WorldWideScience

Sample records for bionic learning network

  1. Learning Networks, Networked Learning

    NARCIS (Netherlands)

    Sloep, Peter; Berlanga, Adriana

    2010-01-01

    Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

  2. Bionic Nanosystems

    Science.gov (United States)

    Sebastian Mannoor, Manu

    Direct multidimensional integration of functional electronics and mechanical elements with viable biological systems could allow for the creation of bionic systems and devices possessing unique and advanced capabilities. For example, the ability to three dimensionally integrate functional electronic and mechanical components with biological cells and tissue could enable the creation of bionic systems that can have tremendous impact in regenerative medicine, prosthetics, and human-machine interfaces. However, as a consequence of the inherent dichotomy in material properties and limitations of conventional fabrication methods, the attainment of truly seamless integration of electronic and/or mechanical components with biological systems has been challenging. Nanomaterials engineering offers a general route for overcoming these dichotomies, primarily due to the existence of a dimensional compatibility between fundamental biological functional units and abiotic nanomaterial building blocks. One area of compelling interest for bionic systems is in the field of biomedical sensing, where the direct interfacing of nanosensors onto biological tissue or the human body could stimulate exciting opportunities such as on-body health quality monitoring and adaptive threat detection. Further, interfacing of antimicrobial peptide based bioselective probes onto the bionic nanosensors could offer abilities to detect pathogenic bacteria with bio-inspired selectivity. Most compellingly, when paired with additive manufacturing techniques such as 3D printing, these characteristics enable three dimensional integration and merging of a variety of functional materials including electronic, structural and biomaterials with viable biological cells, in the precise anatomic geometries of human organs, to form three dimensionally integrated, multi-functional bionic hybrids and cyborg devices with unique capabilities. In this thesis, we illustrate these approaches using three representative

  3. Applied Learning Networks (ALN)

    National Research Council Canada - National Science Library

    Bannister, Joseph; Shen, Wei-Min; Touch, Joseph; Hou, Feili; Pingali, Venkata

    2007-01-01

    Applied Learning Networks (ALN) demonstrates that a network protocol can learn to improve its performance over time, showing how to incorporate learning methods into a general class of network protocols...

  4. Learning Networks for Lifelong Learning

    OpenAIRE

    Sloep, Peter

    2009-01-01

    Presentation in a seminar organized by Christopher Hoadley at Penn State University, October 2004.Contains general introduction into the Learning Network Programme and a demonstration of the Netlogo Simulation of a Learning Network.

  5. The application of bionic wavelet transform to speech signal processing in cochlear implants using neural network simulations.

    Science.gov (United States)

    Yao, Jun; Zhang, Yuan-Ting

    2002-11-01

    Cochlear implants (CIs) restore partial hearing to people with severe to profound sensorineural deafness; but there is still a marked performance gap in speech recognition between those who have received cochlear implant and people with a normal hearing capability. One of the factors that may lead to this performance gap is the inadequate signal processing method used in CIs. This paper investigates the application of an improved signal-processing method called bionic wavelet transform (BWT). This method is based upon the auditory model and allows for signal processing. Comparing the neural network simulations on the same experimental materials processed by wavelet transform (WT) and BWT, the application of BWT to speech signal processing in CI has a number of advantages, including: improvement in recognition rates for both consonants and vowels, reduction of the number of required channels, reduction of the average stimulation duration for words, and high noise tolerance. Consonant recognition results in 15 normal hearing subjects show that the BWT produces significantly better performance than the WT (t = -4.36276, p = 0.00065). The BWT has great potential to reduce the performance gap between CI listeners and people with a normal hearing capability in the future.

  6. Learning conditional Gaussian networks

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given...... a configuration of the discrete parents. We assume parameter independence and complete data. Further, to learn the structure of the network, the network score is deduced. We then develop a local master prior procedure, for deriving parameter priors in these networks. This procedure satisfies parameter...... independence, parameter modularity and likelihood equivalence. Bayes factors to be used in model search are introduced. Finally the methods derived are illustrated by a simple example....

  7. Optical function of bionic nanostructure of ZnO

    International Nuclear Information System (INIS)

    Xu, C X; Zhu, G P; Liu, Y J; Sun, X W; Li, X; Liu, J P; Cui, Y P

    2007-01-01

    A novel bionic network nanostructure of zinc oxide (ZnO), which is similar to the microstructure of a butterfly wing, was first fabricated by a vapor-phase transport method using zinc powder as a source. These bionic nanostructures are composed of three ordered multi-aperture gratings. Similar to the optical effect of butterfly wings, the diffraction patterns of the bionic network of ZnO were observed. The mechanism of the optical function was discussed based on the physical model of multi-aperture diffraction

  8. Learning In networks

    Science.gov (United States)

    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.

  9. Bionics in architecture

    Directory of Open Access Journals (Sweden)

    Sugár Viktória

    2017-04-01

    Full Text Available The adaptation of the forms and phenomena of nature is not a recent concept. Observation of natural mechanisms has been a primary source of innovation since prehistoric ages, which can be perceived through the history of architecture. Currently, this idea is coming to the front again through sustainable architecture and adaptive design. Investigating natural innovations and the clear-outness of evolution during the 20th century led to the creation of a separate scientific discipline, Bionics. Architecture and Bionics are strongly related to each other, since the act of building is as old as the human civilization - moreover its first formal and structural source was obviously the surrounding environment. Present paper discusses the definition of Bionics and its connection with the architecture.

  10. Learning Analytics for Networked Learning Models

    Science.gov (United States)

    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…

  11. Research, Boundaries, and Policy in Networked Learning

    DEFF Research Database (Denmark)

    This book presents cutting-edge, peer reviewed research on networked learning organized by three themes: policy in networked learning, researching networked learning, and boundaries in networked learning. The "policy in networked learning" section explores networked learning in relation to policy...... networks, spaces of algorithmic governance and more. The "boundaries in networked learning" section investigates frameworks of students' digital literacy practices, among other important frameworks in digital learning. Lastly, the "research in networked learning" section delves into new research methods...

  12. Bionic machines and systems

    Energy Technology Data Exchange (ETDEWEB)

    Halme, A.; Paanajaervi, J. (eds.)

    2004-07-01

    Introduction Biological systems form a versatile and complex entirety on our planet. One evolutionary branch of primates, called humans, has created an extraordinary skill, called technology, by the aid of which it nowadays dominate life on the planet. Humans use technology for producing and harvesting food, healthcare and reproduction, increasing their capability to commute and communicate, defending their territory etc., and to develop more technology. As a result of this, humans have become much technology dependent, so that they have been forced to form a specialized class of humans, called engineers, who take care of the knowledge of technology developing it further and transferring it to later generations. Until now, technology has been relatively independent from biology, although some of its branches, e.g. biotechnology and biomedical engineering, have traditionally been in close contact with it. There exist, however, an increasing interest to expand the interface between technology and biology either by directly utilizing biological processes or materials by combining them with 'dead' technology, or by mimicking in technological solutions the biological innovations created by evolution. The latter theme is in focus of this report, which has been written as the proceeding of the post-graduate seminar 'Bionic Machines and Systems' held at HUT Automation Technology Laboratory in autumn 2003. The underlaying idea of the seminar was to analyze biological species by considering them as 'robotic machines' having various functional subsystems, such as for energy, motion and motion control, perception, navigation, mapping and localization. We were also interested about intelligent capabilities, such as learning and communication, and social structures like swarming behavior and its mechanisms. The word 'bionic machine' comes from the book which was among the initial material when starting our mission to the fascinating world

  13. Social Interaction in Learning Networks

    OpenAIRE

    Sloep, Peter

    2009-01-01

    The original publication is available from www.springerlink.com. Sloep, P. (2009). Social Interaction in Learning Networks. In R. Koper (Ed.), Learning Network Services for Professional Development (pp 13-15). Berlin, Germany: Springer Verlag.

  14. Learning Networks for Professional Development & Lifelong Learning

    NARCIS (Netherlands)

    Brouns, Francis; Sloep, Peter

    2009-01-01

    Brouns, F., & Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation of the Learning Network Programme for a Korean delegation of Chonnam National University and Dankook University (researchers dr. Jeeheon Ryu and dr. Minjeong Kim and a Group of PhD and

  15. Coming Soon: The Bionic Man

    Science.gov (United States)

    Woodard, Colin

    2006-01-01

    This article describes the latest advancement in the development of prosthetic arms. Bionic researchers are making significant advances in creating more agile prosthetics that users can control via their own nervous system. The bionic arm, which is still under development, can not only execute complex, thought-controlled movements, but also can…

  16. Accelerating Learning By Neural Networks

    Science.gov (United States)

    Toomarian, Nikzad; Barhen, Jacob

    1992-01-01

    Electronic neural networks made to learn faster by use of terminal teacher forcing. Method of supervised learning involves addition of teacher forcing functions to excitations fed as inputs to output neurons. Initially, teacher forcing functions are strong enough to force outputs to desired values; subsequently, these functions decay with time. When learning successfully completed, terminal teacher forcing vanishes, and dynamics or neural network become equivalent to those of conventional neural network. Simulated neural network with terminal teacher forcing learned to produce close approximation of circular trajectory in 400 iterations.

  17. Redes de aprendizaje, aprendizaje en red Learning Networks, Networked Learning

    Directory of Open Access Journals (Sweden)

    Peter Sloep

    2011-10-01

    Full Text Available Las redes de aprendizaje (Learning Networks son redes sociales en línea mediante las cuales los participantes comparten información y colaboran para crear conocimiento. De esta manera, estas redes enriquecen la experiencia de aprendizaje en cualquier contexto de aprendizaje, ya sea de educación formal (en escuelas o universidades o educación no-formal (formación profesional. Aunque el concepto de aprendizaje en red suscita el interés de diferentes actores del ámbito educativo, aún existen muchos interrogantes sobre cómo debe diseñarse el aprendizaje en red para facilitar adecuadamente la educación y la formación. El artículo toma este interrogante como punto de partida, y posteriormente aborda cuestiones como la dinámica de la evolución de las redes de aprendizaje, la importancia de fomentar la confianza entre los participantes y el papel central que desempeña el perfil de usuario en la construcción de la confianza, así como el apoyo entre compañeros. Además, se elabora el proceso de diseño de una red de aprendizaje, y se describe un ejemplo en el contexto universitario. Basándonos en la investigación que actualmente se lleva a cabo en nuestro propio centro y en otros lugares, el capítulo concluye con una visión del futuro de las redes de aprendizaje.Learning Networks are on-line social networks through which users share knowledge with each other and jointly develop new knowledge. This way, Learning Networks may enrich the experience of formal, school-based learning and form a viable setting for professional development. Although networked learning enjoys an increasing interest, many questions remain on how exactly learning in such networked contexts can contribute to successful education and training. Put differently, how should networked learning be designed best to facilitate education and training? Taking this as its point of departure, the chapter addresses such issues as the dynamic evolution of Learning Networks

  18. [Bionics: limits and perspectives].

    Science.gov (United States)

    Grassmann, P

    1990-07-01

    Bionics, based on analogies between living beings and technical systems, neglect fundamental differences, e.g., on the one hand, technology uses high temperatures, a mean closed to all living beings and, on the other hand, the cells of all organisms keep high autonomy. The first fact makes it possible, e.g., to construct airplanes three or four powers of ten heavier than the heaviest birds, whereas the second fact enables each cell to reproduce itself, to restore lost limbs or even the whole organism, far beyond the reach of technology. The symbiosis of organisms and technical installations (biotechnology) or, on a higher level, of mankind and environment, may be a guiding star for future development.

  19. Neural networks and statistical learning

    CERN Document Server

    Du, Ke-Lin

    2014-01-01

    Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

  20. Workplace Learning in Informal Networks

    Science.gov (United States)

    Milligan, Colin; Littlejohn, Allison; Margaryan, Anoush

    2014-01-01

    Learning does not stop when an individual leaves formal education, but becomes increasingly informal, and deeply embedded within other activities such as work. This article describes the challenges of informal learning in knowledge intensive industries, highlighting the important role of personal learning networks. The article argues that…

  1. Bionic models for identification of biological systems

    Science.gov (United States)

    Gerget, O. M.

    2017-01-01

    This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described.

  2. Bionic intrafascicular interfaces for recording and stimulating peripheral nerve fibers.

    Science.gov (United States)

    Jung, Ranu; Abbas, James J; Kuntaegowdanahalli, Sathyakumar; Thota, Anil K

    2018-01-01

    The network of peripheral nerves presents extraordinary potential for modulating and/or monitoring the functioning of internal organs or the brain. The degree to which these pathways can be used to influence or observe neural activity patterns will depend greatly on the quality and specificity of the bionic interface. The anatomical organization, which consists of multiple nerve fibers clustered into fascicles within a nerve bundle, presents opportunities and challenges that may necessitate insertion of electrodes into individual fascicles to achieve the specificity that may be required for many clinical applications. This manuscript reviews the current state-of-the-art in bionic intrafascicular interfaces, presents specific concerns for stimulation and recording, describes key implementation considerations and discusses challenges for future designs of bionic intrafascicular interfaces.

  3. Professional Learning Networks Designed for Teacher Learning

    Science.gov (United States)

    Trust, Torrey

    2012-01-01

    In the information age, students must learn to navigate and evaluate an expanding network of information. Highly effective teachers model this process of information analysis and knowledge acquisition by continually learning through collaboration, professional development, and studying pedagogical techniques and best practices. Many teachers have…

  4. [The bionic hand].

    Science.gov (United States)

    Surke, Carsten; Ducommun Dit Boudry, Pascal; Vögelin, Esther

    2015-08-01

    The loss of the upper extremity implicates a grave insult in the life of the involved person. To compensate for the loss of function different powered prosthetic devices are available. Ever since their first development 70 years ago numerous improvements in terms of size, weight and wearing comfort have been developed, but issues regarding the control of upper extremity prostheses remain. Slow grasping speed, limited grip positions and especially failure to provide a sensory feedback limit the acceptance in patients. Recent developments are aimed to allow a more intuitive control of the prosthetic device and to provide a sensory feedback to the amputee. Targeted reinnervation reassignes existing muscles to different peripheral nerves thereby enabling them to fulfill alternate functions. Implanting electrodes into muscle bellies of the forearm allows a more accurate control of the prosthesis. Promising results are being achieved by implanting nerve electrodes by establishing bilateral communication between patient and prosthesis. The following review summarizes the current developments of bionic prostheses in the upper extremity.

  5. Learning Python network programming

    CERN Document Server

    Sarker, M O Faruque

    2015-01-01

    If you're a Python developer or a system administrator with Python experience and you're looking to take your first steps in network programming, then this book is for you. Basic knowledge of Python is assumed.

  6. Language Choice & Global Learning Networks

    Directory of Open Access Journals (Sweden)

    Dennis Sayers

    1995-05-01

    Full Text Available How can other languages be used in conjunction with English to further intercultural and multilingual learning when teachers and students participate in computer-based global learning networks? Two portraits are presented of multilingual activities in the Orillas and I*EARN learning networks, and are discussed as examples of the principal modalities of communication employed in networking projects between distant classes. Next, an important historical precedent --the social controversy which accompanied the introduction of telephone technology at the end of the last century-- is examined in terms of its implications for language choice in contemporary classroom telecomputing projects. Finally, recommendations are offered to guide decision making concerning the role of language choice in promoting collaborative critical inquiry.

  7. Professional Learning Networks Taking Off

    Science.gov (United States)

    Flanigan, Robin L.

    2012-01-01

    Busy educators who want to ask advice, offer opinions, and engage in discussions with colleagues increasingly turn to professional learning networks (PLNs)--online communities that allow the sharing of lesson plans, teaching strategies, and student work, as well as collaboration across grade levels and departments. As budget cuts limit…

  8. Learning Networks for Lifelong Competence Development

    NARCIS (Netherlands)

    Koper, Rob; Stefanov, Krassen

    2006-01-01

    Koper, R., & Stefanov, K. (Eds.) (2006). Learning networks for lifelong competence development. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development. March, 30-31, 2006. Sofia, Bulgaria: TENCompentence Conference. Retrieved June 30th, 2006, from

  9. Research Progress on Geometric Texturing and Function Based on Bionic Theory

    Directory of Open Access Journals (Sweden)

    TAN Na

    2018-01-01

    Full Text Available Biological researches have attracted wide attentions in recent years, mimicking the morphology of creatures, learning the function of morphology and applying it to the engineering area have become the research focus. Investigating the special function of bionic surface texturing based on geometric morphology can provide us ideas to optimize surface properties of materials,extend the materials service life, widen the scope of application of materials, and improve the application values of materials. Through studying on the principle and application of hydrophobicity, bionic drag reduction, antifriction and wear-resistant due to the geometric morphology, the different functions of bionic texturing were explored, the future development directions of focusing on bionic patterns preparation and mechanism exploration were clarified, and further the surface treatment on materials was carried out to prepare the materials with more superior properties.

  10. Changing Conditions for Networked Learning?

    DEFF Research Database (Denmark)

    Ryberg, Thomas

    2011-01-01

    of social technologies. I argue that we are seeing the emergence of new architectures and scales of participation, collaboration and networking e.g. through interesting formations of learning networks at different levels of scale, for different purposes and often bridging boundaries such as formal/informal...... (flash activites or event driven streams of information and activities (such as conference events or global protests)). Likewise, we can observe that novel types of collaboration and participation seem to be emerging or solidifying (such as Wikipedia or collectively and dynamically produced online...... in describing the novel pedagogical potentials of these new technologies and practices (e.g. in debates around virtual learning environments versus personal learning environment). Likewise, I shall briefly discuss the notions of ‘digital natives’ or ‘the net generation’ from a critical perspective...

  11. Blending Formal and Informal Learning Networks for Online Learning

    Science.gov (United States)

    Czerkawski, Betül C.

    2016-01-01

    With the emergence of social software and the advance of web-based technologies, online learning networks provide invaluable opportunities for learning, whether formal or informal. Unlike top-down, instructor-centered, and carefully planned formal learning settings, informal learning networks offer more bottom-up, student-centered participatory…

  12. "Bionic Woman" (2007): Gender, Disability and Cyborgs

    Science.gov (United States)

    Quinlan, Margaret M.; Bates, Benjamin R.

    2009-01-01

    This paper explores a representation of overlapping categories of gender, disability and cyborgs in "Bionic Woman" (2007). The television show "Bionic Woman" (2007) is a popular culture representation that uniquely brings together these categories. Three themes emerged from an analysis of blogger discourse surrounding the show. The themes reveal…

  13. 3D Printed Bionic Nanodevices

    Science.gov (United States)

    Kong, Yong Lin; Gupta, Maneesh K.; Johnson, Blake N.; McAlpine, Michael C.

    2016-01-01

    Summary The ability to three-dimensionally interweave biological and functional materials could enable the creation of bionic devices possessing unique and compelling geometries, properties, and functionalities. Indeed, interfacing high performance active devices with biology could impact a variety of fields, including regenerative bioelectronic medicines, smart prosthetics, medical robotics, and human-machine interfaces. Biology, from the molecular scale of DNA and proteins, to the macroscopic scale of tissues and organs, is three-dimensional, often soft and stretchable, and temperature sensitive. This renders most biological platforms incompatible with the fabrication and materials processing methods that have been developed and optimized for functional electronics, which are typically planar, rigid and brittle. A number of strategies have been developed to overcome these dichotomies. One particularly novel approach is the use of extrusion-based multi-material 3D printing, which is an additive manufacturing technology that offers a freeform fabrication strategy. This approach addresses the dichotomies presented above by (1) using 3D printing and imaging for customized, hierarchical, and interwoven device architectures; (2) employing nanotechnology as an enabling route for introducing high performance materials, with the potential for exhibiting properties not found in the bulk; and (3) 3D printing a range of soft and nanoscale materials to enable the integration of a diverse palette of high quality functional nanomaterials with biology. Further, 3D printing is a multi-scale platform, allowing for the incorporation of functional nanoscale inks, the printing of microscale features, and ultimately the creation of macroscale devices. This blending of 3D printing, novel nanomaterial properties, and ‘living’ platforms may enable next-generation bionic systems. In this review, we highlight this synergistic integration of the unique properties of nanomaterials with

  14. Just the Facts: Personal Learning Networks

    Science.gov (United States)

    Nussbaum-Beach, Sheryl

    2013-01-01

    One has heard about personal learning networks (PLNs), but what are they and how are they different than professional learning communities (PLCs)? Find out how PLNs can help a teacher pursue his/her own professional interests and be a better teacher. This article answers questions related to PLNs such as: (1) What are personal learning networks?;…

  15. Collective Learning in Games through Social Networks

    NARCIS (Netherlands)

    Kosterman, S.; Gierasimczuk, N.; Armentano, M.G.; Monteserin, A.; Tang, J.; Yannibelli, V.

    2015-01-01

    This paper argues that combining social networks communication and games can positively influence the learning behavior of players. We propose a computational model that combines features of social network learning (communication) and game-based learning (strategy reinforcement). The focus is on

  16. The Integration of Personal Learning Environments & Open Network Learning Environments

    Science.gov (United States)

    Tu, Chih-Hsiung; Sujo-Montes, Laura; Yen, Cherng-Jyh; Chan, Junn-Yih; Blocher, Michael

    2012-01-01

    Learning management systems traditionally provide structures to guide online learners to achieve their learning goals. Web 2.0 technology empowers learners to create, share, and organize their personal learning environments in open network environments; and allows learners to engage in social networking and collaborating activities. Advanced…

  17. 3D printed bionic ears.

    Science.gov (United States)

    Mannoor, Manu S; Jiang, Ziwen; James, Teena; Kong, Yong Lin; Malatesta, Karen A; Soboyejo, Winston O; Verma, Naveen; Gracias, David H; McAlpine, Michael C

    2013-06-12

    The ability to three-dimensionally interweave biological tissue with functional electronics could enable the creation of bionic organs possessing enhanced functionalities over their human counterparts. Conventional electronic devices are inherently two-dimensional, preventing seamless multidimensional integration with synthetic biology, as the processes and materials are very different. Here, we present a novel strategy for overcoming these difficulties via additive manufacturing of biological cells with structural and nanoparticle derived electronic elements. As a proof of concept, we generated a bionic ear via 3D printing of a cell-seeded hydrogel matrix in the anatomic geometry of a human ear, along with an intertwined conducting polymer consisting of infused silver nanoparticles. This allowed for in vitro culturing of cartilage tissue around an inductive coil antenna in the ear, which subsequently enables readout of inductively-coupled signals from cochlea-shaped electrodes. The printed ear exhibits enhanced auditory sensing for radio frequency reception, and complementary left and right ears can listen to stereo audio music. Overall, our approach suggests a means to intricately merge biologic and nanoelectronic functionalities via 3D printing.

  18. Bionics by examples 250 scenarios from classical to modern times

    CERN Document Server

    Nachtigall, Werner

    2015-01-01

    Bionics means learning from the nature for the development of technology. The science of "bionics" itself is classified into several sections, from materials and structures over procedures and processes until evolution and optimization. Not all these areas, or only a few, are really known in the public and also in scientific literature. This includes the Lotus-effect, converted to the contamination-reduction of fassades and the shark-shed-effect, converted to the  resistance-reduction of airplanes. However, there are hundreds of highly interesting examples that contain the transformation of principles of the nature into technology. From the large number of these examples, 250 were selected for the present book according to "prehistory", "early-history", "classic" and "modern time". Most examples are new. Every example includes a printed page in a homogeneous arrangement. The examples from the field "modern time" are joint in blocks corresponding to the sub-disciplines of bionics.

  19. Identifying Gatekeepers in Online Learning Networks

    Science.gov (United States)

    Gursakal, Necmi; Bozkurt, Aras

    2017-01-01

    The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

  20. Social Networks: Rational Learning and Information Aggregation

    Science.gov (United States)

    2009-09-01

    information spreads in social networks and whether the information is efficiently aggregated in large societies. The models developed in this thesis allow us...network). We characterize equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be...beliefs there will be asymptotic learning in almost all reasonable social networks . Furthermore we provide bounds on the speed of learning for some

  1. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    as in the previous Bayesian approaches and in addition allows learning of node specific link properties similar to that in the modularity objective. We employ a new relaxation method for efficient inference in these generative models that allows us to learn the behavior of very large networks. We compare the link...... prediction performance of the learning based approaches and other widely used link prediction approaches in 14 networks ranging from medium size to large networks with more than a million nodes. While link prediction is typically well above chance for all networks, we find that the learning based mixed......Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...

  2. Network anomaly detection a machine learning perspective

    CERN Document Server

    Bhattacharyya, Dhruba Kumar

    2013-01-01

    With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents mach

  3. Personalized Learning Network Teaching Model

    Science.gov (United States)

    Feng, Zhou

    Adaptive learning system on the salient features, expounded personalized learning is adaptive learning system adaptive to learners key to learning. From the perspective of design theory, put forward an adaptive learning system to learn design thinking individual model, and using data mining techniques, the initial establishment of personalized adaptive systems model of learning.

  4. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    Abstract. We study the effect of learning dynamics on network topology. Firstly, a network of dis- crete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plastic- ity (STDP).

  5. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates ...

  6. deal: A Package for Learning Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Susanne G. Boettcher

    2003-12-01

    Full Text Available deal is a software package for use with R. It includes several methods for analysing data using Bayesian networks with variables of discrete and/or continuous types but restricted to conditionally Gaussian networks. Construction of priors for network parameters is supported and their parameters can be learned from data using conjugate updating. The network score is used as a metric to learn the structure of the network and forms the basis of a heuristic search strategy. deal has an interface to Hugin.

  7. Learning-parameter adjustment in neural networks

    Science.gov (United States)

    Heskes, Tom M.; Kappen, Bert

    1992-06-01

    We present a learning-parameter adjustment algorithm, valid for a large class of learning rules in neural-network literature. The algorithm follows directly from a consideration of the statistics of the weights in the network. The characteristic behavior of the algorithm is calculated, both in a fixed and a changing environment. A simple example, Widrow-Hoff learning for statistical classification, serves as an illustration.

  8. Social network dynamics in international students' learning

    OpenAIRE

    Cox, A.M.; Taha, N.

    2010-01-01

    The potential for the internationalisation of UK HE to bring diverse viewpoints and perspectives into the curriculum has not been fully realised. One of the many obstacles to this may be our lack of understanding of how international students use and build social networks for learning, information sharing and support, and how this impacts on engagement and learning. The literature suggests various ways in which network positions and learning might be associated. In this study we used a range ...

  9. A Collaborative Learning Network Approach to Improvement: The CUSP Learning Network.

    Science.gov (United States)

    Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J

    2015-04-01

    Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.

  10. Stochastic Variational Learning in Recurrent Spiking Networks

    Directory of Open Access Journals (Sweden)

    Danilo eJimenez Rezende

    2014-04-01

    Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  11. Network Learning and Innovation in SME Formal Networks

    Directory of Open Access Journals (Sweden)

    Jivka Deiters

    2013-02-01

    Full Text Available The driver for this paper is the need to better understand the potential for learning and innovation that networks canprovide especially for small and medium sized enterprises (SMEs which comprise by far the majority of enterprises in the food sector. With the challenges the food sector is facing in the near future, learning and innovation or more focused, as it is being discussed in the paper, ‘learning for innovation’ are not just opportunities but pre‐conditions for the sustainability of the sector. Network initiatives that could provide appropriate support involve social interaction and knowledge exchange, learning, competence development, and coordination (organization and management of implementation. The analysis identifies case studies in any of these orientations which serve different stages of the innovation process: invention and implementation. The variety of network case studies cover networks linked to a focus group for training, research, orconsulting, networks dealing with focused market oriented product or process development, promotional networks, and networks for open exchange and social networking.

  12. Quantitative learning strategies based on word networks

    Science.gov (United States)

    Zhao, Yue-Tian-Yi; Jia, Zi-Yang; Tang, Yong; Xiong, Jason Jie; Zhang, Yi-Cheng

    2018-02-01

    Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.

  13. Learning dynamic Bayesian networks with mixed variables

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learn....... An automated procedure for specifying prior distributions for the parameters in a dynamic Bayesian network is presented. It is a simple extension of the procedure for the ordinary Bayesian networks. Finally the W¨olfer?s sunspot numbers are analyzed....

  14. Deep learning in neural networks: an overview.

    Science.gov (United States)

    Schmidhuber, Jürgen

    2015-01-01

    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

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

    Science.gov (United States)

    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.

  16. Brain Networks of Explicit and Implicit Learning

    Science.gov (United States)

    Yang, Jing; Li, Ping

    2012-01-01

    Are explicit versus implicit learning mechanisms reflected in the brain as distinct neural structures, as previous research indicates, or are they distinguished by brain networks that involve overlapping systems with differential connectivity? In this functional MRI study we examined the neural correlates of explicit and implicit learning of artificial grammar sequences. Using effective connectivity analyses we found that brain networks of different connectivity underlie the two types of learning: while both processes involve activation in a set of cortical and subcortical structures, explicit learners engage a network that uses the insula as a key mediator whereas implicit learners evoke a direct frontal-striatal network. Individual differences in working memory also differentially impact the two types of sequence learning. PMID:22952624

  17. Learning Bayesian networks for discrete data

    KAUST Repository

    Liang, Faming

    2009-02-01

    Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

  18. Edmodo social learning network for elementary school mathematics learning

    Science.gov (United States)

    Ariani, Y.; Helsa, Y.; Ahmad, S.; Prahmana, RCI

    2017-12-01

    A developed instructional media can be as printed media, visual media, audio media, and multimedia. The development of instructional media can also take advantage of technological development by utilizing Edmodo social network. This research aims to develop a digital classroom learning model using Edmodo social learning network for elementary school mathematics learning which is practical, valid and effective in order to improve the quality of learning activities. The result of this research showed that the prototype of mathematics learning device for elementary school students using Edmodo was in good category. There were 72% of students passed the assessment as a result of Edmodo learning. Edmodo has become a promising way to engage students in a collaborative learning process.

  19. Simplified Learning Scheme For Analog Neural Network

    Science.gov (United States)

    Eberhardt, Silvio P.

    1991-01-01

    Synaptic connections adjusted one at a time in small increments. Simplified gradient-descent learning scheme for electronic neural-network processor less efficient than better-known back-propagation scheme, but offers two advantages: easily implemented in circuitry because data-access circuitry separated from learning circuitry; and independence of data-access circuitry makes possible to implement feedforward as well as feedback networks, including those of multiple-attractor type. Important in such applications as recognition of patterns.

  20. Designing spaces for the networked learning landscape.

    Science.gov (United States)

    Nordquist, Jonas; Laing, Andrew

    2015-04-01

    The concept of the learning landscape is used to explore the range of learning environments needed at multiple scales to better align with changes in the medical education curriculum. Four key scales that correspond to important types of learning spaces are identified: the classroom, the building, the campus and the city. "In-between" spaces are identified as growing in importance given changing patterns of learning and the use of information technology. Technology is altering how learning takes place in a wider variety of types of spaces as it is interwoven into every aspect of learning. An approach to planning learning environments which recognizes the need to think of networks of learning spaces connected across multiple scales is proposed. The focus is shifted from singular spaces to networks of inter-connected virtual and digital environments. A schematic model comprising the networked learning landscape, intended as a guide to planning that emphasizes relationships between the changing curriculum and its alignment with learning environments at multiple scales is proposed in this work. The need for higher levels of engagement of faculty, administrators and students in defining the briefs for the design of new kinds of medical education environments is highlighted.

  1. Learning drifting concepts with neural networks

    NARCIS (Netherlands)

    Biehl, Michael; Schwarze, Holm

    1993-01-01

    The learning of time-dependent concepts with a neural network is studied analytically and numerically. The linearly separable target rule is represented by an N-vector, whose time dependence is modelled by a random or deterministic drift process. A single-layer network is trained online using

  2. Electronic Social Networks, Teaching, and Learning

    Science.gov (United States)

    Pidduck, Anne Banks

    2010-01-01

    This paper explores the relationship between electronic social networks, teaching, and learning. Previous studies have shown a strong positive correlation between student engagement and learning. By extending this work to engage instructors and add an electronic component, our study shows possible teaching improvement as well. In particular,…

  3. NASA Engineering Network Lessons Learned

    Data.gov (United States)

    National Aeronautics and Space Administration — The NASA Lessons Learned system provides access to official, reviewed lessons learned from NASA programs and projects. These lessons have been made available to the...

  4. Logic Learning in Hopfield Networks

    OpenAIRE

    Sathasivam, Saratha; Abdullah, Wan Ahmad Tajuddin Wan

    2008-01-01

    Synaptic weights for neurons in logic programming can be calculated either by using Hebbian learning or by Wan Abdullah's method. In other words, Hebbian learning for governing events corresponding to some respective program clauses is equivalent with learning using Wan Abdullah's method for the same respective program clauses. In this paper we will evaluate experimentally the equivalence between these two types of learning through computer simulations.

  5. Review of intelligent bionic vision navigation

    Science.gov (United States)

    Wu, Peng; Mu, Rongjun; Deng, Yanpeng

    2017-11-01

    With the popularization of intelligent equipment such as UAV (Unmanned Aerial Vehicle) and UV (Unmanned Vehicle), their demands for autonomy, independence and intelligence of navigation gradually increase, and traditional navigation methods can't meet this demand. In order to make a thorough study, a review of intelligent bionic vision navigation methods is made on its background, research status and related fields. Through the analysis and summarization of the above information, the development trend of intelligent bionic vision navigation is pointed out, and its advantages and disadvantages are discussed.

