (ADS)

Tsuda, Koji

2009-12-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Do written mandatory accreditation standards for residential care positively model learning organizations? Textual and critical discourse analysis.

PubMed

Bell, Erica; Robinson, Andrew; See, Catherine

2013-11-01

Unprecedented global population ageing accompanied by increasing complexity of aged care present major challenges of quality in aged care. In the business literature, Senge's theory of adaptive learning organisations offers a model of organisational quality. However, while accreditation of national standards is an increasing mechanism for achieving quality in aged care, there are anecdotal concerns it creates a 'minimum standards compliance mentality' and no evidence about whether it reinforces learning organisations. The research question was 'Do mandatory national accreditation standards for residential aged care, as they are written, positively model learning organisations?'. Automatic text analysis was combined with critical discourse analysis to analyse the presence of learning concepts from Senge's learning organisation theory in an exhaustive sample of national accreditation standards from 7 countries. The two stages of analysis were: (1) quantitative mapping of the presence of learning organisation concepts in standards using Bayesian-based textual analytics software and (2) qualitative critical discourse analysis to further examine how the language of standards so identified may be modelling learning organisation concepts. The learning concepts 'training', 'development', 'knowledge', and 'systems' are present with relative frequencies of 19%, 11%, 10%, and 10% respectively in the 1944 instances, in paragraph-sized text blocks, considered. Concepts such as 'team', 'integration', 'learning', 'change' and 'innovation' occur with 7%, 6%, 5%, 5%, and 1% relative frequencies respectively. Learning concepts tend to co-occur with negative rather than positive sentiment language in the 3176 instances in text blocks containing sentiment language. Critical discourse analysis suggested that standards generally use the language of organisational change and learning in limited ways that appear to model 'learning averse' communities of practice and organisational cultures. The aged care quality challenge and the role of standards need rethinking. All standards implicitly or explicitly model an organisation of some type. If standards can model a limited and negative learning organisation language, they could model a well-developed and positive learning organisation language. In the context of the global aged care crisis, the modelling of learning organisations is probably critical for minimal competence in residential aged care and certainly achievable in the language of standards. Copyright © 2013 Elsevier Ltd. All rights reserved.

Durability performance of submerged concrete structures - phase 2.

DOT National Transportation Integrated Search

2015-09-01

This project determined that severe corrosion of steel can occur in the submerged : portions of reinforced concrete structures in marine environments. Field studies of decommissioned : pilings from Florida bridges revealed multiple instances of stron...

Progressions of Qualitative Models as a Foundation for Intelligent Learning Environments

DTIC Science & Technology

1986-05-01

knowledge form is that in addition to being efficient and powerul knowledge structures for studeiis to possess, they are also efficient and powerful ...reason "on their feet" about circuit behavibr, and is potentially a very powerful instructional task. Conventionally, however, troubleshooting is preceded...also be applied to a light bulb. 4. Kowledge differentiation -- The student learns about the differences &1 r 41 between two concepts. For instance

Transfer Learning for Adaptive Relation Extraction

DTIC Science & Technology

2011-09-13

other NLP tasks, however, supervised learning approach fails when there is not a sufficient amount of labeled data for training, which is often the case...always 12 Syntactic Pattern Relation Instance Relation Type (Subtype) arg-2 arg-1 Arab leaders OTHER-AFF (Ethnic) his father PER-SOC (Family) South...for x. For sequence labeling tasks in NLP , linear-chain conditional random field has been rather suc- cessful. It is an undirected graphical model in

Changes in Instructional System Design (ISD): Improving Training Product Delivery to United States Army Soldiers

DTIC Science & Technology

2005-03-18

simulation. This model is a basis of what is called discovery learning. Discovery learning is constructionist method of instruction, which is a concept in...2005 PAGES: 48 CLASSIFICATION: Unclassified The purpose of this study is to identify methods that could speed up the instructional system design...became obvious as the enemy attacked using asymmetric means and methods . For instance: during the war, a mine identification-training product was

Policing and COIN Operations: Lessons Learned, Strategies, and Future Directions

DTIC Science & Technology

2011-01-01

from the U.S. Reserves forces). In other instances such as the efforts in Southeast Asia, “medical (and veterinarian ) capabilities were very...The analysis found significant shortages and vulnerabilities in the following areas: Equipment.  Immature logistics capability and

Learning about Proteins

MedlinePlus

... day, kids need to eat about 0.5 grams of protein for every pound (0.5 kilograms) they weigh. That's a gram for every 2 pounds (1 kilogram) you weigh. ... adult size. Adults, for instance, need about 60 grams per day. To figure out your protein needs, ...

On Multiple Zagreb Indices of TiO2 Nanotubes.

PubMed

Malik, Mehar Ali; Imran, Muhammad

2015-01-01

The First and Second Zagreb indices were first introduced by I. Gutman and N. Trinajstic in 1972. It is reported that these indices are useful in the study of anti-inflammatory activities of certain chemical instances, and in elsewhere. Recently, the first and second multiple Zagreb indices of a graph were introduced by Ghorbani and Azimi in 2012. In this paper, we calculate the Zagreb indices and the multiplicative versions of the Zagreb indices of an infinite class of Titania nanotubes TiO(2)[m,n].

Learning, attentional control, and action video games.

PubMed

Green, C S; Bavelier, D

2012-03-20

While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on 'action video games' produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. Copyright © 2012 Elsevier Ltd. All rights reserved.

Learning, attentional control and action video games

PubMed Central

Green, C.S.; Bavelier, D.

2012-01-01

While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on ‘action video games’ produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. PMID:22440805

Accelerated Learning: Undergraduate Research Experiences at the Texas A&M Cyclotron Institute

NASA Astrophysics Data System (ADS)

Yennello, S. J.

The Texas A&M Cyclotron Institute (TAMU CI) has had an NSF funded Research Experiences for Undergraduates program since 2004. Each summer about a dozen students from across the country join us for the 10-week program. They are each imbedded in one of the research groups of the TAMU CI and given their own research project. While the main focus of their effort is their individual research project, we also have other activities to broaden their experience. For instance, one of those activities has been involvement in a dedicated group experiment. Because not every experimental group will run during those 10 weeks and the fact that some of the students are in theory research groups, a group research experience allows everyone to actually be involved in an experiment using the accelerator. In stark contrast to the REU students' very focused experience during the summer, Texas A&M undergraduates can be involved in research projects at the Cyclotron throughout the year, often for multiple years. This extended exposure enables Texas A&M students to have a learning experience that cannot be duplicated without a local accelerator. The motivation for the REU program was to share this accelerator experience with students who do not have that opportunity at their home institution.

How many music centers are in the brain?

PubMed

Altenmüller, E O

2001-06-01

When reviewing the literature on brain substrates of music processing, a puzzling variety of findings can be stated. The traditional view of a left-right dichotomy of brain organization--assuming that in contrast to language, music is primarily processed in the right hemisphere--was challenged 20 years ago, when the influence of music education on brain lateralization was demonstrated. Modern concepts emphasize the modular organization of music cognition. According to this viewpoint, different aspects of music are processed in different, although partly overlapping neuronal networks of both hemispheres. However, even when isolating a single "module," such as, for example, the perception of contours, the interindividual variance of brain substrates is enormous. To clarify the factors contributing to this variability, we conducted a longitudinal experiment comparing the effects of procedural versus explicit music teaching on brain networks. We demonstrated that cortical activation during music processing reflects the auditory "learning biography," the personal experiences accumulated over time. Listening to music, learning to play an instrument, formal instruction, and professional training result in multiple, in many instances multisensory, representations of music, which seem to be partly interchangeable and rapidly adaptive. In summary, as soon as we consider "real music" apart from laboratory experiments, we have to expect individually formed and quickly adaptive brain substrates, including widely distributed neuronal networks in both hemispheres.

A Model for Semantic Equivalence Discovery for Harmonizing Master Data

NASA Astrophysics Data System (ADS)

Piprani, Baba

IT projects often face the challenge of harmonizing metadata and data so as to have a "single" version of the truth. Determining equivalency of multiple data instances against the given type, or set of types, is mandatory in establishing master data legitimacy in a data set that contains multiple incarnations of instances belonging to the same semantic data record . The results of a real-life application define how measuring criteria and equivalence path determination were established via a set of "probes" in conjunction with a score-card approach. There is a need for a suite of supporting models to help determine master data equivalency towards entity resolution—including mapping models, transform models, selection models, match models, an audit and control model, a scorecard model, a rating model. An ORM schema defines the set of supporting models along with their incarnation into an attribute based model as implemented in an RDBMS.

Forming an ad-hoc nearby storage, based on IKAROS and social networking services

NASA Astrophysics Data System (ADS)

Filippidis, Christos; Cotronis, Yiannis; Markou, Christos

2014-06-01

We present an ad-hoc "nearby" storage, based on IKAROS and social networking services, such as Facebook. By design, IKAROS is capable to increase or decrease the number of nodes of the I/O system instance on the fly, without bringing everything down or losing data. IKAROS is capable to decide the file partition distribution schema, by taking on account requests from the user or an application, as well as a domain or a Virtual Organization policy. In this way, it is possible to form multiple instances of smaller capacity higher bandwidth storage utilities capable to respond in an ad-hoc manner. This approach, focusing on flexibility, can scale both up and down and so can provide more cost effective infrastructures for both large scale and smaller size systems. A set of experiments is performed comparing IKAROS with PVFS2 by using multiple clients requests under HPC IOR benchmark and MPICH2.

Capturing Rehearsals to Facilitate Reflection

ERIC Educational Resources Information Center

Albayrak, Meltem; Smith, Brian K.

2004-01-01

Many learning environments involve rituals for rehearsal and reflection. Musicians, for instance, spend countless hours practicing scales and adjusting their bodies to increase their skills. But they do more than simply practice: They also play for instructors and others who can provide valuable critiques of their performances. Architectural…

Robot Training Through Incremental Learning

DTIC Science & Technology

2011-04-18

Turing Associates, Ann Arbor, MI 48103 ABSTRACT The real world is too complex and variable to directly program an autonomous ground robot’s...11 th Conf. Uncertainty in Artificial Intelligence, 338-45 (1995). [6] J. Cleary and L. Trigg, “K*: An Instance-based learner using an entropic

Strategic Avoidance: Can Universities Learn from Other Sectors?

ERIC Educational Resources Information Center

Kerr, Greg; Hosie, Peter

2013-01-01

Universities live in interesting times. For instance, government policies in Australia are allowing for more deregulation of the student market while overseas universities are entering the Australian domestic market. In an environment of increasing uncertainty, sound strategic planning is important. Processes including benchmarking, environmental…

Who's Afraid of Something New?

ERIC Educational Resources Information Center

Learning Today, 1973

1973-01-01

Metro High, St. Louis, is described in pictures and words. The school, started in 1972, is a small, experimental school, which encourages self instruction, community involvement, and the use of real situations as learning experiences. The Zoology class, for instance, visits the zoo to study the animals. (SM)

Why Teach Playwriting?

ERIC Educational Resources Information Center

Levy, Jonathan

2001-01-01

Ponders what students might learn from a course in playwriting: the ability to think in vivid instances and moments; the habit of close observation of the details of human behavior; the ability to think through and see through cliches; making hard choices; sympathy; dreaming within limits; and concision. (SR)

Bacterial computing: a form of natural computing and its applications.

PubMed

Lahoz-Beltra, Rafael; Navarro, Jorge; Marijuán, Pedro C

2014-01-01

The capability to establish adaptive relationships with the environment is an essential characteristic of living cells. Both bacterial computing and bacterial intelligence are two general traits manifested along adaptive behaviors that respond to surrounding environmental conditions. These two traits have generated a variety of theoretical and applied approaches. Since the different systems of bacterial signaling and the different ways of genetic change are better known and more carefully explored, the whole adaptive possibilities of bacteria may be studied under new angles. For instance, there appear instances of molecular "learning" along the mechanisms of evolution. More in concrete, and looking specifically at the time dimension, the bacterial mechanisms of learning and evolution appear as two different and related mechanisms for adaptation to the environment; in somatic time the former and in evolutionary time the latter. In the present chapter it will be reviewed the possible application of both kinds of mechanisms to prokaryotic molecular computing schemes as well as to the solution of real world problems.

Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

PubMed

Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

2016-01-01

Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

The Canadian Corps in the Great War: A Learning Organization in Action

DTIC Science & Technology

2016-04-04

resembled Garvin’s learning cycle, but it did so unevenly throughout the Corps. For instance, the 1st Division’s attacks benefited from improved artillery...Passchendaele, convinced that the cost of winning the battle outweighed the potential benefit . Currie’s unique position as a semi-independent Corps... Wine in New Bottles: A Comparison of British and Canadian Preparations for the Battle of Arras." In Vimy Ridge: A Canadian Reassessment, edited by

Decentralized operating procedures for orchestrating data and behavior across distributed military systems and assets

NASA Astrophysics Data System (ADS)

Peach, Nicholas

2011-06-01

In this paper, we present a method for a highly decentralized yet structured and flexible approach to achieve systems interoperability by orchestrating data and behavior across distributed military systems and assets with security considerations addressed from the beginning. We describe an architecture of a tool-based design of business processes called Decentralized Operating Procedures (DOP) and the deployment of DOPs onto run time nodes, supporting the parallel execution of each DOP at multiple implementation nodes (fixed locations, vehicles, sensors and soldiers) throughout a battlefield to achieve flexible and reliable interoperability. The described method allows the architecture to; a) provide fine grain control of the collection and delivery of data between systems; b) allow the definition of a DOP at a strategic (or doctrine) level by defining required system behavior through process syntax at an abstract level, agnostic of implementation details; c) deploy a DOP into heterogeneous environments by the nomination of actual system interfaces and roles at a tactical level; d) rapidly deploy new DOPs in support of new tactics and systems; e) support multiple instances of a DOP in support of multiple missions; f) dynamically add or remove run-time nodes from a specific DOP instance as missions requirements change; g) model the passage of, and business reasons for the transmission of each data message to a specific DOP instance to support accreditation; h) run on low powered computers with lightweight tactical messaging. This approach is designed to extend the capabilities of existing standards, such as the Generic Vehicle Architecture (GVA).

Representing Learning With Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

Probabilistic graphical models are being used widely in artificial intelligence, for instance, in diagnosis and expert systems, as a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. Their development and use spans several fields including artificial intelligence, decision theory and statistics, and provides an important bridge between these communities. This paper shows by way of example that these models can be extended to machine learning, neural networks and knowledge discovery by representing the notion of a sample on the graphical model. Not only does this allow a flexible variety of learning problems to be represented, it also provides the means for representing the goal of learning and opens the way for the automatic development of learning algorithms from specifications.

Working memory accuracy for multiple targets is driven by reward expectation and stimulus contrast with different time-courses.

PubMed

Klink, P Christiaan; Jeurissen, Danique; Theeuwes, Jan; Denys, Damiaan; Roelfsema, Pieter R

2017-08-22

The richness of sensory input dictates that the brain must prioritize and select information for further processing and storage in working memory. Stimulus salience and reward expectations influence this prioritization but their relative contributions and underlying mechanisms are poorly understood. Here we investigate how the quality of working memory for multiple stimuli is determined by priority during encoding and later memory phases. Selective attention could, for instance, act as the primary gating mechanism when stimuli are still visible. Alternatively, observers might still be able to shift priorities across memories during maintenance or retrieval. To distinguish between these possibilities, we investigated how and when reward cues determine working memory accuracy and found that they were only effective during memory encoding. Previously learned, but currently non-predictive, color-reward associations had a similar influence, which gradually weakened without reinforcement. Finally, we show that bottom-up salience, manipulated through varying stimulus contrast, influences memory accuracy during encoding with a fundamentally different time-course than top-down reward cues. While reward-based effects required long stimulus presentation, the influence of contrast was strongest with brief presentations. Our results demonstrate how memory resources are distributed over memory targets and implicates selective attention as a main gating mechanism between sensory and memory systems.

Multiple Instance Fuzzy Inference

DTIC Science & Technology

2015-12-02

very small probabilities. To compute Pr(t | Bi) for a given bag Bi, a conjunction measure of all its instances Bij , j = 1, . . . ,M is computed using...the noisy-or operator Pr(t | Bi) = 1− ∏ 1≤ j ≤M (1− Pr(Bij ∈ t)), (2.5) where Pr(Bij ∈ t) is computed from a Gaussian distribution centred at the concept...Xnk to target concept Ci, and its computed using Pr(Xnk ∈ Ci) = e−( ∑D j =1 sij(xnkj−cij)2) (2.9) In (4.5), sij is a scaling parameter that weights the

Effect of improving the usability of an e-learning resource: a randomized trial.

PubMed

Davids, Mogamat Razeen; Chikte, Usuf M E; Halperin, Mitchell L

2014-06-01

Optimizing the usability of e-learning materials is necessary to reduce extraneous cognitive load and maximize their potential educational impact. However, this is often neglected, especially when time and other resources are limited. We conducted a randomized trial to investigate whether a usability evaluation of our multimedia e-learning resource, followed by fixing of all problems identified, would translate into improvements in usability parameters and learning by medical residents. Two iterations of our e-learning resource [version 1 (V1) and version 2 (V2)] were compared. V1 was the first fully functional version and V2 was the revised version after all identified usability problems were addressed. Residents in internal medicine and anesthesiology were randomly assigned to one of the versions. Usability was evaluated by having participants complete a user satisfaction questionnaire and by recording and analyzing their interactions with the application. The effect on learning was assessed by questions designed to test the retention and transfer of knowledge. Participants reported high levels of satisfaction with both versions, with good ratings on the System Usability Scale and adjective rating scale. In contrast, analysis of video recordings revealed significant differences in the occurrence of serious usability problems between the two versions, in particular in the interactive HandsOn case with its treatment simulation, where there was a median of five serious problem instances (range: 0-50) recorded per participant for V1 and zero instances (range: 0-1) for V2 (P < 0.001). There were no differences in tests of retention or transfer of knowledge between the two versions. In conclusion, usability evaluation followed by a redesign of our e-learning resource resulted in significant improvements in usability. This is likely to translate into improved motivation and willingness to engage with the learning material. In this population of relatively high-knowledge participants, learning scores were similar across the two versions. Copyright © 2014 The American Physiological Society.

