The Effect of Virtual versus Traditional Learning in Achieving Competency-Based Skills

ERIC Educational Resources Information Center

Mosalanejad, Leili; Shahsavari, Sakine; Sobhanian, Saeed; Dastpak, Mehdi

2012-01-01

Background: By rapid developing of the network technology, the internet-based learning methods are substituting the traditional classrooms making them expand to the virtual network learning environment. The purpose of this study was to determine the effectiveness of virtual systems on competency-based skills of first-year nursing students.…

Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network.

PubMed

Jeng, J T; Lee, T T

2000-01-01

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

Social Networks-Based Adaptive Pairing Strategy for Cooperative Learning

ERIC Educational Resources Information Center

Chuang, Po-Jen; Chiang, Ming-Chao; Yang, Chu-Sing; Tsai, Chun-Wei

2012-01-01

In this paper, we propose a grouping strategy to enhance the learning and testing results of students, called Pairing Strategy (PS). The proposed method stems from the need of interactivity and the desire of cooperation in cooperative learning. Based on the social networks of students, PS provides members of the groups to learn from or mimic…

Synchronized Pair Configuration in Virtualization-Based Lab for Learning Computer Networks

ERIC Educational Resources Information Center

Kongcharoen, Chaknarin; Hwang, Wu-Yuin; Ghinea, Gheorghita

2017-01-01

More studies are concentrating on using virtualization-based labs to facilitate computer or network learning concepts. Some benefits are lower hardware costs and greater flexibility in reconfiguring computer and network environments. However, few studies have investigated effective mechanisms for using virtualization fully for collaboration.…

Formation of Community-Based Hypertension Practice Networks: Success, Obstacles, and Lessons Learned

PubMed Central

Dart, Richard A.; Egan, Brent M.

2014-01-01

Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper we review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O’QUIN) project, located at the Medical University of South Carolina. We highlight key lessons learned and new directions to be explored. PMID:24666425

Late Departures from Paper-Based to Supported Networked Learning in South Africa: Lessons Learned

ERIC Educational Resources Information Center

Kok, Illasha; Beter, Petra; Esterhuizen, Hennie

2018-01-01

Fragmented connectivity in South Africa is the dominant barrier for digitising initiatives. New insights surfaced when a university-based nursing programme introduced tablets within a supportive network learning environment. A qualitative, explorative design investigated adult nurses' experiences of the realities when moving from paper-based…

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

Networked Learning for Agricultural Extension: A Framework for Analysis and Two Cases

ERIC Educational Resources Information Center

Kelly, Nick; Bennett, John McLean; Starasts, Ann

2017-01-01

Purpose: This paper presents economic and pedagogical motivations for adopting information and communications technology (ICT)- mediated learning networks in agricultural education and extension. It proposes a framework for networked learning in agricultural extension and contributes a theoretical and case-based rationale for adopting the…

Learning Networks--Enabling Change through Community Action Research

ERIC Educational Resources Information Center

Bleach, Josephine

2016-01-01

Learning networks are a critical element of ethos of the community action research approach taken by the Early Learning Initiative at the National College of Ireland, a community-based educational initiative in the Dublin Docklands. Key criteria for networking, whether at local, national or international level, are the individual's and…

Integrating XQuery-Enabled SCORM XML Metadata Repositories into an RDF-Based E-Learning P2P Network

ERIC Educational Resources Information Center

Qu, Changtao; Nejdl, Wolfgang

2004-01-01

Edutella is an RDF-based E-Learning P2P network that is aimed to accommodate heterogeneous learning resource metadata repositories in a P2P manner and further facilitate the exchange of metadata between these repositories based on RDF. Whereas Edutella provides RDF metadata repositories with a quite natural integration approach, XML metadata…

Formation of community-based hypertension practice networks: success, obstacles, and lessons learned.

PubMed

Dart, Richard A; Egan, Brent M

2014-06-01

Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper, the authors review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O'QUIN) project, located at the Medical University of South Carolina. Key lessons learned and new directions to be explored are highlighted. ©2014 Wiley Periodicals, Inc.

GA-based fuzzy reinforcement learning for control of a magnetic bearing system.

PubMed

Lin, C T; Jou, C P

2000-01-01

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

Students' Feedback of mDPBL Approach and the Learning Impact towards Computer Networks Teaching and Learning

ERIC Educational Resources Information Center

Winarno, Sri; Muthu, Kalaiarasi Sonai; Ling, Lew Sook

2018-01-01

This study presents students' feedback and learning impact on design and development of a multimedia learning in Direct Problem-Based Learning approach (mDPBL) for Computer Networks in Dian Nuswantoro University, Indonesia. This study examined the usefulness, contents and navigation of the multimedia learning as well as learning impacts towards…

The networked student: A design-based research case study of student constructed personal learning environments in a middle school science course

NASA Astrophysics Data System (ADS)

Drexler, Wendy

This design-based research case study applied a networked learning approach to a seventh grade science class at a public school in the southeastern United States. Students adapted emerging Web applications to construct personal learning environments for in-depth scientific inquiry of poisonous and venomous life forms. The personal learning environments constructed used Application Programming Interface (API) widgets to access, organize, and synthesize content from a number of educational Internet resources and social network connections. This study examined the nature of personal learning environments; the processes students go through during construction, and patterns that emerged. The project was documented from both an instructional and student-design perspective. Findings revealed that students applied the processes of: practicing digital responsibility; practicing digital literacy; organizing content; collaborating and socializing; and synthesizing and creating. These processes informed a model of the networked student that will serve as a framework for future instructional designs. A networked learning approach that incorporates these processes into future designs has implications for student learning, teacher roles, professional development, administrative policies, and delivery. This work is significant in that it shifts the focus from technology innovations based on tools to student empowerment based on the processes required to support learning. It affirms the need for greater attention to digital literacy and responsibility in K12 schools as well as consideration for those skills students will need to achieve success in the 21st century. The design-based research case study provides a set of design principles for teachers to follow when facilitating student construction of personal learning environments.

Using smart mobile devices in social-network-based health education practice: a learning behavior analysis.

PubMed

Wu, Ting-Ting

2014-06-01

Virtual communities provide numerous resources, immediate feedback, and information sharing, enabling people to rapidly acquire information and knowledge and supporting diverse applications that facilitate interpersonal interactions, communication, and sharing. Moreover, incorporating highly mobile and convenient devices into practice-based courses can be advantageous in learning situations. Therefore, in this study, a tablet PC and Google+ were introduced to a health education practice course to elucidate satisfaction of learning module and conditions and analyze the sequence and frequency of learning behaviors during the social-network-based learning process. According to the analytical results, social networks can improve interaction among peers and between educators and students, particularly when these networks are used to search for data, post articles, engage in discussions, and communicate. In addition, most nursing students and nursing educators expressed a positive attitude and satisfaction toward these innovative teaching methods, and looked forward to continuing the use of this learning approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Institutionalizing Community-Based Learning and Research: The Case for External Networks

ERIC Educational Resources Information Center

Shrader, Elizabeth; Saunders, Mary Anne; Marullo, Sam; Benatti, Sylvia; Weigert, Kathleen Maas

2008-01-01

Conversations continue as to whether and how community-based learning and research (CBLR) can be most effectively integrated into the mission and practice of institutions of higher education (IHEs). In 2005, eight District of Columbia- (DC-) area universities affiliated with the Community Research and Learning (CoRAL) Network engaged in a planning…

Super-resolution reconstruction of MR image with a novel residual learning network algorithm

NASA Astrophysics Data System (ADS)

Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu

2018-04-01

Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

A European Languages Virtual Network Proposal

NASA Astrophysics Data System (ADS)

García-Peñalvo, Francisco José; González-González, Juan Carlos; Murray, Maria

ELVIN (European Languages Virtual Network) is a European Union (EU) Lifelong Learning Programme Project aimed at creating an informal social network to support and facilitate language learning. The ELVIN project aims to research and develop the connection between social networks, professional profiles and language learning in an informal educational context. At the core of the ELVIN project, there will be a web 2.0 social networking platform that connects employees/students for language practice based on their own professional/academic needs and abilities, using all relevant technologies. The ELVIN remit involves the examination of both methodological and technological issues inherent in achieving a social-based learning platform that provides the user with their own customized Personal Learning Environment for EU language acquisition. ELVIN started in November 2009 and this paper presents the project aims and objectives as well as the development and implementation of the web platform.

White blood cells identification system based on convolutional deep neural learning networks.

PubMed

Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

2017-11-16

White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

Hypermedia-Assisted Instruction and Second Language Learning: A Semantic-Network-Based Approach.

ERIC Educational Resources Information Center

Liu, Min

This literature review examines a hypermedia learning environment from a semantic network basis and the application of such an environment to second language learning. (A semantic network is defined as a conceptual representation of knowledge in human memory). The discussion is organized under the following headings and subheadings: (1) Advantages…

Praxis-based research networks: An emerging paradigm for research that is rigorous, relevant, and inclusive.

PubMed

Werner, James J; Stange, Kurt C

2014-01-01

Practice-based research networks (PBRNs) have developed a grounded approach to conducting practice-relevant and translational research in community practice settings. Seismic shifts in the health care landscape are shaping PBRNs that work across organizational and institutional margins to address complex problems. Praxis-based research networks combine PBRN knowledge generation with multistakeholder learning, experimentation, and application of practical knowledge. The catalytic processes in praxis-based research networks are cycles of action and reflection based on experience, observation, conceptualization, and experimentation by network members and partners. To facilitate co-learning and solution-building, these networks have a flexible architecture that allows pragmatic inclusion of stakeholders based on the demands of the problem and the needs of the network. Praxis-based research networks represent an evolving trend that combines the core values of PBRNs with new opportunities for relevance, rigor, and broad participation. © Copyright 2014 by the American Board of Family Medicine.

Learning In networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1995-01-01

Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deterministic nodes. Chain graphs are defined as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simplification process or to generate learning algorithms.

The Evolution of Networked Computing in the Teaching of Japanese as a Foreign Language.

ERIC Educational Resources Information Center

Harrison, Richard

1998-01-01

Reviews the evolution of Internet-based projects in Japanese computer-assisted language learning and suggests future directions in which the field may develop, based on emerging network technology and learning theory. (Author/VWL)

Lending, Learning, Leading: Developing Results-Based Leaders in Opportunity Finance

ERIC Educational Resources Information Center

Annie E. Casey Foundation, 2015

2015-01-01

This report tells the story of the CDFI Leadership Learning Network, a Casey Foundation initiative to equip leaders of community development finance institutions with the tools of results-based leadership (RBL). The Foundation shares lessons learned from the network, core RBL concepts and profiles of CDFI leaders as they apply RBL skills and tools…

Sea ice classification using fast learning neural networks

NASA Technical Reports Server (NTRS)

Dawson, M. S.; Fung, A. K.; Manry, M. T.

1992-01-01

A first learning neural network approach to the classification of sea ice is presented. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) were tested on simulated data sets based on the known dominant scattering characteristics of the target class. Four classes were used in the data simulation: open water, thick lossy saline ice, thin saline ice, and multiyear ice. The BP network was unable to consistently converge to less than 25 percent error while the FL method yielded an average error of approximately 1 percent on the first iteration of training. The fast learning method presented can significantly reduce the CPU time necessary to train a neural network as well as consistently yield higher classification accuracy than BP networks.

Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

PubMed

López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

2015-04-15

Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

Evaluation of an Interactive Case-Based Online Network (ICON) in a Problem Based Learning Environment

ERIC Educational Resources Information Center

Nathoo, Arif N.; Goldhoff, Patricia; Quattrochi, James J.

2005-01-01

Purpose: This study sought to assess the introduction of a web-based innovation in medical education that complements traditional problem-based learning curricula. Utilizing the case method as its fundamental educational approach, the Interactive Case-based Online Network (ICON) allows students to interact with each other, faculty and a virtual…

Dialogue, Language and Identity: Critical Issues for Networked Management Learning

ERIC Educational Resources Information Center

Ferreday, Debra; Hodgson, Vivien; Jones, Chris

2006-01-01

This paper draws on the work of Mikhail Bakhtin and Norman Fairclough to show how dialogue is central to the construction of identity in networked management learning. The paper is based on a case study of a networked management learning course in higher education and attempts to illustrate how participants negotiate issues of difference,…

Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

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

Ondrej Linda; Todd Vollmer; Jason Wright

Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrainedmore » computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.« less

A high-capacity model for one shot association learning in the brain

PubMed Central

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs. PMID:25426060

A high-capacity model for one shot association learning in the brain.

PubMed

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs.

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis[W

PubMed Central

Ma, Chuang; Xin, Mingming; Feldmann, Kenneth A.; Wang, Xiangfeng

2014-01-01

Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning–based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive “noninformative” genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained “informative” genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing–based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress–related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes. PMID:24520154

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

PubMed

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

2015-12-01

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

A reinforcement learning-based architecture for fuzzy logic control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

Evolutionary neural networks for anomaly detection based on the behavior of a program.

PubMed

Han, Sang-Jun; Cho, Sung-Bae

2006-06-01

The process of learning the behavior of a given program by using machine-learning techniques (based on system-call audit data) is effective to detect intrusions. Rule learning, neural networks, statistics, and hidden Markov models (HMMs) are some of the kinds of representative methods for intrusion detection. Among them, neural networks are known for good performance in learning system-call sequences. In order to apply this knowledge to real-world problems successfully, it is important to determine the structures and weights of these call sequences. However, finding the appropriate structures requires very long time periods because there are no suitable analytical solutions. In this paper, a novel intrusion-detection technique based on evolutionary neural networks (ENNs) is proposed. One advantage of using ENNs is that it takes less time to obtain superior neural networks than when using conventional approaches. This is because they discover the structures and weights of the neural networks simultaneously. Experimental results with the 1999 Defense Advanced Research Projects Agency (DARPA) Intrusion Detection Evaluation (IDEVAL) data confirm that ENNs are promising tools for intrusion detection.

Identifying Students' Difficulties When Learning Technical Skills via a Wireless Sensor Network

ERIC Educational Resources Information Center

Wang, Jingying; Wen, Ming-Lee; Jou, Min

2016-01-01

Practical training and actual application of acquired knowledge and techniques are crucial for the learning of technical skills. We established a wireless sensor network system (WSNS) based on the 5E learning cycle in a practical learning environment to improve students' reflective abilities and to reduce difficulties for the learning of technical…

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

PubMed

Finnegan, Alex; Song, Jun S

2017-10-01

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

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

PubMed Central

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

2015-01-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting. PMID:25837826

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

PubMed

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

2015-04-01

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

Neural network based speech synthesizer: A preliminary report

NASA Technical Reports Server (NTRS)

Villarreal, James A.; Mcintire, Gary

1987-01-01

A neural net based speech synthesis project is discussed. The novelty is that the reproduced speech was extracted from actual voice recordings. In essence, the neural network learns the timing, pitch fluctuations, connectivity between individual sounds, and speaking habits unique to that individual person. The parallel distributed processing network used for this project is the generalized backward propagation network which has been modified to also learn sequences of actions or states given in a particular plan.

A novel time series link prediction method: Learning automata approach

NASA Astrophysics Data System (ADS)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2017-09-01

Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

Language, Learning, and Identity in Social Networking Sites for Language Learning: The Case of Busuu

ERIC Educational Resources Information Center

Alvarez Valencia, Jose Aldemar

2014-01-01

Recent progress in the discipline of computer applications such as the advent of web-based communication, afforded by the Web 2.0, has paved the way for novel applications in language learning, namely, social networking. Social networking has challenged the area of Computer Mediated Communication (CMC) to expand its research palette in order to…

What Online Networks Offer: "Online Network Compositions and Online Learning Experiences of Three Ethnic Groups"

ERIC Educational Resources Information Center

Lecluijze, Suzanne Elisabeth; de Haan, Mariëtte; Ünlüsoy, Asli

2015-01-01

This exploratory study examines ethno-cultural diversity in youth's narratives regarding their "online" learning experiences while also investigating how these narratives can be understood from the analysis of their online network structure and composition. Based on ego-network data of 79 respondents this study compared the…

Assessment of Matrix Multiplication Learning with a Rule-Based Analytical Model--"A Bayesian Network Representation"

ERIC Educational Resources Information Center

Zhang, Zhidong

2016-01-01

This study explored an alternative assessment procedure to examine learning trajectories of matrix multiplication. It took rule-based analytical and cognitive task analysis methods specifically to break down operation rules for a given matrix multiplication. Based on the analysis results, a hierarchical Bayesian network, an assessment model,…

Prediction in Health Domain Using Bayesian Networks Optimization Based on Induction Learning Techniques

NASA Astrophysics Data System (ADS)

Felgaer, Pablo; Britos, Paola; García-Martínez, Ramón

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

Query-based learning for aerospace applications.

PubMed

Saad, E W; Choi, J J; Vian, J L; Wunsch, D C Ii

2003-01-01

Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper investigates the use of both inversion techniques for QBL learning, and introduces an original heuristic to select the inversion target values for continuous network inversion method. Efficiency and generalization was further enhanced by employing node decoupled extended Kalman filter (NDEKF) training and a causality index (CI) as a means to reduce the input search dimensionality. The benefits of the overall QBL approach are experimentally demonstrated in two aerospace applications: a classification problem with large input space and a control distribution problem.

The Influence of Adult Learners' Self-Directed Learning Readiness and Network Literacy on Online Learning Effectiveness: A Study of Civil Servants in Taiwan

ERIC Educational Resources Information Center

Lai, Horng-Ji

2011-01-01

This study examined the effect of civil servants' Self-Directed Learning Readiness (SDLR) and network literacy on their online learning effectiveness in a web-based training program. Participants were 283 civil servants enrolled in an asynchronous online learning program through an e-learning portal provided by the Regional Civil Service…

node2vec: Scalable Feature Learning for Networks

PubMed Central

Grover, Aditya; Leskovec, Jure

2016-01-01

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node’s network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks. PMID:27853626

Salient object detection based on multi-scale contrast.

PubMed

Wang, Hai; Dai, Lei; Cai, Yingfeng; Sun, Xiaoqiang; Chen, Long

2018-05-01

Due to the development of deep learning networks, a salient object detection based on deep learning networks, which are used to extract the features, has made a great breakthrough compared to the traditional methods. At present, the salient object detection mainly relies on very deep convolutional network, which is used to extract the features. In deep learning networks, an dramatic increase of network depth may cause more training errors instead. In this paper, we use the residual network to increase network depth and to mitigate the errors caused by depth increase simultaneously. Inspired by image simplification, we use color and texture features to obtain simplified image with multiple scales by means of region assimilation on the basis of super-pixels in order to reduce the complexity of images and to improve the accuracy of salient target detection. We refine the feature on pixel level by the multi-scale feature correction method to avoid the feature error when the image is simplified at the above-mentioned region level. The final full connection layer not only integrates features of multi-scale and multi-level but also works as classifier of salient targets. The experimental results show that proposed model achieves better results than other salient object detection models based on original deep learning networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Network Learning for Educational Change. Professional Learning

ERIC Educational Resources Information Center

Veugelers, Wiel, Ed.; O'Hair, Mary John, Ed.

2005-01-01

School-university networks are becoming an important method to enhance educational renewal and student achievement. Networks go beyond tensions of top-down versus bottom-up, school development and professional development of individuals, theory and practice, and formal and informal organizational structures. The theoretical base of networking…

Deep learning for computational chemistry.

PubMed

Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

2017-06-15

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Deep learning for computational chemistry

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

Goh, Garrett B.; Hodas, Nathan O.; Vishnu, Abhinav

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. Inmore » this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.« less

The research of "blind" spot in the LVQ network

NASA Astrophysics Data System (ADS)

Guo, Zhanjie; Nan, Shupo; Wang, Xiaoli

2017-04-01

Nowadays competitive neural network has been widely used in the pattern recognition, classification and other aspects, and show the great advantages compared with the traditional clustering methods. But the competitive neural networks still has inadequate in many aspects, and it needs to be further improved. Based on the learning Vector Quantization Network proposed by Learning Kohonen [1], this paper resolve the issue of the large training error, when there are "blind" spots in a network through the introduction of threshold value learning rules and finally programs the realization with Matlab.

Pattern learning with deep neural networks in EMG-based speech recognition.

PubMed

Wand, Michael; Schultz, Tanja

2014-01-01

We report on classification of phones and phonetic features from facial electromyographic (EMG) data, within the context of our EMG-based Silent Speech interface. In this paper we show that a Deep Neural Network can be used to perform this classification task, yielding a significant improvement over conventional Gaussian Mixture models. Our central contribution is the visualization of patterns which are learned by the neural network. With increasing network depth, these patterns represent more and more intricate electromyographic activity.

Learning polynomial feedforward neural networks by genetic programming and backpropagation.

PubMed

Nikolaev, N Y; Iba, H

2003-01-01

This paper presents an approach to learning polynomial feedforward neural networks (PFNNs). The approach suggests, first, finding the polynomial network structure by means of a population-based search technique relying on the genetic programming paradigm, and second, further adjustment of the best discovered network weights by an especially derived backpropagation algorithm for higher order networks with polynomial activation functions. These two stages of the PFNN learning process enable us to identify networks with good training as well as generalization performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.

Evaluation of a Web-based social network electronic game in enhancing mental health literacy for young people.

PubMed

Li, Tim M H; Chau, Michael; Wong, Paul W C; Lai, Eliza S Y; Yip, Paul S F

2013-05-15

Internet-based learning programs provide people with massive health care information and self-help guidelines on improving their health. The advent of Web 2.0 and social networks renders significant flexibility to embedding highly interactive components, such as games, to foster learning processes. The effectiveness of game-based learning on social networks has not yet been fully evaluated. The aim of this study was to assess the effectiveness of a fully automated, Web-based, social network electronic game on enhancing mental health knowledge and problem-solving skills of young people. We investigated potential motivational constructs directly affecting the learning outcome. Gender differences in learning outcome and motivation were also examined. A pre/posttest design was used to evaluate the fully automated Web-based intervention. Participants, recruited from a closed online user group, self-assessed their mental health literacy and motivational constructs before and after completing the game within a 3-week period. The electronic game was designed according to cognitive-behavioral approaches. Completers and intent-to-treat analyses, using multiple imputation for missing data, were performed. Regression analysis with backward selection was employed when examining the relationship between knowledge enhancement and motivational constructs. The sample included 73 undergraduates (42 females) for completers analysis. The gaming approach was effective in enhancing young people's mental health literacy (d=0.65). The finding was also consistent with the intent-to-treat analysis, which included 127 undergraduates (75 females). No gender differences were found in learning outcome (P=.97). Intrinsic goal orientation was the primary factor in learning motivation, whereas test anxiety was successfully alleviated in the game setting. No gender differences were found on any learning motivation subscales (P>.10). We also found that participants' self-efficacy for learning and performance, as well as test anxiety, significantly affected their learning outcomes, whereas other motivational subscales were statistically nonsignificant. Electronic games implemented through social networking sites appear to effectively enhance users' mental health literacy.

Networked Learning: Design Considerations for Online Instructors

ERIC Educational Resources Information Center

Czerkawski, Betul C.

2016-01-01

The considerable increase in web-based knowledge networks in the past two decades is strongly influencing learning environments. Learning entails information retrieval, use, communication, and production, and is strongly enriched by socially mediated discussions, debates, and collaborative activities. It is becoming critical for educators to…

Learning Tools for Knowledge Nomads: Using Personal Digital Assistants (PDAs) in Web-based Learning Environments.

ERIC Educational Resources Information Center

Loh, Christian Sebastian

2001-01-01

Examines how mobile computers, or personal digital assistants (PDAs), can be used in a Web-based learning environment. Topics include wireless networks on college campuses; online learning; Web-based learning technologies; synchronous and asynchronous communication via the Web; content resources; Web connections; and collaborative learning. (LRW)

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

PubMed

Li, Can; Belkin, Daniel; Li, Yunning; Yan, Peng; Hu, Miao; Ge, Ning; Jiang, Hao; Montgomery, Eric; Lin, Peng; Wang, Zhongrui; Song, Wenhao; Strachan, John Paul; Barnell, Mark; Wu, Qing; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei

2018-06-19

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.

Experiences of Pioneers Facilitating Teacher Networks for Professional Development

ERIC Educational Resources Information Center

Hanraets, Irene; Hulsebosch, Joitske; de Laat, Maarten

2011-01-01

This study presents an exploration into facilitation practices of teacher professional development networks. Stimulating networked learning amongst teachers is a powerful way of creating an informal practice-based learning space driven by teacher needs. As such, it presents an additional channel (besides more formal traditional professional…

Learning in stochastic neural networks for constraint satisfaction problems

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Adorf, Hans-Martin

1989-01-01

Researchers describe a newly-developed artificial neural network algorithm for solving constraint satisfaction problems (CSPs) which includes a learning component that can significantly improve the performance of the network from run to run. The network, referred to as the Guarded Discrete Stochastic (GDS) network, is based on the discrete Hopfield network but differs from it primarily in that auxiliary networks (guards) are asymmetrically coupled to the main network to enforce certain types of constraints. Although the presence of asymmetric connections implies that the network may not converge, it was found that, for certain classes of problems, the network often quickly converges to find satisfactory solutions when they exist. The network can run efficiently on serial machines and can find solutions to very large problems (e.g., N-queens for N as large as 1024). One advantage of the network architecture is that network connection strengths need not be instantiated when the network is established: they are needed only when a participating neural element transitions from off to on. They have exploited this feature to devise a learning algorithm, based on consistency techniques for discrete CSPs, that updates the network biases and connection strengths and thus improves the network performance.

Fuzzy and neural control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

Considering a Twitter-Based Professional Learning Network in Literacy Education

ERIC Educational Resources Information Center

Colwell, Jamie; Hutchison, Amy C.

2018-01-01

This study explored how 26 preservice secondary content teachers perceived their experiences participating in and developing a Twitter-based professional learning network focused on disciplinary literacy. Participants completed blog reflections and anonymous online surveys to reflect on their experiences, which served as data for this study. A…

Doctoral Students' Identity Positioning in Networked Learning Environments

ERIC Educational Resources Information Center

Koole, Marguerite; Stack, Sara

2016-01-01

In this study, the authors explored identity positioning as perceived by doctoral learners in online, networked-learning environments. The study examined two distance doctoral programs at a Canadian university. It was a qualitative study based on methodologies involving open coding and discourse analysis. The social positioning cycle, based on…

Sentiment classification technology based on Markov logic networks

NASA Astrophysics Data System (ADS)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

NASA Astrophysics Data System (ADS)

Yan, Fei; Tian, Fuli; Shi, Zhongke

2016-10-01

Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

Discovery of Action Patterns and User Correlations in Task-Oriented Processes for Goal-Driven Learning Recommendation

ERIC Educational Resources Information Center

Zhou, Xiaokang; Chen, Jian; Wu, Bo; Jin, Qun

2014-01-01

With the high development of social networks, collaborations in a socialized web-based learning environment has become increasing important, which means people can learn through interactions and collaborations in communities across social networks. In this study, in order to support the enhanced collaborative learning, two important factors, user…

Enhancing Formal E-Learning with Edutainment on Social Networks

ERIC Educational Resources Information Center

Labus, A.; Despotovic-Zrakic, M.; Radenkovic, B.; Bogdanovic, Z.; Radenkovic, M.

2015-01-01

This paper reports on the investigation of the possibilities of enhancing the formal e-learning process by harnessing the potential of informal game-based learning on social networks. The goal of the research is to improve the outcomes of the formal learning process through the design and implementation of an educational game on a social network…

Using Social Networks to Enhance Teaching and Learning Experiences in Higher Learning Institutions

ERIC Educational Resources Information Center

Balakrishnan, Vimala

2014-01-01

The paper first explores the factors that affect the use of social networks to enhance teaching and learning experiences among students and lecturers, using structured questionnaires prepared based on the Push-Pull-Mooring framework. A total of 455 students and lecturers from higher learning institutions in Malaysia participated in this study.…

Bridging the Gap between Students and Computers: Supporting Activity Awareness for Network Collaborative Learning with GSM Network

ERIC Educational Resources Information Center

Liu, C.-C.; Tao, S.-Y.; Nee, J.-N.

2008-01-01

The internet has been widely used to promote collaborative learning among students. However, students do not always have access to the system, leading to doubt in the interaction among the students, and reducing the effectiveness of collaborative learning, since the web-based collaborative learning environment relies entirely on the availability…

Competition-Based Learning: A Model for the Integration of Competitions with Project-Based Learning Using Open Source LMS

ERIC Educational Resources Information Center

Issa, Ghassan; Hussain, Shakir M.; Al-Bahadili, Hussein

2014-01-01

In an effort to enhance the learning process in higher education, a new model for Competition-Based Learning (CBL) is presented. The new model utilizes two well-known learning models, namely, the Project-Based Learning (PBL) and competitions. The new model is also applied in a networked environment with emphasis on collective learning as well as…

SISL and SIRL: Two knowledge dissemination models with leader nodes on cooperative learning networks

NASA Astrophysics Data System (ADS)

Li, Jingjing; Zhang, Yumei; Man, Jiayu; Zhou, Yun; Wu, Xiaojun

2017-02-01

Cooperative learning is one of the most effective teaching methods, which has been widely used. Students' mutual contact forms a cooperative learning network in this process. Our previous research demonstrated that the cooperative learning network has complex characteristics. This study aims to investigating the dynamic spreading process of the knowledge in the cooperative learning network and the inspiration of leaders in this process. To this end, complex network transmission dynamics theory is utilized to construct the knowledge dissemination model of a cooperative learning network. Based on the existing epidemic models, we propose a new susceptible-infected-susceptible-leader (SISL) model that considers both students' forgetting and leaders' inspiration, and a susceptible-infected-removed-leader (SIRL) model that considers students' interest in spreading and leaders' inspiration. The spreading threshold λcand its impact factors are analyzed. Then, numerical simulation and analysis are delivered to reveal the dynamic transmission mechanism of knowledge and leaders' role. This work is of great significance to cooperative learning theory and teaching practice. It also enriches the theory of complex network transmission dynamics.

High-throughput Bayesian Network Learning using Heterogeneous Multicore Computers

PubMed Central

Linderman, Michael D.; Athalye, Vivek; Meng, Teresa H.; Asadi, Narges Bani; Bruggner, Robert; Nolan, Garry P.

2017-01-01

Aberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20–50 nodes). In this paper, we present a novel graphics processing unit (GPU)-accelerated implementation of a Monte Carlo Markov Chain-based algorithm for learning BNs that is up to 7.5-fold faster than current general-purpose processor (GPP)-based implementations. The GPU-based implementation is just one of several implementations within the larger application, each optimized for a different input or machine configuration. We describe the methodology we use to build an extensible application, assembled from these variants, that can target a broad range of heterogeneous systems, e.g., GPUs, multicore GPPs. Specifically we show how we use the Merge programming model to efficiently integrate, test and intelligently select among the different potential implementations. PMID:28819655

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Ricketts, Daniel; Kotaru, Raj; Thomas, Robert; Noga, Donald F. (Technical Monitor); Kankam, Mark D. (Technical Monitor)

2000-01-01

In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.