  6. Functionality for learning networks: lessons learned from social web applications

    NARCIS (Netherlands)

    Berlanga, Adriana; Sloep, Peter; Brouns, Francis; Van Rosmalen, Peter; Bitter-Rijpkema, Marlies; Koper, Rob

    2007-01-01

    Berlanga, A. J., Sloep, P., Brouns, F., Van Rosmalen, P., Bitter-Rijpkema, M., & Koper, R. (2007). Functionality for learning networks: lessons learned from social web applications. Proceedings of the ePortfolio 2007 Conference. October, 18-19, 2007, Maastricht, The Netherlands. [See also

  7. Machine Learning Topological Invariants with Neural Networks

    Science.gov (United States)

    Zhang, Pengfei; Shen, Huitao; Zhai, Hui

    2018-02-01

    In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

  8. SUSTAIN: a network model of category learning.

    Science.gov (United States)

    Love, Bradley C; Medin, Douglas L; Gureckis, Todd M

    2004-04-01

    SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

  9. Distance learning, problem based learning and dynamic knowledge networks.

    Science.gov (United States)

    Giani, U; Martone, P

    1998-06-01

    This paper is an attempt to develop a distance learning model grounded upon a strict integration of problem based learning (PBL), dynamic knowledge networks (DKN) and web tools, such as hypermedia documents, synchronous and asynchronous communication facilities, etc. The main objective is to develop a theory of distance learning based upon the idea that learning is a highly dynamic cognitive process aimed at connecting different concepts in a network of mutually supporting concepts. Moreover, this process is supposed to be the result of a social interaction that has to be facilitated by the web. The model was tested by creating a virtual classroom of medical and nursing students and activating a learning session on the concept of knowledge representation in health sciences.

  10. Personalizing Access to Learning Networks

    DEFF Research Database (Denmark)

    Dolog, Peter; Simon, Bernd; Nejdl, Wolfgang

    2008-01-01

    In this article, we describe a Smart Space for Learning™ (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address in this fra......In this article, we describe a Smart Space for Learning™ (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address...... in this framework, enabling personalized access to federated learning repositories with a vast number of learning offers. Our infrastructure includes personalization strategies both at the query and the query results level. Query rewriting is based on learning and language preferences; rule-based and ranking...

  11. Network learning: episodes of interorganizational learning towards a collective performance goal

    OpenAIRE

    Gibb, Jenny; Suñé Torrents, Albert; Albers, Sascha

    2017-01-01

    Little is known about learning processes in horizontal networks. This study focuses on networks as learning entities, i.e. learning by multiple organizations as a group, and the mechanisms involved in developing and addressing a network-level performance goal. By using a narrative approach, we gather in-depth primary data from network members to examine: how do firms engage in network learning? and, how is network learning coordinated towards a performance goal in a horizontal inter-firm netw...

  12. Unfolding Perspectives on Networked Professional Learning: Exploring Ties and Time

    Science.gov (United States)

    de Laat, Maarten; Strijbos, Jan-Willem

    2014-01-01

    Networked learning and learning networks are commonplace concepts in most contemporary discourse on learning in the 21st century. This special issue provides a collection of studies that address the need for a growing body of empirical work to extent the limited understanding of the use and benefits of networks in relation to learning and…

  13. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  14. A Decomposition Algorithm for Learning Bayesian Network Structures from Data

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Cordero Hernandez, Jorge

    2008-01-01

    It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

  15. Neural network models of learning and adaptation

    Science.gov (United States)

    Denker, John S.

    1986-10-01

    Recent work has applied ideas from many fields including biology, physics and computer science, in order to understand how a highly interconnected network of simple processing elements can perform useful computation. Such networks can be used as associative memories, or as analog computers to solve optimization problems. This article reviews the workings of a standard model with particular emphasis on various schemes for learning and adaptation.

  16. Learning Maneuvers Using Neural Network Models

    Science.gov (United States)

    1994-08-07

    parametric function approximators such as neural networks ( Tesauro 1991). The prediction process runs in a series of epochs. Each epoch ends when a...function approximator such as a neural network. This technique has recently been used successfully on a large complex problem, Backgammon, by Tesauro (1991...Morgan Kaufman. Tesauro , G. J. (1991). Practical Issues in Temporal Difference Learning. Report RC 17223 (76307), IBM T. J. Watson Research Center

  17. Evolution of associative learning in chemical networks.

    Directory of Open Access Journals (Sweden)

    Simon McGregor

    Full Text Available Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning - the ability to detect correlated features of the environment - has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the 'memory traces' of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells.

  18. Deep Learning in Neural Networks: An Overview

    OpenAIRE

    Schmidhuber, Juergen

    2014-01-01

    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpr...

  19. Conditions for Productive Learning in Network Learning Environments

    DEFF Research Database (Denmark)

    Ponti, M.; Dirckinck-Holmfeld, Lone; Lindström, B.

    2004-01-01

    are designed without a deep understanding of the pedagogical, communicative and collaborative conditions embedded in networked learning. Despite the existence of good theoretical views pointing to a social understanding of learning, rather than a traditional individualistic and information processing approach......The Kaleidoscope1 Jointly Executed Integrating Research Project (JEIRP) on Conditions for Productive Networked Learning Environments is developing and elaborating conceptual understandings of Computer Supported Collaborative Learning (CSCL) emphasizing the use of cross-cultural comparative...... approaches of case studies in different concrete higher educational settings and existing practices. The analyses are based in a socio-cultural approach in a broad sense (Engestrøm (1987), Wenger (1998), Dirckinck-Holmfeld and Fibiger (2002)) and are concerned with the following aspects and objects of study...

  20. Distributed Extreme Learning Machine for Nonlinear Learning over Network

    Directory of Open Access Journals (Sweden)

    Songyan Huang

    2015-02-01

    Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

  1. Reinforcement learning account of network reciprocity.

    Directory of Open Access Journals (Sweden)

    Takahiro Ezaki

    Full Text Available Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node's degree. Thus, we significantly extend previously obtained numerical results.

  2. Portability and networked learning environments

    NARCIS (Netherlands)

    Collis, Betty; de Diana, I.P.F.

    1994-01-01

    Abstract The portability of educational software is defined as the likelihood of software usage, with or without adaptation, in an educational environment different from that for which it was originally designed and produced. Barriers and research relevant to the portability of electronic learning

  3. Lifelong learning in a network

    NARCIS (Netherlands)

    Jochems, Wim; Koper, Rob

    2005-01-01

    Paper accepted for ODLAA conference (Open and Distance Learning Association of Australia), titled 'breaking down barriers', Adelaide, 9-11 November, Australia. Will be published as a chapter in the book: 'Breaking Down Boundaries: The International Experience in Open, Distance and Flexible

  4. Lifelong learning in a network

    OpenAIRE

    Jochems, Wim; Koper, Rob

    2005-01-01

    Paper accepted for ODLAA conference (Open and Distance Learning Association of Australia), titled 'breaking down barriers', Adelaide, 9-11 November, Australia. Will be published as a chapter in the book: 'Breaking Down Boundaries: The International Experience in Open, Distance and Flexible Education'

  5. Bionic Humans Using EAP as Artificial Muscles Reality and Challenges

    OpenAIRE

    Bar-Cohen, Yoseph

    2004-01-01

    For many years, the idea of a human with bionic muscles immediately conjures up science fiction images of a TV series superhuman character that was implanted with bionic muscles and portrayed with strength and speed far superior to any normal human. As fantastic as this idea may seem, recent developments in electroactive polymers (EAP) may one day make such bionics possible. Polymers that exhibit large displacement in response to stimulation that is other than electrical signa...

  6. Unraveling networked learning initiatives: an analytic framework

    NARCIS (Netherlands)

    Rusman, Ellen; Prinsen, Fleur; Vermeulen, Marjan

    2016-01-01

    Networked learning happens naturally within the social systems of which we are all part. However, in certain circumstances individuals may want to actively take initiative to initiate interaction with others they are not yet regularly in exchange with. This may be the case when external influences

  7. Learning in Networks for Sustainable Development

    NARCIS (Netherlands)

    Lansu, Angelique; Boon, Jo; Sloep, Peter; Van Dam-Mieras, Rietje

    2010-01-01

    The didactic model of remote internships described in this study provides the flexibility needed to support networked learners, i.e. to facilitate the development and subsequent assessment of their competences. The heterogeneity of the participants (students, employers, tutors) in the learning

  8. Social Networking Services in E-Learning

    Science.gov (United States)

    Weber, Peter; Rothe, Hannes

    2016-01-01

    This paper is a report on the findings of a study conducted on the use of the social networking service NING in a cross-location e-learning setting named "Net Economy." We describe how we implemented NING as a fundamental part of the setting through a special phase concept and team building approach. With the help of user statistics, we…

  9. On local optima in learning bayesian networks

    DEFF Research Database (Denmark)

    Dalgaard, Jens; Kocka, Tomas; Pena, Jose

    2003-01-01

    This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness...

  10. Social Networking Sites as a Learning Tool

    Science.gov (United States)

    Sanchez-Casado, Noelia; Cegarra Navarro, Juan Gabriel; Wensley, Anthony; Tomaseti-Solano, Eva

    2016-01-01

    Purpose: Over the past few years, social networking sites (SNSs) have become very useful for firms, allowing companies to manage the customer-brand relationships. In this context, SNSs can be considered as a learning tool because of the brand knowledge that customers develop from these relationships. Because of the fact that knowledge in…

  11. Fermentation based carbon nanotube bionic functional composites

    OpenAIRE

    Valentini, Luca; Bon, Silvia Bittolo; Signetti, Stefano; Tripathi, Manoj; Iacob, Erica; Pugno, Nicola M.

    2016-01-01

    The exploitation of the processes used by microorganisms to digest nutrients for their growth can be a viable method for the formation of a wide range of so called biogenic materials that have unique mechanical and physical properties that are not produced by abiotic processes. Based on grape must and bread fermentation, a bionic composite made of carbon nanotubes (CNTs) and single-cell fungi, the Saccharomyces cerevisiae yeast extract, was prepared by fermentation of such microorganisms at r...

  12. Research Progress on Geometric Texturing and Function Based on Bionic Theory

    OpenAIRE

    TAN Na; XING Zhi-guo; WANG Hai-dou; WANG Xiao-li; JIN Guo; XU Bin-shi

    2018-01-01

    Biological researches have attracted wide attentions in recent years, mimicking the morphology of creatures, learning the function of morphology and applying it to the engineering area have become the research focus. Investigating the special function of bionic surface texturing based on geometric morphology can provide us ideas to optimize surface properties of materials,extend the materials service life, widen the scope of application of materials, and improve the application values of mate...

  13. Collaborative Supervised Learning for Sensor Networks

    Science.gov (United States)

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

    2011-01-01

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

  14. Multifocal microlens for bionic compound eye

    Science.gov (United States)

    Cao, Axiu; Wang, Jiazhou; Pang, Hui; Zhang, Man; Shi, Lifang; Deng, Qiling; Hu, Song

    2017-10-01

    Bionic compound eye optical element composed of multi-dimensional sub-eye microlenses plays an important role in miniaturizing the volume and weight of an imaging system. In this manuscript, we present a novel structure of the bionic compound eye with multiple focal lengths. By the division of the microlens into two concentric radial zones including the inner zone and the outer zone with independent radius, the sub-eye which is a multi-level micro-scale structure can be formed with multiple focal lengths. The imaging capability of the structure has been simulated. The results show that the optical information in different depths can be acquired by the structure. Meanwhile, the parameters including aperture and radius of the two zones, which have an influence on the imaging quality have been analyzed and discussed. With the increasing of the ratio of inner and outer aperture, the imaging quality of the inner zone is becoming better, and instead the outer zone will become worse. In addition, through controlling the radius of the inner and outer zone independently, the design of sub-eye with different focal lengths can be realized. With the difference between the radius of the inner and outer zone becoming larger, the imaging resolution of the sub-eye will decrease. Therefore, the optimization of the multifocal structure should be carried out according to the actual imaging quality demands. Meanwhile, this study can provide references for the further applications of multifocal microlens in bionic compound eye.

  15. Factors that influence cooperation in networks for innovation and learning

    NARCIS (Netherlands)

    Sie, Rory; Bitter-Rijpkema, Marlies; Stoyanov, Slavi; Sloep, Peter

    2018-01-01

    Networked cooperation fails if the available partnerships remain opaque. A literature review and Delphi study uncovered the elements of a fruitful partnership. They relate to personality, diversity, cooperation, and management. Innovation networks and learning networks share the same cooperative

  16. Design of a Networked Learning Master Environment for Professionals

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone

    2010-01-01

    The paper is presenting the overall learning design of MIL (Master in ICT and Learning). The learning design is integrating a number of principles: 1. Principles of problem and project based learning 2. Networked learning / learning in communities of practice. The paper will discuss how these pri...

  17. Reconstructing Causal Biological Networks through Active Learning.

    Directory of Open Access Journals (Sweden)

    Hyunghoon Cho

    Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

  18. PARTNERS IN LEARNING NETWORK FOR UKRAINIAN TEACHERS

    Directory of Open Access Journals (Sweden)

    K. Sereda

    2011-05-01

    Full Text Available The network «Partners in Learning Network» is presented in the article – the Ukrainian segment of global educational community. PILN is created with support of the Microsoft company for teachers who use information communication technology in their professional work. The PILN's purpose and value for Ukrainian teachers, for their professional dialogue and collaboration are described in the article. Functions of PILN's communities for teacher’s cooperation, the joint decision of questions and an exchange of ideas and of technique, teaching tools for increase of level of ICT introduction in educational process are described.

  19. Optimal control learning with artificial neural networks

    International Nuclear Information System (INIS)

    Martinez, J.M.; Parey, C.; Houkari, M.

    1993-01-01

    This paper shows neural networks capabilities in optimal control applications of non linear dynamic systems. Our method is issued of a classical method concerning the direct research of the optimal control using gradient techniques. We show that neural approach and backpropagation paradigm are able to solve efficiently equations relative to necessary conditions for an optimizing solution. We have taken into account the known capabilities of multi layered networks in approximation functions. And for dynamic systems, we have generalized the indirect learning of inverse model adaptive architecture that is capable to define an optimal control in relation to a temporal criterion. (orig.)

  20. Machine Learning for ATLAS DDM Network Metrics

    CERN Document Server

    Lassnig, Mario; The ATLAS collaboration; Vamosi, Ralf

    2016-01-01

    The increasing volume of physics data is posing a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from our ongoing automation efforts. First, we describe our framework for distributed data management and network metrics, automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

  1. Learning and coordinating in a multilayer network.

    Science.gov (United States)

    Lugo, Haydée; San Miguel, Maxi

    2015-01-14

    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.

  2. Learning of N-layers neural network

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný

    2005-01-01

    Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

  3. [Analogies and analogy research in technical biology and bionics].

    Science.gov (United States)

    Nachtigall, Werner

    2010-01-01

    The procedural approaches of Technical Biology and Bionics are characterized, and analogy research is identified as their common basis. The actual creative aspect in bionical research lies in recognizing and exploiting technically oriented analogies underlying a specific biological prototype to indicate a specific technical application.

  4. Characteristic imsets for learning Bayesian network structure

    Czech Academy of Sciences Publication Activity Database

    Hemmecke, R.; Lindner, S.; Studený, Milan

    2012-01-01

    Roč. 53, č. 9 (2012), s. 1336-1349 ISSN 0888-613X R&D Projects: GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * essential graph * standard imset * characteristic imset * LP relaxation of a polytope Subject RIV: BA - General Mathematics Impact factor: 1.729, year: 2012 http://library.utia.cas.cz/separaty/2012/MTR/studeny-0382596.pdf

  5. Learning Methods for Radial Basis Functions Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Kudová, Petra

    2005-01-01

    Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005

  6. Learning in Neural Networks: VLSI Implementation Strategies

    Science.gov (United States)

    Duong, Tuan Anh

    1995-01-01

    Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.

  7. Boltzmann learning of parameters in cellular neural networks

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    1992-01-01

    The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified ...... by unsupervised adaptation of an image segmentation cellular network. The learning rule is applied to adaptive segmentation of satellite imagery......The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified...

  8. A neural network with modular hierarchical learning

    Science.gov (United States)

    Baldi, Pierre F. (Inventor); Toomarian, Nikzad (Inventor)

    1994-01-01

    This invention provides a new hierarchical approach for supervised neural learning of time dependent trajectories. The modular hierarchical methodology leads to architectures which are more structured than fully interconnected networks. The networks utilize a general feedforward flow of information and sparse recurrent connections to achieve dynamic effects. The advantages include the sparsity of units and connections, the modular organization. A further advantage is that the learning is much more circumscribed learning than in fully interconnected systems. The present invention is embodied by a neural network including a plurality of neural modules each having a pre-established performance capability wherein each neural module has an output outputting present results of the performance capability and an input for changing the present results of the performance capabilitiy. For pattern recognition applications, the performance capability may be an oscillation capability producing a repeating wave pattern as the present results. In the preferred embodiment, each of the plurality of neural modules includes a pre-established capability portion and a performance adjustment portion connected to control the pre-established capability portion.

  9. THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

    OpenAIRE

    Mohd Ishak Bin Ismail; Ruzaini Bin Abdullah Arshah

    2016-01-01

    Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by stude...

  10. A neural network model which combines unsupervised and supervised learning.

    Science.gov (United States)

    Hsieh, K R; Chen, W T

    1993-01-01

    A neural network that combines unsupervised and supervised learning for pattern recognition is proposed. The network is a hierarchical self-organization map, which is trained by unsupervised learning at first. When the network fails to recognize similar patterns, supervised learning is applied to teach the network to give different scaling factors for different features so as to discriminate similar patterns. Simulation results show that the model obtains good generalization capability as well as sharp discrimination between similar patterns.

  11. Fastest learning in small-world neural networks

    International Nuclear Information System (INIS)

    Simard, D.; Nadeau, L.; Kroeger, H.

    2005-01-01

    We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition

  12. The Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

    . The Design, Experience and Practice of Networked Learning will prove indispensable reading for researchers, teachers, consultants, and instructional designers in higher and continuing education; for those involved in staff and educational development, and for those studying post graduate qualifications...... in learning and teaching. This, the second volume in the Springer Book Series on Researching Networked Learning, is based on a selection of papers presented at the 2012 Networked Learning Conference held in Maastricht, The Netherlands....

  13. Machine learning for identifying botnet network traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2013-01-01

    . Due to promise of non-invasive and resilient detection, botnet detection based on network traffic analysis has drawn a special attention of the research community. Furthermore, many authors have turned their attention to the use of machine learning algorithms as the mean of inferring botnet......-related knowledge from the monitored traffic. This paper presents a review of contemporary botnet detection methods that use machine learning as a tool of identifying botnet-related traffic. The main goal of the paper is to provide a comprehensive overview on the field by summarizing current scientific efforts....... The contribution of the paper is three-fold. First, the paper provides a detailed insight on the existing detection methods by investigating which bot-related heuristic were assumed by the detection systems and how different machine learning techniques were adapted in order to capture botnet-related knowledge...

  14. Results of total hip arthroplasty using a bionic hip stem.

    Science.gov (United States)

    Fokter, Samo K; Sarler, Taras; Strahovnik, Andrej; Repše-Fokter, Alenka

    2015-06-01

    The trabecular-orientated bionic hip stem was designed to mimic the natural force transmission through the femur in total hip arthroplasty, resulting in supposedly longer prosthesis survivability. The aim of this study was to compare the second-generation bionic hip stem to a standard uncemented hip stem. A group of 18 patients (21 hips) who underwent total hip arthroplasty with a bionic stem (bionic group) was compared with a historic group of 12 patients (12 hips) treated with standard anatomic hip stem (control group). During the first year after the procedure, the densitometric measurements of the bone around the prosthesis were taken. Radiographic and clinical assessments were additionally performed preoperatively and at the three month, six month, one year and three year follow-ups in the bionic group. In the bionic group, one patient was revised for aseptic loosening and 16 patients (19 hips) were available to the final follow-up. A significant decrease of bone mineral density was found in Gruen zones 3, 4 and 5 in the bionic group, and in zone 7 in both groups. The bionic group had a significantly higher bone mineral density in Gruen zone 1 at the one year follow-up. At the final follow-up, all prostheses were radiologically stable in both groups. Provided that a good implant position is achieved, comparable short-term results can be obtained using a bionic stem. Still, a decrease of bone mineral density in Gruen zone 7 occurred in both groups. Further studies are required to determine survivability of the bionic stem.

  15. Tissue bionics: examples in biomimetic tissue engineering

    International Nuclear Information System (INIS)

    Green, David W

    2008-01-01

    Many important lessons can be learnt from the study of biological form and the functional design of organisms as design criteria for the development of tissue engineering products. This merging of biomimetics and regenerative medicine is termed 'tissue bionics'. Clinically useful analogues can be generated by appropriating, modifying and mimicking structures from a diversity of natural biomatrices ranging from marine plankton shells to sea urchin spines. Methods in biomimetic materials chemistry can also be used to fabricate tissue engineering scaffolds with added functional utility that promise human tissues fit for the clinic

  16. Study of controlled motion bionic mini robot

    Science.gov (United States)

    Politov, E. N.; Rukavitsyn, A. N.

    2017-10-01

    The article describes a dynamic model of a bionic mini-robot capable of moving on a rough surface or separately from it. Differential equations describing the robot’s motion in the phases of flight and movement on the support surface were obtained. The debalance’s angular velocity was used as the controlled parameter. The results of numerical modelling of the equations of motion supported theoretical conclusions on the character of the dependence of height and length of a jump on the frequency of rotation. It was simultaneously established that the shape of the trajectory of the center of mass depends on the controlled parameter.

  17. Tissue bionics: examples in biomimetic tissue engineering.

    Science.gov (United States)

    Green, David W

    2008-09-01

    Many important lessons can be learnt from the study of biological form and the functional design of organisms as design criteria for the development of tissue engineering products. This merging of biomimetics and regenerative medicine is termed 'tissue bionics'. Clinically useful analogues can be generated by appropriating, modifying and mimicking structures from a diversity of natural biomatrices ranging from marine plankton shells to sea urchin spines. Methods in biomimetic materials chemistry can also be used to fabricate tissue engineering scaffolds with added functional utility that promise human tissues fit for the clinic.

  18. Tissue bionics: examples in biomimetic tissue engineering

    Energy Technology Data Exchange (ETDEWEB)

    Green, David W [Bone and Joint Research Group, Developmental Origins of Health and Disease, General Hospital, University of Southampton, SO16 6YD (United Kingdom)], E-mail: Hindoostuart@googlemail.com

    2008-09-01

    Many important lessons can be learnt from the study of biological form and the functional design of organisms as design criteria for the development of tissue engineering products. This merging of biomimetics and regenerative medicine is termed 'tissue bionics'. Clinically useful analogues can be generated by appropriating, modifying and mimicking structures from a diversity of natural biomatrices ranging from marine plankton shells to sea urchin spines. Methods in biomimetic materials chemistry can also be used to fabricate tissue engineering scaffolds with added functional utility that promise human tissues fit for the clinic.

  19. The quest for the bionic arm.

    Science.gov (United States)

    Hutchinson, Douglas T

    2014-06-01

    The current state of research of upper extremity prosthetic devices is focused on creating a complete prosthesis with full motor and sensory function that will provide amputees with a near-normal human arm. Although advances are being made rapidly, many hurdles remain to be overcome before a functional, so-called bionic arm is a reality. Acquiring signals via nerve or muscle inputs will require either a reliable wireless device or direct wiring through an osseous-integrated implant. The best way to tap into the "knowledge" present in the peripheral nerve is yet to be determined. Copyright 2014 by the American Academy of Orthopaedic Surgeons.

  20. How and What Do Academics Learn through Their Personal Networks

    Science.gov (United States)

    Pataraia, Nino; Margaryan, Anoush; Falconer, Isobel; Littlejohn, Allison

    2015-01-01

    This paper investigates the role of personal networks in academics' learning in relation to teaching. Drawing on in-depth interviews with 11 academics, this study examines, first, how and what academics learn through their personal networks; second, the perceived value of networks in relation to academics' professional development; and, third,…

  1. Threshold Learning Dynamics in Social Networks

    Science.gov (United States)

    González-Avella, Juan Carlos; Eguíluz, Victor M.; Marsili, Matteo; Vega-Redondo, Fernado; San Miguel, Maxi

    2011-01-01

    Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take with respect to an important issue, typically confront external signals to the information gathered from their contacts. Economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist separated by sharp discontinuous transitions. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally observed in different social networks (both complex or regular), but limited interaction is found to promote correct learning by enlarging the parameter region where it occurs. PMID:21637714

  2. Leading to learn in networks of practice: Two leadership strategies

    NARCIS (Netherlands)

    Soekijad, M.; van den Hooff, B.J.; Agterberg, L.C.M.; Huysman, M.H.

    2011-01-01

    This paper outlines two leadership strategies to support organizational learning through networks of practice (NOPs). An in-depth case study in a development organization reveals that network leaders cope with a learning tension between management involvement and emergent learning processes by

  3. Improved Adjoint-Operator Learning For A Neural Network

    Science.gov (United States)

    Toomarian, Nikzad; Barhen, Jacob

    1995-01-01

    Improved method of adjoint-operator learning reduces amount of computation and associated computational memory needed to make electronic neural network learn temporally varying pattern (e.g., to recognize moving object in image) in real time. Method extension of method described in "Adjoint-Operator Learning for a Neural Network" (NPO-18352).

  4. Learning as Issue Framing in Agricultural Innovation Networks

    Science.gov (United States)

    Tisenkopfs, Talis; Kunda, Ilona; Šumane, Sandra

    2014-01-01

    Purpose: Networks are increasingly viewed as entities of learning and innovation in agriculture. In this article we explore learning as issue framing in two agricultural innovation networks. Design/methodology/approach: We combine frame analysis and social learning theories to analyse the processes and factors contributing to frame convergence and…

  5. Graphite Oxide to Graphene. Biomaterials to Bionics.

    Science.gov (United States)

    Thompson, Brianna C; Murray, Eoin; Wallace, Gordon G

    2015-12-09

    The advent of implantable biomaterials has revolutionized medical treatment, allowing the development of the fields of tissue engineering and medical bionic devices (e.g., cochlea implants to restore hearing, vagus nerve stimulators to control Parkinson's disease, and cardiac pace makers). Similarly, future materials developments are likely to continue to drive development in treatment of disease and disability, or even enhancing human potential. The material requirements for implantable devices are stringent. In all cases they must be nontoxic and provide appropriate mechanical integrity for the application at hand. In the case of scaffolds for tissue regeneration, biodegradability in an appropriate time frame may be required, and for medical bionics electronic conductivity is essential. The emergence of graphene and graphene-family composites has resulted in materials and structures highly relevant to the expansion of the biomaterials inventory available for implantable medical devices. The rich chemistries available are able to ensure properties uncovered in the nanodomain are conveyed into the world of macroscopic devices. Here, the inherent properties of graphene, along with how graphene or structures containing it interface with living cells and the effect of electrical stimulation on nerves and cells, are reviewed. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. ERT Conditions for Productive Learning in Networked Learning Environments: Leadership Report

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone

    This report provides a concluding account of the activities within the European Research Team: Conditions for Productive Learning in Networked Learning Environmentments......This report provides a concluding account of the activities within the European Research Team: Conditions for Productive Learning in Networked Learning Environmentments...

  7. A Team Formation and Project-based Learning Support Service for Social Learning Networks

    NARCIS (Netherlands)

    Spoelstra, Howard; Van Rosmalen, Peter; Van de Vrie, Evert; Obreza, Matija; Sloep, Peter

    2014-01-01

    The Internet affords new approaches to learning. Geographically dispersed self-directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks

  8. Structure Learning in Power Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-01-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time – which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  9. Researching Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

    Hodgson, Vivien; de Laat, Maarten; McConnell, David

    2014-01-01

    and final section draws attention to a growing topic of interest within networked learning: that of networked learning in informal practices. In addition, we provide a reflection on the theories, methods and settings featured in the networked learning research of the chapters. We conclude the introduction...... by discussing four main themes that have emerged from our reading of the chapters and which we believe are important in taking forward the theory of networked learning. They are as follows: practice as epistemology; the coupling of learning contexts (the relationship and connection of learning contexts......In the introductory chapter, we explore how networked learning has developed in recent years by summarising and discussing the research presented in the chapters of the book. The chapters are structured in three sections, each highlighting a particular aspect of practice. The first section focuses...

  10. Implementation of an infrastructure for networked learning

    DEFF Research Database (Denmark)

    Nyvang, Tom; Bygholm, Ann

    2011-01-01

    What are the conditions under which institutional actors decide upon Information and Communication Technology strategies for networked learning purposes? The question is discussed within the frame of a case study of the decision process during a shift from one learning platform to another...... in an education at Aalborg University. The aim is to explicate and understand the multiplicity of issues involved and to point the possible ways of handling such decision processes. On the one hand, the analysis shows that the predominant reasons for deciding to change are dissatisfaction with the existing system...... which is slow due to (too) many levels, lack of esthetic design, lack of coherence in practices and, generally, lack of relevant content. On the other hand, the predominant arguments for choosing the new system are more related to issues of operation, support, and management. We argue that the issues...

  11. Learning by Knowledge Networking across Cultures

    DEFF Research Database (Denmark)

    Wangel, Arne; Stærdahl, Jens; Bransholm Pedersen, Kirsten

    2005-01-01

    some of the obstacles into resources for knowledge sharing. However, students have stressed their positive experience of cross-cultural communication. While a joint course of three week duration by itself may involve only limited cross-cultural learning, serving primarily as an introduction to a long......-term field study, the course efficiently initiates the involvement of the students into, and their interaction with, the socio-political and cultural context of the host country. Thus, learning across cultures requires a longer term process whereby mixed teams leave the classroom, collect data together...... in the field, negotiate and agree on the analysis, and sustain the exchange of knowledge, possibly through virtual peer-to-peer networking....

  12. WEB BASED LEARNING OF COMPUTER NETWORK COURSE

    Directory of Open Access Journals (Sweden)

    Hakan KAPTAN

    2004-04-01

    Full Text Available As a result of developing on Internet and computer fields, web based education becomes one of the area that many improving and research studies are done. In this study, web based education materials have been explained for multimedia animation and simulation aided Computer Networks course in Technical Education Faculties. Course content is formed by use of university course books, web based education materials and technology web pages of companies. Course content is formed by texts, pictures and figures to increase motivation of students and facilities of learning some topics are supported by animations. Furthermore to help working principles of routing algorithms and congestion control algorithms simulators are constructed in order to interactive learning

  13. Learning Reproducibility with a Yearly Networking Contest

    KAUST Repository

    Canini, Marco

    2017-08-10

    Better reproducibility of networking research results is currently a major goal that the academic community is striving towards. This position paper makes the case that improving the extent and pervasiveness of reproducible research can be greatly fostered by organizing a yearly international contest. We argue that holding a contest undertaken by a plurality of students will have benefits that are two-fold. First, it will promote hands-on learning of skills that are helpful in producing artifacts at the replicable-research level. Second, it will advance the best practices regarding environments, testbeds, and tools that will aid the tasks of reproducibility evaluation committees by and large.

  14. Statistical and machine learning approaches for network analysis

    CERN Document Server

    Dehmer, Matthias

    2012-01-01

    Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

  15. Stochastic Online Learning in Dynamic Networks under Unknown Models

    Science.gov (United States)

    2016-08-02

    Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for...12211 Research Triangle Park, NC 27709-2211 Online learning , multi-armed bandit, dynamic networks REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S... Online Learning in Dynamic Networks under Unknown Models Report Title This research aims to develop fundamental theories and practical algorithms for

  16. A Team Formation and Project-based Learning Support Service for Social Learning Networks

    OpenAIRE

    Spoelstra, Howard; Van Rosmalen, Peter; Van de Vrie, Evert; Obreza, Matija; Sloep, Peter

    2014-01-01

    The Internet affords new approaches to learning. Geographically dispersed self-directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks do not readily support effective, coherence-creating and motivating learning settings. It is argued that providing project-based learning opportunities and team formation services can help overcome...

  17. Graduate Employability: The Perspective of Social Network Learning

    Science.gov (United States)

    Chen, Yong

    2017-01-01

    This study provides a conceptual framework for understanding how the graduate acquire employability through the social network in the Chinese context, using insights from the social network theory. This paper builds a conceptual model of the relationship among social network, social network learning and the graduate employability, and uses…

  18. Navigation Support for Learners in Informal Learning Networks

    NARCIS (Netherlands)

    Drachsler, Hendrik

    2009-01-01

    Drachsler, H. (2009). Navigation Support for Learners in Informal Learning Networks. Unpublished doctoral thesis. Oktober, 16, 2009, Heerlen, The Netherlands: Open University of the Netherlands/CELSTEC.

  19. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

    OpenAIRE

    Radford, Alec; Metz, Luke; Chintala, Soumith

    2015-01-01

    In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they ar...

  20. Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

    CERN Document Server

    Hu, Fei

    2012-01-01

    Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

  1. New designing of E-Learning systems with using network learning

    OpenAIRE

    Malayeri, Amin Daneshmand; Abdollahi, Jalal

    2010-01-01

    One of the most applied learning in virtual spaces is using E-Learning systems. Some E-Learning methodologies has been introduced, but the main subject is the most positive feedback from E-Learning systems. In this paper, we introduce a new methodology of E-Learning systems entitle "Network Learning" with review of another aspects of E-Learning systems. Also, we present benefits and advantages of using these systems in educating and fast learning programs. Network Learning can be programmable...

  2. Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

    NARCIS (Netherlands)

    Poell, R.F.; Moorsel, M.A.A.H. van

    1996-01-01

    This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of

  3. Recent Progress in Bionic Condensate Microdrop Self-Propelling Surfaces.

    Science.gov (United States)

    Gong, Xiaojing; Gao, Xuefeng; Jiang, Lei

    2017-12-01

    Bionic condensate microdrop self-propelling (CMDSP) surfaces are attracting increased attention as novel, low-adhesivity superhydrophobic surfaces due to their value in fundamental research and technological innovation, e.g., for enhancing heat transfer, energy-effective antifreezing, and electrostatic energy harvesting. Here, the focus is on recent progress in bionic CMDSP surfaces. Metal-based CMDSP surfaces, which are the most promising in their respective fields, are highlighted for use in future applications. The selected topics are divided into four sections: biological prototypes, mechanism and construction rules, fabrication, and applications of metal-based CMDSP surfaces. Finally, the challenges and future development trends in bionic CMDSP surfaces are envisioned, especially the utilization of potential bionic inspiration in the design of more advanced CMDSP surfaces. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Bionic Design, Materials and Performance of Bone Tissue Scaffolds

    Directory of Open Access Journals (Sweden)

    Tong Wu

    2017-10-01

    Full Text Available Design, materials, and performance are important factors in the research of bone tissue scaffolds. This work briefly describes the bone scaffolds and their anatomic structure, as well as their biological and mechanical characteristics. Furthermore, we reviewed the characteristics of metal materials, inorganic materials, organic polymer materials, and composite materials. The importance of the bionic design in preoperative diagnosis models and customized bone scaffolds was also discussed, addressing both the bionic structure design (macro and micro structure and the bionic performance design (mechanical performance and biological performance. Materials and performance are the two main problems in the development of customized bone scaffolds. Bionic design is an effective way to solve these problems, which could improve the clinical application of bone scaffolds, by creating a balance between mechanical performance and biological performance.