Effect of improving the usability of an e-learning resource: a randomized trial

PubMed Central

Chikte, Usuf M. E.; Halperin, Mitchell L.

2014-01-01

Optimizing the usability of e-learning materials is necessary to reduce extraneous cognitive load and maximize their potential educational impact. However, this is often neglected, especially when time and other resources are limited. We conducted a randomized trial to investigate whether a usability evaluation of our multimedia e-learning resource, followed by fixing of all problems identified, would translate into improvements in usability parameters and learning by medical residents. Two iterations of our e-learning resource [version 1 (V1) and version 2 (V2)] were compared. V1 was the first fully functional version and V2 was the revised version after all identified usability problems were addressed. Residents in internal medicine and anesthesiology were randomly assigned to one of the versions. Usability was evaluated by having participants complete a user satisfaction questionnaire and by recording and analyzing their interactions with the application. The effect on learning was assessed by questions designed to test the retention and transfer of knowledge. Participants reported high levels of satisfaction with both versions, with good ratings on the System Usability Scale and adjective rating scale. In contrast, analysis of video recordings revealed significant differences in the occurrence of serious usability problems between the two versions, in particular in the interactive HandsOn case with its treatment simulation, where there was a median of five serious problem instances (range: 0–50) recorded per participant for V1 and zero instances (range: 0–1) for V2 (P < 0.001). There were no differences in tests of retention or transfer of knowledge between the two versions. In conclusion, usability evaluation followed by a redesign of our e-learning resource resulted in significant improvements in usability. This is likely to translate into improved motivation and willingness to engage with the learning material. In this population of relatively high-knowledge participants, learning scores were similar across the two versions. PMID:24913451

Nursing Students' Qualitative Experiences in the Medical-Surgical Clinical Learning Environment: A Cross-Cultural Integrative Review.

PubMed

Hooven, Katie

2015-08-01

The nature of the clinical learning environment has a huge impact on student learning. For instance, research has supported the idea that a positive learning environment increases student learning. Therefore, the ability to gain information from the student perspective about the learning environment is essential to nursing education. This article reviews qualitative research on nursing students' experiences of the clinical learning environment. The significance of the issue, the purpose of the integrative review, the methods used in the literature search, and the results of the review are presented. Seventeen studies from 12 countries are identified for review, and six common themes are discussed. An exhaustive literature review revealed that among the 17 articles evaluated, six themes were common. The findings indicate the need to continue quality improvement to advance clinical education. Copyright 2015, SLACK Incorporated.

The Development of Causal Categorization

ERIC Educational Resources Information Center

Hayes, Brett K.; Rehder, Bob

2012-01-01

Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs.…

CaMELS: In silico prediction of calmodulin binding proteins and their binding sites.

PubMed

Abbasi, Wajid Arshad; Asif, Amina; Andleeb, Saiqa; Minhas, Fayyaz Ul Amir Afsar

2017-09-01

Due to Ca 2+ -dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet-lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet-lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large-margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM-binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome-wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif-based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub-sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels. © 2017 Wiley Periodicals, Inc.

Conceptual Learning with Multiple Graphical Representations: Intelligent Tutoring Systems Support for Sense-Making and Fluency-Building Processes

ERIC Educational Resources Information Center

Rau, Martina A.

2013-01-01

Most learning environments in the STEM disciplines use multiple graphical representations along with textual descriptions and symbolic representations. Multiple graphical representations are powerful learning tools because they can emphasize complementary aspects of complex learning contents. However, to benefit from multiple graphical…

Using Commercial-off-the-Shelf Computer Games to Train and Educate Complexity and Complex Decision-Making

DTIC Science & Technology

2008-09-01

Jean Piaget is one of the pioneers of constructivist learning theory , Piaget states that knowledge is constructed and learning occurs through an...the mechanics of each game. For instance, if a training program is developed around the u.S. Army’s America ’ s Army computer games then little funds...gathering and maintaining the data needed. and C04󈧏pIeting and reviewing this collection of information. Send OOIT’II’lents regarding thi s burden

Individual Differences in Learner Controlled CAI.

ERIC Educational Resources Information Center

Judd, Wilson A.; And Others

Two assumptions in support of learner-controlled computer-assisted instruction (CAI) are that (1) instruction administered under learner control will be less aversive than if administered under program control, and (2) the student is sufficiently aware of his learning state to make, in most instances, his own instructional decisions. Some 130…

ICT and Constructivist Strategies Instruction for Science and Mathematics Education

ERIC Educational Resources Information Center

Kong, Ng Wai; Lai, Kong Sow

2005-01-01

Concept learning in science and mathematics had often times been taught based on assumptions of alternative concepts or even in some instances based on misconceptions. Some educational researchers favour a constructivist approach in teaching science and mathematics. The constructivist literature existing makes use of alternative conceptions as…

The Progression of Podcasting/Vodcasting in a Technical Physics Class

ERIC Educational Resources Information Center

Glanville, Y. J.

2010-01-01

Technology such as Microsoft PowerPoint presentations, clickers, podcasting, and learning management suites is becoming prevalent in classrooms. Instructors are using these media in both large lecture hall settings and small classrooms with just a handful of students. Traditionally, each of these media is instructor driven. For instance,…

The Artistic Nature of the High School Principal.

ERIC Educational Resources Information Center

Ritschel, Robert E.

The role of high school principals can be compared to that of composers of music. For instance, composers put musical components together into a coherent whole; similarly, principals organize high schools by establishing class schedules, assigning roles to subordinates, and maintaining a safe and orderly learning environment. Second, composers…

Invasive insect herbivores as disrupters of chemically-mediated tritrophic interactions: effects of herbivore density and parasitoid learning

USDA-ARS?s Scientific Manuscript database

Invasive species of insect herbivores have the potential to interfere with native multitrophic interactions when they invade new environments. For instance, exotic herbivores can affect the chemical cues emitted by plants and disrupt attraction of natural enemies mediated by herbivore-induced plant ...

Real Authentic Learning

ERIC Educational Resources Information Center

Murphy, Stephen H.

2009-01-01

Stonington High School in Pawcatuck, Connecticut, adopted an alternating day (A/B) block schedule in 1998. The shift in schedule has resulted in less movement throughout the building and fewer instances of disruptive behaviors. In addition, the initiatives that Stonington has been able to implement have caused the daily attendance rate to increase…

Piaget for Chemists: Explaining What "Good" Students Cannot Understand

ERIC Educational Resources Information Center

Herron, J. Dudley

1975-01-01

Attributes learning difficulties in introductory chemistry to the thesis that many students have not reached the formal operations level of intellectual development. Cites instances to support this thesis, outlines an instructional procedure to overcome the difficulty, and presents a list of competencies that can be expected of these students. (GS)

Content Delivery in the "Blogosphere"

ERIC Educational Resources Information Center

Ferdig, Richard E.; Trammell, Kaye D.

2005-01-01

While a few educators have already started using blogs in the classroom, more have focused on the potential of blogging in teaching and learning (Shachtman 2002; Embrey 2002). For instance, some claim that blogs may further democratize the Internet, addressing some of the concerns under-girding the digital divide (Carroll 2003). This article…

Shadow-Reading: Affordances for Imitation in the Language Classroom

ERIC Educational Resources Information Center

de Guerrero, María C. M.; Commander, Millie

2013-01-01

Imitation has a fundamental role in learning and development within Vygotskyan sociocultural theory. In this study, we adopt a sociocultural theory view of imitation as an intentional, meaningful, and transformative process leading learners to higher developmental levels. The study centers on instances of imitation that occurred as adult learners…

Goofy Guide Game: Affordances and Constraints for Engagement and Oral Communication in English

ERIC Educational Resources Information Center

Enticknap-Seppänen, Kaisa

2017-01-01

This study investigates tourism undergraduates' perceptions of learning engagement and oral communication in English through their experiences of testing a pilot purpose-designed educational digital game. Reflecting the implementation of digitalization strategy in universities of applied sciences in Finland, it examines whether single instances of…

The Design and Analysis of Efficient Learning Algorithms

DTIC Science & Technology

1991-01-01

will be c-close to the target concept with high probability. (Technically, their approach needs some minor modifications to handle, for instance, a...test in the sense that if cii = cik = cik then nothing can be concluded about the relative depth of 17, and Fk . However, our next lemnas give

Anglophone Popular Culture in the Mexican University English Curriculum.

ERIC Educational Resources Information Center

Zoreda, Margaret Lee

This essay proposes instances of how Anglophone popular culture can offer a place for nurturing critical encounters in the context of learning English. It delineates the theoretical bases that reveal popular culture as a fundamental indicator of society and, using Anglophone movies and stories, analyzes the pedagogical possibilities for…

Phonotactics and Morphophonology in Early Child Language: Evidence from Dutch

ERIC Educational Resources Information Center

Zamuner, Tania S.; Kerkhoff, Annemarie; Fikkert, Paula

2012-01-01

This research investigates children's knowledge of how surface pronunciations of lexical items vary according to their phonological and morphological context. Dutch-learning children aged 2.5 and 3.5 years were tested on voicing neutralization and morphophonological alternations. For instance, voicing does not alternate between the pair…

Designing Instructional Text in a Conversational Style: A Meta-Analysis

ERIC Educational Resources Information Center

Ginns, Paul; Martin, Andrew J.; Marsh, Herbert W.

2013-01-01

This article reviews research on the effects of conversational style on learning. Studies of conversational style have variously investigated "personalization" through changing instances of first-person address to second or third person, including sentences that directly address the learner; including more polite forms of address; and…

Wanting and Liking: Components of Situated Motivation Constructs?

ERIC Educational Resources Information Center

Palmer, David

2017-01-01

Brain studies have revealed that 2 neurological systems, one for "wanting" and one for "liking," are responsible for many instances of motivated behavior. If wanting and liking are fundamental elements of motivation, then we should also expect to see them represented in educational models of motivation for learning. However, it…

Learning the Language of Difference: The Dictionary in the High School.

ERIC Educational Resources Information Center

Willinsky, John

1987-01-01

Reports on dictionaries' power to misrepresent gender. Examines the definitions of three terms (clitoris, penis, and vagina) in eight leading high school dictionaries. Concludes that the absence of certain female gender-related terms represents another instance of institutionalized silence about the experience of women. (MM)

In Providing Supports for Students, Language Matters

ERIC Educational Resources Information Center

Jung, Lee Ann

2017-01-01

What's the difference between accommodations and modifications, and why does the distinction matter? In this article, professor Lee Ann Jung explains that accommodations "provide access to the general curriculum but do not fundamentally alter the learning goal or grade level standard." For instance, if the purpose of an assessment is to…

Making a Connection between Computational Modeling and Educational Research.

ERIC Educational Resources Information Center

Carbonaro, Michael

2003-01-01

Bruner, Goodnow, and Austin's (1956) research on concept development is reexamined from a connectionist perspective. A neural network was constructed which associates positive and negative instances of a concept with corresponding attribute values. Results suggest the simultaneous learning of attributes guided the network in constructing a faster…

Nurturing and Testing Translation Competence for Text-Translating

ERIC Educational Resources Information Center

Aubakirova, Karlygash Adilkhanovna

2016-01-01

The article analyzes the problems of contemporary professional education. As its instance, we examine the developmental scheme for training professional translators. Optimal ways of organizing the learning process are suggested from the point of view of the competence approach, which is widely recognized for training a modern specialist. The…

Multiple Intelligence and Digital Learning Awareness of Prospective B.Ed Teachers

ERIC Educational Resources Information Center

Gracious, F. L. Antony; Shyla, F. L. Jasmine Anne

2012-01-01

The present study Multiple Intelligence and Digital Learning Awareness of prospective B.Ed teachers was probed to find the relationship between Multiple Intelligence and Digital Learning Awareness of Prospective B.Ed Teachers. Data for the study were collected using self made Multiple Intelligence Inventory and Digital Learning Awareness Scale.…

The implementation of multiple intelligences based teaching model to improve mathematical problem solving ability for student of junior high school

NASA Astrophysics Data System (ADS)

Fasni, Nurli; Fatimah, Siti; Yulanda, Syerli

2017-05-01

This research aims to achieve some purposes such as: to know whether mathematical problem solving ability of students who have learned mathematics using Multiple Intelligences based teaching model is higher than the student who have learned mathematics using cooperative learning; to know the improvement of the mathematical problem solving ability of the student who have learned mathematics using Multiple Intelligences based teaching model., to know the improvement of the mathematical problem solving ability of the student who have learned mathematics using cooperative learning; to know the attitude of the students to Multiple Intelligences based teaching model. The method employed here is quasi-experiment which is controlled by pre-test and post-test. The population of this research is all of VII grade in SMP Negeri 14 Bandung even-term 2013/2014, later on two classes of it were taken for the samples of this research. A class was taught using Multiple Intelligences based teaching model and the other one was taught using cooperative learning. The data of this research were gotten from the test in mathematical problem solving, scale questionnaire of the student attitudes, and observation. The results show the mathematical problem solving of the students who have learned mathematics using Multiple Intelligences based teaching model learning is higher than the student who have learned mathematics using cooperative learning, the mathematical problem solving ability of the student who have learned mathematics using cooperative learning and Multiple Intelligences based teaching model are in intermediate level, and the students showed the positive attitude in learning mathematics using Multiple Intelligences based teaching model. As for the recommendation for next author, Multiple Intelligences based teaching model can be tested on other subject and other ability.

Enhancing project-oriented learning by joining communities of practice and opening spaces for relatedness

NASA Astrophysics Data System (ADS)

Pascual, R.

2010-03-01

This article describes an extension to project-oriented learning to increase social construction of knowledge and learning. The focus is on: (a) maximising opportunities for students to share their knowledge with practitioners by joining communities of practice, and (b) increasing their intrinsic motivation by creating conditions for student's relatedness. The case study considers a last year capstone course in Mechanical Engineering. The work addresses innovative practices of active learning and beyond project-oriented learning through: (a) the development of a web-based decision support system, (b) meetings between the communities of students, maintenance engineers and academics, and (c) new off-campus group instances. The author hypothesises that this multi-modal approach increases deep learning and social impact of the educational process. Surveys to the actors support a successful achievement of the educational goals. The methodology can easily be extended to further improve the learning process.

Supervised Learning for Dynamical System Learning.

PubMed

Hefny, Ahmed; Downey, Carlton; Gordon, Geoffrey J

2015-01-01

Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to incorporate prior information such as sparsity or structure. To address this problem, we present a new view of dynamical system learning: we show how to learn dynamical systems by solving a sequence of ordinary supervised learning problems, thereby allowing users to incorporate prior knowledge via standard techniques such as L 1 regularization. Many existing spectral methods are special cases of this new framework, using linear regression as the supervised learner. We demonstrate the effectiveness of our framework by showing examples where nonlinear regression or lasso let us learn better state representations than plain linear regression does; the correctness of these instances follows directly from our general analysis.

Rural Renaissance. Revitalizing Small High Schools.

ERIC Educational Resources Information Center

Ford, Edmund A.

Written in 1961, this document presents the rationales and applications of what were and still are, in most instances, considered innovative practices. Subjects discussed are building designs, teaching machines, educational television, flexible scheduling, multiple classes and small-group techniques, teacher assistants, shared services, and…

Promoting stair use: single versus multiple stair-riser messages.

PubMed

Webb, Oliver J; Eves, Frank F

2005-09-01

Message banners attached to stair risers produced a significant increase in pedestrian stair use, exceeding effects previously reported for conventional posters. Multiple instances of the same message banner, however, were as effective as banners featuring different messages. Therefore, greater visibility, rather than message variety, appears to account for the superiority of the banner format. Our findings indicate the feasibility of simple stair-use promotion campaigns based around the repetition of a single message.

Interleaved Practice in Multi-Dimensional Learning Tasks: Which Dimension Should We Interleave?

ERIC Educational Resources Information Center

Rau, Martina A.; Aleven, Vincent; Rummel, Nikol

2013-01-01

Research shows that multiple representations can enhance student learning. Many curricula use multiple representations across multiple task types. The temporal sequence of representations and task types is likely to impact student learning. Research on contextual interference shows that interleaving learning tasks leads to better learning results…

Correcting evaluation bias of relational classifiers with network cross validation

DOE PAGES

Neville, Jennifer; Gallagher, Brian; Eliassi-Rad, Tina; ...

2011-01-04

Recently, a number of modeling techniques have been developed for data mining and machine learning in relational and network domains where the instances are not independent and identically distributed (i.i.d.). These methods specifically exploit the statistical dependencies among instances in order to improve classification accuracy. However, there has been little focus on how these same dependencies affect our ability to draw accurate conclusions about the performance of the models. More specifically, the complex link structure and attribute dependencies in relational data violate the assumptions of many conventional statistical tests and make it difficult to use these tests to assess themore » models in an unbiased manner. In this work, we examine the task of within-network classification and the question of whether two algorithms will learn models that will result in significantly different levels of performance. We show that the commonly used form of evaluation (paired t-test on overlapping network samples) can result in an unacceptable level of Type I error. Furthermore, we show that Type I error increases as (1) the correlation among instances increases and (2) the size of the evaluation set increases (i.e., the proportion of labeled nodes in the network decreases). Lastly, we propose a method for network cross-validation that combined with paired t-tests produces more acceptable levels of Type I error while still providing reasonable levels of statistical power (i.e., 1–Type II error).« less

Combined Diffusion Tensor Imaging and Apparent Transverse Relaxation Rate Differentiate Parkinson Disease and Atypical Parkinsonism.