Pervasive Knowledge, Social Networks, and Cloud Computing: E-Learning 2.0

ERIC Educational Resources Information Center

Anshari, Muhammad; Alas, Yabit; Guan, Lim Sei

2015-01-01

Embedding Web 2.0 in learning processes has extended learning from traditional based learning-centred to a collaborative based learning-centred institution that emphasises learning anywhere and anytime. While deploying Semantic Web into e-learning offers a broader spectrum of pervasive knowledge acquisition to enrich users' experience in learning.…

Inter-firm Networks, Organizational Learning and Knowledge Updating: An Empirical Study

NASA Astrophysics Data System (ADS)

Zhang, Su-rong; Wang, Wen-ping

In the era of knowledge-based economy which information technology develops rapidly, the rate of knowledge updating has become a critical factor for enterprises to gaining competitive advantage .We build an interactional theoretical model among inter-firm networks, organizational learning and knowledge updating thereby and demonstrate it with empirical study at last. The result shows that inter-firm networks and organizational learning is the source of knowledge updating.

Development of the self-learning machine for creating models of microprocessor of single-phase earth fault protection devices in networks with isolated neutral voltage above 1000 V

NASA Astrophysics Data System (ADS)

Utegulov, B. B.; Utegulov, A. B.; Meiramova, S.

2018-02-01

The paper proposes the development of a self-learning machine for creating models of microprocessor-based single-phase ground fault protection devices in networks with an isolated neutral voltage higher than 1000 V. Development of a self-learning machine for creating models of microprocessor-based single-phase earth fault protection devices in networks with an isolated neutral voltage higher than 1000 V. allows to effectively implement mathematical models of automatic change of protection settings. Single-phase earth fault protection devices.

A Deep Neural Network Model for Rainfall Estimation UsingPolarimetric WSR-88DP Radar Observations

NASA Astrophysics Data System (ADS)

Tan, H.; Chandra, C. V.; Chen, H.

2016-12-01

Rainfall estimation based on radar measurements has been an important topic for a few decades. Generally, radar rainfall estimation is conducted through parametric algorisms such as reflectivity-rainfall relation (i.e., Z-R relation). On the other hand, neural networks are developed for ground rainfall estimation based on radar measurements. This nonparametric method, which takes into account of both radar observations and rainfall measurements from ground rain gauges, has been demonstrated successfully for rainfall rate estimation. However, the neural network-based rainfall estimation is limited in practice due to the model complexity and structure, data quality, as well as different rainfall microphysics. Recently, the deep learning approach has been introduced in pattern recognition and machine learning areas. Compared to traditional neural networks, the deep learning based methodologies have larger number of hidden layers and more complex structure for data representation. Through a hierarchical learning process, the high level structured information and knowledge can be extracted automatically from low level features of the data. In this paper, we introduce a novel deep neural network model for rainfall estimation based on ground polarimetric radar measurements .The model is designed to capture the complex abstractions of radar measurements at different levels using multiple layers feature identification and extraction. The abstractions at different levels can be used independently or fused with other data resource such as satellite-based rainfall products and/or topographic data to represent the rain characteristics at certain location. In particular, the WSR-88DP radar and rain gauge data collected in Dallas - Fort Worth Metroplex and Florida are used extensively to train the model, and for demonstration purposes. Quantitative evaluation of the deep neural network based rainfall products will also be presented, which is based on an independent rain gauge network.

Mining Learning Social Networks for Cooperative Learning with Appropriate Learning Partners in a Problem-Based Learning Environment

ERIC Educational Resources Information Center

Chen, Chih-Ming; Chang, Chia-Cheng

2014-01-01

Many studies have identified web-based cooperative learning as an increasingly popular educational paradigm with potential to increase learner satisfaction and interactions. However, peer-to-peer interaction often suffers barriers owing to a failure to explore useful social interaction information in web-based cooperative learning environments.…

Internal and External Pressures: How Does an Organic Cotton Production Network Learn to Keep Its Hybrid Nature?

ERIC Educational Resources Information Center

Turcato, Carolina; Barin-Cruz, Luciano; Pedrozo, Eugenio Avila

2012-01-01

Purpose: This study aims to investigate how an organic cotton production network learns to maintain its hybrid network and its sustainability in the face of internal and external pressures. Design/methodology/approach: A qualitative case study was conducted in Justa Trama, a Brazilian-based organic cotton production network formed by six members…

Get Networked and Spy Your Languages

ERIC Educational Resources Information Center

Rico, Mercedes; Ferreira, Paula; Dominguez, Eva M.; Coppens, Julian

2012-01-01

Our proposal describes ISPY, a multilateral European K2 language project based on the development of an Online Networking Platform for Language Learning (http://www.ispy-project.com/). Supported by the Lifelong Learning European Programme, the platform aims to help young adults across Europe, secondary and vocational school programs, learn a new…

Network-based machine learning and graph theory algorithms for precision oncology.

PubMed

Zhang, Wei; Chien, Jeremy; Yong, Jeongsik; Kuang, Rui

2017-01-01

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

A Networked Learning Model for Construction of Personal Learning Environments in Seventh Grade Life Science

ERIC Educational Resources Information Center

Drexler, Wendy

2010-01-01

The purpose of this design-based research case study was to apply a networked learning approach to a seventh grade science class at a public school in the southeastern United States. Students adapted Web applications to construct personal learning environments for in-depth scientific inquiry of poisonous and venomous life forms. API widgets were…

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

PubMed

Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck

2015-11-01

This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.

Approximate reasoning-based learning and control for proximity operations and docking in space

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Jani, Yashvant; Lea, Robert N.

1991-01-01

A recently proposed hybrid-neutral-network and fuzzy-logic-control architecture is applied to a fuzzy logic controller developed for attitude control of the Space Shuttle. A model using reinforcement learning and learning from past experience for fine-tuning its knowledge base is proposed. Two main components of this approximate reasoning-based intelligent control (ARIC) model - an action-state evaluation network and action selection network are described as well as the Space Shuttle attitude controller. An ARIC model for the controller is presented, and it is noted that the input layer in each network includes three nodes representing the angle error, angle error rate, and bias node. Preliminary results indicate that the controller can hold the pitch rate within its desired deadband and starts to use the jets at about 500 sec in the run.

Spatial features of synaptic adaptation affecting learning performance.

PubMed

Berger, Damian L; de Arcangelis, Lucilla; Herrmann, Hans J

2017-09-08

Recent studies have proposed that the diffusion of messenger molecules, such as monoamines, can mediate the plastic adaptation of synapses in supervised learning of neural networks. Based on these findings we developed a model for neural learning, where the signal for plastic adaptation is assumed to propagate through the extracellular space. We investigate the conditions allowing learning of Boolean rules in a neural network. Even fully excitatory networks show very good learning performances. Moreover, the investigation of the plastic adaptation features optimizing the performance suggests that learning is very sensitive to the extent of the plastic adaptation and the spatial range of synaptic connections.

An Approach Based on Social Network Analysis Applied to a Collaborative Learning Experience

ERIC Educational Resources Information Center

Claros, Iván; Cobos, Ruth; Collazos, César A.

2016-01-01

The Social Network Analysis (SNA) techniques allow modelling and analysing the interaction among individuals based on their attributes and relationships. This approach has been used by several researchers in order to measure the social processes in collaborative learning experiences. But oftentimes such measures were calculated at the final state…

A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

PubMed

Wang, Quan; Rothkopf, Constantin A; Triesch, Jochen

2017-08-01

The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.

A common neural network differentially mediates direct and social fear learning.

PubMed

Lindström, Björn; Haaker, Jan; Olsson, Andreas

2018-02-15

Across species, fears often spread between individuals through social learning. Yet, little is known about the neural and computational mechanisms underlying social learning. Addressing this question, we compared social and direct (Pavlovian) fear learning showing that they showed indistinguishable behavioral effects, and involved the same cross-modal (self/other) aversive learning network, centered on the amygdala, the anterior insula (AI), and the anterior cingulate cortex (ACC). Crucially, the information flow within this network differed between social and direct fear learning. Dynamic causal modeling combined with reinforcement learning modeling revealed that the amygdala and AI provided input to this network during direct and social learning, respectively. Furthermore, the AI gated learning signals based on surprise (associability), which were conveyed to the ACC, in both learning modalities. Our findings provide insights into the mechanisms underlying social fear learning, with implications for understanding common psychological dysfunctions, such as phobias and other anxiety disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning

PubMed Central

2018-01-01

Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma based on gene coexpression network. Using multiple microarray, gene coexpression network analysis was performed to identify survival-related networks. A deep learning based risk stratification model was constructed with representative genes of these networks. The model was validated in two test sets. Survival analysis was performed using the output of the model to evaluate whether it could predict patients' survival independent of clinicopathological variables. Five networks were significantly associated with patients' survival. Considering prognostic significance and representativeness, genes of the two survival-related networks were selected for input of the model. The output of the model was significantly associated with patients' survival in two test sets and training set (p < 0.00001, p < 0.0001 and p = 0.02 for training and test sets 1 and 2, resp.). In multivariate analyses, the model was associated with patients' prognosis independent of other clinicopathological features. Our study presents a new perspective on incorporating gene coexpression networks into the gene expression signature and clinical application of deep learning in genomic data science for prognosis prediction. PMID:29581968

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Analysis and Visualization of Relations in eLearning

NASA Astrophysics Data System (ADS)

Dráždilová, Pavla; Obadi, Gamila; Slaninová, Kateřina; Martinovič, Jan; Snášel, Václav

The popularity of eLearning systems is growing rapidly; this growth is enabled by the consecutive development in Internet and multimedia technologies. Web-based education became wide spread in the past few years. Various types of learning management systems facilitate development of Web-based courses. Users of these courses form social networks through the different activities performed by them. This chapter focuses on searching the latent social networks in eLearning systems data. These data consist of students activity records wherein latent ties among actors are embedded. The social network studied in this chapter is represented by groups of students who have similar contacts and interact in similar social circles. Different methods of data clustering analysis can be applied to these groups, and the findings show the existence of latent ties among the group members. The second part of this chapter focuses on social network visualization. Graphical representation of social network can describe its structure very efficiently. It can enable social network analysts to determine the network degree of connectivity. Analysts can easily determine individuals with a small or large amount of relationships as well as the amount of independent groups in a given network. When applied to the field of eLearning, data visualization simplifies the process of monitoring the study activities of individuals or groups, as well as the planning of educational curriculum, the evaluation of study processes, etc.

Learning and coding in biological neural networks

NASA Astrophysics Data System (ADS)

Fiete, Ila Rani

How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and theoretical results on the scalability of this rule show that learning with stochastic gradient ascent may be adequately fast to explain learning in the bird. Finally, we address the more general issue of the scalability of stochastic gradient learning on quadratic cost surfaces in linear systems, as a function of system size and task characteristics, by deriving analytical expressions for the learning curves.

Usefulness of an Internet-based thematic learning network: comparison of effectiveness with traditional teaching.

PubMed

Coma Del Corral, María Jesús; Guevara, José Cordero; Luquin, Pedro Abáigar; Peña, Horacio J; Mateos Otero, Juan José

2006-03-01

UniNet is an Internet-based thematic network for a virtual community of users (VCU). It supports one multidisciplinary community of doctoral students, who receive most of the courses on the network. The evident advantages of distance learning by Internet, in terms of costs, comfort, etc., require a previous evaluation of the system, focusing on the learning outcomes of the student. The aim was to evaluate the real learning of the students of doctorate courses, by comparing the effectiveness of distance learning in UniNet with traditional classroom-based teaching. Five doctorate courses were taught simultaneously to two independent groups of students in two ways: one, through the UniNet Network, and the other in a traditional classroom. The academic knowledge of students was evaluated at the beginning and end of each course. The difference in score was considered as a knowledge increase. The comparison was made using Student's t-test for independent groups. There were no significant statistical differences in the outcomes of the two groups of students. This suggests that both teaching systems were equivalent in increasing the knowledge of the students. Both educational methods, the traditional system and the online system in a thematic network, are effective and similar for increasing knowledge.

Intelligent deflection routing in buffer-less networks.

PubMed

Haeri, Soroush; Trajković, Ljiljana

2015-02-01

Deflection routing is employed to ameliorate packet loss caused by contention in buffer-less architectures such as optical burst-switched networks. The main goal of deflection routing is to successfully deflect a packet based only on a limited knowledge that network nodes possess about their environment. In this paper, we present a framework that introduces intelligence to deflection routing (iDef). iDef decouples the design of the signaling infrastructure from the underlying learning algorithm. It consists of a signaling and a decision-making module. Signaling module implements a feedback management protocol while the decision-making module implements a reinforcement learning algorithm. We also propose several learning-based deflection routing protocols, implement them in iDef using the ns-3 network simulator, and compare their performance.

A neural network model for credit risk evaluation.

PubMed

Khashman, Adnan

2009-08-01

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

Functional networks inference from rule-based machine learning models.

PubMed

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The implementation of our network inference protocol is available at: http://ico2s.org/software/funel.html.

Student Learning Networks on Residential Field Courses: Does Size Matter?

ERIC Educational Resources Information Center

Langan, A. Mark; Cullen, W. Rod; Shuker, David M.

2008-01-01

This article describes learner and tutor reports of a learning network that formed during the completion of investigative projects on a residential field course. Staff and students recorded project-related interactions, who they were with and how long they lasted over four phases during the field course. An enquiry based learning format challenged…

Learning through Social Networking Sites--The Critical Role of the Teacher

ERIC Educational Resources Information Center

Callaghan, Noelene; Bower, Matt

2012-01-01

This comparative case study examined factors affecting behaviour and learning in social networking sites (SNS). The behaviour and learning of two classes completing identical SNS based modules of work was observed and compared. All student contributions to the SNS were analysed, with the cognitive process dimension of the Revised Bloom's Taxonomy…

A Learning Content Authoring Approach Based on Semantic Technologies and Social Networking: An Empirical Study

ERIC Educational Resources Information Center

Nesic, Sasa; Gasevic, Dragan; Jazayeri, Mehdi; Landoni, Monica

2011-01-01

Semantic web technologies have been applied to many aspects of learning content authoring including semantic annotation, semantic search, dynamic assembly, and personalization of learning content. At the same time, social networking services have started to play an important role in the authoring process by supporting authors' collaborative…

Gross domestic product estimation based on electricity utilization by artificial neural network

NASA Astrophysics Data System (ADS)

Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.

2018-01-01

The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method

PubMed Central

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-01-01

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli, and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs. PMID:29113310

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

PubMed

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Expert networks in CLIPS

NASA Technical Reports Server (NTRS)

Hruska, S. I.; Dalke, A.; Ferguson, J. J.; Lacher, R. C.

1991-01-01

Rule-based expert systems may be structurally and functionally mapped onto a special class of neural networks called expert networks. This mapping lends itself to adaptation of connectionist learning strategies for the expert networks. A parsing algorithm to translate C Language Integrated Production System (CLIPS) rules into a network of interconnected assertion and operation nodes has been developed. The translation of CLIPS rules to an expert network and back again is illustrated. Measures of uncertainty similar to those rules in MYCIN-like systems are introduced into the CLIPS system and techniques for combining and hiring nodes in the network based on rule-firing with these certainty factors in the expert system are presented. Several learning algorithms are under study which automate the process of attaching certainty factors to rules.

How to Trigger Emergence and Self-Organisation in Learning Networks

NASA Astrophysics Data System (ADS)

Brouns, Francis; Fetter, Sibren; van Rosmalen, Peter

The previous chapters of this section discussed why the social structure of Learning Networks is important and present guidelines on how to maintain and allow the emergence of communities in Learning Networks. Chapter 2 explains how Learning Networks rely on social interaction and active participations of the participants. Chapter 3 then continues by presenting guidelines and policies that should be incorporated into Learning Network Services in order to maintain existing communities by creating conditions that promote social interaction and knowledge sharing. Chapter 4 discusses the necessary conditions required for knowledge sharing to occur and to trigger communities to self-organise and emerge. As pointed out in Chap. 4, ad-hoc transient communities facilitate the emergence of social interaction in Learning Networks, self-organising them into communities, taking into account personal characteristics, community characteristics and general guidelines. As explained in Chap. 4 community members would benefit from a service that brings suitable people together for a specific purpose, because it will allow the participant to focus on the knowledge sharing process by reducing the effort or costs. In the current chapter, we describe an example of a peer support Learning Network Service based on the mechanism of peer tutoring in ad-hoc transient communities.

Developing Online Learning Resources: Big Data, Social Networks, and Cloud Computing to Support Pervasive Knowledge

ERIC Educational Resources Information Center

Anshari, Muhammad; Alas, Yabit; Guan, Lim Sei

2016-01-01

Utilizing online learning resources (OLR) from multi channels in learning activities promise extended benefits from traditional based learning-centred to a collaborative based learning-centred that emphasises pervasive learning anywhere and anytime. While compiling big data, cloud computing, and semantic web into OLR offer a broader spectrum of…

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

PubMed Central

Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.

2016-01-01

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566

Incremental Support Vector Machine Framework for Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Awad, Mariette; Jiang, Xianhua; Motai, Yuichi

2006-12-01

Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

Supporting Teachers in Designing CSCL Activities: A Case Study of Principle-Based Pedagogical Patterns in Networked Second Language Classrooms

ERIC Educational Resources Information Center

Wen, Yun; Looi, Chee-Kit; Chen, Wenli

2012-01-01

This paper proposes the identification and use of principle-based pedagogical patterns to help teachers to translate design principles into actionable teaching activities, and to scaffold student learning with sufficient flexibility and creativity. A set of pedagogical patterns for networked Second language (L2) learning, categorized and…

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

Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming.

PubMed

Ostrowski, M; Paulevé, L; Schaub, T; Siegel, A; Guziolowski, C

2016-11-01

Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

PubMed Central

Mustapha, Ibrahim; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A.; Sali, Aduwati; Mohamad, Hafizal

2015-01-01

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. PMID:26287191

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

PubMed

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

Evaluation of a Web-Based Social Network Electronic Game in Enhancing Mental Health Literacy for Young People

PubMed Central

Li, Tim MH; Wong, Paul WC; Lai, Eliza SY; Yip, Paul SF

2013-01-01

Background Internet-based learning programs provide people with massive health care information and self-help guidelines on improving their health. The advent of Web 2.0 and social networks renders significant flexibility to embedding highly interactive components, such as games, to foster learning processes. The effectiveness of game-based learning on social networks has not yet been fully evaluated. Objectives The aim of this study was to assess the effectiveness of a fully automated, Web-based, social network electronic game on enhancing mental health knowledge and problem-solving skills of young people. We investigated potential motivational constructs directly affecting the learning outcome. Gender differences in learning outcome and motivation were also examined. Methods A pre/posttest design was used to evaluate the fully automated Web-based intervention. Participants, recruited from a closed online user group, self-assessed their mental health literacy and motivational constructs before and after completing the game within a 3-week period. The electronic game was designed according to cognitive-behavioral approaches. Completers and intent-to-treat analyses, using multiple imputation for missing data, were performed. Regression analysis with backward selection was employed when examining the relationship between knowledge enhancement and motivational constructs. Results The sample included 73 undergraduates (42 females) for completers analysis. The gaming approach was effective in enhancing young people’s mental health literacy (d=0.65). The finding was also consistent with the intent-to-treat analysis, which included 127 undergraduates (75 females). No gender differences were found in learning outcome (P=.97). Intrinsic goal orientation was the primary factor in learning motivation, whereas test anxiety was successfully alleviated in the game setting. No gender differences were found on any learning motivation subscales (P>.10). We also found that participants’ self-efficacy for learning and performance, as well as test anxiety, significantly affected their learning outcomes, whereas other motivational subscales were statistically nonsignificant. Conclusions Electronic games implemented through social networking sites appear to effectively enhance users’ mental health literacy. PMID:23676714

Artificial neuron-glia networks learning approach based on cooperative coevolution.

PubMed

Mesejo, Pablo; Ibáñez, Oscar; Fernández-Blanco, Enrique; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana B

2015-06-01

Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.

Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images.

PubMed

Khellal, Atmane; Ma, Hongbin; Fei, Qing

2018-05-09

The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.

Fast detection of the fuzzy communities based on leader-driven algorithm

NASA Astrophysics Data System (ADS)

Fang, Changjian; Mu, Dejun; Deng, Zhenghong; Hu, Jun; Yi, Chen-He

2018-03-01

In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.

Fixed Point Learning Based Intelligent Traffic Control System

NASA Astrophysics Data System (ADS)

Zongyao, Wang; Cong, Sui; Cheng, Shao

2017-10-01

Fixed point learning has become an important tool to analyse large scale distributed system such as urban traffic network. This paper presents a fixed point learning based intelligence traffic network control system. The system applies convergence property of fixed point theorem to optimize the traffic flow density. The intelligence traffic control system achieves maximum road resources usage by averaging traffic flow density among the traffic network. The intelligence traffic network control system is built based on decentralized structure and intelligence cooperation. No central control is needed to manage the system. The proposed system is simple, effective and feasible for practical use. The performance of the system is tested via theoretical proof and simulations. The results demonstrate that the system can effectively solve the traffic congestion problem and increase the vehicles average speed. It also proves that the system is flexible, reliable and feasible for practical use.

Network-based stochastic semisupervised learning.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-03-01

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.

PubMed

Kusumoto, Dai; Lachmann, Mark; Kunihiro, Takeshi; Yuasa, Shinsuke; Kishino, Yoshikazu; Kimura, Mai; Katsuki, Toshiomi; Itoh, Shogo; Seki, Tomohisa; Fukuda, Keiichi

2018-06-05

Deep learning technology is rapidly advancing and is now used to solve complex problems. Here, we used deep learning in convolutional neural networks to establish an automated method to identify endothelial cells derived from induced pluripotent stem cells (iPSCs), without the need for immunostaining or lineage tracing. Networks were trained to predict whether phase-contrast images contain endothelial cells based on morphology only. Predictions were validated by comparison to immunofluorescence staining for CD31, a marker of endothelial cells. Method parameters were then automatically and iteratively optimized to increase prediction accuracy. We found that prediction accuracy was correlated with network depth and pixel size of images to be analyzed. Finally, K-fold cross-validation confirmed that optimized convolutional neural networks can identify endothelial cells with high performance, based only on morphology. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Synchronization of heteroclinic circuits through learning in coupled neural networks

NASA Astrophysics Data System (ADS)

Selskii, Anton; Makarov, Valeri A.

2016-01-01

The synchronization of oscillatory activity in neural networks is usually implemented by coupling the state variables describing neuronal dynamics. Here we study another, but complementary mechanism based on a learning process with memory. A driver network, acting as a teacher, exhibits winner-less competition (WLC) dynamics, while a driven network, a learner, tunes its internal couplings according to the oscillations observed in the teacher. We show that under appropriate training the learner can "copy" the coupling structure and thus synchronize oscillations with the teacher. The replication of the WLC dynamics occurs for intermediate memory lengths only, consequently, the learner network exhibits a phenomenon of learning resonance.

Incorporation of perception-based information in robot learning using fuzzy reinforcement learning agents

NASA Astrophysics Data System (ADS)

Zhou, Changjiu; Meng, Qingchun; Guo, Zhongwen; Qu, Wiefen; Yin, Bo

2002-04-01

Robot learning in unstructured environments has been proved to be an extremely challenging problem, mainly because of many uncertainties always present in the real world. Human beings, on the other hand, seem to cope very well with uncertain and unpredictable environments, often relying on perception-based information. Furthermore, humans beings can also utilize perceptions to guide their learning on those parts of the perception-action space that are actually relevant to the task. Therefore, we conduct a research aimed at improving robot learning through the incorporation of both perception-based and measurement-based information. For this reason, a fuzzy reinforcement learning (FRL) agent is proposed in this paper. Based on a neural-fuzzy architecture, different kinds of information can be incorporated into the FRL agent to initialise its action network, critic network and evaluation feedback module so as to accelerate its learning. By making use of the global optimisation capability of GAs (genetic algorithms), a GA-based FRL (GAFRL) agent is presented to solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can perform a more effective global search. Different GAFRL agents are constructed and verified by using the simulation model of a physical biped robot. The simulation analysis shows that the biped learning rate for dynamic balance can be improved by incorporating perception-based information on biped balancing and walking evaluation. The biped robot can find its application in ocean exploration, detection or sea rescue activity, as well as military maritime activity.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

PubMed Central

Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

2017-01-01

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction. PMID:28672867

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

PubMed

Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

2017-06-26

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Competitive STDP Learning of Overlapping Spatial Patterns.

PubMed

Krunglevicius, Dalius

2015-08-01

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron's responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron's forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.

Direct heuristic dynamic programming for damping oscillations in a large power system.

PubMed

Lu, Chao; Si, Jennie; Xie, Xiaorong

2008-08-01

This paper applies a neural-network-based approximate dynamic programming method, namely, the direct heuristic dynamic programming (direct HDP), to a large power system stability control problem. The direct HDP is a learning- and approximation-based approach to addressing nonlinear coordinated control under uncertainty. One of the major design parameters, the controller learning objective function, is formulated to directly account for network-wide low-frequency oscillation with the presence of nonlinearity, uncertainty, and coupling effect among system components. Results include a novel learning control structure based on the direct HDP with applications to two power system problems. The first case involves static var compensator supplementary damping control, which is used to provide a comprehensive evaluation of the learning control performance. The second case aims at addressing a difficult complex system challenge by providing a new solution to a large interconnected power network oscillation damping control problem that frequently occurs in the China Southern Power Grid.

The Effect of Social Interaction on Learning Engagement in a Social Networking Environment

ERIC Educational Resources Information Center

Lu, Jie; Churchill, Daniel

2014-01-01

This study investigated the impact of social interactions among a class of undergraduate students on their learning engagement in a social networking environment. Thirteen undergraduate students enrolled in a course in a university in Hong Kong used an Elgg-based social networking platform throughout a semester to develop their digital portfolios…

"A Lifelong Classroom": Social Studies Educators' Engagement with Professional Learning Networks on Twitter

ERIC Educational Resources Information Center

Noble, Anna; McQuillan, Patrick; Littenberg-Tobias, Josh

2016-01-01

Growing numbers of educators are using social media platforms to connect with other educators to form professional learning networks. These networks serve as alternative sources of professional development for teachers who seek to enrich their professional growth beyond school-based programs. This study aims to add to the small but growing body of…

Analysing the Correlation between Social Network Analysis Measures and Performance of Students in Social Network-Based Engineering Education

ERIC Educational Resources Information Center

Putnik, Goran; Costa, Eric; Alves, Cátia; Castro, Hélio; Varela, Leonilde; Shah, Vaibhav

2016-01-01

Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment,…

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

Transformational Leadership & Professional Development for Digitally Rich Learning Environments: A Case Study of the Galileo Educational Network.

ERIC Educational Resources Information Center

Jacobsen, Michele; Clifford, Pat; Friesen, Sharon

The Galileo Educational Network is an innovative educational reform initiative that brings learning to learners. Expert teachers work alongside teachers and students in schools to create new images of engaged learning, technology integration and professional development. This case study is based on the nine schools involved with Galileo in…

Perception Evolution Network Based on Cognition Deepening Model--Adapting to the Emergence of New Sensory Receptor.

PubMed

Xing, Youlu; Shen, Furao; Zhao, Jinxi

2016-03-01

The proposed perception evolution network (PEN) is a biologically inspired neural network model for unsupervised learning and online incremental learning. It is able to automatically learn suitable prototypes from learning data in an incremental way, and it does not require the predefined prototype number or the predefined similarity threshold. Meanwhile, being more advanced than the existing unsupervised neural network model, PEN permits the emergence of a new dimension of perception in the perception field of the network. When a new dimension of perception is introduced, PEN is able to integrate the new dimensional sensory inputs with the learned prototypes, i.e., the prototypes are mapped to a high-dimensional space, which consists of both the original dimension and the new dimension of the sensory inputs. In the experiment, artificial data and real-world data are used to test the proposed PEN, and the results show that PEN can work effectively.

Adaptive control of nonlinear system using online error minimum neural networks.

PubMed

Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

2016-11-01

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

KDU E-Community Network.

ERIC Educational Resources Information Center

Jonhendro; Ching, Goh Bee; Wahab, Rohazna; Leng, Wang Meei; Aun, Jimmy Tan Lip; Yeoh, Eugene; Hock, Oon; Koo, W. K.

2001-01-01

Describes an education initiative developed by a company in Malaysia, the KDU, to implement a student-centered, teacher-facilitated, educational technology-enabled and knowledge-based learning environment. Explains the KDU e-Community Network that enables passive, interactive, collaborative, and constructivist learning for a variety of…

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Correlational Neural Networks.

PubMed

Chandar, Sarath; Khapra, Mitesh M; Larochelle, Hugo; Ravindran, Balaraman

2016-02-01

Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)-based approaches and autoencoder (AE)-based approaches. CCA-based approaches learn a joint representation by maximizing correlation of the views when projected to the common subspace. AE-based methods learn a common representation by minimizing the error of reconstructing the two views. Each of these approaches has its own advantages and disadvantages. For example, while CCA-based approaches outperform AE-based approaches for the task of transfer learning, they are not as scalable as the latter. In this work, we propose an AE-based approach, correlational neural network (CorrNet), that explicitly maximizes correlation among the views when projected to the common subspace. Through a series of experiments, we demonstrate that the proposed CorrNet is better than AE and CCA with respect to its ability to learn correlated common representations. We employ CorrNet for several cross-language tasks and show that the representations learned using it perform better than the ones learned using other state-of-the-art approaches.

Stable architectures for deep neural networks

NASA Astrophysics Data System (ADS)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

Artificial neural networks and approximate reasoning for intelligent control in space

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1991-01-01

A method is introduced for learning to refine the control rules of approximate reasoning-based controllers. A reinforcement-learning technique is used in conjunction with a multi-layer neural network model of an approximate reasoning-based controller. The model learns by updating its prediction of the physical system's behavior. The model can use the control knowledge of an experienced operator and fine-tune it through the process of learning. Some of the space domains suitable for applications of the model such as rendezvous and docking, camera tracking, and tethered systems control are discussed.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

On the asymptotic equivalence between differential Hebbian and temporal difference learning.

PubMed

Kolodziejski, Christoph; Porr, Bernd; Wörgötter, Florentin

2009-04-01

In this theoretical contribution, we provide mathematical proof that two of the most important classes of network learning-correlation-based differential Hebbian learning and reward-based temporal difference learning-are asymptotically equivalent when timing the learning with a modulatory signal. This opens the opportunity to consistently reformulate most of the abstract reinforcement learning framework from a correlation-based perspective more closely related to the biophysics of neurons.