  5. Bionic Design, Materials and Performance of Bone Tissue Scaffolds.

    Science.gov (United States)

    Wu, Tong; Yu, Suihuai; Chen, Dengkai; Wang, Yanen

    2017-10-17

    Design, materials, and performance are important factors in the research of bone tissue scaffolds. This work briefly describes the bone scaffolds and their anatomic structure, as well as their biological and mechanical characteristics. Furthermore, we reviewed the characteristics of metal materials, inorganic materials, organic polymer materials, and composite materials. The importance of the bionic design in preoperative diagnosis models and customized bone scaffolds was also discussed, addressing both the bionic structure design (macro and micro structure) and the bionic performance design (mechanical performance and biological performance). Materials and performance are the two main problems in the development of customized bone scaffolds. Bionic design is an effective way to solve these problems, which could improve the clinical application of bone scaffolds, by creating a balance between mechanical performance and biological performance.

  6. Evaluation of Advanced Bionics high resolution mode.

    Science.gov (United States)

    Buechner, Andreas; Frohne-Buechner, Carolin; Gaertner, Lutz; Lesinski-Schiedat, Anke; Battmer, Rolf-Dieter; Lenarz, Thomas

    2006-07-01

    The objective of this paper is to evaluate the advantages of the Advanced Bionic high resolution mode for speech perception, through a retrospective analysis. Forty-five adult subjects were selected who had a minimum experience of three months' standard mode (mean of 10 months) before switching to high resolution mode. Speech perception was tested in standard mode immediately before fitting with high resolution mode, and again after a maximum of six months high resolution mode usage (mean of two months). A significant improvement was found, between 11 and 17%, depending on the test material. The standard mode preference does not give any indication about the improvement when switching to high resolution. Users who are converted within any study achieve a higher performance improvement than those converted in the clinical routine. This analysis proves the significant benefits of high resolution mode for users, and also indicates the need for guidelines for individual optimization of parameter settings in a high resolution mode program.

  7. The Bionic Clicker Mark I & II.

    Science.gov (United States)

    Magee, Elliott G; Ourselin, S; Nikitichev, Daniil; Vercauteren, T; Vanhoestenberghe, Anne

    2017-08-14

    In this manuscript, we present two 'Bionic Clicker' systems, the first designed to demonstrate electromyography (EMG) based control systems for educational purposes and the second for research purposes. EMG based control systems pick up electrical signals generated by muscle activation and use these as inputs for controllers. EMG controllers are widely used in prosthetics to control limbs. The Mark I (MK I) clicker allows the wearer to change the slide of a presentation by raising their index finger. It is built around a microcontroller and a bio-signals shield. It generated a lot of interest from both the public and research community. The Mark II (MK II) device presented here was designed to be a cheaper, sleeker, and more customizable system that can be easily modified and directly transmit EMG data. It is built using a wireless capable microcontroller and a muscle sensor.

  8. Cooperative Learning for Distributed In-Network Traffic Classification

    Science.gov (United States)

    Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

    2017-04-01

    Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

  9. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Directory of Open Access Journals (Sweden)

    Chernoded Andrey

    2017-01-01

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

  10. Bionics in Engineering Education Considerations, Experiences and Conclusions

    Directory of Open Access Journals (Sweden)

    Ralf Neurohr

    2008-01-01

    Full Text Available During recent years bionics, a new discipline which is in charge with the transfer of the principles of construction, regulation, interaction and organisation of biology into innovative technical solutions, has attracted significant interest from various industries. Based on this request for bionic expertise in engineering, the faculty for teaching engineering in foreign languages (FILS at ‘Politehnica’ University of Bucharest started a course in bionics in SS 2007, which was supported by the expertise of the German ‘Bionik-Kompetenz-Netz’, one of the leading organizations in bionics. This is the report on the considerations involved in the course concept, the first experiences with the students' acceptance, some conclusions and future perspectives for extending bionics activities at ‘Politehnica’. Finally, within the last section, the evaluation of a questionnaire, filled in by the students at the end of the course, will be presented. In order to avoid any confusion, considering overlapping or mixing up with other bio-disciplines related to technology, the paper starts with a short introduction, explaining the principles of bionics and providing a clear definition of the field.

  11. The Practices of Student Network as Cooperative Learning in Ethiopia

    Science.gov (United States)

    Reda, Weldemariam Nigusse; Hagos, Girmay Tsegay

    2015-01-01

    Student network is a teaching strategy introduced as cooperative learning to all educational levels above the upper primary schools (grade 5 and above) in Ethiopia. The study was, therefore, aimed at investigating to what extent the student network in Ethiopia is actually practiced in line with the principles of cooperative learning. Consequently,…

  12. Adjoint-Operator Learning For A Neural Network

    Science.gov (United States)

    Barhen, Jacob; Toomarian, Nikzad

    1993-01-01

    Electronic neural networks made to synthesize initially unknown mathematical models of time-dependent phenomena or to learn temporally evolving patterns by use of algorithms based on adjoint operators. Algorithms less complicated, involve less computation and solve learning equations forward in time possibly simultaneously with equations of evolution of neural network, thereby both increasing computational efficiency and making real-time applications possible.

  13. Learning Networks--Enabling Change through Community Action Research

    Science.gov (United States)

    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…

  14. "Getting Practical" and the National Network of Science Learning Centres

    Science.gov (United States)

    Chapman, Georgina; Langley, Mark; Skilling, Gus; Walker, John

    2011-01-01

    The national network of Science Learning Centres is a co-ordinating partner in the Getting Practical--Improving Practical Work in Science programme. The principle of training provision for the "Getting Practical" programme is a cascade model. Regional trainers employed by the national network of Science Learning Centres trained the cohort of local…

  15. Towards a Social Networks Model for Online Learning & Performance

    Science.gov (United States)

    Chung, Kon Shing Kenneth; Paredes, Walter Christian

    2015-01-01

    In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

  16. Social networks as ICT collaborative and supportive learning media ...

    African Journals Online (AJOL)

    The paper investigated the social networks as ICT collaborative and supportive learning media utilisation within the Nigerian educational system. The concept of ICT was concisely explained vis-à-vis the social network concept, theory and collaborative and supportive learning media utilisation. Different types of social ...

  17. The Fire Learning Network: A promising conservation strategy for forestry

    Science.gov (United States)

    Bruce E. Goldstein; William H. Butler; R. Bruce. Hull

    2010-01-01

    Conservation Learning Networks (CLN) are an emerging conservation strategy for addressing complex resource management challenges that face the forestry profession. The US Fire Learning Network (FLN) is a successful example of a CLN that operates on a national scale. Developed in 2001 as a partnership between The Nature Conservancy, the US Forest Service, and land-...

  18. The Relationships Between Policy, Boundaries and Research in Networked Learning

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Sinclair, Christine

    2016-01-01

    The biennial Networked Learning Conference is an established locus for work on practice, research and epistemology in the field of networked learning. That work continues between the conferences through the researchers’ own networks, ‘hot seat’ debates, and through publications, especially...... for the Networked Learning Conference are all peer-reviewed, and as they have turned into chapters for this book, each has been re-reviewed by the editors and other authors. The result is a genuinely collegial distillation of themes from a stimulating conference; a snapshot of a time when national and international...

  19. Peer Learning Network: Implementing and Sustaining Cooperative Learning by Teacher Collaboration

    Science.gov (United States)

    Miquel, Ester; Duran, David

    2017-01-01

    This article describes an in-service teachers', staff-development model "Peer Learning Network" and presents results about its efficiency. "Peer Learning Network" promotes three levels of peer learning simultaneously (among pupils, teachers, and schools). It supports pairs of teachers from several schools, who are linked…

  20. Functionally-Specific Changes in Sensorimotor Networks following Motor Learning

    Directory of Open Access Journals (Sweden)

    David J Ostry

    2011-10-01

    Full Text Available The perceptual changes induced by motor learning are important in understanding the adaptive mechanisms and global functions of the human brain. In the present study, we document the neural substrates of this sensory plasticity by combining work on motor learning using a robotic manipulandum with resting-state fMRI measures of learning and psychophysical measures of perceptual function. We show that motor learning results in long-lasting changes to somatosensory areas of the brain. We have developed a new technique for incorporating behavioral measures into resting-state connectivity analyses. The method allows us to identify networks whose functional connectivity changes with learning and specifically to dissociate changes in connectivity that are related to motor learning from those that are related perceptual changes that occur in conjunction with learning. Using this technique we identify a new network in motor learning involving second somatosensory cortex, ventral premotor and supplementary motor cortex whose activation is specifically related to sensory changes that occur in association with learning. The sensory networks that are strengthened in motor learning are similar to those involved in perceptual learning and decision making, which suggests that the process of motor learning engages the perceptual learning network.

  1. Sea ice classification using fast learning neural networks

    Science.gov (United States)

    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.

  2. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

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

  3. PAC learning algorithms for functions approximated by feedforward networks

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.; Protopopescu, V. [Oak Ridge National Lab., TN (United States). Center for Engineering Systems Advanced Research

    1996-06-01

    The authors present a class of efficient algorithms for PAC learning continuous functions and regressions that are approximated by feedforward networks. The algorithms are applicable to networks with unknown weights located only in the output layer and are obtained by utilizing the potential function methods of Aizerman et al. Conditions relating the sample sizes to the error bounds are derived using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can be directly adapted to concept learning problems.

  4. Robust Learning of Fixed-Structure Bayesian Networks

    OpenAIRE

    Diakonikolas, Ilias; Kane, Daniel; Stewart, Alistair

    2016-01-01

    We investigate the problem of learning Bayesian networks in an agnostic model where an $\\epsilon$-fraction of the samples are adversarially corrupted. Our agnostic learning model is similar to -- in fact, stronger than -- Huber's contamination model in robust statistics. In this work, we study the fully observable Bernoulli case where the structure of the network is given. Even in this basic setting, previous learning algorithms either run in exponential time or lose dimension-dependent facto...

  5. Environmental Design for a Structured Network Learning Society

    Science.gov (United States)

    Chang, Ben; Cheng, Nien-Heng; Deng, Yi-Chan; Chan, Tak-Wai

    2007-01-01

    Social interactions profoundly impact the learning processes of learners in traditional societies. The rapid rise of the Internet using population has been the establishment of numerous different styles of network communities. Network societies form when more Internet communities are established, but the basic form of a network society, especially…

  6. Structure of Small World Innovation Network and Learning Performance

    Directory of Open Access Journals (Sweden)

    Shuang Song

    2014-01-01

    Full Text Available This paper examines the differences of learning performance of 5 MNCs (multinational corporations that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having m dimensions, which denotes the heterogeneous knowledge about the reality. We further set n innovative individuals that are mutually interactive and own unique knowledge about the reality. A longer (shorter distance between the knowledge of the individual and the reality denotes a lower (higher knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

  7. Enhancing Formal E-Learning with Edutainment on Social Networks

    Science.gov (United States)

    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…

  8. EduCamp Colombia: Social Networked Learning for Teacher Training

    Directory of Open Access Journals (Sweden)

    Diego Ernesto Leal Fonseca

    2011-03-01

    Full Text Available This paper describes a learning experience called EduCamp, which was launched by the Ministry of Education of Colombia in 2007, based on emerging concepts such as e-Learning 2.0, connectivism, and personal learning environments. An EduCamp proposes an unstructured collective learning experience, which intends to make palpable the possibilities of social software tools in learning and interaction processes while demonstrating face-to-face organizational forms that reflect social networked learning ideas. The experience opens new perspectives for the design of technology training workshops and for the development of lifelong learning experiences.

  9. The teacher as designer? What is the role of ‘learning design’ in networked learning?

    DEFF Research Database (Denmark)

    Konnerup, Ulla; Ryberg, Thomas; Sørensen, Mia Thyrre

    2018-01-01

    (TEL), networked learning, designs for learning and draw out their development and branching to understand potentially different ontological or epistemological roots they draw on. Further, we wish to inquire into how the area of ‘Learning Design’ relate to or distances itself from the philosophy...... and values of networked learning.......This paper explores various strands of ‘Learning Design’ and the understandings of Learning Design that have been developing or are emerging across research fields. We aim to understand the differences and similarities that have developed within various areas, such as Technology Enhanced Learning...

  10. Learning OpenStack networking (Neutron)

    CERN Document Server

    Denton, James

    2014-01-01

    If you are an OpenStack-based cloud operator with experience in OpenStack Compute and nova-network but are new to Neutron networking, then this book is for you. Some networking experience is recommended, and a physical network infrastructure is required to provide connectivity to instances and other network resources configured in the book.

  11. Theoretical framework on selected core issues on conditions for productive learning in networked learning environments

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone; Svendsen, Brian Møller; Ponti, Marisa

    The report documents and summarises the elements and dimensions that have been identified to describe and analyse the case studies collected in the Kaleidoscope Jointly Executed Integrating Research Project (JEIRP) on Conditions for productive learning in network learning environments....

  12. Intra-Organizational Learning Networks within Knowledge-Intensive Learning Environments

    Science.gov (United States)

    Skerlavaj, M.; Dimovski, V.; Mrvar, A.; Pahor, M.

    2010-01-01

    Organizational learning contributes to organizational performance. One research question that remains inadequately explained is how learning occurs. Can it be explained by using the acquisition or participation perspectives? Or is there a need for some other view? This paper suggests that learning networks form an important learning environment…

  13. THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

    Directory of Open Access Journals (Sweden)

    Mohd Ishak Bin Ismail

    2016-02-01

    Full Text Available Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by students in higher learning to communicate to each other, to carry out academic collaboration and sharing resources. Learning through social networking sites is based on the social interaction which learning are emphasizing on students, real world resources, active students` participation, diversity of learning resources and the use of digital tools to deliver meaningful learning. Many studies found the positive, neutral and negative impact of social networking sites on academic performance. Thus, this study will determine the relationship between Facebook usage and academic achievement. Also, it will investigate the association of social capital and academic collaboration to Facebook usage.

  14. Crashworthiness Design for Bionic Bumper Structures Inspired by Cattail and Bamboo

    OpenAIRE

    Xu, Tao; Liu, Nian; Yu, Zhenglei; Xu, Tianshuang; Zou, Meng

    2017-01-01

    Many materials in nature exhibit excellent mechanical properties. In this study, we evaluated the bionic bumper structure models by using nonlinear finite element (FE) simulations for their crashworthiness under full-size impact loading. The structure contained the structural characteristics of cattail and bamboo. The results indicated that the bionic design enhances the specific energy absorption (SEA) of the bumper. The numerical results showed that the bionic cross-beam and bionic box of t...

  15. Learning oncogenetic networks by reducing to mixed integer linear programming.

    Science.gov (United States)

    Shahrabi Farahani, Hossein; Lagergren, Jens

    2013-01-01

    Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

  16. "Bionic Man" Showcases Medical Research | NIH MedlinePlus the Magazine

    Science.gov (United States)

    ... page please turn JavaScript on. Feature: The Bionic Man Meet the Bionic Man Past Issues / Fall 2014 Table of Contents The ... medical imaging, visit www.nibib.nih.gov "Bionic Man" Showcases Medical Research The National Institute of Biomedical ...

  17. Learning Local Components to Understand Large Bayesian Networks

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge

    2009-01-01

    Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....

  18. Fermentation based carbon nanotube multifunctional bionic composites

    Science.gov (United States)

    Valentini, Luca; Bon, Silvia Bittolo; Signetti, Stefano; Tripathi, Manoj; Iacob, Erica; Pugno, Nicola M.

    2016-06-01

    The exploitation of the processes used by microorganisms to digest nutrients for their growth can be a viable method for the formation of a wide range of so called biogenic materials that have unique properties that are not produced by abiotic processes. Here we produced living hybrid materials by giving to unicellular organisms the nutrient to grow. Based on bread fermentation, a bionic composite made of carbon nanotubes (CNTs) and a single-cell fungi, the Saccharomyces cerevisiae yeast extract, was prepared by fermentation of such microorganisms at room temperature. Scanning electron microscopy analysis suggests that the CNTs were internalized by the cell after fermentation bridging the cells. Tensile tests on dried composite films have been rationalized in terms of a CNT cell bridging mechanism where the strongly enhanced strength of the composite is governed by the adhesion energy between the bridging carbon nanotubes and the matrix. The addition of CNTs also significantly improved the electrical conductivity along with a higher photoconductive activity. The proposed process could lead to the development of more complex and interactive structures programmed to self-assemble into specific patterns, such as those on strain or light sensors that could sense damage or convert light stimulus in an electrical signal.

  19. EDITORIAL: Special issue on medical bionics Special issue on medical bionics

    Science.gov (United States)

    Shepherd, Robert K.; D, Ph

    2009-12-01

    This special section of the Journal of Neural Engineering contains eight invited papers presented as part of the inaugural conference `Medical Bionics: A New Paradigm for Human Health' held in the beautiful seaside village of Lorne, Victoria, Australia from 16-19 November 2008. This meeting formed part of the Sir Mark Oliphant International Conference Series (www.oliphant.org.au) and was generously supported by the Department of Innovation, Industry, Science and Research of the Australian Government, the Australian Academy of Science and the Australian Academy of Technological Sciences and Engineering. This meeting was designed to bring experts from a variety of scientific, engineering and clinical disciplines together in a unique environment to discuss current progress in the field of medical bionics and to develop the concepts and techniques required to build the next generation of devices. The field is rapidly expanding, with new engineering solutions for neurological disorders being developed at an astonishing rate. Successful application of emerging engineering technologies into medical bionics devices requires a multidisciplinary research environment in order to deliver clinical solutions that are both safe and effective. Clinical success stories to date include spinal cord stimulators for the management of chronic neurological pain; auditory prostheses that allow the profoundly deaf to hear; and deep brain stimulation to negate movement disorders in Parkinson's disease. Other research programs currently undergoing clinical trials include devices that allow paraplegics to stand and even walk; brain-machine interfaces that provide quadriplegic patients with rudimentary control of a computer but may ultimately provide control of wheel chairs and artificial limbs; devices that detect and suppress epileptic seizures using brief trains of electrical stimulation; and retinal prostheses that will provide vision to the blind. The future for medical bionics is indeed

  20. Shared learning in supply networks: evidence from an emerging market supply network

    NARCIS (Netherlands)

    K.J. Mason (Katy); I. Oshri (Ilan); S. Leek

    2009-01-01

    textabstractPurpose – Firms face the challenge of developing learning capabilities that enable them to work as part of an effective business network. While an extensive literature examines learning capabilities within the firm, little attention has been given to shared learning that occurs between

  1. Efficient learning strategy of Chinese characters based on network approach.

    Directory of Open Access Journals (Sweden)

    Xiaoyong Yan

    Full Text Available We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

  2. Efficient learning strategy of Chinese characters based on network approach.

    Science.gov (United States)

    Yan, Xiaoyong; Fan, Ying; Di, Zengru; Havlin, Shlomo; Wu, Jinshan

    2013-01-01

    We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW) strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

  3. Hybrid E-Learning Tool TransLearning: Video Storytelling to Foster Vicarious Learning within Multi-Stakeholder Collaboration Networks

    Science.gov (United States)

    van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.

    2016-01-01

    E-learning and storytelling approaches can support informal vicarious learning within geographically widely distributed multi-stakeholder collaboration networks. This case study evaluates hybrid e-learning and video-storytelling approach "TransLearning" by investigation into how its storytelling e-tool supported informal vicarious…

  4. One pass learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2016-01-01

    Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance. Copyright

  5. Tweetstorming PLNs: Using Twitter to Brainstorm about Personal Learning Networks

    NARCIS (Netherlands)

    Sie, Rory; Boursinou, Eleni; Rajagopal, Kamakshi; Pataraia, Nino

    2012-01-01

    Sie, R., Boursinou, E., Rajagopal, K., & Pataraia, N. (2011). Tweetstorming PLNs: Using Twitter to Brainstorm about Personal Learning Networks. In Proceedings of The PLE Conference 2011. July, 10-12, 2011, Southampton, UK.

  6. Using machine learning, neural networks and statistics to predict bankruptcy

    NARCIS (Netherlands)

    Pompe, P.P.M.; Feelders, A.J.; Feelders, A.J.

    1997-01-01

    Recent literature strongly suggests that machine learning approaches to classification outperform "classical" statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees, and neural networks in predicting corporate bankruptcy. Linear

  7. Personal Profiles: Enhancing Social Interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter; Fetter, Sibren

    2009-01-01

    Berlanga, A. J., Bitter-Rijpkema, M., Brouns, F., Sloep, P. B., & Fetter, S. (2011). Personal Profiles: Enhancing Social Interaction in Learning Networks. International Journal of Web Based Communities, 7(1), 66-82.

  8. Thermodynamic efficiency of learning a rule in neural networks

    Science.gov (United States)

    Goldt, Sebastian; Seifert, Udo

    2017-11-01

    Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

  9. Recursive Autoconvolution for Unsupervised Learning of Convolutional Neural Networks

    OpenAIRE

    Knyazev, Boris; Barth, Erhardt; Martinetz, Thomas

    2016-01-01

    In visual recognition tasks, such as image classification, unsupervised learning exploits cheap unlabeled data and can help to solve these tasks more efficiently. We show that the recursive autoconvolution operator, adopted from physics, boosts existing unsupervised methods by learning more discriminative filters. We take well established convolutional neural networks and train their filters layer-wise. In addition, based on previous works we design a network which extracts more than 600k fea...

  10. Finite-sample based learning algorithms for feedforward networks

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.; Protopopescu, V.; Mann, R.C.; Oblow, E.M. [Oak Ridge National Lab., TN (United States); Iyengar, S.S. [Louisiana State Univ., Baton Rouge, LA (United States). Dept. of Computer Science

    1995-04-01

    We discuss two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by FeedForward Networks (FFN). The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can also be directly applied to concept learning problems. A main distinguishing feature of the this work is that the sample sizes are based on explicit algorithms rather than information-based methods.

  11. Learning algorithms for feedforward networks based on finite samples

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.; Protopopescu, V.; Mann, R.C.; Oblow, E.M.; Iyengar, S.S.

    1994-09-01

    Two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by feedforward networks, are discussed. The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can be directly adapted to concept learning problems.

  12. Social Media and Social Networking Applications for Teaching and Learning

    Science.gov (United States)

    Yeo, Michelle Mei Ling

    2014-01-01

    This paper aims to better understand the experiences of the youth and the educators with the tapping of social media like YouTube videos and the social networking application of Facebook for teaching and learning. This paper is interested in appropriating the benefits of leveraging of social media and networking applications like YouTube and…

  13. Social Software: Participants' Experience Using Social Networking for Learning

    Science.gov (United States)

    Batchelder, Cecil W.

    2010-01-01

    Social networking tools used in learning provides instructional design with tools for transformative change in education. This study focused on defining the meanings and essences of social networking through the lived common experiences of 7 college students. The problem of the study was a lack of learner voice in understanding the value of social…

  14. Nurturing Global Collaboration and Networked Learning in Higher Education

    Science.gov (United States)

    Cronin, Catherine; Cochrane, Thomas; Gordon, Averill

    2016-01-01

    We consider the principles of communities of practice (CoP) and networked learning in higher education, illustrated with a case study. iCollab has grown from an international community of practice connecting students and lecturers in seven modules across seven higher education institutions in six countries, to a global network supporting the…

  15. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    structures, protein–protein interaction networks, social interactions, the Internet, and so on can be described by complex networks [1–5]. Recent developments in the understanding of complex networks has led to deeper insights about their origin and other properties [1–5]. One common realization that emerges from these ...

  16. A Newton-type neural network learning algorithm

    International Nuclear Information System (INIS)

    Ivanov, V.V.; Puzynin, I.V.; Purehvdorzh, B.

    1993-01-01

    First- and second-order learning methods for feed-forward multilayer networks are considered. A Newton-type algorithm is proposed and compared with the common back-propagation algorithm. It is shown that the proposed algorithm provides better learning quality. Some recommendations for their usage are given. 11 refs.; 1 fig.; 1 tab

  17. Networking for English Literature Class: Cooperative Learning in Chinese Context

    Science.gov (United States)

    Li, Huiyin

    2017-01-01

    This action research was conducted to investigate the efficacy of networking, an adjusted cooperative learning method employed in an English literature class for non-English majors in China. Questionnaire was administered online anonymously to college students after a 14-week cooperative learning in literature class in a Chinese university, aiming…

  18. Social Networks and Performance in Distributed Learning Communities

    Science.gov (United States)

    Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.

    2012-01-01

    Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this…

  19. Language Learning through Social Networks: Perceptions and Reality

    Science.gov (United States)

    Lin, Chin-Hsi; Warschauer, Mark; Blake, Robert

    2016-01-01

    Language Learning Social Network Sites (LLSNSs) have attracted millions of users around the world. However, little is known about how people participate in these sites and what they learn from them. This study investigated learners' attitudes, usage, and progress in a major LLSNS through a survey of 4,174 as well as 20 individual case studies. The…

  20. Cortical electrophysiological network dynamics of feedback learning

    NARCIS (Netherlands)

    Cohen, M.X.; Wilmes, K.A.; van de Vijver, I.

    2011-01-01

    Understanding the neurophysiological mechanisms of learning is important for both fundamental and clinical neuroscience. We present a neurophysiologically inspired framework for understanding cortical mechanisms of feedback-guided learning. This framework is based on dynamic changes in systems-level

  1. Semi-Supervised Deep Learning for Fully Convolutional Networks

    OpenAIRE

    Baur, Christoph; Albarqouni, Shadi; Navab, Nassir

    2017-01-01

    Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there i...

  2. Crashworthiness Design for Bionic Bumper Structures Inspired by Cattail and Bamboo

    Directory of Open Access Journals (Sweden)

    Tao Xu

    2017-01-01

    Full Text Available Many materials in nature exhibit excellent mechanical properties. In this study, we evaluated the bionic bumper structure models by using nonlinear finite element (FE simulations for their crashworthiness under full-size impact loading. The structure contained the structural characteristics of cattail and bamboo. The results indicated that the bionic design enhances the specific energy absorption (SEA of the bumper. The numerical results showed that the bionic cross-beam and bionic box of the bionic bumper have a significant effect on the crashworthiness of the structure. The crush deformation of bionic cross-beam and box bumper model was reduced by 33.33%, and the total weight was reduced by 44.44%. As the energy absorption capacity under lateral impact, the bionic design can be used in the future bumper body.

  3. Crashworthiness Design for Bionic Bumper Structures Inspired by Cattail and Bamboo.

    Science.gov (United States)

    Xu, Tao; Liu, Nian; Yu, Zhenglei; Xu, Tianshuang; Zou, Meng

    2017-01-01

    Many materials in nature exhibit excellent mechanical properties. In this study, we evaluated the bionic bumper structure models by using nonlinear finite element (FE) simulations for their crashworthiness under full-size impact loading. The structure contained the structural characteristics of cattail and bamboo. The results indicated that the bionic design enhances the specific energy absorption (SEA) of the bumper. The numerical results showed that the bionic cross-beam and bionic box of the bionic bumper have a significant effect on the crashworthiness of the structure. The crush deformation of bionic cross-beam and box bumper model was reduced by 33.33%, and the total weight was reduced by 44.44%. As the energy absorption capacity under lateral impact, the bionic design can be used in the future bumper body.

  4. A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks

    NARCIS (Netherlands)

    De Jong, Tim; Fuertes, Alba; Schmeits, Tally; Specht, Marcus; Koper, Rob

    2008-01-01

    De Jong, T., Fuertes, A., Schmeits, T., Specht, M., & Koper, R. (2009). A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks. In D. Goh (Ed.), Multiplatform E-Learning Systems and Technologies: Mobile Devices for Ubiquitous ICT-Based Education (pp.

  5. Facilitating participation:From the EML web site to the Learning Network for Learning Design

    NARCIS (Netherlands)

    Hummel, Hans; Tattersall, Colin; Burgos, Daniel; Brouns, Francis; Kurvers, Hub; Koper, Rob

    2004-01-01

    Please refer to original publication: Hummel, H., Tattersall, C., Burgos, D., Brouns, F., Kurvers, H., & Koper, R. (2005). Facilitating participation: From the EML website to the Learning Network for Learning Design. Interactive Learning Environments,13(1-2), 55-69

  6. The Mobile Learning Network: Getting Serious about Games Technologies for Learning

    Science.gov (United States)

    Petley, Rebecca; Parker, Guy; Attewell, Jill

    2011-01-01

    The Mobile Learning Network currently in its third year, is a unique collaborative initiative encouraging and enabling the introduction of mobile learning in English post-14 education. The programme, funded jointly by the Learning and Skills Council and participating colleges and schools and supported by LSN has involved nearly 40,000 learners and…

  7. Deep learning with convolutional neural network in radiology.

    Science.gov (United States)

    Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

    2018-04-01

    Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

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

    Science.gov (United States)

    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.

  9. Understanding the Context of Learning in an Online Social Network for Health Professionals' Informal Learning.

    Science.gov (United States)

    Li, Xin; Gray, Kathleen; Verspoor, Karin; Barnett, Stephen

    2017-01-01

    Online social networks (OSN) enable health professionals to learn informally, for example by sharing medical knowledge, or discussing practice management challenges and clinical issues. Understanding the learning context in OSN is necessary to get a complete picture of the learning process, in order to better support this type of learning. This study proposes critical contextual factors for understanding the learning context in OSN for health professionals, and demonstrates how these contextual factors can be used to analyse the learning context in a designated online learning environment for health professionals.

  10. Evolution of individual versus social learning on social networks.

    Science.gov (United States)

    Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

    2015-03-06

    A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  11. SOCIAL NETWORKS AS A MEANS OF LEARNING PROCESS

    Directory of Open Access Journals (Sweden)

    T. Arhipova

    2015-02-01

    Full Text Available This paper presents an analysis of social networks in terms of their possible use in the education system. The integration of new information and communication technologies with the technologies of learning is gradually changing the concept of modern education and promotes educational environment focused on the interests and personal development, achievement of her current levels of education, internationalization and increasing access to educational resources, creating conditions for mobility of students and teachers improving the quality of education and the formation of a single educational space. The peculiarity of such an environment is to provide creative research activity of the teacher and students in the learning process. Network services provide the means by which students can act as active creators of media content. The paper presents the results of a study of the advantages and disadvantages of using web communities in the educational process. Articulated pedagogical conditions of the effective organization of educational process in the virtual learning environment using social networks. The experience of the use of social networks in the learning process of the university. Such networking technologies, such as forums, blogs, wikis, educational portals and automated systems for distance learning, having undoubted didactic and methodological advantages, inferior social networks in terms of involving users in their communication space, as well as compliance with the intellectual, creative and social needs.

  12. Learning Networks using Learning Design. A firt collection of papers

    NARCIS (Netherlands)

    Koper, Rob; Spoelstra, Howard; Burgos, Daniel

    2004-01-01

    CONTENT
    THE LEARNING DESIGN SPECIFICATION. INTRODUCTION
    1. Modeling units of study from a pedagogical perspective. The pedagogical meta-model behind EML 2. Representing the Learning Design of Units of Learning 3. Educational Modelling Language: Modelling reusable, interoperable, rich and

  13. Teachers' Self-Initiated Professional Learning through Personal Learning Networks

    Science.gov (United States)

    Tour, Ekaterina

    2017-01-01

    It is widely acknowledged that to be able to teach language and literacy with digital technologies, teachers need to engage in relevant professional learning. Existing formal models of professional learning are often criticised for being ineffective. In contrast, informal and self-initiated forms of learning have been recently recognised as…

  14. Deterministic learning enhanced neutral network control of unmanned helicopter

    Directory of Open Access Journals (Sweden)

    Yiming Jiang

    2016-11-01

    Full Text Available In this article, a neural network–based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design.

  15. Approximation methods for efficient learning of Bayesian networks

    CERN Document Server

    Riggelsen, C

    2008-01-01

    This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

  16. Prognostic Bayesian networks I: rationale, learning procedure, and clinical use.

    Science.gov (United States)

    Verduijn, Marion; Peek, Niels; Rosseel, Peter M J; de Jonge, Evert; de Mol, Bas A J M

    2007-12-01

    Prognostic models are tools to predict the future outcome of disease and disease treatment, one of the fundamental tasks in clinical medicine. This article presents the prognostic Bayesian network (PBN) as a new type of prognostic model that builds on the Bayesian network methodology, and implements a dynamic, process-oriented view on prognosis. A PBN describes the mutual relationships between variables that come into play during subsequent stages of a care process and a clinical outcome. A dedicated procedure for inducing these networks from clinical data is presented. In this procedure, the network is composed of a collection of local supervised learning models that are recursively learned from the data. The procedure optimizes performance of the network's primary task, outcome prediction, and handles the fact that patients may drop out of the process in earlier stages. Furthermore, the article describes how PBNs can be applied to solve a number of information problems that are related to medical prognosis.