PubMed

Du, G; Lewis, M M; Kanekar, S; Sterling, N W; He, L; Kong, L; Li, R; Huang, X

2017-05-01

Both diffusion tensor imaging and the apparent transverse relaxation rate have shown promise in differentiating Parkinson disease from atypical parkinsonism (particularly multiple system atrophy and progressive supranuclear palsy). The objective of the study was to assess the ability of DTI, the apparent transverse relaxation rate, and their combination for differentiating Parkinson disease, multiple system atrophy, progressive supranuclear palsy, and controls. A total of 106 subjects (36 controls, 35 patients with Parkinson disease, 16 with multiple system atrophy, and 19 with progressive supranuclear palsy) were included. DTI and the apparent transverse relaxation rate measures from the striatal, midbrain, limbic, and cerebellar regions were obtained and compared among groups. The discrimination performance of DTI and the apparent transverse relaxation rate among groups was assessed by using Elastic-Net machine learning and receiver operating characteristic curve analysis. Compared with controls, patients with Parkinson disease showed significant apparent transverse relaxation rate differences in the red nucleus. Compared to those with Parkinson disease, patients with both multiple system atrophy and progressive supranuclear palsy showed more widespread changes, extending from the midbrain to striatal and cerebellar structures. The pattern of changes, however, was different between the 2 groups. For instance, patients with multiple system atrophy showed decreased fractional anisotropy and an increased apparent transverse relaxation rate in the subthalamic nucleus, whereas patients with progressive supranuclear palsy showed an increased mean diffusivity in the hippocampus. Combined, DTI and the apparent transverse relaxation rate were significantly better than DTI or the apparent transverse relaxation rate alone in separating controls from those with Parkinson disease/multiple system atrophy/progressive supranuclear palsy; controls from those with Parkinson disease; those with Parkinson disease from those with multiple system atrophy/progressive supranuclear palsy; and those with Parkinson disease from those with multiple system atrophy; but not those with Parkinson disease from those with progressive supranuclear palsy, or those with multiple system atrophy from those with progressive supranuclear palsy. DTI and the apparent transverse relaxation rate provide different but complementary information for different parkinsonisms. Combined DTI and apparent transverse relaxation rate may be a superior marker for the differential diagnosis of parkinsonisms. © 2017 by American Journal of Neuroradiology.

Kepler-90 system (Artist's Concept)

NASA Image and Video Library

2017-12-14

Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light years from Earth. The planet was discovered in data from NASA's Kepler Space Telescope. The newly-discovered Kepler-90i -- a sizzling hot, rocky planet that orbits its star once every 14.4 days -- was found using machine learning from Google. Machine learning is an approach to artificial intelligence in which computers "learn." In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded changes in starlight caused by planets beyond our solar system, known as exoplanets. https://photojournal.jpl.nasa.gov/catalog/PIA22192

Detecting Distributed SQL Injection Attacks in a Eucalyptus Cloud Environment

NASA Technical Reports Server (NTRS)

Kebert, Alan; Barnejee, Bikramjit; Solano, Juan; Solano, Wanda

2013-01-01

The cloud computing environment offers malicious users the ability to spawn multiple instances of cloud nodes that are similar to virtual machines, except that they can have separate external IP addresses. In this paper we demonstrate how this ability can be exploited by an attacker to distribute his/her attack, in particular SQL injection attacks, in such a way that an intrusion detection system (IDS) could fail to identify this attack. To demonstrate this, we set up a small private cloud, established a vulnerable website in one instance, and placed an IDS within the cloud to monitor the network traffic. We found that an attacker could quite easily defeat the IDS by periodically altering its IP address. To detect such an attacker, we propose to use multi-agent plan recognition, where the multiple source IPs are considered as different agents who are mounting a collaborative attack. We show that such a formulation of this problem yields a more sophisticated approach to detecting SQL injection attacks within a cloud computing environment.

Scaffolding Executive Function Capabilities via Play-&-Learn Software for Preschoolers

ERIC Educational Resources Information Center

Axelsson, Anton; Andersson, Richard; Gulz, Agneta

2016-01-01

Educational software in the form of games or so called "computer assisted intervention" for young children has become increasingly common receiving a growing interest and support. Currently there are, for instance, more than 1,000 iPad apps tagged for preschool. Thus, it has become increasingly important to empirically investigate…

Can the Use of Web-Based Comic Strip Creation Tool Facilitate EFL Learners' Grammar and Sentence Writing?

ERIC Educational Resources Information Center

Kilickaya, Ferit; Krajka, Jaroslaw

2012-01-01

Both teacher- and learner-made computer visuals are quite extensively reported in Computer-Assisted Language Learning literature, for instance, filming interviews, soap operas or mini-documentaries, creating storyboard projects, authoring podcasts and vodcasts, designing digital stories. Such student-made digital assets are used to present to…

Technology Integration in Science Classrooms: Framework, Principles, and Examples

ERIC Educational Resources Information Center

Kim, Minchi C.; Freemyer, Sarah

2011-01-01

A great number of technologies and tools have been developed to support science learning and teaching. However, science teachers and researchers point out numerous challenges to implementing such tools in science classrooms. For instance, guidelines, lesson plans, Web links, and tools teachers can easily find through Web-based search engines often…

Effective Web Videoconferencing for Proctoring Online Oral Exams: A Case Study at Scale in Brazil

ERIC Educational Resources Information Center

Okada, Alexandra; Scott, Peter; Mendonça, Murilo

2015-01-01

The challenging of assessing formal and informal online learning at scale includes various issues. Many universities who are now promoting "Massive Online Open Courses" (MOOC), for instance, focus on relatively informal assessment of participant competence, which is not highly "quality assured". This paper reports best…

Peer-Teaching in the Secondary Music Ensemble

ERIC Educational Resources Information Center

Johnson, Erik

2015-01-01

Peer-teaching is an instructional technique that has been used by teachers world-wide to successfully engage, exercise and deepen student learning. Yet, in some instances, teachers find the application of peer-teaching in large music ensembles at the secondary level to be daunting. This article is meant to be a practical resource for secondary…

Using Web Server Logs to Track Users through the Electronic Forest

ERIC Educational Resources Information Center

Coombs, Karen A.

2005-01-01

This article analyzes server logs, providing helpful information in making decisions about Web-based services. The author indicates, as a result of analyzing server logs, several interesting things about the users' behavior were learned. The resulting findings are discussed in this article. Certain pages of the author's Web site, for instance, are…

Bringing the Teacher into Teacher Preparation: Learning from Mentor Teachers in Joint Methods Activities

ERIC Educational Resources Information Center

Wood, Marcy B.; Turner, Erin E.

2015-01-01

Studies of mathematics teacher preparation frequently lament the divide between the more theoretically based university methods course and the practically grounded classroom field experience. In many instances, attempts to mediate this gap involve creating hybrid or third spaces, which seek to dissipate the differences in knowledge status as…

Incorporating Children's Literature into the Content Reading Classroom.

ERIC Educational Resources Information Center

Goerss, Betty L.

The trend in many schools is to move away from using the textbook exclusively in content area classrooms and move toward the integration of various pieces of children's literature, in many instances as a thematic unit. Using a thematic approach and incorporating trade books provides students with opportunities for cumulative learning and the…

Practical Elements in Danish Engineering Programmes, Including the European Project Semester

ERIC Educational Resources Information Center

Hansen, Jorgen

2012-01-01

In Denmark, all engineering programmes in HE have practical elements; for instance, at Bachelor's level, an internship is an integrated part of the programme. Furthermore, Denmark has a long-established tradition of problem-based and project-organized learning, and a large part of students' projects, including their final projects, is done in…

The Formation Experiment in the Age of Hypermedia and Distance Learning

ERIC Educational Resources Information Center

Giest, Hartmut

2004-01-01

Searching for an adequate method to investigate human development (especially the development of theoretical thinking) Vygotsky and his collaborators developed the causal genetic method. The basic idea of this method consists in the investigation of psychic functions and structures by their formation under controlled conditions (for instance via a…

Practicing Collaboration: What We Learn from a Cohort that Functions Well

ERIC Educational Resources Information Center

Ross, Dorene D.; Stafford, Lynn; Church-Pupke, Penny; Bondy, Elizabeth

2006-01-01

Students in the Unified Elementary/Special Education Program at the University of Florida are organized into cohort groups, a common recommendation within the special education teacher education literature. Although highly effective in some instances, the literature also documents numerous problems in the use of cohorts. The current study was…

Transverse Compounding in the Discriminative Learning of Moderately and Mildly Mentally Retarded Adolescents.

ERIC Educational Resources Information Center

Barnes, T. R.; Zeaman, D.

1983-01-01

Results of a study with 10 moderately retarded adolescents on the salience of transverse compound stimuli (combinations of positive and negative cues) were interpreted as an instance of developmental changes in unlearned stimulus salience hierarchies. The low saliency of transverse compounds was suggested to be related to reading difficulties.…

Call mimicry by eastern towhees and its significance in relation to auditory learning

Treesearch

Jon S. Greenlaw; Clifford E. Shackelford; Raymond E. Brown

1998-01-01

The authors document cases of eastern towhees (Pipilo erythrophthalmus) using mimicked alarm calls from three presumptive models (blue jay (Cyanocitta cristata), brown thrasher (Toxostoma rufum), and American robin (Turdus migratorius)). In four instances, male towhees employed heterospecific calls without substitution in their own call repertoires. Three birds (New...

Issues in Education: Language Building Blocks for Climbing the Learning Tree

ERIC Educational Resources Information Center

Pandey, Anita

2012-01-01

Language is the essence of humanity and the backbone of early childhood education. Academic content clusters on it. Math, science, and social studies, for instance, are best taught through "content area language." Critical thinking and other key math, listening, and reading comprehension skills are mirrored in language. Not surprisingly, spoken…

Unintended Results of Using Instructional Media: A Study of Second- and Third-Graders.

ERIC Educational Resources Information Center

Flanagan, Robin

Much of the research on classroom use of educational media has been hampered by difficulties in isolating a single element of the medium--television programming, for instance--that influences behavior in a reliable way. Still, each medium facilitates a particular type of learning environment, and the collective characteristics of those…

FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection.

PubMed

Noto, Keith; Brodley, Carla; Slonim, Donna

2012-01-01

Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called "normal" instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach.

FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection

PubMed Central

Brodley, Carla; Slonim, Donna

2011-01-01

Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called “normal” instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach. PMID:22639542

Hemispheric dissociation of reward processing in humans: insights from deep brain stimulation.

PubMed

Palminteri, Stefano; Serra, Giulia; Buot, Anne; Schmidt, Liane; Welter, Marie-Laure; Pessiglione, Mathias

2013-01-01

Rewards have various effects on human behavior and multiple representations in the human brain. Behaviorally, rewards notably enhance response vigor in incentive motivation paradigms and bias subsequent choices in instrumental learning paradigms. Neurally, rewards affect activity in different fronto-striatal regions attached to different motor effectors, for instance in left and right hemispheres for the two hands. Here we address the question of whether manipulating reward-related brain activity has local or general effects, with respect to behavioral paradigms and motor effectors. Neuronal activity was manipulated in a single hemisphere using unilateral deep brain stimulation (DBS) in patients with Parkinson's disease. Results suggest that DBS amplifies the representation of reward magnitude within the targeted hemisphere, so as to affect the behavior of the contralateral hand specifically. These unilateral DBS effects on behavior include both boosting incentive motivation and biasing instrumental choices. Furthermore, using computational modeling we show that DBS effects on incentive motivation can predict DBS effects on instrumental learning (or vice versa). Thus, we demonstrate the feasibility of causally manipulating reward-related neuronal activity in humans, in a manner that is specific to a class of motor effectors but that generalizes to different computational processes. As these findings proved independent from therapeutic effects on parkinsonian motor symptoms, they might provide insight into DBS impact on non-motor disorders, such as apathy or hypomania. Copyright © 2013 Elsevier Ltd. All rights reserved.

Active Learning of Classification Models with Likert-Scale Feedback.

PubMed

Xue, Yanbing; Hauskrecht, Milos

2017-01-01

Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone.

Active Learning of Classification Models with Likert-Scale Feedback

PubMed Central

Xue, Yanbing; Hauskrecht, Milos

2017-01-01

Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone. PMID:28979827

Learning Negotiation Policies Using IB3 and Bayesian Networks

NASA Astrophysics Data System (ADS)

Nalepa, Gislaine M.; Ávila, Bráulio C.; Enembreck, Fabrício; Scalabrin, Edson E.

This paper presents an intelligent offer policy in a negotiation environment, in which each agent involved learns the preferences of its opponent in order to improve its own performance. Each agent must also be able to detect drifts in the opponent's preferences so as to quickly adjust itself to their new offer policy. For this purpose, two simple learning techniques were first evaluated: (i) based on instances (IB3) and (ii) based on Bayesian Networks. Additionally, as its known that in theory group learning produces better results than individual/single learning, the efficiency of IB3 and Bayesian classifier groups were also analyzed. Finally, each decision model was evaluated in moments of concept drift, being the drift gradual, moderate or abrupt. Results showed that both groups of classifiers were able to effectively detect drifts in the opponent's preferences.

Multiple Repair Sequences in Everyday Conversations Involving People with Parkinson's Disease

ERIC Educational Resources Information Center

Griffiths, Sarah; Barnes, Rebecca; Britten, Nicky; Wilkinson, Ray

2015-01-01

Background: Features of dysarthria associated with Parkinson's disease (PD), such as low volume, variable rate of speech and increased pauses, impact speaker intelligibility. Those affected report restricted interactional participation, although this area is under explored. Aims: To examine naturally occurring instances of problems with…

Speaking across Levels--Generating and Addressing Levels Confusion in Discourse

ERIC Educational Resources Information Center

Stieff, Mike; Ryu, Minjung; Yip, Jason C.

2013-01-01

Reasoning across descriptive levels is a fundamental component of scientific reasoning, particularly in chemistry. Repeatedly, students are seen to confuse features applicable to one level across multiple levels despite instruction. Although many instances of such "levels confusion" have been documented, little is known about the…

Junior Secondary School Students' Conceptions about Plate Tectonics

ERIC Educational Resources Information Center

Mills, Reece; Tomas, Louisa; Lewthwaite, Brian

2017-01-01

There are ongoing calls for research that identifies students' conceptions about geographical phenomena. In response, this study investigates junior secondary school students' (N = 95) conceptions about plate tectonics. Student response data was generated from semi-structured interviews-about-instances and a two-tiered multiple-choice test…

A leitmotif of contemporary mycology has challenges and benefits for plant pathologists

USDA-ARS?s Scientific Manuscript database

Multiple traditional species names for plant pathogenic fungi have been supplemented with new names that delimit formerly cryptic species. In other instances, isolates within a species are clearly differentiated by both phylogeny and distinctive pathogenic traits and are assigned sub-specific design...

Facilitating Multiple Intelligences through Multimodal Learning Analytics

ERIC Educational Resources Information Center

Perveen, Ayesha

2018-01-01

This paper develops a theoretical framework for employing learning analytics in online education to trace multiple learning variations of online students by considering their potential of being multiple intelligences based on Howard Gardner's 1983 theory of multiple intelligences. The study first emphasizes the need to facilitate students as…

Mesolimbic confidence signals guide perceptual learning in the absence of external feedback

PubMed Central

Guggenmos, Matthias; Wilbertz, Gregor; Hebart, Martin N; Sterzer, Philipp

2016-01-01

It is well established that learning can occur without external feedback, yet normative reinforcement learning theories have difficulties explaining such instances of learning. Here, we propose that human observers are capable of generating their own feedback signals by monitoring internal decision variables. We investigated this hypothesis in a visual perceptual learning task using fMRI and confidence reports as a measure for this monitoring process. Employing a novel computational model in which learning is guided by confidence-based reinforcement signals, we found that mesolimbic brain areas encoded both anticipation and prediction error of confidence—in remarkable similarity to previous findings for external reward-based feedback. We demonstrate that the model accounts for choice and confidence reports and show that the mesolimbic confidence prediction error modulation derived through the model predicts individual learning success. These results provide a mechanistic neurobiological explanation for learning without external feedback by augmenting reinforcement models with confidence-based feedback. DOI: http://dx.doi.org/10.7554/eLife.13388.001 PMID:27021283

Implementation of Multiple Intelligences Supported Project-Based Learning in EFL/ESL Classrooms

ERIC Educational Resources Information Center

Bas, Gokhan

2008-01-01

This article deals with the implementation of Multiple Intelligences supported Project-Based learning in EFL/ESL Classrooms. In this study, after Multiple Intelligences supported Project-based learning was presented shortly, the implementation of this learning method into English classrooms. Implementation process of MI supported Project-based…

An Investigation between Multiple Intelligences and Learning Styles

ERIC Educational Resources Information Center

Sener, Sabriye; Çokçaliskan, Ayten

2018-01-01

Exploring learning style and multiple intelligence type of learners can enable the students to identify their strengths and weaknesses and learn from them. It is also very important for teachers to understand their learners' learning styles and multiple intelligences since they can carefully identify their goals and design activities that can…

Memories of good deeds past: The reinforcing power of prosocial behavior in children.

PubMed

Tasimi, Arber; Young, Liane

2016-07-01

Does considering one's past prosociality affect future behavior? Prior research has revealed instances in which adults engage in additional prosocial behavior-moral reinforcement-as well as instances in which adults engage in worse behavior-moral licensing. The current study examined the developmental origins of these effects by testing whether 6- to 8-year-old children (N=225) are more or less generous after recalling their own good deeds. Children were asked to recount a time when they were nice, were mean, or watched a movie. Children behaved more generously after recalling a time when they were nice. We show that this boost in generosity was not simply the result of instructing children to consider nice behavior; children's giving did not increase after recalling others' good deeds. We also show that, even after recounting multiple instances of their past goodness, children continue to behave more generously. These findings suggest that doing good leads to more good in children. Copyright © 2016 Elsevier Inc. All rights reserved.

Evidence for the Concerted Evolution between Short Linear Protein Motifs and Their Flanking Regions

PubMed Central

Chica, Claudia; Diella, Francesca; Gibson, Toby J.

2009-01-01

Background Linear motifs are short modules of protein sequences that play a crucial role in mediating and regulating many protein–protein interactions. The function of linear motifs strongly depends on the context, e.g. functional instances mainly occur inside flexible regions that are accessible for interaction. Sometimes linear motifs appear as isolated islands of conservation in multiple sequence alignments. However, they also occur in larger blocks of sequence conservation, suggesting an active role for the neighbouring amino acids. Results The evolution of regions flanking 116 functional linear motif instances was studied. The conservation of the amino acid sequence and order/disorder tendency of those regions was related to presence/absence of the instance. For the majority of the analysed instances, the pairs of sequences conserving the linear motif were also observed to maintain a similar local structural tendency and/or to have higher local sequence conservation when compared to pairs of sequences where one is missing the linear motif. Furthermore, those instances have a higher chance to co–evolve with the neighbouring residues in comparison to the distant ones. Those findings are supported by examples where the regulation of the linear motif–mediated interaction has been shown to depend on the modifications (e.g. phosphorylation) at neighbouring positions or is thought to benefit from the binding versatility of disordered regions. Conclusion The results suggest that flanking regions are relevant for linear motif–mediated interactions, both at the structural and sequence level. More interestingly, they indicate that the prediction of linear motif instances can be enriched with contextual information by performing a sequence analysis similar to the one presented here. This can facilitate the understanding of the role of these predicted instances in determining the protein function inside the broader context of the cellular network where they arise. PMID:19584925

Error reduction in EMG signal decomposition

PubMed Central

Kline, Joshua C.