Social networks predict selective observation and information spread in ravens

PubMed Central

Rubenstein, Daniel I.; Bugnyar, Thomas; Hoppitt, William; Mikus, Nace; Schwab, Christine

2016-01-01

Animals are predicted to selectively observe and learn from the conspecifics with whom they share social connections. Yet, hardly anything is known about the role of different connections in observation and learning. To address the relationships between social connections, observation and learning, we investigated transmission of information in two raven (Corvus corax) groups. First, we quantified social connections in each group by constructing networks on affiliative interactions, aggressive interactions and proximity. We then seeded novel information by training one group member on a novel task and allowing others to observe. In each group, an observation network based on who observed whose task-solving behaviour was strongly correlated with networks based on affiliative interactions and proximity. Ravens with high social centrality (strength, eigenvector, information centrality) in the affiliative interaction network were also central in the observation network, possibly as a result of solving the task sooner. Network-based diffusion analysis revealed that the order that ravens first solved the task was best predicted by connections in the affiliative interaction network in a group of subadult ravens, and by social rank and kinship (which influenced affiliative interactions) in a group of juvenile ravens. Our results demonstrate that not all social connections are equally effective at predicting the patterns of selective observation and information transmission. PMID:27493780

Networked Learning and Network Science: Potential Applications to Health Professionals' Continuing Education and Development.

PubMed

Margolis, Alvaro; Parboosingh, John

2015-01-01

Prior interpersonal relationships and interactivity among members of professional associations may impact the learning process in continuing medical education (CME). On the other hand, CME programs that encourage interactivity between participants may impact structures and behaviors in these professional associations. With the advent of information and communication technologies, new communication spaces have emerged that have the potential to enhance networked learning in national and international professional associations and increase the effectiveness of CME for health professionals. In this article, network science, based on the application of network theory and other theories, is proposed as an approach to better understand the contribution networking and interactivity between health professionals in professional communities make to their learning and adoption of new practices over time. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

Prototype-Incorporated Emotional Neural Network.

PubMed

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

Agent-based traffic management and reinforcement learning in congested intersection network.

DOT National Transportation Integrated Search

2012-08-01

This study evaluates the performance of traffic control systems based on reinforcement learning (RL), also called approximate dynamic programming (ADP). Two algorithms have been selected for testing: 1) Q-learning and 2) approximate dynamic programmi...

DCS-Neural-Network Program for Aircraft Control and Testing

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

2006-01-01

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning.

PubMed

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C M

2016-03-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The "competition" (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest--ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. Copyright © 2015 Elsevier Ltd. All rights reserved.

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning

PubMed Central

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C.M.

2016-01-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The “competition” (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest – ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. PMID:26866283

Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

NASA Astrophysics Data System (ADS)

Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

2017-12-01

In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

Superior Generalization Capability of Hardware-Learing Algorithm Developed for Self-Learning Neuron-MOS Neural Networks

NASA Astrophysics Data System (ADS)

Kondo, Shuhei; Shibata, Tadashi; Ohmi, Tadahiro

1995-02-01

We have investigated the learning performance of the hardware backpropagation (HBP) algorithm, a hardware-oriented learning algorithm developed for the self-learning architecture of neural networks constructed using neuron MOS (metal-oxide-semiconductor) transistors. The solution to finding a mirror symmetry axis in a 4×4 binary pixel array was tested by computer simulation based on the HBP algorithm. Despite the inherent restrictions imposed on the hardware-learning algorithm, HBP exhibits equivalent learning performance to that of the original backpropagation (BP) algorithm when all the pertinent parameters are optimized. Very importantly, we have found that HBP has a superior generalization capability over BP; namely, HBP exhibits higher performance in solving problems that the network has not yet learnt.

Best Practices in Service Learning: Building a National Community College Network, 1994-1997. AACC Project Brief.

ERIC Educational Resources Information Center

Robinson, Gail; Barnett, Lynn

As part of the Learn and Serve America Program of the Corporation for National Service, the American Association of Community Colleges (AACC) has helped develop campus-based programs that have instigated a growing community college service learning network. Ten colleges, selected in a national competition for grants ranging from $2,000 to $12,000…

Lessons Learnt from and Sustainability of Adopting a Personal Learning Environment & Network (Ple&N)

ERIC Educational Resources Information Center

Tsui, Eric; Sabetzadeh, Farzad

2014-01-01

This paper describes the feedback from the configuration and deployment of a Personal Learning Environment & Network (PLE&N) tool to support peer-based social learning for university students and graduates. An extension of an earlier project in which a generic and PLE&N was deployed for all learners, the current PLE&N is a…

A Comparative Study on Cooperative Learning in Multimedia and Network Environment Used by English Majors between China Mainland and Taiwan

ERIC Educational Resources Information Center

Xue, Gong; Lingling, Liu

2018-01-01

This paper first based on the theory of cooperative learning research. It analyses the characteristics and advantages of cooperative learning under the multimedia network environment. And then take China Three Gorges University and Taiwan I-Shou University English major students for example, using questionnaires and interviews to investigate the…

Blogs and Social Network Sites as Activity Systems: Exploring Adult Informal Learning Process through Activity Theory Framework

ERIC Educational Resources Information Center

Heo, Gyeong Mi; Lee, Romee

2013-01-01

This paper uses an Activity Theory framework to explore adult user activities and informal learning processes as reflected in their blogs and social network sites (SNS). Using the assumption that a web-based space is an activity system in which learning occurs, typical features of the components were investigated and each activity system then…

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

PubMed

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

Using deep learning in image hyper spectral segmentation, classification, and detection

NASA Astrophysics Data System (ADS)

Zhao, Xiuying; Su, Zhenyu

2018-02-01

Recent years have shown that deep learning neural networks are a valuable tool in the field of computer vision. Deep learning method can be used in applications like remote sensing such as Land cover Classification, Detection of Vehicle in Satellite Images, Hyper spectral Image classification. This paper addresses the use of the deep learning artificial neural network in Satellite image segmentation. Image segmentation plays an important role in image processing. The hue of the remote sensing image often has a large hue difference, which will result in the poor display of the images in the VR environment. Image segmentation is a pre processing technique applied to the original images and splits the image into many parts which have different hue to unify the color. Several computational models based on supervised, unsupervised, parametric, probabilistic region based image segmentation techniques have been proposed. Recently, one of the machine learning technique known as, deep learning with convolution neural network has been widely used for development of efficient and automatic image segmentation models. In this paper, we focus on study of deep neural convolution network and its variants for automatic image segmentation rather than traditional image segmentation strategies.

Neural network-based model reference adaptive control system.

PubMed

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

Sustaining Teacher Control in a Blog-Based Personal Learning Environment

ERIC Educational Resources Information Center

Tomberg, Vladimir; Laanpere, Mart; Ley, Tobias; Normak, Peeter

2013-01-01

Various tools and services based on Web 2.0 (mainly blogs, wikis, social networking tools) are increasingly used in formal education to create personal learning environments, providing self-directed learners with more freedom, choice, and control over their learning. In such distributed and personalized learning environments, the traditional role…

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

Facilitating the Development of School-Based Learning Networks

ERIC Educational Resources Information Center

Kubiak, Chris; Bertram, Joan

2010-01-01

Purpose: This paper aims to contribute to the knowledge base on leading and facilitating the growth of school improvement networks by describing the activities and challenges faced by network leaders. Design/methodology/approach: A total of 19 co-leaders from 12 networks were interviewed using a semi-structured schedule about the growth of their…

The value of prior knowledge in machine learning of complex network systems.

PubMed

Ferranti, Dana; Krane, David; Craft, David

2017-11-15

Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available. The simulations are based on Boolean networks-directed graphs with 0/1 node states and logical node update rules-which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics and as such provide a useful framework for developing machine-learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine-learning algorithms to predict network steady-state node values ('phenotypes') and perturbation responses ('drug effects'). Links to codes and datasets here: https://gray.mgh.harvard.edu/people-directory/71-david-craft-phd. dcraft@broadinstitute.org. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Students' perceptions of a blended learning experience in dental education.

PubMed

Varthis, S; Anderson, O R

2018-02-01

"Flipped" instructional sequencing is a new instructional method where online instruction precedes the group meeting, allowing for more sophisticated learning through discussion and critical thinking during the in-person class session; a novel approach studied in this research. The purpose of this study was to document dental students' perceptions of flipped-based blended learning and to apply a new method of displaying their perceptions based on Likert-scale data analysis using a network diagramming method known as an item correlation network diagram (ICND). In addition, this article aimed to encourage institutions or course directors to consider self-regulated learning and social constructivism as a theoretical framework when blended learning is incorporated in dental curricula. Twenty (second year) dental students at a Northeastern Regional Dental School in the United States participated in this study. A Likert scale was administered before and after the learning experience to obtain evidence of their perceptions of its quality and educational merits. Item correlation network diagrams, based on the intercorrelations amongst the responses to the Likert-scale items, were constructed to display students' changes in perceptions before and after the learning experience. Students reported positive perceptions of the blended learning, and the ICND analysis of their responses before and after the learning experience provided insights into their social (group-based) cognition about the learning experience. The ICNDs are considered evidence of social or group-based cognition, because they are constructed from evidence obtained using intercorrelations of the total group responses to the Likert-scale items. The students positively received blended learning in dental education, and the ICND analyses demonstrated marked changes in their social cognition of the learning experience based on the pre- and post-Likert survey data. Self-regulated learning and social constructivism are encouraged as useful theoretical frameworks for a blended learning approach. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Ontology-Based Multimedia Authoring Tool for Adaptive E-Learning

ERIC Educational Resources Information Center

Deng, Lawrence Y.; Keh, Huan-Chao; Liu, Yi-Jen

2010-01-01

More video streaming technologies supporting distance learning systems are becoming popular among distributed network environments. In this paper, the authors develop a multimedia authoring tool for adaptive e-learning by using characterization of extended media streaming technologies. The distributed approach is based on an ontology-based model.…

The Development of an E-Learning-Based Learning Service for MKDP Curriculum and Learning at the Indonesia University of Education

ERIC Educational Resources Information Center

Rusman

2016-01-01

E-learning is a general term used to refer to computer-enhanced learning based that facilitates whoever, wherever, and whenever the person is to be able to learn more fun, easier and cheaper by using Internet. In other words, E-learning is the use of network technologies to create, foster, deliver, and facilitate learning, anytime and anywhere. It…

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

PubMed

Wachinger, Christian; Reuter, Martin; Klein, Tassilo

2018-04-15

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Sampling from complex networks using distributed learning automata

NASA Astrophysics Data System (ADS)

Rezvanian, Alireza; Rahmati, Mohammad; Meybodi, Mohammad Reza

2014-02-01

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others.

Active Learning in Engineering Education: a (re)introduction

NASA Astrophysics Data System (ADS)

Lima, Rui M.; Andersson, Pernille Hammar; Saalman, Elisabeth

2017-01-01

The informal network 'Active Learning in Engineering Education' (ALE) has been promoting Active Learning since 2001. ALE creates opportunity for practitioners and researchers of engineering education to collaboratively learn how to foster learning of engineering students. The activities in ALE are centred on the vision that learners construct their knowledge based on meaningful activities and knowledge. In 2014, the steering committee of the ALE network reinforced the need to discuss the meaning of Active Learning and that was the base for this proposal for a special issue. More than 40 submissions were reviewed by the European Journal of Engineering Education community and this theme issue ended up with eight contributions, which are different both in their research and Active Learning approaches. These different Active Learning approaches are aligned with the different approaches that can be increasingly found in indexed journals.

Nonvolatile Memory Materials for Neuromorphic Intelligent Machines.

PubMed

Jeong, Doo Seok; Hwang, Cheol Seong

2018-04-18

Recent progress in deep learning extends the capability of artificial intelligence to various practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis. While such DNN is virtually built on contemporary data centers of the von Neumann architecture, physical (in part) DNN of non-von Neumann architecture, also known as neuromorphic computing, can remarkably improve learning and inference efficiency. Particularly, resistance-based nonvolatile random access memory (NVRAM) highlights its handy and efficient application to the multiply-accumulate (MAC) operation in an analog manner. Here, an overview is given of the available types of resistance-based NVRAMs and their technological maturity from the material- and device-points of view. Examples within the strategy are subsequently addressed in comparison with their benchmarks (virtual DNN in deep learning). A spiking neural network (SNN) is another type of neural network that is more biologically plausible than the DNN. The successful incorporation of resistance-based NVRAM in SNN-based neuromorphic computing offers an efficient solution to the MAC operation and spike timing-based learning in nature. This strategy is exemplified from a material perspective. Intelligent machines are categorized according to their architecture and learning type. Also, the functionality and usefulness of NVRAM-based neuromorphic computing are addressed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback.

PubMed

Koush, Yury; Meskaldji, Djalel-E; Pichon, Swann; Rey, Gwladys; Rieger, Sebastian W; Linden, David E J; Van De Ville, Dimitri; Vuilleumier, Patrik; Scharnowski, Frank

2017-02-01

Most mental functions are associated with dynamic interactions within functional brain networks. Thus, training individuals to alter functional brain networks might provide novel and powerful means to improve cognitive performance and emotions. Using a novel connectivity-neurofeedback approach based on functional magnetic resonance imaging (fMRI), we show for the first time that participants can learn to change functional brain networks. Specifically, we taught participants control over a key component of the emotion regulation network, in that they learned to increase top-down connectivity from the dorsomedial prefrontal cortex, which is involved in cognitive control, onto the amygdala, which is involved in emotion processing. After training, participants successfully self-regulated the top-down connectivity between these brain areas even without neurofeedback, and this was associated with concomitant increases in subjective valence ratings of emotional stimuli of the participants. Connectivity-based neurofeedback goes beyond previous neurofeedback approaches, which were limited to training localized activity within a brain region. It allows to noninvasively and nonpharmacologically change interconnected functional brain networks directly, thereby resulting in specific behavioral changes. Our results demonstrate that connectivity-based neurofeedback training of emotion regulation networks enhances emotion regulation capabilities. This approach can potentially lead to powerful therapeutic emotion regulation protocols for neuropsychiatric disorders. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Gamification of Learning Deactivates the Default Mode Network

PubMed Central

Howard-Jones, Paul A.; Jay, Tim; Mason, Alice; Jones, Harvey

2016-01-01

We hypothesized that embedding educational learning in a game would improve learning outcomes, with increased engagement and recruitment of cognitive resources evidenced by increased activation of working memory network (WMN) and deactivation of default mode network (DMN) regions. In an fMRI study, we compared activity during periods of learning in three conditions that were increasingly game-like: Study-only (when periods of learning were followed by an exemplar question together with its correct answer), Self-quizzing (when periods of learning were followed by a multiple choice question in return for a fixed number of points) and Game-based (when, following each period of learning, participants competed with a peer to answer the question for escalating, uncertain rewards). DMN hubs deactivated as conditions became more game-like, alongside greater self-reported engagement and, in the Game-based condition, higher learning scores. These changes did not occur with any detectable increase in WMN activity. Additionally, ventral striatal activation was associated with responding to questions and receiving positive question feedback. Results support the significance of DMN deactivation for educational learning, and are aligned with recent evidence suggesting DMN and WMN activity may not always be anti-correlated. PMID:26779054

Gamification of Learning Deactivates the Default Mode Network.

PubMed

Howard-Jones, Paul A; Jay, Tim; Mason, Alice; Jones, Harvey

2015-01-01

We hypothesized that embedding educational learning in a game would improve learning outcomes, with increased engagement and recruitment of cognitive resources evidenced by increased activation of working memory network (WMN) and deactivation of default mode network (DMN) regions. In an fMRI study, we compared activity during periods of learning in three conditions that were increasingly game-like: Study-only (when periods of learning were followed by an exemplar question together with its correct answer), Self-quizzing (when periods of learning were followed by a multiple choice question in return for a fixed number of points) and Game-based (when, following each period of learning, participants competed with a peer to answer the question for escalating, uncertain rewards). DMN hubs deactivated as conditions became more game-like, alongside greater self-reported engagement and, in the Game-based condition, higher learning scores. These changes did not occur with any detectable increase in WMN activity. Additionally, ventral striatal activation was associated with responding to questions and receiving positive question feedback. Results support the significance of DMN deactivation for educational learning, and are aligned with recent evidence suggesting DMN and WMN activity may not always be anti-correlated.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment

PubMed Central

Uddin, Raihan; Singh, Shiva M.

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning. PMID:29066959

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

PubMed

Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

PubMed

Gilra, Aditya; Gerstner, Wulfram

2017-11-27

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network

PubMed Central

Gerstner, Wulfram

2017-01-01

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically. PMID:29173280

A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems.

PubMed

Yin, Weiwei; Garimalla, Swetha; Moreno, Alberto; Galinski, Mary R; Styczynski, Mark P

2015-08-28

There are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). However, complex animal model systems typically have significant limitations on cohort sizes, number of samples, and the ability to perform follow-up and validation experiments. These constraints are particularly problematic for many current network learning approaches, which require large numbers of samples and may predict many more regulatory relationships than actually exist. Here, we test the idea that by leveraging the accuracy and efficiency of classifiers, we can construct high-quality networks that capture important interactions between variables in datasets with few samples. We start from a previously-developed tree-like Bayesian classifier and generalize its network learning approach to allow for arbitrary depth and complexity of tree-like networks. Using four diverse sample networks, we demonstrate that this approach performs consistently better at low sample sizes than the Sparse Candidate Algorithm, a representative approach for comparison because it is known to generate Bayesian networks with high positive predictive value. We develop and demonstrate a resampling-based approach to enable the identification of a viable root for the learned tree-like network, important for cases where the root of a network is not known a priori. We also develop and demonstrate an integrated resampling-based approach to the reduction of variable space for the learning of the network. Finally, we demonstrate the utility of this approach via the analysis of a transcriptional dataset of a malaria challenge in a non-human primate model system, Macaca mulatta, suggesting the potential to capture indicators of the earliest stages of cellular differentiation during leukopoiesis. We demonstrate that by starting from effective and efficient approaches for creating classifiers, we can identify interesting tree-like network structures with significant ability to capture the relationships in the training data. This approach represents a promising strategy for inferring networks with high positive predictive value under the constraint of small numbers of samples, meeting a need that will only continue to grow as more high-throughput studies are applied to complex model systems.

The Research on Informal Learning Model of College Students Based on SNS and Case Study

NASA Astrophysics Data System (ADS)

Lu, Peng; Cong, Xiao; Bi, Fangyan; Zhou, Dongdai

2017-03-01

With the rapid development of network technology, informal learning based on online become the main way for college students to learn a variety of subject knowledge. The favor to the SNS community of students and the characteristics of SNS itself provide a good opportunity for the informal learning of college students. This research first analyzes the related research of the informal learning and SNS, next, discusses the characteristics of informal learning and theoretical basis. Then, it proposed an informal learning model of college students based on SNS according to the support role of SNS to the informal learning of students. Finally, according to the theoretical model and the principles proposed in this study, using the Elgg and related tools which is the open source SNS program to achieve the informal learning community. This research is trying to overcome issues such as the lack of social realism, interactivity, resource transfer mode in the current network informal learning communities, so as to provide a new way of informal learning for college students.

Biologically Inspired SNN for Robot Control.

PubMed

Nichols, Eric; McDaid, Liam J; Siddique, Nazmul

2013-02-01

This paper proposes a spiking-neural-network-based robot controller inspired by the control structures of biological systems. Information is routed through the network using facilitating dynamic synapses with short-term plasticity. Learning occurs through long-term synaptic plasticity which is implemented using the temporal difference learning rule to enable the robot to learn to associate the correct movement with the appropriate input conditions. The network self-organizes to provide memories of environments that the robot encounters. A Pioneer robot simulator with laser and sonar proximity sensors is used to verify the performance of the network with a wall-following task, and the results are presented.

Detecting and preventing error propagation via competitive learning.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2013-05-01

Semisupervised learning is a machine learning approach which is able to employ both labeled and unlabeled samples in the training process. It is an important mechanism for autonomous systems due to the ability of exploiting the already acquired information and for exploring the new knowledge in the learning space at the same time. In these cases, the reliability of the labels is a crucial factor, because mislabeled samples may propagate wrong labels to a portion of or even the entire data set. This paper has the objective of addressing the error propagation problem originated by these mislabeled samples by presenting a mechanism embedded in a network-based (graph-based) semisupervised learning method. Such a procedure is based on a combined random-preferential walk of particles in a network constructed from the input data set. The particles of the same class cooperate among them, while the particles of different classes compete with each other to propagate class labels to the whole network. Computer simulations conducted on synthetic and real-world data sets reveal the effectiveness of the model. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

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

Potok, Thomas E; Schuman, Catherine D; Young, Steven R

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determinemore » network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.« less

On the applicability of STDP-based learning mechanisms to spiking neuron network models

NASA Astrophysics Data System (ADS)

Sboev, A.; Vlasov, D.; Serenko, A.; Rybka, R.; Moloshnikov, I.

2016-11-01

The ways to creating practically effective method for spiking neuron networks learning, that would be appropriate for implementing in neuromorphic hardware and at the same time based on the biologically plausible plasticity rules, namely, on STDP, are discussed. The influence of the amount of correlation between input and output spike trains on the learnability by different STDP rules is evaluated. A usability of alternative combined learning schemes, involving artificial and spiking neuron models is demonstrated on the iris benchmark task and on the practical task of gender recognition.

Impact of censoring on learning Bayesian networks in survival modelling.

PubMed

Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola

2009-11-01

Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from data can be used to learn from censored survival data in the presence of light censoring (up to 20%) by treating censored cases as event-free. Given intermediate or heavy censoring, the learnt models become tuned to the majority class and would thus require a different approach.

Ventral striatal network connectivity reflects reward learning and behavior in patients with Parkinson's disease.

PubMed

Petersen, Kalen; Van Wouwe, Nelleke; Stark, Adam; Lin, Ya-Chen; Kang, Hakmook; Trujillo-Diaz, Paula; Kessler, Robert; Zald, David; Donahue, Manus J; Claassen, Daniel O

2018-01-01

A subgroup of Parkinson's disease (PD) patients treated with dopaminergic therapy develop compulsive reward-driven behaviors, which can result in life-altering morbidity. The mesocorticolimbic dopamine network guides reward-motivated behavior; however, its role in this treatment-related behavioral phenotype is incompletely understood. Here, mesocorticolimbic network function in PD patients who develop impulsive and compulsive behaviors (ICB) in response to dopamine agonists was assessed using BOLD fMRI. The tested hypothesis was that network connectivity between the ventral striatum and the limbic cortex is elevated in patients with ICB and that reward-learning proficiency reflects the extent of mesocorticolimbic network connectivity. To evaluate this hypothesis, 3.0T BOLD-fMRI was applied to measure baseline functional connectivity on and off dopamine agonist therapy in age and sex-matched PD patients with (n = 19) or without (n = 18) ICB. An incentive-based task was administered to a subset of patients (n = 20) to quantify positively or negatively reinforced learning. Whole-brain voxelwise analyses and region-of-interest-based mixed linear effects modeling were performed. Elevated ventral striatal connectivity to the anterior cingulate gyrus (P = 0.013), orbitofrontal cortex (P = 0.034), insula (P = 0.044), putamen (P = 0.014), globus pallidus (P < 0.01), and thalamus (P < 0.01) was observed in patients with ICB. A strong trend for elevated amygdala-to-midbrain connectivity was found in ICB patients on dopamine agonist. Ventral striatum-to-subgenual cingulate connectivity correlated with reward learning (P < 0.01), but not with punishment-avoidance learning. These data indicate that PD-ICB patients have elevated network connectivity in the mesocorticolimbic network. Behaviorally, proficient reward-based learning is related to this enhanced limbic and ventral striatal connectivity. Hum Brain Mapp 39:509-521, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Recognition of Tacit Skills: Sustaining Learning Outcomes in Adult Learning and Work Re-Entry

ERIC Educational Resources Information Center

Evans, Karen; Kersh, Natasha; Kontiainen, Seppo

2004-01-01

This paper is based on the project "Recognition of Tacit Skills and Knowledge in Work Re-entry" carried out as a part of the ESRC-funded Research Network "Improving Incentives to Learning in the Workplace". The network aims to contribute to improved practice among a wide range of practitioners. The study has investigated the part played by tacit…

Social Networks and the Building of Learning Communities: An Experimental Study of a Social MOOC

ERIC Educational Resources Information Center

de Lima, Mariana; Zorrilla, Marta

2017-01-01

This study aimed to analyze the student's behaviour in relation to their degree of commitment, participation, and contribution in a MOOC based on a social learning approach. Interaction data was collected on the learning platform and in social networks, both of which were used in the third edition of a social MOOC course. This data was then…

Neural network error correction for solving coupled ordinary differential equations

NASA Technical Reports Server (NTRS)

Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

1992-01-01

A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

A Learning Framework for Winner-Take-All Networks with Stochastic Synapses.

PubMed

Mostafa, Hesham; Cauwenberghs, Gert

2018-06-01

Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this letter, we show that a biologically motivated model based on multilayer winner-take-all circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task and a semisupervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.

Reducing weight precision of convolutional neural networks towards large-scale on-chip image recognition

NASA Astrophysics Data System (ADS)

Ji, Zhengping; Ovsiannikov, Ilia; Wang, Yibing; Shi, Lilong; Zhang, Qiang

2015-05-01

In this paper, we develop a server-client quantization scheme to reduce bit resolution of deep learning architecture, i.e., Convolutional Neural Networks, for image recognition tasks. Low bit resolution is an important factor in bringing the deep learning neural network into hardware implementation, which directly determines the cost and power consumption. We aim to reduce the bit resolution of the network without sacrificing its performance. To this end, we design a new quantization algorithm called supervised iterative quantization to reduce the bit resolution of learned network weights. In the training stage, the supervised iterative quantization is conducted via two steps on server - apply k-means based adaptive quantization on learned network weights and retrain the network based on quantized weights. These two steps are alternated until the convergence criterion is met. In this testing stage, the network configuration and low-bit weights are loaded to the client hardware device to recognize coming input in real time, where optimized but expensive quantization becomes infeasible. Considering this, we adopt a uniform quantization for the inputs and internal network responses (called feature maps) to maintain low on-chip expenses. The Convolutional Neural Network with reduced weight and input/response precision is demonstrated in recognizing two types of images: one is hand-written digit images and the other is real-life images in office scenarios. Both results show that the new network is able to achieve the performance of the neural network with full bit resolution, even though in the new network the bit resolution of both weight and input are significantly reduced, e.g., from 64 bits to 4-5 bits.

Developing and Assessing Teachers' Knowledge of Game-Based Learning

ERIC Educational Resources Information Center

Shah, Mamta; Foster, Aroutis

2015-01-01

Research focusing on the development and assessment of teacher knowledge in game-based learning is in its infancy. A mixed-methods study was undertaken to educate pre-service teachers in game-based learning using the Game Network Analysis (GaNA) framework. Fourteen pre-service teachers completed a methods course, which prepared them in game…

Memory and learning behaviors mimicked in nanogranular SiO2-based proton conductor gated oxide-based synaptic transistors

NASA Astrophysics Data System (ADS)

Wan, Chang Jin; Zhu, Li Qiang; Zhou, Ju Mei; Shi, Yi; Wan, Qing

2013-10-01

In neuroscience, signal processing, memory and learning function are established in the brain by modifying ionic fluxes in neurons and synapses. Emulation of memory and learning behaviors of biological systems by nanoscale ionic/electronic devices is highly desirable for building neuromorphic systems or even artificial neural networks. Here, novel artificial synapses based on junctionless oxide-based protonic/electronic hybrid transistors gated by nanogranular phosphorus-doped SiO2-based proton-conducting films are fabricated on glass substrates by a room-temperature process. Short-term memory (STM) and long-term memory (LTM) are mimicked by tuning the pulse gate voltage amplitude. The LTM process in such an artificial synapse is due to the proton-related interfacial electrochemical reaction. Our results are highly desirable for building future neuromorphic systems or even artificial networks via electronic elements.In neuroscience, signal processing, memory and learning function are established in the brain by modifying ionic fluxes in neurons and synapses. Emulation of memory and learning behaviors of biological systems by nanoscale ionic/electronic devices is highly desirable for building neuromorphic systems or even artificial neural networks. Here, novel artificial synapses based on junctionless oxide-based protonic/electronic hybrid transistors gated by nanogranular phosphorus-doped SiO2-based proton-conducting films are fabricated on glass substrates by a room-temperature process. Short-term memory (STM) and long-term memory (LTM) are mimicked by tuning the pulse gate voltage amplitude. The LTM process in such an artificial synapse is due to the proton-related interfacial electrochemical reaction. Our results are highly desirable for building future neuromorphic systems or even artificial networks via electronic elements. Electronic supplementary information (ESI) available. See DOI: 10.1039/c3nr02987e

Enhancing Teaching and Learning: How Cognitive Research Can Help

ERIC Educational Resources Information Center

Bowman, Margo; Frame, Debra L.; Kennette, Lynne N.

2013-01-01

Pedagogical considerations should be guided by empirical, brain-based research on the human information processing system. People build and organize knowledge into a network-like system that connects related information. As learning occurs, learners expand the network to accommodate new information. Instructional strategies can be used to maximize…

Enhanced Learning through Electronic Communities: A Research Review.

ERIC Educational Resources Information Center

Burgstahler, Sheryl; Swift, Catherine

This report, in support of the project "Enhanced Learning through Electronic Communities," investigated successful practices of electronic communities. A literature review was conducted and a survey was sent to 15 system operators of networks that had a community-based focus with ancillary educational components and networks that focused primarily…

Enhancement of Teaching and Learning of the Fundamentals of Nuclear Engineering Using Multimedia Courseware.