  17. Design and Testing of a Bionic Dancing Prosthesis.

    Science.gov (United States)

    Rouse, Elliott J; Villagaray-Carski, Nathan C; Emerson, Robert W; Herr, Hugh M

    2015-01-01

    Traditionally, prosthetic leg research has focused on improving mobility for activities of daily living. Artistic expression such as dance, however, is not a common research topic and consequently prosthetic technology for dance has been severely limited for the disabled. This work focuses on investigating the ankle joint kinetics and kinematics during a Latin-American dance to provide unique motor options for disabled individuals beyond those of daily living. The objective of this study was to develop a control system for a bionic ankle prosthesis that outperforms conventional prostheses when dancing the rumba. The biomechanics of the ankle joint of a non-amputee, professional dancer were acquired for the development of the bionic control system. Subsequently, a professional dancer who received a traumatic transtibial amputation in April 2013 tested the bionic dance prosthesis and a conventional, passive prosthesis for comparison. The ability to provide similar torque-angle behavior of the biological ankle was assessed to quantify the biological realism of the prostheses. The bionic dancing prosthesis overlapped with 37 ± 6% of the non-amputee ankle torque and ankle angle data, compared to 26 ± 2% for the conventional, passive prosthesis, a statistically greater overlap (p = 0.01). This study lays the foundation for quantifying unique, expressive activity modes currently unavailable to individuals with disabilities. Future work will focus on an expansion of the methods and types of dance investigated in this work.

  18. Design and Testing of a Bionic Dancing Prosthesis.

    Directory of Open Access Journals (Sweden)

    Elliott J Rouse

    Full Text Available Traditionally, prosthetic leg research has focused on improving mobility for activities of daily living. Artistic expression such as dance, however, is not a common research topic and consequently prosthetic technology for dance has been severely limited for the disabled. This work focuses on investigating the ankle joint kinetics and kinematics during a Latin-American dance to provide unique motor options for disabled individuals beyond those of daily living. The objective of this study was to develop a control system for a bionic ankle prosthesis that outperforms conventional prostheses when dancing the rumba. The biomechanics of the ankle joint of a non-amputee, professional dancer were acquired for the development of the bionic control system. Subsequently, a professional dancer who received a traumatic transtibial amputation in April 2013 tested the bionic dance prosthesis and a conventional, passive prosthesis for comparison. The ability to provide similar torque-angle behavior of the biological ankle was assessed to quantify the biological realism of the prostheses. The bionic dancing prosthesis overlapped with 37 ± 6% of the non-amputee ankle torque and ankle angle data, compared to 26 ± 2% for the conventional, passive prosthesis, a statistically greater overlap (p = 0.01. This study lays the foundation for quantifying unique, expressive activity modes currently unavailable to individuals with disabilities. Future work will focus on an expansion of the methods and types of dance investigated in this work.

  19. BIOCONAID System (Bionic Control of Acceleration Induced Dimming). Final Report.

    Science.gov (United States)

    Rogers, Dana B.; And Others

    The system described represents a new technique for enhancing the fidelity of flight simulators during high acceleration maneuvers. This technique forces the simulator pilot into active participation and energy expenditure similar to the aircraft pilot undergoing actual accelerations. The Bionic Control of Acceleration Induced Dimming (BIOCONAID)…

  20. [The white coat as a cape: doctors, superheroes and bionics].

    Science.gov (United States)

    Engelberts, Connie E; Mevius, Lucas

    2013-01-01

    To study the relationship between doctors and comic books, cartoons, superheroes and bionic prosthetic organs. Descriptive survey. For this study, 341 doctors and medical students filled in a digital survey in the autumn of 2013. The questionnaire contained questions about comic books and cartoons, their superheroes, prosthetic organs and about bionic and non-bionic super powers. As a child more than half of the participants read comic books regularly or often, and most watched cartoons regularly or often. Now their childhood interest in this subject has mostly been lost. In their youth, Suske & Wiske were the favourite, and now it is Donald Duck. The number of doctors with a favourite superhero decreased as aged increased from 52% to 37%. The care givers entertain lively fantasies about having bionic superpowers. According to the participants, the idea doctor would have ultrasonic eyes and all sorts of other super senses. Ninety-one per cent thought that 'the development of prosthetic organs is not a waste of money'. If Batman and Superman come to blows, Catwoman wins.

  1. Biologically plausible learning in neural networks with modulatory feedback.

    Science.gov (United States)

    Grant, W Shane; Tanner, James; Itti, Laurent

    2017-04-01

    Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new learning rule designed around the complications of learning modulatory feedback and composed of three simple concepts grounded in physiologically plausible evidence. Using border ownership as a prototypical example, we show that a Hebbian learning rule fails to properly learn modulatory connections, while our proposed rule correctly learns a stimulus-driven model. To the authors' knowledge, this is the first time a border ownership network has been learned. Additionally, we show that the rule can be used as a drop-in replacement for a Hebbian learning rule to learn a biologically consistent model of orientation selectivity, a network which lacks any modulatory connections. Our results predict that the mechanisms we use are integral for learning modulatory connections in the brain and furthermore that modulatory connections have a strong dependence on inhibition. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  2. Learning flexible sensori-motor mappings in a complex network.

    Science.gov (United States)

    Vasilaki, Eleni; Fusi, Stefano; Wang, Xiao-Jing; Senn, Walter

    2009-02-01

    Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem.

  3. Connection Dynamics in Learning Networks: Games, Agents and Social Network Visualization

    NARCIS (Netherlands)

    Angehrn, Albert; Maxwell, Katrina; Sereno, Bertrand

    2007-01-01

    This paper addresses the challenge of enhancing social interaction through value-adding connections among the online members of Learning Networks. We report on our exploration of three types of connection dynamics: (1) features enabling network member to visualize and browse through relationship

  4. Networking for Learning The role of Networking in a Lifelong Learner's Professional Development

    OpenAIRE

    Rajagopal, Kamakshi

    2016-01-01

    This dissertation discusses the role the social activity of networking plays in lifelong learners’ professional and personal continuous development. The main hypothesis of this thesis is that networking is a learning strategy for lifelong learners, in which conversations are key activities through which they reassess their held thoughts and make sense of their experiences together with others.

  5. Networking for Learning The role of Networking in a Lifelong Learner's Professional Development

    NARCIS (Netherlands)

    Rajagopal, Kamakshi

    2016-01-01

    This dissertation discusses the role the social activity of networking plays in lifelong learners’ professional and personal continuous development. The main hypothesis of this thesis is that networking is a learning strategy for lifelong learners, in which conversations are key activities through

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

    NARCIS (Netherlands)

    Lecluijze, Susanne Elisabeth; de Haan, M.J.; Ünlüsoy, A.

    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

  7. Learning Errors by Radial Basis Function Neural Networks and Regularization Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Vidnerová, Petra

    2009-01-01

    Roč. 1, č. 2 (2009), s. 49-57 ISSN 2005-4262 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network * RBF networks * regularization * learning Subject RIV: IN - Informatics, Computer Science http://www.sersc.org/journals/IJGDC/vol2_no1/5.pdf

  8. Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks.

    Science.gov (United States)

    Dosovitskiy, Alexey; Fischer, Philipp; Springenberg, Jost Tobias; Riedmiller, Martin; Brox, Thomas

    2016-09-01

    Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.

  9. Globally Networked Collaborative Learning in Industrial Design

    Science.gov (United States)

    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…

  10. Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Hasan A. A. Al-Rawi

    2014-01-01

    Full Text Available Cognitive radio (CR enables unlicensed users (or secondary users, SUs to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs. Reinforcement learning (RL is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs’ network performance without significantly jeopardizing PUs’ network performance, specifically SUs’ interference to PUs.

  11. Reinforcement learning for routing in cognitive radio ad hoc networks.

    Science.gov (United States)

    Al-Rawi, Hasan A A; Yau, Kok-Lim Alvin; Mohamad, Hafizal; Ramli, Nordin; Hashim, Wahidah

    2014-01-01

    Cognitive radio (CR) enables unlicensed users (or secondary users, SUs) to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs). Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs' network performance without significantly jeopardizing PUs' network performance, specifically SUs' interference to PUs.

  12. Structural Bionic Design for Digging Shovel of Cassava Harvester Considering Soil Mechanics

    Directory of Open Access Journals (Sweden)

    Shihao Liu

    2014-01-01

    Full Text Available In order to improve the working performance of cassava harvester, structural bionic design for its digging shovel was conducted. Taking the oriental mole cricket's paws as bionic prototype, a new structural bionic design method for digging shovel was established, which considers the morphology-configuration-function coupling bionic. A comprehensive performance comparison method was proposed, which is used to select the bionic design schemes. The proposed bionic design method was used to improve digging shovel structure of a digging-pulling style cassava harvester, and nine bionic-type digging shovels were obtained with considering the impact of soil mechanics. After conducting mechanical properties comparative analysis for bionic-type digging shovels, the bionic design rules were summed up, and the optimal design scheme of digging shovel was obtained through combining the proposed comprehensive performance comparison method with Analytic Hierarchy Process (AHP. Studies have shown that bionic design method not only can improve the overall mechanical properties of digging shovel, but also can help to improve the harvesting effect of cassava harvester, which provides a new idea for crops harvesting machinery's structural optimization design.

  13. Up the ANTe: Understanding Entrepreneurial Leadership Learning through Actor-Network Theory

    Science.gov (United States)

    Smith, Sue; Kempster, Steve; Barnes, Stewart

    2017-01-01

    This article explores the role of educators in supporting the development of entrepreneurial leadership learning by creating peer learning networks of owner-managers of small businesses. Using actor-network theory, the authors think through the process of constructing and maintaining a peer learning network (conceived of as an actor-network) and…

  14. Teachers' Motives for Learning in Networks: Costs, Rewards and Community Interest

    Science.gov (United States)

    van den Beemt, Antoine; Ketelaar, Evelien; Diepstraten, Isabelle; de Laat, Maarten

    2018-01-01

    Background: This paper discusses teachers' perspectives on learning networks and their motives for participating in these networks. Although it is widely held that teachers' learning may be developed through learning networks, not all teachers participate in such networks. Purpose: The theme of reciprocity, central to studies in the area of…

  15. Delivery of E-Learning through Social Learning Networks

    Science.gov (United States)

    Dafoulas, Georgios A.; Shokri, Azam

    2014-01-01

    Over the past two decades policies and speculations have been evident about the importance of internet use including technologies in education and learning at all levels to individuals and societies. The purposes, theories and ways in which learning with technologies ought to be conceptualised and functionalised is generating an increased body of…

  16. Learning-induced pattern classification in a chaotic neural network

    International Nuclear Information System (INIS)

    Li, Yang; Zhu, Ping; Xie, Xiaoping; He, Guoguang; Aihara, Kazuyuki

    2012-01-01

    In this Letter, we propose a Hebbian learning rule with passive forgetting (HLRPF) for use in a chaotic neural network (CNN). We then define the indices based on the Euclidean distance to investigate the evolution of the weights in a simplified way. Numerical simulations demonstrate that, under suitable external stimulations, the CNN with the proposed HLRPF acts as a fuzzy-like pattern classifier that performs much better than an ordinary CNN. The results imply relationship between learning and recognition. -- Highlights: ► Proposing a Hebbian learning rule with passive forgetting (HLRPF). ► Defining indices to investigate the evolution of the weights simply. ► The chaotic neural network with HLRPF acts as a fuzzy-like pattern classifier. ► The pattern classifier ability of the network is improved much.

  17. A Theoretical Design for Learning Model Addressing the Networked Society

    DEFF Research Database (Denmark)

    Nielsen, Janni; Levinsen, Karin Tweddell; Sørensen, Birgitte Holm

    2010-01-01

    The transition from the industrial to the networked society produces contradictions that challenges the educational system and force it to adapt to new conditions. In a Danish virtual Master in Information and Communication Technologies and Learning (MIL) these contradictions appear as a field....... The context for the experiment is MIL's course on Interaction Design. The orchestration is operationalized as a 4-hour script that builds on classic role-play designed as an open ended explorative task. The script of the teams' tasks is designed to facilitate the teams' ongoing negotiation and structuring...... which enables students to develop Networked Society competencies and maintain progression in the learning process also during the online periods. Additionally we suggest that our model contributes to the innovation of a networked society's design for learning....

  18. Neural network models of learning and categorization in multigame experiments

    Directory of Open Access Journals (Sweden)

    Davide eMarchiori

    2011-12-01

    Full Text Available Previous research has shown that regret-driven neural networks predict behavior in repeated completely mixed games remarkably well, substantially equating the performance of the most accurate established models of learning. This result prompts the question of what is the added value of modeling learning through neural networks. We submit that this modeling approach allows for models that are able to distinguish among and respond differently to different payoff structures. Moreover, the process of categorization of a game is implicitly carried out by these models, thus without the need of any external explicit theory of similarity between games. To validate our claims, we designed and ran two multigame experiments in which subjects faced, in random sequence, different instances of two completely mixed 2x2 games. Then, we tested on our experimental data two regret-driven neural network models, and compared their performance with that of other established models of learning and Nash equilibrium.

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

    Science.gov (United States)

    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.

  20. Lifelong learning networks for sustainable regional development

    NARCIS (Netherlands)

    De Kraker, Joop; Cörvers, Ron; Ruelle, Christine; Valkering, Pieter

    2010-01-01

    Sustainable regional development is a participatory, multi-actor process, involving a diversity of societal stakeholders, administrators, policy makers, practitioners and scientific experts. In this process, mutual and collective learning plays a major role as participants have to exchange and

  1. Network Enabled - Unresolved Residual Analysis and Learning (NEURAL)

    Science.gov (United States)

    Temple, D.; Poole, M.; Camp, M.

    Since the advent of modern computational capacity, machine learning algorithms and techniques have served as a method through which to solve numerous challenging problems. However, for machine learning methods to be effective and robust, sufficient data sets must be available; specifically, in the space domain, these are generally difficult to acquire. Rapidly evolving commercial space-situational awareness companies boast the capability to collect hundreds of thousands nightly observations of resident space objects (RSOs) using a ground-based optical sensor network. This provides the ability to maintain custody of and characterize thousands of objects persistently. With this information available, novel deep learning techniques can be implemented. The technique discussed in this paper utilizes deep learning to make distinctions between nightly data collects with and without maneuvers. Implementation of these techniques will allow the data collected from optical ground-based networks to enable well informed and timely the space domain decision making.

  2. Biologically-inspired Learning in Pulsed Neural Networks

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    1999-01-01

    Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...... that useful conclusions as to the future of on-chip learning can be drawn from this work....

  3. Can Learning Collaboratives Support Implementation by Rewiring Professional Networks?

    Science.gov (United States)

    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.

  4. Can Learning Collaboratives Support Implementation By Rewiring Professional Networks?

    Science.gov (United States)

    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

  5. On polyhedral approximations of polytopes for learning Bayesian networks

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Haws, D.C.

    2013-01-01

    Roč. 4, č. 1 (2013), s. 59-92 ISSN 1309-3452 R&D Projects: GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : Bayesian network structure * integer programming * standard imset * characteristic imset * LP relaxation Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2013/MTR/studeny-on polyhedral approximations of polytopes for learning bayesian networks.pdf

  6. Machine learning using a higher order correlation network

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Y.C.; Doolen, G.; Chen, H.H.; Sun, G.Z.; Maxwell, T.; Lee, H.Y.

    1986-01-01

    A high-order correlation tensor formalism for neural networks is described. The model can simulate auto associative, heteroassociative, as well as multiassociative memory. For the autoassociative model, simulation results show a drastic increase in the memory capacity and speed over that of the standard Hopfield-like correlation matrix methods. The possibility of using multiassociative memory for a learning universal inference network is also discussed. 9 refs., 5 figs.

  7. LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORK

    OpenAIRE

    Mücella ÖZBAY KARAKUŞ; Orhan ER

    2013-01-01

    This paper reports results of artificial neural network for robot navigation tasks. Machine learning methods have proven usability in many complex problems concerning mobile robots control. In particular we deal with the well-known strategy of navigating by “wall-following”. In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks. The PNN result was compared with the results of the Logistic Perceptron, Multilayer Perceptron, Mixture of Ex...

  8. Gamification of Learning Deactivates the Default Mode Network.

    Science.gov (United States)

    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.

  9. Learning and Generalisation in Neural Networks with Local Preprocessing

    OpenAIRE

    Kutsia, Merab

    2007-01-01

    We study learning and generalisation ability of a specific two-layer feed-forward neural network and compare its properties to that of a simple perceptron. The input patterns are mapped nonlinearly onto a hidden layer, much larger than the input layer, and this mapping is either fixed or may result from an unsupervised learning process. Such preprocessing of initially uncorrelated random patterns results in the correlated patterns in the hidden layer. The hidden-to-output mapping of the net...

  10. "FORCE" learning in recurrent neural networks as data assimilation

    Science.gov (United States)

    Duane, Gregory S.

    2017-12-01

    It is shown that the "FORCE" algorithm for learning in arbitrarily connected networks of simple neuronal units can be cast as a Kalman Filter, with a particular state-dependent form for the background error covariances. The resulting interpretation has implications for initialization of the learning algorithm, leads to an extension to include interactions between the weight updates for different neurons, and can represent relationships within groups of multiple target output signals.

  11. The Use Of Social Networking Sites For Learning In Institutions Of Higher Learning

    Directory of Open Access Journals (Sweden)

    Mange Gladys Nkatha

    2015-08-01

    Full Text Available Abstract Institutions of higher learning are facing greater challenges to change and subjected to various transformations in the surrounding environment including technology. These challenge and motivate them to explore new ways to improve their teaching approaches. This study sought to investigate the use of social networking site in institutions of higher learning. To this end two objectives were formulated 1 to investigate the current state of the use of social networking sites by the students 2 investigate how social networking sites can be used to promote authentic learning in institutions of higher learning. The study adopted exploratory approach using descriptive survey design where a sample of 10 67 students were picked from Jomo Kenyatta University of Agriculture and Technology JKUAT main campus. The findings indicate the use of social networking sites is a viable option as the students are not only members of social networking sites but also that majority have access to the requisite technological devices. Additionally recommendations for ensuring authentic learning were presented. The researcher recommends the exploration of the leveraging of the existing social networking sites for learning in conjunction with key stakeholders.

  12. The Role of Electronic Learning Technology in Networks Systems

    International Nuclear Information System (INIS)

    Abd ELhamid, A.; Ayad, N.M.A.; Fouad, Y.; Abdelkader, T.

    2016-01-01

    Recently, Electronic Learning Technology (ELT) has been widely spread as one of the new technologies in the world through using Information and Communication Technology (ICT). One of the strategies of ELT is Simulation, for instance Military and Medical simulations that are used to avoid risks and reduce Costs. A wireless communication network refers to any network not physically connected by cables, which enables the desired convenience and mobility for the user. Wireless communication networks have been useful in areas such as commerce, education and defense. According to the nature of a particular application, they can be used in home-based and industrial systems or in commercial and military environments. Historically, Mobile Ad-hoc Networks (MANET) have primarily been used for tactical military network related applications to improve battlefield communications/ survivability. MANET is a collection of wireless nodes that can dynamically be set up anywhere and anytime without using any pre-existing network infrastructure. Mobility in wireless networks basically refers to nodes changing its point of attachment to the network. Also, how the end terminals can move, there are many mobility models described the movement of nodes, many researchers use the Random Way point Mobility Model (RWPM). In this paper, a Graphical User Interface (GUI) for RWPM simulation is introduced as a proposal to be used through ELT Project. In the research area of computer and communications networks, simulation is a very useful technique for the behavior of networks

  13. Few-shot learning in deep networks through global prototyping.

    Science.gov (United States)

    Blaes, Sebastian; Burwick, Thomas

    2017-10-01

    Training a deep convolution neural network (CNN) to succeed in visual object classification usually requires a great number of examples. Here, starting from such a pre-learned CNN, we study the task of extending the network to classify additional categories on the basis of only few examples ("few-shot learning"). We find that a simple and fast prototype-based learning procedure in the global feature layers ("Global Prototype Learning", GPL) leads to some remarkably good classification results for a large portion of the new classes. It requires only up to ten examples for the new classes to reach a plateau in performance. To understand this few-shot learning performance resulting from GPL as well as the performance of the original network, we use the t-SNE method (Maaten and Hinton, 2008) to visualize clusters of object category examples. This reveals the strong connection between classification performance and data distribution and explains why some new categories only need few examples for learning while others resist good classification results even when trained with many more examples. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Learning about knowledge: A complex network approach

    International Nuclear Information System (INIS)

    Fontoura Costa, Luciano da

    2006-01-01

    An approach to modeling knowledge acquisition in terms of walks along complex networks is described. Each subset of knowledge is represented as a node, and relations between such knowledge are expressed as edges. Two types of edges are considered, corresponding to free and conditional transitions. The latter case implies that a node can only be reached after visiting previously a set of nodes (the required conditions). The process of knowledge acquisition can then be simulated by considering the number of nodes visited as a single agent moves along the network, starting from its lowest layer. It is shown that hierarchical networks--i.e., networks composed of successive interconnected layers--are related to compositions of the prerequisite relationships between the nodes. In order to avoid deadlocks--i.e., unreachable nodes--the subnetwork in each layer is assumed to be a connected component. Several configurations of such hierarchical knowledge networks are simulated and the performance of the moving agent quantified in terms of the percentage of visited nodes after each movement. The Barabasi-Albert and random models are considered for the layer and interconnecting subnetworks. Although all subnetworks in each realization have the same number of nodes, several interconnectivities, defined by the average node degree of the interconnection networks, have been considered. Two visiting strategies are investigated: random choice among the existing edges and preferential choice to so far untracked edges. A series of interesting results are obtained, including the identification of a series of plateaus of knowledge stagnation in the case of the preferential movement strategy in the presence of conditional edges

  15. Assessment of Learning in Digital Interactive Social Networks: A Learning Analytics Approach

    Science.gov (United States)

    Wilson, Mark; Gochyyev, Perman; Scalise, Kathleen

    2016-01-01

    This paper summarizes initial field-test results from data analytics used in the work of the Assessment and Teaching of 21st Century Skills (ATC21S) project, on the "ICT Literacy--Learning in digital networks" learning progression. This project, sponsored by Cisco, Intel and Microsoft, aims to help educators around the world enable…

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

    Science.gov (United States)

    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.…

  17. Learning in rich networks involves both positive and negative associations.

    Science.gov (United States)

    Roembke, Tanja C; Wasserman, Edward A; McMurray, Bob

    2016-08-01

    Adaptive behaviors are believed to be shaped by both positive (the strengthening of correct associations) and negative (the pruning of incorrect associations or the building of inhibitory associations) forms of associative learning. However, there has been little direct documentation of how these basic processes participate in the learning of rich associative networks that support cognitive behaviors like categorization. Although negative associative learning is an important component of theories of development, it is not clear whether it involves acquiring specific (experience-dependent) content or represents a more general aspect of (experience-expectant) development. The authors thus trained pigeons on a complex many-to-many learning paradigm previously established as an analog to human word learning. Pigeons learned to map 16 objects onto 16 distinct report tokens; the authors manipulated the amount of negative associative learning that could occur by restricting which tokens were available as incorrect options. In testing, accuracy was lower on trials with foils that had not been presented with a target than on trials with previously experienced foils. Moreover, when the correct token was withheld, pigeons preferred foils novel to the target object over previously experienced foils. A second experiment replicated these results and further found that these effects only emerged after some positive associations had been acquired. Findings indicate that the learning of rich associative networks does not depend solely on positive associative learning, but also on negative associative learning; this conclusion has important implications for basic learning theories in both animals and humans, as well as for theories of development. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  18. Understanding Knowledge Network, Learning and Connectivism

    Science.gov (United States)

    AlDahdouh, Alaa A.; Osório, António J.; Caires, Susana

    2015-01-01

    Behaviorism, Cognitivism, Constructivism and other growing theories such as Actor-Network and Connectivism are circulating in the educational field. For each, there are allies who stand behind research evidence and consistency of observation. Meantime, those existing theories dominate the field until the background is changed or new concrete…

  19. Deep belief networks learn context dependent behavior.

    Directory of Open Access Journals (Sweden)

    Florian Raudies

    Full Text Available With the goal of understanding behavioral mechanisms of generalization, we analyzed the ability of neural networks to generalize across context. We modeled a behavioral task where the correct responses to a set of specific sensory stimuli varied systematically across different contexts. The correct response depended on the stimulus (A,B,C,D and context quadrant (1,2,3,4. The possible 16 stimulus-context combinations were associated with one of two responses (X,Y, one of which was correct for half of the combinations. The correct responses varied symmetrically across contexts. This allowed responses to previously unseen stimuli (probe stimuli to be generalized from stimuli that had been presented previously. By testing the simulation on two or more stimuli that the network had never seen in a particular context, we could test whether the correct response on the novel stimuli could be generated based on knowledge of the correct responses in other contexts. We tested this generalization capability with a Deep Belief Network (DBN, Multi-Layer Perceptron (MLP network, and the combination of a DBN with a linear perceptron (LP. Overall, the combination of the DBN and LP had the highest success rate for generalization.

  20. Deep belief networks learn context dependent behavior.

    Science.gov (United States)

    Raudies, Florian; Zilli, Eric A; Hasselmo, Michael E

    2014-01-01

    With the goal of understanding behavioral mechanisms of generalization, we analyzed the ability of neural networks to generalize across context. We modeled a behavioral task where the correct responses to a set of specific sensory stimuli varied systematically across different contexts. The correct response depended on the stimulus (A,B,C,D) and context quadrant (1,2,3,4). The possible 16 stimulus-context combinations were associated with one of two responses (X,Y), one of which was correct for half of the combinations. The correct responses varied symmetrically across contexts. This allowed responses to previously unseen stimuli (probe stimuli) to be generalized from stimuli that had been presented previously. By testing the simulation on two or more stimuli that the network had never seen in a particular context, we could test whether the correct response on the novel stimuli could be generated based on knowledge of the correct responses in other contexts. We tested this generalization capability with a Deep Belief Network (DBN), Multi-Layer Perceptron (MLP) network, and the combination of a DBN with a linear perceptron (LP). Overall, the combination of the DBN and LP had the highest success rate for generalization.

  1. Will Learning Social Inclusion Assist Rural Networks

    Science.gov (United States)

    Marchant, Jillian

    2013-01-01

    Current research on social networks in some rural communities reports continuing demise despite efforts to build resilient communities. Several factors are identified as contributing to social decline including globalisation and rural social characteristics. Particular rural social characteristics, such as strong social bonds among members of…

  2. Markov Chain Monte Carlo Bayesian Learning for Neural Networks

    Science.gov (United States)

    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.

  3. Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks

    OpenAIRE

    Airola, Rasmus; Hager, Kristoffer

    2017-01-01

    The use of machine learning and specifically neural networks is a growing trend in software development, and has grown immensely in the last couple of years in the light of an increasing need to handle big data and large information flows. Machine learning has a broad area of application, such as human-computer interaction, predicting stock prices, real-time translation, and self driving vehicles. Large companies such as Microsoft and Google have already implemented machine learning in some o...

  4. Neural Network Learning as an Inverse Problem

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra

    2005-01-01

    Roč. 13, č. 5 (2005), s. 551-559 ISSN 1367-0751 R&D Projects: GA AV ČR 1ET100300517 Institutional research plan: CEZ:AV0Z10300504 Keywords : learning from data * generalization * empirical error functional * inverse problem * evaluation operator * kernel methods Subject RIV: BA - General Mathematics Impact factor: 0.382, year: 2005

  5. Experiment in Collaborative Learning Network for Enhanced ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Since 1961, CUSO has sent approximately 11 000 Canadians abroad to work at the local level on various development issues. CUSO is now in the process of merging with Voluntary Services Overseas (VSO) Canada, and is seeking to validate the importance of knowledge sharing and collaborative learning in ...

  6. Virtual learning networks for sustainable development

    NARCIS (Netherlands)

    De Kraker, Joop; Cörvers, Ron

    2010-01-01

    Sustainable development is a participatory, multi-actor process. In this process, learning plays a major role as participants have to exchange and integrate a diversity of perspectives and types of knowledge and expertise in order to arrive at innovative, jointly supported solutions. Virtual

  7. Differential theory of learning for efficient neural network pattern recognition

    Science.gov (United States)

    Hampshire, John B., II; Vijaya Kumar, Bhagavatula

    1993-09-01

    We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

  8. Idea Management: Perspectives from Leadership, Learning, and Network Theory

    NARCIS (Netherlands)

    D. Deichmann (Dirk)

    2012-01-01

    textabstractIn this dissertation, we focus on how leadership styles, individual learning behaviors, and social network structures drive or inhibit organizational members to repeatedly generate and develop innovative ideas. Taking the idea management programs of three multinational companies as the

  9. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

    Science.gov (United States)

    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…

  10. Recommending Learning Activities in Social Network Using Data Mining Algorithms

    Science.gov (United States)

    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…

  11. Critical Facilities for Active Participation in Learning Networks

    NARCIS (Netherlands)

    Hummel, Hans; Tattersall, Colin; Burgos, Daniel; Brouns, Francis; Kurvers, Hub; Koper, Rob

    2005-01-01

    Please use the following citation: Hummel, H. G. K., Tattersall, C., Burgos, D., Brouns, F. M. R., Kurvers, H. J., & Koper, E. J. R. (2006). Critical facilities for active participation in learning networks. Int. J. Web Based Communities, 2, 1, 81-99. This article is an extended version (with

  12. Social Capital Theory: Implications for Women's Networking and Learning

    Science.gov (United States)

    Alfred, Mary V.

    2009-01-01

    This chapter describes social capital theory as a framework for exploring women's networking and social capital resources. It presents the foundational assumptions of the theory, the benefits and risks of social capital engagement, a feminist critique of social capital, and the role of social capital in adult learning.

  13. The role of learning networks in agricultural extension service delivery

    African Journals Online (AJOL)

    This paper seeks to present the findings of a study based on learning networks conducted in nine provinces of South Africa during 2013. The aim of the study was to establish to what extent this tool is known or used in the provinces of South Africa. The information was important in order to assist decision makers in future ...

  14. Ad Hoc Transient Groups: Instruments for Awareness in Learning Networks

    NARCIS (Netherlands)

    Fetter, Sibren; Rajagopal, Kamakshi; Berlanga, Adriana; Sloep, Peter

    2011-01-01

    Fetter, S., Rajagopal, K., Berlanga, A. J., & Sloep, P. B. (2011). Ad Hoc Transient Groups: Instruments for Awareness in Learning Networks. In W. Reinhardt, T. D. Ullmann, P. Scott, V. Pammer, O. Conlan, & A. J. Berlanga (Eds.), Proceedings of the 1st European Workshop on Awareness and Reflection in

  15. Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter

    2009-01-01

    Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. (2009). Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring. Presentation at the IADIS international conference on Cognition and Exploratory in Digital Age (CELDA 2009). November, 20-22, 2009, Rome, Italy.

  16. Learning Neuroscience: An Interactive Case-Based Online Network (ICON).

    Science.gov (United States)

    Quattrochi, James J.; Pasquale, Susan; Cerva, Barbara; Lester, John E.

    2002-01-01

    Presents an interactive, case-based online network (ICON) that provides a learning environment that integrates student thinking across different concentration tracks and allows students to get away from interpreting vast amounts of available information, move toward selecting useful information, recognize discriminating findings, and build a…

  17. IP Addressing: Problem-Based Learning Approach on Computer Networks

    Science.gov (United States)

    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…

  18. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    Energy Technology Data Exchange (ETDEWEB)

    Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  19. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    International Nuclear Information System (INIS)

    Bornholdt, S.

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback

  20. Application of Bionic Design to FRP T-Joints

    Science.gov (United States)

    Luo, Guang-Min; Kuo, Chia-Hung

    2017-09-01

    We applied the concepts of bionics to enhance the mechanical strength of fiberglass reinforced plastic T-joints. The failure modes of the designed arthrosis-like and gum-like joints were determined using three-point bending tests and numerical simulations and compared with those of normal T-joints bonded using structural adhesives. In the simulation, we used cohesive elements to simulate the adhesive interface of the structural adhesive. The experimental and simulation results show that the arthrosis-like joint can effectively delay the failure progress and enhance the bonding strength of T-joints, thus confirming that an appropriate bionic design can effectively control the bonding properties of structural adhesives.

  1. Learner Views about Cooperative Learning in Social Learning Networks

    Science.gov (United States)

    Cankaya, Serkan; Yunkul, Eyup

    2018-01-01

    The purpose of this study was to reveal the attitudes and views of university students about the use of Edmodo as a cooperative learning environment. In the research process, the students were divided into groups of 4 or 5 within the scope of a course given in the department of Computer Education and Instructional Technology. For each group,…

  2. Dictionary Networking in an LSP Learning Context

    DEFF Research Database (Denmark)

    Nielsen, Sandro

    2007-01-01

    Dictionaries have long been an indispensable part of learning the factual and linguistic content of a subject-field, i.e. the relevant LSP. Both teachers and students refer to and use printed and electronic specialised dictionaries as tools when teaching and learning the structure, terminology...... and usage of a subject-field, particularly when they have to read, write or translate domain-specific texts. The modern theory of dictionary functions presented in Bergenholtz and Tarp (2002) opens up exciting new possibilities for theoretical and practical lexicography and encourages lexicographers...... to adopt a new way of thinking when planning and compiling dictionaries. The function of a dictionary is to assist a particular user group with specific characteristics in order to fulfil the complex needs that arise in a particular type of use-situation. This entails a study of the extra...

  3. Real time hardware vision processing for a bionic eye

    OpenAIRE

    Josh, Horace Edmund

    2017-01-01

    A recent objective in medical bionics research is to develop visual prostheses - devices that could potentially restore the sight of blind individuals. The Monash Vision Group is currently working towards implementing a fully autonomous direct-to-brain vision implant called the Gennaris. Although research in this field is progressing quickly, initial implementations of these devices will be quite naive, offering very basic levels of vision. The vision is anticipated to be binary - that is wit...

  4. Numerical Study on Hydrodynamic Performance of Bionic Caudal Fin

    OpenAIRE

    Kai Zhou; Junkao Liu; Weishan Chen

    2016-01-01

    In this work, numerical simulations are conducted to reveal the hydrodynamic mechanism of caudal fin propulsion. In the modeling of a bionic caudal fin, a universal kinematics model with three degrees of freedom is adopted and the flexible deformation in the spanwise direction is considered. Navier-Stokes equations are used to solve the unsteady fluid flow and dynamic mesh method is applied to track the locomotion. The force coefficients, torque coefficient, and flow field characteristics are...