2014-01-01

Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors. We set out to classify and reduce these errors by analyzing 1,061 motor-unit action-potential trains (MUAPTs), obtained by decomposing surface EMG (sEMG) signals recorded during human voluntary contractions. Decomposition errors were classified into two general categories: location errors representing variability in the temporal localization of each motor-unit firing instance and identification errors consisting of falsely detected or missed firing instances. To mitigate these errors, we developed an error-reduction algorithm that combines multiple decomposition estimates to determine a more probable estimate of motor-unit firing instances with fewer errors. The performance of the algorithm is governed by a trade-off between the yield of MUAPTs obtained above a given accuracy level and the time required to perform the decomposition. When applied to a set of sEMG signals synthesized from real MUAPTs, the identification error was reduced by an average of 1.78%, improving the accuracy to 97.0%, and the location error was reduced by an average of 1.66 ms. The error-reduction algorithm in this study is not limited to any specific decomposition strategy. Rather, we propose it be used for other decomposition methods, especially when analyzing precise motor-unit firing instances, as occurs when measuring synchronization. PMID:25210159

A Multimodal Assessment of Behavioral and Cognitive Deficits in Abused and Neglected Preschoolers.

ERIC Educational Resources Information Center

Hoffman-Plotkin, Debbie; Twentyman, Craig T.

1984-01-01

Multiple measures of social and cognitive functioning were obtained to investigate whether abused and neglected children demonstrate serious psychological disturbances following instances of child maltreatment. Participants were 42 preschool children who had a previous history of physical abuse, serious neglect, or no maltreatment. (Author/RH)

Using "Flatland 2: Sphereland" to Help Teach Motion and Multiple Dimensions

NASA Astrophysics Data System (ADS)

Caplan, Seth; Johnson, Dano; Vondracek, Mark

2015-01-01

The 1884 book Flatland: A Romance of Many Dimensions,1 written by Edwin Abbott, has captured the interest of numerous generations, and has also been used in schools to help students learn and think about the concept of dimension in a creative, fun way. In 2007, a film was released called "Flatland: The Movie,"2 and over one million students have watched it worldwide, primarily in mathematics classes. Since then, a sequel to the "Flatland" movie was released in 2012, entitled "Flatland 2: Sphereland."3 A primary goal of this sequel is to expand the use of the movie beyond mathematics classes and into physics classes because a central premise to "Sphereland" is the notion of warped space. This latest movie provides an engaging and interesting visual way for students to think about both dimension and motion through warped space. In addition, basic motion concepts such as speed and acceleration can be studied by students in introductory physics classes, for instance, by using frame-by-frame analysis of various scenes in the movie.

Shared Medical Imaging Repositories.

PubMed

Lebre, Rui; Bastião, Luís; Costa, Carlos

2018-01-01

This article describes the implementation of a solution for the integration of ownership concept and access control over medical imaging resources, making possible the centralization of multiple instances of repositories. The proposed architecture allows the association of permissions to repository resources and delegation of rights to third entities. It includes a programmatic interface for management of proposed services, made available through web services, with the ability to create, read, update and remove all components resulting from the architecture. The resulting work is a role-based access control mechanism that was integrated with Dicoogle Open-Source Project. The solution has several application scenarios like, for instance, collaborative platforms for research and tele-radiology services deployed at Cloud.

Virtual machine provisioning, code management, and data movement design for the Fermilab HEPCloud Facility

NASA Astrophysics Data System (ADS)

Timm, S.; Cooper, G.; Fuess, S.; Garzoglio, G.; Holzman, B.; Kennedy, R.; Grassano, D.; Tiradani, A.; Krishnamurthy, R.; Vinayagam, S.; Raicu, I.; Wu, H.; Ren, S.; Noh, S.-Y.

2017-10-01

The Fermilab HEPCloud Facility Project has as its goal to extend the current Fermilab facility interface to provide transparent access to disparate resources including commercial and community clouds, grid federations, and HPC centers. This facility enables experiments to perform the full spectrum of computing tasks, including data-intensive simulation and reconstruction. We have evaluated the use of the commercial cloud to provide elasticity to respond to peaks of demand without overprovisioning local resources. Full scale data-intensive workflows have been successfully completed on Amazon Web Services for two High Energy Physics Experiments, CMS and NOνA, at the scale of 58000 simultaneous cores. This paper describes the significant improvements that were made to the virtual machine provisioning system, code caching system, and data movement system to accomplish this work. The virtual image provisioning and contextualization service was extended to multiple AWS regions, and to support experiment-specific data configurations. A prototype Decision Engine was written to determine the optimal availability zone and instance type to run on, minimizing cost and job interruptions. We have deployed a scalable on-demand caching service to deliver code and database information to jobs running on the commercial cloud. It uses the frontiersquid server and CERN VM File System (CVMFS) clients on EC2 instances and utilizes various services provided by AWS to build the infrastructure (stack). We discuss the architecture and load testing benchmarks on the squid servers. We also describe various approaches that were evaluated to transport experimental data to and from the cloud, and the optimal solutions that were used for the bulk of the data transport. Finally, we summarize lessons learned from this scale test, and our future plans to expand and improve the Fermilab HEP Cloud Facility.

Virtual Machine Provisioning, Code Management, and Data Movement Design for the Fermilab HEPCloud Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Timm, S.; Cooper, G.; Fuess, S.

The Fermilab HEPCloud Facility Project has as its goal to extend the current Fermilab facility interface to provide transparent access to disparate resources including commercial and community clouds, grid federations, and HPC centers. This facility enables experiments to perform the full spectrum of computing tasks, including data-intensive simulation and reconstruction. We have evaluated the use of the commercial cloud to provide elasticity to respond to peaks of demand without overprovisioning local resources. Full scale data-intensive workflows have been successfully completed on Amazon Web Services for two High Energy Physics Experiments, CMS and NOνA, at the scale of 58000 simultaneous cores.more » This paper describes the significant improvements that were made to the virtual machine provisioning system, code caching system, and data movement system to accomplish this work. The virtual image provisioning and contextualization service was extended to multiple AWS regions, and to support experiment-specific data configurations. A prototype Decision Engine was written to determine the optimal availability zone and instance type to run on, minimizing cost and job interruptions. We have deployed a scalable on-demand caching service to deliver code and database information to jobs running on the commercial cloud. It uses the frontiersquid server and CERN VM File System (CVMFS) clients on EC2 instances and utilizes various services provided by AWS to build the infrastructure (stack). We discuss the architecture and load testing benchmarks on the squid servers. We also describe various approaches that were evaluated to transport experimental data to and from the cloud, and the optimal solutions that were used for the bulk of the data transport. Finally, we summarize lessons learned from this scale test, and our future plans to expand and improve the Fermilab HEP Cloud Facility.« less

International Seminar--History and Social Science Textbook (Santiago de Chile, 2008)

ERIC Educational Resources Information Center

Online Submission, 2009

2009-01-01

The School Textbook Division from the Chilean Ministry of Education has carried out experience-sharing instances and reflection processes oriented to improve the quality of textbooks, adapting them to new learning strategies, to the demands of the school system as well as to the state-of-the-art in research in education worldwide. Since the…

Developing Future University Structures: New Funding and Legal Models. Policy Commentary

ERIC Educational Resources Information Center

Stanfield, Glynne

2009-01-01

The last decade has seen significant changes in the interaction between publicly funded higher education institutions and the private sector. This has led not only to collaborations between the public and the private sectors but also to the public higher education sector seeking to learn from and, in some instances, to replicate the private…

28 Days Later: Twitter Hashtags as "Just in Time" Teacher Professional Development

ERIC Educational Resources Information Center

Greenhalgh, Spencer P.; Koehler, Matthew J.

2017-01-01

Researchers have argued that Twitter has potential to support high-quality professional development (PD) that can respond to teachers' questions and concerns just in time and "on the spot." Yet, very little attention has been paid to instances where Twitter has made just-in-time learning possible. In this paper, we examine one instance…

In the Name of Liberation: Notes on Governmentality, Entrepreneurial Education, and Lifelong Learning

ERIC Educational Resources Information Center

Dahlstedt, Magnus; Hertzberg, Fredrik

2014-01-01

The focus of this article is the growing importance of entrepreneurship in the context of Swedish education policy. Departing from Foucault's concept of governmentality, this article analyzes some of the main ideas in the discourse on entrepreneurship education in Sweden and points out its specifics, as an instance of the broader educational and…

Using Television in Distance Education. Papers on Information Technology No. 245.

ERIC Educational Resources Information Center

Bates, A. W.

The presentational power of television gives it unique teaching characteristics and it is a source of a wide variety of learning material that would be unavailable to learners in any other way. For instance, it can be used to: (1) demonstrate experiments or experimental situations; (2) explain principles involving movement over space and/or time;…

Academic Outcomes for North Carolina Virtual Public School Credit Recovery Students. REL 2017-177

ERIC Educational Resources Information Center

Stallings, D. T.; Weiss, Sara P.; Maser, Robert H.; Stanhope, Daniel; Starcke, Matthew; Li, Difei

2016-01-01

Across the Regional Educational Laboratory Southeast Region there is growing interest in strengthening the presence of online learning in all public schools to help equalize education opportunities for all students and prepare students for a digital future. For instance, the North Carolina General Assembly has required that the state transition to…

Comparing International Textbooks to Develop Historical Thinking

ERIC Educational Resources Information Center

DeRose, John J.

2007-01-01

Students have frequently expressed curiosity about the way past events involving the United States were viewed by other nations. For instance, students have often wondered how World War II is presented to students in Germany, or what students in Japan learn about the dropping of the atomic bombs. To help his students look at events from a global…

Indicators of School Crime and Safety: 2016. NCES 2017-064/NCJ 250650

ERIC Educational Resources Information Center

Zhang, Anlan; Wang, Ke; Zhang, Jizhi; Oudekerk, Barbara A.

2017-01-01

The nation's schools should be safe havens for teaching and learning, free of crime and violence. Any instance of crime or violence at school not only affects the individuals involved, but also may disrupt the educational process and affect bystanders, the school itself, and the surrounding community. Establishing reliable indicators of the…

Indicators of School Crime and Safety: 2015. NCES 2016-079/NCJ 249758

ERIC Educational Resources Information Center

Zhang, Anlan; Musu-Gillette, Lauren; Oudekerk, Barbara A.

2016-01-01

Our nation's schools should be safe havens for teaching and learning, free of crime and violence. Any instance of crime or violence at school not only affects the individuals involved, but also may disrupt the educational process and affect bystanders, the school itself, and the surrounding community (Brookmeyer, Fanti, and Henrich 2006;…

Overcoming Misconceptions in Quantum Mechanics with the Time Evolution Operator

ERIC Educational Resources Information Center

Quijas, P. C. Garcia; Aguilar, L. M. Arevalo

2007-01-01

Recently, there have been many efforts to use the research techniques developed in the field of physics education research to improve the teaching and learning of quantum mechanics. In particular, part of this research is focusing on misconceptions held by students. For instance, a set of misconceptions is associated with the concept of stationary…

"Living on the Edge": A Case of School Reform Working for Disadvantaged Young Adolescents

ERIC Educational Resources Information Center

Smyth, John; McInerney, Peter

2007-01-01

This paper describes an instance of a disadvantaged (urban) Australian government school that realized it had little alternative but to try new approaches; "old ways" were not working. The paper describes an ensemble of school reform practices, philosophies and strategies that give young adolescents genuine ownership of their learning.…

Comments on "Reflections on 'A Review of Trends in Serious Gaming'"

ERIC Educational Resources Information Center

Young, Michael F.; Slota, Stephen T.; Lai, Benedict

2012-01-01

In large measure the authors agree with Tobias and Fletcher's (2012) comments stating that clearer operational definitions of game features are needed to enable research on games and learning. The authors cannot accept that games are a subset of simulations, preferring to identify instances when games and simulations overlap and when they do not.…

Does Imagined Practice Help in Learning a Motor Skill?

ERIC Educational Resources Information Center

Winters, Lynn; Reisberg, Daniel

Several studies have shown an improvement in the performance of motor skills following imagined performance of the skill, or "mental practice." One unresolved issue has centered on whether the effect being observed is in fact a practice effect. As one alternative, the effect may be a simple instance of planning when to use a skill, or…

Text Categorization for Multi-Page Documents: A Hybrid Naive Bayes HMM Approach.

ERIC Educational Resources Information Center

Frasconi, Paolo; Soda, Giovanni; Vullo, Alessandro

Text categorization is typically formulated as a concept learning problem where each instance is a single isolated document. This paper is interested in a more general formulation where documents are organized as page sequences, as naturally occurring in digital libraries of scanned books and magazines. The paper describes a method for classifying…

Understanding and building wilderness management partnerships with indigenous peoples and communities

Treesearch

Gregory F. Hansen

2011-01-01

Learning about, understanding, and working with native cultures can be rewarding as well as enlightening. Such endeavors can also be time consuming, difficult, and even frustrating in certain instances. However, if coordinated carefully and managed properly, at the end of the day such efforts can ultimately result in innumerable benefits to native communities, land...

The Effects of Behavioral Objectives on Learning: A Review of Empirical Studies.

ERIC Educational Resources Information Center

Duchastel, Philippe C.; Merrill, Paul F.

In a review of over 25 empirical investigations of effects of communicating behavioral objectives to students, several trends were apparent. Advance knowledge of behavioral objectives led to improved posttest performance in five of ten studies and to improved retention in two of three instances. Only two of seven studies found an interaction…

Is There a Crisis in International Learning? The "Three Freedoms" Paradox

ERIC Educational Resources Information Center

Shoemaker, Adam

2011-01-01

This paper explores creative responses to global educational, financial and ethical crises. The focus is the potential intersection between academic, Internet and media freedoms. At base, it asks whether there are rights (of definition, use and control) associated with each of these. For instance, is unfettered access to the Internet a human right…

Revisiting a Student-Oriented Curriculum in the Nigerian Secondary School System

ERIC Educational Resources Information Center

Tawo, R. E.; Arikpo, B.; Asuquo, E.

2013-01-01

The increasing gap between what students learn and what they remember has agitated the minds of educators in recent times. The apparent gap is that teaching tends to be more theoretical than practical. For instance an apparent absence of relevant instructional materials, skilled vocational education personnel and the current lack of awareness on…

Indicators of School Crime and Safety: 2014. NCES 2015-072/NCJ 248036

ERIC Educational Resources Information Center

Robers, Simone; Zhang, Anlan; Morgan, Rachel E.

2015-01-01

Our nation's schools should be safe havens for teaching and learning, free of crime and violence. Any instance of crime or violence at school not only affects the individuals involved, but also may disrupt the educational process and affect bystanders, the school itself, and the surrounding community (Brookmeyer, Fanti, and Henrich 2006;…

Verb Biases Are Shaped through Lifelong Learning

ERIC Educational Resources Information Center

Ryskin, Rachel A.; Qi, Zhenghan; Duff, Melissa C.; Brown-Schmidt, Sarah

2017-01-01

Verbs often participate in more than 1 syntactic structure, but individual verbs can be biased in terms of whether they are used more often with 1 structure or the other. For instance, in a sentence such as "Bop the bunny with the flower," the phrase "with the flower" is more likely to indicate an instrument with which to…

Play to Learn: Self-Directed Home Language Literacy Acquisition through Online Games

ERIC Educational Resources Information Center

Eisenchlas, Susana A.; Schalley, Andrea C.; Moyes, Gordon

2016-01-01

Home language literacy education in Australia has been pursued predominantly through Community Language Schools. At present, some 1,000 of these, attended by over 100,000 school-age children, cater for 69 of the over 300 languages spoken in Australia. Despite good intentions, these schools face a number of challenges. For instance, children may…

Extensible Adaptable Simulation Systems: Supporting Multiple Fidelity Simulations in a Common Environment

NASA Technical Reports Server (NTRS)

McLaughlin, Brian J.; Barrett, Larry K.

2012-01-01

Common practice in the development of simulation systems is meeting all user requirements within a single instantiation. The Joint Polar Satellite System (JPSS) presents a unique challenge to establish a simulation environment that meets the needs of a diverse user community while also spanning a multi-mission environment over decades of operation. In response, the JPSS Flight Vehicle Test Suite (FVTS) is architected with an extensible infrastructure that supports the operation of multiple observatory simulations for a single mission and multiple mission within a common system perimeter. For the JPSS-1 satellite, multiple fidelity flight observatory simulations are necessary to support the distinct user communities consisting of the Common Ground System development team, the Common Ground System Integration & Test team, and the Mission Rehearsal Team/Mission Operations Team. These key requirements present several challenges to FVTS development. First, the FVTS must ensure all critical user requirements are satisfied by at least one fidelity instance of the observatory simulation. Second, the FVTS must allow for tailoring of the system instances to function in diverse operational environments from the High-security operations environment at NOAA Satellite Operations Facility (NSOF) to the ground system factory floor. Finally, the FVTS must provide the ability to execute sustaining engineering activities on a subset of the system without impacting system availability to parallel users. The FVTS approach of allowing for multiple fidelity copies of observatory simulations represents a unique concept in simulator capability development and corresponds to the JPSS Ground System goals of establishing a capability that is flexible, extensible, and adaptable.