ERIC Educational Resources Information Center

Keyvan, Shahla A.; Pickard, Rodney; Song, Xiaolong

1997-01-01

Computer-aided instruction incorporating interactive multimedia and network technologies can boost teaching effectiveness and student learning. This article describes the development and implementation of network server-based interactive multimedia courseware for a fundamental course in nuclear engineering. A student survey determined that 80% of…

Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

ERIC Educational Resources Information Center

Ye, Qiang

2010-01-01

Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

"Lost in Space": The Role of Social Networking in University-Based Entrepreneurial Learning

ERIC Educational Resources Information Center

Lockett, Nigel; Quesada-Pallarès, Carla; Williams-Middleton, Karen; Padilla-Meléndez, Antonio; Jack, Sarah

2017-01-01

While entrepreneurship education increasingly uses various means to connect students to the "real world", the impact of social networking on learning remains underexplored. This qualitative study of student entrepreneurs in the United Kingdom and Sweden shows that their entrepreneurial journey becomes increasingly complex, requiring…

Unsupervised learning of digit recognition using spike-timing-dependent plasticity

PubMed Central

Diehl, Peter U.; Cook, Matthew

2015-01-01

In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN) can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns), since most such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks. PMID:26941637

Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction

PubMed Central

2014-01-01

Background Network-based learning algorithms for automated function prediction (AFP) are negatively affected by the limited coverage of experimental data and limited a priori known functional annotations. As a consequence their application to model organisms is often restricted to well characterized biological processes and pathways, and their effectiveness with poorly annotated species is relatively limited. A possible solution to this problem might consist in the construction of big networks including multiple species, but this in turn poses challenging computational problems, due to the scalability limitations of existing algorithms and the main memory requirements induced by the construction of big networks. Distributed computation or the usage of big computers could in principle respond to these issues, but raises further algorithmic problems and require resources not satisfiable with simple off-the-shelf computers. Results We propose a novel framework for scalable network-based learning of multi-species protein functions based on both a local implementation of existing algorithms and the adoption of innovative technologies: we solve “locally” the AFP problem, by designing “vertex-centric” implementations of network-based algorithms, but we do not give up thinking “globally” by exploiting the overall topology of the network. This is made possible by the adoption of secondary memory-based technologies that allow the efficient use of the large memory available on disks, thus overcoming the main memory limitations of modern off-the-shelf computers. This approach has been applied to the analysis of a large multi-species network including more than 300 species of bacteria and to a network with more than 200,000 proteins belonging to 13 Eukaryotic species. To our knowledge this is the first work where secondary-memory based network analysis has been applied to multi-species function prediction using biological networks with hundreds of thousands of proteins. Conclusions The combination of these algorithmic and technological approaches makes feasible the analysis of large multi-species networks using ordinary computers with limited speed and primary memory, and in perspective could enable the analysis of huge networks (e.g. the whole proteomes available in SwissProt), using well-equipped stand-alone machines. PMID:24843788

A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.

PubMed

Liu, Jia; Gong, Maoguo; Qin, Kai; Zhang, Puzhao

2018-03-01

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.

Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity

NASA Astrophysics Data System (ADS)

Mizusaki, Beatriz E. P.; Agnes, Everton J.; Erichsen, Rubem; Brunnet, Leonardo G.

2017-08-01

The plastic character of brain synapses is considered to be one of the foundations for the formation of memories. There are numerous kinds of such phenomenon currently described in the literature, but their role in the development of information pathways in neural networks with recurrent architectures is still not completely clear. In this paper we study the role of an activity-based process, called pre-synaptic dependent homeostatic scaling, in the organization of networks that yield precise-timed spiking patterns. It encodes spatio-temporal information in the synaptic weights as it associates a learned input with a specific response. We introduce a correlation measure to evaluate the precision of the spiking patterns and explore the effects of different inhibitory interactions and learning parameters. We find that large learning periods are important in order to improve the network learning capacity and discuss this ability in the presence of distinct inhibitory currents.

Machine learning vortices at the Kosterlitz-Thouless transition

NASA Astrophysics Data System (ADS)

Beach, Matthew J. S.; Golubeva, Anna; Melko, Roger G.

2018-01-01

Efficient and automated classification of phases from minimally processed data is one goal of machine learning in condensed-matter and statistical physics. Supervised algorithms trained on raw samples of microstates can successfully detect conventional phase transitions via learning a bulk feature such as an order parameter. In this paper, we investigate whether neural networks can learn to classify phases based on topological defects. We address this question on the two-dimensional classical XY model which exhibits a Kosterlitz-Thouless transition. We find significant feature engineering of the raw spin states is required to convincingly claim that features of the vortex configurations are responsible for learning the transition temperature. We further show a single-layer network does not correctly classify the phases of the XY model, while a convolutional network easily performs classification by learning the global magnetization. Finally, we design a deep network capable of learning vortices without feature engineering. We demonstrate the detection of vortices does not necessarily result in the best classification accuracy, especially for lattices of less than approximately 1000 spins. For larger systems, it remains a difficult task to learn vortices.

Co-Attention Based Neural Network for Source-Dependent Essay Scoring

ERIC Educational Resources Information Center

Zhang, Haoran; Litman, Diane

2018-01-01

This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliable score prediction of…

Design and Evaluation of a Widget-Based Dashboard for Awareness Support in Research Networks

ERIC Educational Resources Information Center

Reinhardt, Wolfgang; Mletzko, Christian; Drachsler, Hendrik; Sloep, Peter B.

2014-01-01

In this article, we describe the rationale, design and evaluation of a widget-based dashboard to support scholars' awareness of their Research Networks. We introduce the concept of a Research Network and discuss Personal Research Environments that are built of as a development parallel to Personal Learning Environments. Based on the results…

On Deep Learning for Trust-Aware Recommendations in Social Networks.

PubMed

Deng, Shuiguang; Huang, Longtao; Xu, Guandong; Wu, Xindong; Wu, Zhaohui

2017-05-01

With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

PubMed

Kulkarni, Shruti R; Rajendran, Bipin

2018-07-01

We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Community-Based Research Networks: Development and Lessons Learned in an Emerging Field.

ERIC Educational Resources Information Center

Stoecker, Randy; Ambler, Susan H.; Cutforth, Nick; Donohue, Patrick; Dougherty, Dan; Marullo, Sam; Nelson, Kris S.; Stutts, Nancy B.

2003-01-01

Compares seven multi-institutional community-based research networks in Appalachia; Colorado; District of Columbia; Minneapolis-St. Paul; Philadelphia; Richmond, Virginia; and Trenton, New Jersey. After reviewing the histories of the networks, conducts a comparative SWOT analysis, showing their common and unique strengths, weaknesses,…

Learning in neural networks based on a generalized fluctuation theorem

NASA Astrophysics Data System (ADS)

Hayakawa, Takashi; Aoyagi, Toshio

2015-11-01

Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.

A Machine Learning Concept for DTN Routing

NASA Technical Reports Server (NTRS)

Dudukovich, Rachel; Hylton, Alan; Papachristou, Christos

2017-01-01

This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the concepts of Contact Graph Routing, Q-routing and Naive Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN (Delay-Tolerant Networking) protocols is discussed. Finally, initial simulation setup and results are given.

Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links.

PubMed

Sardi, Shira; Vardi, Roni; Goldental, Amir; Sheinin, Anton; Uzan, Herut; Kanter, Ido

2018-03-23

Physical models typically assume time-independent interactions, whereas neural networks and machine learning incorporate interactions that function as adjustable parameters. Here we demonstrate a new type of abundant cooperative nonlinear dynamics where learning is attributed solely to the nodes, instead of the network links which their number is significantly larger. The nodal, neuronal, fast adaptation follows its relative anisotropic (dendritic) input timings, as indicated experimentally, similarly to the slow learning mechanism currently attributed to the links, synapses. It represents a non-local learning rule, where effectively many incoming links to a node concurrently undergo the same adaptation. The network dynamics is now counterintuitively governed by the weak links, which previously were assumed to be insignificant. This cooperative nonlinear dynamic adaptation presents a self-controlled mechanism to prevent divergence or vanishing of the learning parameters, as opposed to learning by links, and also supports self-oscillations of the effective learning parameters. It hints on a hierarchical computational complexity of nodes, following their number of anisotropic inputs and opens new horizons for advanced deep learning algorithms and artificial intelligence based applications, as well as a new mechanism for enhanced and fast learning by neural networks.

Agent-based real-time signal coordination in congested networks.

DOT National Transportation Integrated Search

2014-01-01

This study is the continuation of a previous NEXTRANS study on agent-based reinforcement : learning methods for signal coordination in congested networks. In the previous study, the : formulation of a real-time agent-based traffic signal control in o...

Fault-tolerant nonlinear adaptive flight control using sliding mode online learning.

PubMed

Krüger, Thomas; Schnetter, Philipp; Placzek, Robin; Vörsmann, Peter

2012-08-01

An expanded nonlinear model inversion flight control strategy using sliding mode online learning for neural networks is presented. The proposed control strategy is implemented for a small unmanned aircraft system (UAS). This class of aircraft is very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties and of course system failures. Therefore, these systems mark a sensible testbed to evaluate fault-tolerant, adaptive flight control strategies. Within this work the concept of feedback linearization is combined with feed forward neural networks to compensate for inversion errors and other nonlinear effects. Backpropagation-based adaption laws of the network weights are used for online training. Within these adaption laws the standard gradient descent backpropagation algorithm is augmented with the concept of sliding mode control (SMC). Implemented as a learning algorithm, this nonlinear control strategy treats the neural network as a controlled system and allows a stable, dynamic calculation of the learning rates. While considering the system's stability, this robust online learning method therefore offers a higher speed of convergence, especially in the presence of external disturbances. The SMC-based flight controller is tested and compared with the standard gradient descent backpropagation algorithm in the presence of system failures. Copyright © 2012 Elsevier Ltd. All rights reserved.

Markov Chain Monte Carlo Bayesian Learning for Neural Networks

NASA Technical Reports Server (NTRS)

Goodrich, Michael S.

2011-01-01

Conventional training methods for neural networks involve starting al a random location in the solution space of the network weights, navigating an error hyper surface to reach a minimum, and sometime stochastic based techniques (e.g., genetic algorithms) to avoid entrapment in a local minimum. It is further typically necessary to preprocess the data (e.g., normalization) to keep the training algorithm on course. Conversely, Bayesian based learning is an epistemological approach concerned with formally updating the plausibility of competing candidate hypotheses thereby obtaining a posterior distribution for the network weights conditioned on the available data and a prior distribution. In this paper, we developed a powerful methodology for estimating the full residual uncertainty in network weights and therefore network predictions by using a modified Jeffery's prior combined with a Metropolis Markov Chain Monte Carlo method.

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks

PubMed Central

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S.

2017-01-01

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a=(u,v) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages. PMID:28771201

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

PubMed

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

2017-08-03

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

PubMed

Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

2016-10-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

Recurrent neural networks for breast lesion classification based on DCE-MRIs

NASA Astrophysics Data System (ADS)

Antropova, Natasha; Huynh, Benjamin; Giger, Maryellen

2018-02-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in breast cancer screening, cancer staging, and monitoring response to therapy. Recently, deep learning methods are being rapidly incorporated in image-based breast cancer diagnosis and prognosis. However, most of the current deep learning methods make clinical decisions based on 2-dimentional (2D) or 3D images and are not well suited for temporal image data. In this study, we develop a deep learning methodology that enables integration of clinically valuable temporal components of DCE-MRIs into deep learning-based lesion classification. Our work is performed on a database of 703 DCE-MRI cases for the task of distinguishing benign and malignant lesions, and uses the area under the ROC curve (AUC) as the performance metric in conducting that task. We train a recurrent neural network, specifically a long short-term memory network (LSTM), on sequences of image features extracted from the dynamic MRI sequences. These features are extracted with VGGNet, a convolutional neural network pre-trained on a large dataset of natural images ImageNet. The features are obtained from various levels of the network, to capture low-, mid-, and high-level information about the lesion. Compared to a classification method that takes as input only images at a single time-point (yielding an AUC = 0.81 (se = 0.04)), our LSTM method improves lesion classification with an AUC of 0.85 (se = 0.03).

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

PubMed Central

Staras, Kevin

2016-01-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture. PMID:27760125

Neural networks for learning and prediction with applications to remote sensing and speech perception

NASA Astrophysics Data System (ADS)

Gjaja, Marin N.

1997-11-01

Neural networks for supervised and unsupervised learning are developed and applied to problems in remote sensing, continuous map learning, and speech perception. Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART networks synthesize fuzzy logic and neural networks, and supervised ARTMAP networks incorporate ART modules for prediction and classification. New ART and ARTMAP methods resulting from analyses of data structure, parameter specification, and category selection are developed. Architectural modifications providing flexibility for a variety of applications are also introduced and explored. A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on fuzzy ARTMAP, is developed. System capabilities are tested on a challenging remote sensing problem, prediction of vegetation classes in the Cleveland National Forest from spectral and terrain features. After training at the pixel level, performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, back propagation neural networks, and K-nearest neighbor algorithms. Best performance is obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. This work forms the foundation for additional studies exploring fuzzy ARTMAP's capability to estimate class mixture composition for non-homogeneous sites. Exploratory simulations apply ARTMAP to the problem of learning continuous multidimensional mappings. A novel system architecture retains basic ARTMAP properties of incremental and fast learning in an on-line setting while adding components to solve this class of problems. The perceptual magnet effect is a language-specific phenomenon arising early in infant speech development that is characterized by a warping of speech sound perception. An unsupervised neural network model is proposed that embodies two principal hypotheses supported by experimental data--that sensory experience guides language-specific development of an auditory neural map and that a population vector can predict psychological phenomena based on map cell activities. Model simulations show how a nonuniform distribution of map cell firing preferences can develop from language-specific input and give rise to the magnet effect.

Analytical reasoning task reveals limits of social learning in networks.

PubMed

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

2014-04-06

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

Developing 21st century skills through the use of student personal learning networks

NASA Astrophysics Data System (ADS)

Miller, Robert D.

This research was conducted to study the development of 21st century communication, collaboration, and digital literacy skills of students at the high school level through the use of online social network tools. The importance of this study was based on evidence high school and college students are not graduating with the requisite skills of communication, collaboration, and digital literacy skills yet employers see these skills important to the success of their employees. The challenge addressed through this study was how high schools can integrate social network tools into traditional learning environments to foster the development of these 21st century skills. A qualitative research study was completed through the use of case study. One high school class in a suburban high performing town in Connecticut was selected as the research site and the sample population of eleven student participants engaged in two sets of interviews and learned through the use social network tools for one semester of the school year. The primary social network tools used were Facebook, Diigo, Google Sites, Google Docs, and Twitter. The data collected and analyzed partially supported the transfer of the theory of connectivism at the high school level. The students actively engaged in collaborative learning and research. Key results indicated a heightened engagement in learning, the development of collaborative learning and research skills, and a greater understanding of how to use social network tools for effective public communication. The use of social network tools with high school students was a positive experience that led to an increased awareness of the students as to the benefits social network tools have as a learning tool. The data supported the continued use of social network tools to develop 21st century communication, collaboration, and digital literacy skills. Future research in this area may explore emerging social network tools as well as the long term impact these tools have on the development of lifelong learning skills and quantitative data linked to student learning.

Single image super-resolution based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia

2018-03-01

We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series.

PubMed

Klimovskaia, Anna; Ganscha, Stefan; Claassen, Manfred

2016-12-01

Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.

Artificial Neural Networks as an Architectural Design Tool-Generating New Detail Forms Based On the Roman Corinthian Order Capital

NASA Astrophysics Data System (ADS)

Radziszewski, Kacper

2017-10-01

The following paper presents the results of the research in the field of the machine learning, investigating the scope of application of the artificial neural networks algorithms as a tool in architectural design. The computational experiment was held using the backward propagation of errors method of training the artificial neural network, which was trained based on the geometry of the details of the Roman Corinthian order capital. During the experiment, as an input training data set, five local geometry parameters combined has given the best results: Theta, Pi, Rho in spherical coordinate system based on the capital volume centroid, followed by Z value of the Cartesian coordinate system and a distance from vertical planes created based on the capital symmetry. Additionally during the experiment, artificial neural network hidden layers optimal count and structure was found, giving results of the error below 0.2% for the mentioned before input parameters. Once successfully trained artificial network, was able to mimic the details composition on any other geometry type given. Despite of calculating the transformed geometry locally and separately for each of the thousands of surface points, system could create visually attractive and diverse, complex patterns. Designed tool, based on the supervised learning method of machine learning, gives possibility of generating new architectural forms- free of the designer’s imagination bounds. Implementing the infinitely broad computational methods of machine learning, or Artificial Intelligence in general, not only could accelerate and simplify the design process, but give an opportunity to explore never seen before, unpredictable forms or everyday architectural practice solutions.

Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

PubMed

Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

2015-01-01

Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

Graph Representations of Flow and Transport in Fracture Networks using Machine Learning

NASA Astrophysics Data System (ADS)

Srinivasan, G.; Viswanathan, H. S.; Karra, S.; O'Malley, D.; Godinez, H. C.; Hagberg, A.; Osthus, D.; Mohd-Yusof, J.

2017-12-01

Flow and transport of fluids through fractured systems is governed by the properties and interactions at the micro-scale. Retaining information about the micro-structure such as fracture length, orientation, aperture and connectivity in mesh-based computational models results in solving for millions to billions of degrees of freedom and quickly renders the problem computationally intractable. Our approach depicts fracture networks graphically, by mapping fractures to nodes and intersections to edges, thereby greatly reducing computational burden. Additionally, we use machine learning techniques to build simulators on the graph representation, trained on data from the mesh-based high fidelity simulations to speed up computation by orders of magnitude. We demonstrate our methodology on ensembles of discrete fracture networks, dividing up the data into training and validation sets. Our machine learned graph-based solvers result in over 3 orders of magnitude speedup without any significant sacrifice in accuracy.

Good Features to Correlate for Visual Tracking

NASA Astrophysics Data System (ADS)

Gundogdu, Erhan; Alatan, A. Aydin

2018-05-01

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.

Motor imagery learning modulates functional connectivity of multiple brain systems in resting state.

PubMed

Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

2014-01-01

Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.

Mobile robots exploration through cnn-based reinforcement learning.

PubMed

Tai, Lei; Liu, Ming

2016-01-01

Exploration in an unknown environment is an elemental application for mobile robots. In this paper, we outlined a reinforcement learning method aiming for solving the exploration problem in a corridor environment. The learning model took the depth image from an RGB-D sensor as the only input. The feature representation of the depth image was extracted through a pre-trained convolutional-neural-networks model. Based on the recent success of deep Q-network on artificial intelligence, the robot controller achieved the exploration and obstacle avoidance abilities in several different simulated environments. It is the first time that the reinforcement learning is used to build an exploration strategy for mobile robots through raw sensor information.

Remote Labs and Game-Based Learning for Process Control

ERIC Educational Resources Information Center

Zualkernan, Imran A.; Husseini, Ghaleb A.; Loughlin, Kevin F.; Mohebzada, Jamshaid G.; El Gaml, Moataz

2013-01-01

Social networking platforms and computer games represent a natural informal learning environment for the current generation of learners in higher education. This paper explores the use of game-based learning in the context of an undergraduate chemical engineering remote laboratory. Specifically, students are allowed to manipulate chemical…

Globally Networked Collaborative Learning in Industrial Design

ERIC Educational Resources Information Center

Bohemia, Erik; Ghassan, Aysar

2012-01-01

This article explores project-based cross-cultural and cross-institutional learning. Using Web 2.0 technologies, this project involved more than 240 students and eighteen academic staff from seven international universities. The focus of this article relates to a project-based learning activity named "The Gift". At each institution the…

Networked Teacher Professional Development: The Case of Globaloria

ERIC Educational Resources Information Center

Whitehouse, Pamela

2011-01-01

The purpose of this paper is to explore a teacher professional development program embedded in a networked learning environment, and to offer an emerging model and analytic matrix of 21st century teacher professional development. The Globaloria program is based on theories of learning by design and facilitates teachers and students as they create…

Information Resources Usage in Project Management Digital Learning System

ERIC Educational Resources Information Center

Davidovitch, Nitza; Belichenko, Margarita; Kravchenko, Yurii

2017-01-01

The article combines a theoretical approach to structuring knowledge that is based on the integrated use of fuzzy semantic network theory predicates, Boolean functions, theory of complexity of network structures and some practical aspects to be considered in the distance learning at the university. The paper proposes a methodological approach that…

Developmental Impact Evaluation for Facilitating Learning in Innovation Networks

ERIC Educational Resources Information Center

Saari, Eveliina; Kallio, Katri

2011-01-01

Innovations and new project ideas often emerge in networks of researchers, users of research, and societal actors. This article analyzes and discusses how research scientists learn to conduct research that has an impact on their clients, scientific community, and society. We describe a new developmental impact evaluation method based on the theory…

IP Addressing: Problem-Based Learning Approach on Computer Networks

ERIC Educational Resources Information Center

Jevremovic, Aleksandar; Shimic, Goran; Veinovic, Mladen; Ristic, Nenad

2017-01-01

The case study presented in this paper describes the pedagogical aspects and experience gathered while using an e-learning tool named IPA-PBL. Its main purpose is to provide additional motivation for adopting theoretical principles and procedures in a computer networks course. In the proposed model, the sequencing of activities of the learning…

Design of a Competitive and Collaborative Learning Strategy in a Communication Networks Course

ERIC Educational Resources Information Center

Regueras, L. M.; Verdu, E.; Verdu, M. J.; de Castro, J. P.

2011-01-01

In this paper, an educational methodology based on collaborative and competitive learning is proposed. The suggested approach has been successfully applied to an undergraduate communication networks course, which is part of the core curriculum of the three-year degree in telecommunications engineering at the University of Valladolid in Spain. This…

Introduction to the Special Issue on "Advances in Research on Networked Learning."

ERIC Educational Resources Information Center

Banks, Sheena; Goodyear, Peter; Hodgson, Vivian; McConnell, David

2003-01-01

Taken together, the articles in this special issue contribute to the collective sense of recent progress in research on networked learning in higher education. In combination, they help explain some of the key relationships between teachers' and learners' intentions and experiences, the affordances of text-based communications technologies and…

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

PubMed

Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

2017-11-01

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Learning Biological Networks via Bootstrapping with Optimized GO-based Gene Similarity

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

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2010-08-02

Microarray gene expression data provide a unique information resource for learning biological networks using "reverse engineering" methods. However, there are a variety of cases in which we know which genes are involved in a given pathology of interest, but we do not have enough experimental evidence to support the use of fully-supervised/reverse-engineering learning methods. In this paper, we explore a novel semi-supervised approach in which biological networks are learned from a reference list of genes and a partial set of links for these genes extracted automatically from PubMed abstracts, using a knowledge-driven bootstrapping algorithm. We show how new relevant linksmore » across genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. We describe an application of this approach to the TGFB pathway as a case study and show how the ensuing results prove the feasibility of the approach as an alternate or complementary technique to fully supervised methods.« less

Deep Learning: A Primer for Radiologists.

PubMed

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts

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

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2011-02-18

Transcriptional regulatory networks are being determined using “reverse engineering” methods that infer connections based on correlations in gene state. Corroboration of such networks through independent means such as evidence from the biomedical literature is desirable. Here, we explore a novel approach, a bootstrapping version of our previous Cross-Ontological Analytic method (XOA) that can be used for semi-automated annotation and verification of inferred regulatory connections, as well as for discovery of additional functional relationships between the genes. First, we use our annotation and network expansion method on a biological network learned entirely from the literature. We show how new relevant linksmore » between genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. Second, we apply our method to annotation, verification, and expansion of a set of regulatory connections found by the Context Likelihood of Relatedness algorithm.« less

Deep Visual Attention Prediction

NASA Astrophysics Data System (ADS)

Wang, Wenguan; Shen, Jianbing

2018-05-01

In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

An Indoor Positioning Technique Based on a Feed-Forward Artificial Neural Network Using Levenberg-Marquardt Learning Method

NASA Astrophysics Data System (ADS)

Pahlavani, P.; Gholami, A.; Azimi, S.

2017-09-01

This paper presents an indoor positioning technique based on a multi-layer feed-forward (MLFF) artificial neural networks (ANN). Most of the indoor received signal strength (RSS)-based WLAN positioning systems use the fingerprinting technique that can be divided into two phases: the offline (calibration) phase and the online (estimation) phase. In this paper, RSSs were collected for all references points in four directions and two periods of time (Morning and Evening). Hence, RSS readings were sampled at a regular time interval and specific orientation at each reference point. The proposed ANN based model used Levenberg-Marquardt algorithm for learning and fitting the network to the training data. This RSS readings in all references points and the known position of these references points was prepared for training phase of the proposed MLFF neural network. Eventually, the average positioning error for this network using 30% check and validation data was computed approximately 2.20 meter.

Holography as deep learning

NASA Astrophysics Data System (ADS)

Gan, Wen-Cong; Shu, Fu-Wen

Quantum many-body problem with exponentially large degrees of freedom can be reduced to a tractable computational form by neural network method [G. Carleo and M. Troyer, Science 355 (2017) 602, arXiv:1606.02318.] The power of deep neural network (DNN) based on deep learning is clarified by mapping it to renormalization group (RG), which may shed lights on holographic principle by identifying a sequence of RG transformations to the AdS geometry. In this paper, we show that any network which reflects RG process has intrinsic hyperbolic geometry, and discuss the structure of entanglement encoded in the graph of DNN. We find the entanglement structure of DNN is of Ryu-Takayanagi form. Based on these facts, we argue that the emergence of holographic gravitational theory is related to deep learning process of the quantum-field theory.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

PubMed

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-04-10

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

PubMed Central

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-01-01

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks. PMID:28394270

Expanding the Reach of Continuing Educational Offerings Through a Web-Based Virtual Network: The Experience of InspireNet.

PubMed

Frisch, Noreen C; Atherton, Pat; Borycki, Elizabeth M; Mickelson, Grace; Black, Agnes; Novak Lauscher, Helen; Cordeiro, Jennifer

2017-01-01

Virtual platforms using webinars, e-posters, e-newsletters, wikis and blogs connect people who have common interests in new ways. When those individuals are healthcare providers, a professional network that operates on a virtual platform can support their needs for learning, professional development and information currency. The practice of e-learning for continuing professional development is emerging , particularly in nursing where shift work shift inhibits their ability to attend conferences and classes. This article reports the experience of the InspireNet network that provided e-learning models to: 1) provide opportunities for healthcare providers to organize themselves into learning communities through development of electronic communities of practice; 2) support learning on demand; and 3) dramatically increase the reach of educational offerings.

Dynamically stable associative learning: a neurobiologically based ANN and its applications

NASA Astrophysics Data System (ADS)

Vogl, Thomas P.; Blackwell, Kim L.; Barbour, Garth; Alkon, Daniel L.

1992-07-01

Most currently popular artificial neural networks (ANN) are based on conceptions of neuronal properties that date back to the 1940s and 50s, i.e., to the ideas of McCullough, Pitts, and Hebb. Dystal is an ANN based on current knowledge of neurobiology at the cellular and subcellular level. Networks based on these neurobiological insights exhibit the following advantageous properties: (1) A theoretical storage capacity of bN non-orthogonal memories, where N is the number of output neurons sharing common inputs and b is the number of distinguishable (gray shade) levels. (2) The ability to learn, store, and recall associations among noisy, arbitrary patterns. (3) A local synaptic learning rule (learning depends neither on the output of the post-synaptic neuron nor on a global error term), some of whose consequences are: (4) Feed-forward, lateral, and feed-back connections (as well as time-sensitive connections) are possible without alteration of the learning algorithm; (5) Storage allocation (patch creation) proceeds dynamically as associations are learned (self- organizing); (6) The number of training set presentations required for learning is small (< 10) and does not change with pattern size or content; and (7) The network exhibits monotonic convergence, reaching equilibrium (fully trained) values without oscillating. The performance of Dystal on pattern completion tasks such as faces with different expressions and/or corrupted by noise, and on reading hand-written digits (98% accuracy) and hand-printed Japanese Kanji (90% accuracy) is demonstrated.

[GSH fermentation process modeling using entropy-criterion based RBF neural network model].

PubMed

Tan, Zuoping; Wang, Shitong; Deng, Zhaohong; Du, Guocheng

2008-05-01

The prediction accuracy and generalization of GSH fermentation process modeling are often deteriorated by noise existing in the corresponding experimental data. In order to avoid this problem, we present a novel RBF neural network modeling approach based on entropy criterion. It considers the whole distribution structure of the training data set in the parameter learning process compared with the traditional MSE-criterion based parameter learning, and thus effectively avoids the weak generalization and over-learning. Then the proposed approach is applied to the GSH fermentation process modeling. Our results demonstrate that this proposed method has better prediction accuracy, generalization and robustness such that it offers a potential application merit for the GSH fermentation process modeling.

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

ICADx: interpretable computer aided diagnosis of breast masses

NASA Astrophysics Data System (ADS)

Kim, Seong Tae; Lee, Hakmin; Kim, Hak Gu; Ro, Yong Man

2018-02-01

In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework. The proposed framework is devised with a generative adversarial network, which consists of interpretable diagnosis network and synthetic lesion generative network to learn the relationship between malignancy and a standardized description (BI-RADS). The lesion generative network and the interpretable diagnosis network compete in an adversarial learning so that the two networks are improved. The effectiveness of the proposed method was validated on public mammogram database. Experimental results showed that the proposed ICADx framework could provide the interpretability of mass as well as mass classification. It was mainly attributed to the fact that the proposed method was effectively trained to find the relationship between malignancy and interpretations via the adversarial learning. These results imply that the proposed ICADx framework could be a promising approach to develop the CADx system.

Inversion of surface parameters using fast learning neural networks

NASA Technical Reports Server (NTRS)

Dawson, M. S.; Olvera, J.; Fung, A. K.; Manry, M. T.

1992-01-01

A neural network approach to the inversion of surface scattering parameters is presented. Simulated data sets based on a surface scattering model are used so that the data may be viewed as taken from a completely known randomly rough surface. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) are tested on the simulated backscattering data. The RMS error of training the FL network is found to be less than one half the error of the BP network while requiring one to two orders of magnitude less CPU time. When applied to inversion of parameters from a statistically rough surface, the FL method is successful at recovering the surface permittivity, the surface correlation length, and the RMS surface height in less time and with less error than the BP network. Further applications of the FL neural network to the inversion of parameters from backscatter measurements of an inhomogeneous layer above a half space are shown.

Social learning strategies modify the effect of network structure on group performance.

PubMed

Barkoczi, Daniel; Galesic, Mirta

2016-10-07

The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.

Social learning strategies modify the effect of network structure on group performance

NASA Astrophysics Data System (ADS)

Barkoczi, Daniel; Galesic, Mirta

2016-10-01

The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.

A neural network prototyping package within IRAF

NASA Technical Reports Server (NTRS)

Bazell, D.; Bankman, I.

1992-01-01

We outline our plans for incorporating a Neural Network Prototyping Package into the IRAF environment. The package we are developing will allow the user to choose between different types of networks and to specify the details of the particular architecture chosen. Neural networks consist of a highly interconnected set of simple processing units. The strengths of the connections between units are determined by weights which are adaptively set as the network 'learns'. In some cases, learning can be a separate phase of the user cycle of the network while in other cases the network learns continuously. Neural networks have been found to be very useful in pattern recognition and image processing applications. They can form very general 'decision boundaries' to differentiate between objects in pattern space and they can be used for associative recall of patterns based on partial cures and for adaptive filtering. We discuss the different architectures we plan to use and give examples of what they can do.

Organizing Project-Based Learning in Work Contexts: A Cross-Cultural Cross Analysis of Data from Two Projects

ERIC Educational Resources Information Center

Poell, Rob F.; Yorks, Lyle; Marsick, Victoria J.