  5. Bionic catalysis of porphyrin for electrochemical detection of nucleic acids

    International Nuclear Information System (INIS)

    Li Jie; Lei Jianping; Wang Quanbo; Wang Peng; Ju Huangxian

    2012-01-01

    Highlights: ► This is the first application of bionic catalysis of porphyrin as detection probe in bioanalysis. ► Porphyrin–DNA–gold nanoparticle probe is synthesized. ► Binding model between FeTMPyP and DNA is verified. ► The detection probe shows excellent electrocatalytic behaviors toward the reduction of O 2 . ► The biosensor exhibited good performance with wide linear range and high specificity. - Abstract: A novel electrochemical strategy was designed for the detection of DNA based on the bionic catalysis of porphyrin. The detection probe was prepared via the assembly of thiolated double strand DNA (dsDNA) with gold nanoparticles (AuNPs), and then interacted with cationic iron (III) meso-tetrakis (N-methylphyridinum-4-yl) porphyrin (FeTMPyP) via groove binding along the dsDNA surface. The resulting nanocomplex was characterized with transmission electron microscopy, UV–vis absorption and circular dichroism spectroscopy. The FeTMPyP–DNA–AuNPs probe on gold electrode demonstrated the excellent electrocatalytic behaviors toward the reduction of O 2 due to the largely loading of FeTMPyP and good conductivity. Based on bionic catalysis of porphyrin for the reduction of O 2 , the resulting biosensor exhibited a good performance for the detection of DNA with a wide linear range from 1 × 10 −12 to 1 × 10 −8 mol L −1 and detection limit of 2.5 × 10 −13 mol L −1 at the signal/noise of 3. More importantly, the biosensor presented excellent ability to discriminate the perfectly complementary target and the mismatched stand. This strategy could be conveniently extended for detection of other biomolecules. To the best of our knowledge, this is the first application of bionic catalysis of porphyrin as detection probe and opens new opportunities for sensitive detection of biorecognition events.

  6. Design and Simulation for Bionic Mechanical Arm in Jujube Transplanter

    OpenAIRE

    Sun, Yonghua; Wang, Wei; Zong, Wangyuan; Zhang, Hong

    2010-01-01

    International audience; In this paper an automatic bionic mechanical arm of jujube transplanter has been designed and simulated with Pro/E and ADAMS software. The device can achieve the work of clamping—sending—setting the sapling and support the sapling to guarantee it perpendicularity in setting process. Design the structure of manipulator utilizing the simulation of hand working. There is 5-DOF at the manipulator to achieve simulating. Constitute dynamics mathematical model and estimated i...

  7. Machine learning of network metrics in ATLAS Distributed Data Management

    CERN Document Server

    AUTHOR|(INSPIRE)INSPIRE-00218873; The ATLAS collaboration; Toler, Wesley; Vamosi, Ralf; Bogado Garcia, Joaquin Ignacio

    2017-01-01

    The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our m...

  8. Erosion resistance of bionic functional surfaces inspired from desert scorpions.

    Science.gov (United States)

    Zhiwu, Han; Junqiu, Zhang; Chao, Ge; Li, Wen; Ren, Luquan

    2012-02-07

    In this paper, a bionic method is presented to improve the erosion resistance of machine components. Desert scorpion (Androctonus australis) is a typical animal living in sandy deserts, and may face erosive action of blowing sand at a high speed. Based on the idea of bionics and biologic experimental techniques, the mechanisms of the sand erosion resistance of desert scorpion were investigated. Results showed that the desert scorpions used special microtextures such as bumps and grooves to construct the functional surfaces to achieve the erosion resistance. In order to understand the erosion resistance mechanisms of such functional surfaces, the combination of computational and experimental research were carried out in this paper. The Computational Fluid Dynamics (CFD) method was applied to predict the erosion performance of the bionic functional surfaces. The result demonstrated that the microtextured surfaces exhibited better erosion resistance than the smooth surfaces. The further erosion tests indicated that the groove surfaces exhibited better erosion performance at 30° injection angle. In order to determine the effect of the groove dimensions on the erosion resistance, regression analysis of orthogonal multinomials was also performed under a certain erosion condition, and the regression equation between the erosion rate and groove distance, width, and height was established.

  9. Nanobionics: the impact of nanotechnology on implantable medical bionic devices.

    Science.gov (United States)

    Wallace, G G; Higgins, M J; Moulton, S E; Wang, C

    2012-08-07

    The nexus of any bionic device can be found at the electrode-cellular interface. Overall efficiency is determined by our ability to transfer electronic information across that interface. The nanostructure imparted to electrodes plays a critical role in controlling the cascade of events that determines the composition and structure of that interface. With commonly used conductors: metals, carbon and organic conducting polymers, a number of approaches that promote control over structure in the nanodomain have emerged in recent years with subsequent studies revealing a critical dependency between nanostructure and cellular behaviour. As we continue to develop our understanding of how to create and characterise electromaterials in the nanodomain, this is expected to have a profound effect on the development of next generation bionic devices. In this review, we focus on advances in fabricating nanostructured electrodes that present new opportunities in the field of medical bionics. We also briefly evaluate the interactions of living cells with the nanostructured electromaterials, in addition to highlighting emerging tools used for nanofabrication and nanocharacterisation of the electrode-cellular interface.

  10. Drag reduction through self-texturing compliant bionic materials

    Science.gov (United States)

    Liu, Eryong; Li, Longyang; Wang, Gang; Zeng, Zhixiang; Zhao, Wenjie; Xue, Qunji

    2017-01-01

    Compliant fish skin is effectively in reducing drag, thus the design and application of compliant bionic materials may be a good choice for drag reduction. Here we consider the drag reduction of compliant bionic materials. First, ZnO and PDMS mesh modified with n-octadecane were prepared, the drag reduction of self-texturing compliant n-octadecane were studied. The results show that the mesh modified by ZnO and PDMS possess excellent lipophilic and hydrophobic, thus n-octadecane at solid, semisolid and liquid state all have good adhesion with modified mesh. The states of n-octadecane changed with temperature, thus, the surface contact angle and adhesive force all varies obviously at different state. The contact angle decreases with temperature, the adhesive force shows a lower value at semisolid state. Furthermore, the drag testing results show that the compliant n-octadecane film is more effectively in drag reduction than superhydrophobic ZnO/PDMS film, indicating that the drag reduction mechanism of n-octadecane is significantly different with superhydrophobic film. Further research shows that the water flow leads to self-texturing of semisolid state n-octadecane, which is similar with compliant fish skin. Therefore, the compliant bionic materials of semisolid state n-octadecane with regular bulge plays a major role in the drag reduction.

  11. Transformative Reality: improving bionic vision with robotic sensing.

    Science.gov (United States)

    Lui, Wen Lik Dennis; Browne, Damien; Kleeman, Lindsay; Drummond, Tom; Li, Wai Ho

    2012-01-01

    Implanted visual prostheses provide bionic vision with very low spatial and intensity resolution when compared against healthy human vision. Vision processing converts camera video to low resolution imagery for bionic vision with the aim of preserving salient features such as edges. Transformative Reality extends and improves upon traditional vision processing in three ways. Firstly, a combination of visual and non-visual sensors are used to provide multi-modal data of a person's surroundings. This enables the sensing of features that are difficult to sense with only a camera. Secondly, robotic sensing algorithms construct models of the world in real time. This enables the detection of complex features such as navigable empty ground or people. Thirdly, models are visually rendered so that visually complex entities such as people can be effectively represented in low resolution. Preliminary simulated prosthetic vision trials, where a head mounted display is used to constrain a subject's vision to 25×25 binary phosphenes, suggest that Transformative Reality provides functional bionic vision for tasks such as indoor navigation, object manipulation and people detection in scenes where traditional processing is unusable.

  12. Advances in Propulsive Bionic Feet and Their Actuation Principles

    Directory of Open Access Journals (Sweden)

    Pierre Cherelle

    2014-07-01

    Full Text Available In the past decades, researchers have deeply studied pathological and nonpathological gait to understand the human ankle function during walking. These efforts resulted in the development of new lower limb prosthetic devices aiming at raising the 3C-level (control, comfort, and cosmetics of amputees. Thanks to the technological advances in engineering and mechatronics, challenges in the field of prosthetics have become an important source of interest for roboticists. Currently, most of the bionic feet are still on a research level but show promising results and a preview of tomorrow's commercial prosthetic devices. In this paper, the authors present the current state-of-the-art and the latest advances in propulsive bionic feet with its actuation principles. The context of this review study is outlined followed by a brief description of the basics in human biomechanics and criteria for new prosthetic designs. A new categorization based on the actuation principle of propulsive ankle-foot prostheses is proposed. Based on simulations, the general principles and benefits of each actuation method are explained. The corresponding latest advances in propulsive bionic feet are presented together with their main characteristics and scientific outcomes. The authors also propose to the reader a comparison analysis of the presented devices with a discussion of the general tendencies in new prosthetic feet.

  13. Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback.

    Science.gov (United States)

    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.

  14. A Kohonen Network for Modeling Students' Learning Styles in Web 2.0 Collaborative Learning Systems

    Science.gov (United States)

    Zatarain-Cabada, Ramón; Barrón-Estrada, M. Lucia; Zepeda-Sánchez, Leopoldo; Sandoval, Guillermo; Osorio-Velazquez, J. Moises; Urias-Barrientos, J. E.

    The identification of the best learning style in an Intelligent Tutoring System must be considered essential as part of the success in the teaching process. In many implementations of automatic classifiers finding the right student learning style represents the hardest assignment. The reason is that most of the techniques work using expert groups or a set of questionnaires which define how the learning styles are assigned to students. This paper presents a novel approach for automatic learning styles classification using a Kohonen network. The approach is used by an author tool for building Intelligent Tutoring Systems running under a Web 2.0 collaborative learning platform. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the author tool.

  15. Deep Belief Networks Learn Context Dependent Behavior

    OpenAIRE

    Raudies, Florian; Zilli, Eric A.; Hasselmo, Michael E.

    2014-01-01

    With the goal of understanding behavioral mechanisms of generalization, we analyzed the ability of neural networks to generalize across context. We modeled a behavioral task where the correct responses to a set of specific sensory stimuli varied systematically across different contexts. The correct response depended on the stimulus (A,B,C,D) and context quadrant (1,2,3,4). The possible 16 stimulus-context combinations were associated with one of two responses (X,Y), one of which was correct f...

  16. Some Learning Properties of Modular Network SOMs

    Science.gov (United States)

    Takeda, Manabu; Ikeda, Kazushi; Furukawa, Tetsuo

    The Modular Network Self-Organizing Map (mnSOM) is a generalization of the SOM, where each node represents a parametric function such as a multi-layer perceptron or another SOM. Since given datasets are, in general, fewer than nodes, some nodes never win in competition and have to update their parameters from the winners in the neighborhood. This is a process that can be regarded as interpolation. This study derives the interpolation curve between winners in simple cases and discusses the distribution of winners based on the neighborhood function.

  17. Evaluation of the functional status of learning networks based on the dimensions defining communities of practice

    NARCIS (Netherlands)

    Meijs, Celeste; Prinsen, Fleur; De Laat, Maarten

    2017-01-01

    Abstract: Learning in professional networks is gaining popularity in teachers’ professional development. To study how teachers evaluated their networks, we developed a questionnaire called the ‘network barometer’ to inquire functioning according to three dimensions based on communities of

  18. Advanced Learning Technologies and Learning Networks and Their Impact on Future Aerospace Workforce

    Science.gov (United States)

    Noor, Ahmed K. (Compiler)

    2003-01-01

    This document contains the proceedings of the training workshop on Advanced Learning Technologies and Learning Networks and their impact on Future Aerospace Workforce. The workshop was held at the Peninsula Workforce Development Center, Hampton, Virginia, April 2 3, 2003. The workshop was jointly sponsored by Old Dominion University and NASA. Workshop attendees came from NASA, other government agencies, industry, and universities. The objectives of the workshop were to: 1) provide broad overviews of the diverse activities related to advanced learning technologies and learning environments, and 2) identify future directions for research that have high potential for aerospace workforce development. Eighteen half-hour overviewtype presentations were made at the workshop.

  19. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

    Science.gov (United States)

    Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L.; Schulz, Andreas L.; Ohl, Frank W.; Lommerzheim, Marcel; Neumann, Heiko

    2016-01-01

    Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

  20. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

    Directory of Open Access Journals (Sweden)

    Christian Jarvers

    2016-11-01

    Full Text Available Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e. tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN, which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that

  1. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning.

    Science.gov (United States)

    Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L; Schulz, Andreas L; Ohl, Frank W; Lommerzheim, Marcel; Neumann, Heiko

    2016-01-01

    Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

  2. Students' Personal Networks in Virtual and Personal Learning Environments: A Case Study in Higher Education Using Learning Analytics Approach

    Science.gov (United States)

    Casquero, Oskar; Ovelar, Ramón; Romo, Jesús; Benito, Manuel; Alberdi, Mikel

    2016-01-01

    The main objective of this paper is to analyse the effect of the affordances of a virtual learning environment and a personal learning environment (PLE) in the configuration of the students' personal networks in a higher education context. The results are discussed in light of the adaptation of the students to the learning network made up by two…

  3. Effective learning in recurrent max-min neural networks.

    Science.gov (United States)

    Loe, Kia Fock; Teow, Loo Nin

    1998-04-01

    Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-valued domains. As such, neural networks that employ max-min activation functions have been a subject of interest in recent years. Since max-min functions are not strictly differentiable, we propose a mathematically sound learning method based on using Fourier convergence analysis of side-derivatives to derive a gradient descent technique for max-min error functions. We then propose a novel recurrent max-min neural network model that is trained to perform grammatical inference as an application example. Comparisons made between this model and recurrent sigmoidal neural networks show that our model not only performs better in terms of learning speed and generalization, but that its final weight configuration allows a deterministic finite automation (DFA) to be extracted in a straightforward manner. In essence, we are able to demonstrate that our proposed gradient descent technique does allow max-min neural networks to learn effectively.

  4. Supervised dictionary learning for inferring concurrent brain networks.

    Science.gov (United States)

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

    2015-10-01

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

  5. Mimicking Nature´s way of organizing in industry: a network learning perspective

    DEFF Research Database (Denmark)

    Ulhøi, John Parm; Madsen, Henning

    to reconsider organisational learning as being both an internal as well as an external phenomenon. By bringing network learning into an existing interorganisational setting (such as industrial ecology) new potentials for increased learning emerge for the participating companies. The concept of network learning...

  6. Outsmarting neural networks: an alternative paradigm for machine learning

    Energy Technology Data Exchange (ETDEWEB)

    Protopopescu, V.; Rao, N.S.V.

    1996-10-01

    We address three problems in machine learning, namely: (i) function learning, (ii) regression estimation, and (iii) sensor fusion, in the Probably and Approximately Correct (PAC) framework. We show that, under certain conditions, one can reduce the three problems above to the regression estimation. The latter is usually tackled with artificial neural networks (ANNs) that satisfy the PAC criteria, but have high computational complexity. We propose several computationally efficient PAC alternatives to ANNs to solve the regression estimation. Thereby we also provide efficient PAC solutions to the function learning and sensor fusion problems. The approach is based on cross-fertilizing concepts and methods from statistical estimation, nonlinear algorithms, and the theory of computational complexity, and is designed as part of a new, coherent paradigm for machine learning.

  7. Wavelet-cellular neural network architecture and learning algorithm

    Science.gov (United States)

    Bal, Abdullah; Ucan, Osman N.; Pastaci, Halit; Alam, Mohammad S.

    2004-04-01

    Cellular Neural Networks (CNN) provides fast parallel computational capability for image processing applications. The behavior of the CNN is defined by two template matrices. In this paper, adjustment of these template-matrix coefficients have been realized using supervised learning algorithm based on back-propagation technique and wavelet function. Back-propagation algorithm has been modified for dynamic behavior of CNN. Wavelet function is utilized to provide the activation function derivation in this learning algorithm. The supervised learning algorithm is then executed to obtain a compact CNN architecture, called as Wave-CNN. The proposed new learning algorithm and Wave-CNN architecture performance have been tested for 2D image processing applications.

  8. SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

    Science.gov (United States)

    Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T

    2015-02-01

    Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks. Copyright © 2014 Elsevier Inc. All rights reserved.

  9. Learning discriminative functional network features of schizophrenia

    Science.gov (United States)

    Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar

    2017-03-01

    Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

  10. Learning by Knowledge Networking across Cultures

    DEFF Research Database (Denmark)

    Wangel, Arne; Stærdahl, Jens; Bransholm Pedersen, Kirsten

    2005-01-01

    ) and environmental impact assessment (EIA) in Malaysia 1998-2003 has sought to address these needs for new competences. Differences in educational background and the work culture of the participants have presented difficulties during these courses, in particular in terms of achieving a mixed team building to turn......Engineers and planners working in trans-national production and aid project interventions in Third World countries must be able to 're-invent' technological systems across cultures and plan and build the capacities of their counterparts. A series of joint courses on cleaner production (CP...... some of the obstacles into resources for knowledge sharing. However, students have stressed their positive experience of cross-cultural communication. While a joint course of three week duration by itself may involve only limited cross-cultural learning, serving primarily as an introduction to a long...

  11. Functional networks inference from rule-based machine learning models.

    Science.gov (United States)

    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

  12. Social Networking Sites and Addiction: Ten Lessons Learned.

    Science.gov (United States)

    Kuss, Daria J; Griffiths, Mark D

    2017-03-17

    Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i) social networking and social media use are not the same; (ii) social networking is eclectic; (iii) social networking is a way of being; (iv) individuals can become addicted to using social networking sites; (v) Facebook addiction is only one example of SNS addiction; (vi) fear of missing out (FOMO) may be part of SNS addiction; (vii) smartphone addiction may be part of SNS addiction; (viii) nomophobia may be part of SNS addiction; (ix) there are sociodemographic differences in SNS addiction; and (x) there are methodological problems with research to date. These are discussed in turn. Recommendations for research and clinical applications are provided.

  13. Social Networking Sites and Addiction: Ten Lessons Learned

    Directory of Open Access Journals (Sweden)

    Daria J. Kuss

    2017-03-01

    Full Text Available Online social networking sites (SNSs have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i social networking and social media use are not the same; (ii social networking is eclectic; (iii social networking is a way of being; (iv individuals can become addicted to using social networking sites; (v Facebook addiction is only one example of SNS addiction; (vi fear of missing out (FOMO may be part of SNS addiction; (vii smartphone addiction may be part of SNS addiction; (viii nomophobia may be part of SNS addiction; (ix there are sociodemographic differences in SNS addiction; and (x there are methodological problems with research to date. These are discussed in turn. Recommendations for research and clinical applications are provided.

  14. Social Networking Sites and Addiction: Ten Lessons Learned

    Science.gov (United States)

    Kuss, Daria J.; Griffiths, Mark D.

    2017-01-01

    Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i) social networking and social media use are not the same; (ii) social networking is eclectic; (iii) social networking is a way of being; (iv) individuals can become addicted to using social networking sites; (v) Facebook addiction is only one example of SNS addiction; (vi) fear of missing out (FOMO) may be part of SNS addiction; (vii) smartphone addiction may be part of SNS addiction; (viii) nomophobia may be part of SNS addiction; (ix) there are sociodemographic differences in SNS addiction; and (x) there are methodological problems with research to date. These are discussed in turn. Recommendations for research and clinical applications are provided. PMID:28304359

  15. Learning to play Go using recursive neural networks.

    Science.gov (United States)

    Wu, Lin; Baldi, Pierre

    2008-11-01

    Go is an ancient board game that poses unique opportunities and challenges for artificial intelligence. Currently, there are no computer Go programs that can play at the level of a good human player. However, the emergence of large repositories of games is opening the door for new machine learning approaches to address this challenge. Here we develop a machine learning approach to Go, and related board games, focusing primarily on the problem of learning a good evaluation function in a scalable way. Scalability is essential at multiple levels, from the library of local tactical patterns, to the integration of patterns across the board, to the size of the board itself. The system we propose is capable of automatically learning the propensity of local patterns from a library of games. Propensity and other local tactical information are fed into recursive neural networks, derived from a probabilistic Bayesian network architecture. The recursive neural networks in turn integrate local information across the board in all four cardinal directions and produce local outputs that represent local territory ownership probabilities. The aggregation of these probabilities provides an effective strategic evaluation function that is an estimate of the expected area at the end, or at various other stages, of the game. Local area targets for training can be derived from datasets of games played by human players. In this approach, while requiring a learning time proportional to N(4), skills learned on a board of size N(2) can easily be transferred to boards of other sizes. A system trained using only 9 x 9 amateur game data performs surprisingly well on a test set derived from 19 x 19 professional game data. Possible directions for further improvements are briefly discussed.

  16. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

    Science.gov (United States)

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

  17. Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

    Science.gov (United States)

    Covi, Erika; Brivio, Stefano; Serb, Alexander; Prodromakis, Themis; Fanciulli, Marco; Spiga, Sabina

    2016-01-01

    Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%. PMID:27826226

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

    Science.gov (United States)

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

    2014-04-06

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

  19. Research on the Integration of Bionic Geometry Modeling and Simulation of Robot Foot Based on Characteristic Curve

    Science.gov (United States)

    He, G.; Zhu, H.; Xu, J.; Gao, K.; Zhu, D.

    2017-09-01

    The bionic research of shape is an important aspect of the research on bionic robot, and its implementation cannot be separated from the shape modeling and numerical simulation of the bionic object, which is tedious and time-consuming. In order to improve the efficiency of shape bionic design, the feet of animals living in soft soil and swamp environment are taken as bionic objects, and characteristic skeleton curve, section curve, joint rotation variable, position and other parameters are used to describe the shape and position information of bionic object’s sole, toes and flipper. The geometry modeling of the bionic object is established by using the parameterization of characteristic curves and variables. Based on this, the integration framework of parametric modeling and finite element modeling, dynamic analysis and post-processing of sinking process in soil is proposed in this paper. The examples of bionic ostrich foot and bionic duck foot are also given. The parametric modeling and integration technique can achieve rapid improved design based on bionic object, and it can also greatly improve the efficiency and quality of robot foot bionic design, and has important practical significance to improve the level of bionic design of robot foot’s shape and structure.

  20. Implementing e-network-supported inquiry learning in science

    DEFF Research Database (Denmark)

    Williams, John; Cowie, Bronwen; Khoo, Elaine

    2013-01-01

    The successful implementation of electronically networked (e-networked) tools to support an inquiry-learning approach in secondary science classrooms is dependent on a range of factors spread between teachers, schools, and students. The teacher must have a clear understanding of the nature......-construct knowledge using a wide range of resources for meaning making and expression of ideas. These outcomes were, however, contingent on the interplay of teacher understanding of the nature of science inquiry and school provision of an effective technological infrastructure and support for flexible curriculum...

  1. Parameter learning in MTE networks using incomplete data

    DEFF Research Database (Denmark)

    Fernández, Antonio; Langseth, Helge; Nielsen, Thomas Dyhre

    a considerable computational burden as well as the inability to handle missing values in the training data. In this paper we describe an EM-based algorithm for learning the maximum likelihood parameters of an MTE network when confronted with incomplete data. In order to overcome the computational difficulties we......Bayesian networks with mixtures of truncated exponentials (MTEs) are gaining popularity as a flexible modelling framework for hybrid domains. MTEs support efficient and exact inference algorithms, but estimating an MTE from data has turned out to be a difficult task. Current methods suffer from...

  2. Rethinking learning networks collaborative possibilities for a Deleuzian century

    CERN Document Server

    Kamp, Annelies

    2013-01-01

    In the face of today's complex policy challenges, various forms of 'joining-up' - networking, collaborating, partnering - have become key responses. However, institutions often fail to take advantage of the full benefits that joining-up offers. In this book, the author draws on ethnographic research into learning networks in post compulsory education and training in the state of Victoria, Australia, to explore why this might be the case and presents an argument for rethinking how joining-up works in practice. Throughout the book, Deleuzian concepts are engaged to forge a 'little complicating m

  3. Are deep neural networks really learning relevant features?

    DEFF Research Database (Denmark)

    Kereliuk, Corey Mose; Larsen, Jan; Sturm, Bob L.

    In recent years deep neural networks (DNNs) have become a popular choice for audio content analysis. This may be attributed to various factors including advancements in training algorithms, computational power, and the potential for DNNs to implicitly learn a set of feature detectors. We have...... drawn based on training/testing using the GTZAN dataset, which is now known to contain several flaws including replicated observations and artists. We illustrate how considering these flaws dramatically changes the results, which leads one to question the degree to which the learned frame-level features...

  4. Perspectives on Advanced Learning Technologies and Learning Networks and Future Aerospace Workforce Environments

    Science.gov (United States)

    Noor, Ahmed K. (Compiler)

    2003-01-01

    An overview of the advanced learning technologies is given in this presentation along with a brief description of their impact on future aerospace workforce development. The presentation is divided into five parts (see Figure 1). In the first part, a brief historical account of the evolution of learning technologies is given. The second part describes the current learning activities. The third part describes some of the future aerospace systems, as examples of high-tech engineering systems, and lists their enabling technologies. The fourth part focuses on future aerospace research, learning and design environments. The fifth part lists the objectives of the workshop and some of the sources of information on learning technologies and learning networks.

  5. Bionic prosthetic hands: A review of present technology and future aspirations.

    Science.gov (United States)

    Clement, R G E; Bugler, K E; Oliver, C W

    2011-12-01

    Bionic prosthetic hands are rapidly evolving. An in-depth knowledge of this field of medicine is currently only required by a small number of individuals working in highly specialist units. However, with improving technology it is likely that the demand for and application of bionic hands will continue to increase and a wider understanding will be necessary. We review the literature and summarise the important advances in medicine, computing and engineering that have led to the development of currently available bionic hand prostheses. The bionic limb of today has progressed greatly since the hook prostheses that were introduced centuries ago. We discuss the ways that major functions of the human hand are being replicated artificially in modern bionic hands. Despite the impressive advances bionic prostheses remain an inferior replacement to their biological counterparts. Finally we discuss some of the key areas of research that could lead to vast improvements in bionic limb functionality that may one day be able to fully replicate the biological hand or perhaps even surpass its innate capabilities. It is important for the healthcare community to have an understanding of the development of bionic hands and the technology underpinning them as this area of medicine will expand. Copyright © 2011 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.

  6. Bionic Design for Reducing Adhesive Resistance of the Ridger Inspired by a Boar's Head

    Science.gov (United States)

    Li, Jianqiao; Yan, Yunpeng; Chirende, Benard; Wu, Xuejiao; Wang, Zhaoliang

    2017-01-01

    The main feature of the boar's head used to root around for food is the front part, which is similar to the ridger in terms of function, load, and environment. In this paper, the boar's head was selected as the biological prototype for developing a new ridger. The point cloud of the head was captured by a 3D scanner, and then, the head surface was reconstructed using 3D coordinates. The characteristic curves of the front part of the boar's head were extracted, and then, five cross-sectional curves and one vertical section curve were fitted. Based on the fitted curves, five kinds of bionic ridgers were designed. The penetrating resistances of the bionic ridgers and traditional ridger were tested at different speeds in an indoor soil bin. The test results showed that bionic ridger B had the best penetrating resistance reduction ratio of 16.67% at 4.2 km/h velocity. In order to further evaluate the performance of the best bionic ridger (bionic ridger B), both the bionic ridger and traditional ridger were tested in a field under the same working conditions. The field results indicate that the bionic ridger reduces penetrating resistance by 6.91% compared to the traditional ridger, and the test results validate that the bionic ridger has an effect on reducing penetrating resistance. PMID:28757796

  7. Machine learning based Intelligent cognitive network using fog computing

    Science.gov (United States)

    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.

  8. A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

    Directory of Open Access Journals (Sweden)

    Satchidananda Dehuri

    2011-01-01

    dimensional-space where linear separability is possible. Moreover, the proposed HCFLNN combines the best attribute of particle swarm optimization (PSO, back propagation learning (BP learning, and functional link neural networks (FLNNs. The proposed method eliminates the need of hidden layer by expanding the input patterns using Chebyshev orthogonal polynomials. We have shown its effectiveness of classifying the unknown pattern using the publicly available datasets obtained from UCI repository. The computational results are then compared with functional link neural network (FLNN with a generic basis functions, PSO-based FLNN, and EFLN. From the comparative study, we observed that the performance of the HCFLNN outperforms FLNN, PSO-based FLNN, and EFLN in terms of classification accuracy.

  9. Learning document semantic representation with hybrid deep belief network.

    Science.gov (United States)

    Yan, Yan; Yin, Xu-Cheng; Li, Sujian; Yang, Mingyuan; Hao, Hong-Wei

    2015-01-01

    High-level abstraction, for example, semantic representation, is vital for document classification and retrieval. However, how to learn document semantic representation is still a topic open for discussion in information retrieval and natural language processing. In this paper, we propose a new Hybrid Deep Belief Network (HDBN) which uses Deep Boltzmann Machine (DBM) on the lower layers together with Deep Belief Network (DBN) on the upper layers. The advantage of DBM is that it employs undirected connection when training weight parameters which can be used to sample the states of nodes on each layer more successfully and it is also an effective way to remove noise from the different document representation type; the DBN can enhance extract abstract of the document in depth, making the model learn sufficient semantic representation. At the same time, we explore different input strategies for semantic distributed representation. Experimental results show that our model using the word embedding instead of single word has better performance.

  10. Analysis of North American Newspaper Coverage of Bionics Using the Disability Studies Framework

    Directory of Open Access Journals (Sweden)

    Sonum Panesar

    2014-02-01

    Full Text Available Bionics are a set of technology products that are constantly evolving. Bionics are proposed as body add-ons or replacement for many body parts (ears, eyes, knees, neural prostheses, joints, muscles, kidney, liver, cartilage lungs, discs, pancreas, dental pulp, skin, hippocampus, legs and hands, and functions such as speech. Two main applications of bionic products are discussed; one being for the restoration of body abilities to a species-typical norm and the other being the addition of abilities to the body that are not species-typical. Disabled people are one main group perceived to be in need of therapeutic interventions that use various bionic products. So far, therapeutic interventions are about restoration to the species-typical norm. However, therapeutic bionic products increasingly give the wearer beyond normal body abilities (therapeutic enhancements. Many so-called non-disabled people want the same enhanced body-abilities especially through non-invasive bionic products (e.g., non-invasive brain machine interfaces, exoskeletons. The media has the ability to shape public perceptions with numerous consequences. The purpose of this study was to provide quantitative and qualitative data on how bionic technologies and its users are portrayed in North American newspapers. Data was obtained from 1977 to 2013 from the Canadian Newsstand complete database which covers over 300 English language Canadian newspapers and two Canadian newspapers, one with national focus (The Globe and Mail and one with local focus (Calgary Herald, and from 1980–2013 from one American newspaper with national reach (The New York Times. The study found (a an almost always positive portrayal of bionics; (b coverage of bionics mostly within a medical framework; (c a predominantly stereotypical and negative portrayal of individuals with disabilities; and (d a hierarchy of worthiness between different assistive devices such as a reporting bias favoring artificial legs

  11. Estimation of the age and amount of brown rice plant hoppers based on bionic electronic nose use.

    Science.gov (United States)

    Xu, Sai; Zhou, Zhiyan; Lu, Huazhong; Luo, Xiwen; Lan, Yubin; Zhang, Yang; Li, Yanfang

    2014-09-29

    The brown rice plant hopper (BRPH), Nilaparvata lugens (Stal), is one of the most important insect pests affecting rice and causes serious damage to the yield and quality of rice plants in Asia. This study used bionic electronic nose technology to sample BRPH volatiles, which vary in age and amount. Principal component analysis (PCA), linear discrimination analysis (LDA), probabilistic neural network (PNN), BP neural network (BPNN) and loading analysis (Loadings) techniques were used to analyze the sampling data. The results indicate that the PCA and LDA classification ability is poor, but the LDA classification displays superior performance relative to PCA. When a PNN was used to evaluate the BRPH age and amount, the classification rates of the training set were 100% and 96.67%, respectively, and the classification rates of the test set were 90.67% and 64.67%, respectively. When BPNN was used for the evaluation of the BRPH age and amount, the classification accuracies of the training set were 100% and 48.93%, respectively, and the classification accuracies of the test set were 96.67% and 47.33%, respectively. Loadings for BRPH volatiles indicate that the main elements of BRPHs' volatiles are sulfur-containing organics, aromatics, sulfur-and chlorine-containing organics and nitrogen oxides, which provide a reference for sensors chosen when exploited in specialized BRPH identification devices. This research proves the feasibility and broad application prospects of bionic electronic noses for BRPH recognition.

  12. Neural network representation and learning of mappings and their derivatives

    Science.gov (United States)

    White, Halbert; Hornik, Kurt; Stinchcombe, Maxwell; Gallant, A. Ronald

    1991-01-01

    Discussed here are recent theorems proving that artificial neural networks are capable of approximating an arbitrary mapping and its derivatives as accurately as desired. This fact forms the basis for further results establishing the learnability of the desired approximations, using results from non-parametric statistics. These results have potential applications in robotics, chaotic dynamics, control, and sensitivity analysis. An example involving learning the transfer function and its derivatives for a chaotic map is discussed.

  13. Learning gene regulatory networks from only positive and unlabeled data

    Directory of Open Access Journals (Sweden)

    Elkan Charles

    2010-05-01

    Full Text Available Abstract Background Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact. Results A recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement. Conclusions Compared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.

  14. Neural Network Machine Learning and Dimension Reduction for Data Visualization

    Science.gov (United States)

    Liles, Charles A.

    2014-01-01

    Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

  15. Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.

    Directory of Open Access Journals (Sweden)

    Insoo Sohn

    Full Text Available 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.

  16. Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.

    Science.gov (United States)

    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.

  17. Learning Effectiveness of the NASA Digital Learning Network

    Science.gov (United States)

    Hix, Billy

    2005-01-01

    Student participation in actual investigations which develop inquiry and intellectual skills has long been regarded as an essential component of science instructions (Schwab, 1962; White, 1999). Such investigations give students an opportunity to appreciate the spirit of science and promote an understanding of the nature of science. However, classroom research conducted over the past 20 years describes science teaching as primarily teacher centered. Typical instruction consists of whole class, noninteractive activities in which individual seatwork has constituted the bulk of classroom interactions (Tobin and Gallagher, 1997). Students typically learn science from textbooks and lectures. Their main motivation is to do reasonably well on tests and examinations (Layman, 1999). During the past five years, infrastructure constraints have reduced to the point that many schools systems can now afford low cost, high quality video conferencing equipment (International Society for Technology in Education, 2003). This study investigates the use of interactive video conferencing vs. face to face interaction with hands-on, inquiry based activities. Some basic questions to be addressed are: How does the delivery method impact the students understanding of the goals of the experiment? Are students explanation of the strategies of experimentation different based on the method of instruction that was provided. Do students engaged in a workshop with the instructor in the room vs. an instructor over video conferencing have different perception of the understanding of the subject materials?

  18. Bifurcation and category learning in network models of oscillating cortex

    Science.gov (United States)

    Baird, Bill

    1990-06-01

    A genetic model of oscillating cortex, which assumes “minimal” coupling justified by known anatomy, is shown to function as an associative memory, using previously developed theory. The network has explicit excitatory neurons with local inhibitory interneuron feedback that forms a set of nonlinear oscillators coupled only by long-range excitatory connections. Using a local Hebb-like learning rule for primary and higher-order synapses at the ends of the long-range connections, the system learns to store the kinds of oscillation amplitude patterns observed in olfactory and visual cortex. In olfaction, these patterns “emerge” during respiration by a pattern forming phase transition which we characterize in the model as a multiple Hopf bifurcation. We argue that these bifurcations play an important role in the operation of real digital computers and neural networks, and we use bifurcation theory to derive learning rules which analytically guarantee CAM storage of continuous periodic sequences-capacity: N/2 Fourier components for an N-node network-no “spurious” attractors.

  19. The Challenges to Connectivist Learning on Open Online Networks: Learning Experiences during a Massive Open Online Course

    Directory of Open Access Journals (Sweden)

    Rita Kop

    2011-03-01

    Full Text Available Self-directed learning on open online networks is now a possibility as communication and resources can be combined to create learning environments. But is it really? There are some challenges that might prevent learners from having a quality learning experience. This paper raises questions on levels of learner autonomy, presence, and critical literacies required in active connectivist learning.

  20. Practice of Connectivism As Learning Theory: Enhancing Learning Process Through Social Networking Site (Facebook

    Directory of Open Access Journals (Sweden)

    Fahriye Altınay Aksal

    2013-12-01

    Full Text Available The impact of the digital age within learning and social interaction has been growing rapidly. The realm of digital age and computer mediated communication requires reconsidering instruction based on collaborative interactive learning process and socio-contextual experience for learning. Social networking sites such as facebook can help create group space for digital dialogue to inform, question and challenge within a frame of connectivism as learning theory within the digital age. The aim of this study is to elaborate the practice of connectivism as learning theory in terms of internship course. Facebook group space provided social learning platform for dialogue and negotiation beside the classroom learning and teaching process in this study. The 35 internship students provided self-reports within a frame of this qualitative research. This showed how principles of theory practiced and how this theory and facebook group space contribute learning, selfleadership, decision making and reflection skills. As the research reflects a practice of new theory based on action research, learning is not individualistic attempt in the digital age as regards the debate on learning in digital age within a frame of connectivism

  1. Empirical Models of Social Learning in a Large, Evolving Network.

    Directory of Open Access Journals (Sweden)

    Ayşe Başar Bener

    Full Text Available This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals' access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1 attraction homophily causes individuals to form ties on the basis of attribute similarity, 2 aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3 social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.

  2. Generalized Hybrid Constructive Learning Algorithm for Multioutput RBF Networks.

    Science.gov (United States)

    Qian, Xusheng; Huang, He; Chen, Xiaoping; Huang, Tingwen

    2017-11-01

    An efficient generalized hybrid constructive (GHC) learning algorithm for multioutput radial basis function (RBF) networks is proposed to obtain a compact network with good generalization capability. By this algorithm, one can train the adjustable parameters and determine the optimal network structure simultaneously. First, an initialization method based on the growing and pruning algorithm is utilized to select the important initial hidden neurons and candidate ones. Then, by introducing a generalized hidden matrix, a structured parameter optimization algorithm is presented to train multioutput RBF network with fixed size, which combines Levenberg-Marquardt (LM) algorithm with least-square method together. Beginning from an appropriate number of hidden neurons, new neurons chosen from the candidates are added one by one each time when the training entraps into local minima. By incorporating an improved incremental constructive scheme, the training is built on previous results after adding new neurons such that the GHC learning algorithm avoids a trial-and-error procedure. Furthermore, based on the improved computation for LM training, the memory limitation problem is solved. The computational complexity analysis and experimental results demonstrate that better performance is efficiently achieved by this algorithm.

  3. Empirical Models of Social Learning in a Large, Evolving Network.

    Science.gov (United States)

    Bener, Ayşe Başar; Çağlayan, Bora; Henry, Adam Douglas; Prałat, Paweł

    2016-01-01

    This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals' access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.

  4. Distributed reinforcement learning for adaptive and robust network intrusion response

    Science.gov (United States)

    Malialis, Kleanthis; Devlin, Sam; Kudenko, Daniel

    2015-07-01

    Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.

  5. Visual Tracking Utilizing Object Concept from Deep Learning Network

    Science.gov (United States)

    Xiao, C.; Yilmaz, A.; Lia, S.

    2017-05-01

    Despite having achieved good performance, visual tracking is still an open area of research, especially when target undergoes serious appearance changes which are not included in the model. So, in this paper, we replace the appearance model by a concept model which is learned from large-scale datasets using a deep learning network. The concept model is a combination of high-level semantic information that is learned from myriads of objects with various appearances. In our tracking method, we generate the target's concept by combining the learned object concepts from classification task. We also demonstrate that the last convolutional feature map can be used to generate a heat map to highlight the possible location of the given target in new frames. Finally, in the proposed tracking framework, we utilize the target image, the search image cropped from the new frame and their heat maps as input into a localization network to find the final target position. Compared to the other state-of-the-art trackers, the proposed method shows the comparable and at times better performance in real-time.

  6. Networked learning in, for, and with the world

    DEFF Research Database (Denmark)

    Nørgård, Rikke Toft; Mor, Yishay; Bengtsen, Søren Smedegaard

    2018-01-01

    With the so-called ‘Mode 3’ university as overarching framework (Barnett, 2004; Bengtsen & Nørgård, 2016; Barnett & Bengtsen, 2017; Nørgård, Olesen & Toft-Nielsen, 2018) this chapter considers how traditional forms of and formats for teaching and learning within higher education can be rethought,......’ in higher education. In the following sections, we will describe these transformations of university being, before considering some of the new challenges, opportunities, and potentials of teaching and learning in and through hybrid networks in the Mode 3 institution......., opportunities, and potentials to the teaching and learning that takes place at the university. Through history, and across different present national contexts and cultures, the ‘being’ of the university and its livelihood and mandate has changed (Wright, 2016; Barnett, 2018). Through these transformations where......, reconfigured, and redesigned in order to facilitate valuable, meaningful and relevant hybrid networked learning in, for, and with the world. What it means to ‘be’ a university is changing and the university is a ‘being’ that in itself is changing (Barnett, 2011), something also offering challenges...

  7. Learning Bayesian networks from survival data using weighting censored instances.

    Science.gov (United States)

    Stajduhar, Ivan; Dalbelo-Basić, Bojana

    2010-08-01

    Different survival data pre-processing procedures and adaptations of existing machine-learning techniques have been successfully applied to numerous fields in clinical medicine. Zupan et al. (2000) proposed handling censored survival data by assigning distributions of outcomes to shortly observed censored instances. In this paper, we applied their learning technique to two well-known procedures for learning Bayesian networks: a search-and-score hill-climbing algorithm and a constraint-based conditional independence algorithm. The method was thoroughly tested in a simulation study and on the publicly available clinical dataset GBSG2. We compared it to learning Bayesian networks by treating censored instances as event-free and to Cox regression. The results on model performance suggest that the weighting approach performs best when dealing with intermediate censoring. There is no significant difference between the model structures learnt using either the weighting approach or by treating censored instances as event-free, regardless of censoring. Copyright 2010 Elsevier Inc. All rights reserved.

  8. Learning a Dilated Residual Network for SAR Image Despeckling

    Science.gov (United States)

    Zhang, Qiang; Yuan, Qiangqiang; Li, Jie; Yang, Zhen; Ma, Xiaoshuang

    2018-01-01

    In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows superior performance over the state-of-the-art methods on both quantitative and visual assessments, especially for strong speckle noise.

  9. Deep learning for steganalysis via convolutional neural networks

    Science.gov (United States)

    Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu

    2015-03-01

    Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.

  10. Learning a Dilated Residual Network for SAR Image Despeckling

    Directory of Open Access Journals (Sweden)

    Qiang Zhang

    2018-01-01

    Full Text Available In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN. SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and a residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows a superior performance over the state-of-the-art methods in both quantitative and visual assessments, especially for strong speckle noise.

  11. Machine learning of network metrics in ATLAS Distributed Data Management

    Science.gov (United States)

    Lassnig, Mario; Toler, Wesley; Vamosi, Ralf; Bogado, Joaquin; ATLAS Collaboration

    2017-10-01

    The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for networkaware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

  12. Bionic Humans Using EAP as Artificial Muscles Reality and Challenges

    Directory of Open Access Journals (Sweden)

    Yoseph Bar-Cohen

    2008-11-01

    Full Text Available For many years, the idea of a human with bionic muscles immediately conjures up science fiction images of a TV series superhuman character that was implanted with bionic muscles and portrayed with strength and speed far superior to any normal human. As fantastic as this idea may seem, recent developments in electroactive polymers (EAP may one day make such bionics possible. Polymers that exhibit large displacement in response to stimulation that is other than electrical signal were known for many years. Initially, EAP received relatively little attention due to their limited actuation capability. However, in the recent years, the view of the EAP materials has changed due to the introduction of effective new materials that significantly surpassed the capability of the widely used piezoelectric polymer, PVDF. As this technology continues to evolve, novel mechanisms that are biologically inspired are expected to emerge. EAP materials can potentially provide actuation with lifelike response and more flexible configurations. While further improvements in performance and robustness are still needed, there already have been several reported successes. In recognition of the need for cooperation in this multidisciplinary field, the author initiated and organized a series of international forums that are leading to a growing number of research and development projects and to great advances in the field. In 1999, he challenged the worldwide science and engineering community of EAP experts to develop a robotic arm that is actuated by artificial muscles to win a wrestling match against a human opponent. In this paper, the field of EAP as artificial muscles will be reviewed covering the state of the art, the challenges and the vision for the progress in future years.

  13. Investigating the Educational Value of Social Learning Networks: A Quantitative Analysis

    Science.gov (United States)

    Dafoulas, Georgios; Shokri, Azam

    2016-01-01

    Purpose: The emergence of Education 2.0 enabled technology-enhanced learning, necessitating new pedagogical approaches, while e-learning has evolved into an instrumental pedagogy of collaboration through affordances of social media. Social learning networks and ubiquitous learning enabled individual and group learning through social engagement and…

  14. Transformation: Structure/space studies in bionics and space design

    Science.gov (United States)

    Gruber, Petra; Imhof, Barbara

    2007-02-01

    This paper discusses the architectural design project "Transformation Structure Space", which was carried out at the Department of Building Construction HB2 in 2004. The goal of the study was to find innovative solutions for space system design through the application of bionic (biomimetic) approaches. Using specific research both fields as the foundation, five different architectural projects based on a scientific-technological concept were developed. The introduction of natural role models into the design process and the development of the application in space and the respective setting proved to be a difficult task within the timeframe of a design program, nonetheless all of the projects show very innovative aspects.

  15. The Emergence of the Open Networked ``i-Learning'' Model

    Science.gov (United States)

    Elia, Gianluca

    The most significant forces that are changing the business world and the society behaviors in this beginning of the twenty-first century can be identified into the globalization of the economy, technological evolution and convergence, change of the workers' expectations, workplace diversity and mobility, and mostly, knowledge and learning as major organizational assets. But which type of ­learning dynamics must be nurtured and pursued within the organizations, today, in order to generate valuable knowledge and its effective applications? After a brief discussion on the main changes observable in management, ICT and society/workplace in the last years, this chapter aims to answer to this question, through the proposition of the “Π-shaped” profile (a new professional archetype for leading change), and through the discussion of the open networked “i-Learning” model (a new framework to “incubate” innovation in learning processes). Actually, the “i” stands for “innovation” (to highlight the nature of the impact on traditional ­learning model), but also it stands for “incubation” (to underline the urgency to have new environments in which incubating new professional profiles). Specifically, the main key characteristics at the basis of the innovation of the learning processes will be ­presented and described, by highlighting the managerial, technological and societal aspects of their nature. A set of operational guidelines will be also ­provided to ­activate and sustain the innovation process, so implementing changes in the strategic dimensions of the model. Finally, the “i-Learning Radar” is presented as an operational tool to design, communicate and control an “i-Learning experience”. This tool is represented by a radar diagram with six strategic dimensions of a ­learning initiative.

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

    Science.gov (United States)

    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…

  17. Home and away : learning in and learning from organisational networks in Europe

    NARCIS (Netherlands)

    Docherty, P.; Huzzard, T.; Leede, J. de

    2003-01-01

    This report is a comparative analysis of the various learning networks established within the Innoflex Project. The report recaps on the central argument underpinning Innoflex, namely that traditional ways of organising workplaces and traditional styles of management cannot achieve the commitment,

  18. Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

    Science.gov (United States)

    Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran

    2012-01-01

    Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers

  19. A fully connected network of Bernoulli units with correlation learning

    Science.gov (United States)

    Dente, J. A.; Vilela Mendes, R.

    1996-02-01

    Biological evidence suggests that pattern recognition and associative memory in the mammalian nervous system operates through the establishment of spatio-temporal patterns of activity and not by the evolution towards an equilibrium point as in attractor neural networks. Information is carried by the space-time correlation of the activity intensities rather than by the details of individual neuron signals. Furthermore the fast recognition times that are achieved with relatively slow biological neurons seem to be associated to the chaotic nature of the basal nervous activity. To copy the biology hardware may not be technologically sound, but to look for inspiration in the efficient biological information processing methods is an idea that deserves consideration. Inspired by the mechanisms at work in the mammalian olfactory system we study a network where, in the absence of external inputs, the units have a dynamics of the Bernoulli shift type. When an external signal is presented, the pattern of excitation bursts depends on the learning history of the network. Association and pattern identification in the network operates by the selection, by the external stimulus, of distinct invariant measures in the chaotic system. The simplicity of the node dynamics, that is chosen, allows a reasonable analytical control of the network behavior.

  20. Learning Wireless Networks' Topologies Using Asymmetric Granger Causality

    Science.gov (United States)

    Laghate, Mihir; Cabric, Danijela

    2018-02-01

    Sharing spectrum with a communicating incumbent user (IU) network requires avoiding interference to IU receivers. But since receivers are passive when in the receive mode and cannot be detected, the network topology can be used to predict the potential receivers of a currently active transmitter. For this purpose, this paper proposes a method to detect the directed links between IUs of time multiplexing communication networks from their transmission start and end times. It models the response mechanism of commonly used communication protocols using Granger causality: the probability of an IU starting a transmission after another IU's transmission ends increases if the former is a receiver of the latter. This paper proposes a non-parametric test statistic for detecting such behavior. To help differentiate between a response and the opportunistic access of available spectrum, the same test statistic is used to estimate the response time of each link. The causal structure of the response is studied through a discrete time Markov chain that abstracts the IUs' medium access protocol and focuses on the response time and response probability of 2 IUs. Through NS-3 simulations, it is shown that the proposed algorithm outperforms existing methods in accurately learning the topologies of infrastructure-based networks and that it can infer the directed data flow in ad hoc networks with finer time resolution than an existing method.

  1. A Hierarchical Network of Provably Optimal Learning Control Systems: Extensions of the Associative Control Process (ACP) Network

    Science.gov (United States)

    1993-01-01

    learning systems have been found to work well on difficult problems. Tesauro (1990, 1992) has applied these ideas successfully to the problem of play- ing...Proceedings of the American Control Conference. Boston, MA. Tesauro , G. (1990). Neurogammon: A neural-network backgammon program. Pro- Adaptive...Conference on Neural Networks, 3, 33-40. Tesauro , G. (1992). Practical issues in temporal difference learning. Machine Learning, 8(3/4), 257-277. Thrun, S

  2. Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

    Science.gov (United States)

    Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang

    2018-03-01

    Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.

  3. Model of Learning Organizational Development of Primary School Network under the Office of Basic Education Commission

    Science.gov (United States)

    Sai-rat, Wipa; Tesaputa, Kowat; Sriampai, Anan

    2015-01-01

    The objectives of this study were 1) to study the current state of and problems with the Learning Organization of the Primary School Network, 2) to develop a Learning Organization Model for the Primary School Network, and 3) to study the findings of analyses conducted using the developed Learning Organization Model to determine how to develop the…

  4. Personal Learning Network Clusters: A Comparison between Mathematics and Computer Science Students

    Science.gov (United States)

    Harding, Ansie; Engelbrecht, Johann

    2015-01-01

    "Personal learning environments" (PLEs) and "personal learning networks" (PLNs) are well-known concepts. A personal learning network "cluster" is a small group of people who regularly interact academically and whose PLNs have a non-empty intersection that includes all the other members. At university level PLN…

  5. Language Views on Social Networking Sites for Language Learning: The Case of Busuu

    Science.gov (United States)

    Álvarez Valencia, José Aldemar

    2016-01-01

    Social networking has compelled the area of computer-assisted language learning (CALL) to expand its research palette and account for new virtual ecologies that afford language learning and socialization. This study focuses on Busuu, a social networking site for language learning (SNSLL), and analyzes the views of language that are enacted through…

  6. Comparison between extreme learning machine and wavelet neural networks in data classification

    Science.gov (United States)

    Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

    2017-03-01

    Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

  7. Environmental Monitoring Networks Optimization Using Advanced Active Learning Algorithms

    Science.gov (United States)

    Kanevski, Mikhail; Volpi, Michele; Copa, Loris

    2010-05-01

    The problem of environmental monitoring networks optimization (MNO) belongs to one of the basic and fundamental tasks in spatio-temporal data collection, analysis, and modeling. There are several approaches to this problem, which can be considered as a design or redesign of monitoring network by applying some optimization criteria. The most developed and widespread methods are based on geostatistics (family of kriging models, conditional stochastic simulations). In geostatistics the variance is mainly used as an optimization criterion which has some advantages and drawbacks. In the present research we study an application of advanced techniques following from the statistical learning theory (SLT) - support vector machines (SVM) and the optimization of monitoring networks when dealing with a classification problem (data are discrete values/classes: hydrogeological units, soil types, pollution decision levels, etc.) is considered. SVM is a universal nonlinear modeling tool for classification problems in high dimensional spaces. The SVM solution is maximizing the decision boundary between classes and has a good generalization property for noisy data. The sparse solution of SVM is based on support vectors - data which contribute to the solution with nonzero weights. Fundamentally the MNO for classification problems can be considered as a task of selecting new measurement points which increase the quality of spatial classification and reduce the testing error (error on new independent measurements). In SLT this is a typical problem of active learning - a selection of the new unlabelled points which efficiently reduce the testing error. A classical approach (margin sampling) to active learning is to sample the points closest to the classification boundary. This solution is suboptimal when points (or generally the dataset) are redundant for the same class. In the present research we propose and study two new advanced methods of active learning adapted to the solution of

  8. A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

    Directory of Open Access Journals (Sweden)

    Karim El-Laithy

    2011-01-01

    Full Text Available An integration of both the Hebbian-based and reinforcement learning (RL rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

  9. Self-Learning Power Control in Wireless Sensor Networks.

    Science.gov (United States)

    Chincoli, Michele; Liotta, Antonio

    2018-01-27

    Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.

  10. Deschooling Society? A Lifelong Learning Network for Sustainable Communities, Urban Regeneration and Environmental Technologies

    Directory of Open Access Journals (Sweden)

    John Blewitt

    2010-11-01

    Full Text Available The complexity and multifaceted nature of sustainable lifelong learning can be effectively addressed by a broad network of providers working co-operatively and collaboratively. Such a network involving the third, public and private sector bodies must realise the full potential of accredited flexible and blended formal learning, contextual opportunities offered by enablers of informal and non formal learning and the affordances derived from the various loose and open spaces that can make social learning effective. Such a conception informs the new Lifelong Learning Network Consortium on Sustainable Communities, Urban Regeneration and Environmental Technologies established and led by the Lifelong Learning Centre at Aston University. This paper offers a radical, reflective and political evaluation of its first year in development arguing that networked learning of this type could prefigure a new model for lifelong learning and sustainable education that renders the city itself a creative medium for transformative learning and sustainability.

  11. The Impacts of Network Centrality and Self-Regulation on an E-Learning Environment with the Support of Social Network Awareness

    Science.gov (United States)

    Lin, Jian-Wei; Huang, Hsieh-Hong; Chuang, Yuh-Shy

    2015-01-01

    An e-learning environment that supports social network awareness (SNA) is a highly effective means of increasing peer interaction and assisting student learning by raising awareness of social and learning contexts of peers. Network centrality profoundly impacts student learning in an SNA-related e-learning environment. Additionally,…

  12. Investigation of the Effect of Dimple Bionic Nonsmooth Surface on Tire Antihydroplaning

    Directory of Open Access Journals (Sweden)

    Haichao Zhou

    2015-01-01

    Full Text Available Inspired by the idea that bionic nonsmooth surfaces (BNSS reduce fluid adhesion and resistance, the effect of dimple bionic nonsmooth structure arranged in tire circumferential grooves surface on antihydroplaning performance was investigated by using Computational Fluid Dynamics (CFD. The physical model of the object (model of dimple bionic nonsmooth surface distribution, hydroplaning model and SST k-ω turbulence model are established for numerical analysis of tire hydroplaning. By virtue of the orthogonal table L16(45, the parameters of dimple bionic nonsmooth structure design compared to the smooth structure were analyzed, and the priority level of the experimental factors as well as the best combination within the scope of the experiment was obtained. The simulation results show that dimple bionic nonsmooth structure can reduce water flow resistance by disturbing the eddy movement in boundary layers. Then, optimal type of dimple bionic nonsmooth structure is arranged on the bottom of tire circumferential grooves for hydroplaning performance analysis. The results show that the dimple bionic nonsmooth structure effectively decreases the tread hydrodynamic pressure when driving on water film and increases the tire hydroplaning velocity, thus improving tire antihydroplaning performance.

  13. Cell-bionics: tools for real-time sensor processing.

    Science.gov (United States)

    Toumazou, Chris; Cass, Tony

    2007-08-29

    The accurate monitoring of the physiological status of cells, tissues and whole organisms demands a new generation of devices capable of providing accurate data in real time with minimal perturbation of the system being measured. To deliver on the promise of cell-bionics advances over the past decade in miniaturization, analogue signal processing, low-power electronics, materials science and protein engineering need to be brought together. In this paper we summarize recent advances in our research that is moving us in this direction. Two areas in particular are highlighted: the exploitation of the physical properties inherent in semiconductor devices to perform very low power on chip signal processing and the use of gene technology to tailor proteins for sensor applications. In the context of engineered tissues, cell-bionics could offer the ability to monitor the precise physiological state of the construct, both during 'manufacture' and post-implantation. Monitoring during manufacture, particularly by embedded devices, would offer quality assurance of the materials components and the fabrication process. Post-implantation monitoring would reveal changes in the underlying physiology as a result of the tissue construct adapting to its new environment.

  14. Effective connectivity analysis of default mode network based on the Bayesian network learning approach

    Science.gov (United States)

    Li, Rui; Chen, Kewei; Zhang, Nan; Fleisher, Adam S.; Li, Yao; Wu, Xia

    2009-02-01

    This work proposed to use the linear Gaussian Bayesian network (BN) to construct the effective connectivity model of the brain's default mode network (DMN), a set of regions characterized by more increased neural activity during rest-state than most goal-oriented tasks. In a complete unsupervised data-driven manner, Bayesian information criterion (BIC) based learning approach was utilized to identify a highest scored network whose nodes (brain regions) were selected based on the result from the group independent component analysis (Group ICA) examining the DMN. We put forward to adopt the statistical significance testing method for regression coefficients used in stepwise regression analysis to further refine the network identified by BIC. The final established BN, learned from the functional magnetic resonance imaging (fMRI) data acquired from 12 healthy young subjects during rest-state, revealed that the hippocampus (HC) was the most influential brain region that affected activities in all other regions included in the BN. In contrast, the posterior cingulate cortex (PCC) was influenced by other regions, but had no reciprocal effects on any other region. Overall, the configuration of our BN illustrated that a prominent connection from HC to PCC existed in the DMN.

  15. Social Learning Networks: Build Mobile Learning Networks Based on Collaborative Services

    Science.gov (United States)

    Huang, Jeff J. S.; Yang, Stephen J. H.; Huang, Yueh-Min; Hsiao, Indy Y. T.

    2010-01-01

    Recently, the rising of Web 2.0 has made online community gradually become popular, like Facebook, blog, etc. As a result, the online knowledge sharing network formed by interpersonal interaction is now a major character of Web 2.0, and therefore, by this trend, we try to build up a collaborative service mechanism and further set up an analysis…

  16. Didactic Networks: A Proposal for e-learning Content Generation

    Directory of Open Access Journals (Sweden)

    F. Javier Del Alamo

    2010-12-01

    Full Text Available The Didactic Networks proposed in this paper are based on previous publications in the field of the RSR (Rhetorical-Semantic Relations. The RSR is a set of primitive relations used for building a specific kind of semantic networks for artificial intelligence applications on the web: the RSN (Rhetorical-Semantic Networks. We bring into focus the RSR application in the field of elearning, by defining Didactic Networks as a new set of semantic patterns oriented to the development of elearning applications. The different lines we offer in our research fall mainly into three levels: (1 The most basic one is in the field of computational linguistics and related to Logical Operations on RSR (RSR Inverses and plurals, RSR combinations, etc, once they have been created. The application of Walter Bosma's results regarding rhetorical distance application and treatment as semantic weighted networks is one of the important issues here. (2 In parallel, we have been working on the creation of a knowledge representation and storage model and data architecture capable of supporting the definition of knowledge networks based on RSR. (3 The third strategic line is in the meso-level, the formulation of a molecular structure of knowledge based on the most frequently used patterns. The main contribution at this level is the set of Fundamental Cognitive Networks (FCN as an application of Novak's mental maps proposal. This paper is part of this third intermediate level, and the Fundamental Didactic Networks (FDN are the result of the application of rhetorical theory procedures to the instructional theory. We have formulated a general set of RSR capable of building discourse, making it possible to express any concept, procedure or principle in terms of knowledge nodes and RSRs. The Instructional knowledge can then be elaborated in the same way. This network structure expressing the instructional knowledge in terms of RSR makes the objective of developing web-learning

  17. Networked curricula: fostering transnational partnership in open and distance learning

    Directory of Open Access Journals (Sweden)

    María Luz Cacheiro-González

    2013-05-01

    Full Text Available Transnational Networked Curricula (TNC provides many benefits to the institutions that offer them as well as to the different stakeholders involved, not only the students but also the academics, the institutions as a whole, and the wider society. Supporting Higher Education Institutions in enhancing and implementing international networked practices in virtual campus building is the main aim of the NetCU project, which has been developed by the EADTU, in partnership with 14 member organizations, from 2009 to 2012. The project outcomes intend to facilitate the future set-up of networked curricula in Higher Education institutions and potentially lead to more transnational partnerships in Open and Distance Education (ODE and blended learning, showing challenges, obstacles and ways to overcome them. This paper presents the main products developed in the project, assesses its completeness and usage, and discusses on the challenges of curricula networking starting from the ideas and opinions shared in different stakeholders workshops organized under the NetCU project.

  18. Network-based stochastic competitive learning approach to disambiguation in collaborative networks

    Science.gov (United States)

    Christiano Silva, Thiago; Raphael Amancio, Diego

    2013-03-01

    Many patterns have been uncovered in complex systems through the application of concepts and methodologies of complex networks. Unfortunately, the validity and accuracy of the unveiled patterns are strongly dependent on the amount of unavoidable noise pervading the data, such as the presence of homonymous individuals in social networks. In the current paper, we investigate the problem of name disambiguation in collaborative networks, a task that plays a fundamental role on a myriad of scientific contexts. In special, we use an unsupervised technique which relies on a particle competition mechanism in a networked environment to detect the clusters. It has been shown that, in this kind of environment, the learning process can be improved because the network representation of data can capture topological features of the input data set. Specifically, in the proposed disambiguating model, a set of particles is randomly spawned into the nodes constituting the network. As time progresses, the particles employ a movement strategy composed of a probabilistic convex mixture of random and preferential walking policies. In the former, the walking rule exclusively depends on the topology of the network and is responsible for the exploratory behavior of the particles. In the latter, the walking rule depends both on the topology and the domination levels that the particles impose on the neighboring nodes. This type of behavior compels the particles to perform a defensive strategy, because it will force them to revisit nodes that are already dominated by them, rather than exploring rival territories. Computer simulations conducted on the networks extracted from the arXiv repository of preprint papers and also from other databases reveal the effectiveness of the model, which turned out to be more accurate than traditional clustering methods.

  19. Research Notes ~ Development of a Defense Learning Network for the Canadian Department of National Defense

    Directory of Open Access Journals (Sweden)

    Dennis Margueratt

    2003-10-01

    Full Text Available The idea of an online learning network for members of the Canadian Department of National Defence (DND has surfaced several times over the past decade and a half, but has never reached the level of development seen in the current Defence Learning Network (DLN initiative. Past attempts at creating a learning network failed primarily because of the lack of a champion within DND’s senior leadership, and the ability of traditional residential learning to meet the training and education needs of the Department. Recently, however, the rising cost of residential learning, coupled with recognition of the benefits afforded by distance learning, particularly learning flexibility and the ability of learners to engaged in requisite learning at their home base rather than at dispersed locations across Canada, have greatly enhanced the attractiveness of distance learning as a viable learning delivery option.

  20. The neighborhood MCMC sampler for learning Bayesian networks

    Science.gov (United States)

    Alyami, Salem A.; Azad, A. K. M.; Keith, Jonathan M.

    2016-07-01

    Getting stuck in local maxima is a problem that arises while learning Bayesian networks (BNs) structures. In this paper, we studied a recently proposed Markov chain Monte Carlo (MCMC) sampler, called the Neighbourhood sampler (NS), and examined how efficiently it can sample BNs when local maxima are present. We assume that a posterior distribution f(N,E|D) has been defined, where D represents data relevant to the inference, N and E are the sets of nodes and directed edges, respectively. We illustrate the new approach by sampling from such a distribution, and inferring BNs. The simulations conducted in this paper show that the new learning approach substantially avoids getting stuck in local modes of the distribution, and achieves a more rapid rate of convergence, compared to other common algorithms e.g. the MCMC Metropolis-Hastings sampler.

  1. Scholarly information discovery in the networked academic learning environment

    CERN Document Server

    Li, LiLi

    2014-01-01

    In the dynamic and interactive academic learning environment, students are required to have qualified information literacy competencies while critically reviewing print and electronic information. However, many undergraduates encounter difficulties in searching peer-reviewed information resources. Scholarly Information Discovery in the Networked Academic Learning Environment is a practical guide for students determined to improve their academic performance and career development in the digital age. Also written with academic instructors and librarians in mind who need to show their students how to access and search academic information resources and services, the book serves as a reference to promote information literacy instructions. This title consists of four parts, with chapters on the search for online and printed information via current academic information resources and services: part one examines understanding information and information literacy; part two looks at academic information delivery in the...

  2. A geometric view on learning Bayesian network structures

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Vomlel, Jiří; Hemmecke, R.

    2010-01-01

    Roč. 51, č. 5 (2010), s. 578-586 ISSN 0888-613X. [PGM 2008] R&D Projects: GA AV ČR(CZ) IAA100750603; GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : learning Bayesian networks * standard imset * inclusion neighborhood * geometric neighborhood * GES algorithm Subject RIV: BA - General Mathematics Impact factor: 1.679, year: 2010 http://library.utia.cas.cz/separaty/2010/MTR/studeny-0342804.pdf

  3. Supervised learning of probability distributions by neural networks

    Science.gov (United States)

    Baum, Eric B.; Wilczek, Frank

    1988-01-01

    Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

  4. Satellite -Based Networks for U-Health & U-Learning

    Science.gov (United States)

    Graschew, G.; Roelofs, T. A.; Rakowsky, S.; Schlag, P. M.

    2008-08-01

    The use of modern Information and Communication Technologies (ICT) as enabling tools for healthcare services (eHealth) introduces new ways of creating ubiquitous access to high-level medical care for all, anytime and anywhere (uHealth). Satellite communication constitutes one of the most flexible methods of broadband communication offering high reliability and cost-effectiveness of connections meeting telemedicine communication requirements. Global networks and the use of computers for educational purposes stimulate and support the development of virtual universities for e-learning. Especially real-time interactive applications can play an important role in tailored and personalised services.

  5. Networking European Universities through e-learning (reviewed text)

    OpenAIRE

    Dlouhá, Jana

    2008-01-01

    Virtual Campus for a Sustainable Europe (VCSE) network has been selected to be part of the EC DG EAC Inventory of innovative good practice on education for sustainable development. The main purpose of the Inventory is to show concrete examples which have been implemented in the Member States under the concept of ESD in formal and non-formal learning contexts and which are at the forefront as regards innovative approaches. Projects/programmes selected as innovative good practice will be use...

  6. Semantic Web, Reusable Learning Objects, Personal Learning Networks in Health: Key Pieces for Digital Health Literacy.

    Science.gov (United States)

    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.

  7. Socio-semantic Networks of Research Publications in the Learning Analytics Community

    NARCIS (Netherlands)

    Fazeli, Soude; Drachsler, Hendrik; Sloep, Peter

    2013-01-01

    Fazeli, S., Drachsler, H., & Sloep, P. B. (2013, April). Socio-semantic Networks of Research Publications in the Learning Analytics Community. Presentation at the Learning Analystic and Knowelege (LAK13), Leuven, Belgium.

  8. Approximation Methods for Inference and Learning in Belief Networks: Progress and Future Directions

    National Research Council Canada - National Science Library

    Pazzan, Michael

    1997-01-01

    .... In this research project, we have investigated methods and implemented algorithms for efficiently making certain classes of inference in belief networks, and for automatically learning certain...

  9. Improving Accessibility for Seniors in a Life-Long Learning Network: A Usability Study of Learning Websites

    Science.gov (United States)

    Gu, Xiaoqing; Ding, Rui; Fu, Shirong

    2011-01-01

    Senior citizens are comparatively vulnerable in accessing learning opportunities offered on the Internet due to usability problems in current web design. In an effort to build a senior-friendly learning web as a part of the Life-long Learning Network in Shanghai, usability studies of two websites currently available to Shanghai senior citizens…

  10. Understanding the Construction of Personal Learning Networks to Support Non-Formal Workplace Learning of Training Professionals

    Science.gov (United States)

    Manning, Christin

    2013-01-01

    Workers in the 21st century workplace are faced with rapid and constant developments that place a heavy demand on them to continually learn beyond what the Human Resources and Training groups can meet. As a consequence, professionals must rely on non-formal learning approaches through the development of a personal learning network to keep…

  11. Learning for Work and Professional Development: The Significance of Informal Learning Networks of Digital Media Industry Professionals

    Science.gov (United States)

    Campana, Joe

    2014-01-01

    Informal learning networks play a key role in the skill and professional development of professionals, working in micro-businesses within Australia's digital media industry, as they do not have access to learning and development or human resources sections that can assist in mapping their learning pathway. Professionals working in this environment…

  12. Design Guidelines for Collaboration and Participation with Examples from the LN4LD (Learning Network for Learning Design)

    NARCIS (Netherlands)

    Burgos, Daniel; Hummel, Hans; Tattersall, Colin; Brouns, Francis; Koper, Rob

    2007-01-01

    Burgos, D., Hummel, H. G. K., Tattersall, C., Brouns, F., & Koper, R. (2009). Design Guidelines for Collaboration and Participation with Examples from the LN4LD (Learning Network for Learning Design). In L. Lockyer, S. Bennett, S. Agostinho & B. Harper (Eds.), Handbook of Research on Learning Design

  13. Classification of CT brain images based on deep learning networks.

    Science.gov (United States)

    Gao, Xiaohong W; Hui, Rui; Tian, Zengmin

    2017-01-01

    While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease. Towards this end, three categories of CT images (N = 285) are clustered into three groups, which are AD, lesion (e.g. tumour) and normal ageing. In addition, considering the characteristics of this collection with larger thickness along the direction of depth (z) (~3-5 mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification accuracy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, lesion and normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6 ± 1.10, 86.3 ± 1.04, 85.2 ± 1.60, 83.1 ± 0.35 for 2D CNN, 2D SIFT, 2D KAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information

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

    Science.gov (United States)

    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…

  15. Goals, Motivation for, and Outcomes of Personal Learning through Networks: Results of a Tweetstorm

    Science.gov (United States)

    Sie, Rory L. L.; Pataraia, Nino; Boursinou, Eleni; Rajagopal, Kamakshi; Margaryan, Anoush; Falconer, Isobel; Bitter-Rijpkema, Marlies; Littlejohn, Allison; Sloep, Peter B.

    2013-01-01

    Recent developments in the use of social media for learning have posed serious challenges for learners. The information overload that these online social tools create has changed the way learners learn and from whom they learn. An investigation of learners' goals, motivations and expected outcomes when using a personal learning network is…

  16. SME Innovation and Learning: The Role of Networks and Crisis Events

    Science.gov (United States)

    Saunders, Mark N. K.; Gray, David E; Goregaokar, Harshita

    2014-01-01

    Purpose: The purpose of this paper is to contribute to the literature on innovation and entrepreneurial learning by exploring how SMEs learn and innovate, how they use both formal and informal learning and in particular the role of networks and crisis events within their learning experience. Design/methodology/approach: Mixed method study,…

  17. The JOYN 2.0 project social networking and language learning: some preliminary insights

    OpenAIRE

    Riordan, Elaine; James, Phil

    2012-01-01

    peer-reviewed The introduction of social networking in language learning is becoming increasingly important, and as a result, teachers require new skills in their newfound roles as moderators of informal online learning. This paper presents details about a two-year EU Lifelong Learning funded project, namely JOYN 2.0, which aims to promote language learning through social networking http://www.joynlanguages.eu/. The JOYN 2.0 projects examines the process of moderating lan...

  18. Influence of Multiple Bionic Unit Coupling on Sliding Wear of Laser-Processed Gray Cast Iron

    Science.gov (United States)

    Zhang, Haifeng; Zhang, Peng; Sui, Qi; Zhao, Kai; Zhou, Hong; Ren, Luquan

    2017-04-01

    In this study, in effort to improve the sliding wear resistance of gray cast iron under wet lubrication conditions, specimens with different bionic units were manufactured and modified according to bionic theory. Inspired by the structure and appearance of biological wear-resistant skin, two kinds of bionic units were processed by laser on the specimen surfaces. We investigated the wear resistance properties of the samples via indentation method and then observed the wear surface morphology of specimens and the stress distributions. The results indicated that coupling the bionic units enhanced the wear resistance of the cast iron considerably compared to the other samples. We also determined the mechanism of wear resistance improvement according to the results.

  19. Simulated effect on the compressive and shear mechanical properties of bionic integrated honeycomb plates.

    Science.gov (United States)

    He, Chenglin; Chen, Jinxiang; Wu, Zhishen; Xie, Juan; Zu, Qiao; Lu, Yun

    2015-05-01

    Honeycomb plates can be applied in many fields, including furniture manufacturing, mechanical engineering, civil engineering, transportation and aerospace. In the present study, we discuss the simulated effect on the mechanical properties of bionic integrated honeycomb plates by investigating the compressive and shear failure modes and the mechanical properties of trabeculae reinforced by long or short fibers. The results indicate that the simulated effect represents approximately 80% and 70% of the compressive and shear strengths, respectively. Compared with existing bionic samples, the mass-specific strength was significantly improved. Therefore, this integrated honeycomb technology remains the most effective method for the trial manufacturing of bionic integrated honeycomb plates. The simulated effect of the compressive rigidity is approximately 85%. The short-fiber trabeculae have an advantage over the long-fiber trabeculae in terms of shear rigidity, which provides new evidence for the application of integrated bionic honeycomb plates. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Bionic Design of Wind Turbine Blade Based on Long-Eared Owl’s Airfoil

    Directory of Open Access Journals (Sweden)

    Weijun Tian

    2017-01-01

    Full Text Available The main purpose of this paper is to demonstrate a bionic design for the airfoil of wind turbines inspired by the morphology of Long-eared Owl’s wings. Glauert Model was adopted to design the standard blade and the bionic blade, respectively. Numerical analysis method was utilized to study the aerodynamic characteristics of the airfoils as well as the blades. Results show that the bionic airfoil inspired by the airfoil at the 50% aspect ratio of the Long-eared Owl’s wing gives rise to a superior lift coefficient and stalling performance and thus can be beneficial to improving the performance of the wind turbine blade. Also, the efficiency of the bionic blade in wind turbine blades tests increases by 12% or above (up to 44% compared to that of the standard blade. The reason lies in the bigger pressure difference between the upper and lower surface which can provide stronger lift.

  1. Architectural solutions in terms of Bionic urban environment of Olympic Sochi

    Directory of Open Access Journals (Sweden)

    Olga V. Kiba

    2010-10-01

    Full Text Available The principle of architectural bionics, suitable for Olympic Sochi environment was used for promenade designing. The complex solution includes promenade areas and a ‘winter and summer’ platform.

  2. Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.

    Directory of Open Access Journals (Sweden)

    Xia Jiang

    Full Text Available The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS datasets, which involve millions of single nucleotide polymorphism (SNPs, where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects.We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer's dataset, we investigated the performance of MBS-IGain.When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer's dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is significant because we have increasingly

  3. Learning random networks for compression of still and moving images

    Science.gov (United States)

    Gelenbe, Erol; Sungur, Mert; Cramer, Christopher

    1994-01-01

    Image compression for both still and moving images is an extremely important area of investigation, with numerous applications to videoconferencing, interactive education, home entertainment, and potential applications to earth observations, medical imaging, digital libraries, and many other areas. We describe work on a neural network methodology to compress/decompress still and moving images. We use the 'point-process' type neural network model which is closer to biophysical reality than standard models, and yet is mathematically much more tractable. We currently achieve compression ratios of the order of 120:1 for moving grey-level images, based on a combination of motion detection and compression. The observed signal-to-noise ratio varies from values above 25 to more than 35. The method is computationally fast so that compression and decompression can be carried out in real-time. It uses the adaptive capabilities of a set of neural networks so as to select varying compression ratios in real-time as a function of quality achieved. It also uses a motion detector which will avoid retransmitting portions of the image which have varied little from the previous frame. Further improvements can be achieved by using on-line learning during compression, and by appropriate compensation of nonlinearities in the compression/decompression scheme. We expect to go well beyond the 250:1 compression level for color images with good quality levels.

  4. Learning free energy landscapes using artificial neural networks

    Science.gov (United States)

    Sidky, Hythem; Whitmer, Jonathan K.

    2018-03-01

    Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.

  5. Learning free energy landscapes using artificial neural networks.

    Science.gov (United States)

    Sidky, Hythem; Whitmer, Jonathan K

    2018-03-14

    Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.

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

    Science.gov (United States)

    Finnegan, Alex; Song, Jun S

    2017-10-01

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

  7. Emergence and expansion of cosmic space in BIonic system

    Energy Technology Data Exchange (ETDEWEB)

    Sepehri, A., E-mail: alireza.sepehri@uk.ac.ir [Faculty of Physics, Shahid Bahonar University, P.O. Box 76175, Kerman (Iran, Islamic Republic of); Rahaman, Farook, E-mail: rahaman@iucaa.ernet.in [Department of Mathematics, Jadavpur University, Kolkata 700032, West Bengal (India); Pradhan, Anirudh, E-mail: pradhan@iucaa.ernet.in [Department of Mathematics, GLA University, Mathura-281 406, U.P. (India); Sardar, Iftikar Hossain, E-mail: iftikar.spm@gmail.com [Department of Mathematics, Jadavpur University, Kolkata 700032, West Bengal (India)

    2015-02-04

    Recently, Padmanabhan [ (arXiv:1206.4916 [hep-th])] argued that the expansion rate of the universe can be thought of as the emergence of space as cosmic time progresses and is related to the difference between the surface degrees of freedom on the holographic horizon and the bulk degrees of freedom inside. The main question arises as to what is the origin of emergence of space in 4D universe. We answer this question in BIonic system. The BIon is a configuration in flat space of a D-brane and a parallel anti-D-brane connected by a thin shell wormhole with F-string charge. We propose a new model that allows that all degrees of freedom inside and outside the universe are controlled by the evolutions of BIon in extra dimension and tend to degrees of freedom of black F-string in string theory or black M2-brane in M-theory.

  8. Psychosocial Impact of the Bionic Pancreas During Summer Camp.

    Science.gov (United States)

    Weissberg-Benchell, Jill; Hessler, Danielle; Polonsky, William H; Fisher, Lawrence

    2016-07-01

    The psychosocial impact of the bionic pancreas (BP) was assessed among children attending diabetes camp. Nineteen children were randomly assigned for 5 days to the BP condition and 5 days to the control condition in a crossover design. Significant reductions in hypoglycemic fear and regimen burden were found. Children felt less burdened or worried about diabetes and felt freer to do things they enjoyed while using the BP. Children wished the BP responded to out of range numbers faster and expressed annoyance about carrying around the necessary equipment. Children may experience improved psychosocial outcomes following use of BP while expressing key areas of user concern. Future studies in less controlled environments with larger sample sizes can determine if these findings are generalizable to other groups. © 2016 Diabetes Technology Society.

  9. Medicalization: current concept and future directions in a bionic society.

    Science.gov (United States)

    Maturo, Antonio

    2012-01-01

    The article illustrates the main features of the concept of medicalization, starting from its theoretical roots. Although it is the process of extending the medical gaze on human conditions, it appears that medicalization cannot be strictly connected to medical imperialism anymore. Other "engines" of medicalization are influential: consumers, biotechnology and managed care. The growth of research and theoretical reflections on medicalization has led to the proposal of other parallel concepts like pharmaceuticalization, genetization and biomedicalization. These new theoretical tools could be useful in the analysis of human enhancement. Human enhancement can be considered as the use of biomedical technology to improve performance on a human being who is not in need of a cure: a practice that is increasingly spreading in what might be defined as a "bionic society".

  10. Emergence and expansion of cosmic space in BIonic system

    International Nuclear Information System (INIS)

    Sepehri, A.; Rahaman, Farook; Pradhan, Anirudh; Sardar, Iftikar Hossain

    2015-01-01

    Recently, Padmanabhan [ (arXiv:1206.4916 [hep-th])] argued that the expansion rate of the universe can be thought of as the emergence of space as cosmic time progresses and is related to the difference between the surface degrees of freedom on the holographic horizon and the bulk degrees of freedom inside. The main question arises as to what is the origin of emergence of space in 4D universe. We answer this question in BIonic system. The BIon is a configuration in flat space of a D-brane and a parallel anti-D-brane connected by a thin shell wormhole with F-string charge. We propose a new model that allows that all degrees of freedom inside and outside the universe are controlled by the evolutions of BIon in extra dimension and tend to degrees of freedom of black F-string in string theory or black M2-brane in M-theory

  11. Bionic balance organs: progress in the development of vestibular prostheses.

    Science.gov (United States)

    Smith, Paul F

    2017-09-01

    The vestibular system is a sensory system that is critically important in humans for gaze and image stability as well as postural control. Patients with complete bilateral vestibular loss are severely disabled and experience a poor quality of life. There are very few effective treatment options for patients with no vestibular function. Over the last 10 years, rapid progress has been made in developing artificial 'vestibular implants' or 'prostheses', based on cochlear implant technology. As of 2017, 13 patients worldwide have received vestibular implants and the results are encouraging. Vestibular implants are now becoming part of an increasing effort to develop artificial, bionic sensory systems, and this paper provides a review of the progress in this area.

  12. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

    Directory of Open Access Journals (Sweden)

    Yuexiang Li

    2018-02-01

    Full Text Available Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1, lesion dermoscopic feature extraction (task 2 and lesion classification (task 3. A deep learning framework consisting of two fully convolutional residual networks (FCRN is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

  13. Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning

    KAUST Repository

    Aljaafari, Nura

    2018-04-15

    The study and the analysis of marine ecosystems is a significant part of the marine science research. These systems are valuable resources for fisheries, improving water quality and can even be used in drugs production. The investigation of ichthyoplankton inhabiting these ecosystems is also an important research field. Ichthyoplankton are fish in their early stages of life. In this stage, the fish have relatively similar shape and are small in size. The currently used way of identifying them is not optimal. Marine scientists typically study such organisms by sending a team that collects samples from the sea which is then taken to the lab for further investigation. These samples need to be studied by an expert and usually end needing a DNA sequencing. This method is time-consuming and requires a high level of experience. The recent advances in AI have helped to solve and automate several difficult tasks which motivated us to develop a classification tool for ichthyoplankton. We show that using machine learning techniques, such as generative adversarial networks combined with transfer learning solves such a problem with high accuracy. We show that using traditional machine learning algorithms fails to solve it. We also give a general framework for creating a classification tool when the dataset used for training is a limited dataset. We aim to build a user-friendly tool that can be used by any user for the classification task and we aim to give a guide to the researchers so that they can follow in creating a classification tool.

  14. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network.

    Science.gov (United States)

    Li, Yuexiang; Shen, Linlin

    2018-02-11

    Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

  15. Fuzzy comprehensive evaluation model of interuniversity collaborative learning based on network

    Science.gov (United States)

    Wenhui, Ma; Yu, Wang

    2017-06-01

    Learning evaluation is an effective method, which plays an important role in the network education evaluation system. But most of the current network learning evaluation methods still use traditional university education evaluation system, which do not take into account of web-based learning characteristics, and they are difficult to fit the rapid development of interuniversity collaborative learning based on network. Fuzzy comprehensive evaluation method is used to evaluate interuniversity collaborative learning based on the combination of fuzzy theory and analytic hierarchy process. Analytic hierarchy process is used to determine the weight of evaluation factors of each layer and to carry out the consistency check. According to the fuzzy comprehensive evaluation method, we establish interuniversity collaborative learning evaluation mathematical model. The proposed scheme provides a new thought for interuniversity collaborative learning evaluation based on network.

  16. Design and Implementation of a Bionic Mimosa Robot with Delicate Leaf Swing Behavior

    OpenAIRE

    Chung-Liang Chang; Jin-Long Shie

    2014-01-01

    This study designed and developed a bionic mimosa robot with delicate leaf swing behaviors. For different swing behaviors, this study developed a variety of situations, in which the bionic mimosa robot would display different postures. The core technologies used were Shape Memory Alloys (SMAs), plastic material, and an intelligent control device. The technology particularly focused on the SMAs memory processing bend mode, directional guidance, and the position of SMAs installed inside the pla...

  17. Bionic design methodology for wear reduction of bulk solids handling equipment

    OpenAIRE

    Chen, G.; Schott, D.L.; Lodewijks, G.

    2016-01-01

    Large-scale handling of particulate solids can cause severe wear on bulk solids handling equipment surfaces. Wear reduces equipment life span and increases maintenance cost. Examples of traditional methods to reduce wear of bulk solids handling equipment include optimizing transport operations and utilizing resistant materials. To our knowledge, the so-called bionic design has not been utilized. Bionic design is the application of biological models, systems, or elements to modern engineering....

  18. The US Fire Learning Network: Springing a Rigidity Trap through Multiscalar Collaborative Networks

    Directory of Open Access Journals (Sweden)

    William Hale. Butler

    2010-09-01

    Full Text Available Wildland fire management in the United States is caught in a rigidity trap, an inability to apply novelty and innovation in the midst of crisis. Despite wide recognition that public agencies should engage in ecological fire restoration, fire suppression still dominates planning and management, and restoration has failed to gain traction. The U.S. Fire Learning Network (FLN, a multiscalar collaborative endeavor established in 2002 by federal land management agencies and The Nature Conservancy, offers the potential to overcome barriers that inhibit restoration planning and management. By circulating people, planning products, and information among landscape- and regional-scale collaboratives, this network has facilitated the development and dissemination of innovative approaches to ecological fire restoration. Through experimentation and innovation generated in the network, the FLN has fostered change by influencing fire and land management plans as well as federal policy. We suggest that multiscalar collaborative planning networks such as the FLN can facilitate overcoming the rigidity traps that prevent resource management agencies from responding to complex cross-scalar problems.

  19. Learning-induced synchronization and plasticity of a developing neural network.

    Science.gov (United States)

    Chao, T C; Chen, C M

    2005-12-01

    Learning-induced synchronization of a neural network at various developing stages is studied by computer simulations using a pulse-coupled neural network model in which the neuronal activity is simulated by a one-dimensional map. Two types of Hebbian plasticity rules are investigated and their differences are compared. For both models, our simulations show a logarithmic increase in the synchronous firing frequency of the network with the culturing time of the neural network. This result is consistent with recent experimental observations. To investigate how to control the synchronization behavior of a neural network after learning, we compare the occurrence of synchronization for four networks with different designed patterns under the influence of an external signal. The effect of such a signal on the network activity highly depends on the number of connections between neurons. We discuss the synaptic plasticity and enhancement effects for a random network after learning at various developing stages.

  20. Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

    Science.gov (United States)

    Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

    2016-01-01

    This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information.

  1. Breast image feature learning with adaptive deconvolutional networks

    Science.gov (United States)

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

    2012-03-01

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

  2. A survey on social networks to determine requirements for Learning Networks for professional development of university staff

    NARCIS (Netherlands)

    Brouns, Francis; Berlanga, Adriana; Fetter, Sibren; Bitter-Rijpkema, Marlies; Van Bruggen, Jan; Sloep, Peter

    2009-01-01

    Brouns, F., Berlanga, A. J., Fetter, S., Bitter-Rijpkema, M. E., Van Bruggen, J. M., & Sloep, P. B. (2011). A survey on social networks to determine requirements for Learning Networks for professional development of university staff. International Journal of Web Based Communities, 7(3), 298-311.

  3. Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

    Science.gov (United States)

    Peng, Yefei

    2010-01-01

    An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

  4. Enhancing Teaching and Learning Wi-Fi Networking Using Limited Resources to Undergraduates

    Science.gov (United States)

    Sarkar, Nurul I.

    2013-01-01

    Motivating students to learn Wi-Fi (wireless fidelity) wireless networking to undergraduate students is often difficult because many students find the subject rather technical and abstract when presented in traditional lecture format. This paper focuses on the teaching and learning aspects of Wi-Fi networking using limited hardware resources. It…

  5. Enriching Professional Learning Networks: A Framework for Identification, Reflection, and Intention

    Science.gov (United States)

    Krutka, Daniel G.; Carpenter, Jeffrey Paul; Trust, Torrey

    2017-01-01

    Many educators in the 21st century utilize social media platforms to enrich professional learning networks (PLNs). PLNs are uniquely personalized networks that can support participatory and continuous learning. Social media services can mediate professional engagements with a wide variety of people, spaces and tools that might not otherwise be…

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

    Science.gov (United States)

    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…

  7. Implementation of a Framework for Collaborative Social Networks in E-Learning

    Science.gov (United States)

    Maglajlic, Seid

    2016-01-01

    This paper describes the implementation of a framework for the construction and utilization of social networks in ELearning. These social networks aim to enhance collaboration between all E-Learning participants (i.e. both traineeto-trainee and trainee-to-tutor communication are targeted). E-Learning systems that include a so-called "social…

  8. Professional Online Presence and Learning Networks: Educating for Ethical Use of Social Media

    Science.gov (United States)

    Forbes, Dianne

    2017-01-01

    In a teacher education context, this study considers the use of social media for building a professional online presence and learning network. This article provides an overview of uses of social media in teacher education, presents a case study of key processes in relation to professional online presence and learning networks, and highlights…

  9. Social Networks, Communication Styles, and Learning Performance in a CSCL Community

    Science.gov (United States)

    Cho, Hichang; Gay, Geri; Davidson, Barry; Ingraffea, Anthony

    2007-01-01

    The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning…

  10. Feature Biases in Early Word Learning: Network Distinctiveness Predicts Age of Acquisition

    Science.gov (United States)

    Engelthaler, Tomas; Hills, Thomas T.

    2017-01-01

    Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge…

  11. Foundation Year Students' Perceptions of Using Social Network Sites for Learning English in the Saudi Context

    Science.gov (United States)

    AlShoaibi, Rana; Shukri, Nadia

    2017-01-01

    The major aim of this study is to better understand the university students' perceptions and attitudes towards using social network sites for learning English as well as to identify if there is a difference between male and female university students in terms of using social networking sites for learning English inside and outside the classroom.…

  12. Learning and Model-checking Networks of I/O Automata

    DEFF Research Database (Denmark)

    Mao, Hua; Jaeger, Manfred

    2012-01-01

    We introduce a new statistical relational learning (SRL) approach in which models for structured data, especially network data, are constructed as networks of communicating nite probabilistic automata. Leveraging existing automata learning methods from the area of grammatical inference, we can le...

  13. The Role of Action Research in the Development of Learning Networks for Entrepreneurs

    Science.gov (United States)

    Brett, Valerie; Mullally, Martina; O'Gorman, Bill; Fuller-Love, Nerys

    2012-01-01

    Developing sustainable learning networks for entrepreneurs is the core objective of the Sustainable Learning Networks in Ireland and Wales (SLNIW) project. One research team drawn from the Centre for Enterprise Development and Regional Economy at Waterford Institute of Technology and the School of Management and Business from Aberystwyth…

  14. Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning

    NARCIS (Netherlands)

    Villmann, T.; Biehl, M.; Villmann, A.; Saralajew, S.

    2017-01-01

    The advantage of prototype based learning vector quantizers are the intuitive and simple model adaptation as well as the easy interpretability of the prototypes as class representatives for the class distribution to be learned. Although they frequently yield competitive performance and show robust

  15. Pedagogy framework design in social networked-based learning: Focus on children with learning difficulties

    Directory of Open Access Journals (Sweden)

    Samira Sadat Sajadi

    2014-09-01

    Full Text Available This paper presents an investigation on the theory of constructivism applicable for learners with learning difficulties, specifically learners with Attention Deficit Hyperactivity Disorder (ADHD. The primary objective of this paper is to determine whether a constructivist technology enhanced learning pedagogy could be used to help ADHD learners cope with their educational needs within a social-media learning environment. Preliminary work is stated here, in which we are seeking evidence to determine the viability of a constructivist approach for learners with ADHD. The novelty of this research lies in the proposals to support ADHD learners to overcome their weaknesses with appropriate pedagogically sound interventions. As a result, a framework has been designed to illuminate areas in which constructivist pedagogies require to address the limitations of ADHD learners. An analytical framework addressing the suitability of a constructivist learning for ADHD is developed from a combination of literature and expert advice from those involved in the education of learners with ADHD. This analytical framework is married to a new model of pedagogy, which the authors have derived from literature analysis. Future work will expand this model to develop a constructivist social network-based learning and eventually test it in specialist schools with ADHD learners.

  16. The fluid control mechanism of bionic structural heterogeneous composite materials and its potential application in enhancing pump efficiency

    Directory of Open Access Journals (Sweden)

    Limei Tian

    2015-11-01

    Full Text Available Studies have shown that the structure of dolphin skin controls fluid media dynamically. Gaining inspiration from this phenomenon, a kind of bionic structural heterogeneous composite material was designed. The bionic structural heterogeneous composite material is composed of two materials: a rigid metal base layer with bionic structures and an elastic polymer surface layer with the corresponding mirror structures. The fluid control mechanism of the bionic structural heterogeneous composite material was investigated using a fluid–solid interaction method in ANSYS Workbench. The results indicated that the bionic structural heterogeneous composite material’s fluid control mechanism is its elastic deformation, which is caused by the coupling action between the elastic surface material and the bionic structure. This deformation can decrease the velocity gradient of the fluid boundary layer through changing the fluid–solid actual contact surface and reduce the frictional force. The bionic structural heterogeneous composite material can also absorb some energy through elastic deformation and avoid energy loss. The bionic structural heterogeneous composite material was applied to the impeller of a centrifugal pump in a contrast experiment, increasing the pump efficiency by 5% without changing the hydraulic model of the impeller. The development of this bionic structural heterogeneous composite material will be straightforward from an engineering point of view, and it will have valuable practical applications.

  17. Effect of electrical pulse treatment on the thermal fatigue resistance of bionic compacted graphite cast iron processed in water

    International Nuclear Information System (INIS)

    Liu, Yan; Zhou, Hong; Su, Hang; Yang, Chunyan; Cheng, Jingyan; Zhang, Peng; Ren, Luquan

    2012-01-01

    Highlights: ► Electrical pulse treatment can reduce cracks on bionic units before thermal fatigue tests. ► Electrical pulse treatment can reduce crack sources during thermal fatigue tests. ► Thermal fatigue resistance of bionic units processed in water is enhanced. ► Thermal fatigue resistance of bionic CGI processed in water is improved. -- Abstract: In order to further enhance the thermal fatigue resistance of bionic compacted graphite cast iron (CGI) which is processed by laser in water, the electrical pulse treatment is applied to improve the thermal fatigue resistance of bionic units. The results show that the electrical pulse treatment causes the supersaturated carbon atoms located in the lattice of austenite to react with the iron atoms to form the Fe 3 C. The microstructures of the bionic units processed in water are refined by the electrical pulse treatment. The cracks on the bionic units are reduced by the electrical pulse treatment before the thermal fatigue tests; and during the tests, the thermal fatigue resistance of bionic units is therefore enhanced by reducing the crack sources. By this way, the thermal fatigue resistance of bionic CGI processed in water is improved.

  18. Networked Learning and Design Based Research for welfare innovation through further education

    DEFF Research Database (Denmark)

    Østergaard, Rina; Sorensen, Elsebeth Korsgaard

    2014-01-01

    This paper sets out on a reflective journey to investigate, theoretically, the potential of a marriage between Networked Learning (NL) and Design Based Research (DBR) (Barab & Squire, 2004) in a creative and innovative pedagogical practice for welfare professionals. With reference to theoretical...... views on Innovative Learning (IL) and Networked Learning (NL) the paper discusses how it may be possible to gain knowledge that may help and qualify the development of creative innovative and ICT based learning designs for the future. To discuss this question the authors of the paper explore...... the entities of a model, which integrate the above mentioned relationships in learning designs. The suggested networked model offers possibilities of innovative learning in further educations. At the same time – in parallel – the suggested networked model offers possibilities of data generation to be used...

  19. Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties

    KAUST Repository

    Alharbi, Basma Mohammed

    2017-02-07

    Location-Based Social Networks (LBSNs) capture individuals whereabouts for a large portion of the population. To utilize this data for user (location)-similarity based tasks, one must map the raw data into a low-dimensional uniform feature space. However, due to the nature of LBSNs, many users have sparse and incomplete check-ins. In this work, we propose to overcome this issue by leveraging the network of friends, when learning the new feature space. We first analyze the impact of friends on individuals\\'s mobility, and show that individuals trajectories are correlated with thoseof their friends and friends of friends (2-hop friends) in an online setting. Based on our observation, we propose a mixed-membership model that infers global mobility patterns from users\\' check-ins and their network of friends, without impairing the model\\'s complexity. Our proposed model infers global patterns and learns new representations for both usersand locations simultaneously. We evaluate the inferred patterns and compare the quality of the new user representation against baseline methods on a social link prediction problem.

  20. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

    Science.gov (United States)

    Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

    2017-03-01

    Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Smart Social Networking: 21st Century Teaching and Learning Skills

    Directory of Open Access Journals (Sweden)

    Helen B. Boholano

    2017-06-01

    Full Text Available Education in the 21st century highlights globalization and internationalization. Preservice teachers in the 21st century are technology savvy. To effectively engage and teach generation Z students, preservice teachers will help the educational system meet this requirement. The educational systems must be outfitted with a prerequisite of ICT resources both hardware and software, and curricula must be designed to promote a collaborative learner-centered environment to which students will relate and respond. This study determines the 21st century skills possessed by the pre-service teachers in terms of social networking. Pre-service teachers use computers in very advanced ways, but educators must remember that they still need guidance to use technology safely and effectively. Through social media the pre-service teachers can use a multitude of applications, including Web 2.0, for their projects. Smart social networking requires critical-thinking skills and the ability to integrate and evaluate real-world scenarios and authentic learning skills for validation.

  2. Deep learning networks for stock market analysis and prediction : methodology, data representations, and case studies.

    OpenAIRE

    Chong, E.; Han, C.; Park, F.C.

    2017-01-01

    We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the m...

  3. Do technologies have politics? The new paradigm and pedagogy in networked learning

    OpenAIRE

    Jones, Chris

    2001-01-01

    This paper explores the relationships between the technologies deployed in networked and e-Learning and the pedagogies and politics associated with them. Networked learning and the related move to e-Learning are coincident with the globalisation, commodification and massification of Higher Education. It examines the hard and soft forms of technological determinism (TD) found in the current advocacy of technological futures for Higher Education. Hard TD claims that new technologies bring about...

  4. Deschooling Society? A Lifelong Learning Network for Sustainable Communities, Urban Regeneration and Environmental Technologies

    OpenAIRE

    Blewitt, John

    2010-01-01

    The complexity and multifaceted nature of sustainable lifelong learning can be effectively addressed by a broad network of providers working co-operatively and collaboratively. Such a network involving the third, public and private sector bodies must realise the full potential of accredited flexible and blended formal learning, contextual opportunities offered by enablers of informal and non formal learning and the affordances derived from the various loose and open spaces that can make socia...

  5. Social Network Analysis in E-Learning Environments: A Preliminary Systematic Review

    Science.gov (United States)

    Cela, Karina L.; Sicilia, Miguel Ángel; Sánchez, Salvador

    2015-01-01

    E-learning occupies an increasingly prominent place in education. It provides the learner with a rich virtual network where he or she can exchange ideas and information and create synergies through interactions with other members of the network, whether fellow learners or teachers. Social network analysis (SNA) has proven extremely powerful at…

  6. Social Networking Tools and Teacher Education Learning Communities: A Case Study

    Science.gov (United States)

    Poulin, Michael T.

    2014-01-01

    Social networking tools have become an integral part of a pre-service teacher's educational experience. As a result, the educational value of social networking tools in teacher preparation programs must be examined. The specific problem addressed in this study is that the role of social networking tools in teacher education learning communities…

  7. To Enhance Collaborative Learning and Practice Network Knowledge with a Virtualization Laboratory and Online Synchronous Discussion

    Science.gov (United States)

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

    2014-01-01

    Recently, various computer networking courses have included additional laboratory classes in order to enhance students' learning achievement. However, these classes need to establish a suitable laboratory where each student can connect network devices to configure and test functions within different network topologies. In this case, the Linux…

  8. A Social Network Analysis of Teaching and Research Collaboration in a Teachers' Virtual Learning Community

    Science.gov (United States)

    Lin, Xiaofan; Hu, Xiaoyong; Hu, Qintai; Liu, Zhichun

    2016-01-01

    Analysing the structure of a social network can help us understand the key factors influencing interaction and collaboration in a virtual learning community (VLC). Here, we describe the mechanisms used in social network analysis (SNA) to analyse the social network structure of a VLC for teachers and discuss the relationship between face-to-face…

  9. Network Training for a Boy with Learning Disabilities and Behaviours That Challenge

    Science.gov (United States)

    Cooper, Kate; McElwee, Jennifer

    2016-01-01

    Background: Network Training is an intervention that draws upon systemic ideas and behavioural principles to promote positive change in networks of support for people defined as having a learning disability. To date, there are no published case studies looking at the outcomes of Network Training. Materials and Methods: This study aimed to…

  10. A novel Bayesian learning method for information aggregation in modular neural networks

    DEFF Research Database (Denmark)

    Wang, Pan; Xu, Lida; Zhou, Shang-Ming

    2010-01-01

    Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight...... benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling....

  11. CosmoQuest Collaborative: Galvanizing a Dynamic Professional Learning Network

    Science.gov (United States)

    Cobb, Whitney; Bracey, Georgia; Buxner, Sanlyn; Gay, Pamela L.; Noel-Storr, Jacob; CosmoQuest Team

    2016-10-01

    The CosmoQuest Collaboration offers in-depth experiences to diverse audiences around the nation and the world through pioneering citizen science in a virtual research facility. An endeavor between universities, research institutes, and NASA centers, CosmoQuest brings together scientists, educators, researchers, programmers—and citizens of all ages—to explore and make sense of our solar system and beyond. Leveraging human networks to expand NASA science, scaffolded by an educational framework that inspires lifelong learners, CosmoQuest engages citizens in analyzing and interpreting real NASA data, inspiring questions and defining problems.The QuestionLinda Darling-Hammond calls for professional development to be: "focused on the learning and teaching of specific curriculum content [i.e. NGSS disciplinary core ideas]; organized around real problems of practice [i.e. NGSS science and engineering practices] … [and] connected to teachers' collaborative work in professional learning community...." (2012) In light of that, what is the unique role CosmoQuest's virtual research facility can offer NASA STEM education?A Few AnswersThe CosmoQuest Collaboration actively engages scientists in education, and educators (and learners) in science. CosmoQuest uses social channels to empower and expand NASA's learning community through a variety of media, including science and education-focused hangouts, virtual star parties, and social media. In addition to creating its own supportive, standards-aligned materials, CosmoQuest offers a hub for excellent resources and materials throughout NASA and the larger astronomy community.In support of CosmoQuest citizen science opportunities, CQ initiatives (Learning Space, S-ROSES, IDEASS, Educator Zone) will be leveraged and shared through the CQPLN. CosmoQuest can be present and alive in the awareness its growing learning community.Finally, to make the CosmoQuest PLN truly relevant, it aims to encourage partnerships between scientists

  12. The Network Operations Control Center upgrade task: Lessons learned

    Science.gov (United States)

    Sherif, J. S.; Tran, T.-L.; Lee, S.

    1994-01-01

    This article synthesizes and describes the lessons learned from the Network Operations Control Center (NOCC) upgrade project, from the requirements phase through development and test and transfer. At the outset, the NOCC upgrade was being performed simultaneously with two other interfacing and dependent upgrades at the Signal Processing Center (SPC) and Ground Communications Facility (GCF), thereby adding a significant measure of complexity to the management and overall coordination of the development and transfer-to-operations (DTO) effort. Like other success stories, this project carried with it the traditional elements of top management support and exceptional dedication of cognizant personnel. Additionally, there were several NOCC-specific reasons for success, such as end-to-end system engineering, adoption of open-system architecture, thorough requirements management, and use of appropriate off-the-shelf technologies. On the other hand, there were several difficulties, such as ill-defined external interfaces, transition issues caused by new communications protocols, ambivalent use of two sets of policies and standards, and mistailoring of the new JPL management standard (due to the lack of practical guidelines). This article highlights the key lessons learned, as a means of constructive suggestions for the benefit of future projects.

  13. Accelerating Innovation Through Coopetition: The Innovation Learning Network Experience.

    Science.gov (United States)

    McCarthy, Chris; Ford Carleton, Penny; Krumpholz, Elizabeth; Chow, Marilyn P

    Coopetition, the simultaneous pursuit of cooperation and competition, is a growing force in the innovation landscape. For some organizations, the primary mode of innovation continues to be deeply secretive and highly competitive, but for others, a new style of shared challenges, shared purpose, and shared development has become a superior, more efficient way of working to accelerate innovation capabilities and capacity. Over the last 2 decades, the literature base devoted to coopetition has gradually expanded. However, the field is still in its infancy. The majority of coopetition research is qualitative, primarily consisting of case studies. Few studies have addressed the nonprofit sector or service industries such as health care. The authors believe that this article may offer a unique perspective on coopetition in the context of a US-based national health care learning alliance designed to accelerate innovation, the Innovation Learning Network or ILN. The mission of the ILN is to "Share the joy and pain of innovation," accelerating innovation by sharing solutions, teaching techniques, and cultivating friendships. These 3 pillars (sharing, teaching, and cultivating) form the foundation for coopetition within the ILN. Through the lens of coopetition, we examine the experience of the ILN over the last 10 years and provide case examples that illustrate the benefits and challenges of coopetition in accelerating innovation in health care.

  14. Structure Learning and Statistical Estimation in Distribution Networks - Part I

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-02-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as those related to demand response, outage detection and management, and improved load-monitoring. In this two part paper, inspired by proliferation of the metering technology, we discuss estimation problems in structurally loopy but operationally radial distribution grids from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. In Part I, the objective is to learn the operational layout of the grid. Part II of this paper presents algorithms that estimate load statistics or line parameters in addition to learning the grid structure. Further, Part II discusses the problem of structure estimation for systems with incomplete measurement sets. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time– which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  15. Memory and learning in a class of neural network models

    International Nuclear Information System (INIS)

    Wallace, D.J.

    1986-01-01

    The author discusses memory and learning properties of the neural network model now identified with Hopfield's work. The model, how it attempts to abstract some key features of the nervous system, and the sense in which learning and memory are identified in the model are described. A brief report is presented on the important role of phase transitions in the model and their implications for memory capacity. The results of numerical simulations obtained using the ICL Distributed Array Processors at Edinburgh are presented. A summary is presented on how the fraction of images which are perfectly stored, depends on the number of nodes and the number of nominal images which one attempts to store using the prescription in Hopfield's paper. Results are presented on the second phase transition in the model, which corresponds to almost total loss of storage capacity as the number of nominal images is increased. Results are given on the performance of a new iterative algorithm for exact storage of up to N images in an N node model

  16. Characterization of synchronized bursts in cultured hippocampal neuronal networks with learning training on microelectrode arrays.

    Science.gov (United States)

    Li, Yanling; Zhou, Wei; Li, Xiangning; Zeng, Shaoqun; Liu, Man; Luo, Qingming

    2007-06-15

    Spontaneous synchronized bursts seem to play a key role in brain functions such as learning and memory. Still controversial is the characterization of spontaneous synchronized bursts in neuronal networks after learning training, whether depression or promotion. By taking advantages of the main features of the microelectrode array (MEA) technology (i.e. multisite recordings, stable and long-term coupling with the biological preparation), we analyzed changes of spontaneous synchronized bursts in cultured hippocampal neuronal networks after learning training. And for this purpose, a learning model at networking level on MEA system was constructed, and analysis of spontaneous synchronized burst activity modulation was presented. Preliminary results show that, the number of burst was increased by 154%, burst duration was increased by 35%, and the number of spikes per burst was increased by 124%, while interburst interval decreased by 44% with learning. In particular, correlation and synchrony of neuronal activities in networks were enhanced by 51% and 36%, respectively, with learning. In contrast, dynamic properties of neuronal networks were not changed much when the network was under "non-learning" condition. These results indicate that firing, association and synchrony of spontaneous bursts in neuronal networks were promoted by learning. Furthermore, from these observations, we are encouraged to think of a more engineered system based on in vitro hippocampal neurons, as a novel sensitive system for electrophysiological evaluations.

  17. A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data.

    Science.gov (United States)

    Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

    2013-06-01

    Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph--a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.

  18. Paradoxes of Social Networking in a Structured Web 2.0 Language Learning Community

    Science.gov (United States)

    Loiseau, Mathieu; Zourou, Katerina

    2012-01-01

    This paper critically inquires into social networking as a set of mechanisms and associated practices developed in a structured Web 2.0 language learning community. This type of community can be roughly described as learning spaces featuring (more or less) structured language learning resources displaying at least some notions of language learning…

  19. Effects of the ISIS Recommender System for Navigation Support in Self-Organised Learning Networks

    Science.gov (United States)

    Drachsler, Hendrik; Hummel, Hans; van den Berg, Bert; Eshuis, Jannes; Waterink, Wim; Nadolski, Rob; Berlanga, Adriana; Boers, Nanda; Koper, Rob

    2009-01-01

    The need to support users of the Internet with the selection of information is becoming more important. Learners in complex, self-organising Learning Networks have similar problems and need guidance to find and select most suitable learning activities, in order to attain their lifelong learning goals in the most efficient way. Several research…

  20. An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction

    NARCIS (Netherlands)

    Commu, Charlotte; Theelen, Mathilde; Treur, J.

    2017-01-01

    In this study, an adaptive temporal-causal network model is present-ed for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The

  1. Service-Learning Project in a First-Year Seminar: A Social Network Analysis

    Science.gov (United States)

    Teymuroglu, Zeynep

    2013-01-01

    Understanding the effects of a service-learning component on the classroom culture, socially and academically, brings a novel perspective to designing, executing, and assessing these types of active-learning projects. This paper evaluates the success of a service-learning project from a perspective of social networks by investigating the question:…

  2. An Analysis of Social Network Websites for Language Learning: Implications for Teaching and Learning English as a Second Language

    Science.gov (United States)

    Liu, M.; Abe, K.; Cao, M. W.; Liu, S.; Ok, D. U.; Park, J.; Parrish, C.; Sardegna, V. G.

    2015-01-01

    Although educators are excited about the potential of social network sites for language learning (SNSLL), there is a lack of understanding of how SNSLL can be used to facilitate teaching and learning for English as Second language (ESL) instructors and students. The purpose of this study was to examine the affordances of four selected SNSLL…

  3. Influence of face-to-face meetings on virtual community activity: the case of Learning Network for Learning Design

    NARCIS (Netherlands)

    Burgos, Daniel; Hummel, Hans; Tattersall, Colin; Brouns, Francis; Kurvers, Hub; Koper, Rob

    2005-01-01

    Burgos, D., Hummel, H., Tattersall, C., Brouns, F., Kurvers, H., & Koper, R. (2006). Influence of face-to-face meetings on virtual community activity: the case of Learning Network for Learning Design. Proceedings of IADIS International Conference Web Based Communities 2006. February, 16-18,2006, San

  4. Developing student engagement in networked teaching and learning practices through problem- and project-based learning approaches

    DEFF Research Database (Denmark)

    Lerche Nielsen, Jørgen; Andreasen, Lars Birch

    2012-01-01

    This paper focuses on how learner engagement can be facilitated through use of social media and communication technologies. The discussions are based on the Danish Master’s Programme of ICT and Learning (MIL), where students study in groups within a networked learning structure. The paper reflects...... on the challenges for students as both independent and interconnected learners....

  5. Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns.

    Science.gov (United States)

    Yamashita, Masahiro; Kawato, Mitsuo; Imamizu, Hiroshi

    2015-01-05

    Individual learning performance of cognitive function is related to functional connections within 'task-activated' regions where activities increase during the corresponding cognitive tasks. On the other hand, since any brain region is connected with other regions and brain-wide networks, learning is characterized by modulations in connectivity between networks with different functions. Therefore, we hypothesized that learning performance is determined by functional connections among intrinsic networks that include both task-activated and less-activated networks. Subjects underwent resting-state functional MRI and a short period of training (80-90 min) in a working memory task on separate days. We calculated functional connectivity patterns of whole-brain intrinsic networks and examined whether a sparse linear regression model predicts a performance plateau from the individual patterns. The model resulted in highly accurate predictions (R(2) = 0.73, p = 0.003). Positive connections within task-activated networks, including the left fronto-parietal network, accounted for nearly half (48%) of the contribution ratio to the prediction. Moreover, consistent with our hypothesis, connections of the task-activated networks with less-activated networks showed a comparable contribution (44%). Our findings suggest that learning performance is potentially constrained by system-level interactions within task-activated networks as well as those between task-activated and less-activated networks.

  6. RELATION BETWEEN COOPERATION AND ORGANIZATIONAL LEARNING WITH THE COMPETITIVENESS IN AN INTERORGANIZATIONAL NETWORK

    Directory of Open Access Journals (Sweden)

    Paulo Cesar Zonta

    2015-05-01

    Full Text Available The study analyzed the relationship between cooperation and organizational learning with competitiveness in a small and medium enterprises (SME network, with business of the groups of the Commercial and Industrial Association of Chapecó (ACIC. The methodology used was quantitative, with the factorial analysis. Currently, ACIC has 14 groups and 236 SME´s nucleated, developing joint activities of economic and social sustainability in Chapecó. The theoretical study raised concepts already endorsed by the scientific community on interorganizational networks, competitiveness, cooperation and organizational learning. The results demonstrated that indicators related to cooperation and learning in horizontal networks are characterized as antecedents of competitiveness in organizational networks, and that there is a positive correlation between the constructs cooperation and organizational learning with competitiveness construct. The study confirms the belief that small businesses associated in networks can increase their competitiveness, thus contributing to regional development.

  7. A Formal Verification Model for Performance Analysis of Reinforcement Learning Algorithms Applied t o Dynamic Networks

    OpenAIRE

    Shrirang Ambaji KULKARNI; Raghavendra G . RAO

    2017-01-01

    Routing data packets in a dynamic network is a difficult and important problem in computer networks. As the network is dynamic, it is subject to frequent topology changes and is subject to variable link costs due to congestion and bandwidth. Existing shortest path algorithms fail to converge to better solutions under dynamic network conditions. Reinforcement learning algorithms posses better adaptation techniques in dynamic environments. In this paper we apply model based Q-Routing technique ...

  8. Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

    Science.gov (United States)

    Sadeghi, Zahra

    2016-09-01

    In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

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

    Directory of Open Access Journals (Sweden)

    Xiaolei Ma

    2017-04-01

    Full Text Available 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.

  10. Dialogic e-learning2learn: creating global digital networks and educational knowledge building architectures across diversity

    DEFF Research Database (Denmark)

    Sorensen, Elsebeth Korsgaard

    2007-01-01

    Abstract: Purpose – The purpose of this paper is to address the challenge and potential of online higher and continuing education, of fostering and promoting, in a global perspective across time and space, democratic values working for a better world. Design/methodology/approach – The paper...... presents a generalized dialogic learning architecture of networked collaborative learning and makes a plea for a theory-informed networked collaborative learning architecture and methodology appropriate for adult learners in higher and continuing education. Findings – Values include mutual political...... and evaluation of the implementation of the pedagogical architecture into a Danish Master Programme....

  11. On the Use of Machine Learning for Identifying Botnet Network Traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    contemporary approaches use machine learning techniques for identifying malicious traffic. This paper presents a survey of contemporary botnet detection methods that rely on machine learning for identifying botnet network traffic. The paper provides a comprehensive overview on the existing scientific work thus...... contributing to the better understanding of capabilities, limitations and opportunities of using machine learning for identifying botnet traffic. Furthermore, the paper outlines possibilities for the future development of machine learning-based botnet detection systems....

  12. THE USE OF SOCIAL NETWORKS IN THE PROCESS OF LEARNING ENGLISH AS A SECOND LANGUAGE

    Directory of Open Access Journals (Sweden)

    Halyna I. Sotska

    2018-02-01

    Full Text Available In the recent decade many changes in the process of education took place because of the development of information and communication technologies. Online social groups tend to be used by teachers and students for formal (study and informal (personal communication purposes. An efficient teacher may turn social networks into an effective tool, encouraging students to communicate in the target language. With the help of social networks the teacher can activate students in the process of learning, create situations for better understanding and perceiving the material. The use of such approaches as blended learning, corporative learning and active learning helps make the classes more attractive and effective. Moreover, social networks can help in the development of students’ creativity, provision of feedback and cooperative learning. The article deals with the question of influence of Massive online open courses on effectiveness of the educational process for students who learn English as a second language.

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

    Science.gov (United States)

    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.

  14. Bio-Tribology Properties of Bionic Carp Scale Morphology on Ti6A14V Surface

    Science.gov (United States)

    Wang, W.; Y Wei, X.; Meng, K.; Zhong, L. H.; Wang, Y.; Yu, X. H.

    2017-12-01

    In order to improve the bio-tribology properties of Ti6A14V surface, the bionic carp scale appearance pattern on Ti6A14V surface was prepared by laser surface texturing technology. The ball-disc reciprocating linear tribological experiment under different lubricants with dry friction was carried out by MRTR multifunction friction and wear testing machine using ZrO2/Ti6A14V as friction pair. The wear scar morphology of the sample surface was observed by SEM. The results show that for dry friction, the friction factor of the bionic carp scale morphology Ti6A14V reduces by 0.23 than those without bionic carp scale morphology, a decline of 45%. Under different lubrication conditions, the friction factors of samples with the bionic carp scale are increased in varying degrees with the increase of size of bionic texturing. The friction factor with same specimen under different lubrication conditions according to the ascending order are 0.5g/dl of sodium hyaluronate +0.5g/dl-γglobulin and 0.5g/dl mixed aqueous solution of sodium hyaluronate solution and artificial saliva. The wear volume also showed a similar variation.

  15. A Numerical Study of Aerodynamic Performance and Noise of a Bionic Airfoil Based on Owl Wing

    Directory of Open Access Journals (Sweden)

    Xiaomin Liu

    2014-08-01

    Full Text Available Noise reduction and efficiency enhancement are the two important directions in the development of the multiblade centrifugal fan. In this study, we attempt to develop a bionic airfoil based on the owl wing and investigate its aerodynamic performance and noise-reduction mechanism at the relatively low Reynolds number. Firstly, according to the geometric characteristics of the owl wing, a bionic airfoil is constructed as the object of study at Reynolds number of 12,300. Secondly, the large eddy simulation (LES with the Smagorinsky model is adopted to numerically simulate the unsteady flow fields around the bionic airfoil and the standard NACA0006 airfoil. And then, the acoustic sources are extracted from the unsteady flow field data, and the Ffowcs Williams-Hawkings (FW-H equation based on Lighthill's acoustic theory is solved to predict the propagation of these acoustic sources. The numerical results show that the lift-to-drag ratio of bionic airfoil is higher than that of the traditional NACA 0006 airfoil because of its deeply concave lower surface geometry. Finally, the sound field of the bionic airfoil is analyzed in detail. The distribution of the A-weighted sound pressure levels, the scaled directivity of the sound, and the distribution of dP/dt on the airfoil surface are provided so that the characteristics of the acoustic sources could be revealed.

  16. Bionic Design for Mars Sampling Scoop Inspired by Himalayan Marmot Claw

    Directory of Open Access Journals (Sweden)

    Long Xue

    2016-01-01

    Full Text Available Cave animals are often adapted to digging and life underground, with claw toes similar in structure and function to a sampling scoop. In this paper, the clawed toes of the Himalayan marmot were selected as a biological prototype for bionic research. Based on geometric parameter optimization of the clawed toes, a bionic sampling scoop for use on Mars was designed. Using a 3D laser scanner, the point cloud data of the second front claw toe was acquired. Parametric equations and contour curves for the claw were then built with cubic polynomial fitting. We obtained 18 characteristic curve equations for the internal and external contours of the claw. A bionic sampling scoop was designed according to the structural parameters of Curiosity’s sampling shovel and the contours of the Himalayan marmot’s claw. Verifying test results showed that when the penetration angle was 45° and the sampling speed was 0.33 r/min, the bionic sampling scoops’ resistance torque was 49.6% less than that of the prototype sampling scoop. When the penetration angle was 60° and the sampling speed was 0.22 r/min, the resistance torque of the bionic sampling scoop was 28.8% lower than that of the prototype sampling scoop.

  17. Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic System

    Directory of Open Access Journals (Sweden)

    A. A. Salama

    2015-03-01

    Full Text Available In this paper, we present a review of different recommender system algorithms that are utilized in social networks based e-Learning systems. Future research will include our proposed our e-Learning system that utilizes Recommender System and Social Network. Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache in [21, 22, 23] and Salama et al. in [24-66].The purpose of this paper is to utilize a neutrosophic set to analyze social networks data conducted through learning activities.

  18. Numerical Study on Hydrodynamic Performance of Bionic Caudal Fin

    Directory of Open Access Journals (Sweden)

    Kai Zhou

    2016-01-01

    Full Text Available In this work, numerical simulations are conducted to reveal the hydrodynamic mechanism of caudal fin propulsion. In the modeling of a bionic caudal fin, a universal kinematics model with three degrees of freedom is adopted and the flexible deformation in the spanwise direction is considered. Navier-Stokes equations are used to solve the unsteady fluid flow and dynamic mesh method is applied to track the locomotion. The force coefficients, torque coefficient, and flow field characteristics are extracted and analyzed. Then the thrust efficiency is calculated. In order to verify validity and feasibility of the algorithm, hydrodynamic performance of flapping foil is analyzed. The present results of flapping foil compare well with those in experimental researches. After that, the influences of amplitude of angle of attack, amplitude of heave motion, Strouhal number, and spanwise flexibility are analyzed. The results show that, the performance can be improved by adjusting the motion and flexibility parameters. The spanwise flexibility of caudal fin can increase thrust force with high propulsive efficiency.

  19. Biomimetic approaches to bionic touch through a peripheral nerve interface.

    Science.gov (United States)

    Saal, Hannes P; Bensmaia, Sliman J

    2015-12-01

    State-of-the-art prosthetic hands nearly match the dexterity of the human hand, and sophisticated approaches have been developed to control them intuitively. However, grasping and dexterously manipulating objects relies heavily on the sense of touch, without which we would struggle to perform even the most basic activities of daily living. Despite the importance of touch, not only in motor control but also in affective communication and embodiment, the restoration of touch through bionic hands is still in its infancy, a shortcoming that severely limits their effectiveness. Here, we focus on approaches to restore the sense of touch through an electrical interface with the peripheral nerve. First, we describe devices that can be chronically implanted in the nerve to electrically activate nerve fibers. Second, we discuss how these interfaces have been used to convey basic somatosensory feedback. Third, we review what is known about how the somatosensory nerve encodes information about grasped objects in intact limbs and discuss how these natural neural codes can be exploited to convey artificial tactile feedback. Finally, we offer a blueprint for how these codes could be implemented in a neuroprosthetic device to deliver rich, natural, and versatile tactile sensations. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  20. Research on anti crack mechanism of bionic coupling brake disc

    Science.gov (United States)

    Shi, Lifeng; Yang, Xiao; Zheng, Lingnan; Wu, Can; Ni, Jing

    2017-09-01

    According to the biological function of fatigue resistance possessed by biology, this study designed a Bionic Coupling Brake Disc (BCBD) which can inhibit crack propagation as the result of improving fatigue property. Thermal stress field of brake disc was calculated under emergency working condition, and circumferential and radial stress field which lead to fatigue failure of brake disc were investigated simultaneously. Results showed that the maximum temperature of surface reached 890°C and the maximum residual tensile stress was 207 Mpa when the initial velocity of vehicle was 200 km/h. Based on the theory of elastic plastic fracture mechanics, the crack opening displacement and the crack front J integrals of the BCBD and traditional brake disc (TBD) with pre-cracking were calculated, and the strength of crack front was compared. Results revealed the growth behavior of fatigue crack located on surface of brake disc, and proved the anti fatigue resistance of BCBD was better, and the strength of crack resistance of BCBD was much stronger than that of TBD. This simulation research provided significant references for optimization and manufacturing of BCBD.

  1. A bionic camera-based polarization navigation sensor.

    Science.gov (United States)

    Wang, Daobin; Liang, Huawei; Zhu, Hui; Zhang, Shuai

    2014-07-21

    Navigation and positioning technology is closely related to our routine life activities, from travel to aerospace. Recently it has been found that Cataglyphis (a kind of desert ant) is able to detect the polarization direction of skylight and navigate according to this information. This paper presents a real-time bionic camera-based polarization navigation sensor. This sensor has two work modes: one is a single-point measurement mode and the other is a multi-point measurement mode. An indoor calibration experiment of the sensor has been done under a beam of standard polarized light. The experiment results show that after noise reduction the accuracy of the sensor can reach up to 0.3256°. It is also compared with GPS and INS (Inertial Navigation System) in the single-point measurement mode through an outdoor experiment. Through time compensation and location compensation, the sensor can be a useful alternative to GPS and INS. In addition, the sensor also can measure the polarization distribution pattern when it works in multi-point measurement mode.

  2. Nanocarbon-Coated Porous Anodic Alumina for Bionic Devices

    Directory of Open Access Journals (Sweden)

    Morteza Aramesh

    2015-08-01

    Full Text Available A highly-stable and biocompatible nanoporous electrode is demonstrated herein. The electrode is based on a porous anodic alumina which is conformally coated with an ultra-thin layer of diamond-like carbon. The nanocarbon coating plays an essential role for the chemical stability and biocompatibility of the electrodes; thus, the coated electrodes are ideally suited for biomedical applications. The corrosion resistance of the proposed electrodes was tested under extreme chemical conditions, such as in boiling acidic/alkali environments. The nanostructured morphology and the surface chemistry of the electrodes were maintained after wet/dry chemical corrosion tests. The non-cytotoxicity of the electrodes was tested by standard toxicity tests using mouse fibroblasts and cortical neurons. Furthermore, the cell–electrode interaction of cortical neurons with nanocarbon coated nanoporous anodic alumina was studied in vitro. Cortical neurons were found to attach and spread to the nanocarbon coated electrodes without using additional biomolecules, whilst no cell attachment was observed on the surface of the bare anodic alumina. Neurite growth appeared to be sensitive to nanotopographical features of the electrodes. The proposed electrodes show a great promise for practical applications such as retinal prostheses and bionic implants in general.

  3. Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory

    DEFF Research Database (Denmark)

    Lüders, Benno; Schläger, Mikkel; Korach, Aleksandra

    2017-01-01

    a new task is learned. This paper takes a step in overcoming this limitation by building on the recently proposed Evolving Neural Turing Machine (ENTM) approach. In the ENTM, neural networks are augmented with an external memory component that they can write to and read from, which allows them to store...... it easier to find unused memory location and therefor facilitates the evolution of continual learning networks. Our results suggest that augmenting evolving networks with an external memory component is not only a viable mechanism for adaptive behaviors in neuroevolution but also allows these networks...

  4. Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

    Directory of Open Access Journals (Sweden)

    Alexandru D. Iordan

    2018-01-01

    Full Text Available Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on “resting-state” networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA and 20 older adults (OA were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of

  5. Cooperation in networks where the learning environment differs from the interaction environment.

    Science.gov (United States)

    Zhang, Jianlei; Zhang, Chunyan; Chu, Tianguang; Weissing, Franz J

    2014-01-01

    We study the evolution of cooperation in a structured population, combining insights from evolutionary game theory and the study of interaction networks. In earlier studies it has been shown that cooperation is difficult to achieve in homogeneous networks, but that cooperation can get established relatively easily when individuals differ largely concerning the number of their interaction partners, such as in scale-free networks. Most of these studies do, however, assume that individuals change their behaviour in response to information they receive on the payoffs of their interaction partners. In real-world situations, subjects do not only learn from their interaction partners, but also from other individuals (e.g. teachers, parents, or friends). Here we investigate the implications of such incongruences between the 'interaction network' and the 'learning network' for the evolution of cooperation in two paradigm examples, the Prisoner's Dilemma game (PDG) and the Snowdrift game (SDG). Individual-based simulations and an analysis based on pair approximation both reveal that cooperation will be severely inhibited if the learning network is very different from the interaction network. If the two networks overlap, however, cooperation can get established even in case of considerable incongruence between the networks. The simulations confirm that cooperation gets established much more easily if the interaction network is scale-free rather than random-regular. The structure of the learning network has a similar but much weaker effect. Overall we conclude that the distinction between interaction and learning networks deserves more attention since incongruences between these networks can strongly affect both the course and outcome of the evolution of cooperation.

  6. Monitoring of Students' Interaction in Online Learning Settings by Structural Network Analysis and Indicators.

    Science.gov (United States)

    Ammenwerth, Elske; Hackl, Werner O

    2017-01-01

    Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

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

    2017-08-01

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

  9. ANA, automatic natural learning of a semantic network

    International Nuclear Information System (INIS)

    Enguehard, Chantal

    1992-01-01

    The objective of this research thesis is the automatic extraction of terminology and the study of its automatic structuring in order to produce a semantic network. Such an operation is applied to text corpus representing knowledge on a specific field in order to select the relevant technical vocabulary regarding this field. Thus, the author developed a method and a software for the automatic acquisition of terminology items. The author first gives an overview of systems and methods of document indexing and of thesaurus elaboration, and a brief presentation of the state-of-the-art of learning. Then, he discusses some drawbacks of computer systems of natural language processing which are using large knowledge sources such as grammars and dictionaries. After a presentation of the adopted approach and of some hypotheses, the author defines objects and operators which are necessary for an easier data handling, presents the knowledge acquisition process, and finally precisely describes the system computerization. Some results are assessed and discussed, and limitations and perspectives are commented [fr

  10. Image aesthetic quality evaluation using convolution neural network embedded learning

    Science.gov (United States)

    Li, Yu-xin; Pu, Yuan-yuan; Xu, Dan; Qian, Wen-hua; Wang, Li-peng

    2017-11-01

    A way of embedded learning convolution neural network (ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.

  11. Construction of Course Ubiquitous Learning Based on Network

    Science.gov (United States)

    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…

  12. Design and Implementation of a Bionic Mimosa Robot with Delicate Leaf Swing Behavior

    Directory of Open Access Journals (Sweden)

    Chung-Liang Chang

    2014-12-01

    Full Text Available This study designed and developed a bionic mimosa robot with delicate leaf swing behaviors. For different swing behaviors, this study developed a variety of situations, in which the bionic mimosa robot would display different postures. The core technologies used were Shape Memory Alloys (SMAs, plastic material, and an intelligent control device. The technology particularly focused on the SMAs memory processing bend mode, directional guidance, and the position of SMAs installed inside the plastic material. Performance analysis and evaluation were conducted using two SMAs for mimosa opening/closing behaviors. Finally, by controlling the mimosa behavior with a micro-controller, the optimal strain swing behavior was realized through fuzzy logic control in order to display the different postures of mimosa under different situations. The proposed method is applicable to micro-bionic robot systems, entertainment robots, biomedical engineering, and architectural aesthetics-related fields in the future.

  13. Effects of setting angle and chord length on performance of four blades bionic wind turbine

    Science.gov (United States)

    Yang, Z. X.; Li, G. S.; Song, L.; Bai, Y. F.

    2017-11-01

    With the energy crisis and the increasing environmental pollution, more and more efforts have been made about wind power development. In this paper, a four blades bionic wind turbine was proposed, and the outline of wind turbine was constructed by the fitted curve. This paper attempted to research the effects of setting angle and chord length on performance of four blades bionic wind turbine by computational fluid dynamics (CFD) simulation. The results showed that the setting angle and chord length of the bionic wind turbine has some significant effects on the efficiency of the wind turbine, and within the range of wind speed from 7 m/s to 15 m/s, the wind turbine achieved maximum efficiency when the setting angle is 31 degree and the chord length is 125 mm. The conclusion will work as a guideline for the improvement of wind turbine design

  14. A Hybrid Bionic Image Sensor Achieving FOV Extension and Foveated Imaging

    Directory of Open Access Journals (Sweden)

    Qun Hao

    2018-03-01

    Full Text Available Based on bionic compound eye and human foveated imaging mechanisms, a hybrid bionic image sensor (HBIS is proposed in this paper to extend the field of view (FOV with high resolution. First, the hybrid bionic imaging model was developed and the structure parameters of the HBIS were deduced. Second, the properties of the HBIS were simulated, including FOV extension, super-resolution imaging, foveal ratio and so on. Third, a prototype of the HBIS was developed to validate the theory. Imaging experiments were carried out, and the results are in accordance with the simulations, proving the potential of the HBIS for large FOV and high-resolution imaging with low cost.

  15. Bionic optimization in structural design stochastically based methods to improve the performance of parts and assemblies

    CERN Document Server

    Gekeler, Simon

    2016-01-01

    The book provides suggestions on how to start using bionic optimization methods, including pseudo-code examples of each of the important approaches and outlines of how to improve them. The most efficient methods for accelerating the studies are discussed. These include the selection of size and generations of a study’s parameters, modification of these driving parameters, switching to gradient methods when approaching local maxima, and the use of parallel working hardware. Bionic Optimization means finding the best solution to a problem using methods found in nature. As Evolutionary Strategies and Particle Swarm Optimization seem to be the most important methods for structural optimization, we primarily focus on them. Other methods such as neural nets or ant colonies are more suited to control or process studies, so their basic ideas are outlined in order to motivate readers to start using them. A set of sample applications shows how Bionic Optimization works in practice. From academic studies on simple fra...

  16. Effect of using peer tutoring to support knowledge sharing in Learning Networks: A cognitive load perspective

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Sloep, Peter

    2010-01-01

    Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2010). Effect of using peer tutoring to support knowledge sharing in Learning Networks: A cognitive load perspective. ICO-Toogdag. November, 4, 2010, Amstelveen, The Netherlands: VU Amsterdam.

  17. Mechanisms of peer tutoring on optimizing cognitive load during knowledge sharing in learning networks

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Sloep, Peter

    2010-01-01

    Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2010, 15 April). Mechanisms of peer tutoring on optimizing cognitive load during knowledge sharing in learning networks. Presentation at NELLL Colloqium, Heerlen, The Netherlands: Open University of the Netherlands.

  18. Effect of using peer tutoring to support knowledge sharing in Learning Networks: A cognitive load perspective

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Sloep, Peter

    2010-01-01

    Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2010, 4 November). Effect of using peer tutoring to support knowledge sharing in Learning Networks: A cognitive load perspective. Presentation at ICO-Toogdag, Amstelveen, The Netherlands: VU Amsterdam.

  19. Designing optimal peer support to alleviate learner cognitive load in Learning Networks

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Sloep, Peter

    2012-01-01

    Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2012, 21 July). Designing optimal peer support to alleviate learner cognitive load in Learning Networks. Presentation at IADIS International Conference Web-Based Communities and Social Media 2012, Lisbon, Portugal.

  20. Incidental and intentional learning of verbal episodic material differentially modifies functional brain networks.

    Directory of Open Access Journals (Sweden)

    Marie-Therese Kuhnert

    Full Text Available Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.