The Answering Process for Multiple-Choice Questions in Collaborative Learning: A Mathematical Learning Model Analysis

ERIC Educational Resources Information Center

Nakamura, Yasuyuki; Nishi, Shinnosuke; Muramatsu, Yuta; Yasutake, Koichi; Yamakawa, Osamu; Tagawa, Takahiro

2014-01-01

In this paper, we introduce a mathematical model for collaborative learning and the answering process for multiple-choice questions. The collaborative learning model is inspired by the Ising spin model and the model for answering multiple-choice questions is based on their difficulty level. An intensive simulation study predicts the possibility of…

Incremental classification learning for anomaly detection in medical images

NASA Astrophysics Data System (ADS)

Giritharan, Balathasan; Yuan, Xiaohui; Liu, Jianguo

2009-02-01

Computer-aided diagnosis usually screens thousands of instances to find only a few positive cases that indicate probable presence of disease.The amount of patient data increases consistently all the time. In diagnosis of new instances, disagreement occurs between a CAD system and physicians, which suggests inaccurate classifiers. Intuitively, misclassified instances and the previously acquired data should be used to retrain the classifier. This, however, is very time consuming and, in some cases where dataset is too large, becomes infeasible. In addition, among the patient data, only a small percentile shows positive sign, which is known as imbalanced data.We present an incremental Support Vector Machines(SVM) as a solution for the class imbalance problem in classification of anomaly in medical images. The support vectors provide a concise representation of the distribution of the training data. Here we use bootstrapping to identify potential candidate support vectors for future iterations. Experiments were conducted using images from endoscopy videos, and the sensitivity and specificity were close to that of SVM trained using all samples available at a given incremental step with significantly improved efficiency in training the classifier.

Contextual remapping in visual search after predictable target-location changes.

PubMed

Conci, Markus; Sun, Luning; Müller, Hermann J

2011-07-01

Invariant spatial context can facilitate visual search. For instance, detection of a target is faster if it is presented within a repeatedly encountered, as compared to a novel, layout of nontargets, demonstrating a role of contextual learning for attentional guidance ('contextual cueing'). Here, we investigated how context-based learning adapts to target location (and identity) changes. Three experiments were performed in which, in an initial learning phase, observers learned to associate a given context with a given target location. A subsequent test phase then introduced identity and/or location changes to the target. The results showed that contextual cueing could not compensate for target changes that were not 'predictable' (i.e. learnable). However, for predictable changes, contextual cueing remained effective even immediately after the change. These findings demonstrate that contextual cueing is adaptive to predictable target location changes. Under these conditions, learned contextual associations can be effectively 'remapped' to accommodate new task requirements.

Learning Nursing in the Workplace Community: The Generation of Professional Capital

NASA Astrophysics Data System (ADS)

Gobbi, Mary

This chapter explores the connections between learning, working and professional communities in nursing. It draws on experiences and research in nursing practice and education, where not only do isolated professionals learn as a result of their actions for patients and others, but those professionals are part of a community whose associated networks enable learning to occur. Several characteristics of this professional community are shared with those found in Communities of Practice (CoPs) (Lave and Wenger, 1991; Wenger, 1998), but the balance and importance of many elements can differ. For instance, whilst Lave and Wenger (1991) describe many aspects of situated learning in CoPs that apply to nurses, their model is of little help in understanding the ways in which other professions as well as patients/clients and carers influence the development of nursing practice. Therefore, I shall argue that it is not just the Community of Practice that we need to consider

Learning mechanisms in multidisciplinary teamwork with real customers and open-ended problems

NASA Astrophysics Data System (ADS)

Heikkinen, Juho; Isomöttönen, Ville

2015-11-01

Recently, there has been a trend towards adding a multidisciplinary or multicultural element to traditional monodisciplinary project courses in computing and engineering. In this article, we examine the implications of multidisciplinarity for students' learning experiences during a one-semester project course for real customers. We use a qualitative research approach and base our analysis on students' learning reports on three instances of a project course titled Multidisciplinary working life project. The main contribution of this article is the unified theoretical picture of the learning mechanisms stemming from multidisciplinarity. Our main conclusions are that (1) students generally have a positive view of multidisciplinarity; (2) multidisciplinary teams enable students to better identify their own expertise, which leads to increased occupational identity; and (3) learning experiences are not fixed, as team spirit and student attitude play an important role in how students react to challenging situations arising from introduction of the multidisciplinarity.

Science identity construction through extraordinary professional development experiences

NASA Astrophysics Data System (ADS)

McLain, Bradley David

Despite great efforts and expenditures to promote science literacy and STEM career choices, the U.S. continues to lag behind other countries in science education, diminishing our capacity for STEM leadership and our ability to make informed decisions in the face of multiple looming global issues. I suggest that positive science identity construction (the integration of science into one's sense of self so that it becomes a source of inspiration and contributes to lifelong learning) is critical for promoting durable science literacy and pro-science choices. Therefore, the focus of this study was extraordinary professional development experiences for science educators that may significantly impact their sense of self. My hypothesis was that such experiences could positively impact educators' science and science educator identities, and potentially enhance their capacities to impact student science identities. The first part of this hypothesis is examined in this study. Further, I suggest that first-person narratives play an important role in science identity construction. Presenting a new conceptual model that connects experiential learning theory to identity theory through the narrative study of lives, I explored the impacts of subjectively regarded extraordinary professional development experiences on the science identity and science educator identity construction processes for a cohort of fifteen K-12 science teachers during a science-learning-journey to explore the volcanoes of Hawaii. I used a case study research approach under the broader umbrella of a hermeneutic phenomenology to consider four individual cases as lived experiences and to consider the journey as a phenomenon unto itself. Findings suggest science and science educator identities are impacted by such an experience but with marked variability in magnitude and nature. Evidence also suggests important impacts on their other identities. In most instances, science-related impacts were secondary to and/or embedded within the more holistic physical, intellectual, and emotional impacts. Rather than only targeting specific learning goals, as traditional professional development programs often do, this immersive experiential learning program integrated a wide range of human experience that were important factors, most notably, risk, social connections, permission and agency, and emotions in connection with more targeted science learning. Implications for future research and practice are discussed.

Multiple Learning Approaches in the Professional Development of School Leaders -- Theoretical Perspectives and Empirical Findings on Self-assessment and Feedback

ERIC Educational Resources Information Center

Huber, Stephan Gerhard

2013-01-01

This article investigates the use of multiple learning approaches and different modes and types of learning in the (continuous) professional development (PD) of school leaders, particularly the use of self-assessment and feedback. First, formats and multiple approaches to professional learning are described. Second, a possible approach to…

The effect of multiple intelligence-based learning towards students’ concept mastery and interest in learning matter

NASA Astrophysics Data System (ADS)

Pratiwi, W. N.; Rochintaniawati, D.; Agustin, R. R.

2018-05-01

This research was focused on investigating the effect of multiple intelligence -based learning as a learning approach towards students’ concept mastery and interest in learning matter. The one-group pre-test - post-test design was used in this research towards a sample which was according to the suitable situation of the research sample, n = 13 students of the 7th grade in a private school in Bandar Seri Begawan. The students’ concept mastery was measured using achievement test and given at the pre-test and post-test, meanwhile the students’ interest level was measured using a Likert Scale for interest. Based on the analysis of the data, the result shows that the normalized gain was .61, which was considered as a medium improvement. in other words, students’ concept mastery in matter increased after being taught using multiple intelligence-based learning. The Likert scale of interest shows that most students have a high interest in learning matter after being taught by multiple intelligence-based learning. Therefore, it is concluded that multiple intelligence – based learning helped in improving students’ concept mastery and gain students’ interest in learning matter.

Successful Learning with Multiple Graphical Representations and Self-Explanation Prompts

ERIC Educational Resources Information Center

Rau, Martina A.; Aleven, Vincent; Rummel, Nikol

2015-01-01

Research shows that multiple external representations can significantly enhance students' learning. Most of this research has focused on learning with text and 1 additional graphical representation. However, real instructional materials often employ multiple "graphical" representations (MGRs) in addition to text. An important open…

Task-switching cost and repetition priming: two overlooked confounds in the first-set procedure of the Sternberg paradigm and how they affect memory set-size effects.

PubMed

Jou, Jerwen

2014-10-01

Subjects performed Sternberg-type memory recognition tasks (Sternberg paradigm) in four experiments. Category-instance names were used as learning and testing materials. Sternberg's original experiments demonstrated a linear relation between reaction time (RT) and memory-set size (MSS). A few later studies found no relation, and other studies found a nonlinear relation (logarithmic) between the two variables. These deviations were used as evidence undermining Sternberg's serial scan theory. This study identified two confounding variables in the fixed-set procedure of the paradigm (where multiple probes are presented at test for a learned memory set) that could generate a MSS RT function that was either flat or logarithmic rather than linearly increasing. These two confounding variables were task-switching cost and repetition priming. The former factor worked against smaller memory sets and in favour of larger sets whereas the latter factor worked in the opposite way. Results demonstrated that a null or a logarithmic RT-to-MSS relation could be the artefact of the combined effects of these two variables. The Sternberg paradigm has been used widely in memory research, and a thorough understanding of the subtle methodological pitfalls is crucial. It is suggested that a varied-set procedure (where only one probe is presented at test for a learned memory set) is a more contamination-free procedure for measuring the MSS effects, and that if a fixed-set procedure is used, it is worthwhile examining the RT function of the very first trials across the MSSs, which are presumably relatively free of contamination by the subsequent trials.

Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology.

PubMed

Karbasi, Amin; Salavati, Amir Hesam; Vetterli, Martin

2018-04-01

The connectivity of a neuronal network has a major effect on its functionality and role. It is generally believed that the complex network structure of the brain provides a physiological basis for information processing. Therefore, identifying the network's topology has received a lot of attentions in neuroscience and has been the center of many research initiatives such as Human Connectome Project. Nevertheless, direct and invasive approaches that slice and observe the neural tissue have proven to be time consuming, complex and costly. As a result, the inverse methods that utilize firing activity of neurons in order to identify the (functional) connections have gained momentum recently, especially in light of rapid advances in recording technologies; It will soon be possible to simultaneously monitor the activities of tens of thousands of neurons in real time. While there are a number of excellent approaches that aim to identify the functional connections from firing activities, the scalability of the proposed techniques plays a major challenge in applying them on large-scale datasets of recorded firing activities. In exceptional cases where scalability has not been an issue, the theoretical performance guarantees are usually limited to a specific family of neurons or the type of firing activities. In this paper, we formulate the neural network reconstruction as an instance of a graph learning problem, where we observe the behavior of nodes/neurons (i.e., firing activities) and aim to find the links/connections. We develop a scalable learning mechanism and derive the conditions under which the estimated graph for a network of Leaky Integrate and Fire (LIf) neurons matches the true underlying synaptic connections. We then validate the performance of the algorithm using artificially generated data (for benchmarking) and real data recorded from multiple hippocampal areas in rats.

Evaluating Multiple Levels of an Interaction Fidelity Continuum on Performance and Learning in Near-Field Training Simulations.

PubMed

Bhargava, Ayush; Bertrand, Jeffrey W; Gramopadhye, Anand K; Madathil, Kapil C; Babu, Sabarish V

2018-04-01

With costs of head-mounted displays (HMDs) and tracking technology decreasing rapidly, various virtual reality applications are being widely adopted for education and training. Hardware advancements have enabled replication of real-world interactions in virtual environments to a large extent, paving the way for commercial grade applications that provide a safe and risk-free training environment at a fraction of the cost. But this also mandates the need to develop more intrinsic interaction techniques and to empirically evaluate them in a more comprehensive manner. Although there exists a body of previous research that examines the benefits of selected levels of interaction fidelity on performance, few studies have investigated the constituent components of fidelity in a Interaction Fidelity Continuum (IFC) with several system instances and their respective effects on performance and learning in the context of a real-world skills training application. Our work describes a large between-subjects investigation conducted over several years that utilizes bimanual interaction metaphors at six discrete levels of interaction fidelity to teach basic precision metrology concepts in a near-field spatial interaction task in VR. A combined analysis performed on the data compares and contrasts the six different conditions and their overall effects on performance and learning outcomes, eliciting patterns in the results between the discrete application points on the IFC. With respect to some performance variables, results indicate that simpler restrictive interaction metaphors and highest fidelity metaphors perform better than medium fidelity interaction metaphors. In light of these results, a set of general guidelines are created for developers of spatial interaction metaphors in immersive virtual environments for precise fine-motor skills training simulations.

The Continuity of Metaphor: Evidence from Temporal Gestures

ERIC Educational Resources Information Center

Walker, Esther; Cooperrider, Kensy

2016-01-01

Reasoning about bedrock abstract concepts such as time, number, and valence relies on spatial metaphor and often on multiple spatial metaphors for a single concept. Previous research has documented, for instance, both future-in-front and future-to-right metaphors for time in English speakers. It is often assumed that these metaphors, which appear…

Mapping Habitat Connectivity for Multiple Rare, Threatened, and Endangered Species on and Around Military Installations

DTIC Science & Technology

2011-05-01

choice model based on foraying behavior to predict natal dispersal destinations. We counted instances in which a female occupied the breeding position...movement for pollen dispersal by honey bees. Ecology 74:493-500. Müller, J., J. Stadler, R. Brandl., 2009. Composition versus physiognomy of vegetation

Generating nonlinear FM chirp radar signals by multiple integrations

DOEpatents

Doerry, Armin W [Albuquerque, NM

2011-02-01

A phase component of a nonlinear frequency modulated (NLFM) chirp radar pulse can be produced by performing digital integration operations over a time interval defined by the pulse width. Each digital integration operation includes applying to a respectively corresponding input parameter value a respectively corresponding number of instances of digital integration.

Improving Multiple Surface Range Estimation of a 3-Dimensional FLASH LADAR in the Presence of Atmospheric Turbulence

DTIC Science & Technology

2013-06-01

during the design process. For instance, the detector could be calibrated with incoherent il- lumination and a separate calibration could be performed...Poisson dis- tribution is often employed as a statistical distribution for the detected images. How- ever, due to the highly coherent nature of laser

Effects of a Differential Observing Response on Intraverbal Performance of Preschool Children: A Preliminary Investigation

ERIC Educational Resources Information Center

Kisamore, April N.; Karsten, Amanda M.; Mann, Charlotte C.; Conde, Kerry Ann

2013-01-01

Axe (2008) speculated that some instances of intraverbal responding might be associated with limited or delayed acquisition because they require discrimination of multiple components of verbal stimuli. Past studies suggest that acquisition of responses under control of complex, multicomponent antecedent stimuli (e.g., conditional or compound…

Appendix A. Borderlands Site Database

Treesearch

A.C. MacWilliams

2006-01-01

The database includes modified components of the Arizona State Museum Site Recording System (Arizona State Museum 1993) and the New Mexico NMCRIS User?s Guide (State of New Mexico 1993). When sites contain more than one recorded component, these instances were entered separately with the result that many sites have multiple entries. Information for this database...

RS-Forest: A Rapid Density Estimator for Streaming Anomaly Detection.

PubMed

Wu, Ke; Zhang, Kun; Fan, Wei; Edwards, Andrea; Yu, Philip S

Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree is defined on the tree node into which an instance falls. Each incoming instance in a data stream is scored by the density estimates averaged over all trees in the forest. Two strategies, statistical attribute range estimation of high probability guarantee and dual node profiles for rapid model update, are seamlessly integrated into RS-Forest to systematically address the ever-evolving nature of data streams. We derive the theoretical upper bound for the proposed algorithm and analyze its asymptotic properties via bias-variance decomposition. Empirical comparisons to the state-of-the-art methods on multiple benchmark datasets demonstrate that the proposed method features high detection rate, fast response, and insensitivity to most of the parameter settings. Algorithm implementations and datasets are available upon request.

RS-Forest: A Rapid Density Estimator for Streaming Anomaly Detection

PubMed Central

Wu, Ke; Zhang, Kun; Fan, Wei; Edwards, Andrea; Yu, Philip S.

2015-01-01

Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree is defined on the tree node into which an instance falls. Each incoming instance in a data stream is scored by the density estimates averaged over all trees in the forest. Two strategies, statistical attribute range estimation of high probability guarantee and dual node profiles for rapid model update, are seamlessly integrated into RS-Forest to systematically address the ever-evolving nature of data streams. We derive the theoretical upper bound for the proposed algorithm and analyze its asymptotic properties via bias-variance decomposition. Empirical comparisons to the state-of-the-art methods on multiple benchmark datasets demonstrate that the proposed method features high detection rate, fast response, and insensitivity to most of the parameter settings. Algorithm implementations and datasets are available upon request. PMID:25685112

Kepler-90 System Compared to Our Solar System (Artist's Concept)

NASA Image and Video Library

2017-12-14

Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light years from Earth. The planet was discovered in data from NASA's Kepler Space Telescope. This artist's concept depicts the Kepler-90 system compared with our own solar system. The newly-discovered Kepler-90i -- a sizzling hot, rocky planet that orbits its star once every 14.4 days -- was found using machine learning from Google. Machine learning is an approach to artificial intelligence in which computers "learn." In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded changes in starlight caused by planets beyond our solar system, known as exoplanets. https://photojournal.jpl.nasa.gov/catalog/PIA22193

Artificial Intelligence and NASA Data Used to Discover Eighth Planet Circling Distant Star

NASA Image and Video Library

2017-12-12

Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light years from Earth. The planet was discovered in data from NASA’s Kepler space telescope. The newly-discovered Kepler-90i -- a sizzling hot, rocky planet that orbits its star once every 14.4 days -- was found by researchers from Google and The University of Texas at Austin using machine learning. Machine learning is an approach to artificial intelligence in which computers “learn.” In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded signals from planets beyond our solar system, known as exoplanets. Video Credit: NASA Ames Research Center / Google

The Necessity of Machine Learning and Epistemology in the Development of Categorization Theories: A Case Study in Prototype-Exemplar Debate

NASA Astrophysics Data System (ADS)

Gagliardi, Francesco

In the present paper we discuss some aspects of the development of categorization theories concerning cognitive psychology and machine learning. We consider the thirty-year debate between prototype-theory and exemplar-theory in the studies of cognitive psychology regarding the categorization processes. We propose this debate is ill-posed, because it neglects some theoretical and empirical results of machine learning about the bias-variance theorem and the existence of some instance-based classifiers which can embed models subsuming both prototype and exemplar theories. Moreover this debate lies on a epistemological error of pursuing a, so called, experimentum crucis. Then we present how an interdisciplinary approach, based on synthetic method for cognitive modelling, can be useful to progress both the fields of cognitive psychology and machine learning.

Measuring students' self-regulated learning in professional education: bridging the gap between event and aptitude measurements.