2009-01-01

The authors describe research aimed at developing a more comprehensive framework for project-based learning in work contexts. This grows out of a cross-cultural reanalysis of data from two previous studies using two different frameworks: actor-centered learning network theory and a critical pragmatist lens on action reflection learning. Findings…

Pupils' Views on an ICT-Based Learning Environment in Health Learning

ERIC Educational Resources Information Center

Räihä, Teija; Tossavainen, Kerttu; Enkenberg, Jorma; Turunen, Hannele

2014-01-01

This paper presents a study that examined pupils' views on an ICT-based learning environment in health learning. The study was a part of the wider European Network of Health Promoting Schools programme (ENHPS; since 2008, Schools for Health in Europe, SHE) in Finland, and particularly its sub-project, From Puijo to the World with Health Lunch,…

Deinterlacing using modular neural network

NASA Astrophysics Data System (ADS)

Woo, Dong H.; Eom, Il K.; Kim, Yoo S.

2004-05-01

Deinterlacing is the conversion process from the interlaced scan to progressive one. While many previous algorithms that are based on weighted-sum cause blurring in edge region, deinterlacing using neural network can reduce the blurring through recovering of high frequency component by learning process, and is found robust to noise. In proposed algorithm, input image is divided into edge and smooth region, and then, to each region, one neural network is assigned. Through this process, each neural network learns only patterns that are similar, therefore it makes learning more effective and estimation more accurate. But even within each region, there are various patterns such as long edge and texture in edge region. To solve this problem, modular neural network is proposed. In proposed modular neural network, two modules are combined in output node. One is for low frequency feature of local area of input image, and the other is for high frequency feature. With this structure, each modular neural network can learn different patterns with compensating for drawback of counterpart. Therefore it can adapt to various patterns within each region effectively. In simulation, the proposed algorithm shows better performance compared with conventional deinterlacing methods and single neural network method.

Boosting compound-protein interaction prediction by deep learning.

PubMed

Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

2016-11-01

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

NASA Astrophysics Data System (ADS)

Kong, Changduk; Lim, Semyeong; Kim, Keunwoo

2013-03-01

The Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data. The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP (Feed Forward Back Propagation) is used. In order to user's friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB.

Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

NASA Astrophysics Data System (ADS)

Calvin Frans Mariel, Wahyu; Mariyah, Siti; Pramana, Setia

2018-03-01

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm’s ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

PubMed

Zenke, Friedemann; Ganguli, Surya

2018-06-01

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Helping Children Actively Design How They Learn about Health and Wellness: The Institute of Play Tests an Online Social Networking Tool within a Game-Based School Curriculum. Program Results Report

ERIC Educational Resources Information Center

Parker, Susan

2011-01-01

Youth rarely receive opportunities to craft their own strategies around health and wellness within contexts relevant to them. From 2009 to 2010, the Institute of Play, based in New York, developed Being Me, a social networking site, to enable sixth-graders at the Quest to Learn public school to explore, discover and document a range of ideas…

Toward a Learner-Centered System for Adult Learning

ERIC Educational Resources Information Center

Hermans, Henry; Kalz, Marco; Koper, Rob

2013-01-01

Purpose: The purpose of this paper is to present an e-learning system that integrates the use of concepts of virtual learning environments, personal learning environments, and social network sites. The system is based on a learning model which comprises and integrates three learning contexts for the adult learner: the formal, instructional…

Experiential Learning-Based vs. Lecture-Based Discussion: The Impact of Degree of Participation and Student Characteristics on Comprehension and Retention.

ERIC Educational Resources Information Center

Specht, Pamela Hammers

1985-01-01

The article describes a study conducted to determine whether experiential learning-based discussion is more effective than lecture-based discussion in facilitating understanding of material presented in a typical undergraduate business course, specifically organizational communication networks. (CT)

The Comparison of Students' Satisfaction between Ubiquitous and Web-Based Learning Environments

ERIC Educational Resources Information Center

Virtanen, Mari Aulikki; Kääriäinen, Maria; Liikanen, Eeva; Haavisto, Elina

2017-01-01

Higher education is moving towards digitalized learning. The rapid development of technological resources, devices and wireless networks enables more flexible opportunities to study and learn in innovative learning environments. New technologies enable combining of authentic and virtual learning spaces and digital resources as multifunctional…

An Architecture for Case-Based Learning

ERIC Educational Resources Information Center

Cifuentes, Laurent; Mercer, Rene; Alverez, Omar; Bettati, Riccardo

2010-01-01

We report on the design, development, implementation, and evaluation of a case-based instructional environment designed for learning network engineering skills for cybersecurity. We describe the societal problem addressed, the theory-based solution, and the preliminary testing and evaluation of that solution. We identify an architecture for…

Center for development technology and program in technology and human affairs. [emphasizing technology-based networks

NASA Technical Reports Server (NTRS)

Wong, M. D.

1974-01-01

The role of technology in nontraditional higher education with particular emphasis on technology-based networks is analyzed nontraditional programs, institutions, and consortia are briefly reviewed. Nontraditional programs which utilize technology are studied. Technology-based networks are surveyed and analyzed with regard to kinds of students, learning locations, technology utilization, interinstitutional relationships, cost aspects, problems, and future outlook.

The scientific learning approach using multimedia-based maze game to improve learning outcomes

NASA Astrophysics Data System (ADS)

Setiawan, Wawan; Hafitriani, Sarah; Prabawa, Harsa Wara

2016-02-01

The objective of curriculum 2013 is to improve the quality of education in Indonesia, which leads to improving the quality of learning. The scientific approach and supported empowerment media is one approach as massaged of curriculum 2013. This research aims to design a labyrinth game based multimedia and apply in the scientific learning approach. This study was conducted in one of the Vocational School in Subjects of Computer Network on 2 (two) classes of experimental and control. The method used Mix Method Research (MMR) which combines qualitative in multimedia design, and quantitative in the study of learning impact. The results of a survey showed that the general of vocational students like of network topology material (68%), like multimedia (74%), and in particular, like interactive multimedia games and flash (84%). Multimediabased maze game developed good eligibility based on media and material aspects of each value 840% and 82%. Student learning outcomes as a result of using a scientific approach to learning with a multimediabased labyrinth game increase with an average of gain index about (58%) and higher than conventional multimedia with index average gain of 0.41 (41%). Based on these results the scientific approach to learning by using multimediabased labyrinth game can improve the quality of learning and increase understanding of students. Multimedia of learning based labyrinth game, which developed, got a positive response from the students with a good qualification level (75%).

Pre-Service Teachers' and Teacher-Educators' Experiences and Attitudes toward Using Social Networking Sites for Collaborative Learning

ERIC Educational Resources Information Center

Soomro, Kamal Ahmed; Kale, Ugur; Zai, Sajid Yousuf

2014-01-01

Extensive use of social networking sites by students and teachers makes educators and researchers to think whether they can be incorporated in instructional process to facilitate students' learning. This survey-based study records and examines Pakistani pre-service teachers' and teacher-educators' current uses of Facebook, and their attitudes…

A Co-Citation Network of Young Children's Learning with Technology

ERIC Educational Resources Information Center

Tang, Kai-Yu; Li, Ming-Chaun; Hsin, Ching-Ting; Tsai, Chin-Chung

2016-01-01

This paper used a novel literature review approach--co-citation network analysis--to illuminate the latent structure of 87 empirical papers in the field of young children's learning with technology (YCLT). Based on the document co-citation analysis, a total of 206 co-citation relationships among the 87 papers were identified and then graphically…

An Online Social Networking Approach to Reinforce Learning of Rocks and Minerals

ERIC Educational Resources Information Center

Kennelly, Patrick

2009-01-01

Numerous and varied methods are used in introductory Earth science and geology classes to help students learn about rocks and minerals, such as classroom lectures, laboratory specimen identification, and field trips. This paper reports on a method using online social networking. The choice of this forum was based on two criteria. First, many…

Pursuing Excellence in Teacher Preparation: Evidence of Institutional Change from TNE Learning Network Universities

ERIC Educational Resources Information Center

Rogers Poliakoff, Anne; Dailey, Caitlin Rose; White, Robin

2011-01-01

The purpose of this report is to document evidence of institutional change in teacher preparation among universities participating in the Teachers for a New Era (TNE) Learning Network. The report is based upon a cross-case analysis of individual case studies of nine universities, conducted by Academy for Educational Development (AED) researchers.…

Roles of Course Facilitators, Learners, and Technology in the Flow of Information of a cMOOC

ERIC Educational Resources Information Center

Skrypnyk, Oleksandra; Joksimovic, Srec´ko; Kovanovic, Vitomir; Gas?evic, Dragan; Dawson, Shane

2015-01-01

Distributed Massive Open Online Courses (MOOCs) are based on the premise that online learning occurs through a network of interconnected learners. The teachers' role in distributed courses extends to forming such a network by facilitating communication that connects learners and their separate personal learning environments scattered around the…

Recommending Learning Activities in Social Network Using Data Mining Algorithms

ERIC Educational Resources Information Center

Mahnane, Lamia

2017-01-01

In this paper, we show how data mining algorithms (e.g. Apriori Algorithm (AP) and Collaborative Filtering (CF)) is useful in New Social Network (NSN-AP-CF). "NSN-AP-CF" processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the users through mining the frequent episodes by the…

Cultural and Pedagogical Implications of a Global E-Learning Programme

ERIC Educational Resources Information Center

Selinger, Michelle

2004-01-01

An eleven country internal evaluation of the Cisco Networking Academy program across Europe, the Middle East and Africa, revealed a number of issues related to the globalization of e-learning. The Academy program is a 280-hour web-based course that teaches students to install, maintain and troubleshoot computer networks. It was developed in the US…

Models of Community Learning Networks in Canada = Modeles de reseaux d'apprentissage communautaires au Canada.

ERIC Educational Resources Information Center

Human Resources Development Canada, Hull (Quebec). Office of Learning Technologies.

Canada-based community learning networks (CLNs) were examined to provide an operational definition of CLNs, design a framework for their review and analysis, and identify best practices in CLNs. Data were collected from three sources: interviews with 16 key stakeholders in CLNs, literature review, and case studies of five Canadian CLNs. The…

Impact of Collaborative Project-Based Learning on Self-Efficacy of Urban Minority Students in Engineering

ERIC Educational Resources Information Center

Chen, Pearl; Hernandez, Anthony; Dong, Jane

2015-01-01

This paper presents an interdisciplinary research project that studies the impact of collaborative project-based learning (CPBL) on the development of self-efficacy of students from various ethnic groups in an undergraduate senior-level computer networking class. Grounded in social constructivist and situated theories of learning, the study…

Supporting Evidence Use in Networked Professional Learning: The Role of the Middle Leader

ERIC Educational Resources Information Center

LaPointe-McEwan, Danielle; DeLuca, Christopher; Klinger, Don A.

2017-01-01

Background: In Canada, contemporary collaborative professional learning models for educators utilise multiple forms of evidence to inform practice. Commonly, two forms of evidence are prioritised: (a) research-based evidence and (b) classroom-based evidence of student learning. In Ontario, the integration of these two forms of evidence within…

Elements of Network-Based Assessment

ERIC Educational Resources Information Center

Gibson, David

2007-01-01

Elements of network-based assessment systems are envisioned based on recent advances in knowledge and practice in learning theory, assessment design and delivery, and semantic web interoperability. The architecture takes advantage of the meditating role of technology as well as recent models of assessment systems. This overview of the elements…

A Q-Learning-Based Delay-Aware Routing Algorithm to Extend the Lifetime of Underwater Sensor Networks.

PubMed

Jin, Zhigang; Ma, Yingying; Su, Yishan; Li, Shuo; Fu, Xiaomei

2017-07-19

Underwater sensor networks (UWSNs) have become a hot research topic because of their various aquatic applications. As the underwater sensor nodes are powered by built-in batteries which are difficult to replace, extending the network lifetime is a most urgent need. Due to the low and variable transmission speed of sound, the design of reliable routing algorithms for UWSNs is challenging. In this paper, we propose a Q-learning based delay-aware routing (QDAR) algorithm to extend the lifetime of underwater sensor networks. In QDAR, a data collection phase is designed to adapt to the dynamic environment. With the application of the Q-learning technique, QDAR can determine a global optimal next hop rather than a greedy one. We define an action-utility function in which residual energy and propagation delay are both considered for adequate routing decisions. Thus, the QDAR algorithm can extend the network lifetime by uniformly distributing the residual energy and provide lower end-to-end delay. The simulation results show that our protocol can yield nearly the same network lifetime, and can reduce the end-to-end delay by 20-25% compared with a classic lifetime-extended routing protocol (QELAR).

A Q-Learning-Based Delay-Aware Routing Algorithm to Extend the Lifetime of Underwater Sensor Networks

PubMed Central

Ma, Yingying; Su, Yishan; Li, Shuo; Fu, Xiaomei

2017-01-01

Underwater sensor networks (UWSNs) have become a hot research topic because of their various aquatic applications. As the underwater sensor nodes are powered by built-in batteries which are difficult to replace, extending the network lifetime is a most urgent need. Due to the low and variable transmission speed of sound, the design of reliable routing algorithms for UWSNs is challenging. In this paper, we propose a Q-learning based delay-aware routing (QDAR) algorithm to extend the lifetime of underwater sensor networks. In QDAR, a data collection phase is designed to adapt to the dynamic environment. With the application of the Q-learning technique, QDAR can determine a global optimal next hop rather than a greedy one. We define an action-utility function in which residual energy and propagation delay are both considered for adequate routing decisions. Thus, the QDAR algorithm can extend the network lifetime by uniformly distributing the residual energy and provide lower end-to-end delay. The simulation results show that our protocol can yield nearly the same network lifetime, and can reduce the end-to-end delay by 20–25% compared with a classic lifetime-extended routing protocol (QELAR). PMID:28753951

Deep Constrained Siamese Hash Coding Network and Load-Balanced Locality-Sensitive Hashing for Near Duplicate Image Detection.

PubMed

Hu, Weiming; Fan, Yabo; Xing, Junliang; Sun, Liang; Cai, Zhaoquan; Maybank, Stephen

2018-09-01

We construct a new efficient near duplicate image detection method using a hierarchical hash code learning neural network and load-balanced locality-sensitive hashing (LSH) indexing. We propose a deep constrained siamese hash coding neural network combined with deep feature learning. Our neural network is able to extract effective features for near duplicate image detection. The extracted features are used to construct a LSH-based index. We propose a load-balanced LSH method to produce load-balanced buckets in the hashing process. The load-balanced LSH significantly reduces the query time. Based on the proposed load-balanced LSH, we design an effective and feasible algorithm for near duplicate image detection. Extensive experiments on three benchmark data sets demonstrate the effectiveness of our deep siamese hash encoding network and load-balanced LSH.

Bankruptcy prediction based on financial ratios using Jordan Recurrent Neural Networks: a case study in Polish companies

NASA Astrophysics Data System (ADS)

Hardinata, Lingga; Warsito, Budi; Suparti

2018-05-01

Complexity of bankruptcy causes the accurate models of bankruptcy prediction difficult to be achieved. Various prediction models have been developed to improve the accuracy of bankruptcy predictions. Machine learning has been widely used to predict because of its adaptive capabilities. Artificial Neural Networks (ANN) is one of machine learning which proved able to complete inference tasks such as prediction and classification especially in data mining. In this paper, we propose the implementation of Jordan Recurrent Neural Networks (JRNN) to classify and predict corporate bankruptcy based on financial ratios. Feedback interconnection in JRNN enable to make the network keep important information well allowing the network to work more effectively. The result analysis showed that JRNN works very well in bankruptcy prediction with average success rate of 81.3785%.

Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

PubMed Central

Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

2014-01-01

Background Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. Methodology/Principal Findings We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. Conclusions/Significance These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning. PMID:24465577

Sparse dictionary learning of resting state fMRI networks.

PubMed

Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

2012-07-02

Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

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

PubMed

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

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

Netlearning and Learning through Networks

ERIC Educational Resources Information Center

Wiberg, Mikael

2007-01-01

Traditional non-computerized learning environments are typically founded on an understanding of learning as acquiring silence for an effective "individual learning process". Recently, it has also been reported that the high expectations for the impact of computer-based technology on educational practice have not been realized. This paper…

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

PubMed Central

Lindén, Henrik; Lansner, Anders

2016-01-01

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison. PMID:27213810

Analytical reasoning task reveals limits of social learning in networks

PubMed Central

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

2014-01-01

Social learning—by observing and copying others—is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an ‘unreflective copying bias’, which limits their social learning to the output, rather than the process, of their peers’ reasoning—even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning. PMID:24501275

Ensemble learning in fixed expansion layer networks for mitigating catastrophic forgetting.

PubMed

Coop, Robert; Mishtal, Aaron; Arel, Itamar

2013-10-01

Catastrophic forgetting is a well-studied attribute of most parameterized supervised learning systems. A variation of this phenomenon, in the context of feedforward neural networks, arises when nonstationary inputs lead to loss of previously learned mappings. The majority of the schemes proposed in the literature for mitigating catastrophic forgetting were not data driven and did not scale well. We introduce the fixed expansion layer (FEL) feedforward neural network, which embeds a sparsely encoding hidden layer to help mitigate forgetting of prior learned representations. In addition, we investigate a novel framework for training ensembles of FEL networks, based on exploiting an information-theoretic measure of diversity between FEL learners, to further control undesired plasticity. The proposed methodology is demonstrated on a basic classification task, clearly emphasizing its advantages over existing techniques. The architecture proposed can be enhanced to address a range of computational intelligence tasks, such as regression problems and system control.

Genetic learning in rule-based and neural systems

NASA Technical Reports Server (NTRS)

Smith, Robert E.

1993-01-01

The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

Drive Control Scheme of Electric Power Assisted Wheelchair Based on Neural Network Learning of Human Wheelchair Operation Characteristics

NASA Astrophysics Data System (ADS)

Tanohata, Naoki; Seki, Hirokazu

This paper describes a novel drive control scheme of electric power assisted wheelchairs based on neural network learning of human wheelchair operation characteristics. “Electric power assisted wheelchair” which enhances the drive force of the operator by employing electric motors is expected to be widely used as a mobility support system for elderly and disabled people. However, some handicapped people with paralysis of the muscles of one side of the body cannot maneuver the wheelchair as desired because of the difference in the right and left input force. Therefore, this study proposes a neural network learning system of such human wheelchair operation characteristics and a drive control scheme with variable distribution and assistance ratios. Some driving experiments will be performed to confirm the effectiveness of the proposed control system.

Adaptive enhancement of learning protocol in hippocampal cultured networks grown on multielectrode arrays

PubMed Central

Pimashkin, Alexey; Gladkov, Arseniy; Mukhina, Irina; Kazantsev, Victor

2013-01-01

Learning in neuronal networks can be investigated using dissociated cultures on multielectrode arrays supplied with appropriate closed-loop stimulation. It was shown in previous studies that weakly respondent neurons on the electrodes can be trained to increase their evoked spiking rate within a predefined time window after the stimulus. Such neurons can be associated with weak synaptic connections in nearby culture network. The stimulation leads to the increase in the connectivity and in the response. However, it was not possible to perform the learning protocol for the neurons on electrodes with relatively strong synaptic inputs and responding at higher rates. We proposed an adaptive closed-loop stimulation protocol capable to achieve learning even for the highly respondent electrodes. It means that the culture network can reorganize appropriately its synaptic connectivity to generate a desired response. We introduced an adaptive reinforcement condition accounting for the response variability in control stimulation. It significantly enhanced the learning protocol to a large number of responding electrodes independently on its base response level. We also found that learning effect preserved after 4–6 h after training. PMID:23745105

Geometrical structure of Neural Networks: Geodesics, Jeffrey's Prior and Hyper-ribbons

NASA Astrophysics Data System (ADS)

Hayden, Lorien; Alemi, Alex; Sethna, James

2014-03-01

Neural networks are learning algorithms which are employed in a host of Machine Learning problems including speech recognition, object classification and data mining. In practice, neural networks learn a low dimensional representation of high dimensional data and define a model manifold which is an embedding of this low dimensional structure in the higher dimensional space. In this work, we explore the geometrical structure of a neural network model manifold. A Stacked Denoising Autoencoder and a Deep Belief Network are trained on handwritten digits from the MNIST database. Construction of geodesics along the surface and of slices taken from the high dimensional manifolds reveal a hierarchy of widths corresponding to a hyper-ribbon structure. This property indicates that neural networks fall into the class of sloppy models, in which certain parameter combinations dominate the behavior. Employing this information could prove valuable in designing both neural network architectures and training algorithms. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No . DGE-1144153.

Can Learning Collaboratives Support Implementation by Rewiring Professional Networks?

PubMed

Bunger, Alicia C; Hanson, Rochelle F; Doogan, Nathan J; Powell, Byron J; Cao, Yiwen; Dunn, Jerry

2016-01-01

This study examined how a learning collaborative focusing on trauma-focused CBT (TF-CBT) impacted advice-seeking patterns between clinicians and three key learning sources: (1) training experts who share technical knowledge about TF-CBT, (2) peers from other participating organizations who share their implementation experiences, and (3) colleagues from their own agency who provide social and professional support. Based on surveys administered to 132 clinicians from 32 agencies, participants' professional networks changed slightly over time by forming new advice-seeking relationships with training experts. While small, these changes at the clinician-level yielded substantial changes in the structure of the regional advice network.

Can Learning Collaboratives Support Implementation By Rewiring Professional Networks?

PubMed Central

Hanson, Rochelle F.; Doogan, Nathan J.; Powell, Byron J.; Cao, Yiwen; Dunn, Jerry

2015-01-01

This study examined how a learning collaborative focusing on Trauma-Focused CBT (TF-CBT) impacted advice-seeking patterns between clinicians and three key learning sources: (1) training experts who share technical knowledge about TF-CBT, (2) peers from other participating organizations who share their implementation experiences, and (3) colleagues from their own agency who provide social and professional support. Based on surveys administered to 132 clinicians from 32 agencies, participants’ professional networks changed slightly over time by forming new advice-seeking relationships with training experts. While small, these changes at the clinician-level yielded substantial changes in the structure of the regional advice network. PMID:25542237

A statewide biomedical communications network for South Carolina.

PubMed

Mangiaracina, J; Sawyer, W A

1976-03-01

In 1972, the Medical University of South Carolina was awarded a contract to establish 4 Area Health Education Centers in South Carolina. These centers, based in community hospitals, provide residency programs, clinical instruction for students, and continuing education programs for health professionals. In late 1974, contractual agreements between the Medical University of South Carolina's Library/Learning Resource Center and the Area Health Education Centers were negotiated to provide book and nonbook learning materials to all health practitioners in South Carolina. The history and the functions of the resulting network and evaluation of audiovisual and self-instructional learning materials procured and distributed by the network are described.

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

PubMed

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

PubMed

Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan

2017-12-20

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.

PubMed

Sohn, Insoo; Liu, Huaping; Ansari, Nirwan

2015-01-01

An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.

Proceedings for the Annual Symposium and Exhibition on Situational Awareness in the Tactical Air Environment, (2nd), Held at Patuxent River, Maryland, on 3-4 June 1997

DTIC Science & Technology

1997-06-01

made based on a learning mechanism. Traditional statistical regression and neural network approaches offer some utility, but suffer from practical...Columbus, OH. Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive, skill- based , and affective theories of learning outcomes to new...and Feature Effects 151 Enhanced Spatial State Feedback for Night Vision Goggle Displays 159 Statistical Network Applications of Decision Aiding for

Evaluation of multilayer perceptron algorithms for an analysis of network flow data

NASA Astrophysics Data System (ADS)

Bieniasz, Jedrzej; Rawski, Mariusz; Skowron, Krzysztof; Trzepiński, Mateusz

2016-09-01

The volume of exchanged information through IP networks is larger than ever and still growing. It creates a space for both benign and malicious activities. The second one raises awareness on security network devices, as well as network infrastructure and a system as a whole. One of the basic tools to prevent cyber attacks is Network Instrusion Detection System (NIDS). NIDS could be realized as a signature-based detector or an anomaly-based one. In the last few years the emphasis has been placed on the latter type, because of the possibility of applying smart and intelligent solutions. An ideal NIDS of next generation should be composed of self-learning algorithms that could react on known and unknown malicious network activities respectively. In this paper we evaluated a machine learning approach for detection of anomalies in IP network data represented as NetFlow records. We considered Multilayer Perceptron (MLP) as the classifier and we used two types of learning algorithms - Backpropagation (BP) and Particle Swarm Optimization (PSO). This paper includes a comprehensive survey on determining the most optimal MLP learning algorithm for the classification problem in application to network flow data. The performance, training time and convergence of BP and PSO methods were compared. The results show that PSO algorithm implemented by the authors outperformed other solutions if accuracy of classifications is considered. The major disadvantage of PSO is training time, which could be not acceptable for larger data sets or in real network applications. At the end we compared some key findings with the results from the other papers to show that in all cases results from this study outperformed them.

Chinese lexical networks: The structure, function and formation

NASA Astrophysics Data System (ADS)

Li, Jianyu; Zhou, Jie; Luo, Xiaoyue; Yang, Zhanxin

2012-11-01

In this paper Chinese phrases are modeled using complex networks theory. We analyze statistical properties of the networks and find that phrase networks display some important features: not only small world and the power-law distribution, but also hierarchical structure and disassortative mixing. These statistical traits display the global organization of Chinese phrases. The origin and formation of such traits are analyzed from a macroscopic Chinese culture and philosophy perspective. It is interesting to find that Chinese culture and philosophy may shape the formation and structure of Chinese phrases. To uncover the structural design principles of networks, network motif patterns are studied. It is shown that they serve as basic building blocks to form the whole phrase networks, especially triad 38 (feed forward loop) plays a more important role in forming most of the phrases and other motifs. The distinct structure may not only keep the networks stable and robust, but also be helpful for information processing. The results of the paper can give some insight into Chinese language learning and language acquisition. It strengthens the idea that learning the phrases helps to understand Chinese culture. On the other side, understanding Chinese culture and philosophy does help to learn Chinese phrases. The hub nodes in the networks show the close relationship with Chinese culture and philosophy. Learning or teaching the hub characters, hub-linking phrases and phrases which are meaning related based on motif feature should be very useful and important for Chinese learning and acquisition.

Embedding Entrepreneurial Thinking into Fluids-related Courses: Small Changes Lead to Positive Results

NASA Astrophysics Data System (ADS)

Carnasciali, Maria-Isabel

2017-11-01

Many fluid dynamics instructors have embraced student-centered learning pedagogies (Active & Collaborative Learning (ACL) and Problem/Project Based Learning (PBL)) to promote learning and increase student engagement. A growing effort in engineering education calls to equip students with entrepreneurial skills needed to drive innovation. The Kern Entrepreneurial Engineering Network (KEEN) defines entrepreneurial mindset based on three key attributes: curiosity, connections, and creating value. Elements of ACL and PBL have been used to embed Entrepreneurial Thinking concepts into two fluids-related subjects: 1) an introductory thermal-fluid systems course, and 2) thermo-fluids laboratory. Assessment of students' work reveal an improvement in student learning. Course Evaluations and Surveys indicate an increased perceived-value of course content. Training and development made possible through funding from the Kern Entrepreneurial Engineering Network and the Bucknall Excellence in Teaching Award.

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.

Teaching with technology: free Web resources for teaching and learning.

PubMed

Wink, Diane M; Smith-Stoner, Marilyn

2011-01-01

In this bimonthly series, the department editor examines how nurse educators can use Internet and Web-based computer technologies such as search, communication, collaborative writing tools; social networking, and social bookmarking sites; virtual worlds; and Web-based teaching and learning programs. In this article, the department editor and her coauthor describe free Web-based resources that can be used to support teaching and learning.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Convergent Technologies in Distance Learning Delivery.

ERIC Educational Resources Information Center

Wheeler, Steve

1999-01-01

Describes developments in British education in distance learning technologies. Highlights include networking the rural areas; communication, community, and paradigm shifts; digital compression techniques and telematics; Web-based material delivered over the Internet; system flexibility; social support; learning support; videoconferencing; and…

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

ERIC Educational Resources Information Center

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

Stochastic associative memory

NASA Astrophysics Data System (ADS)

Baumann, Erwin W.; Williams, David L.

1993-08-01

Artificial neural networks capable of learning and recalling stochastic associations between non-deterministic quantities have received relatively little attention to date. One potential application of such stochastic associative networks is the generation of sensory 'expectations' based on arbitrary subsets of sensor inputs to support anticipatory and investigate behavior in sensor-based robots. Another application of this type of associative memory is the prediction of how a scene will look in one spectral band, including noise, based upon its appearance in several other wavebands. This paper describes a semi-supervised neural network architecture composed of self-organizing maps associated through stochastic inter-layer connections. This 'Stochastic Associative Memory' (SAM) can learn and recall non-deterministic associations between multi-dimensional probability density functions. The stochastic nature of the network also enables it to represent noise distributions that are inherent in any true sensing process. The SAM architecture, training process, and initial application to sensor image prediction are described. Relationships to Fuzzy Associative Memory (FAM) are discussed.

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

DOE PAGES

Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

2017-02-01

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

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

Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less

Informal interprofessional learning: visualizing the clinical workplace.

PubMed

Wagter, Judith Martine; van de Bunt, Gerhard; Honing, Marina; Eckenhausen, Marina; Scherpbier, Albert

2012-05-01

Daily collaboration of senior doctors, residents and nurses involves a major potential for sharing knowledge between professionals. Therefore, more attention needs to be paid to informal learning to create strategies and appropriate conditions for enhancing and effectuating informal learning in the workplace. The aim of this study is to visualize and describe patterns of informal interprofessional learning relations among staff in complex care. Questionnaires with four network questions - recognized as indicators of informal learning in the clinical workplace - were handed out to intensive and medium care unit (ICU/MCU) staff members (N = 108), of which 77% were completed and returned. Data were analyzed using social network analysis and Mokken scale analysis. Densities, tie strength and reciprocity of the four networks created show MCU and ICU nurses as subgroups within the ward and reveal central but relatively one-sided relations of senior doctors with nurses and residents. Based on the analyses, we formulated a scale of intensity of informal learning relations that can be used to understand and stimulate informal interprofessional learning.

Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction.

PubMed

Gupta, Shashank; Pawar, Sachin; Ramrakhiyani, Nitin; Palshikar, Girish Keshav; Varma, Vasudeva

2018-06-13

Social media is a useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from Twitter. Medical information extraction from social media is challenging, mainly due to short and highly informal nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction rely on supervised learning methods, which suffer from labeled data scarcity problem. The state-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which is Long-Short-Term-Memory network (LSTM). Deep neural networks, due to their large number of free parameters rely heavily on large annotated corpora for learning the end task. But in the real-world, it is hard to get large labeled data, mainly due to the heavy cost associated with the manual annotation. To this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction. In this study, we tackle the problem of labeled data scarcity for Adverse Drug Reaction mention extraction from social media and propose a novel semi-supervised learning based method which can leverage large unlabeled corpus available in abundance on the web. Through empirical study, we demonstrate that our proposed method outperforms fully supervised learning based baseline which relies on large manually annotated corpus for a good performance.

Evolutionary image simplification for lung nodule classification with convolutional neural networks.