PubMed

Endedijk, Maaike D; Brekelmans, Mieke; Sleegers, Peter; Vermunt, Jan D

Self-regulated learning has benefits for students' academic performance in school, but also for expertise development during their professional career. This study examined the validity of an instrument to measure student teachers' regulation of their learning to teach across multiple and different kinds of learning events in the context of a postgraduate professional teacher education programme. Based on an analysis of the literature, we developed a log with structured questions that could be used as a multiple-event instrument to determine the quality of student teachers' regulation of learning by combining data from multiple learning experiences. The findings showed that this structured version of the instrument measured student teachers' regulation of their learning in a valid and reliable way. Furthermore, with the aid of the Structured Learning Report individual differences in student teachers' regulation of learning could be discerned. Together the findings indicate that a multiple-event instrument can be used to measure regulation of learning in multiple contexts for various learning experiences at the same time, without the necessity of relying on students' ability to rate themselves across all these different experiences. In this way, this instrument can make an important contribution to bridging the gap between two dominant approaches to measure SRL, the traditional aptitude and event measurement approach.

Cyber Situation Awareness through Instance-Based Learning: Modeling the Security Analyst in a Cyber-Attack Scenario

DTIC Science & Technology

2012-01-01

Chocolate Avenue Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: cust@igi-global.com Web site: http://www.igi-global.com Copyright © 2011...Lawrence Erlbaum Associates. Anderson, J. R., & Lebiere, C. (2003). The New- ell test for a theory of mind. The Behavioral and Brain Sciences, 26(5

Instance-Based Learning: Integrating Sampling and Repeated Decisions from Experience

ERIC Educational Resources Information Center

Gonzalez, Cleotilde; Dutt, Varun

2011-01-01

In decisions from experience, there are 2 experimental paradigms: sampling and repeated-choice. In the sampling paradigm, participants sample between 2 options as many times as they want (i.e., the stopping point is variable), observe the outcome with no real consequences each time, and finally select 1 of the 2 options that cause them to earn or…

Concept-Focused Teaching: Using Big Ideas to Guide Instruction in Science

ERIC Educational Resources Information Center

Olson, Joanne K.

2008-01-01

One of the main problems we face in science teaching is that students are learning isolated facts and missing central concepts. For instance, consider what you know about life cycles. Chances are that you remember something about butterflies and stages, such as egg, larva, pupa, adult. But what's the take-home idea that we should have learned…

The Shape of Things: The Origin of Young Children's Knowledge of the Names and Properties of Geometric Forms

ERIC Educational Resources Information Center

Verdine, Brian N.; Lucca, Kelsey R.; Golinkoff, Roberta M.; Hirsh-Pasek, Kathryn; Newcombe, Nora S.

2016-01-01

How do toddlers learn the names of geometric forms? Previous work suggests that preschoolers have fragmentary knowledge and that defining properties are not understood until well into elementary school. The current study investigated when children first begin to understand shape names and how they apply those labels to unusual instances. We tested…

In Search of a Better Understanding of Social Presence: An Investigation into How Researchers Define Social Presence

ERIC Educational Resources Information Center

Lowenthal, Patrick R.; Snelson, Chareen

2017-01-01

Research on social presence and online learning continues to grow. But to date, researchers continue to define and conceptualize social presence very differently. For instance, at a basic level, some conceptualize social presence as one of three presences within a Community of Inquiry, while others do not. Given this problem, we analyzed how…

I Guess the Joke Was on Me: A Reality Rub Reclaiming Intervention

ERIC Educational Resources Information Center

Hoyt, Lisa; Fecser, Frank A.

2011-01-01

Ian is a 17-year-old student attending a therapeutic school in an urban district. He was referred less than a year ago due to behavioral and academic issues at the comprehensive high school. One instance in his art class, Ian overreacted when his teacher misinterpreted his humor, leading to a major school crisis. Learning to recognize the early…

Scientific and Ethical Reflections on Academic Corruption in Universities: On the Science Research Evaluation System in China's Universities

ERIC Educational Resources Information Center

Xiaochun, Wu; Dan, Jia

2007-01-01

A study of the science research activities in China's institutions of higher learning in recent years indicates that there is a major connection between the current instances of corruption in scientific research at colleges and universities and the evaluations system for scientific research implemented at many of the colleges and universities.…

Analysis of the Dynamics of the Sharing Knowledge between Cooperating Teacher and Teacher-in-Training: The Partners' Respective Roles

ERIC Educational Resources Information Center

Liliane, Portelance; Colette, Gervais

2009-01-01

Collaboration is becoming increasingly important in the realm of education (Novoa, 2004). For instance, as soon as training is undertaken, the future teacher must develop an ability to cooperate in a pedagogical context. However, in order to learn to make a relevant contribution to a teaching team's undertakings and to provide innovative…

An Assessment of Teachers' Abilities to Support Blended Learning Implementation in Tanzanian Secondary Schools

ERIC Educational Resources Information Center

Kihoza, Patrick D.; Zlotnikova, Irina; Bada, Joseph Kizito; Kalegele, Khamisi

2016-01-01

The purpose of this study was to describe instances of pedagogical practices of teachers using ICTs and the enhancements of practices using traditional methods, to more fundamental changes in their approach to teaching. Using a mixed method, the research examined the impact of increased education level on the ICT use competence perception and the…

Credit Recovery in a Virtual School: Affordances of Online Learning for the At-Risk Student

ERIC Educational Resources Information Center

Oliver, Kevin; Kellogg, Shaun

2015-01-01

This paper summarizes evaluation findings about a high school credit recovery (CR) program as solicited by a statesponsored virtual school in the United States. Student and teacher surveys explained why CR students failed previous instances of face-to-face courses and defined how the online CR model helped these learners overcome both internal…

Learning from Others in 9-18-Month-Old Infants

ERIC Educational Resources Information Center

Goubet, Nathalie; Rochat, Philippe; Maire-Leblond, Celine; Poss, Sarah

2006-01-01

The use of an adult as a resource for help and instruction in a problem solving situation was examined in 9, 14, and 18-month-old infants. Infants were placed in various situations ranging from a simple means-end task where a toy was placed beyond infants' prehensile space on a mat, to instances where an attractive toy was placed inside closed…

English Vocabulary Instruction in Six Early Childhood Classrooms in Hong Kong

ERIC Educational Resources Information Center

Lau, Carrie; Rao, Nirmala

2013-01-01

Vocabulary instruction during English language learning was observed for one week in six classrooms (three K2 classes for four-year olds and three K3 classes for five-year olds) from three kindergartens in two districts of Hong Kong. From 23 sessions of observations and 535 minutes of data, field notes were coded to identify instances of…

Advanced Quality Control Theory for Training and Education: A Guide to Optimizing Training and Education Efforts

ERIC Educational Resources Information Center

Heppler, Brad

2008-01-01

This is a book about quality and how to control quality through deliberate actions on the part of the professionals developing and implementing the instances of instruction available at an organization. Quality control theory favors no particular learning philosophy and is only directed towards aspects of how, what, where and when measurements are…

Digital Diversity: A Basic Tool with Lots of Uses

ERIC Educational Resources Information Center

Coy, Mary

2006-01-01

In this article the author relates how the digital camera has altered the way she teaches and the way her students learn. She also emphasizes the importance for teachers to have software that can edit, print, and incorporate photos. She cites several instances in which a digital camera can be used: (1) PowerPoint presentations; (2) Open house; (3)…

Attention Switching and Multimedia Learning: The Impact of Executive Resources on the Integrative Comprehension of Texts and Pictures

ERIC Educational Resources Information Center

Baadte, Christiane; Rasch, Thorsten; Honstein, Helena

2015-01-01

The ability to flexibly allocate attention to goal-relevant information is pivotal for the completion of high-level cognitive processes. For instance, in comprehending illustrated texts, the reader permanently has to switch the attentional focus between the text and the corresponding picture in order to extract relevant information from both…

Feeling and Thinking about Studio Practices: Exploring Dissonance in Semi-Structured Interviews with Students in Higher Education Music

ERIC Educational Resources Information Center

Burwell, Kim

2017-01-01

While studio-based instrumental and vocal learning is widely regarded as both important and effective in higher education music, research to date has offered little concrete information about studio practices that students have regarded as ineffective. Two recent case studies investigated what appear to be exceptional instances in which students…

The Role of the Dorsal Anterior Cingulate in Evaluating Behavior for Achieving Gains and Avoiding Losses

ERIC Educational Resources Information Center

Magno, Elena; Simoes-Franklin, Cristina; Robertson, Ian H.; Garavan, Hugh

2009-01-01

Effective goal-directed behavior relies on a network of regions including anterior cingulate cortex and ventral striatum to learn from negative outcomes in order to improve performance. We employed fMRI to determine if this frontal-striatal system is also involved in instances of behavior that do not presume negative circumstances. Participants…

Handbook 2006-2007: Federal Student Aid. Volume 3--Calculating Awards & Packaging

ERIC Educational Resources Information Center

US Department of Education, 2006

2006-01-01

Every eligible program, including graduate programs, must have a defined academic year. Award limits are generally connected to a period of time. For instance, all of the programs except Federal Work-Study have a maximum amount that can be awarded for an academic year or award year. This handbook is a resource for learning about Federal Student…

A lifelong learning hyper-heuristic method for bin packing.

PubMed

Sim, Kevin; Hart, Emma; Paechter, Ben

2015-01-01

We describe a novel hyper-heuristic system that continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; and representative problems and heuristics are incorporated into a self-sustaining network of interacting entities inspired by methods in artificial immune systems. The network is plastic in both its structure and content, leading to the following properties: it exploits existing knowledge captured in the network to rapidly produce solutions; it can adapt to new problems with widely differing characteristics; and it is capable of generalising over the problem space. The system is tested on a large corpus of 3,968 new instances of 1D bin-packing problems as well as on 1,370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to previous approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable.

Learning a Nonmediated Route for Response Selection in Task Switching

PubMed Central

Schneider, Darryl W.; Logan, Gordon D.

2015-01-01

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 percentage 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. PMID:25663003

Not Only Size Matters: Early-Talker and Late-Talker Vocabularies Support Different Word-Learning Biases in Babies and Networks.

PubMed

Colunga, Eliana; Sims, Clare E

2017-02-01

In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2-year-olds seem to intuit the whole range of things in a category from hearing a single instance named-they have word-learning biases. This is not the case for children with relatively small vocabularies (late talkers). We present a computational model that accounts for the emergence of word-learning biases in children at both ends of the vocabulary spectrum based solely on vocabulary structure. The results of Experiment 1 show that late-talkers' and early-talkers' noun vocabularies have different structures and that neural networks trained on the vocabularies of individual late talkers acquire different word-learning biases than those trained on early-talker vocabularies. These models make novel predictions about the word-learning biases in these two populations. Experiment 2 tests these predictions on late- and early-talking toddlers in a novel noun generalization task. Copyright © 2016 Cognitive Science Society, Inc.

Horizontal and vertical combination of multi-tenancy patterns in service-oriented applications

NASA Astrophysics Data System (ADS)

Mietzner, Ralph; Leymann, Frank; Unger, Tobias

2011-02-01

Software as a service (SaaS) providers exploit economies of scale by offering the same instance of an application to multiple customers typically in a single-instance multi-tenant architecture model. Therefore the applications must be scalable, multi-tenant aware and configurable. In this article, we show how the services in a service-oriented SaaS application can be deployed using different multi-tenancy patterns. We describe how services in different multi-tenancy patterns can be composed on the application level. In addition to that, we also describe how these multi-tenancy patterns can be applied to middleware and hardware components. We then show with some real world examples how the different multi-tenancy patterns can be combined.

In vitro molecular machine learning algorithm via symmetric internal loops of DNA.

PubMed

Lee, Ji-Hoon; Lee, Seung Hwan; Baek, Christina; Chun, Hyosun; Ryu, Je-Hwan; Kim, Jin-Woo; Deaton, Russell; Zhang, Byoung-Tak

2017-08-01

Programmable biomolecules, such as DNA strands, deoxyribozymes, and restriction enzymes, have been used to solve computational problems, construct large-scale logic circuits, and program simple molecular games. Although studies have shown the potential of molecular computing, the capability of computational learning with DNA molecules, i.e., molecular machine learning, has yet to be experimentally verified. Here, we present a novel molecular learning in vitro model in which symmetric internal loops of double-stranded DNA are exploited to measure the differences between training instances, thus enabling the molecules to learn from small errors. The model was evaluated on a data set of twenty dialogue sentences obtained from the television shows Friends and Prison Break. The wet DNA-computing experiments confirmed that the molecular learning machine was able to generalize the dialogue patterns of each show and successfully identify the show from which the sentences originated. The molecular machine learning model described here opens the way for solving machine learning problems in computer science and biology using in vitro molecular computing with the data encoded in DNA molecules. Copyright © 2017. Published by Elsevier B.V.

Learning Multiplication Using Indonesian Traditional Game in Third Grade

ERIC Educational Resources Information Center

Prahmana, Rully Charitas Indra; Zulkardi; Hartono, Yusuf

2012-01-01

Several previous researches showed that students had difficulty in understanding the basic concept of multiplication. Students are more likely to be introduced by using formula without involving the concept itself. This underlies the researcher to design a learning trajectory of learning multiplication using Permainan Tradisional Tepuk Bergambar…

The Community as Classroom: Multiple Perspectives on Student Learning.

ERIC Educational Resources Information Center

Kerrigan, Seanna; Gelmon, Sherrill; Spring, Amy

2003-01-01

Reports on the multiple perspectives of students, community members, and faculty to document the affect of student participation in service-learning courses. The study examined in this article used a large sample size and multiple qualitative and quantitative methods over several years. The results indicate that service learning affects students…

Multiple Intelligences in Virtual and Traditional Skill Instructional Learning Environments

ERIC Educational Resources Information Center

McKethan, Robert; Rabinowitz, Erik; Kernodle, Michael W.

2010-01-01

The purpose of this investigation was to examine (a) how Multiple Intelligence (MI) strengths correlate to learning in virtual and traditional environments and (b) the effectiveness of learning with and without an authority figure in attendance. Participants (N=69) were randomly assigned to four groups, administered the Multiple Intelligences…

Learned helplessness in the multiple sclerosis population.

PubMed

McGuinness, S

1996-06-01

The purpose of this cross-sectional, descriptive study was to describe the relationships between learned helplessness and disease status, functional and social disability, and disease activity in the multiple sclerosis population. Additionally, the relationships between learned helplessness and age, disease duration, education and marital and employment status were evaluated. Self-report instruments with established validity and reliability in the multiple sclerosis population were used to collect the data. Learned helplessness was significantly positively correlated with social and functional disability. Although not significant at the .05 level, disease status and disease activity were also positively correlated with learned helplessness. Additionally, unemployed individuals were more likely to be helpless than employed individuals. Overall, the results suggest that learned helplessness is related to negative health indicators in the multiple sclerosis population. Nursing interventions to decrease or prevent learned helplessness may be appropriate in this population.

Agent-Based Learning Environments as a Research Tool for Investigating Teaching and Learning.

ERIC Educational Resources Information Center

Baylor, Amy L.

2002-01-01

Discusses intelligent learning environments for computer-based learning, such as agent-based learning environments, and their advantages over human-based instruction. Considers the effects of multiple agents; agents and research design; the use of Multiple Intelligent Mentors Instructing Collaboratively (MIMIC) for instructional design for…

Cross-platform learning: on the nature of children's learning from multiple media platforms.

PubMed

Fisch, Shalom M

2013-01-01

It is increasingly common for an educational media project to span several media platforms (e.g., TV, Web, hands-on materials), assuming that the benefits of learning from multiple media extend beyond those gained from one medium alone. Yet research typically has investigated learning from a single medium in isolation. This paper reviews several recent studies to explore cross-platform learning (i.e., learning from combined use of multiple media platforms) and how such learning compares to learning from one medium. The paper discusses unique benefits of cross-platform learning, a theoretical mechanism to explain how these benefits might arise, and questions for future research in this emerging field. Copyright © 2013 Wiley Periodicals, Inc., A Wiley Company.

Exploring Contextual Models in Chemical Patent Search

NASA Astrophysics Data System (ADS)

Urbain, Jay; Frieder, Ophir

We explore the development of probabilistic retrieval models for integrating term statistics with entity search using multiple levels of document context to improve the performance of chemical patent search. A distributed indexing model was developed to enable efficient named entity search and aggregation of term statistics at multiple levels of patent structure including individual words, sentences, claims, descriptions, abstracts, and titles. The system can be scaled to an arbitrary number of compute instances in a cloud computing environment to support concurrent indexing and query processing operations on large patent collections.

Recognition of emotions using multimodal physiological signals and an ensemble deep learning model.

PubMed

Yin, Zhong; Zhao, Mengyuan; Wang, Yongxiong; Yang, Jingdong; Zhang, Jianhua

2017-03-01

Using deep-learning methodologies to analyze multimodal physiological signals becomes increasingly attractive for recognizing human emotions. However, the conventional deep emotion classifiers may suffer from the drawback of the lack of the expertise for determining model structure and the oversimplification of combining multimodal feature abstractions. In this study, a multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) is proposed for recognizing emotions, in which the deep structure is identified based on a physiological-data-driven approach. Each SAE consists of three hidden layers to filter the unwanted noise in the physiological features and derives the stable feature representations. An additional deep model is used to achieve the SAE ensembles. The physiological features are split into several subsets according to different feature extraction approaches with each subset separately encoded by a SAE. The derived SAE abstractions are combined according to the physiological modality to create six sets of encodings, which are then fed to a three-layer, adjacent-graph-based network for feature fusion. The fused features are used to recognize binary arousal or valence states. DEAP multimodal database was employed to validate the performance of the MESAE. By comparing with the best existing emotion classifier, the mean of classification rate and F-score improves by 5.26%. The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Older Adults with Mild Cognitive Impairments Show Less Driving Errors after a Multiple Sessions Simulator Training Program but Do Not Exhibit Long Term Retention.