PubMed

Lückehe, Daniel; von Voigt, Gabriele

2018-05-29

Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

A Social Learning Management System Supporting Feedback for Incorrect Answers Based on Social Network Services

ERIC Educational Resources Information Center

Son, Jiseong; Kim, Jeong-Dong; Na, Hong-Seok; Baik, Doo-Kwon

2016-01-01

In this research, we propose a Social Learning Management System (SLMS) enabling real-time and reliable feedback for incorrect answers by learners using a social network service (SNS). The proposed system increases the accuracy of learners' assessment results by using a confidence scale and a variety of social feedback that is created and shared…

A Dynamic Bayesian Network Based Structural Learning towards Automated Handwritten Digit Recognition

NASA Astrophysics Data System (ADS)

Pauplin, Olivier; Jiang, Jianmin

Pattern recognition using Dynamic Bayesian Networks (DBNs) is currently a growing area of study. In this paper, we present DBN models trained for classification of handwritten digit characters. The structure of these models is partly inferred from the training data of each class of digit before performing parameter learning. Classification results are presented for the four described models.

Challenges to the Learning Organization in the Context of Generational Diversity and Social Networks

ERIC Educational Resources Information Center

Kaminska, Renata; Borzillo, Stefano

2018-01-01

Purpose: The purpose of this paper is to gain a better understanding of the challenges to the emergence of a learning organization (LO) posed by a context of generational diversity and an enterprise social networking system (ESNS). Design/methodology/approach: This study uses a qualitative methodology based on an analysis of 20 semi-structured…

Research Trends in the Study of ICT Based Learning Communities: A Bibliometric Analysis

ERIC Educational Resources Information Center

Hernández, Jonathan Bermúdez; Chalela, Salim; Arias, Jackeline Valencia; Arias, Alejandro Valencia

2017-01-01

The current opportunities to develop and acquire knowledge in the network, Information and Communications Technology (ICT) play a major role in the learning process. This research offers a bibliometric analysis in order to examine the state of the research activity carried out in relation to the learning communities based on ICT. The indicators…

Effect of Bayesian Student Modeling on Academic Achievement in Foreign Language Teaching (University Level English Preparatory School Example)

ERIC Educational Resources Information Center

Aslan, Burak Galip; Öztürk, Özlem; Inceoglu, Mustafa Murat

2014-01-01

Considering the increasing importance of adaptive approaches in CALL systems, this study implemented a machine learning based student modeling middleware with Bayesian networks. The profiling approach of the student modeling system is based on Felder and Silverman's Learning Styles Model and Felder and Soloman's Index of Learning Styles…

An Autonomous Mobile Agent-Based Distributed Learning Architecture: A Proposal and Analytical Analysis

ERIC Educational Resources Information Center

Ahmed, Iftikhar; Sadeq, Muhammad Jafar

2006-01-01

Current distance learning systems are increasingly packing highly data-intensive contents on servers, resulting in the congestion of network and server resources at peak service times. A distributed learning system based on faded information field (FIF) architecture that employs mobile agents (MAs) has been proposed and simulated in this work. The…

Neural Correlates of Motor Learning, Transfer of Learning, and Learning to Learn

PubMed Central

Seidler, Rachael D.

2009-01-01

Recent studies on the neural bases of sensorimotor adaptation demonstrate that the cerebellar and striatal thalamocortical pathways contribute to early learning. Transfer of learning involves a reduction in the contribution of early learning networks, and increased reliance on the cerebellum. The neural correlates of learning to learn remain to be determined, but likely involve enhanced functioning of general aspects of early learning. PMID:20016293

Dynamic reconfiguration of human brain functional networks through neurofeedback.

PubMed

Haller, Sven; Kopel, Rotem; Jhooti, Permi; Haas, Tanja; Scharnowski, Frank; Lovblad, Karl-Olof; Scheffler, Klaus; Van De Ville, Dimitri

2013-11-01

Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity. Copyright © 2013 Elsevier Inc. All rights reserved.

Deep convolutional neural network based antenna selection in multiple-input multiple-output system

NASA Astrophysics Data System (ADS)

Cai, Jiaxin; Li, Yan; Hu, Ying

2018-03-01

Antenna selection of wireless communication system has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity in large-scale Multiple-Input MultipleOutput antenna systems. Recently, deep learning based methods have achieved promising performance for large-scale data processing and analysis in many application fields. This paper is the first attempt to introduce the deep learning technique into the field of Multiple-Input Multiple-Output antenna selection in wireless communications. First, the label of attenuation coefficients channel matrix is generated by minimizing the key performance indicator of training antenna systems. Then, a deep convolutional neural network that explicitly exploits the massive latent cues of attenuation coefficients is learned on the training antenna systems. Finally, we use the adopted deep convolutional neural network to classify the channel matrix labels of test antennas and select the optimal antenna subset. Simulation experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based wireless antenna selection.

An Adaptive Deghosting Method in Neural Network-Based Infrared Detectors Nonuniformity Correction

PubMed Central

Li, Yiyang; Jin, Weiqi; Zhu, Jin; Zhang, Xu; Li, Shuo

2018-01-01

The problems of the neural network-based nonuniformity correction algorithm for infrared focal plane arrays mainly concern slow convergence speed and ghosting artifacts. In general, the more stringent the inhibition of ghosting, the slower the convergence speed. The factors that affect these two problems are the estimated desired image and the learning rate. In this paper, we propose a learning rate rule that combines adaptive threshold edge detection and a temporal gate. Through the noise estimation algorithm, the adaptive spatial threshold is related to the residual nonuniformity noise in the corrected image. The proposed learning rate is used to effectively and stably suppress ghosting artifacts without slowing down the convergence speed. The performance of the proposed technique was thoroughly studied with infrared image sequences with both simulated nonuniformity and real nonuniformity. The results show that the deghosting performance of the proposed method is superior to that of other neural network-based nonuniformity correction algorithms and that the convergence speed is equivalent to the tested deghosting methods. PMID:29342857

An Adaptive Deghosting Method in Neural Network-Based Infrared Detectors Nonuniformity Correction.

PubMed

Li, Yiyang; Jin, Weiqi; Zhu, Jin; Zhang, Xu; Li, Shuo

2018-01-13

The problems of the neural network-based nonuniformity correction algorithm for infrared focal plane arrays mainly concern slow convergence speed and ghosting artifacts. In general, the more stringent the inhibition of ghosting, the slower the convergence speed. The factors that affect these two problems are the estimated desired image and the learning rate. In this paper, we propose a learning rate rule that combines adaptive threshold edge detection and a temporal gate. Through the noise estimation algorithm, the adaptive spatial threshold is related to the residual nonuniformity noise in the corrected image. The proposed learning rate is used to effectively and stably suppress ghosting artifacts without slowing down the convergence speed. The performance of the proposed technique was thoroughly studied with infrared image sequences with both simulated nonuniformity and real nonuniformity. The results show that the deghosting performance of the proposed method is superior to that of other neural network-based nonuniformity correction algorithms and that the convergence speed is equivalent to the tested deghosting methods.

SVM-based tree-type neural networks as a critic in adaptive critic designs for control.

PubMed

Deb, Alok Kanti; Jayadeva; Gopal, Madan; Chandra, Suresh

2007-07-01

In this paper, we use the approach of adaptive critic design (ACD) for control, specifically, the action-dependent heuristic dynamic programming (ADHDP) method. A least squares support vector machine (SVM) regressor has been used for generating the control actions, while an SVM-based tree-type neural network (NN) is used as the critic. After a failure occurs, the critic and action are retrained in tandem using the failure data. Failure data is binary classification data, where the number of failure states are very few as compared to the number of no-failure states. The difficulty of conventional multilayer feedforward NNs in learning this type of classification data has been overcome by using the SVM-based tree-type NN, which due to its feature to add neurons to learn misclassified data, has the capability to learn any binary classification data without a priori choice of the number of neurons or the structure of the network. The capability of the trained controller to handle unforeseen situations is demonstrated.

Infrastructure to support learning health systems: are we there yet? Innovative solutions and lessons learned from American Recovery and Reinvestment Act CER investments.

PubMed

Holve, Erin; Segal, Courtney

2014-11-01

The 11 big health data networks participating in the AcademyHealth Electronic Data Methods Forum represent cutting-edge efforts to harness the power of big health data for research and quality improvement. This paper is a comparative case study based on site visits conducted with a subset of these large infrastructure grants funded through the Recovery Act, in which four key issues emerge that can inform the evolution of learning health systems, including the importance of acknowledging the challenges of scaling specialized expertise needed to manage and run CER networks; the delicate balance between privacy protections and the utility of distributed networks; emerging community engagement strategies; and the complexities of developing a robust business model for multi-use networks.

Learning by Doing Approach in the Internet Environment to Improve the Teaching Efficiency of Information Technology

NASA Astrophysics Data System (ADS)

Zhang, X.-S.; Xie, Hua

This paper presents a learning-by-doing method in the Internet environment to enhance the results of information technology education by experimental work in the classroom of colleges. In this research, an practical approach to apply the "learning by doing" paradigm in Internet-based learning, both for higher educational environments and life-long training systems, taking into account available computer and network resources, such as blogging, podcasting, social networks, wiki etc. We first introduce the different phases in the learning process, which aimed at showing to the readers that the importance of the learning by doing paradigm, which is not implemented in many Internet-based educational environments. Secondly, we give the concept of learning by doing in the different perfective. Then, we identify the most important trends in this field, and give a real practical case for the application of this approach. The results show that the attempt methods are much better than traditional teaching methods.

Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain

NASA Astrophysics Data System (ADS)

Virkar, Yogesh S.; Shew, Woodrow L.; Restrepo, Juan G.; Ott, Edward

2016-10-01

Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model composed of two interacting networks: a model neural network interconnected by synapses that undergo spike-timing-dependent plasticity; and a model glial network interconnected by gap junctions that diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks

PubMed Central

Li, Ming; Miao, Chunyan; Leung, Cyril

2015-01-01

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches. PMID:26690162

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks.

PubMed

Li, Ming; Miao, Chunyan; Leung, Cyril

2015-12-04

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.

Effective Prediction of Errors by Non-native Speakers Using Decision Tree for Speech Recognition-Based CALL System

NASA Astrophysics Data System (ADS)

Wang, Hongcui; Kawahara, Tatsuya

CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the lexicon and language model, or the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students learning Japanese shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.

Designing Personalized Learning Products for Middle School Mathematics: The Case for Networked Learning Games

ERIC Educational Resources Information Center

Evans, Michael A.; Pruett, Jordan; Chang, Mido; Nino, Miguel

2014-01-01

Middle school mathematics education is subject to ongoing reform based on advances in digital instructional technologies, especially learning games, leading to recent calls for investment in "personalized learning." Through an extensive literature review, this investigation identified three priority areas that should be taken into…

Learning in networks: individual teacher learning versus organizational learning in a regional health-promoting schools network.

PubMed

Flaschberger, Edith; Gugglberger, Lisa; Dietscher, Christina

2013-12-01

To change a school into a health-promoting organization, organizational learning is required. The evaluation of an Austrian regional health-promoting schools network provides qualitative data on the views of the different stakeholders on learning in this network (steering group, network coordinator and representatives of the network schools; n = 26). Through thematic analysis and deep-structure analyses, the following three forms of learning in the network were identified: (A) individual learning through input offered by the network coordination, (B) individual learning between the network schools, i.e. through exchange between the representatives of different schools and (C) learning within the participating schools, i.e. organizational learning. Learning between (B) or within the participating schools (C) seems to be rare in the network; concepts of individual teacher learning are prevalent. Difficulties detected relating to the transfer of information from the network to the member schools included barriers to organizational learning such as the lack of collaboration, coordination and communication in the network schools, which might be effects of the school system in which the observed network is located. To ensure connectivity of the information offered by the network, more emphasis should be put on linking health promotion to school development and the core processes of schools.

A network-based training environment: a medical image processing paradigm.

PubMed

Costaridou, L; Panayiotakis, G; Sakellaropoulos, P; Cavouras, D; Dimopoulos, J

1998-01-01

The capability of interactive multimedia and Internet technologies is investigated with respect to the implementation of a distance learning environment. The system is built according to a client-server architecture, based on the Internet infrastructure, composed of server nodes conceptually modelled as WWW sites. Sites are implemented by customization of available components. The environment integrates network-delivered interactive multimedia courses, network-based tutoring, SIG support, information databases of professional interest, as well as course and tutoring management. This capability has been demonstrated by means of an implemented system, validated with digital image processing content, specifically image enhancement. Image enhancement methods are theoretically described and applied to mammograms. Emphasis is given to the interactive presentation of the effects of algorithm parameters on images. The system end-user access depends on available bandwidth, so high-speed access can be achieved via LAN or local ISDN connections. Network based training offers new means of improved access and sharing of learning resources and expertise, as promising supplements in training.

Transfer of Learning Relates to Intrinsic Connectivity between Hippocampus, Ventromedial Prefrontal Cortex, and Large-Scale Networks

PubMed Central

Gerraty, Raphael T.; Davidow, Juliet Y.; Wimmer, G. Elliott; Kahn, Itamar

2014-01-01

An important aspect of adaptive learning is the ability to flexibly use past experiences to guide new decisions. When facing a new decision, some people automatically leverage previously learned associations, while others do not. This variability in transfer of learning across individuals has been demonstrated repeatedly and has important implications for understanding adaptive behavior, yet the source of these individual differences remains poorly understood. In particular, it is unknown why such variability in transfer emerges even among homogeneous groups of young healthy participants who do not vary on other learning-related measures. Here we hypothesized that individual differences in the transfer of learning could be related to relatively stable differences in intrinsic brain connectivity, which could constrain how individuals learn. To test this, we obtained a behavioral measure of memory-based transfer outside of the scanner and on a separate day acquired resting-state functional MRI images in 42 participants. We then analyzed connectivity across independent component analysis-derived brain networks during rest, and tested whether intrinsic connectivity in learning-related networks was associated with transfer. We found that individual differences in transfer were related to intrinsic connectivity between the hippocampus and the ventromedial prefrontal cortex, and between these regions and large-scale functional brain networks. Together, the findings demonstrate a novel role for intrinsic brain dynamics in flexible learning-guided behavior, both within a set of functionally specific regions known to be important for learning, as well as between these regions and the default and frontoparietal networks, which are thought to serve more general cognitive functions. PMID:25143610

The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept.

PubMed

Zsuga, Judit; Biro, Klara; Papp, Csaba; Tajti, Gabor; Gesztelyi, Rudolf

2016-02-01

Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS). (c) 2016 APA, all rights reserved).

Centrality Measures and Academic Achievement in Computerized Classroom Social Networks: An Empirical Investigation

ERIC Educational Resources Information Center

Reychav, Iris; Raban, Daphne Ruth; McHaney, Roger

2018-01-01

The current empirical study examines relationships between network measures and learning performance from a social network analysis perspective. We collected computerized, networking data to analyze how 401 junior high students connected to classroom peers using text- and video-based material on iPads. Following a period of computerized…

Development and Evaluation of a City-Wide Wireless Weather Sensor Network

ERIC Educational Resources Information Center

Chang, Ben; Wang, Hsue-Yie; Peng, Tian-Yin; Hsu, Ying-Shao

2010-01-01

This project analyzed the effectiveness of a city-wide wireless weather sensor network, the Taipei Weather Science Learning Network (TWIN), in facilitating elementary and junior high students' study of weather science. The network, composed of sixty school-based weather sensor nodes and a centralized weather data archive server, provides students…

Learning Time-Varying Coverage Functions

PubMed Central

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2015-01-01

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data. PMID:25960624

Learning Time-Varying Coverage Functions.

PubMed

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2014-12-08

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data.

Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware

PubMed Central

Knight, James C.; Tully, Philip J.; Kaplan, Bernhard A.; Lansner, Anders; Furber, Steve B.

2016-01-01

SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models. PMID:27092061

Neural networks for continuous online learning and control.

PubMed

Choy, Min Chee; Srinivasan, Dipti; Cheu, Ruey Long

2006-11-01

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem.

On adaptive learning rate that guarantees convergence in feedforward networks.

PubMed

Behera, Laxmidhar; Kumar, Swagat; Patnaik, Awhan

2006-09-01

This paper investigates new learning algorithms (LF I and LF II) based on Lyapunov function for the training of feedforward neural networks. It is observed that such algorithms have interesting parallel with the popular backpropagation (BP) algorithm where the fixed learning rate is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. LF II, a modified version of LF I, has been introduced with an aim to avoid local minima. This modification also helps in improving the convergence speed in some cases. Conditions for achieving global minimum for these kind of algorithms have been studied in detail. The performances of the proposed algorithms are compared with BP algorithm and extended Kalman filtering (EKF) on three bench-mark function approximation problems: XOR, 3-bit parity, and 8-3 encoder. The comparisons are made in terms of number of learning iterations and computational time required for convergence. It is found that the proposed algorithms (LF I and II) are much faster in convergence than other two algorithms to attain same accuracy. Finally, the comparison is made on a complex two-dimensional (2-D) Gabor function and effect of adaptive learning rate for faster convergence is verified. In a nutshell, the investigations made in this paper help us better understand the learning procedure of feedforward neural networks in terms of adaptive learning rate, convergence speed, and local minima.

Machine learning based Intelligent cognitive network using fog computing

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

2017-05-01

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

Secure relay selection based on learning with negative externality in wireless networks

NASA Astrophysics Data System (ADS)

Zhao, Caidan; Xiao, Liang; Kang, Shan; Chen, Guiquan; Li, Yunzhou; Huang, Lianfen

2013-12-01

In this paper, we formulate relay selection into a Chinese restaurant game. A secure relay selection strategy is proposed for a wireless network, where multiple source nodes send messages to their destination nodes via several relay nodes, which have different processing and transmission capabilities as well as security properties. The relay selection utilizes a learning-based algorithm for the source nodes to reach their best responses in the Chinese restaurant game. In particular, the relay selection takes into account the negative externality of relay sharing among the source nodes, which learn the capabilities and security properties of relay nodes according to the current signals and the signal history. Simulation results show that this strategy improves the user utility and the overall security performance in wireless networks. In addition, the relay strategy is robust against the signal errors and deviations of some user from the desired actions.

Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device.

PubMed

McKinstry, Jeffrey L; Edelman, Gerald M

2013-01-01

Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions.

Networked Instructional Chemistry: Using Technology To Teach Chemistry

NASA Astrophysics Data System (ADS)

Smith, Stanley; Stovall, Iris

1996-10-01

Networked multimedia microcomputers provide new ways to help students learn chemistry and to help instructors manage the learning environment. This technology is used to replace some traditional laboratory work, collect on-line experimental data, enhance lectures and quiz sections with multimedia presentations, provide prelaboratory training for beginning nonchemistry- major organic laboratory, provide electronic homework for organic chemistry students, give graduate students access to real NMR data for analysis, and provide access to molecular modeling tools. The integration of all of these activities into an active learning environment is made possible by a client-server network of hundreds of computers. This requires not only instructional software but also classroom and course management software, computers, networking, and room management. Combining computer-based work with traditional course material is made possible with software management tools that allow the instructor to monitor the progress of each student and make available an on-line gradebook so students can see their grades and class standing. This client-server based system extends the capabilities of the earlier mainframe-based PLATO system, which was used for instructional computing. This paper outlines the components of a technology center used to support over 5,000 students per semester.

Telemedicine in Western Africa: lessons learned from a pilot project in Mali, perspectives and recommendations.

PubMed

Geissbuhler, Antoine; Ly, Ousmane; Lovis, Christian; L'Haire, Jean-François

2003-01-01

to evaluate the feasibility, potential and risks of an internet-based telemedicine network in developing countries of Western Africa. a project for the development of a national telemedicine network in Mali was initiated in 2001, using internet-based technologies for distance learning and teleconsultations. the telemedicine network has been in productive use for 12 months and has enabled various collaboration channels, including North-South, South-South, and South-North distance learning and teleconsultations. It also unveiled a set of potential problems: a) limited pertinence of North-South collaborations when there are major differences in available resources or socio-cultural contexts between the collaborating parties; b) risk of induced digital divide if the periphery of the health system is not involved in the development of the network, and c) need for the development of local medical contents management skills. the identified risks must be taken into account when designing large-scale telemedicine projects in developing countries and can be mitigated by the fostering of South-South collaboration channels, the use of satellite-based internet connectivity in remote areas, and the valorization of local knowledge and its publication on-line.

Deep learning based state recognition of substation switches

NASA Astrophysics Data System (ADS)

Wang, Jin

2018-06-01

Different from the traditional method which recognize the state of substation switches based on the running rules of electrical power system, this work proposes a novel convolutional neuron network-based state recognition approach of substation switches. Inspired by the theory of transfer learning, we first establish a convolutional neuron network model trained on the large-scale image set ILSVRC2012, then the restricted Boltzmann machine is employed to replace the full connected layer of the convolutional neuron network and trained on our small image dataset of 110kV substation switches to get a stronger model. Experiments conducted on our image dataset of 110kV substation switches show that, the proposed approach can be applicable to the substation to reduce the running cost and implement the real unattended operation.

Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

NASA Astrophysics Data System (ADS)

Zhu, Aichun; Wang, Tian; Snoussi, Hichem

2018-03-01

This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

Historical data learning based dynamic LSP routing for overlay IP/MPLS over WDM networks

NASA Astrophysics Data System (ADS)

Yu, Xiaojun; Xiao, Gaoxi; Cheng, Tee Hiang

2013-08-01

Overlay IP/MPLS over WDM network is a promising network architecture starting to gain wide deployments recently. A desirable feature of such a network is to achieve efficient routing with limited information exchanges between the IP/MPLS and the WDM layers. This paper studies dynamic label switched path (LSP) routing in the overlay IP/MPLS over WDM networks. To enhance network performance while maintaining its simplicity, we propose to learn from the historical data of lightpath setup costs maintained by the IP-layer integrated service provider (ISP) when making routing decisions. Using a novel historical data learning scheme for logical link cost estimation, we develop a new dynamic LSP routing method named Existing Link First (ELF) algorithm. Simulation results show that the proposed algorithm significantly outperforms the existing ones under different traffic loads, with either limited or unlimited numbers of optical ports. Effects of the number of candidate routes, add/drop ratio and the amount of historical data are also evaluated.

An Autonomous Mobile Agent-Based Distributed Learning Architecture-A Proposal and Analytical Analysis

ERIC Educational Resources Information Center

Sadiig, I. Ahmed M. J.

2005-01-01

The traditional learning paradigm involving face-to-face interaction with students is shifting to highly data-intensive electronic learning with the advances in Information and Communication Technology. An important component of the e-learning process is the delivery of the learning contents to their intended audience over a network. A distributed…

Construction of Course Ubiquitous Learning Based on Network

ERIC Educational Resources Information Center

Wang, Xue; Zhang, Wei; Yang, Xinhui

2017-01-01

Ubiquitous learning has been more and more recognized, which describes a new generation of learning from a new point of view. Ubiquitous learning will bring the new teaching practice and teaching reform, which will become an essential way of learning in 21st century. Taking translation course as a case study, this research constructed a system of…

Organisational adaptation in an activist network: social networks, leadership, and change in al-Muhajiroun.

PubMed

Kenney, Michael; Horgan, John; Horne, Cale; Vining, Peter; Carley, Kathleen M; Bigrigg, Michael W; Bloom, Mia; Braddock, Kurt

2013-09-01

Social networks are said to facilitate learning and adaptation by providing the connections through which network nodes (or agents) share information and experience. Yet, our understanding of how this process unfolds in real-world networks remains underdeveloped. This paper explores this gap through a case study of al-Muhajiroun, an activist network that continues to call for the establishment of an Islamic state in Britain despite being formally outlawed by British authorities. Drawing on organisation theory and social network analysis, we formulate three hypotheses regarding the learning capacity and social network properties of al-Muhajiroun (AM) and its successor groups. We then test these hypotheses using mixed methods. Our methods combine quantitative analysis of three agent-based networks in AM measured for structural properties that facilitate learning, including connectedness, betweenness centrality and eigenvector centrality, with qualitative analysis of interviews with AM activists focusing organisational adaptation and learning. The results of these analyses confirm that al-Muhajiroun activists respond to government pressure by changing their operations, including creating new platforms under different names and adjusting leadership roles among movement veterans to accommodate their spiritual leader's unwelcome exodus to Lebanon. Simple as they are effective, these adaptations have allowed al-Muhajiroun and its successor groups to continue their activism in an increasingly hostile environment. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Reinforcement learning for resource allocation in LEO satellite networks.

PubMed

Usaha, Wipawee; Barria, Javier A

2007-06-01

In this paper, we develop and assess online decision-making algorithms for call admission and routing for low Earth orbit (LEO) satellite networks. It has been shown in a recent paper that, in a LEO satellite system, a semi-Markov decision process formulation of the call admission and routing problem can achieve better performance in terms of an average revenue function than existing routing methods. However, the conventional dynamic programming (DP) numerical solution becomes prohibited as the problem size increases. In this paper, two solution methods based on reinforcement learning (RL) are proposed in order to circumvent the computational burden of DP. The first method is based on an actor-critic method with temporal-difference (TD) learning. The second method is based on a critic-only method, called optimistic TD learning. The algorithms enhance performance in terms of requirements in storage, computational complexity and computational time, and in terms of an overall long-term average revenue function that penalizes blocked calls. Numerical studies are carried out, and the results obtained show that the RL framework can achieve up to 56% higher average revenue over existing routing methods used in LEO satellite networks with reasonable storage and computational requirements.

Diverse Region-Based CNN for Hyperspectral Image Classification.

PubMed

Zhang, Mengmeng; Li, Wei; Du, Qian

2018-06-01

Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

Radar signal categorization using a neural network

NASA Technical Reports Server (NTRS)

Anderson, James A.; Gately, Michael T.; Penz, P. Andrew; Collins, Dean R.

1991-01-01

Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID

PubMed Central

Yang, Xiaoping; Chen, Xueying; Xia, Riting; Qian, Zhihong

2018-01-01

Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm–neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS. PMID:29671822

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID.

PubMed

Yang, Xiaoping; Chen, Xueying; Xia, Riting; Qian, Zhihong

2018-04-19

Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm⁻neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS.

Learning and coordinating in a multilayer network

PubMed Central

Lugo, Haydée; Miguel, Maxi San

2015-01-01

We introduce a two layer network model for social coordination incorporating two relevant ingredients: a) different networks of interaction to learn and to obtain a pay-off, and b) decision making processes based both on social and strategic motivations. Two populations of agents are distributed in two layers with intralayer learning processes and playing interlayer a coordination game. We find that the skepticism about the wisdom of crowd and the local connectivity are the driving forces to accomplish full coordination of the two populations, while polarized coordinated layers are only possible for all-to-all interactions. Local interactions also allow for full coordination in the socially efficient Pareto-dominant strategy in spite of being the riskier one. PMID:25585934

Overcoming catastrophic forgetting in neural networks

PubMed Central

Kirkpatrick, James; Pascanu, Razvan; Rabinowitz, Neil; Veness, Joel; Desjardins, Guillaume; Rusu, Andrei A.; Milan, Kieran; Quan, John; Ramalho, Tiago; Grabska-Barwinska, Agnieszka; Hassabis, Demis; Clopath, Claudia; Kumaran, Dharshan; Hadsell, Raia

2017-01-01

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially. PMID:28292907

Distance Learning. Transforming Libraries Number 6.

ERIC Educational Resources Information Center

Jones, William G.

1998-01-01

This report explores the diverse ways that libraries are currently supporting distance learning, both in its traditional and newer Internet-based forms. Based on interviews with administrators and staff, the following "Reports from the Field" reflect developments in public institutions: "UNET (University of Maine System Network)…

Around the Globe.

ERIC Educational Resources Information Center

Wheeler, Steve

2001-01-01

Provides an overview of current activities in Web-based learning and development in Romania. Discusses promotion of online learning by various societies and institutions; the distance courses based on traditional procedures; problems of the current state of distance education, including shortage of teachers and network administration and poor and…

Pushing the Child Centred Approach in Myanmar: The Role of Cross National Policy Networks and the Effects in the Classroom

ERIC Educational Resources Information Center

Lall, Marie

2011-01-01

In Myanmar schools, rote learning is the norm. International aid and education organisations based in the country have been trying to promote the child centred approach (CCA) as a much more progressive form of teaching and learning. The CCA is being rolled out principally through monastic school networks aided by international and national…

Spatiotemporal coding in the cortex: information flow-based learning in spiking neural networks.

PubMed

Deco, G; Schürmann, B

1999-05-15

We introduce a learning paradigm for networks of integrate-and-fire spiking neurons that is based on an information-theoretic criterion. This criterion can be viewed as a first principle that demonstrates the experimentally observed fact that cortical neurons display synchronous firing for some stimuli and not for others. The principle can be regarded as the postulation of a nonparametric reconstruction method as optimization criteria for learning the required functional connectivity that justifies and explains synchronous firing for binding of features as a mechanism for spatiotemporal coding. This can be expressed in an information-theoretic way by maximizing the discrimination ability between different sensory inputs in minimal time.

Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning.

PubMed

Miller, Vonda H; Jansen, Ben H

2008-12-01

Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.

Reverse engineering highlights potential principles of large gene regulatory network design and learning.

PubMed

Carré, Clément; Mas, André; Krouk, Gabriel

2017-01-01

Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10 4 genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data ( Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

NASA Technical Reports Server (NTRS)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Cytopathological image analysis using deep-learning networks in microfluidic microscopy.

PubMed

Gopakumar, G; Hari Babu, K; Mishra, Deepak; Gorthi, Sai Siva; Sai Subrahmanyam, Gorthi R K

2017-01-01

Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing process. Several neural network architectures were designed to provide human expertise to machines. In this paper, we explore and propose the feasibility of using deep-learning networks for cytopathologic analysis by performing the classification of three important unlabeled, unstained leukemia cell lines (K562, MOLT, and HL60). The cell images used in the classification are captured using a low-cost, high-throughput cell imaging technique: microfluidics-based imaging flow cytometry. We demonstrate that without any conventional fine segmentation followed by explicit feature extraction, the proposed deep-learning algorithms effectively classify the coarsely localized cell lines. We show that the designed deep belief network as well as the deeply pretrained convolutional neural network outperform the conventionally used decision systems and are important in the medical domain, where the availability of labeled data is limited for training. We hope that our work enables the development of a clinically significant high-throughput microfluidic microscopy-based tool for disease screening/triaging, especially in resource-limited settings.

Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans

NASA Astrophysics Data System (ADS)

Ramachandran S., Sindhu; George, Jose; Skaria, Shibon; V. V., Varun

2018-02-01

Lung cancer is the leading cause of cancer related deaths in the world. The survival rate can be improved if the presence of lung nodules are detected early. This has also led to more focus being given to computer aided detection (CAD) and diagnosis of lung nodules. The arbitrariness of shape, size and texture of lung nodules is a challenge to be faced when developing these detection systems. In the proposed work we use convolutional neural networks to learn the features for nodule detection, replacing the traditional method of handcrafting features like geometric shape or texture. Our network uses the DetectNet architecture based on YOLO (You Only Look Once) to detect the nodules in CT scans of lung. In this architecture, object detection is treated as a regression problem with a single convolutional network simultaneously predicting multiple bounding boxes and class probabilities for those boxes. By performing training using chest CT scans from Lung Image Database Consortium (LIDC), NVIDIA DIGITS and Caffe deep learning framework, we show that nodule detection using this single neural network can result in reasonably low false positive rates with high sensitivity and precision.

Bio-inspired spiking neural network for nonlinear systems control.

PubMed

Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Experiments on Adaptive Techniques for Host-Based Intrusion Detection

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

DRAELOS, TIMOTHY J.; COLLINS, MICHAEL J.; DUGGAN, DAVID P.

2001-09-01

This research explores four experiments of adaptive host-based intrusion detection (ID) techniques in an attempt to develop systems that can detect novel exploits. The technique considered to have the most potential is adaptive critic designs (ACDs) because of their utilization of reinforcement learning, which allows learning exploits that are difficult to pinpoint in sensor data. Preliminary results of ID using an ACD, an Elman recurrent neural network, and a statistical anomaly detection technique demonstrate an ability to learn to distinguish between clean and exploit data. We used the Solaris Basic Security Module (BSM) as a data source and performed considerablemore » preprocessing on the raw data. A detection approach called generalized signature-based ID is recommended as a middle ground between signature-based ID, which has an inability to detect novel exploits, and anomaly detection, which detects too many events including events that are not exploits. The primary results of the ID experiments demonstrate the use of custom data for generalized signature-based intrusion detection and the ability of neural network-based systems to learn in this application environment.« less

Feature to prototype transition in neural networks

NASA Astrophysics Data System (ADS)

Krotov, Dmitry; Hopfield, John

Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

Digging deeper on "deep" learning: A computational ecology approach.

PubMed

Buscema, Massimo; Sacco, Pier Luigi

2017-01-01

We propose an alternative approach to "deep" learning that is based on computational ecologies of structurally diverse artificial neural networks, and on dynamic associative memory responses to stimuli. Rather than focusing on massive computation of many different examples of a single situation, we opt for model-based learning and adaptive flexibility. Cross-fertilization of learning processes across multiple domains is the fundamental feature of human intelligence that must inform "new" artificial intelligence.

A spiking neural network based on the basal ganglia functional anatomy.

PubMed

Baladron, Javier; Hamker, Fred H

2015-07-01

We introduce a spiking neural network of the basal ganglia capable of learning stimulus-action associations. We model learning in the three major basal ganglia pathways, direct, indirect and hyperdirect, by spike time dependent learning and considering the amount of dopamine available (reward). Moreover, we allow to learn a cortico-thalamic pathway that bypasses the basal ganglia. As a result the system develops new functionalities for the different basal ganglia pathways: The direct pathway selects actions by disinhibiting the thalamus, the hyperdirect one suppresses alternatives and the indirect pathway learns to inhibit common mistakes. Numerical experiments show that the system is capable of learning sets of either deterministic or stochastic rules. Copyright © 2015 Elsevier Ltd. All rights reserved.

Towards representation of a perceptual color manifold using associative memory for color constancy.

PubMed

Seow, Ming-Jung; Asari, Vijayan K

2009-01-01

In this paper, we propose the concept of a manifold of color perception through empirical observation that the center-surround properties of images in a perceptually similar environment define a manifold in the high dimensional space. Such a manifold representation can be learned using a novel recurrent neural network based learning algorithm. Unlike the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete locations in the state space, the dynamics of the proposed learning algorithm represent memory as a nonlinear line of attraction. The region of convergence around the nonlinear line is defined by the statistical characteristics of the training data. This learned manifold can then be used as a basis for color correction of the images having different color perception to the learned color perception. Experimental results show that the proposed recurrent neural network learning algorithm is capable of color balance the lighting variations in images captured in different environments successfully.

Automatic classification of transiently evoked otoacoustic emissions using an artificial neural network.

PubMed

Buller, G; Lutman, M E

1998-08-01

The increasing use of transiently evoked otoacoustic emissions (TEOAE) in large neonatal hearing screening programmes makes a standardized method of response classification desirable. Until now methods have been either subjective or based on arbitrary response characteristics. This study takes an expert system approach to standardize the subjective judgements of an experienced scorer. The method that is developed comprises three stages. First, it transforms TEOAEs from waveforms in the time domain into a simplified parameter set. Second, the parameter set is classified by an artificial neural network that has been taught on a large database TEOAE waveforms and corresponding expert scores. Third, additional fuzzy logic rules automatically detect probable artefacts in the waveforms and synchronized spontaneous emission components. In this way, the knowledge of the experienced scorer is encapsulated in the expert system software and thereafter can be accessed by non-experts. Teaching and evaluation of the neural network was based on TEOAEs from a database totalling 2190 neonatal hearing screening tests. The database was divided into learning and test groups with 820 and 1370 waveforms respectively. From each recorded waveform a set of 12 parameters was calculated, representing signal static and dynamic properties. The artifical network was taught with parameter sets of only the learning groups. Reproduction of the human scorer classification by the neural net in the learning group showed a sensitivity for detecting screen fails of 99.3% (299 from 301 failed results on subjective scoring) and a specificity for detecting screen passes of 81.1% (421 of 519 pass results). To quantify the post hoc performance of the net (generalization), the test group was then presented to the network input. Sensitivity was 99.4% (474 from 477) and specificity was 87.3% (780 from 893). To check the efficiency of the classification method, a second learning group was selected out of the previous test group, and the previous learning group was used as the test group. Repeating learning and test procedures yielded 99.3% sensitivity and 80.7% specificity for reproduction, and 99.4% sensitivity and 86.7% specificity for generalization. In all respects, performance was better than for a previously optimized method based simply on cross-correlation between replicate non-linear waveforms. It is concluded that classification methods based on neural networks show promise for application to large neonatal screening programmes utilizing TEOAEs.

Concept Model on Topological Learning

NASA Astrophysics Data System (ADS)

Ae, Tadashi; Kioi, Kazumasa

2010-11-01

We discuss a new model for concept based on topological learning, where the learning process on the neural network is represented by mathematical topology. The topological learning of neural networks is summarized by a quotient of input space and the hierarchical step induces a tree where each node corresponds to a quotient. In general, the concept acquisition is a difficult problem, but the emotion for a subject is represented by providing the questions to a person. Therefore, a kind of concept is captured by such data and the answer sheet can be mapped into a topology consisting of trees. In this paper, we will discuss a way of mapping the emotional concept to a topological learning model.

Design Patterns for Learning and Assessment: Facilitating the Introduction of a Complex Simulation-Based Learning Environment into a Community of Instructors

NASA Astrophysics Data System (ADS)

Frezzo, Dennis C.; Behrens, John T.; Mislevy, Robert J.

2010-04-01

Simulation environments make it possible for science and engineering students to learn to interact with complex systems. Putting these capabilities to effective use for learning, and assessing learning, requires more than a simulation environment alone. It requires a conceptual framework for the knowledge, skills, and ways of thinking that are meant to be developed, in order to design activities that target these capabilities. The challenges of using simulation environments effectively are especially daunting in dispersed social systems. This article describes how these challenges were addressed in the context of the Cisco Networking Academies with a simulation tool for computer networks called Packet Tracer. The focus is on a conceptual support framework for instructors in over 9,000 institutions around the world for using Packet Tracer in instruction and assessment, by learning to create problem-solving scenarios that are at once tuned to the local needs of their students and consistent with the epistemic frame of "thinking like a network engineer." We describe a layered framework of tools and interfaces above the network simulator that supports the use of Packet Tracer in the distributed community of instructors and students.

Finite time convergent learning law for continuous neural networks.

PubMed

Chairez, Isaac

2014-02-01

This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

Evaluating Bayesian Networks' Precision for Detecting Students' Learning Styles

ERIC Educational Resources Information Center

Garcia, Patricio; Amandi, Analia; Schiaffino, Silvia; Campo, Marcelo

2007-01-01

Students are characterized by different learning styles, focusing on different types of information and processing this information in different ways. One of the desirable characteristics of a Web-based education system is that all the students can learn despite their different learning styles. To achieve this goal we have to detect how students…

An Auto-Scoring Mechanism for Evaluating Problem-Solving Ability in a Web-Based Learning Environment

ERIC Educational Resources Information Center

Chiou, Chuang-Kai; Hwang, Gwo-Jen; Tseng, Judy C. R.

2009-01-01

The rapid development of computer and network technologies has attracted researchers to investigate strategies for and the effects of applying information technologies in learning activities; simultaneously, learning environments have been developed to record the learning portfolios of students seeking web information for problem-solving. Although…

A Program That Acquires Language Using Positive and Negative Feedback.

ERIC Educational Resources Information Center

Brand, James

1987-01-01

Describes the language learning program "Acquire," which is a sample of grammar induction. It is a learning algorithm based on a pattern-matching scheme, using both a positive and negative network to reduce overgeneration. Language learning programs may be useful as tutorials for learning the syntax of a foreign language. (Author/LMO)

The Relationship between the Learning Strategies and Learning Styles in a Hypermedia Environment.

ERIC Educational Resources Information Center

Liu, Min; Reed, W. Michael

Different learning strategies that are used by field-independent (FI) and field-dependent (FD) people in a hypermedia-assisted instructional setting were studied with 63 international college students for whom English was a second language. The treatment was a semantic network-based hypermedia-assisted language-learning environment to help…

Impacts and Characteristics of Computer-Based Science Inquiry Learning Environments for Precollege Students

ERIC Educational Resources Information Center

Donnelly, Dermot F.; Linn, Marcia C.; Ludvigsen, Sten

2014-01-01

The National Science Foundation-sponsored report "Fostering Learning in the Networked World" called for "a common, open platform to support communities of developers and learners in ways that enable both to take advantage of advances in the learning sciences." We review research on science inquiry learning environments (ILEs)…

Implicit Procedural Learning in Fragile X and Down Syndrome

ERIC Educational Resources Information Center

Bussy, G.; Charrin, E.; Brun, A.; Curie, A.; des Portes, V.

2011-01-01

Background: Procedural learning refers to rule-based motor skill learning and storage. It involves the cerebellum, striatum and motor areas of the frontal lobe network. Fragile X syndrome, which has been linked with anatomical abnormalities within the striatum, may result in implicit procedural learning deficit. Methods: To address this issue, a…

A Theory of How Columns in the Neocortex Enable Learning the Structure of the World

PubMed Central

Hawkins, Jeff; Ahmad, Subutai; Cui, Yuwei

2017-01-01

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed. PMID:29118696

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

NASA Astrophysics Data System (ADS)

Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

2018-02-01

Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

Center for Neural Engineering at Tennessee State University, ASSERT Annual Progress Report.

DTIC Science & Technology

1995-07-01

neural networks . Their research topics are: (1) developing frequency dependent oscillatory neural networks ; (2) long term pontentiation learning rules...as applied to spatial navigation; (3) design and build a servo joint robotic arm and (4) neural network based prothesis control. One graduate student

Slow feature analysis: unsupervised learning of invariances.

PubMed

Wiskott, Laurenz; Sejnowski, Terrence J

2002-04-01

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

Web-Based Learning in the Computer-Aided Design Curriculum.

ERIC Educational Resources Information Center

Sung, Wen-Tsai; Ou, S. C.

2002-01-01

Applies principles of constructivism and virtual reality (VR) to computer-aided design (CAD) curriculum, particularly engineering, by integrating network, VR and CAD technologies into a Web-based learning environment that expands traditional two-dimensional computer graphics into a three-dimensional real-time simulation that enhances user…

Adaptive critic learning techniques for engine torque and air-fuel ratio control.

PubMed

Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

2008-08-01

A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

Mastering the game of Go without human knowledge.

PubMed

Silver, David; Schrittwieser, Julian; Simonyan, Karen; Antonoglou, Ioannis; Huang, Aja; Guez, Arthur; Hubert, Thomas; Baker, Lucas; Lai, Matthew; Bolton, Adrian; Chen, Yutian; Lillicrap, Timothy; Hui, Fan; Sifre, Laurent; van den Driessche, George; Graepel, Thore; Hassabis, Demis

2017-10-18

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo's own move selections and also the winner of AlphaGo's games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo.

Mastering the game of Go without human knowledge

NASA Astrophysics Data System (ADS)

Silver, David; Schrittwieser, Julian; Simonyan, Karen; Antonoglou, Ioannis; Huang, Aja; Guez, Arthur; Hubert, Thomas; Baker, Lucas; Lai, Matthew; Bolton, Adrian; Chen, Yutian; Lillicrap, Timothy; Hui, Fan; Sifre, Laurent; van den Driessche, George; Graepel, Thore; Hassabis, Demis

2017-10-01

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo.

Neurons with hysteresis form a network that can learn without any changes in synaptic connection strengths

NASA Astrophysics Data System (ADS)

Hoffmann, Geoffrey W.; Benson, Maurice W.

1986-08-01

A neural network concept derived from an analogy between the immune system and the central nerous system is outlined. The theory is based on a nervous that is slightly more complicated than the conventional McCullogh-Pitts type of neuron, in that it exhibits hysteresis at the single cell level. This added complication is compensated by the fact that a network of such neurons is able to learn without the necessity for any changes in synaptic connection strengths. The learning occurs as a natural consequence of interactions between the network and its enviornment, with environmental stimuli moving the system around in an N-dimensional phase space, until a point in phase space is reached such that the system's responses are appropriate for dealing with the stimuli. Due to the hysteresis associated with each neuron, the system tends to stay in the region of phase space where it is located. The theory includes a role for sleep in learning.

Nonparametric Density Estimation Based on Self-Organizing Incremental Neural Network for Large Noisy Data.

PubMed

Nakamura, Yoshihiro; Hasegawa, Osamu

2017-01-01

With the ongoing development and expansion of communication networks and sensors, massive amounts of data are continuously generated in real time from real environments. Beforehand, prediction of a distribution underlying such data is difficult; furthermore, the data include substantial amounts of noise. These factors make it difficult to estimate probability densities. To handle these issues and massive amounts of data, we propose a nonparametric density estimator that rapidly learns data online and has high robustness. Our approach is an extension of both kernel density estimation (KDE) and a self-organizing incremental neural network (SOINN); therefore, we call our approach KDESOINN. An SOINN provides a clustering method that learns about the given data as networks of prototype of data; more specifically, an SOINN can learn the distribution underlying the given data. Using this information, KDESOINN estimates the probability density function. The results of our experiments show that KDESOINN outperforms or achieves performance comparable to the current state-of-the-art approaches in terms of robustness, learning time, and accuracy.

Neural Network Classifier Architectures for Phoneme Recognition. CRC Technical Note No. CRC-TN-92-001.

ERIC Educational Resources Information Center

Treurniet, William

A study applied artificial neural networks, trained with the back-propagation learning algorithm, to modelling phonemes extracted from the DARPA TIMIT multi-speaker, continuous speech data base. A number of proposed network architectures were applied to the phoneme classification task, ranging from the simple feedforward multilayer network to more…

Influence Function Learning in Information Diffusion Networks.

PubMed

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2014-06-01

Can we learn the influence of a set of people in a social network from cascades of information diffusion? This question is often addressed by a two-stage approach: first learn a diffusion model, and then calculate the influence based on the learned model. Thus, the success of this approach relies heavily on the correctness of the diffusion model which is hard to verify for real world data. In this paper, we exploit the insight that the influence functions in many diffusion models are coverage functions, and propose a novel parameterization of such functions using a convex combination of random basis functions. Moreover, we propose an efficient maximum likelihood based algorithm to learn such functions directly from cascade data, and hence bypass the need to specify a particular diffusion model in advance. We provide both theoretical and empirical analysis for our approach, showing that the proposed approach can provably learn the influence function with low sample complexity, be robust to the unknown diffusion models, and significantly outperform existing approaches in both synthetic and real world data.

Flexible Learning in a Workplace Model: Blended a Motivation to a Lifelong Learner in a Social Network Environment

ERIC Educational Resources Information Center

Na-songkhla, Jaitip

2011-01-01

This paper presents a model of learning in a workplace, in which an online course provides flexibility for staff to learn at their convenient hours. A motivation was brought into an account of the success of learning in a workplace program, based upon Behaviorist learning approach--an online mentor and an accumulated learning activities score was…

Competence-Based Vocational Education and Training (VET): An Approach of Shaping and Networking

ERIC Educational Resources Information Center

Bohne, Christoph; Eicker, Friedhelm; Haseloff, Gesine

2017-01-01

Purpose: The purpose of this paper is to develop a vocational scientific constructivist concept meant for shaping competence-based and networked teaching and learning in vocational education and training (VET). Design/methodology/approach: VET must enable learners to shape work within the context of conceptions based on the development of society.…

Learning spatially coherent properties of the visual world in connectionist networks

NASA Astrophysics Data System (ADS)

Becker, Suzanna; Hinton, Geoffrey E.

1991-10-01

In the unsupervised learning paradigm, a network of neuron-like units is presented with an ensemble of input patterns from a structured environment, such as the visual world, and learns to represent the regularities in that input. The major goal in developing unsupervised learning algorithms is to find objective functions that characterize the quality of the network's representation without explicitly specifying the desired outputs of any of the units. The sort of objective functions considered cause a unit to become tuned to spatially coherent features of visual images (such as texture, depth, shading, and surface orientation), by learning to predict the outputs of other units which have spatially adjacent receptive fields. Simulations show that using an information-theoretic algorithm called IMAX, a network can be trained to represent depth by observing random dot stereograms of surfaces with continuously varying disparities. Once a layer of depth-tuned units has developed, subsequent layers are trained to perform surface interpolation of curved surfaces, by learning to predict the depth of one image region based on depth measurements in surrounding regions. An extension of the basic model allows a population of competing neurons to learn a distributed code for disparity, which naturally gives rise to a representation of discontinuities.

Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images.

PubMed

Ito, Eisuke; Sato, Takaaki; Sano, Daisuke; Utagawa, Etsuko; Kato, Tsuyoshi

2018-06-01

A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps. The detection performance of the proposed method was assessed against a dataset of TEM images containing feline calicivirus particles and compared with several existing detection methods, and the state-of-the-art performance of the developed method for detecting virus was demonstrated. Since our method is based on supervised learning that requires both the input images and their corresponding annotations, it is basically used for detection of already-known viruses. However, the method is highly flexible, and the convolutional networks can adapt themselves to any virus particles by learning automatically from an annotated dataset.

Nonlinear Deep Kernel Learning for Image Annotation.

PubMed

Jiu, Mingyuan; Sahbi, Hichem

2017-02-08

Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

Similarity networks as a knowledge representation for space applications

NASA Technical Reports Server (NTRS)

Bailey, David; Thompson, Donna; Feinstein, Jerald

1987-01-01

Similarity networks are a powerful form of knowledge representation that are useful for many artificial intelligence applications. Similarity networks are used in applications ranging from information analysis and case based reasoning to machine learning and linking symbolic to neural processing. Strengths of similarity networks include simple construction, intuitive object storage, and flexible retrieval techniques that facilitate inferencing. Therefore, similarity networks provide great potential for space applications.

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines.

PubMed

Neftci, Emre O; Pedroni, Bruno U; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

2016-01-01

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware.

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

PubMed Central

Neftci, Emre O.; Pedroni, Bruno U.; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

2016-01-01

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware. PMID:27445650

Transfer of learning relates to intrinsic connectivity between hippocampus, ventromedial prefrontal cortex, and large-scale networks.

PubMed

Gerraty, Raphael T; Davidow, Juliet Y; Wimmer, G Elliott; Kahn, Itamar; Shohamy, Daphna

2014-08-20

An important aspect of adaptive learning is the ability to flexibly use past experiences to guide new decisions. When facing a new decision, some people automatically leverage previously learned associations, while others do not. This variability in transfer of learning across individuals has been demonstrated repeatedly and has important implications for understanding adaptive behavior, yet the source of these individual differences remains poorly understood. In particular, it is unknown why such variability in transfer emerges even among homogeneous groups of young healthy participants who do not vary on other learning-related measures. Here we hypothesized that individual differences in the transfer of learning could be related to relatively stable differences in intrinsic brain connectivity, which could constrain how individuals learn. To test this, we obtained a behavioral measure of memory-based transfer outside of the scanner and on a separate day acquired resting-state functional MRI images in 42 participants. We then analyzed connectivity across independent component analysis-derived brain networks during rest, and tested whether intrinsic connectivity in learning-related networks was associated with transfer. We found that individual differences in transfer were related to intrinsic connectivity between the hippocampus and the ventromedial prefrontal cortex, and between these regions and large-scale functional brain networks. Together, the findings demonstrate a novel role for intrinsic brain dynamics in flexible learning-guided behavior, both within a set of functionally specific regions known to be important for learning, as well as between these regions and the default and frontoparietal networks, which are thought to serve more general cognitive functions. Copyright © 2014 the authors 0270-6474/14/3411297-07$15.00/0.

Overview of deep learning in medical imaging.

PubMed

Suzuki, Kenji

2017-09-01

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

Semantic Web, Reusable Learning Objects, Personal Learning Networks in Health: Key Pieces for Digital Health Literacy.

PubMed

Konstantinidis, Stathis Th; Wharrad, Heather; Windle, Richard; Bamidis, Panagiotis D

2017-01-01

The knowledge existing in the World Wide Web is exponentially expanding, while continuous advancements in health sciences contribute to the creation of new knowledge. There are a lot of efforts trying to identify how the social connectivity can endorse patients' empowerment, while other studies look at the identification and the quality of online materials. However, emphasis has not been put on the big picture of connecting the existing resources with the patients "new habits" of learning through their own Personal Learning Networks. In this paper we propose a framework for empowering patients' digital health literacy adjusted to patients' currents needs by utilizing the contemporary way of learning through Personal Learning Networks, existing high quality learning resources and semantics technologies for interconnecting knowledge pieces. The framework based on the concept of knowledge maps for health as defined in this paper. Health Digital Literacy needs definitely further enhancement and the use of the proposed concept might lead to useful tools which enable use of understandable health trusted resources tailored to each person needs.

A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay.

PubMed

Zhou, Caigen; Zeng, Xiaoqin; Luo, Chaomin; Zhang, Huaguang

In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.

Application of machine learning methods for traffic signs recognition

NASA Astrophysics Data System (ADS)

Filatov, D. V.; Ignatev, K. V.; Deviatkin, A. V.; Serykh, E. V.

2018-02-01

This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition; the approaches involved neural networks to analyze images obtained from a camera in the real-time mode. The first approach is based on a sequential image processing. At the initial stage, with the help of color filters and morphological operations (dilatation and erosion), the area containing the traffic sign is located on the image, then the selected and scaled fragment of the image is analyzed using a feedforward neural network to determine the meaning of the found traffic sign. Learning of the neural network in this approach is carried out using a backpropagation method. The second approach involves convolution neural networks at both stages, i.e. when searching and selecting the area of the image containing the traffic sign, and when determining its meaning. Learning of the neural network in the second approach is carried out using the intersection over union function and a loss function. For neural networks to learn and the proposed algorithms to be tested, a series of videos from a dash cam were used that were shot under various weather and illumination conditions. As a result, the proposed approaches for traffic signs recognition were analyzed and compared by key indicators such as recognition rate percentage and the complexity of neural networks’ learning process.

Heuristic pattern correction scheme using adaptively trained generalized regression neural networks.

PubMed

Hoya, T; Chambers, J A

2001-01-01

In many pattern classification problems, an intelligent neural system is required which can learn the newly encountered but misclassified patterns incrementally, while keeping a good classification performance over the past patterns stored in the network. In the paper, an heuristic pattern correction scheme is proposed using adaptively trained generalized regression neural networks (GRNNs). The scheme is based upon both network growing and dual-stage shrinking mechanisms. In the network growing phase, a subset of the misclassified patterns in each incoming data set is iteratively added into the network until all the patterns in the incoming data set are classified correctly. Then, the redundancy in the growing phase is removed in the dual-stage network shrinking. Both long- and short-term memory models are considered in the network shrinking, which are motivated from biological study of the brain. The learning capability of the proposed scheme is investigated through extensive simulation studies.

MHEG Based Distance Learning System on Information Superhighway.

ERIC Educational Resources Information Center

Lee, SeiHoon; Yoon, KyungSeob; Wang, ChangJong

As the need for distance education grows, requirements for the development of high-speed network-based real-time distance learning systems increases. MHEG-5 is the fifth part of the MHEG (Multimedia and Hypermedia information coding Experts Group) standard, and it defines a final-form representation for application interchange. This paper…

Designing an Adaptive Web-Based Learning System Based on Students' Cognitive Styles Identified Online

ERIC Educational Resources Information Center

Lo, Jia-Jiunn; Chan, Ya-Chen; Yeh, Shiou-Wen

2012-01-01

This study developed an adaptive web-based learning system focusing on students' cognitive styles. The system is composed of a student model and an adaptation model. It collected students' browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF).…

Snail Tales

ERIC Educational Resources Information Center

Phelps, Cynthia L.; Willcockson, Irmgard U.; Houtz, Lynne

2004-01-01

A team of teachers, scientists, and high school students at the University of Texas Health Science Center at Houston has developed activities to teach concepts in learning through an inquiry-based laboratory method. The Learning Education and Research Network (LEARN) activities were field tested at the Society for Neuroscience Conference, in the…

The networked health enterprise: a vision for 2008.

PubMed

Stead, W W

1998-01-01

Informatics and information technology hold the promise of a consumer-centered health enterprise--one that provides quality care at a cost society is willing to pay; one where need-based, adaptive, competency-based learning results in cost-effectiveness of health education; one where team-based health and learning on demand, coupled with monitoring of process outcomes and network access to expertise, guarantee quality. The barriers to this promise are the professional guilds, the cross-subsidies that support the health enterprise of 1998, and the lack of respect for privacy. Collectively, the informatics community needs to develop a compelling vision that will galvanize the health community to action. If the health community does not step up to this challenge, consumers will take advantage of disintermediation. Empowered by the network, they will go outside the system into hands that meet their needs.

Deterministic convergence of chaos injection-based gradient method for training feedforward neural networks.

PubMed

Zhang, Huisheng; Zhang, Ying; Xu, Dongpo; Liu, Xiaodong

2015-06-01

It has been shown that, by adding a chaotic sequence to the weight update during the training of neural networks, the chaos injection-based gradient method (CIBGM) is superior to the standard backpropagation algorithm. This paper presents the theoretical convergence analysis of CIBGM for training feedforward neural networks. We consider both the case of batch learning as well as the case of online learning. Under mild conditions, we prove the weak convergence, i.e., the training error tends to a constant and the gradient of the error function tends to zero. Moreover, the strong convergence of CIBGM is also obtained with the help of an extra condition. The theoretical results are substantiated by a simulation example.

Resting-state Functional Connectivity is an Age-dependent Predictor of Motor Learning Abilities.

PubMed

Mary, Alison; Wens, Vincent; Op de Beeck, Marc; Leproult, Rachel; De Tiège, Xavier; Peigneux, Philippe

2017-10-01

This magnetoencephalography study investigates how ageing modulates the relationship between pre-learning resting-state functional connectivity (rsFC) and subsequent learning. Neuromagnetic resting-state activity was recorded 5 min before motor sequence learning in 14 young (19-30 years) and 14 old (66-70 years) participants. We used a seed-based beta-band power envelope correlation approach to estimate rsFC maps, with the seed located in the right primary sensorimotor cortex. In each age group, the relation between individual rsFC and learning performance was investigated using Pearson's correlation analyses. Our results show that rsFC is predictive of subsequent motor sequence learning but involves different cross-network interactions in the two age groups. In young adults, decreased coupling between the sensorimotor network and the cortico-striato-cerebellar network is associated with better motor learning, whereas a similar relation is found in old adults between the sensorimotor, the dorsal-attentional and the DMNs. Additionally, age-related correlational differences were found in the dorsolateral prefrontal cortex, known to subtend attentional and controlled processes. These findings suggest that motor skill learning depends-in an age-dependent manner-on subtle interactions between resting-state networks subtending motor activity on the one hand, and controlled and attentional processes on the other hand. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Lessons learned from the design of chemical space networks and opportunities for new applications.

PubMed

Vogt, Martin; Stumpfe, Dagmar; Maggiora, Gerald M; Bajorath, Jürgen

2016-03-01

The concept of chemical space is of fundamental relevance in chemical informatics and computer-aided drug discovery. In a series of articles published in the Journal of Computer-Aided Molecular Design, principles of chemical space design were evaluated, molecular networks proposed as an alternative to conventional coordinate-based chemical reference spaces, and different types of chemical space networks (CSNs) constructed and analyzed. Central to the generation of CSNs was the way in which molecular similarity relationships were assessed and a primary focal point was the network-based representation of biologically relevant chemical space. The design and comparison of CSNs based upon alternative similarity measures can be viewed as an evolutionary path with interesting lessons learned along the way. CSN design has matured to the point that such chemical space representations can be used in practice. In this contribution, highlights from the sequence of CSN design efforts are discussed in context, providing a perspective for future practical applications.

Lessons learned from the design of chemical space networks and opportunities for new applications

NASA Astrophysics Data System (ADS)

Vogt, Martin; Stumpfe, Dagmar; Maggiora, Gerald M.; Bajorath, Jürgen

2016-03-01

The concept of chemical space is of fundamental relevance in chemical informatics and computer-aided drug discovery. In a series of articles published in the Journal of Computer- Aided Molecular Design, principles of chemical space design were evaluated, molecular networks proposed as an alternative to conventional coordinate-based chemical reference spaces, and different types of chemical space networks (CSNs) constructed and analyzed. Central to the generation of CSNs was the way in which molecular similarity relationships were assessed and a primary focal point was the network-based representation of biologically relevant chemical space. The design and comparison of CSNs based upon alternative similarity measures can be viewed as an evolutionary path with interesting lessons learned along the way. CSN design has matured to the point that such chemical space representations can be used in practice. In this contribution, highlights from the sequence of CSN design efforts are discussed in context, providing a perspective for future practical applications.

Machine learning molecular dynamics for the simulation of infrared spectra.

PubMed

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

PubMed

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

The Effect of Interactivity and Instructional Exposure on Learning Effectiveness and Knowledge Retention: A Comparative Study of Two U.S. Air Force Computer-Based Training (CBT) Courses for Network User Licensing

DTIC Science & Technology

2003-03-01

sociocultural theory of learning was pioneered by Lev Vygotsky in the early twentieth century Soviet Union. Although his works were not published...Overview ....................................................................................................................... 14 Learning Theories ...and Teaching Strategies .................................................................. 14 Learning Theories and CBT

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.

Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning.