PubMed

Teasdale, Normand; Simoneau, Martin; Hudon, Lisa; Germain Robitaille, Mathieu; Moszkowicz, Thierry; Laurendeau, Denis; Bherer, Louis; Duchesne, Simon; Hudon, Carol

2016-01-01

The driving performance of individuals with mild cognitive impairment (MCI) is suboptimal when compared to healthy older adults. It is expected that the driving will worsen with the progression of the cognitive decline and thus, whether or not these individuals should continue to drive is a matter of debate. The aim of the study was to provide support to the claim that individuals with MCI can benefit from a training program and improve their overall driving performance in a driving simulator. Fifteen older drivers with MCI participated in five training sessions in a simulator (over a 21-day period) and in a 6-month recall session. During training, they received automated auditory feedback on their performance when an error was noted about various maneuvers known to be suboptimal in MCI individuals (for instance, weaving, omitting to indicate a lane change, to verify a blind spot, or to engage in a visual search before crossing an intersection). The number of errors was compiled for eight different maneuvers for all sessions. For the initial five sessions, a gradual and significant decrease in the number of errors was observed, indicating learning and safer driving. The level of performance, however, was not maintained at the 6-month recall session. Nevertheless, the initial learning observed opens up possibilities to undertake more regular interventions to maintain driving skills and safe driving in MCI individuals.

Multiple-choice pretesting potentiates learning of related information.

PubMed

Little, Jeri L; Bjork, Elizabeth Ligon

2016-10-01

Although the testing effect has received a substantial amount of empirical attention, such research has largely focused on the effects of tests given after study. The present research examines the effect of using tests prior to study (i.e., as pretests), focusing particularly on how pretesting influences the subsequent learning of information that is not itself pretested but that is related to the pretested information. In Experiment 1, we found that multiple-choice pretesting was better for the learning of such related information than was cued-recall pretesting or a pre-fact-study control condition. In Experiment 2, we found that the increased learning of non-pretested related information following multiple-choice testing could not be attributed to increased time allocated to that information during subsequent study. Last, in Experiment 3, we showed that the benefits of multiple-choice pretesting over cued-recall pretesting for the learning of related information persist over 48 hours, thus demonstrating the promise of multiple-choice pretesting to potentiate learning in educational contexts. A possible explanation for the observed benefits of multiple-choice pretesting for enhancing the effectiveness with which related nontested information is learned during subsequent study is discussed.

SkICAT: A cataloging and analysis tool for wide field imaging surveys

NASA Technical Reports Server (NTRS)

Weir, N.; Fayyad, U. M.; Djorgovski, S. G.; Roden, J.

1992-01-01

We describe an integrated system, SkICAT (Sky Image Cataloging and Analysis Tool), for the automated reduction and analysis of the Palomar Observatory-ST ScI Digitized Sky Survey. The Survey will consist of the complete digitization of the photographic Second Palomar Observatory Sky Survey (POSS-II) in three bands, comprising nearly three Terabytes of pixel data. SkICAT applies a combination of existing packages, including FOCAS for basic image detection and measurement and SAS for database management, as well as custom software, to the task of managing this wealth of data. One of the most novel aspects of the system is its method of object classification. Using state-of-theart machine learning classification techniques (GID3* and O-BTree), we have developed a powerful method for automatically distinguishing point sources from non-point sources and artifacts, achieving comparably accurate discrimination a full magnitude fainter than in previous Schmidt plate surveys. The learning algorithms produce decision trees for classification by examining instances of objects classified by eye on both plate and higher quality CCD data. The same techniques will be applied to perform higher-level object classification (e.g., of galaxy morphology) in the near future. Another key feature of the system is the facility to integrate the catalogs from multiple plates (and portions thereof) to construct a single catalog of uniform calibration and quality down to the faintest limits of the survey. SkICAT also provides a variety of data analysis and exploration tools for the scientific utilization of the resulting catalogs. We include initial results of applying this system to measure the counts and distribution of galaxies in two bands down to Bj is approximately 21 mag over an approximate 70 square degree multi-plate field from POSS-II. SkICAT is constructed in a modular and general fashion and should be readily adaptable to other large-scale imaging surveys.

Lessons Learned From Developing Three Generations of Remote Sensing Science Data Processing Systems

NASA Technical Reports Server (NTRS)

Tilmes, Curt; Fleig, Albert J.

2005-01-01

The Biospheric Information Systems Branch at NASA s Goddard Space Flight Center has developed three generations of Science Investigator-led Processing Systems for use with various remote sensing instruments. The first system is used for data from the MODIS instruments flown on NASA s Earth Observing Systems @OS) Terra and Aqua Spacecraft launched in 1999 and 2002 respectively. The second generation is for the Ozone Measuring Instrument flying on the EOS Aura spacecraft launched in 2004. We are now developing a third generation of the system for evaluation science data processing for the Ozone Mapping and Profiler Suite (OMPS) to be flown by the NPOESS Preparatory Project (NPP) in 2006. The initial system was based on large scale proprietary hardware, operating and database systems. The current OMI system and the OMPS system being developed are based on commodity hardware, the LINUX Operating System and on PostgreSQL, an Open Source RDBMS. The new system distributes its data archive across multiple server hosts and processes jobs on multiple processor boxes. We have created several instances of this system, including one for operational processing, one for testing and reprocessing and one for applications development and scientific analysis. Prior to receiving the first data from OMI we applied the system to reprocessing information from the Solar Backscatter Ultraviolet (SBUV) and Total Ozone Mapping Spectrometer (TOMS) instruments flown from 1978 until now. The system was able to process 25 years (108,000 orbits) of data and produce 800,000 files (400 GiB) of level 2 and level 3 products in less than a week. We will describe the lessons we have learned and tradeoffs between system design, hardware, operating systems, operational staffing, user support and operational procedures. During each generational phase, the system has become more generic and reusable. While the system is not currently shrink wrapped we believe it is to the point where it could be readily adopted, with substantial cost savings, for other similar tasks.

Multiple Objects Fusion Tracker Using a Matching Network for Adaptively Represented Instance Pairs

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

Multiple-object tracking is affected by various sources of distortion, such as occlusion, illumination variations and motion changes. Overcoming these distortions by tracking on RGB frames, such as shifting, has limitations because of material distortions caused by RGB frames. To overcome these distortions, we propose a multiple-object fusion tracker (MOFT), which uses a combination of 3D point clouds and corresponding RGB frames. The MOFT uses a matching function initialized on large-scale external sequences to determine which candidates in the current frame match with the target object in the previous frame. After conducting tracking on a few frames, the initialized matching function is fine-tuned according to the appearance models of target objects. The fine-tuning process of the matching function is constructed as a structured form with diverse matching function branches. In general multiple object tracking situations, scale variations for a scene occur depending on the distance between the target objects and the sensors. If the target objects in various scales are equally represented with the same strategy, information losses will occur for any representation of the target objects. In this paper, the output map of the convolutional layer obtained from a pre-trained convolutional neural network is used to adaptively represent instances without information loss. In addition, MOFT fuses the tracking results obtained from each modality at the decision level to compensate the tracking failures of each modality using basic belief assignment, rather than fusing modalities by selectively using the features of each modality. Experimental results indicate that the proposed tracker provides state-of-the-art performance considering multiple objects tracking (MOT) and KITTIbenchmarks. PMID:28420194

Using ontology databases for scalable query answering, inconsistency detection, and data integration

PubMed Central

Dou, Dejing

2011-01-01

An ontology database is a basic relational database management system that models an ontology plus its instances. To reason over the transitive closure of instances in the subsumption hierarchy, for example, an ontology database can either unfold views at query time or propagate assertions using triggers at load time. In this paper, we use existing benchmarks to evaluate our method—using triggers—and we demonstrate that by forward computing inferences, we not only improve query time, but the improvement appears to cost only more space (not time). However, we go on to show that the true penalties were simply opaque to the benchmark, i.e., the benchmark inadequately captures load-time costs. We have applied our methods to two case studies in biomedicine, using ontologies and data from genetics and neuroscience to illustrate two important applications: first, ontology databases answer ontology-based queries effectively; second, using triggers, ontology databases detect instance-based inconsistencies—something not possible using views. Finally, we demonstrate how to extend our methods to perform data integration across multiple, distributed ontology databases. PMID:22163378

Blended learning in health education: three case studies.

PubMed

de Jong, Nynke; Savin-Baden, Maggi; Cunningham, Anne Marie; Verstegen, Daniëlle M L

2014-09-01

Blended learning in which online education is combined with face-to-face education is especially useful for (future) health care professionals who need to keep up-to-date. Blended learning can make learning more efficient, for instance by removing barriers of time and distance. In the past distance-based learning activities have often been associated with traditional delivery-based methods, individual learning and limited contact. The central question in this paper is: can blended learning be active and collaborative? Three cases of blended, active and collaborative learning are presented. In case 1 a virtual classroom is used to realize online problem-based learning (PBL). In case 2 PBL cases are presented in Second Life, a 3D immersive virtual world. In case 3 discussion forums, blogs and wikis were used. In all cases face-to-face meetings were also organized. Evaluation results of the three cases clearly show that active, collaborative learning at a distance is possible. Blended learning enables the use of novel instructional methods and student-centred education. The three cases employ different educational methods, thus illustrating diverse possibilities and a variety of learning activities in blended learning. Interaction and communication rules, the role of the teacher, careful selection of collaboration tools and technical preparation should be considered when designing and implementing blended learning.

Influence of Perceptual Saliency Hierarchy on Learning of Language Structures: An Artificial Language Learning Experiment

PubMed Central

Gong, Tao; Lam, Yau W.; Shuai, Lan

2016-01-01

Psychological experiments have revealed that in normal visual perception of humans, color cues are more salient than shape cues, which are more salient than textural patterns. We carried out an artificial language learning experiment to study whether such perceptual saliency hierarchy (color > shape > texture) influences the learning of orders regulating adjectives of involved visual features in a manner either congruent (expressing a salient feature in a salient part of the form) or incongruent (expressing a salient feature in a less salient part of the form) with that hierarchy. Results showed that within a few rounds of learning participants could learn the compositional segments encoding the visual features and the order between them, generalize the learned knowledge to unseen instances with the same or different orders, and show learning biases for orders that are congruent with the perceptual saliency hierarchy. Although the learning performances for both the biased and unbiased orders became similar given more learning trials, our study confirms that this type of individual perceptual constraint could contribute to the structural configuration of language, and points out that such constraint, as well as other factors, could collectively affect the structural diversity in languages. PMID:28066281

Influence of Perceptual Saliency Hierarchy on Learning of Language Structures: An Artificial Language Learning Experiment.

PubMed

Gong, Tao; Lam, Yau W; Shuai, Lan

2016-01-01

Psychological experiments have revealed that in normal visual perception of humans, color cues are more salient than shape cues, which are more salient than textural patterns. We carried out an artificial language learning experiment to study whether such perceptual saliency hierarchy (color > shape > texture) influences the learning of orders regulating adjectives of involved visual features in a manner either congruent (expressing a salient feature in a salient part of the form) or incongruent (expressing a salient feature in a less salient part of the form) with that hierarchy. Results showed that within a few rounds of learning participants could learn the compositional segments encoding the visual features and the order between them, generalize the learned knowledge to unseen instances with the same or different orders, and show learning biases for orders that are congruent with the perceptual saliency hierarchy. Although the learning performances for both the biased and unbiased orders became similar given more learning trials, our study confirms that this type of individual perceptual constraint could contribute to the structural configuration of language, and points out that such constraint, as well as other factors, could collectively affect the structural diversity in languages.

Deformable segmentation via sparse representation and dictionary learning.

PubMed

Zhang, Shaoting; Zhan, Yiqiang; Metaxas, Dimitris N

2012-10-01

"Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling method has been proposed based on sparse learning theory. Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC). SSC is robust to non-Gaussian errors and still preserves individual shape characteristics even when such characteristics is not statistically significant. Although it seems straightforward to incorporate SSC into a deformable segmentation framework as shape priors, the large-scale sparse optimization of SSC has low runtime efficiency, which cannot satisfy clinical requirements. In this paper, we design two strategies to decrease the computational complexity of SSC, making a robust, accurate and efficient deformable segmentation system. (1) When the shape repository contains a large number of instances, which is often the case in 2D problems, K-SVD is used to learn a more compact but still informative shape dictionary. (2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, an affinity propagation method is used to partition the surface into small sub-regions, on which the sparse shape composition is performed locally. Both strategies dramatically decrease the scale of the sparse optimization problem and hence speed up the algorithm. Our method is applied on a diverse set of biomedical image analysis problems. Compared to the original SSC, these two newly-proposed modules not only significant reduce the computational complexity, but also improve the overall accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV).

PubMed

Piette, Elizabeth R; Moore, Jason H

2018-01-01

Machine learning methods and conventions are increasingly employed for the analysis of large, complex biomedical data sets, including genome-wide association studies (GWAS). Reproducibility of machine learning analyses of GWAS can be hampered by biological and statistical factors, particularly so for the investigation of non-additive genetic interactions. Application of traditional cross validation to a GWAS data set may result in poor consistency between the training and testing data set splits due to an imbalance of the interaction genotypes relative to the data as a whole. We propose a new cross validation method, proportional instance cross validation (PICV), that preserves the original distribution of an independent variable when splitting the data set into training and testing partitions. We apply PICV to simulated GWAS data with epistatic interactions of varying minor allele frequencies and prevalences and compare performance to that of a traditional cross validation procedure in which individuals are randomly allocated to training and testing partitions. Sensitivity and positive predictive value are significantly improved across all tested scenarios for PICV compared to traditional cross validation. We also apply PICV to GWAS data from a study of primary open-angle glaucoma to investigate a previously-reported interaction, which fails to significantly replicate; PICV however improves the consistency of testing and training results. Application of traditional machine learning procedures to biomedical data may require modifications to better suit intrinsic characteristics of the data, such as the potential for highly imbalanced genotype distributions in the case of epistasis detection. The reproducibility of genetic interaction findings can be improved by considering this variable imbalance in cross validation implementation, such as with PICV. This approach may be extended to problems in other domains in which imbalanced variable distributions are a concern.

Confidence-Based Feature Acquisition

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; desJardins, Marie; MacGlashan, James

2010-01-01

Confidence-based Feature Acquisition (CFA) is a novel, supervised learning method for acquiring missing feature values when there is missing data at both training (learning) and test (deployment) time. To train a machine learning classifier, data is encoded with a series of input features describing each item. In some applications, the training data may have missing values for some of the features, which can be acquired at a given cost. A relevant JPL example is that of the Mars rover exploration in which the features are obtained from a variety of different instruments, with different power consumption and integration time costs. The challenge is to decide which features will lead to increased classification performance and are therefore worth acquiring (paying the cost). To solve this problem, CFA, which is made up of two algorithms (CFA-train and CFA-predict), has been designed to greedily minimize total acquisition cost (during training and testing) while aiming for a specific accuracy level (specified as a confidence threshold). With this method, it is assumed that there is a nonempty subset of features that are free; that is, every instance in the data set includes these features initially for zero cost. It is also assumed that the feature acquisition (FA) cost associated with each feature is known in advance, and that the FA cost for a given feature is the same for all instances. Finally, CFA requires that the base-level classifiers produce not only a classification, but also a confidence (or posterior probability).

Active Learning in the Middle Grades Classroom: Overcoming the Barriers to Implementation

ERIC Educational Resources Information Center

Edwards, Susan

2015-01-01

The Association for Middle Level Education advocates for instruction that incorporates active learning and multiple learning approaches in middle grades classrooms. The aim of this qualitative study was to examine middle level teachers who are able to implement active learning and multiple learning approaches within the standardized testing and…

Assessment of Innovation Competency: A Thematic Analysis of Upper Secondary School Teachers' Talk

ERIC Educational Resources Information Center

Nielsen, Jan Alexis

2015-01-01

The author employed a 3-step qualitative research design with multiple instances of source validation to capture expert teachers' (n = 28) reflections on which manifest signs they would look for when they asses students' innovation competency. The author reports on the thematic analysis of the recorded talk in interaction that occurred in teacher…

Conjunction Illusions and Conjunction Fallacies in Episodic Memory

ERIC Educational Resources Information Center

Brainerd, C. J.; Holliday, Robyn E.; Nakamura, Koyuki; Reyna, Valerie F.

2014-01-01

Recent research on the overdistribution principle implies that episodic memory is infected by conjunction illusions. These are instances in which an item that was presented in a single context (e.g., List 1) is falsely remembered as having been presented in multiple contexts (e.g., List 1 and List 2). Robust conjunction illusions were detected in…

34 CFR Appendix C to Part 300 - National Instructional Materials Accessibility Standard (NIMAS)

Code of Federal Regulations, 2013 CFR

2013-07-01

... + “-NIMAS”—exact format to be determined). dc:Language (one instance, or multiple in the case of a foreign.../ c. Block elements author Identifies the writer of a work other than this one. Contrast with , which... heading (generally only one, possibly with ), and an intermixture of list items and . If bullets and...

34 CFR Appendix C to Part 300 - National Instructional Materials Accessibility Standard (NIMAS)

Code of Federal Regulations, 2012 CFR

2012-07-01

... + “-NIMAS”—exact format to be determined). dc:Language (one instance, or multiple in the case of a foreign.../ c. Block elements author Identifies the writer of a work other than this one. Contrast with , which... heading (generally only one, possibly with ), and an intermixture of list items and . If bullets and...

34 CFR Appendix C to Part 300 - National Instructional Materials Accessibility Standard (NIMAS)

Code of Federal Regulations, 2014 CFR

2014-07-01

... + “-NIMAS”—exact format to be determined). dc:Language (one instance, or multiple in the case of a foreign.../ c. Block elements author Identifies the writer of a work other than this one. Contrast with , which... heading (generally only one, possibly with ), and an intermixture of list items and . If bullets and...

Computation of Effect Size for Moderating Effects of Categorical Variables in Multiple Regression

ERIC Educational Resources Information Center

Aguinis, Herman; Pierce, Charles A.

2006-01-01

The computation and reporting of effect size estimates is becoming the norm in many journals in psychology and related disciplines. Despite the increased importance of effect sizes, researchers may not report them or may report inaccurate values because of a lack of appropriate computational tools. For instance, Pierce, Block, and Aguinis (2004)…

Effective organizational control: implications for academic medicine.

PubMed

Wilkes, Michael S; Srinivasan, Malathi; Flamholtz, Eric

2005-11-01

This article provides a framework for understanding the nature, role, functioning, design, and effects of organizational oversight systems. Using a case study with elements recognizable to an academic audience, the authors explore how a dean of a fictitious School of Medicine might use organizational control structures to develop effective solutions to global disarray within the academic medical center. Organizational control systems are intended to help influence the behavior of people as members of a formal organization. They are necessary to motivate people toward organizational goals, to coordinate diverse efforts, and to provide feedback about problems. The authors present a model of control to make this process more visible within organizations. They explore the overlap among academic medical centers and large businesses-for instance, each is a billion-dollar enterprise with complex internal and external demands and multiple audiences. The authors identify and describe how to use the key components of an organization's control system: environment, culture, structure, and core control system. Elements of the core control system are identified, described, and explored. These closely articulating elements include planning, operations, measurement, evaluation, and feedback systems. Use of control portfolios is explored to achieve goal-outcome congruence. Additionally, the authors describe how the components of the control system can be used synergistically by academic leadership to create organizational change, congruent with larger organizational goals. The enterprise of medicine is quickly learning from the enterprise of business. Achieving goal-action congruence will better position academic medicine to meet its multiple missions.

Connecting a cognitive architecture to robotic perception

NASA Astrophysics Data System (ADS)

Kurup, Unmesh; Lebiere, Christian; Stentz, Anthony; Hebert, Martial

2012-06-01

We present an integrated architecture in which perception and cognition interact and provide information to each other leading to improved performance in real-world situations. Our system integrates the Felzenswalb et. al. object-detection algorithm with the ACT-R cognitive architecture. The targeted task is to predict and classify pedestrian behavior in a checkpoint scenario, most specifically to discriminate between normal versus checkpoint-avoiding behavior. The Felzenswalb algorithm is a learning-based algorithm for detecting and localizing objects in images. ACT-R is a cognitive architecture that has been successfully used to model human cognition with a high degree of fidelity on tasks ranging from basic decision-making to the control of complex systems such as driving or air traffic control. The Felzenswalb algorithm detects pedestrians in the image and provides ACT-R a set of features based primarily on their locations. ACT-R uses its pattern-matching capabilities, specifically its partial-matching and blending mechanisms, to track objects across multiple images and classify their behavior based on the sequence of observed features. ACT-R also provides feedback to the Felzenswalb algorithm in the form of expected object locations that allow the algorithm to eliminate false-positives and improve its overall performance. This capability is an instance of the benefits pursued in developing a richer interaction between bottom-up perceptual processes and top-down goal-directed cognition. We trained the system on individual behaviors (only one person in the scene) and evaluated its performance across single and multiple behavior sets.

Signature detection and matching for document image retrieval.

PubMed

Zhu, Guangyu; Zheng, Yefeng; Doermann, David; Jaeger, Stefan

2009-11-01

As one of the most pervasive methods of individual identification and document authentication, signatures present convincing evidence and provide an important form of indexing for effective document image processing and retrieval in a broad range of applications. However, detection and segmentation of free-form objects such as signatures from clustered background is currently an open document analysis problem. In this paper, we focus on two fundamental problems in signature-based document image retrieval. First, we propose a novel multiscale approach to jointly detecting and segmenting signatures from document images. Rather than focusing on local features that typically have large variations, our approach captures the structural saliency using a signature production model and computes the dynamic curvature of 2D contour fragments over multiple scales. This detection framework is general and computationally tractable. Second, we treat the problem of signature retrieval in the unconstrained setting of translation, scale, and rotation invariant nonrigid shape matching. We propose two novel measures of shape dissimilarity based on anisotropic scaling and registration residual error and present a supervised learning framework for combining complementary shape information from different dissimilarity metrics using LDA. We quantitatively study state-of-the-art shape representations, shape matching algorithms, measures of dissimilarity, and the use of multiple instances as query in document image retrieval. We further demonstrate our matching techniques in offline signature verification. Extensive experiments using large real-world collections of English and Arabic machine-printed and handwritten documents demonstrate the excellent performance of our approaches.

Executive function skills and academic achievement gains in prekindergarten: Contributions of learning-related behaviors.

PubMed

Nesbitt, Kimberly Turner; Farran, Dale Clark; Fuhs, Mary Wagner

2015-07-01

Although research suggests associations between children's executive function skills and their academic achievement, the specific mechanisms that may help explain these associations in early childhood are unclear. This study examined whether children's (N = 1,103; M age = 54.5 months) executive function skills at the beginning of prekindergarten (pre-K) predict their learning-related behaviors in the classroom and whether these behaviors then mediate associations between children's executive function skills and their pre-K literacy, language, and mathematic gains. Learning-related behaviors were quantified in terms of (a) higher levels of involvement in learning opportunities; (b) greater frequency of participation in activities that require sequential steps; (c) more participation in social-learning interactions; and (d) less instances of being unoccupied, disruptive, or in time out. Results indicated that children's learning-related behaviors mediated associations between executive function skills and literacy and mathematics gains through children's level of involvement, sequential learning behaviors, and disengagement from the classroom. The implications of the findings for early childhood education are discussed. (c) 2015 APA, all rights reserved).

The effectiveness of music as a mnemonic device on recognition memory for people with multiple sclerosis.

PubMed

Moore, Kimberly Sena; Peterson, David A; O'Shea, Geoffrey; McIntosh, Gerald C; Thaut, Michael H

2008-01-01

Research shows that people with multiple sclerosis exhibit learning and memory difficulties and that music can be used successfully as a mnemonic device to aid in learning and memory. However, there is currently no research investigating the effectiveness of music mnemonics as a compensatory learning strategy for people with multiple sclerosis. Participants with clinically definitive multiple sclerosis (N = 38) were given a verbal learning and memory test. Results from a recognition memory task were analyzed that compared learning through music (n = 20) versus learning through speech (n = 18). Preliminary baseline neuropsychological data were collected that measured executive functioning skills, learning and memory abilities, sustained attention, and level of disability. An independent samples t test showed no significant difference between groups on baseline neuropsychological functioning or on recognition task measures. Correlation analyses suggest that music mnemonics may facilitate learning for people who are less impaired by the disease. Implications for future research are discussed.

Iterative-method performance evaluation for multiple vectors associated with a large-scale sparse matrix

NASA Astrophysics Data System (ADS)

Imamura, Seigo; Ono, Kenji; Yokokawa, Mitsuo

2016-07-01

Ensemble computing, which is an instance of capacity computing, is an effective computing scenario for exascale parallel supercomputers. In ensemble computing, there are multiple linear systems associated with a common coefficient matrix. We improve the performance of iterative solvers for multiple vectors by solving them at the same time, that is, by solving for the product of the matrices. We implemented several iterative methods and compared their performance. The maximum performance on Sparc VIIIfx was 7.6 times higher than that of a naïve implementation. Finally, to deal with the different convergence processes of linear systems, we introduced a control method to eliminate the calculation of already converged vectors.

Studying Different Tasks of Implicit Learning across Multiple Test Sessions Conducted on the Web

PubMed Central

Sævland, Werner; Norman, Elisabeth

2016-01-01

Implicit learning is usually studied through individual performance on a single task, with the most common tasks being the Serial Reaction Time (SRT) task, the Dynamic System Control (DSC) task, and Artificial Grammar Learning (AGL). Few attempts have been made to compare performance across different implicit learning tasks within the same study. The current study was designed to explore the relationship between performance on the DSC Sugar factory task and the Alternating Serial Reaction Time (ASRT) task. We also addressed another limitation of 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. In everyday situations, implicit learning is assumed to involve a gradual accumulation of knowledge across several learning episodes over a longer time span. One way to increase the ecological validity of implicit learning experiments could be to present the learning material repeatedly across shorter test sessions. This can most easily be done by using a web-based setup in which participants can access the material 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 multiple session condition. Learning occurred on both tasks, and awareness measures suggested that acquired knowledge was not fully conscious on either of the tasks. Learning and the degree of conscious awareness of the learned regularities were compared across conditions and tasks. On the DSC task, performance was not affected by whether learning had taken place in one or over multiple sessions. On the ASRT task, RT improvement across blocks was larger in the multiple-session condition. Learning in the two tasks was not related. PMID:27375512

Narb-Based Analysis of Tweets Related to United Airlines Controversy: Learning beyond the Media

ERIC Educational Resources Information Center

Mitra, Ananda

2017-01-01

The use of narrative bits--narbs--has been discussed as an alternative means of looking at opinions of those who are producing narbs, for instance, in the form of tweets. The American carrier, United Airlines, came under media attention in April 2016 when a passenger was forcibly removed from a flight. This resulted in a spike in tweets around the…

The Prospects for "E-Learning" Revolution in "Education": A "Philosophical Analysis"

ERIC Educational Resources Information Center

Gunga, Samson O.; Ricketts, Ian W.

2008-01-01

If I lose my key in Canada, for instance, and I search for it in the United Kingdom, how long will I take to find it? This paper argues that problems in education are caused by non-professional teachers who are employed when trained teachers move in search of promotion friendly activities or financially rewarding duties. This shift of focus means…

Classical conditioning in the treatment of psoriasis.

PubMed

Ader, R

2000-11-01

It has been argued that the placebo effect represents a learned response. Research is suggested to address the utility of applying principles derived from classical (Pavlovian) conditioning to the design of drug treatment protocols. In the present instance, it is hypothesized that, by capitalizing on conditioned pharmacotherapeutic responses, it may be possible to reduce the cumulative amount of corticosteroid medication used in the treatment of psoriasis.

The Impact of Language Play-Oriented Tasks with Planned Focus on Form on Iranian EFL Learners' Accuracy in Controlled Writings

ERIC Educational Resources Information Center

Gholami, Javad; Gholizadeh, Mitra

2015-01-01

Language play and its effects on second language learning have been addressed by many scholars in recent years with instances of language play being identified both inside and outside the classroom. However, only a few have integrated language play with classroom tasks, and they just sufficed to the qualitative analyses of the learners'…

Accounting for Discrepancies in Teachers' Attitudes towards Evidence Use and Actual Instances of Evidence Use in Schools

ERIC Educational Resources Information Center

Brown, Chris; Zhang, Dell

2017-01-01

While beneficial, the consistent and regular use of evidence to improve teaching and learning is proving difficult to achieve in practice. This paper attempts to shed new light on this issue by examining the question: "If using evidence to inform teaching practice is rational behaviour, why aren't all teachers engaged in it?" It first…

The Post-9/11 G.I. Bill: A Catalyst to Change Service Voluntary Education Programs

DTIC Science & Technology

2011-06-17

another significant consequence to the enrollment explosion. In many instances classroom spaces, dormitories and laboratories had to be expanded to...meet demand. Classrooms filled to capacity and dormitories were inadequate for this new population of students (including the addition of married...even in Afghanistan.5 Whether VolEd participation occurs in a classroom or through Distance Learning, education is a

DIXIE TEACHERS REPORT THEIR PUPILS LEARN WELL IN MIXED-RACE SCHOOLS. NEGRO STUDENTS LAG AT FIRST BUT STUDY HARD TO CATCH UP WITH WHITES, THEY SAY.

ERIC Educational Resources Information Center

TANNER, JAMES C.

INCLUDED ARE NUMEROUS EXAMPLES SHOWING THAT THE SOUTHERN SEGREGATIONISTS' ARGUMENTS AGAINST SCHOOL INTEGRATION ARE FACTUALLY UNFOUNDED. IN MOST INSTANCES THE PERFORMANCE OF BOTH NEGRO AND WHITE STUDENTS HAS INCREASED MARKEDLY WHEN SCHOOLS ARE INTEGRATED. INITIALLY, NEGRO STUDENTS ARE USUALLY BEHIND THEIR WHITE COUNTERPARTS BY 1 OR 2 YEARS, BUT…

The Dark Matter of Lab Work: Illuminating the Negotiation of Disciplined Perception in Mechanics

ERIC Educational Resources Information Center

Lindwall, Oskar; Lymer, Gustav

2008-01-01

This study examines the practical work of a pair of students and an instructor using probeware in a mechanics lab. The aim of the study is to describe and discuss a type of interactional sequence that we refer to as "dark matter", the ordinary backdrop to the extraordinary sequences that are easily recognizable as clear-cut instances of learning.…

School Completion: What We Learn from Different Measures of Family Background. Longitudinal Surveys of Australian Youth. Research Report 59

ERIC Educational Resources Information Center

Homel, Jacqueline; Mavisakalyan, Astghik; Nguyen, Ha Trong; Ryan, Chris

2012-01-01

This paper examines how disadvantage affects educational outcomes, in this instance, Year 12 completion. While previous studies have found a strong link between parental education or occupational status and Year 12 completion, this research was able to capture a broader set of cultural, material and resource aspects of disadvantage. It did this by…

Deep neural networks for modeling visual perceptual learning.

PubMed

Wenliang, Li; Seitz, Aaron R

2018-05-23

Understanding visual perceptual learning (VPL) has become increasingly more challenging as new phenomena are discovered with novel stimuli and training paradigms. While existing models aid our knowledge of critical aspects of VPL, the connections shown by these models between behavioral learning and plasticity across different brain areas are typically superficial. Most models explain VPL as readout from simple perceptual representations to decision areas and are not easily adaptable to explain new findings. Here, we show that a well-known instance of deep neural network (DNN), while not designed specifically for VPL, provides a computational model of VPL with enough complexity to be studied at many levels of analyses. After learning a Gabor orientation discrimination task, the DNN model reproduced key behavioral results, including increasing specificity with higher task precision, and also suggested that learning precise discriminations could asymmetrically transfer to coarse discriminations when the stimulus conditions varied. In line with the behavioral findings, the distribution of plasticity moved towards lower layers when task precision increased, and this distribution was also modulated by tasks with different stimulus types. Furthermore, learning in the network units demonstrated close resemblance to extant electrophysiological recordings in monkey visual areas. Altogether, the DNN fulfilled predictions of existing theories regarding specificity and plasticity, and reproduced findings of tuning changes in neurons of the primate visual areas. Although the comparisons were mostly qualitative, the DNN provides a new method of studying VPL and can serve as a testbed for theories and assist in generating predictions for physiological investigations. SIGNIFICANCE STATEMENT Visual perceptual learning (VPL) has been found to cause changes at multiple stages of the visual hierarchy. We found that training a deep neural network (DNN) on an orientation discrimination task produced similar behavioral and physiological patterns found in human and monkey experiments. Unlike existing VPL models, the DNN was pre-trained on natural images to reach high performance in object recognition but was not designed specifically for VPL, and yet it fulfilled predictions of existing theories regarding specificity and plasticity, and reproduced findings of tuning changes in neurons of the primate visual areas. When used with care, this unbiased and deep-hierarchical model can provide new ways of studying VPL from behavior to physiology. Copyright © 2018 the authors.

Learning-Related Shifts in Generalization Gradients for Complex Sounds

PubMed Central

Wisniewski, Matthew G.; Church, Barbara A.; Mercado, Eduardo

2010-01-01

Learning to discriminate stimuli can alter how one distinguishes related stimuli. For instance, training an individual to differentiate between two stimuli along a single dimension can alter how that individual generalizes learned responses. In this study, we examined the persistence of shifts in generalization gradients after training with sounds. University students were trained to differentiate two sounds that varied along a complex acoustic dimension. Students subsequently were tested on their ability to recognize a sound they experienced during training when it was presented among several novel sounds varying along this same dimension. Peak shift was observed in Experiment 1 when generalization tests immediately followed training, and in Experiment 2 when tests were delayed by 24 hours. These findings further support the universality of generalization processes across species, modalities, and levels of stimulus complexity. They also raise new questions about the mechanisms underlying learning-related shifts in generalization gradients. PMID:19815929

Learning, Teaching and Ambiguity in Virtual Worlds

NASA Astrophysics Data System (ADS)

Carr, Diane; Oliver, Martin; Burn, Andrew

What might online communities and informal learning practices teach us about virtual world pedagogy? In this chapter we describe a research project in which learning practices in online worlds such as World of Warcraft and Second LifeTM (SL) were investigated. Working within an action research framework, we employed a range of methods to investigate how members of online communities define the worlds they encounter, negotiate the terms of participation, and manage the incremental complexity of game worlds. The implications of such practices for online pedagogy were then explored through teaching in SL. SL eludes simple definitions. Users, or "residents", of SL partake of a range of pleasures and activities - socialising, building, creating and exhibiting art, playing games, exploring, shopping, or running a business, for instance. We argue that the variable nature of SL gives rise to degrees of ambiguity. This ambiguity impacts on inworld social practices, and has significant implications for online teaching and learning.

Machine learning methods in chemoinformatics

PubMed Central

Mitchell, John B O

2014-01-01

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers. WIREs Comput Mol Sci 2014, 4:468–481. How to cite this article: WIREs Comput Mol Sci 2014, 4:468–481. doi:10.1002/wcms.1183 PMID:25285160

Sustainable Assessment for Large Science Classes: Non-Multiple Choice, Randomised Assignments through a Learning Management System

ERIC Educational Resources Information Center

Schultz, Madeleine

2011-01-01

This paper reports on the development of a tool that generates randomised, non-multiple choice assessment within the BlackBoard Learning Management System interface. An accepted weakness of multiple-choice assessment is that it cannot elicit learning outcomes from upper levels of Biggs' SOLO taxonomy. However, written assessment items require…

Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances.

PubMed

Al-Sahaf, Harith; Zhang, Mengjie; Johnston, Mark

2016-01-01

In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.

Development of an Adaptive Learning System with Multiple Perspectives based on Students' Learning Styles and Cognitive Styles

ERIC Educational Resources Information Center

Yang, Tzu-Chi; Hwang, Gwo-Jen; Yang, Stephen Jen-Hwa

2013-01-01

In this study, an adaptive learning system is developed by taking multiple dimensions of personalized features into account. A personalized presentation module is proposed for developing adaptive learning systems based on the field dependent/independent cognitive style model and the eight dimensions of Felder-Silverman's learning style. An…

Syntactic transfer in artificial grammar learning.

PubMed

Beesley, T; Wills, A J; Le Pelley, M E

2010-02-01

In an artificial grammar learning (AGL) experiment, participants were trained with instances of one grammatical structure before completing a test phase in which they were required to discriminate grammatical from randomly created strings. Importantly, the underlying structure used to generate test strings was different from that used to generate the training strings. Despite the fact that grammatical training strings were more similar to nongrammatical test strings than they were to grammatical test strings, this manipulation resulted in a positive transfer effect, as compared with controls trained with nongrammatical strings. It is suggested that training with grammatical strings leads to an appreciation of set variance that aids the detection of grammatical test strings in AGL tasks. The analysis presented demonstrates that it is useful to conceptualize test performance in AGL as a form of unsupervised category learning.