PubMed

Ren, Zhipeng; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Zhipeng Ren; Daoyi Dong; Huaxiong Li; Chunlin Chen; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Ren, Zhipeng

2018-06-01

In this paper, a new training paradigm is proposed for deep reinforcement learning using self-paced prioritized curriculum learning with coverage penalty. The proposed deep curriculum reinforcement learning (DCRL) takes the most advantage of experience replay by adaptively selecting appropriate transitions from replay memory based on the complexity of each transition. The criteria of complexity in DCRL consist of self-paced priority as well as coverage penalty. The self-paced priority reflects the relationship between the temporal-difference error and the difficulty of the current curriculum for sample efficiency. The coverage penalty is taken into account for sample diversity. With comparison to deep Q network (DQN) and prioritized experience replay (PER) methods, the DCRL algorithm is evaluated on Atari 2600 games, and the experimental results show that DCRL outperforms DQN and PER on most of these games. More results further show that the proposed curriculum training paradigm of DCRL is also applicable and effective for other memory-based deep reinforcement learning approaches, such as double DQN and dueling network. All the experimental results demonstrate that DCRL can achieve improved training efficiency and robustness for deep reinforcement learning.

Altered Intrinsic Hippocmapus Declarative Memory Network and Its Association with Impulsivity in Abstinent Heroin Dependent Subjects

PubMed Central

Zhai, Tian-Ye; Shao, Yong-Cong; Xie, Chun-Ming; Ye, En-Mao; Zou, Feng; Fu, Li-Ping; Li, Wen-Jun; Chen, Gang; Chen, Guang-Yu; Zhang, Zheng-Guo; Li, Shi-Jiang; Yang, Zheng

2014-01-01

Converging evidence suggests that addiction can be considered a disease of aberrant learning and memory with impulsive decision-making. In the past decades, numerous studies have demonstrated that drug addiction is involved in multiple memory systems such as classical conditioned drug memory, instrumental learning memory and the habitual learning memory. However, most of these studies have focused on the contributions of non-declarative memory, and declarative memory has largely been neglected in the research of addiction. Based on a recent finding that hippocampus, as a core functioning region of declarative memory, was proved biased the decision-making process based on past experiences by spreading associated reward values throughout memory. Our present study focused on the hippocampus. By utilizing seed-based network analysis on the resting-state functional MRI datasets with the seed hippocampus we tested how the intrinsic hippocampal memory network altered towards drug addiction, and examined how the functional connectivity strength within the altered hippocampal network correlated with behavioral index ‘impulsivity’. Our results demonstrated that HD group showed enhanced coherence between hippocampus which represents declarative memory system and non-declarative rewardguided learning memory system, and also showed attenuated intrinsic functional link between hippocampus and top-down control system, compared to the CN group. This alteration was furthered found to have behavioral significance over the behavioral index ‘impulsivity’ measured with Barratt Impulsiveness Scale (BIS). These results provide insights into the mechanism of declarative memory underlying the impulsive behavior in drug addiction. PMID:25008351

On learning navigation behaviors for small mobile robots with reservoir computing architectures.

PubMed

Antonelo, Eric Aislan; Schrauwen, Benjamin

2015-04-01

This paper proposes a general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) be fixed while only a linear readout output layer is trained. The proposed RC framework builds upon the notion of navigation attractor or behavior that can be embedded in the high-dimensional space of the reservoir after learning. The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory-motor sequence, can be linearly discriminated in the high-dimensional nonlinear space of the dynamic reservoir. Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on the examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal-directed behaviors, in a supervised way, using a hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors toward the goal.

Reviewing the Differences in Size, Composition and Structure between the Personal Networks of High-and Low-Performing Students

ERIC Educational Resources Information Center

Casquero, Oskar; Ovelar, Ramón; Romo, Jesús; Benito, Manuel

2015-01-01

An interesting aspect in the current literature about learning networks is the shift of focus from the understanding of the "whole network" of a course to the examination of the "personal networks" of individual students. This line of research is relatively new, based on small-scale studies and diverse analysis techniques,…

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

NASA Astrophysics Data System (ADS)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

A Generational Opportunity: A 21st Century Learning Content Delivery System

ERIC Educational Resources Information Center

McElroy, Patrick

2007-01-01

This paper describes a collaboratively developed, open marketplace for network-based learning and research content for the higher education community. It explores how available technologies and standards can facilitate a new knowledge creation industry for higher education learning content that engages all stakeholders in new ways. The Advisory…

The GenTechnique Project: Developing an Open Environment for Learning Molecular Genetics.

ERIC Educational Resources Information Center

Calza, R. E.; Meade, J. T.

1998-01-01

The GenTechnique project at Washington State University uses a networked learning environment for molecular genetics learning. The project is developing courseware featuring animation, hyper-link controls, and interactive self-assessment exercises focusing on fundamental concepts. The first pilot course featured a Web-based module on DNA…

Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

NASA Technical Reports Server (NTRS)

Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

2006-01-01

The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

International Cancer Screening Network

Cancer.gov

The International Cancer Screening Network promotes evidence-based cancer screening implementation and evaluation with cooperation from multilateral organizations around the globe. Learn more about how ICSN aims to reduce the global burden of cancer by supporting research and international collaboration.

Improving deep convolutional neural networks with mixed maxout units.

PubMed

Zhao, Hui-Zhen; Liu, Fu-Xian; Li, Long-Yue

2017-01-01

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN) model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.

Learning Bayesian Networks from Correlated Data

NASA Astrophysics Data System (ADS)

Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

2016-05-01

Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

A neural network controller for automated composite manufacturing

NASA Technical Reports Server (NTRS)

Lichtenwalner, Peter F.

1994-01-01

At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.

Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle.

PubMed

Kim, In-Ho; Jeon, Haemin; Baek, Seung-Chan; Hong, Won-Hwa; Jung, Hyung-Jo

2018-06-08

Bridge inspection using unmanned aerial vehicles (UAV) with high performance vision sensors has received considerable attention due to its safety and reliability. As bridges become obsolete, the number of bridges that need to be inspected increases, and they require much maintenance cost. Therefore, a bridge inspection method based on UAV with vision sensors is proposed as one of the promising strategies to maintain bridges. In this paper, a crack identification method by using a commercial UAV with a high resolution vision sensor is investigated in an aging concrete bridge. First, a point cloud-based background model is generated in the preliminary flight. Then, cracks on the structural surface are detected with the deep learning algorithm, and their thickness and length are calculated. In the deep learning method, region with convolutional neural networks (R-CNN)-based transfer learning is applied. As a result, a new network for the 384 collected crack images of 256 × 256 pixel resolution is generated from the pre-trained network. A field test is conducted to verify the proposed approach, and the experimental results proved that the UAV-based bridge inspection is effective at identifying and quantifying the cracks on the structures.

Resource constrained design of artificial neural networks using comparator neural network

NASA Technical Reports Server (NTRS)

Wah, Benjamin W.; Karnik, Tanay S.

1992-01-01

We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

NASA Astrophysics Data System (ADS)

Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

2010-12-01

We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation.

PubMed

Wang, Hongxun; Zhang, Weifang; Sun, Fuqiang; Zhang, Wei

2017-05-18

The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model ( K * approach). The results show that the predictions of MLAs are superior to those of K * approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.

A differential memristive synapse circuit for on-line learning in neuromorphic computing systems

NASA Astrophysics Data System (ADS)

Nair, Manu V.; Muller, Lorenz K.; Indiveri, Giacomo

2017-12-01

Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes. This imposes severe constraints on the length of the pulses transmitted in the network, and on the network’s throughput. Furthermore, most of these circuits do not decouple the currents flowing through memristive devices from the one stimulating the target neuron. This can be a problem when using devices with high conductance values, because of the resulting large currents. In this paper, we propose a novel circuit that decouples the current produced by the memristive device from the one used to stimulate the post-synaptic neuron, by using a novel differential scheme based on the Gilbert normalizer circuit. We show how this circuit is useful for reducing the effect of variability in the memristive devices, and how it is ideally suited for spike-based learning mechanisms that do not require overlapping pre- and post-synaptic pulses. We demonstrate the features of the proposed synapse circuit with SPICE simulations, and validate its learning properties with high-level behavioral network simulations which use a stochastic gradient descent learning rule in two benchmark classification tasks.

Analog Delta-Back-Propagation Neural-Network Circuitry

NASA Technical Reports Server (NTRS)

Eberhart, Silvio

1990-01-01

Changes in synapse weights due to circuit drifts suppressed. Proposed fully parallel analog version of electronic neural-network processor based on delta-back-propagation algorithm. Processor able to "learn" when provided with suitable combinations of inputs and enforced outputs. Includes programmable resistive memory elements (corresponding to synapses), conductances (synapse weights) adjusted during learning. Buffer amplifiers, summing circuits, and sample-and-hold circuits arranged in layers of electronic neurons in accordance with delta-back-propagation algorithm.

International distance-learning outreach: the APEC EINet experience.

PubMed

Kimball, A M; Shih, L; Brown, J; Harris, T G; Pautler, N; Jamieson, R W; Bolles, J; Horwitch, C

2003-01-01

The Emerging Infections Network is a mature electronic network that links Public Health professionals in the Asia Pacific through regular e-mail bulletins and an extensive Web site (http://www.apec.org/infectious). Emerging infections is a new area of study; learning materials help foster education. Our objective is to quantify the response of the network to the introduction of distance-learning materials on the Web site. Distance-learning materials, developed by the University of Washington School of Public Health, were field tested and launched on the site. Publicity was carried out prior to the launch of the materials. Access was tracked prospectively using server counts of page downloads. Web access increased substantially during the month after the materials were launched, especially among Asia based computers. The effect was isolated to the distance-learning pages, and not general to the site. This Web site appears to be responsive to the advertisement and to the materials. Prospective Web-site monitoring proved useful. Copyright 2002 Elsevier Science Ireland Ltd.

Action-Driven Visual Object Tracking With Deep Reinforcement Learning.

PubMed

Yun, Sangdoo; Choi, Jongwon; Yoo, Youngjoon; Yun, Kimin; Choi, Jin Young

2018-06-01

In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control tracking actions is pretrained using various training video sequences and fine-tuned during actual tracking for online adaptation to a change of target and background. The pretraining is done by utilizing deep reinforcement learning (RL) as well as supervised learning. The use of RL enables even partially labeled data to be successfully utilized for semisupervised learning. Through the evaluation of the object tracking benchmark data set, the proposed tracker is validated to achieve a competitive performance at three times the speed of existing deep network-based trackers. The fast version of the proposed method, which operates in real time on graphics processing unit, outperforms the state-of-the-art real-time trackers with an accuracy improvement of more than 8%.

Learning to Live Together: A Review of UNESCO's Associated Schools Project Network

NASA Astrophysics Data System (ADS)

Schweisfurth, Michele

2005-05-01

Some 7400 schools belong to the global network of UNESCO's Associated School Project Network. They are committed to promoting ideals such as human rights, intercultural understanding, peace and environmental protection. This study is based on an extensive review undertaken in 2003. It discusses the origins and analyzes the achievements of the Associated School Project Network in bringing change to schools, communities and national policy. The analysis employs a variety of models of educational innovation and reform in order to assess the horizontal and vertical impact of the Associated School Project Network. It draws general conclusions on the usefulness of such networks for intercultural learning and educational and social change. Key issues include the commitment of stakeholders; the treatment of culturally sensitive issues; cultural interpretations of certain subjects; the value of horizontal networks; and the difficulty of achieving vertical impact on national policy-making.

Inferring causal molecular networks: empirical assessment through a community-based effort.

PubMed

Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

2016-04-01

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

A Learning System for Discriminating Variants of Malicious Network Traffic

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

Beaver, Justin M; Symons, Christopher T; Gillen, Rob

Modern computer network defense systems rely primarily on signature-based intrusion detection tools, which generate alerts when patterns that are pre-determined to be malicious are encountered in network data streams. Signatures are created reactively, and only after in-depth manual analysis of a network intrusion. There is little ability for signature-based detectors to identify intrusions that are new or even variants of an existing attack, and little ability to adapt the detectors to the patterns unique to a network environment. Due to these limitations, the need exists for network intrusion detection techniques that can more comprehensively address both known unknown networkbased attacksmore » and can be optimized for the target environment. This work describes a system that leverages machine learning to provide a network intrusion detection capability that analyzes behaviors in channels of communication between individual computers. Using examples of malicious and non-malicious traffic in the target environment, the system can be trained to discriminate between traffic types. The machine learning provides insight that would be difficult for a human to explicitly code as a signature because it evaluates many interdependent metrics simultaneously. With this approach, zero day detection is possible by focusing on similarity to known traffic types rather than mining for specific bit patterns or conditions. This also reduces the burden on organizations to account for all possible attack variant combinations through signatures. The approach is presented along with results from a third-party evaluation of its performance.« less

Network-Based Leadership Development: A Guiding Framework and Resources for Management Educators

ERIC Educational Resources Information Center

Cullen-Lester, Kristin L.; Woehler, Meredith L.; Willburn, Phil

2016-01-01

Management education and leadership development has traditionally focused on improving human capital (i.e., knowledge, skills, and abilities). Social capital, networks, and networking skills have received less attention. When this content has been incorporated into learning and development experiences, it has often been more ad hoc and has…

Digital Identity Formation: Socially Being Real and Present on Digital Networks

ERIC Educational Resources Information Center

Bozkurt, Aras; Tu, Chih-Hsiung

2016-01-01

Social networks have become popular communication and interaction environments recently. As digital environments, so as ecosystems, they have potential in terms of networked learning as they fulfill some roles such as mediating an environment for digital identity formation and providing social and emotional presence. Based on this phenomenon, the…

Adaptive neural network/expert system that learns fault diagnosis for different structures

NASA Astrophysics Data System (ADS)

Simon, Solomon H.

1992-08-01

Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

Network formation: neighborhood structures, establishment costs, and distributed learning.

PubMed

Chasparis, Georgios C; Shamma, Jeff S

2013-12-01

We consider the problem of network formation in a distributed fashion. Network formation is modeled as a strategic-form game, where agents represent nodes that form and sever unidirectional links with other nodes and derive utilities from these links. Furthermore, agents can form links only with a limited set of neighbors. Agents trade off the benefit from links, which is determined by a distance-dependent reward function, and the cost of maintaining links. When each agent acts independently, trying to maximize its own utility function, we can characterize “stable” networks through the notion of Nash equilibrium. In fact, the introduced reward and cost functions lead to Nash equilibria (networks), which exhibit several desirable properties such as connectivity, bounded-hop diameter, and efficiency (i.e., minimum number of links). Since Nash networks may not necessarily be efficient, we also explore the possibility of “shaping” the set of Nash networks through the introduction of state-based utility functions. Such utility functions may represent dynamic phenomena such as establishment costs (either positive or negative). Finally, we show how Nash networks can be the outcome of a distributed learning process. In particular, we extend previous learning processes to so-called “state-based” weakly acyclic games, and we show that the proposed network formation games belong to this class of games.

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

PubMed

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule.

PubMed

Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

2015-11-01

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

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

Liu, Hui; Song, Yongduan; Xue, Fangzheng

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than themore » SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.« less

Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks

NASA Astrophysics Data System (ADS)

Sun, Xiaofeng; Shen, Shuhan; Lin, Xiangguo; Hu, Zhanyi

2017-10-01

High-resolution remote sensing data classification has been a challenging and promising research topic in the community of remote sensing. In recent years, with the rapid advances of deep learning, remarkable progress has been made in this field, which facilitates a transition from hand-crafted features designing to an automatic end-to-end learning. A deep fully convolutional networks (FCNs) based ensemble learning method is proposed to label the high-resolution aerial images. To fully tap the potentials of FCNs, both the Visual Geometry Group network and a deeper residual network, ResNet, are employed. Furthermore, to enlarge training samples with diversity and gain better generalization, in addition to the commonly used data augmentation methods (e.g., rotation, multiscale, and aspect ratio) in the literature, aerial images from other datasets are also collected for cross-scene learning. Finally, we combine these learned models to form an effective FCN ensemble and refine the results using a fully connected conditional random field graph model. Experiments on the ISPRS 2-D Semantic Labeling Contest dataset show that our proposed end-to-end classification method achieves an overall accuracy of 90.7%, a state-of-the-art in the field.

Reputation-based collaborative network biology.

PubMed

Binder, Jean; Boue, Stephanie; Di Fabio, Anselmo; Fields, R Brett; Hayes, William; Hoeng, Julia; Park, Jennifer S; Peitsch, Manuel C

2015-01-01

A pilot reputation-based collaborative network biology platform, Bionet, was developed for use in the sbv IMPROVER Network Verification Challenge to verify and enhance previously developed networks describing key aspects of lung biology. Bionet was successful in capturing a more comprehensive view of the biology associated with each network using the collective intelligence and knowledge of the crowd. One key learning point from the pilot was that using a standardized biological knowledge representation language such as BEL is critical to the success of a collaborative network biology platform. Overall, Bionet demonstrated that this approach to collaborative network biology is highly viable. Improving this platform for de novo creation of biological networks and network curation with the suggested enhancements for scalability will serve both academic and industry systems biology communities.

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

PubMed Central

Clune, Jeff

2017-01-01

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting. PMID:29145413

Knowledge Management through the Equilibrium Pattern Model for Learning

NASA Astrophysics Data System (ADS)

Sarirete, Akila; Noble, Elizabeth; Chikh, Azeddine

Contemporary students are characterized by having very applied learning styles and methods of acquiring knowledge. This behavior is consistent with the constructivist models where students are co-partners in the learning process. In the present work the authors developed a new model of learning based on the constructivist theory coupled with the cognitive development theory of Piaget. The model considers the level of learning based on several stages and the move from one stage to another requires learners' challenge. At each time a new concept is introduced creates a disequilibrium that needs to be worked out to return back to its equilibrium stage. This process of "disequilibrium/equilibrium" has been analyzed and validated using a course in computer networking as part of Cisco Networking Academy Program at Effat College, a women college in Saudi Arabia. The model provides a theoretical foundation for teaching especially in a complex knowledge domain such as engineering and can be used in a knowledge economy.

Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

NASA Astrophysics Data System (ADS)

Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

2013-03-01

Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

Learning Analytics for Networked Learning Models

ERIC Educational Resources Information Center

Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

2014-01-01

Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

Smoothing of cost function leads to faster convergence of neural network learning

NASA Astrophysics Data System (ADS)

Xu, Li-Qun; Hall, Trevor J.

1994-03-01

One of the major problems in supervised learning of neural networks is the inevitable local minima inherent in the cost function f(W,D). This often makes classic gradient-descent-based learning algorithms that calculate the weight updates for each iteration according to (Delta) W(t) equals -(eta) (DOT)$DELwf(W,D) powerless. In this paper we describe a new strategy to solve this problem, which, adaptively, changes the learning rate and manipulates the gradient estimator simultaneously. The idea is to implicitly convert the local- minima-laden cost function f((DOT)) into a sequence of its smoothed versions {f(beta t)}Ttequals1, which, subject to the parameter (beta) t, bears less details at time t equals 1 and gradually more later on, the learning is actually performed on this sequence of functionals. The corresponding smoothed global minima obtained in this way, {Wt}Ttequals1, thus progressively approximate W-the desired global minimum. Experimental results on a nonconvex function minimization problem and a typical neural network learning task are given, analyses and discussions of some important issues are provided.

A 3D model retrieval approach based on Bayesian networks lightfield descriptor

NASA Astrophysics Data System (ADS)

Xiao, Qinhan; Li, Yanjun

2009-12-01

A new 3D model retrieval methodology is proposed by exploiting a novel Bayesian networks lightfield descriptor (BNLD). There are two key novelties in our approach: (1) a BN-based method for building lightfield descriptor; and (2) a 3D model retrieval scheme based on the proposed BNLD. To overcome the disadvantages of the existing 3D model retrieval methods, we explore BN for building a new lightfield descriptor. Firstly, 3D model is put into lightfield, about 300 binary-views can be obtained along a sphere, then Fourier descriptors and Zernike moments descriptors can be calculated out from binaryviews. Then shape feature sequence would be learned into a BN model based on BN learning algorithm; Secondly, we propose a new 3D model retrieval method by calculating Kullback-Leibler Divergence (KLD) between BNLDs. Beneficial from the statistical learning, our BNLD is noise robustness as compared to the existing methods. The comparison between our method and the lightfield descriptor-based approach is conducted to demonstrate the effectiveness of our proposed methodology.

Towards Scalable Deep Learning via I/O Analysis and Optimization

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

Pumma, Sarunya; Si, Min; Feng, Wu-Chun

Deep learning systems have been growing in prominence as a way to automatically characterize objects, trends, and anomalies. Given the importance of deep learning systems, researchers have been investigating techniques to optimize such systems. An area of particular interest has been using large supercomputing systems to quickly generate effective deep learning networks: a phase often referred to as “training” of the deep learning neural network. As we scale existing deep learning frameworks—such as Caffe—on these large supercomputing systems, we notice that the parallelism can help improve the computation tremendously, leaving data I/O as the major bottleneck limiting the overall systemmore » scalability. In this paper, we first present a detailed analysis of the performance bottlenecks of Caffe on large supercomputing systems. Our analysis shows that the I/O subsystem of Caffe—LMDB—relies on memory-mapped I/O to access its database, which can be highly inefficient on large-scale systems because of its interaction with the process scheduling system and the network-based parallel filesystem. Based on this analysis, we then present LMDBIO, our optimized I/O plugin for Caffe that takes into account the data access pattern of Caffe in order to vastly improve I/O performance. Our experimental results show that LMDBIO can improve the overall execution time of Caffe by nearly 20-fold in some cases.« less

Model-Free Adaptive Control for Unknown Nonlinear Zero-Sum Differential Game.

PubMed

Zhong, Xiangnan; He, Haibo; Wang, Ding; Ni, Zhen

2018-05-01

In this paper, we present a new model-free globalized dual heuristic dynamic programming (GDHP) approach for the discrete-time nonlinear zero-sum game problems. First, the online learning algorithm is proposed based on the GDHP method to solve the Hamilton-Jacobi-Isaacs equation associated with optimal regulation control problem. By setting backward one step of the definition of performance index, the requirement of system dynamics, or an identifier is relaxed in the proposed method. Then, three neural networks are established to approximate the optimal saddle point feedback control law, the disturbance law, and the performance index, respectively. The explicit updating rules for these three neural networks are provided based on the data generated during the online learning along the system trajectories. The stability analysis in terms of the neural network approximation errors is discussed based on the Lyapunov approach. Finally, two simulation examples are provided to show the effectiveness of the proposed method.

Wishart Deep Stacking Network for Fast POLSAR Image Classification.

PubMed

Jiao, Licheng; Liu, Fang

2016-05-11

Inspired by the popular deep learning architecture - Deep Stacking Network (DSN), a specific deep model for polarimetric synthetic aperture radar (POLSAR) image classification is proposed in this paper, which is named as Wishart Deep Stacking Network (W-DSN). First of all, a fast implementation of Wishart distance is achieved by a special linear transformation, which speeds up the classification of POLSAR image and makes it possible to use this polarimetric information in the following Neural Network (NN). Then a single-hidden-layer neural network based on the fast Wishart distance is defined for POLSAR image classification, which is named as Wishart Network (WN) and improves the classification accuracy. Finally, a multi-layer neural network is formed by stacking WNs, which is in fact the proposed deep learning architecture W-DSN for POLSAR image classification and improves the classification accuracy further. In addition, the structure of WN can be expanded in a straightforward way by adding hidden units if necessary, as well as the structure of the W-DSN. As a preliminary exploration on formulating specific deep learning architecture for POLSAR image classification, the proposed methods may establish a simple but clever connection between POLSAR image interpretation and deep learning. The experiment results tested on real POLSAR image show that the fast implementation of Wishart distance is very efficient (a POLSAR image with 768000 pixels can be classified in 0.53s), and both the single-hidden-layer architecture WN and the deep learning architecture W-DSN for POLSAR image classification perform well and work efficiently.

Deep Learning and Its Applications in Biomedicine.

PubMed

Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

2018-02-01

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

Shallow Transits—Deep Learning. I. Feasibility Study of Deep Learning to Detect Periodic Transits of Exoplanets

NASA Astrophysics Data System (ADS)

Zucker, Shay; Giryes, Raja

2018-04-01

Transits of habitable planets around solar-like stars are expected to be shallow, and to have long periods, which means low information content. The current bottleneck in the detection of such transits is caused in large part by the presence of red (correlated) noise in the light curves obtained from the dedicated space telescopes. Based on the groundbreaking results deep learning achieves in many signal and image processing applications, we propose to use deep neural networks to solve this problem. We present a feasibility study, in which we applied a convolutional neural network on a simulated training set. The training set comprised light curves received from a hypothetical high-cadence space-based telescope. We simulated the red noise by using Gaussian Processes with a wide variety of hyper-parameters. We then tested the network on a completely different test set simulated in the same way. Our study proves that very difficult cases can indeed be detected. Furthermore, we show how detection trends can be studied and detection biases quantified. We have also checked the robustness of the neural-network performance against practical artifacts such as outliers and discontinuities, which are known to affect space-based high-cadence light curves. Future work will allow us to use the neural networks to characterize the transit model and identify individual transits. This new approach will certainly be an indispensable tool for the detection of habitable planets in the future planet-detection space missions such as PLATO.

Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect

PubMed Central

Knoblauch, Andreas; Körner, Edgar; Körner, Ursula; Sommer, Friedrich T.

2014-01-01

Although already William James and, more explicitly, Donald Hebb's theory of cell assemblies have suggested that activity-dependent rewiring of neuronal networks is the substrate of learning and memory, over the last six decades most theoretical work on memory has focused on plasticity of existing synapses in prewired networks. Research in the last decade has emphasized that structural modification of synaptic connectivity is common in the adult brain and tightly correlated with learning and memory. Here we present a parsimonious computational model for learning by structural plasticity. The basic modeling units are “potential synapses” defined as locations in the network where synapses can potentially grow to connect two neurons. This model generalizes well-known previous models for associative learning based on weight plasticity. Therefore, existing theory can be applied to analyze how many memories and how much information structural plasticity can store in a synapse. Surprisingly, we find that structural plasticity largely outperforms weight plasticity and can achieve a much higher storage capacity per synapse. The effect of structural plasticity on the structure of sparsely connected networks is quite intuitive: Structural plasticity increases the “effectual network connectivity”, that is, the network wiring that specifically supports storage and recall of the memories. Further, this model of structural plasticity produces gradients of effectual connectivity in the course of learning, thereby explaining various cognitive phenomena including graded amnesia, catastrophic forgetting, and the spacing effect. PMID:24858841

Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior

PubMed Central

Gureckis, Todd M.; Love, Bradley C.

2009-01-01

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predict differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior. PMID:20396653

Landcover Classification Using Deep Fully Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Wang, J.; Li, X.; Zhou, S.; Tang, J.

2017-12-01

Land cover classification has always been an essential application in remote sensing. Certain image features are needed for land cover classification whether it is based on pixel or object-based methods. Different from other machine learning methods, deep learning model not only extracts useful information from multiple bands/attributes, but also learns spatial characteristics. In recent years, deep learning methods have been developed rapidly and widely applied in image recognition, semantic understanding, and other application domains. However, there are limited studies applying deep learning methods in land cover classification. In this research, we used fully convolutional networks (FCN) as the deep learning model to classify land covers. The National Land Cover Database (NLCD) within the state of Kansas was used as training dataset and Landsat images were classified using the trained FCN model. We also applied an image segmentation method to improve the original results from the FCN model. In addition, the pros and cons between deep learning and several machine learning methods were compared and explored. Our research indicates: (1) FCN is an effective classification model with an overall accuracy of 75%; (2) image segmentation improves the classification results with better match of spatial patterns; (3) FCN has an excellent ability of learning which can attains higher accuracy and better spatial patterns compared with several machine learning methods.

PatterNet: a system to learn compact physical design pattern representations for pattern-based analytics

NASA Astrophysics Data System (ADS)

Lutich, Andrey

2017-07-01

This research considers the problem of generating compact vector representations of physical design patterns for analytics purposes in semiconductor patterning domain. PatterNet uses a deep artificial neural network to learn mapping of physical design patterns to a compact Euclidean hyperspace. Distances among mapped patterns in this space correspond to dissimilarities among patterns defined at the time of the network training. Once the mapping network has been trained, PatterNet embeddings can be used as feature vectors with standard machine learning algorithms, and pattern search, comparison, and clustering become trivial problems. PatterNet is inspired by the concepts developed within the framework of generative adversarial networks as well as the FaceNet. Our method facilitates a deep neural network (DNN) to learn directly the compact representation by supplying it with pairs of design patterns and dissimilarity among these patterns defined by a user. In the simplest case, the dissimilarity is represented by an area of the XOR of two patterns. Important to realize that our PatterNet approach is very different to the methods developed for deep learning on image data. In contrast to "conventional" pictures, the patterns in the CAD world are the lists of polygon vertex coordinates. The method solely relies on the promise of deep learning to discover internal structure of the incoming data and learn its hierarchical representations. Artificial intelligence arising from the combination of PatterNet and clustering analysis very precisely follows intuition of patterning/optical proximity correction experts paving the way toward human-like and human-friendly engineering tools.

Protein complexes, big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks.

PubMed

Havugimana, Pierre C; Hu, Pingzhao; Emili, Andrew

2017-10-01

Elucidation of the networks of physical (functional) interactions present in cells and tissues is fundamental for understanding the molecular organization of biological systems, the mechanistic basis of essential and disease-related processes, and for functional annotation of previously uncharacterized proteins (via guilt-by-association or -correlation). After a decade in the field, we felt it timely to document our own experiences in the systematic analysis of protein interaction networks. Areas covered: Researchers worldwide have contributed innovative experimental and computational approaches that have driven the rapidly evolving field of 'functional proteomics'. These include mass spectrometry-based methods to characterize macromolecular complexes on a global-scale and sophisticated data analysis tools - most notably machine learning - that allow for the generation of high-quality protein association maps. Expert commentary: Here, we recount some key lessons learned, with an emphasis on successful workflows, and challenges, arising from our own and other groups' ongoing efforts to generate, interpret and report proteome-scale interaction networks in increasingly diverse biological contexts.

Neural coordination can be enhanced by occasional interruption of normal firing patterns: a self-optimizing spiking neural network model.

PubMed

Woodward, Alexander; Froese, Tom; Ikegami, Takashi

2015-02-01

The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains. Copyright © 2014 Elsevier Ltd. All rights reserved.

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks

PubMed Central

Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

2017-01-01

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks. PMID:28932180

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks.

PubMed

Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

2017-01-01

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.

Automatic learning rate adjustment for self-supervising autonomous robot control

NASA Technical Reports Server (NTRS)

Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

1992-01-01

Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions

PubMed Central

Box, Simon

2014-01-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human ‘player’ to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable. PMID:26064570

Can surgical simulation be used to train detection and classification of neural networks?

PubMed

Zisimopoulos, Odysseas; Flouty, Evangello; Stacey, Mark; Muscroft, Sam; Giataganas, Petros; Nehme, Jean; Chow, Andre; Stoyanov, Danail

2017-10-01

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems.

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions.

PubMed

Box, Simon

2014-12-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human 'player' to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable.