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...
Hodgson, Vivien; de Laat, Maarten; McConnell, David
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...
Appleby, Yvon; Hillier, Yvonne
This paper discusses the contribution that practice-research networks can make to support critical professional development in the Learning and Skills sector in England. By practice-research networks we mean groups or networks which maintain a connection between research and professional practice. These networks stem from the philosophy of…
Sloep, Peter; Berlanga, Adriana
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
Ryberg, Thomas; Sinclair, Christine
the books that include a selection of reworked and peer-reviewed papers from the conference. The 2014 Networked Learning Conference which was held in Edinburgh was characterised by animated dialogue on emergent influences affecting networked teaching and learning building on work established in earlier...
Brett, Valerie; Mullally, Martina; O'Gorman, Bill; Fuller-Love, Nerys
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…
Leroy, Lisa; Rittner, Jessica Levin; Johnson, Karin E; Gerteis, Jessie; Miller, Therese
Collaborative research networks are increasingly used as an effective mechanism for accelerating knowledge transfer into policy and practice. This paper explored the characteristics and collaborative learning approaches of nine health research networks. Semi-structured interviews with representatives from eight diverse US health services research networks conducted between November 2012 and January 2013 and program evaluation data from a ninth. The qualitative analysis assessed each network's purpose, duration, funding sources, governance structure, methods used to foster collaboration, and barriers and facilitators to collaborative learning. The authors reviewed detailed notes from the interviews to distill salient themes. Face-to-face meetings, intentional facilitation and communication, shared vision, trust among members and willingness to work together were key facilitators of collaborative learning. Competing priorities for members, limited funding and lack of long-term support and geographic dispersion were the main barriers to coordination and collaboration across research network members. The findings illustrate the importance of collaborative learning in research networks and the challenges to evaluating the success of research network functionality. Conducting readiness assessments and developing process and outcome evaluation metrics will advance the design and show the impact of collaborative research networks. Copyright © 2017 Longwoods Publishing.
Fazeli, Soude; Drachsler, Hendrik; Sloep, Peter
Fazeli, S., Drachsler, H., & Sloep, P. B. (2013). Socio-semantic Networks of Research Publications in the Learning Analytics Community. In M. d'Aquin, S. Dietze, H. Drachsler, E. Herder, & D. Taibi (Eds.), Linked data challenge, Learning Analytic and Knowledge (LAK13) (pp. 6-10). Vol. 974, Leuven,
Pérez Calle, Elio
The Department of Modern Physics of the University of Science and Technology of China is hosting a Tier-3 centre for the ATLAS experiment. A interdisciplinary team of researchers, engineers and students are devoted to the task of receiving, storing and analysing the scientific data produced by the LHC. In order to achieve the highest performance and to develop a knowledge base shared by all members of the team, the research activities and their coordination are being supported by an array of computing systems. These systems have been designed to foster communication, collaboration and coordination among the members of the team, both face-to-face and remotely, and both in synchronous and asynchronous ways. The result is a collaborative learning-research network whose main objectives are awareness (to get shared knowledge about other's activities and therefore obtain synergies), articulation (to allow a project to be divided, work units to be assigned and then reintegrated) and adaptation (to adapt information technologies to the needs of the group). The main technologies involved are Communication Tools such as web publishing, revision control and wikis, Conferencing Tools such as forums, instant messaging and video conferencing and Coordination Tools, such as time management, project management and social networks. The software toolkit has been deployed by the members of the team and it has been based on free and open source software.
Calle, Elio Pérez
The Department of Modern Physics of the University of Science and Technology of China is hosting a Tier-3 centre for the ATLAS experiment. A interdisciplinary team of researchers, engineers and students are devoted to the task of receiving, storing and analysing the scientific data produced by the LHC. In order to achieve the highest performance and to develop a knowledge base shared by all members of the team, the research activities and their coordination are being supported by an array of computing systems. These systems have been designed to foster communication, collaboration and coordination among the members of the team, both face-to-face and remotely, and both in synchronous and asynchronous ways. The result is a collaborative learning-research network whose main objectives are awareness (to get shared knowledge about other's activities and therefore obtain synergies), articulation (to allow a project to be divided, work units to be assigned and then reintegrated) and adaptation (to adapt information technologies to the needs of the group). The main technologies involved are Communication Tools such as web publishing, revision control and wikis, Conferencing Tools such as forums, instant messaging and video conferencing and Coordination Tools, such as time management, project management and social networks. The software toolkit has been deployed by the members of the team and it has been based on free and open source software.
Østergaard, Rina; Sorensen, Elsebeth Korsgaard
Abstract 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...... 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...... help and qualify the development of innovative DBR and NL designs directed towards the future. Assuming the views outlined and promoted in this paper, the authors claim that researchers in the field as well as welfare professionals in pedagogical, social and health areas, must display creative...
Wright, Steve; Parchoma, Gale
How is the link between learner and technology made in mobile learning? What is the value of the concept of "affordances"? And how does research articulating this concept act to position mobile devices as "technologies for learning"? This literature review used both unstructured and structured search samples of published research on mobile…
PAN Hao; CEN Li; ZHONG Luo
Introduce a method of generation of new units within a cluster and a algorithm of generating new clusters.The model automatically builds up its dynamically growing internal representation structure during the learning process.Comparing model with other typical classification algorithm such as the Kohonen's self-organizing map, the model realizes a multilevel classification of the input pattern with an op tional accuracy and gives a strong support possibility for the parallel computational main processor. The idea is suitable for the high level storage of complex datas struetures for object recognition.
Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan
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…
Full Text Available Melinda M Davis,1,2 Sara Keller,1 Jennifer E DeVoe,1,3 Deborah J Cohen11Department of Family Medicine, 2Oregon Rural Practice-based Research Network, Oregon Health & Science University, Portland, OR, USA; 3OCHIN Practice-based Research Network, Portland, OR, USAAbstract: Practice-based research networks (PBRNs are organizations that involve practicing clinicians in asking and answering clinically relevant research questions. This review explores the origins, characteristics, funding, and lessons learned through practice-based research in the United States. Primary care PBRNs emerged in the USA in the 1970s. Early studies explored the etiology of common problems encountered in primary care practices (eg, headache, miscarriage, demonstrating the gap between research conducted in controlled specialty settings and real-world practices. Over time, national initiatives and an evolving funding climate have shaped PBRN development, contributing to larger networks, a push for shared electronic health records, and the use of a broad range of research methodologies (eg, observational studies, pragmatic randomized controlled trials, continuous quality improvement, participatory methods. Today, there are over 160 active networks registered with the Agency for Healthcare Research and Quality's PBRN Resource Center that engage primary care clinicians, pharmacists, dentists, and other health care professionals in research and quality-improvement initiatives. PBRNs provide an important laboratory for encouraging collaborative research partnerships between academicians and practices or communities to improve population health, conduct comparative effectiveness and patient-centered outcomes research, and study health policy reform. PBRNs continue to face critical challenges that include: (1 adapting to a changing landscape; (2 recruiting and retaining membership; (3 securing infrastructure support; (4 straddling two worlds (academia and community and managing
Brouns, Francis; Sloep, Peter
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
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.
Paolino, Andrea R; McGlynn, Elizabeth A; Lieu, Tracy; Nelson, Andrew F; Prausnitz, Stephanie; Horberg, Michael A; Arterburn, David E; Gould, Michael K; Laws, Reesa L; Steiner, John F
The Patient Outcomes Research to Advance Learning (PORTAL) Network was established with funding from the Patient-Centered Outcomes Research Institute (PCORI) in 2014. The PORTAL team adapted governance structures and processes from past research network collaborations. We will review and outline the structures and processes of the PORTAL governance approach and describe how proactively focusing on priority areas helped us to facilitate an ambitious research agenda. For years a variety of funders have supported large-scale infrastructure grants to promote the use of clinical datasets to answer important comparative effectiveness research (CER) questions. These awards have provided the impetus for health care systems to join forces in creating clinical data research networks. Often, these scientific networks do not develop governance processes proactively or systematically, and address issues only as problems arise. Even if network leaders and collaborators foresee the need to develop governance approaches, they may underestimate the time and effort required to develop sound processes. The resulting delays can impede research progress. Because the PORTAL sites had built trust and a foundation of collaboration by participating with one another in past research networks, essential elements of effective governance such as guiding principles, decision making processes, project governance, data governance, and stakeholders in governance were familiar to PORTAL investigators. This trust and familiarity enabled the network to rapidly prioritize areas that required sound governance approaches: responding to new research opportunities, creating a culture of trust and collaboration, conducting individual studies, within the broader network, assigning responsibility and credit to scientific investigators, sharing data while protecting privacy/security, and allocating resources. The PORTAL Governance Document, complete with a Toolkit of Appendices is included for reference and
Henneberg, Stephan C.; Jiang, Zhizhong; Naudé, Peter
The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987). In thi......The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987......). In this paper, based upon the papers presented at the 22 conferences held to date, we undertake a Social Network Analysis in order to examine the degree of co-publishing that has taken place between this group of researchers. We identify the different components in this database, and examine the large main...
Lin, Xiaofan; Hu, Xiaoyong; Hu, Qintai; Liu, Zhichun
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…
Sloep, P. B. (2013). Networked professional learning. In A. Littlejohn, & A. Margaryan (Eds.), Technology-enhanced Professional Learning: Processes, Practices and Tools (pp. 97–108). London: Routledge.
Penuel, William R.; Bell, Philip; Bevan, Bronwyn; Buffington, Pam; Falk, Joni
This paper explores practical ways to engage two areas of educational scholarship--research on science learning and research on social networks--to inform efforts to plan and support implementation of new standards. The standards, the "Next Generation Science Standards" (NGSS; NGSS Lead States in Next generation science standards: For…
Paul, Heather L.; Guillory, Erika R.
NASA s Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and webcasting. As part of NASA s Strategic Plan to reach the next generation of space explorers, the DLN develops and delivers educational programs that reinforce principles in the areas of science, technology, engineering and mathematics. The DLN has created a series of live education videoconferences connecting the Desert Research and Technology Studies (RATS) field test to students across the United States. The programs are also extended to students around the world via live webcasting. The primary focus of the events is the Vision for Space Exploration. During the programs, Desert RATS engineers and scientists inform and inspire students about the importance of exploration and share the importance of the field test as it correlates with plans to return to the Moon and explore Mars. This paper describes the events that took place in September 2006.
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...
School and classroom structures designed to meet the needs of the industrial past cannot "maintain the temperature required for sustaining life." Recent learning sciences research findings compel educators to invent new learning environments better suited to meet the demands of the 21st century. These new learning environments require…
Kelling, Steve; Gerbracht, Jeff; Fink, Daniel; Lagoze, Carl; Wong, Weng-Keen; Yu, Jun; Damoulas, Theodoros; Gomes, Carla
In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network,...
Martens, Harrie; Vogten, Hubert; Koper, Rob; Tattersall, Colin; Van Rosmalen, Peter; Sloep, Peter; Van Bruggen, Jan; Spoelstra, Howard
Learning Networks Distributed Environment is a prototype of an architecture that allows the sharing and modification of learning materials through a number of transport protocols. The prototype implements a p2p protcol using JXTA.
Geels, Mark J; Thøgersen, Regitze L; Guzman, Carlos A; Ho, Mei Mei; Verreck, Frank; Collin, Nicolas; Robertson, James S; McConkey, Samuel J; Kaufmann, Stefan H E; Leroy, Odile
TRANSVAC was a collaborative infrastructure project aimed at enhancing European translational vaccine research and training. The objective of this four year project (2009-2013), funded under the European Commission's (EC) seventh framework programme (FP7), was to support European collaboration in the vaccine field, principally through the provision of transnational access (TNA) to critical vaccine research and development (R&D) infrastructures, as well as by improving and harmonising the services provided by these infrastructures through joint research activities (JRA). The project successfully provided all available services to advance 29 projects and, through engaging all vaccine stakeholders, successfully laid down the blueprint for the implementation of a permanent research infrastructure for early vaccine R&D in Europe. Copyright © 2015. Published by Elsevier Ltd.
Stoecker, Randy; Ambler, Susan H.; Cutforth, Nick; Donohue, Patrick; Dougherty, Dan; Marullo, Sam; Nelson, Kris S.; Stutts, Nancy B.
Compares seven multi-institutional community-based research networks in Appalachia; Colorado; District of Columbia; Minneapolis-St. Paul; Philadelphia; Richmond, Virginia; and Trenton, New Jersey. After reviewing the histories of the networks, conducts a comparative SWOT analysis, showing their common and unique strengths, weaknesses,…
, trust formation and profiling in Learning Networks, and peer-support among Learning Network participants. This discussion will be interspersed with implementation guidelines for Learning Networks and with a discussion of the more extended case of a Learning Network for Higher Education. Taking into consideration research currently carried out at our own centre and elsewhere, the chapter will close off with a look into the future of Learning Networks.
Full Text Available How is the link between learner and technology made in mobile learning? Whatis the value of the concept of ‘affordances'? And how does research articulatingthis concept act to position mobile devices as ‘technologies for learning'? Thisliterature review used both unstructured and structured search samples of publishedresearch on mobile learning to critically evaluate the prevalence and influenceof the concept of the affordances of mobile technologies. Actor-networktheory is drawn on as a theoretical lens through which to critically considerhow this concept is articulated, and in particular to explore the way it positionsand controls mobile devices as technologies for learning. Parallels in contemporaryaccounts of mobile learning are drawn with classifications of previous discoursesaround the introduction of computers into schools. An alternativeagenda for mobile learning research is suggested with a focus on authentic andinformal contexts rather than controlled experiments.
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....
Spielman, Howard; Schaffer, Scott B; Cohen, Mitchell G; Wu, Hongyu; Vena, Donald A; Collie, Damon; Curro, Frederick A; Thompson, Van P; Craig, Ronald G
The authors aimed to determine the outcome of and factors associated with success and failure of restorations in endodontically treated teeth in patients in practices participating in the Practitioners Engaged in Applied Research and Learning (PEARL) Network. Practitioner-investigators (P-Is) invited the enrollment of all patients seeking care at participating practices who had undergone primary endodontic therapy and restoration in a permanent tooth three to five years earlier. P-Is classified endodontically reated teeth as restorative failures if the restoration was replaced, the restoration needed replacement or the tooth was cracked or fractured. P-Is from 64 practices enrolled in the study 1,298 eligible patients who had endodontically treated teeth that had been restored. The mean (standard deviation) time to follow-up was 3.9 (0.6) years. Of the 1,298 enrolled teeth, P-Is classified 181 (13.9 percent; 95 percent confidence interval [CI], 12.1-15.8 percent) as restorative failures: 44 (3.4 percent) due to cracks or fractures, 57 (4.4 percent) due to replacement of the original restoration for reasons other than fracture and 80 (6.2 percent) due to need for a new restoration. When analyzing the results by means of multivariate logistic regression, the authors found a greater risk of restorative failure to be associated with canines or incisors and premolars (P = .04), intracoronal restorations (P < .01), lack of preoperative proximal contacts (P < .01), presence of periodontal connective-tissue attachment loss (P < .01), younger age (P = .01), Hispanic/Latino ethnicity (P = .04) and endodontic therapy not having been performed by a specialist (P = .04). These results suggest that molars (as opposed to other types of teeth), full-coverage restorations, preoperative proximal contacts, good periodontal health, non-Hispanic/Latino ethnicity, endodontic therapy performed by a specialist and older patient age are associated with restorative success for
Cheng, Adam; Kessler, David; Mackinnon, Ralph; Chang, Todd P; Nadkarni, Vinay M; Hunt, Elizabeth A; Duval-Arnould, Jordan; Lin, Yiqun; Pusic, Martin; Auerbach, Marc
Simulation-based research has grown substantially over the past two decades; however, relatively few published simulation studies are multicenter in nature. Multicenter research confers many distinct advantages over single-center studies, including larger sample sizes for more generalizable findings, sharing resources amongst collaborative sites, and promoting networking. Well-executed multicenter studies are more likely to improve provider performance and/or have a positive impact on patient outcomes. In this manuscript, we offer a step-by-step guide to conducting multicenter, simulation-based research based upon our collective experience with the International Network for Simulation-based Pediatric Innovation, Research and Education (INSPIRE). Like multicenter clinical research, simulation-based multicenter research can be divided into four distinct phases. Each phase has specific differences when applied to simulation research: (1) Planning phase , to define the research question, systematically review the literature, identify outcome measures, and conduct pilot studies to ensure feasibility and estimate power; (2) Project Development phase , when the primary investigator identifies collaborators, develops the protocol and research operations manual, prepares grant applications, obtains ethical approval and executes subsite contracts, registers the study in a clinical trial registry, forms a manuscript oversight committee, and conducts feasibility testing and data validation at each site; (3) Study Execution phase , involving recruitment and enrollment of subjects, clear communication and decision-making, quality assurance measures and data abstraction, validation, and analysis; and (4) Dissemination phase , where the research team shares results via conference presentations, publications, traditional media, social media, and implements strategies for translating results to practice. With this manuscript, we provide a guide to conducting quantitative multicenter
Gustavo Henrique de Araújo Freire
Full Text Available [Portuguese]Apresenta um dos aspectos da tese de que uma rede virtual de aprendizagem (aqui denominada estoques de informação em fluxo facilita a comunicação da informação nos grupos de usuários que dela participa. Aponta que a principal característica da sociedade contemporânea é a aplicação da informação e do conhecimento em um ciclo de realimentação cumulativo da inovação tecnológica. Discute o valor do capital intelectual para o processo de produção social, o que vem a exigir a constante atualização dos estoques dinâmicos de informação armazenados nos indivíduos. Define o papel dos profissionais da informação a partir da responsabilidade social de facilitar a comunicação da informação para um usuário que dela necessita, no processo de construção do seu próprio conhecimento. Propõe que as redes de aprendizagem assumam o papel fundamental de meio não somente na comunicação da informação, mas, especialmente, na criação de possibilidades de produção de novos conhecimentos.[English]Presents an aspect of the thesis in which a virtual learning network (here called information stocks in flow facilitates the communication of information in groups of users that participate in it. Points out that the main characteristic of contemporary society is the application of information and knowledge in a cumulative feedback cycle of technological innovation. Discusses the value of intellectual capital in the process of social production, which means a constant demand of update in dynamic stocks of information stored in individuals. Defines the information professional role beginning with social responsibility in facilitating the communication of information to an user that needs it, in the process of construction of its own knowledge. Proposes that virtual learning networks take on the fundamental task of being a medium, not only as way of communication information, but, specially, in creating possibilities of
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.
Hoette, Vivian L.; Puckett, Andrew W.; Linder, Tyler R.; Heatherly, Sue Ann; Rector, Travis A.; Haislip, Joshua B.; Meredith, Kate; Caughey, Austin L.; Brown, Johnny E.; McCarty, Cameron B.; Whitmore, Kevin T.
Skynet is a worldwide robotic telescope network operated by the University of North Carolina at Chapel Hill with active observing sites on 3 continents. The queue-based observation request system is simple enough to be used by middle school students, but powerful enough to supply data for research scientists. The Skynet Junior Scholars program, funded by the NSF, has teamed up with professional astronomers to engage students from middle school to undergraduates in authentic research projects, from target selection through image analysis and publication of results. Asteroid research is a particularly fruitful area for youth collaboration that reinforces STEM education standards and can allow students to make real contributions to scientific knowledge, e.g., orbit refinement through astrometric submissions to the Minor Planet Center. We have created a set of projects for youth to: 1. Image an asteroid, make a movie, and post it to a gallery; 2. Measure the asteroid’s apparent motion using the Afterglow online image processor; and 3. Image asteroids from two or more telescopes simultaneously to demonstrate parallax. The apparent motion and parallax projects allow students to estimate the distance to their asteroid, as if they were the discoverer of a brand new object in the solar system. Older students may take on advanced projects, such as analyzing uncertainties in asteroid orbital parameters; studying impact probabilities of known objects; observing time-sensitive targets such as Near Earth Asteroids; and even discovering brand new objects in the solar system.Images are acquired from among seven Skynet telescopes in North Carolina, California, Wisconsin, Canada, Australia, and Chile, as well as collaborating observatories such as WestRock in Columbus, Georgia; Stone Edge in El Verano, California; and Astronomical Research Institute in Westfield, Illinois.
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Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation at a NeLLL seminar with Etienne Wenger held at the Open Universiteit Nederland. September, 10, 2009, Heerlen, The Netherlands.
Bessant, John; Barnes, Justin; Morris, Mike; Kaplinsky, Raphael
Research suggests that there are a number of potential advantages to learning in some form of network which include being able to benefit from other’s experience, being able to reduce the risks in experimentation, being able to engage in challenging reflection and in making use of peer group support. Examples of such configurations can be found in regional clusters, in sector groupings, in heterogeneous groups sharing a common topic of interest, in user groups concerned with le...
Marsolo, Keith; Margolis, Peter A; Forrest, Christopher B; Colletti, Richard B; Hutton, John J
We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research. We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests. The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however. We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.
Bettencourt, Luis; Kaiser, David
Based on an a historically documented example of scientific discovery - Feynman diagrams as the main calculational tool of theoretical high energy Physics - we map the time evolution of the social network of early adopters through in the US, UK, Japan and the USSR. The spread of the technique for total number of users in each region is then modelled in terms of epidemic models, highlighting parallel and divergent aspects of this analogy. We also show that transient social arrangements develop as the idea is introduced and learned, which later disappear as the technique becomes common knowledge. Such early transient is characterized by abnormally low connectivity distribution powers and by high clustering. This interesting early non-equilibrium stage of network evolution is captured by a new dynamical model for network evolution, which coincides in its long time limit with familiar preferential aggregation dynamics.
Dietterich, Thomas G.
Machine-learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models.
Devine, Emily Beth; Alfonso-Cristancho, Rafael; Devlin, Allison; Edwards, Todd C; Farrokhi, Ellen T; Kessler, Larry; Lavallee, Danielle C; Patrick, Donald L; Sullivan, Sean D; Tarczy-Hornoch, Peter; Yanez, N David; Flum, David R
To describe the inaugural comparative effectiveness research (CER) cohort study of Washington State's Comparative Effectiveness Research Translation Network (CERTAIN), which compares invasive with noninvasive treatments for peripheral artery disease, and to focus on the patient centeredness of this cohort study by describing it within the context of a newly published conceptual framework for patient-centered outcomes research (PCOR). The peripheral artery disease study was selected because of clinician-identified uncertainty in treatment selection and differences in desired outcomes between patients and clinicians. Patient centeredness is achieved through the "Patient Voices Project," a CERTAIN initiative through which patient-reported outcome (PRO) instruments are administered for research and clinical purposes, and a study-specific patient advisory group where patients are meaningfully engaged throughout the life cycle of the study. A clinician-led research advisory panel follows in parallel. Primary outcomes are PRO instruments that measure function, health-related quality of life, and symptoms, the latter developed with input from the patients. Input from the patient advisory group led to revised retention procedures, which now focus on short-term (3-6 months) follow-up. The research advisory panel is piloting a point-of-care, patient assessment checklist, thereby returning study results to practice. The cohort study is aligned with the tenets of one of the new conceptual frameworks for conducting PCOR. The CERTAIN's inaugural cohort study may serve as a useful model for conducting PCOR and creating a learning health care network. Copyright © 2013 Elsevier Inc. All rights reserved.
Ponti, M.; Dirckinck-Holmfeld, Lone; Lindström, B.
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......: Pedagogical design and the dialectics of the digital artefacts, the concept of collaboration, ethics/trust, identity and the role of scaffolding of networked learning environments. The JEIRP is motivated by the fact that many networked learning environments in various European educational settings...
Ariani, Y.; Helsa, Y.; Ahmad, S.; Prahmana, RCI
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.
Bayer, Péter; Herings, P. Jean-Jacques; Peeters, Ronald; Thuijsman, Frank
This paper studies adaptive learning in the class of weighted network games. This class of games includes applications like research and development within interlinked firms, crime within social networks, the economics of pollution, and defense expenditures within allied nations. We show that for
This design-based research case study applied a networked learning approach to a seventh grade science class at a public school in the southeastern United States. Students adapted emerging Web applications to construct personal learning environments for in-depth scientific inquiry of poisonous and venomous life forms. The personal learning environments constructed used Application Programming Interface (API) widgets to access, organize, and synthesize content from a number of educational Internet resources and social network connections. This study examined the nature of personal learning environments; the processes students go through during construction, and patterns that emerged. The project was documented from both an instructional and student-design perspective. Findings revealed that students applied the processes of: practicing digital responsibility; practicing digital literacy; organizing content; collaborating and socializing; and synthesizing and creating. These processes informed a model of the networked student that will serve as a framework for future instructional designs. A networked learning approach that incorporates these processes into future designs has implications for student learning, teacher roles, professional development, administrative policies, and delivery. This work is significant in that it shifts the focus from technology innovations based on tools to student empowerment based on the processes required to support learning. It affirms the need for greater attention to digital literacy and responsibility in K12 schools as well as consideration for those skills students will need to achieve success in the 21st century. The design-based research case study provides a set of design principles for teachers to follow when facilitating student construction of personal learning environments.
Zhao, Yue-Tian-Yi; Jia, Zi-Yang; Tang, Yong; Xiong, Jason Jie; Zhang, Yi-Cheng
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.
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.
Geok See Ng
Full Text Available In this paper, entropy term is used in the learning phase of a neural network. As learning progresses, more hidden nodes get into saturation. The early creation of such hidden nodes may impair generalisation. Hence entropy approach is proposed to dampen the early creation of such nodes. The entropy learning also helps to increase the importance of relevant nodes while dampening the less important nodes. At the end of learning, the less important nodes can then be eliminated to reduce the memory requirements of the neural network.
Full Text Available The article explores the complementary connections between communities of practice and the ways in which individuals orchestrate their engagement with others to further their professional learning. It does so by reporting on part of a research project conducted in New Zealand on teachers’ online professional learning in a university graduate diploma program on ICT education. Evolving from social constructivist pedagogy for online professional development, the research describes how teachers create their own networks of practice as they blend online and offline interactions with fellow learners and workplace colleagues. Teachers’ perspectives of their professional learning activities challenge the way universities design formal online learning communities and highlight the potential for networked learning in the zones and intersections between professional practice and study.The article extends the concepts of Lave and Wenger’s (1991 communities of practice social theory of learning by considering the role participants play in determining their engagement and connections in and across boundaries between online learning communities and professional practice. It provides insights into the applicability of connectivist concepts for developing online pedagogies to promote socially networked learning and for emphasising the role of the learner in defining their learning pathways.
Peters, Linda D.; Pressey, Andrew D.; Johnston, Wesley J.
To increase understanding of the impact of individuals on organizational learning processes, this paper explores the impact of individual cognition and action on the absorptive capacity process of the wider network. In particular this study shows how contingent factors such as social integration mechanisms and power relationships influence how network members engage in, and benefit from, learning. The use of cognitive consistency and sensemaking theory enables examination of how these conting...
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...... 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...
Soares, Louise M.; Soares, Anthony T.
Brain research has illuminated several areas of the learning process: (1) learning as association; (2) learning as reinforcement; (3) learning as perception; (4) learning as imitation; (5) learning as organization; (6) learning as individual style; and (7) learning as brain activity. The classic conditioning model developed by Pavlov advanced…
Jørgensen, Christian Helms; Warring, Niels
This article presents a theoretical and methodological framework for understanding and researching learning in the workplace. The workplace is viewed in a societal context and the learner is viewed as more than an employee in order to understand the learning process in relation to the learner......'s life history.Moreover we will explain the need to establish a 'double view' by examining learning in the workplace both as an objective and as a subjective reality. The article is mainly theoretical, but can also be of interest to practitioners who wish to understand learning in the workplace both...
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
... little African-led research on the cultural appropriateness and impact of such models of transitional justice. This grant will facilitate the creation and sustainable expansion of an electronically-based research network on options and lessons learned pertaining to transitional justice. A second objective is to build the capacity ...
Sarker, M O Faruque
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.
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.
Aug 7, 2013 ... IDRC's Informaon and Networks (I&N) program is seeking a Research ... The growth of networked technologies has created new opportunies for ... What role do collaborave technologies (e.g., social media) play in social ...
How transformational learning promotes caring, consultation and creativity, and ultimately contributes to sustainable development: Lessons from the Partnership for Education and Research about Responsible Living (PERL) network
Thoresen, Victoria Wyszynski
Oases of learning which are transformative and lead to significant behavioural change can be found around the globe. Transformational learning has helped learners not only to understand what they have been taught but also to re-conceptualise and re-apply this understanding to their daily lives. Unfortunately, as many global reports indicate, inspirational transformational learning approaches for sustainable development are rare and have yet to become the norm - despite calls for such approaches by several outstanding educators and organisations. This article examines three learning approaches developed by the network of the Partnership for Education and Research about Responsible Living (PERL). These approaches are structured around core elements of transformative learning for sustainable development, yet focus particularly on the ability to care, consult with others and be creative. They seem to depend on the learners' ability to articulate their perceptions of sustainable development in relation to their own values and to identify how these are actualised in their daily life. Together with other core elements of transformative learning, an almost magical (not precisely measurable) synergy then emerges. The intensity of this synergy appears to be directly related to the individual learner's understanding of the contradictions, interlinkages and interdependencies of modern society. The impact of this synergy seems to be concurrent with the extent to which the learner engages in a continual learning process with those with whom he/she has contact. The findings of this study suggest that mainstreaming transformational learning for sustainable development in ways that release the "magic synergy of creative caring" can result in the emergence of individuals who are willing and able to move from "business as usual" towards more socially just, economically equitable, and environmentally sensitive behaviour.
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.......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....
Network forensics: random infection vs spreading epidemic , Proceedings of ACM Sigmetrics. 11-JUN-12, London, UK. : , TOTAL: 4 06/09/2016 Received Paper...Multi-modal Social Networks A MRF Learning Approach The work primarily focused on two lines of research. 1. We propose new greedy algorithms...Box 12211 Research Triangle Park, NC 27709-2211 social networks , learning and inference REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT
can be seen as network effects—they are produced, supported and enacted by the network. Hence, the capacity of the interventionist researcher to act in a particular role is neither located within the researcher nor the research project, but in particular socio-material arrangements. Accordingly, roles...
Williams, M. W.
The traditional, small-scale, incremental approach to environmental science is changing as researchers embrace a more integrated and multi-disciplinary approach to understanding how our natural systems work today and how they may respond in the future to forcings such as climate change. In situ networks are evolving in response to these challenges so as to provide the appropriate measurements to develop high-resolution spatial and temporal data sets across a wide range of platforms from microbial measurements to remote sensing. These large programs provide a unique set of challenges when compared to more traditional programs. Here I provide insights learned from my participation in a number of large programs, including NASA EOS, LTER, CZO, NEON, and WSC and how those experiences in environmental science can help us move forward towards more applied applications of environmental science, including sustainability initiatives. I'll chat about the importance of managerial and management skills, which most of us scientists prefer to avoid. I'll also chat about making decisions about what long-term measurements to make and when to stop. Data management is still the weakest part of environmental networks; what needs to be done. We have learned that these networks provide an important knowledge base that can lead to informed decisions leading to environmental, energy, social and cultural sustainability.
Czerkawski, Betül C.
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…
... of networked technologies has created new opportunities for advancing human ... The I&N Research Awardee will ideally explore research questions centred ... Examples of questions include: ... engineering or computer/information science;.
Cantner, Uwe; Rake, Bastian
of scientific publications related to pharmaceutical research and applying social network analysis, we find that both the number of countries and their connectivity increase in almost all disease group specific networks. The cores of the networks consist of high income OECD countries and remain rather stable......Knowledge production and scientific research have become increasingly more collaborative and international, particularly in pharmaceuticals. We analyze this tendency in general and tie formation in international research networks on the country level in particular. Based on a unique dataset...... over time. Using network regression techniques to analyze the network dynamics our results indicate that accumulative advantages based on connectedness and multi-connectivity are positively related to changes in the countries' collaboration intensity whereas various indicators on similarity between...
The key is to develop online learning strategies at each individual node. Specifically, through local information exchange with its neighbors, each...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning...Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for
Labus, A.; Despotovic-Zrakic, M.; Radenkovic, B.; Bogdanovic, Z.; Radenkovic, M.
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…
Neruda, Roman; Kudová, Petra
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
Bernstein, Susan D; Horowitz, Allan J; Man, Martin; Wu, Hongyu; Foran, Denise; Vena, Donald A; Collie, Damon; Matthews, Abigail G; Curro, Frederick A; Thompson, Van P; Craig, Ronald G
The authors undertook a study involving members of a dental practice-based research network to determine the outcome and factors associated with success and failure of endodontic therapy. Members in participating practices (practitioner-investigators [P-Is]) invited the enrollment of all patients seeking treatment in the practice who had undergone primary endodontic therapy and restoration in a permanent tooth three to five years previously. If a patient had more than one tooth so treated, the P-I selected as the index tooth the tooth treated earliest during the three- to five-year period. The authors excluded from the study any teeth that served as abutments for removable partial dentures or overdentures, third molars and teeth undergoing active orthodontic endodontic therapy. The primary outcome was retention of the index tooth. Secondary outcomes, in addition to extraction, that defined failure included clinical or radiographic evidence (or both) of periapical pathosis, endodontic retreatment or pain on percussion. P-Is in 64 network practices enrolled 1,312 patients with a mean (standard deviation) time to follow-up of 3.9 (0.6) years. During that period, 3.3 percent of the index teeth were extracted, 2.2 percent underwent retreatment, 3.6 percent had pain on percussion and 10.6 percent had periapical radiolucencies for a combined failure rate of 19.1 percent. The presence of preoperative periapical radiolucency with a diagnosis of either irreversible pulpitis or necrotic pulp was associated with failure after multivariate analysis, as were multiple canals, male sex and Hispanic/Latino ethnicity. These results suggest that failure rates for endodontic therapy are higher than previously reported in general practices, according to results of studies based on dental insurance claims data. The results of this study can help guide the practitioner in deciding the most appropriate course of therapy for teeth with irreversible pulpitis, necrotic pulp or periapical
Nielsen, Kurt Aagaard; Svensson, Lennart
The authors suggest routines and educational structures that could improve a succesfull learning and education of action research.......The authors suggest routines and educational structures that could improve a succesfull learning and education of action research....
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.
Ahmed R. Abas
Full Text Available In this paper, the adaptive competitive learning (ACL neural network algorithm is proposed. This neural network not only groups similar input feature vectors together but also determines the appropriate number of groups of these vectors. This algorithm uses a new proposed criterion referred to as the ACL criterion. This criterion evaluates different clustering structures produced by the ACL neural network for an input data set. Then, it selects the best clustering structure and the corresponding network architecture for this data set. The selected structure is composed of the minimum number of clusters that are compact and balanced in their sizes. The selected network architecture is efficient, in terms of its complexity, as it contains the minimum number of neurons. Synaptic weight vectors of these neurons represent well-separated, compact and balanced clusters in the input data set. The performance of the ACL algorithm is evaluated and compared with the performance of a recently proposed algorithm in the literature in clustering an input data set and determining its number of clusters. Results show that the ACL algorithm is more accurate and robust in both determining the number of clusters and allocating input feature vectors into these clusters than the other algorithm especially with data sets that are sparsely distributed.
Decuypere, Mathias; Simons, Maarten; Masschelein, Jan
The ongoing development of mobile devices like cell phones, iPods, PDAs, and so on is seen by an increasing number of educationalists as a chance to focus on a new kind of learning. This mobile learning, as it is called, should enable students to learn while on the move. Rather than giving a genealogy of the use of mobile equipment in education,…
Konnerup, Ulla; Dirckinck-Holmfeld, Lone
This article presents visions for community-centred networks and networking among companies, educational and cultural institutions and research based on blended on- and off-line collaboration and communication. Our point of departure is the general vision of networking between government, industry...... and research as formulated in the Triple Helix Model (Etzkowitz 2008). The article draws on a case study of NoEL, a network on e-learning among business, educational and cultural institutions and research, all in all 21 partners from all around Denmark. Focus is how networks and networking change character......’ in Networked Learning, Wenger et al. 2009; The analysis concerns the participation structure and how the network activities connect local work practices and research, and how technology and online communication contribute to a change from participation in offline and physical network activities into online...
Kosterman, S.; Gierasimczuk, N.; Armentano, M.G.; Monteserin, A.; Tang, J.; Yannibelli, V.
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
. 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...
Ductor, L.; Fafchamps, M.; Goyal, S.; van der Leij, M.J.
We study how knowledge about the social network of an individual researcher - as embodied in his coauthor relations - helps us in developing a more accurate prediction of his future productivity. We find that incorporating information about coauthor networks leads to a modest improvement in the
Scharfman, Helen E; Kanner, Andres M; Friedman, Alon; Blümcke, Ingmar; Crocker, Candice E; Cendes, Fernando; Diaz-Arrastia, Ramon; Förstl, Hans; Fenton, André A; Grace, Anthony A; Palop, Jorge; Morrison, Jason; Nehlig, Astrid; Prasad, Asuri; Wilcox, Karen S; Jette, Nathalie; Pohlmann-Eden, Bernd
There is common agreement that many disorders of the central nervous system are 'complex', that is, there are many potential factors that influence the development of the disease, underlying mechanisms, and successful treatment. Most of these disorders, unfortunately, have no cure at the present time, and therapeutic strategies often have debilitating side effects. Interestingly, some of the 'complexities' of one disorder are found in another, and the similarities are often network defects. It seems likely that more discussions of these commonalities could advance our understanding and, therefore, have clinical implications or translational impact. With this in mind, the Fourth International Halifax Epilepsy Conference and Retreat was held as described in the prior paper, and this companion paper focuses on the second half of the meeting. Leaders in various subspecialties of epilepsy research were asked to address aging and dementia or psychosis in people with epilepsy (PWE). Commonalities between autism, depression, aging and dementia, psychosis, and epilepsy were the focus of the presentations and discussion. In the last session, additional experts commented on new conceptualization of translational epilepsy research efforts. Here, the presentations are reviewed, and salient points are highlighted. Copyright © 2017 Elsevier Inc. All rights reserved.
Tu, Chih-Hsiung; Sujo-Montes, Laura; Yen, Cherng-Jyh; Chan, Junn-Yih; Blocher, Michael
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…
Statistical Machine Learning is used in many real-world systems, such as web search, network and power management, online advertising, finance and health services, in which adversaries are incentivized to attack the learner, motivating the urgent need for a better understanding of the security vulnerabilities of adaptive systems. Conversely, research in Computer Security stands to reap great benefits by leveraging learning for building adaptive defenses and even designing intelligent attacks ...
This paper explores roles and interventions in IS action research. I draw upon a four-year research project about electronic medical records, conducted in close collaboration with a community partner. Following a self-reflexive stance, I trace the trajectory of the research engagement...... and the different roles I occupied. To better understand the complex nature of collaboration found within action research projects, I propose conceptualizing action research as a network. The network framework directs our attention to the collective production and the conditions through which roles...... this influences the researcher’s agency....
Liang, Faming; Zhang, Jian
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
Gursakal, Necmi; Bozkurt, Aras
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…
This work concentrates on two topics, networks and game theory, and learning in games. The first part of this thesis looks at network games and the role of incomplete information in such games. It is assumed that players are located on a network and interact with their neighbors in the network.
Stevanovic, Matija; Pedersen, Jens Myrup
. 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...
Hodgson, V.; McConnell, D.
Discusses the theory, nature, and benefits of cooperative learning. Considers the Cooperative Learning and Development Network (CLDN) trial in the JITOL (Just in Time Open Learning) project and examines the relationship between theories about cooperative learning and the reality of a group of professionals participating in a virtual cooperative…
Full Text Available Formation of water hammer, its consequences and possible protection measures are rarely topics, however the problem is significant. Water hammer can form in water supply and pressurized sewage networks, for various reasons. The article presents short theory of water hammer and methodology for calculation of specific parameters. Research of water hammer was performed in real water supply and sewer networks of country. Simulation of water hammer was carried out by turning on and off water pumps in pumping station. Successful measurement of water hammer depends on accuracy of the measurement equipment, therefore during the research surge wave fluctuations were measured with especially high resolution pressure meters. Detailed analysis of water hammer and selection of protecting equipment hydraulic model of water supply network was created. Protection against water hammer helps to avoid breaking of the water network and extend operation time.
Building a virtual global research institute to support maternal and child health ... Learning Initiatives for Network Economies in Asia (LIRNEasia) : Building ... to information and communication technology (ICT) initiatives through its global ...
Anželika Jurkienė; Mindaugas Rimeika
Formation of water hammer, its consequences and possible protection measures are rarely topics, however the problem is significant. Water hammer can form in water supply and pressurized sewage networks, for various reasons. The article presents short theory of water hammer and methodology for calculation of specific parameters. Research of water hammer was performed in real water supply and sewer networks of country. Simulation of water hammer was carried out by turning on and off water pumps...
Bhattacharyya, Dhruba Kumar
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
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 ...
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 learned...
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…
Heskes, Tom M.; Kappen, Bert
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.
Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables
Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J
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.
Murphy, Brandon F.
This paper will focus the on research and testing done on penetrating a network for security purposes. This research will provide the IT security office new methods of attacks across and against a company's network as well as introduce them to new platforms and software that can be used to better assist with protecting against such attacks. Throughout this paper testing and research has been done on two different Linux based operating systems, for attacking and compromising a Windows based host computer. Backtrack 5 and BlackBuntu (Linux based penetration testing operating systems) are two different "attacker'' computers that will attempt to plant viruses and or NASA USRP - Internship Final Report exploits on a host Windows 7 operating system, as well as try to retrieve information from the host. On each Linux OS (Backtrack 5 and BlackBuntu) there is penetration testing software which provides the necessary tools to create exploits that can compromise a windows system as well as other operating systems. This paper will focus on two main methods of deploying exploits 1 onto a host computer in order to retrieve information from a compromised system. One method of deployment for an exploit that was tested is known as a "social engineering" exploit. This type of method requires interaction from unsuspecting user. With this user interaction, a deployed exploit may allow a malicious user to gain access to the unsuspecting user's computer as well as the network that such computer is connected to. Due to more advance security setting and antivirus protection and detection, this method is easily identified and defended against. The second method of exploit deployment is the method mainly focused upon within this paper. This method required extensive research on the best way to compromise a security enabled protected network. Once a network has been compromised, then any and all devices connected to such network has the potential to be compromised as well. With a compromised
Strober, Brad; Veitz-Keenan, Analia; Barna, Julie Ann; Matthews, Abigail G; Vena, Donald; Craig, Ronald G; Curro, Frederick A; Thompson, Van P
The objectives of this randomized comparative effectiveness study conducted by members of the Practitioners Engaged in Applied Research and Learning (PEARL) Network were to determine whether using a resin-modified glass ionomer (RMGI) liner reduces postoperative hypersensitivity (POH) in dentin-bonded Class I and Class II resin-based composite (RBC) restorations, as well as to identify other factors (putative risk factors) associated with increased POH. PEARL Network practitioner-investigators (P-Is) (n = 28) were trained to assess sensitivity determination, enamel and dentin caries activity rankings, evaluation for sleep bruxism, and materials and techniques used. The P-Is enrolled 341 participants who had hypersensitive posterior lesions. Participants were randomly assigned to receive an RBC restoration with or without an RMGI liner before P-Is applied a one-step, self-etching bonding agent. P-Is conducted sensitivity evaluations at baseline, at one and four weeks after treatment, and at all visits according to patient-reported outcomes. P-Is collected complete data regarding 347 restorations (339 participants) at baseline, with 341 (98 percent) (333 participants) recalled at four weeks. Treatment groups were balanced across baseline characteristics and measures. RBC restorations with or without an RMGI liner had the same one-week and four-week POH outcomes, as measured clinically (by means of cold or air stimulation) and according to patient-reported outcomes. Use of an RMGI liner did not reduce clinically measured or patient-reported POH in moderate-depth Class I and Class II restorations. Cold and air clinical stimulation findings were similar between groups. Practical Implications. The time, effort and expense involved in placing an RMGI liner in these moderate-depth RBC restorations may be unnecessary, as the representative liner used did not improve hypersensitivity outcomes.
Panoutsopoulos, Hercules; Donert, Karl; Papoutsis, Panos; Kotsanis, Ioannis
During the last few years, ongoing developments in the technological field of Cloud computing have initiated discourse on the potential of the Cloud to be systematically exploited in educational contexts. Research interest has been stimulated by a range of advantages of Cloud technologies (e.g. adaptability, flexibility, scalability,…
Sol, J.; Beers, P.J.; Oosting, S.J.; Geerling-Eiff, F.A.
The educational experimental project ‘Bridge to the Future’, which took place between 2002 and 2007, aimed primarily at supporting the regional development process by action- oriented student research. The second aim was to develop students’ roles as boundary workers in the co-creation of knowledge
Neruda, Roman; Vidnerová, Petra
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
Danilo eJimenez Rezende
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.
Jimenez Rezende, Danilo; Gerstner, Wulfram
The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward 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 both stationary 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.
Gilbert, Nigel; Ahrweiler, Petra; Pyka, Andreas
According to the organizational learning literature, the greatest competitive advantage a firm has is its ability to learn. In this paper, a framework for modeling learning competence in firms is presented to improve the understanding of managing innovation. Firms with different knowledge stocks attempt to improve their economic performance by engaging in radical or incremental innovation activities and through partnerships and networking with other firms. In trying to vary and/or to stabilize their knowledge stocks by organizational learning, they attempt to adapt to environmental requirements while the market strongly selects on the results. The simulation experiments show the impact of different learning activities, underlining the importance of innovation and learning.
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.
.... 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...
The purpose of this article is to review current research studies focusing on the users of Facebook and their behaviors in social networks. This review is organized into two sections: 1) social-demographic characteristics (Age, Gender, Nationality); 2) personality characteristics (Neuroticism, Extraversion, Openness-to-Experience, Agreeableness, Conscientiousness, Narcissism, Self-esteem). The results showed that the information in the personal profile and online behavior are strongly connect...
Full Text Available The purpose of this article is to review current research studies focusing on the users of Facebook and their behaviors in social networks. This review is organized into two sections: 1 social-demographic characteristics (Age, Gender, Nationality; 2 personality characteristics (Neuroticism, Extraversion, Openness-to-Experience, Agreeableness, Conscientiousness, Narcissism, Self-esteem. The results showed that the information in the personal profile and online behavior are strongly connected with socio-demographic and personality characteristics
Alvarez Valencia, Jose Aldemar
Recent progress in the discipline of computer applications such as the advent of web-based communication, afforded by the Web 2.0, has paved the way for novel applications in language learning, namely, social networking. Social networking has challenged the area of Computer Mediated Communication (CMC) to expand its research palette in order to…
Fiete, Ila Rani
How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and
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.
The paper will address the role of the recent implementation of systems of research evaluation in universities. The role of classic quality control system, the peer review, is to produce the most trustworthy knowledge and at the same time function as a learning system in a peer-to-peer learning p...
Lin, Yu-Tzu; Chen, Ming-Puu; Chang, Chia-Hu; Chang, Pu-Chen
The benefits of social learning have been recognized by existing research. To explore knowledge distribution in social learning and its effects on learning achievement, we developed a social learning platform and explored students' behaviors of peer interactions by the proposed algorithms based on social network analysis. An empirical study was…
This article examines a study of seven learners who logged their experiences on the language leaning social networking site Livemocha over a period of three months. The features of the site are described and the likelihood of their future success is considered. The learners were introduced to the Social Networking Site (SNS) and asked to learn a…
Biehl, Michael; Schwarze, Holm
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
Sathasivam, Saratha; Abdullah, Wan Ahmad Tajuddin Wan
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.
Berlanga, Adriana; Sloep, Peter; Brouns, Francis; Van Rosmalen, Peter; Bitter-Rijpkema, Marlies; Koper, Rob
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
Álvarez Valencia, José Aldemar
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…
Some goals of this network are as follows: Extend U.S. technological leadership in high performance computing and computer communications; Provide wide dissemination and application of the technologies both to the speed and the pace of innovation and to serve the national economy, national security, education, and the global environment; and Spur gains in the U.S. productivity and industrial competitiveness by making high performance computing and networking technologies an integral part of the design and production process. Strategies for achieving these goals are as follows: Support solutions to important scientific and technical challenges through a vigorous R and D effort; Reduce the uncertainties to industry for R and D and use of this technology through increased cooperation between government, industry, and universities and by the continued use of government and government funded facilities as a prototype user for early commercial HPCC products; and Support underlying research, network, and computational infrastructures on which U.S. high performance computing technology is based.
of using networks to create insightful maps of learning discussions. To conclude, I argue that conceptual blending is a powerful framework for constructing "mixed methods" methodologies that may integrate diverse theories and other methodologies with network methodologies.......In recent years a number of researchers within the PER community have started using network analysis as a new methodology to extend our understanding of teaching and learning physics by viewing these as complex systems. In this paper, I give examples of social, cognitive, and action mapping...... networks and how they can be analyzed. In so doing I show how a network can be methodologically described as a set of relations between a set of entities, and how a network can be characterized and analyzed as a mathematical object. Then, as an illustrative example, I discuss a relatively new example...
Mørup, Morten; Hansen, Lars Kai
such as the Modularity, it has recently been shown that latent structure in complex networks is learnable by Bayesian generative link distribution models (Airoldi et al., 2008, Hofman and Wiggins, 2008). In this paper we propose a new generative model that allows representation of latent community structure......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...... 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...
Zhang, Pengfei; Shen, Huitao; Zhai, Hui
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.
Love, Bradley C; Medin, Douglas L; Gureckis, Todd M
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.
Current research on brain activity has many implications for educators. The triune brain concept and the left and right hemisphere concepts are among the many complex theories evolving from experimentation and observation. The triune brain concept suggests that the human forebrain has expanded while retaining three structurally unique formations…
Giani, U; Martone, P
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.
Kaše, Robert; King, Zella; Minbaeva, Dana
; the impact of social networking sites on perceptions of relationships; and ethical issues in organizational network analysis, we propose specific suggestions to bring social network perspectives closer to HRM researchers and practitioners and rebalance our attention to people and to their relationships.......The article features a conversation between Rob Cross and Martin Kilduff about organizational network analysis in research and practice. It demonstrates the value of using social network perspectives in HRM. Drawing on the discussion about managing personal networks; managing the networks of others...
Dolog, Peter; Simon, Bernd; Nejdl, Wolfgang
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...
Energy Innovation Network Solar Energy Innovation Network The Solar Energy Innovation Network grid. Text version The Solar Energy Innovation Network is a collaborative research effort administered (DOE) Solar Energy Technologies Office to develop and demonstrate new ways for solar energy to improve
Omta, S.W.F.; Trienekens, J.H.; Beers, G.
In this first article of the Journal on Chain and Network Science the base-line is set for a discussion on contents and scope of chain and network theory. Chain and network research is clustered into four main ‘streams’: Network theory, social capital theory, supply chain management and business
Computer networks and their services have become an essential part of research and education. Nowadays every modern R&E institution must have a computer network and provide network services to its students and staff. In addition to its internal computer network, every R&E institution must have a
Firdausiah Mansur, Andi Besse; Yusof, Norazah
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…
Hasan A. A. Al-Rawi
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.
Mange Gladys Nkatha
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.
Zeng, Yifeng; Cordero Hernandez, Jorge
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....
Networks provide a powerful lens to understand complex relational entanglements that are transforming social, economic and political life. Through a discussion of the various streams of network research in tourism, this paper argues that policy matters run across and throughout these strands....... Rather than arguing for increased interest in tourism policy network research as a separate subfield, the paper argues for deeper theoretical engagement with the policy dimension in tourism network research. Researchers adopting a network ontology could gain considerable insights and open up new lines...
Houborg, Esben; Munksgaard, Rasmus
Purpose – The purpose of this paper is to map research communities related to heroin-assisted treatment (HAT) and the scientific network they are part of to determine their structure and content. Design/methodology/approach – Co-authorship as the basis for conducting social network analysis....... In total, 11 research communities were constructed with different scientific content. HAT research communities are closely connected to medical, psychiatric, and epidemiological research and very loosely connected to social research. Originality/value – The first mapping of the collaborative network HAT...... researchers using social network methodology...
Abd ELhamid, A.; Ayad, N.M.A.; Fouad, Y.; Abdelkader, T.
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
Vogten, Hubert; Martens, Harrie; Lemmers, Ruud
The TENCompetence project developed a first release of a Learning Network infrastructure to support individuals, groups and organisations in professional competence development. This infrastructure Learning Network infrastructure was released as open source to the community thereby allowing users and organisations to use and contribute to this development as they see fit. The infrastructure consists of client applications providing the user experience and server components that provide the services to these clients. These services implement the domain model (Koper 2006) by provisioning the entities of the domain model (see also Sect. 18.4) and henceforth will be referenced as domain entity services.
predecessor, Gale and Kariv (2003) who generalize the payoff equalization result of Bala and Goyal (1998) in connected social networks (discussed below...requires more notation. Using Bayes’ Rule and the assumption of equal priors on the state θ, we have that the social belief given by observing... Social Networks: Rational Learning and Information Aggregation by Ilan Lobel B.Sc., Pontif́ıcia Universidade Católica do Rio de Janeiro (2004
Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.
Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...
Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui
Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.
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......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...
Ozyildirim, Buse Melis; Avci, Mutlu
Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.
Yun, Xiang; Feng, Xiancheng
It is the research hotspot of current broadband network to combine voice service, data service and broadband audio-video service by IP protocol to transport various real time and mutual services to terminal users (home). Home Networking is a new kind of network and application technology which can provide various services. Home networking is called as Digital Home Network. It means that PC, home entertainment equipment, home appliances, Home wirings, security, illumination system were communicated with each other by some composing network technology, constitute a networking internal home, and connect with WAN by home gateway. It is a new network technology and application technology, and can provide many kinds of services inside home or between homes. Currently, home networking can be divided into three kinds: Information equipment, Home appliances, Communication equipment. Equipment inside home networking can exchange information with outer networking by home gateway, this information communication is bidirectional, user can get information and service which provided by public networking by using home networking internal equipment through home gateway connecting public network, meantime, also can get information and resource to control the internal equipment which provided by home networking internal equipment. Based on the general network model of home networking, there are four functional entities inside home networking: HA, HB, HC, and HD. (1) HA (Home Access) - home networking connects function entity; (2) HB (Home Bridge) Home networking bridge connects function entity; (3) HC (Home Client) - Home networking client function entity; (4) HD (Home Device) - decoder function entity. There are many physical ways to implement four function entities. Based on theses four functional entities, there are reference model of physical layer, reference model of link layer, reference model of IP layer and application reference model of high layer. In the future home network
Li, WenYao; Zhou, Fang; Wu, JianXue; Li, ZhiGuang
Nowadays NGN (Next Generation Network) is the hotspot for discussion and research in IT section. The NGN core technology is the network control technology. The key goal of NGN is to realize the network convergence and evolution. Referring to overlay network model core on Softswitch technology, circuit switch network and IP network convergence realized. Referring to the optical transmission network core on ASTN/ASON, service layer (i.e. IP layer) and optical transmission convergence realized. Together with the distributing feature of NGN network control technology, on NGN platform, overview of combining Softswitch and ASTN/ASON control technology, the solution whether IP should be the NGN core carrier platform attracts general attention, and this is also a QoS problem on NGN end to end. This solution produces the significant practical meaning on equipment development, network deployment, network design and optimization, especially on realizing present network smooth evolving to the NGN. This is why this paper puts forward the research topic on the NGN network control technology. This paper introduces basics on NGN network control technology, then proposes NGN network control reference model, at the same time describes a realizable network structure of NGN. Based on above, from the view of function realization, NGN network control technology is discussed and its work mechanism is analyzed.
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.
Ezaki, Takahiro; Masuda, Naoki
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.
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.
Parraguez Ruiz, Pedro; Maier, Anja
and societal impact. This chapter contributes to the use of network science in empirical studies of design organisations. It focuses on introducing a network-based perspective on the design process and in particular on making use of network science to support design research and practice. The main contribution...... of this chapter is an overview of the methodological challenges and core decision points when embarking on network-based design research, namely defining the overall research purpose and selecting network features. We furthermore highlight the potential for using archival data, the opportunities for navigating...
Canino, Frank J.
The application of learned helplessness theory to achievement is discussed within the context of implications for research in learning disabilities. Finally, the similarities between helpless children and learning disabled students in terms of problems solving and attention are discussed. (Author)
Simard, Patrice Yvon
Fully recurrent (asymmetrical) networks can be used to learn temporal trajectories. The network is unfolded in time, and backpropagation is used to train the weights. The presence of recurrent connections creates internal states in the system which vary as a function of time. The resulting dynamics can provide interesting additional computing power but learning is made more difficult by the existence of internal memories. This study first exhibits the properties of recurrent networks in terms of convergence when the internal states of the system are unknown. A new energy functional is provided to change the weights of the units in order to the control the stability of the fixed points of the network's dynamics. The power of the resultant algorithm is illustrated with the simulation of a content addressable memory. Next, the more general case of time trajectories on a recurrent network is studied. An application is proposed in which trajectories are generated to draw letters as a function of an input. In another application of recurrent systems, a neural network certain temporal properties observed in human callosally sectioned brains. Finally the proposed algorithm for stabilizing dynamics around fixed points is extended to one for stabilizing dynamics around time trajectories. Its effects are illustrated on a network which generates Lisajous curves.
Noor, Ahmed K. (Compiler)
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.
Fine, Marija Futchs
The 7000l Training and Employment Institute offers self-paced instruction through the use of computers and audiovisual materials to young people to improve opportunities for success in the work force. In 1988, four sites were equipped with Apple stand-alone software in an integrated learning system that included courses in reading and math, test…
Burgos, Daniel; Hummel, Hans; Tattersall, Colin; Brouns, Francis; Koper, Rob
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
Sheneman, Leigh; Hintze, Arend
There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines.
Sanchez-Casado, Noelia; Cegarra Navarro, Juan Gabriel; Wensley, Anthony; Tomaseti-Solano, Eva
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…
Weber, Peter; Rothe, Hannes
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…
Barrera, D.; Bunt, G. van de
This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using
Barrera, D.; van de Bunt, G
This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using
Lansu, Angelique; Boon, Jo; Sloep, Peter; Van Dam-Mieras, Rietje
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
Rusman, Ellen; Prinsen, Fleur; Vermeulen, Marjan
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
The increasing availability of Big Data in healthcare encourages investigators to seek answers to big questions. However, nonparametric approaches to analyzing these data can suffer from the curse of dimensionality, and traditional parametric modeling does not necessarily scale. Targeted learning (TL) combines semiparametric methodology with advanced machine learning techniques to provide a sound foundation for extracting information from data. Predictive models, variable importance measures, and treatment benefits and risks can all be addressed within this framework. TL has been applied in a broad range of healthcare settings, including genomics, precision medicine, health policy, and drug safety. This article provides an introduction to the two main components of TL, targeted minimum loss-based estimation and super learning, and gives examples of applications in predictive modeling, variable importance ranking, and comparative effectiveness research.
Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C
Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.
The Defense Logistics Agency's Research and Development Enterprise Division established a network of universities, equipment suppliers, apparel manufacturers, industry consultants and software developers...
Wagstaff, Kiri L.; Rebbapragada, Umaa; Lane, Terran
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.
Sie, Rory; Bitter-Rijpkema, Marlies; Stoyanov, Slavi; Sloep, Peter
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
Fahriye Altınay Aksal
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
Strijbos, Jan-Willem; Fischer, Frank
Research on collaborative learning, both face-to-face and computer-supported, has thrived in the past 10 years. The studies range from outcome-oriented (individual and group learning) to process-oriented (impact of interaction on learning processes, motivation and organisation of collaboration) to mixed studies. Collaborative learning research is…
Kuss, Daria J.; Griffiths, Mark D.
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
Daria J. Kuss
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.
Kuss, Daria J; Griffiths, Mark D
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.
... to craft policies for fruitful integration into the global economy and inclusive growth. ... The grant will support a broad-based research network, the Southern Africa ... researchers based in regional institutions; transforming selected institutions ...
The Comprehensive Oncologic Emergencies Research Network (CONCERN) was established in March 2015 with the goal to accelerate knowledge generation, synthesis and translation of oncologic emergency medicine research through multi-center collaborations.
Jul 8, 2011 ... For the 19 young scholars brought together by the Poverty Research Network, the rewards have been substantial. Lu Ming, who describes his experience with the group as “just fantastic,” likens the network to a bridge – connecting China to Canada, and linking researchers to each other and to scholars ...
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.
function in concert. Consider the behavior of social insects, such as bees and ants. Fish and birds are other examples of animals whose collective...Tropical Watershed, Springer/Kluwer, 83–95, 2005. Lehner, B. and Döll, P.: Development and validation of a global database of lakes, reservoirs and wetlands ...what it would be in an unperturbed network. A biological network with this sensitivity to error would not survive for very long in the wild . For
DV-Hop algorithm is one of the important range-free localization algorithms. It performs better in isotropic density senor networks, however, it brings larger location errors in random distributed networks. According to the localization principle of the DV-Hop algorithm, this paper improves the estimation of average single hop distance by using the Least Equal Square Error, and revises the estimated distance between the unknown node and the anchor node with compensation coefficient considerin...
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.
Noor, Ahmed K. (Compiler)
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.
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.
Lassnig, Mario; The ATLAS collaboration; Vamosi, Ralf
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.
Duong, Tuan Anh
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.
Hemmecke, R.; Lindner, S.; Studený, Milan
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
de Laat, Maarten; Ryberg, Thomas
, actor network theory), learning environments and social media (e.g. LMS, MOOC, Virtual Worlds, Twitter, Facebook), technologies (e.g. phone, laptop, tablet), methodology (e.g. quantitative, qualitative) and related research in the domain of e-learning (e-learning, CSCL, TEL). The findings are placed...
Jarvela, Sanna; Naykki, Piia; Laru, Jari; Luokkanen, Tiina
In our recent research we have explored possibilities to scaffold collaborative learning in higher education with wireless networks and mobile tools. The pedagogical ideas are grounded on concepts of collaborative learning, including the socially shared origin of cognition, as well as self-regulated learning theory. This paper presents our three…
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
Simard, D.; Nadeau, L.; Kroeger, H.
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
Mohd Ishak Bin Ismail; Ruzaini Bin Abdullah Arshah
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...
Full Text Available This paper presents an overview of major trends in archaeological network research through a bibliometric analysis of the full corpus of publications on the topic between 1965 and 2016. It illustrates we can begin identifying the outlines of a new sub-discipline within archaeology with its distinct traditions, including a diversity of research approaches, dedicated events and preferred publication venues. This sub-discipline is at a similar stage of development as historical network research, and we argue that archaeologists and historians alike interested in establishing network research as a key tool for exploring social change will have a greater chance for success to the extent that we actively collaborate, pool resources, engage in common community activities and publications, and learn from each other’s mistakes.
José Pedro Pinto
Full Text Available The study of molecular networks has recently moved into the limelight of biomedical research. While it has certainly provided us with plenty of new insights into cellular mechanisms, the challenge now is how to modify or even restructure these networks. This is especially true for human diseases, which can be regarded as manifestations of distorted states of molecular networks. Of the possible interventions for altering networks, the use of drugs is presently the most feasible. In this mini-review, we present and discuss some exemplary approaches of how analysis of molecular interaction networks can contribute to pharmacology (e.g., by identifying new drug targets or prediction of drug side effects, as well as listing pointers to relevant resources and software to guide future research. We also outline recent progress in the use of drugs for in vitro reprogramming of cells, which constitutes an example par excellence for altering molecular interaction networks with drugs.
Richards, David; Lerche, Carol
Discusses current RLIN (Research Libraries Information Network) communications technology and motivations for change. Goals, topology, hardware, software, and protocol, terminal wiring, and deployment are considered. Sidebars provide a diagram of the current RLIN communications technology and describe the integrated RLIN network. (one reference)…
Kafle, A.; Mukhopadhyay, P.; Nepal, M.; Shyamsundar, P.
In South Asia, a majority of institutions are ill-equipped to undertake research on multi-disciplinary environmental problems, though these problems are increasing at a fast rate and connected to the region's poverty and growth objectives. In this context, the South Asian Network for Development and Environmental Economics (SANDEE) tries to fill a research, training and knowledge gap by building skills in the area of Environment and Development Economics. In this paper, the authors argue that research networks contribute to the growth of sustainability knowledge through (a) knowledge creation, (b) knowledge transfer and (c) knowledge deepening. The paper tries to show the relationship between capacity building, mentorship and research scholarship. It demonstrates that researchers, by associating with the network and its multiple training and mentoring processes, are able to build skills, change curricula and deliver useful knowledge products. The paper discusses the need for interdisciplinary research and the challenges of bridging the gap between research outputs and policy reforms.
González-Avella, Juan Carlos; Eguíluz, Victor M.; Marsili, Matteo; Vega-Redondo, Fernado; San Miguel, Maxi
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
Tisenkopfs, Talis; Kunda, Ilona; Šumane, Sandra
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…
Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.
A. A. Salama
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.
de Laat, Maarten; Ryberg, Thomas
conferences with the aim to describe some general trends and developments in networked learning research as they emerge and fade out over the years. In order to do so the authors use the proceedings of each networked learning conference (from 1998 till 2016) as a compiled dataset. This dataset forms a text...... corpus that has been analysed with Voyant tools (Sinclair and Rockwell 2016) specifically designed for analysing digital texts. Voyant tools are used to generate a set of word clouds (Cirrus) in order to visualise networked learning research-related terms that feature most frequently in each set...
Tobacco Control Research, Dissemination and Networking in Lebanon. The Tobacco ... IDRC “unpacks women's empowerment” at McGill University Conference ... New funding opportunity for gender equality and climate change. IDRC is ...
Ringsberg, Karin C
The Nordic Health Promotion Research Network (NHPRN) was established in 2007 at the Nordic School of Public Health (NHV). This article aims to describe the foundation of the NHPRN, the development and the present status of the work of NHPRN. The NHPRN consists of about 50 senior and junior researchers from all Nordic countries. It is a working network that aims to develop the theoretical understanding of health promotion, to create research cooperation in health promotion from a Nordic perspective and to extend the scope of health promotion through education. Network members meet biannually to discuss and further develop research within the field and are also responsible for the Nordic conference on Health Promotion, organized every 3 years. The NHV hosted the network between 2007 and 2014; and the World Health Organisation (WHO) will assume this role in 2015. © 2015 the Nordic Societies of Public Health.
Gillies, Robyn M.
Cooperative learning is widely recognized as a pedagogical practice that promotes socialization and learning among students from kindergarten through to college level and across different subject areas. Cooperative learning involves students working together to achieve common goals or complete group tasks. Interest in cooperative learning has…
With the continuous development of mobile communication technology, Ad Hoc access network has become a hot research, Ad Hoc access network nodes can be used to expand capacity of multi-hop communication range of mobile communication system, even business adjacent to the community, improve edge data rates. When the ad hoc network is the access network of the internet, the gateway discovery protocol is very important to choose the most appropriate gateway to guarantee the connectivity between ad hoc network and IP based fixed networks. The paper proposes a QoS gateway discovery protocol which uses the time delay and stable route to the gateway selection conditions. And according to the gateway discovery protocol, it also proposes a fast handover scheme which can decrease the handover time and improve the handover efficiency.
Full Text Available Learning is considered as a social activity, a student does not learn only of the teacher and the textbook or only in the classroom, learn also from many other agents related to the media, peers and society in general. And since the explosion of the Internet, the information is within the reach of everyone, is there where the main area of opportunity in new technologies applied to education, as well as taking advantage of recent socialization trends that can be leveraged to improve not only informing of their daily practices, but rather as a tool that explore different branches of education research. One can foresee the future of higher education as a social learning environment, open and collaborative, where people construct knowledge in interaction with others, in a comprehensive manner. The mobility and ubiquity that provide mobile devices enable the connection from anywhere and at any time. In modern educational environments can be expected to facilitate mobile devices in the classroom expansion in digital environments, so that students and teachers can build the teaching-learning process collectively, this partial derivative results in the development of draft research approved by the CONADI in “Universidad Cooperativa de Colombia”, "Social Networks: A teaching strategy in learning environments in higher education."
Poell, R.F.; Moorsel, M.A.A.H. van
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 learning groups in organisations. Four theoretical types of learning projects are distinguished. Four different approaches to the learning climate of work groups are compared to the approach offered by t...
Troi, Anders; Jørgensen, Bo Nørregaard; Larsen, Emil Mahler
Grid Network’s recommendations’, which relate to strengthening and marketing the research infrastructure that will position Denmark as the global hub for Smart Grid development; strengthening basic research into the complex relationships in electric systems with large quantities of independent parties...
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
Spoelstra, Howard; Van Rosmalen, Peter; Van de Vrie, Evert; Obreza, Matija; Sloep, Peter
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
Full Text Available 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 characters are displayed and it is robust to a device-to-device variability of up to +/-30%.
Covi, Erika; Brivio, Stefano; Serb, Alexander; Prodromakis, Themis; Fanciulli, Marco; Spiga, Sabina
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 HfO 2 -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%.
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)
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.
Full Text Available This paper deploys notions of emergence, connections, and designs for learning to conceptualize high school students’ interactions when using online social media as a learning environment. It makes links to chaos and complexity theories and to fractal patterns as it reports on a part of the first author’s action research study, conducted while she was a teacher working in an Australian public high school and completing her PhD. The study investigates the use of a Ning online social network as a learning environment shared by seven classes, and it examines students’ reactions and online activity while using a range of social media and Web 2.0 tools.The authors use Graham Nuthall’s (2007 “lens on learning” to explore the social processes and culture of this shared online classroom. The paper uses his extensive body of research and analyses of classroom learning processes to conceptualize and analyze data throughout the action research cycle. It discusses the pedagogical implications that arise from the use of social media and, in so doing, challenges traditional models of teaching and learning.
Full Text Available The popular deep Q learning algorithm is known to be instability because of the Q-value’s shake and overestimation action values under certain conditions. These issues tend to adversely affect their performance. In this paper, we develop the ensemble network architecture for deep reinforcement learning which is based on value function approximation. The temporal ensemble stabilizes the training process by reducing the variance of target approximation error and the ensemble of target values reduces the overestimate and makes better performance by estimating more accurate Q-value. Our results show that this architecture leads to statistically significant better value evaluation and more stable and better performance on several classical control tasks at OpenAI Gym environment.
AlDahdouh, Alaa A.; Osório, António J.; Caires, Susana
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…
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…
Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume
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
Dalgaard, Jens; Kocka, Tomas; Pena, Jose
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...... is set at maximum, KES corresponds to the greedy equivalence search algorithm (GES). When greediness is kept at minimum, we prove that under mild assumptions KES asymptotically returns any inclusion optimal BN with nonzero probability. Experimental results for both synthetic and real data are reported...
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
Adel, Tameem; de Campos, Cassio P.
We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a ...
Bartlett, E.B.; Uhrig, R.E.
The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given
Balaji, V. [Princeton Univ., NJ (United States). Earth Science Grid Federation (ESGF); Boden, Tom [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Cowley, Dave [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Dart, Eli [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Dattoria, Vince [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Desai, Narayan [Argonne National Lab. (ANL), Argonne, IL (United States); Egan, Rob [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Foster, Ian [Argonne National Lab. (ANL), Argonne, IL (United States); Goldstone, Robin [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Gregurick, Susan [U.S. Dept. of Energy, Washington, DC (United States). Biological Systems Science Division; Houghton, John [U.S. Dept. of Energy, Washington, DC (United States). Biological and Environmental Research (BER) Program; Izaurralde, Cesar [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Johnston, Bill [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Joseph, Renu [U.S. Dept. of Energy, Washington, DC (United States). Climate and Environmental Sciences Division; Kleese-van Dam, Kerstin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lipton, Mary [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Monga, Inder [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Pritchard, Matt [British Atmospheric Data Centre (BADC), Oxon (United Kingdom); Rotman, Lauren [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Strand, Gary [National Center for Atmospheric Research (NCAR), Boulder, CO (United States); Stuart, Cory [Argonne National Lab. (ANL), Argonne, IL (United States); Tatusova, Tatiana [National Inst. of Health (NIH), Bethesda, MD (United States); Tierney, Brian [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Thomas, Brian [Univ. of California, Berkeley, CA (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Zurawski, Jason [Internet2, Washington, DC (United States)
The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet be a highly successful enabler of scientific discovery for over 25 years. In November 2012, ESnet and the Office of Biological and Environmental Research (BER) of the DOE SC organized a review to characterize the networking requirements of the programs funded by the BER program office. Several key findings resulted from the review. Among them: 1) The scale of data sets available to science collaborations continues to increase exponentially. This has broad impact, both on the network and on the computational and storage systems connected to the network. 2) Many science collaborations require assistance to cope with the systems and network engineering challenges inherent in managing the rapid growth in data scale. 3) Several science domains operate distributed facilities that rely on high-performance networking for success. Key examples illustrated in this report include the Earth System Grid Federation (ESGF) and the Systems Biology Knowledgebase (KBase). This report expands on these points, and addresses others as well. The report contains a findings section as well as the text of the case studies discussed at the review.
Bonderup Dohn, Nina; Sime, Julie-Ann; Cranmer, Susan
with a short presentation of each of the chapters. This leads us to identify broader themes which point out significant perspectives and challenges for future research and practice. Among these are social justice, criticality, mobility, new forms of openness and learning in the public arena (all leading themes...
This abstract proposes a discussion of how professional science communication and scientific cooperation can become more efficient through the use of modern social network technology, using the example of Mendeley. Mendeley is a research workflow and collaboration tool which crowdsources real-time research trend information and semantic annotations of research papers in a central data store, thereby creating a "social research network" that is emergent from the research data added to the platform. We describe how Mendeley's model can overcome barriers for collaboration by turning research papers into social objects, making academic data publicly available via an open API, and promoting more efficient collaboration. Central to the success of Mendeley has been the creation of a tool that works for the researcher without the requirement of being part of an explicit social network. Mendeley automatically extracts metadata from research papers, and allows a researcher to annotate, tag and organize their research collection. The tool integrates with the paper writing workflow and provides advanced collaboration options, thus significantly improving researchers' productivity. By anonymously aggregating usage data, Mendeley enables the emergence of social metrics and real-time usage stats on top of the articles' abstract metadata. In this way a social network of collaborators, and people genuinely interested in content, emerges. By building this research network around the article as the social object, a social layer of direct relevance to academia emerges. As science, particularly Earth sciences with their large shared resources, become more and more global, the management and coordination of research is more and more dependent on technology to support these distributed collaborations.
Nind, Melanie; Chapman, Rohhss; Seale, Jane; Tilley, Liz
Background: This study explores the training involved when people with learning disabilities take their place in the community as researchers. This was a theme in a recent UK seminar series where a network of researchers explored pushing the boundaries of participatory research. Method: Academics, researchers with learning disabilities, supporters…
Da Silva, John D; Kazimiroff, Julie; Papas, Athena; Curro, Frederick A; Thompson, Van P; Vena, Donald A; Wu, Hongyu; Collie, Damon; Craig, Ronald G
The authors conducted a study to determine the types, outcomes, risk factors and esthetic assessment of implants and their restorations placed in the general practices of a practice-based research network. All patients who visited network practices three to five years previously and underwent placement of an implant and restoration within the practice were invited to enroll. Practitioner-investigators (P-Is) recorded the status of the implant and restoration, characteristics of the implant site and restoration, presence of peri-implant pathology and an esthetic assessment by the P-I and patient. The P-Is classified implants as failures if the original implant was missing or had been replaced, the implant was mobile or elicited pain on percussion, there was overt clinical or radiographic evidence of pathology or excessive bone loss (> 0.2 millimeter per year after an initial bone loss of 2 mm). They classified restorations as failures if they had been replaced or if there was abutment or restoration fracture. The authors enrolled 922 implants and patients from 87 practices, with a mean (standard deviation) follow-up of 4.2 (0.6) years. Of the 920 implants for which complete data records were available, 64 (7.0 percent) were classified as failures when excessive bone loss was excluded from the analysis. When excessive bone loss was included, 172 implants (18.7 percent) were classified as failures. According to the results of univariate analysis, a history of severe periodontitis, sites with preexisting inflammation or type IV bone, cases of immediate implant placement and placement in the incisor or canine region were associated with implant failure. According to the results of multivariate analysis, sites with preexisting inflammation (odds ratio [OR] = 2.17; 95 percent confidence interval [CI], 1.41-3.34]) or type IV bone (OR = 1.99; 95 percent CI, 1.12-3.55) were associated with a greater risk of implant failure. Of the 908 surviving implants, 20 (2.2 percent) had
Nägele, Andreas; Dejori, Mathäus; Stetter, Martin
Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.
Cankaya, Serkan; Yunkul, Eyup
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,…
How Transformational Learning Promotes Caring, Consultation and Creativity, and Ultimately Contributes to Sustainable Development: Lessons from the Partnership for Education and Research about Responsible Living (PERL) Network
Thoresen, Victoria Wyszynski
Oases of learning which are transformative and lead to significant behavioural change can be found around the globe. Transformational learning has helped learners not only to understand what they have been taught but also to re-conceptualise and re-apply this understanding to their daily lives. Unfortunately, as many global reports indicate,…
Doyle, Elaine; Buckley, Patrick
The evolution of enquiry-based teaching and learning has broadened the range of research carried out by university students. As a result, the boundaries between teaching and learning and academic research are being blurred to a degree not experienced heretofore. This paper examines whether research undertaken as part of course work should fall…
Malayeri, Amin Daneshmand; Abdollahi, Jalal
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...
Poell, R.F.; Moorsel, M.A.A.H. van
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
Consolidating African Research and Education Networking (CORENA) - Phase I. African universities and research institutions possess significant human capacity, but their contribution to national human development as well as their intellectual property output is still very limited. A major cause of this is lack of easy and ...
Goduscheit, René Chester; Rasmussen, Erik Stavnsager; Jørgensen, Jacob Høj
Traditionally, the literature on action research has been aimed at intra-organisational issues. These studies have distinguished between two researcher roles: The problem-solver and the observer. This article addresses the distinct challenges of action research in inter-organisational projects....... In addition to the problem-solver and observer roles, the researcher in an inter-organisational setting can serve as a legitimiser of the project and manage to involve partners that in an ordinary business-to-business setting would not have participated. Based on an action research project in a Danish inter......-organisational network, this article discusses potential pitfalls in the legitimiser role. Lack of clarity in defining the researcher role and project ownership in relation to the funding organisation and the rest of the network can jeopardise the project and potentially the credibility of the researchers. The article...
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.
Kurilovas, Eugenijus; Juskeviciene, Anita; Bireniene, Virginija
The paper aims to present current research on mobile learning activities in Lithuania while implementing flagship EU-funded CCL project on application of tablet computers in education. In the paper, the quality of modern mobile learning activities based on learning personalisation, problem solving, collaboration, and flipped class methods is…
spaces, learning to learn through languages, learners´ stories, qualitative research method Methodology or Methods/Research Instruments or Sources Used A number of semi structured qualitative interviews have been conducted with three learners of Danish as second language. The language learners...... in the paper is on the research process and methodological tools. The goal of this paper is to show, that learners´ stories have a huge potential in researching learning processes. References Benson, P. & D. Nunan (2004). Lerners´ stories. Difference and Diversity in Language Learning. Cambridge University...... to use learners´ stories as a research methodology in the field of learning in general and language learning in particular....
Lindberg, Daniel M; Scribano, Philip V
As foundational work in preparation for a sustainable, multi-center network devoted to child abuse medical research, we recently used a combination of survey and modified Delphi methodologies to determine research priorities for future multi-center studies. Avoiding missed diagnoses, and improving selected/indicated prevention were the topics rated most highly in terms of research priority. Several constructive commentaries in this issue identify the key challenges which must be overcome to ensure a successful network. Indeed, as with the clinical work of child abuse pediatrics, a scientific network will also require constant collaboration within and outside the community of child abuse pediatricians, the wider medical community, and even non-medical professions. Copyright © 2017 Elsevier Ltd. All rights reserved.
Likumahuwa, Sonja; Song, Hui; Singal, Robbie; Weir, Rosy Chang; Crane, Heidi; Muench, John; Sim, Shao-Chee; DeVoe, Jennifer E
This article introduces the Community Health Applied Research Network (CHARN), a practice-based research network of community health centers (CHCs). Established by the Health Resources and Services Administration in 2010, CHARN is a network of 4 community research nodes, each with multiple affiliated CHCs and an academic center. The four nodes (18 individual CHCs and 4 academic partners in 9 states) are supported by a data coordinating center. Here we provide case studies detailing how CHARN is building research infrastructure and capacity in CHCs, with a particular focus on how community practice-academic partnerships were facilitated by the CHARN structure. The examples provided by the CHARN nodes include many of the building blocks of research capacity: communication capacity and "matchmaking" between providers and researchers; technology transfer; research methods tailored to community practice settings; and community institutional review board infrastructure to enable community oversight. We draw lessons learned from these case studies that we hope will serve as examples for other networks, with special relevance for community-based networks seeking to build research infrastructure in primary care settings.
Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.
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.
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.
Fraser, Katie C.
Government policy and academic research both talk about transforming learning through networked technologies – sharing newly available information about the learning context with new partners to support lifelong learning activities, and giving learners increased power and autonomy. This thesis examines how such learning opportunities might be supported. In order to ground these learning opportunities in current educational activity it studies homework, which is an example of a learning activi...
... 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 network are highlighted among which Facebook, ...
Reda, Weldemariam Nigusse; Hagos, Girmay Tsegay
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,…
Chung, Kon Shing Kenneth; Paredes, Walter Christian
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…
Ferreday, Debra; Hodgson, Vivien; Jones, Chris
This paper draws on the work of Mikhail Bakhtin and Norman Fairclough to show how dialogue is central to the construction of identity in networked management learning. The paper is based on a case study of a networked management learning course in higher education and attempts to illustrate how participants negotiate issues of difference,…
Chapman, Georgina; Langley, Mark; Skilling, Gus; Walker, John
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…
Shankle, Dean E.; Shankle, Jeremy P.
Please, cite this publication as: Shankle, D.E., & Shankle, J.P. (2006). Problems in the Deployment of Learning Networks In Small Organizations. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria:
Cornelissen, Frank; de Jong, Tjip; Kessels, Joseph
Purpose: This paper aims to propose a framework which connects perspectives on knowledge and learning to various approaches of social networks studies. The purpose is twofold: providing input for the discourse in organizational studies about the way different views on knowledge and networks drive design choices and activities of researchers,…
Evis Trandafili; Marenglen Biba
Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution...
Maglajlic, Seid; Helic, Denis
and Purpose: The purpose of this research is to shed light on the impact of implicit social networks to the learning outcome of e-learning participants in an industrial setting. Design/methodology/approach: The paper presents a theoretical framework that allows the authors to measure correlation coefficients between the different affiliations that…
Sung, Ko-Yin; Poole, Frederick
This study explored college students' use of a popular smartphone social networking application, WeChat, in a tandem language learning project. The research questions included (1) How do Chinese-English dyads utilize the WeChat app for weekly language learning?, and (2) What are the perceptions of the Chinese-English dyads on the use of the WeChat…
Parsons, M. A.; Mokrane, M.; Sorvari, S.; Treloar, A.; Smith, C.
International data networks enable the sharing of data within and between scientific disciplines and countries and thus provide the foundation for Open Science. Developing effective and sustainable international research data networks is critical for progress in many areas of research and for science to address complex global societal challenges. However, the development and maintenance of effective networks is not always easy, particularly in a context where public resources for science are limited and international cooperation is not a priority for many countries. The global landscape for data sharing in science is complex; many international data networks already exist and have highly variable structures. Some are linked to large intergovernmental research infrastructures, have highly developed centralized services and deal mainly with the data needs of single disciplines. Some are highly distributed, have much less rigid governance structures and provide access to data from many different domains. Most are somewhere between these two extremes and they cover different geographic regions, from regional to global. All provide a mix of data and associated data services which meets the needs of the research community to various extents and this provision depends on a mix of hardware, software, standards and protocols and human skills. These come together, working across national boundaries, in technical and social networks. In all of this, what makes a network function effectively or not is unclear. This means that there is also no simple answer to what can usefully be done at the policy level to promote the development of effective and sustainable data networks. Hence the rational for the present project - to study a variety of currently successful networks, explore the challenges that they are facing and the lessons that can be learned from confronting these challenges, and, where applicable, to translate this analysis into potential policy actions. Detailed
Studený, Milan; Vomlel, Jiří; Hemmecke, R.
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
Cynthia Dedós Reyes
Full Text Available In this research we explored the integration of social media in the process of learning and teaching, in a private higher education institution, in Puerto Rico. Attention was given to the perspectives of teachers and students. The participants —9 part-time teachers and 118 students— were selected based on availability. The results showed that teachers and students alike use social the network You Tube for academic purposes; and use Facebook, Twitter, and blogs for social purposes and entertainment. Results also revealed that there is no significant contrast between the perspectives of teachers and students digital immigrants.
Cagnazzo, Luca; Taticchi, Paolo; Bidini, Gianni; Baglieri, Enzo
New business models and theories are developing nowadays towards collaborative environments direction, and many new tools in sustaining companies involved in these organizations are emerging. Among them, a plethora of methodologies to analyze their needs are already developed for single companies. Few academic works are available about Enterprise Networks (ENs) need analysis. This paper presents the learning from an action research (AR) in the mechatronics sector: AR has been used in order to experience the issue of evaluating network needs and therefore define, develop, and test a complete framework for network evaluation. Reflection on the story in the light of the experience and the theory is presented, as well as extrapolation to a broader context and articulation of usable knowledge.
Albiol, Thierry; Haste, Tim; Dorsselaere, Jean-Pierre van
51 organizations network in SARNET (Severe Accident Research NETwork of Excellence) their capacities of research in order to resolve the most important remaining uncertainties for enhancing, in regard of Severe Accidents (SA), the safety of existing and future Nuclear Power Plants (NPPs). This project, co-funded by the European Commission (EC), has been defined in order to optimise the use of the available means and to constitute sustainable research groups in the European Union. SARNET tackles the fragmentation that exists between the different R and D national programmes, in defining common research programmes and developing common computer tools and methodologies for safety assessment. SARNET comprises most of the actors involved in SA research in Europe (plus Canada). To reach these objectives, all the organizations networked in SARNET contribute to a so-called Joint Programme of Activities (JPA), which consists in: Implementing an advanced communication tool for accessing all project information, fostering exchange of information, and managing documents; Harmonizing and re-orienting the research programmes; Jointly analysing the experimental results provided by research programmes in order to elaborate a common understanding of relevant phenomena; Developing the ASTEC code (integral computer code used to predict the NPP behaviour during a postulated SA), which capitalizes in terms of physical models the knowledge produced within SARNET; Developing Scientific Databases, in which all the results of research programmes are stored in a common format (DATANET); Developing a common methodology for Probabilistic Safety Assessment (PSA) of NPPs; Developing courses and writing a text book on SA for students and researchers; Promoting personnel mobility between various European organizations. After the first period (2004-2008), co-funded by the EC, the network will progressively evolve toward self-sustainability. The bases for such an evolution, still under discussion
Suresh, Sundaram; Savitha, Ramasamy
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...
Isabella Norén Creutz
Full Text Available The objective of this paper is to explore the discourses of learning that are actualized in workplace e-learning. It aims to understand how learning is defined in research within this field. The empirical material consists of academic research articles on e-learning in the workplace, published from 2000 to 2013. The findings are presented as four metaphors highlighting four overlapping time periods with different truth regimes: Celebration, Questioning, Reflection and Dissolution. It is found that learning as a phenomenon tends to be marginalized in relation to the digital technology used. Based on this, we discuss a proposal for a more critical and problematized approach to e-learning, and a deeper understanding of the challenges and opportunities for employees and organizations to acquire knowledge in the digital age.
Dudley Herron, J.; Nurrenbern, Susan C.
Chemical education research is the systematic investigation of learning grounded in a theoretical foundation that focuses on understanding and improving learning of chemistry. This article reviews many activities, changes, and accomplishments that have taken place in this area of scholarly activity despite its relatively recent emergence as a research area. The article describes how the two predominant broad perspectives of learning, behaviorism and constructivism, have shaped and influenced chemical education research design, analysis, and interpretation during the 1900s. Selected research studies illustrate the range of research design strategies and results that have contributed to an increased understanding of learning in chemistry. The article also provides a perspective of current and continuing challenges that researchers in this area face as they strive to bridge the gap between chemistry and education - disciplines with differing theoretical bases and research paradigms.
Albiol, T.; Van Dorsselaere, J. P.; Chaumont, B.; Haste, T.; Journeau, Ch.; Meyer, L.; Sehgal, Bal Raj; Schwinges, Bernd; Beraha, D.; Annunziato, A.; Zeyen, R.
Fifty-one organisations network in SARNET (Severe Accident Research Network of Excellence) their research capacities in order to resolve the most important pending issues for enhancing, with regard to Severe Accidents (SA), the safety of existing and future Nuclear Power Plants (NPPs). This project. co-funded by the European Commission (EC) under the 6. Framework Programme, has been defined in order to optimise the use of the available means and to constitute sustainable research groups in the European Union. SARNET tackles the fragmentation that may exist between the different national R and D programmes, in defining common research programmes and developing common computer tools and methodologies for safety assessment. SARNET comprises most of the organisations involved in SA research in Europe, plus Canada. To reach these objectives, all the organisations networked in SARNET contributed to a joint Programme of Activities, which consisted of: Implementation of an advanced communication tool for accessing all project information, fostering exchange of information, and managing documents; Harmonization and re-orientation of the research programmes, and definition of new ones; Analysis of the experimental results provided by research programmes in order to elaborate a common understanding of relevant phenomena; Development of the ASTEC code (integral computer code used to predict the NPP behaviour during a postulated SA), which capitalizes in terms of physical models the knowledge produced within SARNET; Development of Scientific Databases in which all the results of research programmes are stored in a common format (DATANET); Development of a common methodology for Probabilistic Safety Assessment of NPPs; Development of short courses and writing a textbook on Severe Accidents for students and researchers; Promotion of personnel mobility amongst various European organisations. This paper presents the major achievements after four and a half years of operation of the
Full Text Available The papers in this issue present a convenient snapshot of current research in learning technology, both in their coverage of the issues that concern us and the methods that are being used to investigate them. This issue shows that e-learning researchers are interested in: what technologies are available and explorations of their potential (Nie et al. explore the role of podcasting, how to design technology-mediated learning activities in ways which support specific learning outcomes (Simpson evaluates the role of ‘book raps' in supporting critical thinking, the identification of critical success factors in implementations (Cochrane's observation of three mobile learning projects and how such e-learning initiatives can be sustained within an institutional context (Gunn's examination of the challenges of embedding ‘grass roots' initiatives. Finally e-learning research is concerned with investigating the impact of emerging technologies on education – in this case Traxler's discussion of mobile, largely student-owned, devices. Together these five papers demonstrate the scope of research in learning technology and it is with this in mind that we will soon be referring to this journal by its subtitle: Research in Learning Technology.
Cadima, Rita; Ojeda Rodríguez, Jordi; Monguet Fierro, José María
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 study we analyse two distributed learning communities' social networks in order to understand how characteristics of the social structure can enhance s...
Freeman, Jason; Saad, David
An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives ...
Diakonikolas, Ilias; Kane, Daniel; Stewart, Alistair
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...
Bilal, Alsallakh; Jourabloo, Amin; Ye, Mao; Liu, Xiaoming; Ren, Liu
Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.
Kim, Sonia A.; Blanck, Heidi M.; Cradock, Angie; Gortmaker, Steven
Effective nutrition and obesity policies that improve the food environments in which Americans live, work, and play can have positive effects on the quality of human diets. The Centers for Disease Control and Prevention’s (CDC’s) Nutrition and Obesity Policy Research and Evaluation Network (NOPREN) conducts transdisciplinary practice-based policy research and evaluation to foster understanding of the effectiveness of nutrition policies. The articles in this special collection bring to light a...
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.
García-Dorado, David; Castro-Beiras, Alfonso; Díez, Javier; Gabriel, Rafael; Gimeno-Blanes, Juan R; Ortiz de Landázuri, Manuel; Sánchez, Pedro L; Fernández-Avilés, Francisco
Today, cardiovascular disease is the principal cause of death and hospitalization in Spain, and accounts for an annual healthcare budget of more than 4000 million euros. Consequently, early diagnosis, effective prevention, and the optimum treatment of cardiovascular disease present a significant social and healthcare challenge for the country. In this context, combining all available resources to increase the efficacy and healthcare benefits of scientific research is a priority. This rationale prompted the establishment of the Spanish Cooperative Cardiovascular Disease Research Network, or RECAVA (Red Temática de Investigación Cooperativa en Enfermedades Cardiovasculares), 5 years ago. Since its foundation, RECAVA's activities have focused on achieving four objectives: a) to facilitate contacts between basic, clinical and epidemiological researchers; b) to promote the shared use of advanced technological facilities; c) to apply research results to clinical practice, and d) to train a new generation of translational cardiovascular researchers in Spain. At present, RECAVA consists of 41 research groups and seven shared technological facilities. RECAVA's research strategy is based on a scientific design matrix centered on the most important cardiovascular processes. The level of RECAVA's research activity is reflected in the fact that 28 co-authored articles were published in international journals during the first six months of 2007, with each involving contributions from at least two groups in the network. Finally, RECAVA also participates in the work of the Spanish National Center for Cardiovascular Research, or CNIC (Centro Nacional de Investigación Cardiovascular), and some established Biomedical Research Network Centers, or CIBER (Centros de Investigación Biomédica en RED), with the aim of consolidating the development of a dynamic multidisciplinary research framework that is capable of meeting the growing challenge that cardiovascular disease will present
Hansen, Lars Kai
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...
Dart, Richard A; Egan, Brent M
Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper, the authors review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O'QUIN) project, located at the Medical University of South Carolina. Key lessons learned and new directions to be explored are highlighted. ©2014 Wiley Periodicals, Inc.
Full Text Available There is little profiling academic research on discourse studies in relation to second language learning from a regional perspective. Thisstudy aims at unveiling what, when, where and who constitute scholarly work in research about these two interrelated fields. A dataset wasconfigured from registers taken from Dialnet and studied using specialized text-mining software. Findings revealed myriad research interests,few prolific years and the lack of networking. It is recommended to trace out our research as an ELT community locally and globally.
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.
SCHLEYER, TITUS; BUTLER, BRIAN S.; SONG, MEI; SPALLEK, HEIKO
Science in general, and biomedical research in particular, is becoming more collaborative. As a result, collaboration with the right individuals, teams, and institutions is increasingly crucial for scientific progress. We propose Research Networking Systems (RNS) as a new type of system designed to help scientists identify and choose collaborators, and suggest a corresponding research agenda. The research agenda covers four areas: foundations, presentation, architecture, and evaluation. Foundations includes project-, institution- and discipline-specific motivational factors; the role of social networks; and impression formation based on information beyond expertise and interests. Presentation addresses representing expertise in a comprehensive and up-to-date manner; the role of controlled vocabularies and folksonomies; the tension between seekers’ need for comprehensive information and potential collaborators’ desire to control how they are seen by others; and the need to support serendipitous discovery of collaborative opportunities. Architecture considers aggregation and synthesis of information from multiple sources, social system interoperability, and integration with the user’s primary work context. Lastly, evaluation focuses on assessment of collaboration decisions, measurement of user-specific costs and benefits, and how the large-scale impact of RNS could be evaluated with longitudinal and naturalistic methods. We hope that this article stimulates the human-computer interaction, computer-supported cooperative work, and related communities to pursue a broad and comprehensive agenda for developing research networking systems. PMID:24376309
Schleyer, Titus; Butler, Brian S; Song, Mei; Spallek, Heiko
Science in general, and biomedical research in particular, is becoming more collaborative. As a result, collaboration with the right individuals, teams, and institutions is increasingly crucial for scientific progress. We propose Research Networking Systems (RNS) as a new type of system designed to help scientists identify and choose collaborators, and suggest a corresponding research agenda. The research agenda covers four areas: foundations, presentation, architecture , and evaluation . Foundations includes project-, institution- and discipline-specific motivational factors; the role of social networks; and impression formation based on information beyond expertise and interests. Presentation addresses representing expertise in a comprehensive and up-to-date manner; the role of controlled vocabularies and folksonomies; the tension between seekers' need for comprehensive information and potential collaborators' desire to control how they are seen by others; and the need to support serendipitous discovery of collaborative opportunities. Architecture considers aggregation and synthesis of information from multiple sources, social system interoperability, and integration with the user's primary work context. Lastly, evaluation focuses on assessment of collaboration decisions, measurement of user-specific costs and benefits, and how the large-scale impact of RNS could be evaluated with longitudinal and naturalistic methods. We hope that this article stimulates the human-computer interaction, computer-supported cooperative work, and related communities to pursue a broad and comprehensive agenda for developing research networking systems.
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.
Jean L. Steiner; Timothy Strickland; Peter J.A. Kleinman; Kris Havstad; Thomas B. Moorman; M.Susan Moran; Phil Hellman; Ray B. Bryant; David Huggins; Greg McCarty
While current weather patterns and rapidly accelerated changes in technology often focus attention onÂ short-term trends in agriculture, the fundamental demands on modern agriculture to meet society food, feed, fuel andÂ fiber production while providing the foundation for a healthy environment requires long-term perspective. The Long-Â Term Agroecoystem Research Network...
McElmurry, Beverly J.; Minckley, Barbara B.
Models for collegial networking as a means of increasing the participants' scholarly productivity are presented. A Midwestern historical methodology research interest group is described as an example of the long-term benefits of forming networks of scholars. (MSE)
Full Text Available Peer-to-Peer (P2P networking in a mobile learning environment has become a popular topic of research. One of the new emerging research ideas is on the ability to combine P2P network with server-based network to form a strong efficient portable and compatible network infrastructure. This paper describes a unique mobile network architecture, which reflects the on-campus students’ need for a mobile learning environment. This can be achieved by combining two different networks, client-server and peer-to-peer ad-hoc to form a sold and secure network. This is accomplished by employing one peer within the ad-hoc network to act as an agent-peer to facilitate communication and information sharing between the two networks. It can be implemented without any major changes to the current network technologies, and can combine any wireless protocols such as GPRS, Wi-Fi, Bluetooth, and 3G.
Ida, Katsumi; Nakanishi, Hideya
This report describes the development of a computer network that uses the Integrated Service Digital Network (ISDN) for real-time analysis of experimental plasma physics and nuclear fusion research. Communication speed, 64/128kbps (INS64) or 1.5Mbps (INS1500) per connection, is independent of how busy the network is. When INS-1500 is used, the communication speed, which is proportional to the public telephone connection fee, can be dynamically varied from 64kbps to 1472kbps (depending on how much data are being transferred using the Bandwidth-on-Demand (BOD) function in the ISDN Router. On-demand dial-up and time-out disconnection reduce the public telephone connection fee by 10%-97%. (author)
Mohd Ishak Bin Ismail
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.
Wu, Bing; Xu, WenXia; Ge, Jun
This study is a productivity review on the literature gleaned from SSCI, SCIE databases concerning experience in E-Learning research. The result indicates that the number of literature productions on experience effect in ELearning research is still growing from 2005. The main research development country is Croatia, and from the analysis of the publication year, the number of papers is increasing to the peaking in 2010. And the main source title is British Journal of Educational Technology. In addition the subject area concentrated on Education & Educational Research. Moreover the research focuses on are mainly survey research and empirical research, in order to explore experience effect in E-Learning research. Also the limitations and future research of these research were discussed, so that the direction for further research work can be exploited
Andrews, Christine P.
Researchers have described "service learning" as an ideal way to integrate experiential education into coursework while meeting community needs and imbuing students with civic responsibility. They have advocated service learning in business as a method to implement course concepts and increase student understanding of the external environment. In…
Siebenhüner, Bernd; Rodela, Romina; Ecker, Franz
Social learning studies emerged as part of the ecological economics research agenda rather recently. Questions of how human societies and organisations learn and transition on the basis of environmental knowledge relate to the core ideas of ecological economics with its pluralistic understanding
Porter, Jill; Lacey, Penny
The aim of this book is to provide a source for teachers and other professionals working with children and adults with learning difficulties and disabilities that will enable them to: (1) access selected recent and relevant research in the field of learning difficulties, drawn from a range of disciplines and groups of people; (2) reflect on…
Conklin, James; Lusk, Elizabeth; Harris, Megan; Stolee, Paul
The purpose of this paper is to describe and reflect on the role of knowledge brokers (KBs) in the Seniors Health Research Transfer Network (SHRTN). The paper reviews the relevant literature on knowledge brokering, and then describes the evolving role of knowledge brokering in this knowledge network. The description of knowledge brokering provided here is based on a developmental evaluation program and on the experiences of the authors. Data were gathered through qualitative and quantitative methods, analyzed by the evaluators, and interpreted by network members who participated in sensemaking forums. The results were fed back to the network each year in the form of formal written reports that were widely distributed to network members, as well as through presentations to the network's members. The SHRTN evaluation and our experiences as evaluators and KBs suggest that a SHRTN KB facilitates processes of learning whereby people are connected with tacit or explicit knowledge sources that will help them to resolve work-related challenges. To make this happen, KBs engage in a set of relational, technical, and analytical activities that help communities of practice (CoPs) to develop and operate, facilitate exchanges among people with similar concerns and interests, and help groups and individuals to create, explore, and apply knowledge in their practice. We also suggest that the role is difficult to define, emergent, abstract, episodic, and not fully understood. The KB role within this knowledge network has developed and matured over time. The KB adapts to the social and technical affordances of each situation, and fashions a unique and relevant process to create relationships and promote learning and change. The ability to work with teams and to develop relevant models and feasible approaches are critical KB skills. The KB is a leader who wields influence rather than power, and who is prepared to adopt whatever roles and approaches are needed to bring about a valuable
Shahrabi Farahani, Hossein; Lagergren, Jens
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) ,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.
Wu, Bing; Xu, WenXia; Ge, Jun
This study is a productivity review on the literature gleaned from SSCI, SCIE databases concerning innovation research in E-Learning. The result indicates that the number of literature productions on innovation research in ELearning is still growing from 2005. The main research development country is England, and from the analysis of the publication year, the number of papers is increasing peaking in 25% of the total in 2010. Meanwhile the main source title is British Journal of Educational Technology. In addition the subject area concentrated on Education & Educational Research, Computer Science, Interdisciplinary Applications and Computer Science, Software Engineering. Moreover the research focuses on are mainly conceptual research and empirical research, which were used to explore E-Learning in respective of innovation diffusion theory, also the limitations and future research of these research were discussed for further research.
Sandberg, J.; Maris, M.; Arnedillo Sánchez, I.; Isaías, P.
A review of mobile learning research shows that studies take various research approaches and apply a varied number of research methods, ranging from primarily quantitative and experimental to purely qualitative and descriptive. This paper presents a classification framework to position mobile
Singer, Susan Rundell
This special issue of "Journal of Research in Science Teaching" reflects conclusions and recommendations in the "Discipline-Based Education Research" (DBER) report and makes a substantial contribution to advancing the field. Research on undergraduate science learning is currently a loose affiliation of related fields. The…
Vega-Redondo, F.; Slanina, František; Marsili, M.
Roč. 3, 2-3 (2005), s. 628-638 ISSN 1542-4766 R&D Projects: GA MŠk(CZ) 1P04OCP10.001 Grant - others:MEC(ES) SEJ2004-02170; EU(XE) HPRN-CT-2002-00319 Institutional research plan: CEZ:AV0Z10100520 Keywords : sociophysics * random graphs * networks Subject RIV: BE - Theoretical Physics
Full Text Available In issue 12(1, Jones (2004 in his article ‘Networks and learning: communities, practices and the metaphor of networks' sets out to address three inter-related sets of issues: … firstly that learning technology needs to take account of the wider debate about networks and secondly that research in this field needs to address the theoretical and practical issues raised by advances in the field of networks. A third point is that the idea of the network acts as a powerful metaphor even if we are able to discount any particular theory generated in its support. The network metaphor can act as a unifying concept allowing us to bring together apparently disparate elements of the field.
Hajhashemi, Karim; Anderson, Neil; Jackson, Cliff; Caltabiano, Nerina
Computers, mobile devices and the Internet have enabled a learning environment described as online learning or a variety of other terms such as e-learning. Researchers believe that online learning has become more complex due to learners' sharing and acquiring knowledge at a variety of remote locations, in a variety of modalities. However, advances in technology and the integration of ICT with teaching and learning settings have quickened the growth of online learning and importantly have chan...
F. Javier Del Alamo
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
Koper, Rob; Sloep, Peter
Koper, E.J.R., Sloep, P.B. (2002) Learning Networks connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning. RTD Programma into Learning Technologies 2003-2008. More is different… Heerlen, Nederland: Open Universiteit
van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.
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…
Pamungkas, Bian Dwi
This study aims to examine the contribution of learning methods on learning output, the contribution of facilities and infrastructure on output learning, the contribution of learning resources on learning output, and the contribution of learning methods, the facilities and infrastructure, and learning resources on learning output. The research design is descriptive causative, using a goal-oriented assessment approach in which the assessment focuses on assessing the achievement of a goal. The ...
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.
Kim, Sonia A; Blanck, Heidi M; Cradock, Angie; Gortmaker, Steven
Effective nutrition and obesity policies that improve the food environments in which Americans live, work, and play can have positive effects on the quality of human diets. The Centers for Disease Control and Prevention's (CDC's) Nutrition and Obesity Policy Research and Evaluation Network (NOPREN) conducts transdisciplinary practice-based policy research and evaluation to foster understanding of the effectiveness of nutrition policies. The articles in this special collection bring to light a set of policies that are being used across the United States. They add to the larger picture of policies that can work together over time to improve diet and health.
Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi
Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.
Full Text Available In this article, the author explains how and why one particular qualitative research approach, the naturalistic inquiry paradigm, was implemented in an e-learning research study that investigated the use of the World Wide Web technology in higher education. A framework is presented that situates the research study within the qualitative research literature. The author then justifies how the study was compliant with naturalistic inquiry and concludes by presenting a model for judging the quality of such research. The purpose of this article is to provide an example of how naturalistic inquiry can be implemented in e-learning research that can serve as a guide for researchers undertaking this form of qualitative inquiry. As such, the focus of the article is to illustrate how methodological issues pertaining to naturalistic inquiry were addressed and justified to represent a rigorous research approach rather than presenting the results of the research study.
The NASA Lewis Research Center's Learning Technologies Project (LTP) has responded to requests from local school district technology coordinators to provide content for videoconferencing workshops. Over the past year we have offered three teacher professional development workshops that showcase NASA Lewis-developed educational products and NASA educational Internet sites. In order to determine the direction of our involvement with distance learning, the LTP staff conducted a survey of 500 U.S. schools. We received responses from 72 schools that either currently use distance learning or will be using distance learning in 98-99 school year. The results of the survey are summarized in the article. In addition, the article provides information on distance learners, distance learning technologies, and the NASA Lewis LTP videoconferencing workshops. The LTP staff will continue to offer teacher development workshops through videoconferencing during the 98-99 school year. We hope to add workshops on new educational products as they are developed at NASA Lewis.
Li, Jingjing; Zhang, Yumei; Man, Jiayu; Zhou, Yun; Wu, Xiaojun
Cooperative learning is one of the most effective teaching methods, which has been widely used. Students' mutual contact forms a cooperative learning network in this process. Our previous research demonstrated that the cooperative learning network has complex characteristics. This study aims to investigating the dynamic spreading process of the knowledge in the cooperative learning network and the inspiration of leaders in this process. To this end, complex network transmission dynamics theory is utilized to construct the knowledge dissemination model of a cooperative learning network. Based on the existing epidemic models, we propose a new susceptible-infected-susceptible-leader (SISL) model that considers both students' forgetting and leaders' inspiration, and a susceptible-infected-removed-leader (SIRL) model that considers students' interest in spreading and leaders' inspiration. The spreading threshold λcand its impact factors are analyzed. Then, numerical simulation and analysis are delivered to reveal the dynamic transmission mechanism of knowledge and leaders' role. This work is of great significance to cooperative learning theory and teaching practice. It also enriches the theory of complex network transmission dynamics.
Pompe, P.P.M.; Feelders, A.J.; Feelders, A.J.
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
A comprehensive theoretical framework is proposed for the learning of a class of gradient-type neural networks with an additive Gaussian white noise process. The study is based on stochastic sensitivity analysis techniques, and formal expressions are obtained for stochastic learning laws in terms of functional derivative sensitivity coefficients. The present method, based on Langevin simulation techniques, uses only the internal states of the network and ubiquitous noise to compute the learning information inherent in the stochastic correlation between noise signals and the performance functional. In particular, the method does not require the solution of adjoint equations of the back-propagation type. Thus, the present algorithm has the potential for efficiently learning network weights with significantly fewer computations. Application to an unfolded multi-layered network is described, and the results are compared with those obtained by using a back-propagation method
Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter; Fetter, Sibren
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.
Learning Initiatives for Network Economies in Asia (LIRNEasia) : Building Capacity in ICT Policy ... LIRNEasia seeks to build capacity for evidence-based interventions in the public policy process by persons attuned to the ... Project status.
Deyamport, W. H., III.
In this action research study, eight teachers at an elementary school were trained in the use of Twitter to support the development of a personal learning network as a strategy to address non-differentiated professional development at the school. The main research question for this study was: In what ways, if any, can the use of a…
Song, Huan; Chen, Jinqiang; Cao, Suzhi; Cui, Dandan; Li, Tong; Su, Yuxing
Software defined network is a new type of network architecture, which decouples control plane and data plane of traditional network, has the feature of flexible configurations and is a direction of the next generation terrestrial Internet development. Satellite network is an important part of the space-ground integrated information network, while the traditional satellite network has the disadvantages of difficult network topology maintenance and slow configuration. The application of SDN technology in satellite network can solve these problems that traditional satellite network faces. At present, the research on the application of SDN technology in satellite network is still in the stage of preliminary study. In this paper, we start with introducing the SDN technology and satellite network architecture. Then we mainly introduce software defined satellite network architecture, as well as the comparison of different software defined satellite network architecture and satellite network virtualization. Finally, the present research status and development trend of SDN technology in satellite network are analyzed.
Suchsland, Philippe; Wessel, Stefan
We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.
Goldt, Sebastian; Seifert, Udo
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.
Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo
Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.
Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge
(domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes......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....
Full Text Available e-Learning research is an expanding and diversifying field of study. Specialist research units and departments proliferate. Postgraduate courses recruit well in the UK and overseas, with an increasing focus on critical and research-based aspects of the field, as well as the more obvious professional development requirements. Following this year's launch of a National e-Learning Research Centre, it is timely to debate what the field of study should be prioritising for the future. This discussion piece suggests that the focus should fall on questions that are both clear and tractable for researchers, and likely to have a real impact on learners and practitioners. Suggested questions are based on early findings from a series of JISC-funded projects on e-learning and pedagogy.
Singelis, Theodore M.
This article describes the involvement of undergraduate students in research at the California State University (CSU), Chico funded through an Academic Research Enhancement Award (AREA) from the National Institute on Aging (NIA). CSU, Chico is a "teaching" university and has students with a variety of motivations and abilities. The…
DeFries, J. C.; And Others
Results obtained from the center's six research projects are reviewed, including research on psychometric assessment of twins with reading disabilities, reading and language processes, attention deficit-hyperactivity disorder and executive functions, linkage analysis and physical mapping, computer-based remediation of reading disabilities, and…
Kennedy, Marie R.; Kennedy, David P.; Brancolini, Kristine R.
This article describes for the first time the composition and structure of the personal networks of novice librarian researchers. We used social network analysis to observe if participating in the Institute for Research Design in Librarianship (IRDL) affected the development of the librarians' personal networks and how the networks changed over…
Derrida, B.; Nadal, J.P.
It is possible to construct diluted asymmetric models of neural networks for which the dynamics can be calculated exactly. The authors test several learning schemes, in particular, models for which the values of the synapses remain bounded and depend on the history. Our analytical results on the relative efficiencies of the various learning schemes are qualitatively similar to the corresponding ones obtained numerically on fully connected symmetric networks
Samira Sadat Sajadi
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.
Full Text Available The paper establishes the evolutionary mechanism model of agile supply chain network by means of complex network theory which can be used to describe the growth process of the agile supply chain network and analyze the complexity of the agile supply chain network. After introducing the process and the suitability of taking complex network theory into supply chain network research, the paper applies complex network theory into the agile supply chain network research, analyzes the complexity of agile supply chain network, presents the evolutionary mechanism of agile supply chain network based on complex network theory, and uses Matlab to simulate degree distribution, average path length, clustering coefficient, and node betweenness. Simulation results show that the evolution result displays the scale-free property. It lays the foundations of further research on agile supply chain network based on complex network theory.
Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area. PMID:28570593
Baber, Sikunder Ali
" and shows how a number of professional associations have become as networks of learning to encourage the continuing professional education of both pre-service and in-service teachers in the context of Pakistan. A case of the Mathematics Association of Pakistan (MAP) as a Network of Learning is presented....... The formation and growth of this network can be viewed as developing insights into the improvement of mathematics education in the developing world. The contributions of the association may also add value to the learning of teacher colleagues in other parts of the world. This sharing of the experience may......Importance of the professional development of teachers has been recognized and research has contributed greatly in terms of proposing variety of approaches for the development of teachers,both pre-service and in-service. Among them, networking among teachers, teacher educators,curriculum developers...
Jie, Wan; Wen, Wang
The building of military logistics network is an important issue for the construction of new forces. This paper has thrown out a concept model of 6R military logistics network model based on JIT. Then we conceive of axis spoke y logistics centers network, flexible 6R organizational network, lean 6R military information network based grid. And then the strategy and proposal for the construction of the three sub networks of 6Rmilitary logistics network are given.
Batchelder, Cecil W.
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…
Yeo, Michelle Mei Ling
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…
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 ...
This research is founded on an innovative pedagogical project as part of a higher education lecturer teaching qualification. This project involved redesigning the module 'advanced history taking and physical examination with clinical reasoning', a continuing professional development at a higher education institution. The author undertook an exploration of the literature, considering evidence on teaching styles and the way in which students learn and gain knowledge. The module was redesigned, impelemented and then evaluated by the student participants. Key themes in the evaluation centred on the experiential learning style and experiential teaching style. There are numerous internal and external factors that affect teaching, and student learning. Experiential learning has provided a successful teaching pedagogy when applied to clinical skill acquisition, and has positively benefited the module delivery and pass rate, suggesting it has embedded 'deep learning'. Student feedback was positive, and the redesigned module has had a positive impact on student engagement and the teacher-student interaction.
"The Department of Energy's (DOE) Energy Sciences Network (ESnet) and Internet2 will deploy a high capacity nationwide network that will greatly enhance the capabilities of researchers across the country who participate in the DOE's scientific research efforts." (1 page)
Cohen, M.X.; Wilmes, K.A.; van de Vijver, I.
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
Full Text Available In recent years, learning analytics (LA has attracted a great deal of attention in technology-enhanced learning (TEL research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?, stakeholders (who?, objectives (why?, and methods (how?. It further identifies various challenges and research opportunities in the area of LA in relation to each dimension.
Design research is a broad, practice-based approach to investigating problems of education. This approach can catalyze the development of learning theory by fostering opportunities for transformational change in scholars' interpretation of instructional interactions. Surveying a succession of design-research projects, I explain how challenges in…
This selective review of the second language acquisition and applied linguistics research literature on grammar learning and teaching falls into three categories: where research has had little impact (the non-interface position), modest impact (form-focused instruction), and where it potentially can have a large impact (reconceiving grammar).…
Wolf, Lorraine W.
This chapter discusses the impact of undergraduate research as a form of engaged student learning. It summarizes the gains reported in post-fellowship assessment essays acquired from students participating in the Auburn University Undergraduate Research Fellowship Program. The chapter also discusses the program's efforts to increase opportunities…
This paper outlines the development of a generic Business Research Methods course from a simple name in a box to a full e-Learning web based module. It highlights particular issues surrounding the nature of the discipline and the integration of a large number of cross faculty subject specific research methods courses into a single generic module.…
This article features recent research in science teaching and learning. It presents three current articles of interest in life sciences education, as well as more general and noteworthy publications in education research. URLs are provided for the abstracts or full text of articles. For articles listed as "Abstract available," full text may be…
Greenhow, Christine; Robelia, Beth
All students today are increasingly expected to develop technological fluency, digital citizenship, and other twenty-first century competencies despite wide variability in the quality of learning opportunities schools provide. Social network sites (SNSs) available via the internet may provide promising contexts for learning to supplement…
Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.
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…
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning
Lin, Chin-Hsi; Warschauer, Mark; Blake, Robert
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…
Ivanov, V.V.; Puzynin, I.V.; Purehvdorzh, B.
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
A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits
De Jong, Tim; Fuertes, Alba; Schmeits, Tally; Specht, Marcus; Koper, Rob
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.
Petley, Rebecca; Parker, Guy; Attewell, Jill
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…
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
Li, Xin; Gray, Kathleen; Verspoor, Karin; Barnett, Stephen
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.
Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu
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.
Robyn M. Gillies
Full Text Available Cooperative learning, where students work in small groups to accomplish shared goals, is widely recognized as a teaching strategy that promotes learning and socialization among students from kindergarten through college and across different subject domains. It has been used successfully to promote reading and writing achievements, understanding and conceptual development in science classes, problem-solving in mathematics, and higher-order thinking and learning to name just a few. It has been shown to enhance students' willingness to work cooperatively and productively with others with diverse learning and adjustment needs and to enhance intergroup relations with those from culturally and ethnically different backgrounds. It has also been used as a teaching strategy to assist students to manage conflict and to help students identified as bullies learn appropriate interpersonal skills. In fact, it has been argued that cooperative learning experiences are crucial to preventing and alleviating many of the social problems related to children, adolescents, and young adults. There is no doubt that the benefits attributed to cooperative learning are widespread and numerous and it is the apparent success of this approach to learning that has led to it being acclaimed as one of the greatest educational innovations of recent times. The purpose of this paper is not only to review developments in research on cooperative learning but also to examine the factors that mediate and moderate its success. In particular, the review focuses on the types of student and teacher interactions generated and the key role talk plays in developing student thinking and learning, albeit through the expression of contrasting opinions or constructed shared meaning. The intention is to provide additional insights on how teachers can effectively utilize this pedagogical approach to teaching and learning in their classrooms.
Ann M Hermundstad
Full Text Available The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.
Milačić, Ljubiša; Jović, Srđan; Vujović, Tanja; Miljković, Jovica
The purpose of this research is to develop and apply the artificial neural network (ANN) with extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. The economic growth forecasting was analyzed based on agriculture, manufacturing, industry and services value added in GDP. The results were compared with ANN with back propagation (BP) learning approach since BP could be considered as conventional learning methodology. The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Based on results, it was shown that ANN with ELM learning methodology can be applied effectively in applications of GDP forecasting.
Cobb, Whitney; Bracey, Georgia; Buxner, Sanlyn; Gay, Pamela L.; Noel-Storr, Jacob; CosmoQuest Team
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
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…
Lehmann, Torsten; Woodburn, Robin
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...
Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo
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.
Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E
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.
Wang, Ye; Dragoi, Valentin
Although changes in brain activity during learning have been extensively examined at the single neuron level, the coding strategies employed by cell populations remain mysterious. We examined cell populations in macaque area V4 during a rapid form of perceptual learning that emerges within tens of minutes. Multiple single units and LFP responses were recorded as monkeys improved their performance in an image discrimination task. We show that the increase in behavioral performance during learning is predicted by a tight coordination of spike timing with local population activity. More spike-LFP theta synchronization is correlated with higher learning performance, while high-frequency synchronization is unrelated with changes in performance, but these changes were absent once learning had stabilized and stimuli became familiar, or in the absence of learning. These findings reveal a novel mechanism of plasticity in visual cortex by which elevated low-frequency synchronization between individual neurons and local population activity accompanies the improvement in performance during learning.
Sun, Caihong; Wan, Yuzi; Chen, Yu
Most organizations encourage the formation of teams to accomplish complicated tasks, and vice verse, effective teams could bring lots benefits and profits for organizations. Network structure plays an important role in forming teams. In this paper, we specifically study the dynamics of team formation in large research communities in which knowledge of individuals plays an important role on team performance and individual utility. An agent-based model is proposed, in which heterogeneous agents from research communities are described and empirically tested. Each agent has a knowledge endowment and a preference for both income and leisure. Agents provide a variable input (‘effort’) and their knowledge endowments to production. They could learn from others in their team and those who are not in their team but have private connections in community to adjust their own knowledge endowment. They are allowed to join other teams or work alone when it is welfare maximizing to do so. Various simulation experiments are conducted to examine the impacts of network topology, knowledge diffusion among community network, and team output sharing mechanisms on the dynamics of team formation.
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.
This Paper reports lessons learned and state of knowledge gained from an organizational factors research activity involving commercial nuclear power plants in the United States, through the end of 1991, as seen by the scientists immediately involved in the research. Lessons learned information was gathered from the research teams and individuals using a question and answer format. The following five questions were submitted to each team and individual: (1) What organizational factors appear to influence safety performance in some systematic way, (2) Should organizational factors research focus at the plant level, or should it extend beyond the plant level to the parent company, rate setting commissions, regulatory agencies, (3) How important is having direct access to plants for doing organizational factors research, (4) What lessons have been learned to date as the result of doing organizational factors research in a nuclear regulatory setting, and (5) What organizational research topics and issues should be pursued in the future? Conclusions based on the responses provided for this report are that organizational factors research can be conducted in a regulatory setting and produce useful results. Technologies pioneered in other academic, commercial, and military settings can be adopted for use in a nuclear regulatory setting. The future success of such research depends upon the cooperation of regulators, contractors, and the nuclear industry
Full Text Available In the spring of 2011, the UC San Diego Research Cyberinfrastructure (RCI Implementation Team invited researchers and research teams to participate in a research curation and data management pilot program. This invitation took the form of a campus-wide solicitation. More than two dozen applications were received and, after due deliberation, the RCI Oversight Committee selected five curation-intensive projects. These projects were chosen based on a number of criteria, including how they represented campus research, varieties of topics, researcher engagement, and the various services required. The pilot process began in September 2011, and will be completed in early 2014. Extensive lessons learned from the pilots are being compiled and are being used in the on-going design and implementation of the permanent Research Data Curation Program in the UC San Diego Library. In this paper, we present specific implementation details of these various services, as well as lessons learned. The program focused on many aspects of contemporary scholarship, including data creation and storage, description and metadata creation, citation and publication, and long term preservation and access. Based on the lessons learned in our processes, the Research Data Curation Program will provide a suite of services from which campus users can pick and choose, as necessary. The program will provide support for the data management requirements from national funding agencies.
Full Text Available Welcome to the June issue of EBLIP, our firstto be published with an HTML version as wellas PDFs for each article. I hope you enjoy andfind the alternative formats useful. As usualthe issue comprises an interesting range ofevidence summaries and articles that I hopeyou will find useful in applying evidence toyour practice.When considering evidence, two recent trips toEdinburgh got me thinking about the widerange of study designs or methods that areuseful for generating evidence, and also howwe can learn about their use from otherprofessions.The first trip was as part of the cadre of the LISDREaM project (http://lisresearch.org/dreamproject/.DREaM has been set up by the LISResearch Coalition to develop a sustainableLIS research network in the UK. As part ofthis, a series of workshops aims to introduceLIS practitioners to a wider range of researchmethods, thus expanding the methods used inLIS research. Indeed, a quick scan of thecontents of this issue show a preponderance ofsurveys, interviews, and citation analysis,suggesting that broadening our knowledge ofmethods may well be a useful idea. Theworkshops are highly interactive and, at eachsession experts from outside the LIS disciplineintroduce particular research methods andoutline how they could be used in LISapplications. As a result, I can see the valueand understand when to use research methodssuch as social network analysis, horizonscanning, ethnography, discourse analysis, andrepertory grids – as well as knowing that datamining is something I’m likely to avoid! So farI’ve shared my new knowledge with a PhDstudent who was considering her methodologyand incorporated my new knowledge ofhorizon scanning into a bid for researchfunding. The next (and more exciting step isto think of a situation where I can apply one ofthese methods to examining an aspect of LIS practice.The second trip was the British Association ofCounselling and Psychotherapy ResearchConference, an event which I
There’s a lot of talk about the benefits of deep learning (neural networks) and how it’s the new electricity that will power us into the future. Medical diagnosis, computer vision and speech recognition are all examples of use-cases where neural networks are being applied in our everyday business environment. This begs the question…what are the uses of neural-network applications for cyber security? How does the AI process work when applying neural networks to detect malicious software bombar...
Parnell, James Michael; Robinson, Jennifer C
This paper introduces social network analysis as a versatile method with many applications in nursing research. Social networks have been studied for years in many social science fields. The methods continue to advance but remain unknown to most nursing scholars. Discussion paper. English language and interpreted literature was searched from Ovid Healthstar, CINAHL, PubMed Central, Scopus and hard copy texts from 1965 - 2017. Social network analysis first emerged in nursing literature in 1995 and appears minimally through present day. To convey the versatility and applicability of social network analysis in nursing, hypothetical scenarios are presented. The scenarios are illustrative of three approaches to social network analysis and include key elements of social network research design. The methods of social network analysis are underused in nursing research, primarily because they are unknown to most scholars. However, there is methodological flexibility and epistemological versatility capable of supporting quantitative and qualitative research. The analytic techniques of social network analysis can add new insight into many areas of nursing inquiry, especially those influenced by cultural norms. Furthermore, visualization techniques associated with social network analysis can be used to generate new hypotheses. Social network analysis can potentially uncover findings not accessible through methods commonly used in nursing research. Social networks can be analysed based on individual-level attributes, whole networks and subgroups within networks. Computations derived from social network analysis may stand alone to answer a research question or incorporated as variables into robust statistical models. © 2018 John Wiley & Sons Ltd.
Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff
The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.
Gisiger-Camata, Silvia; Nolan, Timiya S; Vo, Jacqueline B; Bail, Jennifer R; Lewis, Kayla A; Meneses, Karen
The Young Breast Cancer Survivors Network (Network) is an academic and community-based partnership dedicated to education, support, and networking. The Network used a multi-pronged approach via monthly support and networking, annual education seminars, website networking, and individual survivor consultation. Formative and summative evaluations were conducted using group survey and individual survivor interviews for monthly gatherings, annual education meetings, and individual consultation. Google Analytics was applied to evaluate website use. The Network began with 4 initial partnerships and grew to 38 in the period from 2011 to 2017. During this 5-year period, 5 annual meetings (598 attendees), 23 support and networking meetings (373), and 115 individual survivor consultations were conducted. The Network website had nearly 12,000 individual users and more than 25,000 page views. Lessons learned include active community engagement, survivor empowerment, capacity building, social media outreach, and network sustainability. The 5-year experiences with the Network demonstrated that a regional program dedicated to the education, support, networking, and needs of young breast cancer survivors and their families can become a vital part of cancer survivorship services in a community. Strong community support, engagement, and encouragement were vital components to sustain the program.
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?
Henneberg, Stephan C. M.; Ziang, Zhizhong; Naudé, Peter
The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987......). In this paper, based upon the papers presented at the 22 conferences held to date, we undertake a Social Network Analysis in order to examine the degree of co-publishing that has taken place between this group of researchers. We identify the different components in this database, and examine the large main...
The first report of a connection between vocabulary learning and phonological short-term memory was published in 1988 (Baddeley, Papagno, & Vallar, 1988). At that time, both Susan Gathercole and I were involved in longitudinal studies, investigating the relation between nonword repetition and language learning. We both found a connection. Now,…
Bohemia, Erik; Ghassan, Aysar
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…
Holmes, Kathryn; Preston, Greg; Shaw, Kylie; Buchanan, Rachel
Effective professional learning for teachers is fundamental for any school system aiming to make transformative and sustainable change to teacher practice. This paper investigates the efficacy of Twitter as a medium for teachers to participate in professional learning by analysing the tweets of 30 influential users of the popular medium. We find…
Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco
Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.
Smith-Stoner, Marilyn; Molle, Mary E
Nurse educators must continually improve their teaching skills through innovation. However, research about the process used by faculty members to transform their teaching methods is limited. This collaborative study uses classroom action research to describe, analyze, and address problems encountered in implementing cooperative learning in two undergraduate nursing courses. After four rounds of action and reflection, the following themes emerged: students did not understand the need for structured cooperative learning; classroom structure and seating arrangement influenced the effectiveness of activities; highly structured activities engaged the students; and short, targeted activities that involved novel content were most effective. These findings indicate that designing specific activities to prepare students for class is critical to cooperative learning. Copyright 2010, SLACK Incorporated.
Lecluijze, Suzanne Elisabeth; de Haan, Mariëtte; Ünlüsoy, Asli
This exploratory study examines ethno-cultural diversity in youth's narratives regarding their "online" learning experiences while also investigating how these narratives can be understood from the analysis of their online network structure and composition. Based on ego-network data of 79 respondents this study compared the…
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.
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
Full Text Available This paper focuses on the broad outcome of an action research project in which practical theory was developed in the field of networked learning through case-study analysis of learners' experiences and critical evaluation of educational practice. It begins by briefly discussing the pedagogical approach adopted for the case-study course and the action research methodology. It then identifies key dimensions of four interconnected developmental processes–orientation, communication, socialisation and organisation–that were associated with ‘learning to learn' in the course's networked environment, and offers a flavour of participants' experiences in relation to these processes. A number of key evaluation issues that arose are highlighted. Finally, the paper presents the broad conceptual framework for the design and facilitation of process support in networked learning that was derived from this research. The framework proposes a strong, explicit focus on support for process as well as domain learning, and progression from tighter to looser design and facilitation structures for process-focused (as well as domain-focused learning tasks.
Chen, Barry Y. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patterns are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.
In a globalized economy, education and research are becoming increasing international in content and context. Academic and research institutions worldwide try to internationalize their programs by setting formal or informal collaborations. An education that is enhanced by international experiences leads to mobility of the science and technology workforce. Existing academic cultures and research structures are at odds with efforts to internationalize education. For the past 20-30 years, the US has recognized the need to improve the abroad experience of our scientists and technologists: however progress has been slow. Despite a number of both federally and privately supported programs, efforts to scale up the numbers of participants have not been satisfactory. The exchange is imbalanced as more foreign scientists and researchers move to the US than the other way around. There are a number of issues that contribute to this imbalance but we could consider the US academic career system, as defined by its policies and practices, as a barrier to internationalizing the early career faculty experience. Strict curricula, pre-tenure policies and financial commitments discourage students, post doctoral fellows and pre-tenure faculty from taking international leaves to participate in research abroad experiences. Specifically, achieving an international experience requires funding that is not provided by the universities. Furthermore, intellectual property requirements and constraints in pre-tenure probationary periods may discourage students and faculty from collaborations with peers across the Atlantic or Pacific or across the American continent. Environments that support early career networking are not available. This presentation will discuss the increasing need for international collaborations and will explore the need for additional programs, more integration, better conditions and improved infrastructures that can encourage and support mobility of scientists. In addition
John S Liu
Full Text Available The body of literature addressing the phenomenon related to social networking services (SNSs has grown rather fast recently. Through a systematic and quantitative approach, this study identifies the recent SNS research themes, which are the issues discussed by a coherent and growing subset of this literature. A set of academic articles retrieved from the Web of Science database is used as the basis for uncovering the recent themes. We begin the analysis by constructing a citation network which is further separated into groups after applying a widely used clustering method. The resulting clusters all consist of articles coherent in citation relationships. This study suggests eight fast growing recent themes. They span widely encompassing politics, romantic relationships, public relations, journalism, and health. Among them, four focus their issues largely on Twitter, three on Facebook, and one generally on both. While discussions on traditional issues in SNSs such as personality, motivations, self-disclosure, narcissism, etc. continue to lead the pack, the proliferation of the highlighted recent themes in the near future is very likely to happen.
Liu, John S; Ho, Mei Hsiu-Ching; Lu, Louis Y Y
The body of literature addressing the phenomenon related to social networking services (SNSs) has grown rather fast recently. Through a systematic and quantitative approach, this study identifies the recent SNS research themes, which are the issues discussed by a coherent and growing subset of this literature. A set of academic articles retrieved from the Web of Science database is used as the basis for uncovering the recent themes. We begin the analysis by constructing a citation network which is further separated into groups after applying a widely used clustering method. The resulting clusters all consist of articles coherent in citation relationships. This study suggests eight fast growing recent themes. They span widely encompassing politics, romantic relationships, public relations, journalism, and health. Among them, four focus their issues largely on Twitter, three on Facebook, and one generally on both. While discussions on traditional issues in SNSs such as personality, motivations, self-disclosure, narcissism, etc. continue to lead the pack, the proliferation of the highlighted recent themes in the near future is very likely to happen.
What does brain-based research say about how adolescents learn? The 1990s was declared as the Decade of the Brain by President Bush and Congress. With the advancement of MRIs (Magnetic Resonance Imagining) and PET (positron emission tomography) scans, it has become much easier to study live healthy brains. As a result, the concept of…
van den Beemt, Antoine; Ketelaar, Evelien; Diepstraten, Isabelle; de Laat, Maarten
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…
van den Beemt, A.A.J.; Ketelaar, E.; Diepstraten, I.; de Laat, M.
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
Özmen, Büsra; Atici, Bünyamin
In this study, it was aimed to examine the use of learning management systems supported by social networking sites in distance education and to determine the views of learners regarding these platforms. The study group of this study, which uses a qualitative research approach, consists of 15 undergraduate students who resumed their education in…
Taslidere, E.; Cohen, F. S.; Reisman, F. K.
This paper presents the use of wireless sensor networks (WSNs) in educational research as a platform for enhanced pedagogical learning. The aim here with the use of a WSN platform was to go beyond the implementation stage to the real-life application stage, i.e., linking the implementation to real-life applications, where abstract theory and…
Pourmirza, S.; Gardner, M.; Callaghan, V; Augusto, J.C.; Zhang, T.
Although extensive research has been carried out on virtual learning environments and the role of groups and communities in social networks, few studies exist which adequately cover the relationship between these two domains. In this paper, the authors demonstrate the effectiveness of integrating
This article reports on phenomenographic research which explored the qualitative differences in post-secondary students' accounts of their networked learning experiences. Data was generated using semi-structured interviews with a purposive sample of participants. Phenomenographic analysis led to a configuration of variation in students' accounts…
Son, Jiseong; Kim, Jeong-Dong; Na, Hong-Seok; Baik, Doo-Kwon
In this research, we propose a Social Learning Management System (SLMS) enabling real-time and reliable feedback for incorrect answers by learners using a social network service (SNS). The proposed system increases the accuracy of learners' assessment results by using a confidence scale and a variety of social feedback that is created and shared…
On the Creation, Utility and Sustaining of Rare Diseases Research Networks: Lessons learned from the Urea Cycle Disorders Consortium, the Japanese Urea Cycle Disorders Consortium and the European Registry and Network for Intoxication Type Metabolic Diseases.
Summar, Marshall L; Endo, Fumio; Kölker, Stefan
The past two decades has seen a rapid expansion in the scientific and public interest in rare diseases and their treatment. One consequence of this has been the formation of registries/longitudinal natural history studies for these disorders. Given the expense and effort needed to develop and maintain such programs, we describe our experience with three linked registries on the same disease group, urea cycle disorders. The Urea Cycle Disorders Consortium (UCDC) was formed in the U.S. in 2003 in response to a request for application from the National Institutes of Health (NIH); the European Registry and Network for Intoxication Type Metabolic Diseases (E-IMD) was formed in 2011 in response to a request for applications from the Directorate-General for Health and Consumers (DG SANCO) of the EU; and the Japanese Urea Cycle Disorders Consortium (JUCDC) was founded in 2012 as a sister organization to the UCDC and E-IMD. The functions of these groups are to collect natural history data, educate the professional and lay population, develop and test new treatments, and establish networks of excellence for the care for these disorders. The UCDC and JUCDC focus exclusively on urea cycle disorders while the E-IMD includes patients with urea cycle disorders and organic acidurias. More than 1400 patients have been enrolled in the three consortia, and numerous projects have been developed and joint meetings held including an international UCDC/E-IMD/JUCDC Urea Cycle meeting in Barcelona in 2013. This article summarizes some of the experiences from the three groups regarding formation, funding, and models for sustainability. Copyright © 2014 Elsevier Inc. All rights reserved.
Levinsen, Karin; Nielsen, Janni; Sørensen, Birgitte Holm
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 of...... 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....... is continuously decreasing. We teach for deep learning but are confronted by students' cost-benefit strategies when they navigate through the study programme under time pressure. To meet these challenges a Design for Learning Model has been developed. The aim is to provide a scaffold that ensures students......' acquisition of the subject matter within a time limit and at a learning quality that support their deep learning process during a subsequent period of on-line study work. In the process of moving from theory to application the model passes through three stages: 1) Conceptual modelling; 2) Orchestration, and 3...
Full Text Available The real value of innovation consists in its diffusion on industrial network. The factors which affect the diffusion of innovation on industrial network are the topology of industrial network and rules of diffusion. Industrial network is a complex network which has scale-free and small-world characters; its structure has some affection on threshold, length of path, enterprise’s status, and information share of innovation diffusion. Based on the cost and attitude to risk of technical innovation, we present the “avalanche” diffusing model of technical innovation on industrial network.
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.
Li, Yang; Zhu, Ping; Xie, Xiaoping; He, Guoguang; Aihara, Kazuyuki
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.
De Kraker, Joop; Cörvers, Ron; Ruelle, Christine; Valkering, Pieter
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
Ebrahim, Nader Ale
Researchers needs to remove many traditional obstacles to disseminate and outreach their research outputs. Academic social networking allows you to connect with other researchers in your field, share your publications, and get feedback on your non-peer-reviewed work. The academic social networking, making your work more widely discoverable and easily available. The two best known academic social networking are ResearchGate and Academia.edu. These sites offer an instant technique to monitor wh...
Coghlan, David; Coughlan, Paul
The philosophical foundations of action learning research have not received a great deal of attention. In the context of action learning postgraduate and professional programmes in universities, articulation of a philosophy of action learning research seems timely and appropriate. This article explores a philosophy of action learning research,…
This research is about the application of neural networks used in the external radiotherapy domain. The goal is to elaborate a new evaluating system for the radiation dose distributions in heterogeneous environments. The al objective of this work is to build a complete tool kit to evaluate the optimal treatment planning. My st research point is about the conception of an incremental learning algorithm. The interest of my work is to combine different optimizations specialized in the function interpolation and to propose a new algorithm allowing to change the neural network architecture during the learning phase. This algorithm allows to minimise the al size of the neural network while keeping a good accuracy. The second part of my research is to parallelize the previous incremental learning algorithm. The goal of that work is to increase the speed of the learning step as well as the size of the learned dataset needed in a clinical case. For that, our incremental learning algorithm presents an original data decomposition with overlapping, together with a fault tolerance mechanism. My last research point is about a fast and accurate algorithm computing the radiation dose deposit in any heterogeneous environment. At the present time, the existing solutions used are not optimal. The fast solution are not accurate and do not give an optimal treatment planning. On the other hand, the accurate solutions are far too slow to be used in a clinical context. Our algorithm answers to this problem by bringing rapidity and accuracy. The concept is to use a neural network adequately learned together with a mechanism taking into account the environment changes. The advantages of this algorithm is to avoid the use of a complex physical code while keeping a good accuracy and reasonable computation times. (author)
Ryberg, Thomas; Christiansen, Ellen
This paper examines the affordance of the Danish social networking site Mingler.dk for peer-to-peer learning and development. With inspiration from different theoretical frameworks, the authors argue how learning and development in such social online systems can be conceptualised and analysed....... Theoretically the paper defines development in accordance with Vygotsky's concept of the zone of proximal development, and learning in accordance with Wenger's concept of communities of practice. The authors suggest analysing the learning and development taking place on Mingler.dk by using these concepts...... supplemented by the notion of horizontal learning adopted from Engestrm and Wenger. Their analysis shows how horizontal learning happens by crossing boundaries between several sites of engagement, and how the actors' multiple membership enables the community members to draw on a vast amount of resources from...
Margolis, Alvaro; Parboosingh, John
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.
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.
Networks are frequently cited as an important knowledge mobilization strategy; however, there is little empirical research that considers how they connect research and practice. Taking a social network perspective, I explore how central office personnel find, understand and share research knowledge within a research brokering network. This mixed…
Hon, Marc; Stello, Dennis; Yu, Jie
Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary state of oscillating red giants into red giant branch stars and helium-core burning stars by recognizing visual features in their asteroseismic...... frequency spectra. We elaborate further on the deep learning method by developing an improved convolutional neural network classifier. To make our method useful for current and future space missions such as K2, TESS, and PLATO, we train classifiers that are able to classify the evolutionary states of lower...
Learn more about this successful Canada-China collaboration: "A strong competition" · Two-way learning · Declining poverty, rising inequality · One scholar's story ... Mini soap operas foster financial education and inclusion of women in Peru.
Lee, Y.C.; Doolen, G.; Chen, H.H.; Sun, G.Z.; Maxwell, T.; Lee, H.Y.
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.
Kuss, Daria J.; Griffiths, Mark D.
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 onl...
Shen, Li; Lin, Zhouchen; Huang, Qingming
Learning deeper convolutional neural networks becomes a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be gained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, that encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015...
Pfeiffer, Joseph J.
People increasingly communicate through email and social networks to maintain friendships and conduct business, as well as share online content such as pictures, videos and products. Relational machine learning (RML) utilizes a set of observed attributes and network structure to predict corresponding labels for items; for example, to predict individuals engaged in securities fraud, we can utilize phone calls and workplace information to make joint predictions over the individuals. However, in...
Zenke, Friedemann; Ganguli, Surya
A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.
Paul Alexander Howard-Jones
Full Text Available We hypothesised 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.
Howard-Jones, Paul A; Jay, Tim; Mason, Alice; Jones, Harvey
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.
A. S. Potapov
Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.
Duane, Gregory S.
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.
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...
We propose that a general learning system should have three kinds of agents corresponding to sensory, short-term, and long-term memory that implicitly will facilitate context-free and context-sensitive aspects of learning. These three agents perform mututally complementary functions that capture aspects of the human cognition system. We investigate the use of CC1 and CC4 networks for use as models of short-term and sensory memory.
Bunger, Alicia C; Lengnick-Hall, Rebecca
research directions that can disentangle the relationship between learning collaboratives and team networks.
Monteiro, Carlos Anisio; Barroso, Antonio Carlos de Oliveira, E-mail: email@example.com, E-mail: firstname.lastname@example.org [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)
In this article, we investigate the IPEN's scientific collaboration network. Based on publications registered in IPEN's technical and scientific database was extracted a set of authors that developed technical and scientific work on the 2001 to 2010 period, using coauthorship to define the relationship between authors. From the data collected, we used degree centrality indicator in conjunction with two approaches to assess the relationship between collaboration and productivity: normal count, where for each publication that the author appears is added one for the author’s productivity indicator, and fractional count which is added a fractional value according to the total number of publication's authors. We concluded that collaboration for the development of a technical and scientific work has a positive correlation with the researchers productivity, that is, the greater the collaboration greater the productivity. We presented, also, a statistical summary to reveal the total number of publications and the number of IPEN's authors by publication, the average number of IPEN's authors per publication and the average number of publications by IPEN's author, the number of IPEN's authors that not published with no other author of the IPEN and, finally, the number of active and inactive (ex. retirees) researchers of the IPEN, as well as, the number of authors who do not have employment contract with the IPEN. (author)
Monteiro, Carlos Anisio; Barroso, Antonio Carlos de Oliveira
In this article, we investigate the IPEN's scientific collaboration network. Based on publications registered in IPEN's technical and scientific database was extracted a set of authors that developed technical and scientific work on the 2001 to 2010 period, using coauthorship to define the relationship between authors. From the data collected, we used degree centrality indicator in conjunction with two approaches to assess the relationship between collaboration and productivity: normal count, where for each publication that the author appears is added one for the author’s productivity indicator, and fractional count which is added a fractional value according to the total number of publication's authors. We concluded that collaboration for the development of a technical and scientific work has a positive correlation with the researchers productivity, that is, the greater the collaboration greater the productivity. We presented, also, a statistical summary to reveal the total number of publications and the number of IPEN's authors by publication, the average number of IPEN's authors per publication and the average number of publications by IPEN's author, the number of IPEN's authors that not published with no other author of the IPEN and, finally, the number of active and inactive (ex. retirees) researchers of the IPEN, as well as, the number of authors who do not have employment contract with the IPEN. (author)
that comprises it. The main theoretical and empirical approaches that have been used to guide it to date are then briefly described, emphasizing recent debates about interpretivism and decentring. Next, it suggests that a robust and interesting future for network governance requires diversity, rather than...... adherence to a single approach. It is argued that more sophisticated approaches for examining network governance are fashioned through a synthesis of ideas and methods to create an analysis of networks as networks. This is especially the case where some formal analysis of network structure is used...
Liou, Yi-Hwa; Daly, Alan J.
Researchers, educators, and policymakers suggest the use of professional learning communities as one important approach to the improvement of teaching and learning. However, relatively little research examines the interplay of professional interactions (structural social capital) around instructional practices and key elements of professional…
Gardner, Brian; Sporea, Ioana; Grüning, André
Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.
Ladiges, Warren; Ikeno, Yuji; Niedernhofer, Laura; McIndoe, Richard A; Ciol, Marcia A; Ritchey, Jerry; Liggitt, Denny
Geropathology is the study of aging and age-related lesions and diseases in the form of whole necropsies/autopsies, surgical biopsies, histology, and molecular biomarkers. It encompasses multiple subspecialties of geriatrics, anatomic pathology, molecular pathology, clinical pathology, and gerontology. In order to increase the consistency and scope of communication in the histologic and molecular pathology assessment of tissues from preclinical and clinical aging studies, a Geropathology Research Network has been established consisting of pathologists and scientists with expertise in the comparative pathology of aging, the design of aging research studies, biostatistical methods for analysis of aging data, and bioinformatics for compiling and annotating large sets of data generated from aging studies. The network provides an environment to promote learning and exchange of scientific information and ideas for the aging research community through a series of symposia, the development of uniform ways of integrating pathology into aging studies, and the statistical analysis of pathology data. The efforts of the network are ultimately expected to lead to a refined set of sentinel biomarkers of molecular and anatomic pathology that could be incorporated into preclinical and clinical aging intervention studies to increase the relevance and productivity of these types of investigations. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: email@example.com.
Fontoura Costa, Luciano da
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
Wilson, Mark; Gochyyev, Perman; Scalise, Kathleen
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…
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.…
Monterola, Christopher; Saloma, Caesar
We show that the generalization capability of a mature thresholding neural network to process above-threshold disturbances in a noise-free environment is extended to subthreshold disturbances by ambient noise without retraining. The ability to benefit from noise is intrinsic and does not have to be learned separately. Nonlinear dependence of sensitivity with noise strength is significantly narrower than in individual threshold systems. Noise has a minimal effect on network performance for above-threshold signals. We resolve two seemingly contradictory responses of trained networks to noise--their ability to benefit from its presence and their robustness against noisy strong disturbances
Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola
Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from
Airola, Rasmus; Hager, Kristoffer
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...
... process and results of collaborative networking in a particular region and on a specific theme. They will share knowledge in the form of thematic information, best practices, policy analysis, practical methodologies and tools, online courses and seminars, coaching and mentoring, face-to-face exchanges, and workshops.
Xue, Gong; Lingling, Liu
This paper first based on the theory of cooperative learning research. It analyses the characteristics and advantages of cooperative learning under the multimedia network environment. And then take China Three Gorges University and Taiwan I-Shou University English major students for example, using questionnaires and interviews to investigate the…
Networking and Information Technology Research and Development, Executive Office of the President — In the four decades since Federal research first enabled computers to send and receive data over networks, U.S. government research and development R and D in...
Local Governance and ICT Research Network for Africa (LOG-IN Africa) is an emergent pan-African network of researchers and research institutions from nine countries. LOG-IN Africa will assess the current state and outcome of electronic local governance initiatives in Africa, focusing on how information and ...
Full Text Available This paper first based on the theory of cooperative learning research. It analyses the characteristics and advantages of cooperative learning under the multimedia network environment.And then take China Three Gorges University and Taiwan I-Shou University English major students for example, using questionnaires and interviews to investigate the students's cooperative learning in the network environment. Survey results showed that cooperative learning teaching mode has been widely used in English classrooms across the Taiwan Strait. Students think highly of cooperative learning in the multimedia-aided, and it can have a positive effect on learning; but on cooperative learning ability and the specific learning process, students still have some problems.Nowadays,cooperative learning in the network environment has various ways, but there exist certain differences in the learning styles across the Strait. Taiwan students rely more on teachers’ help and teachers feedback, while students in mainland depend mainly on networking and panel discussion. On qualitative analysis of interview is a supplement to the questionnaire and further explore its deeper causes, which provide valuable evidence for the study and learning practice. Finally, according to the comparative analysis ,the author puts forward some constructive suggestions.
Nicola, Wilten; Clopath, Claudia
Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.
Goodrich, Michael S.
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.
Canini, Marco; Crowcroft, Jon
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
De Kraker, Joop; Cörvers, Ron
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
Contribution to Prolearn Summerschool, 7-6-2006; Bled; Slovenia. Slides of the lecture and the 'user questions' we produced in the workshop. The task in the workshop was to identify learning questions that a user could have for the TENCompetence system. These questions should be a) hard to answer
Zhang, Xin; Song, Ding-Li; Yan, Shu
Digital library is a self-development needs for the modern library to meet the development requirements of the times, changing the way services and so on. digital library from the hardware, technology, management and other aspects to objective analysis of the factors of threats to digital library network security. We should face up the problems of digital library network security: digital library network hardware are "not hard", the technology of digital library is relatively lag, digital library management system is imperfect and other problems; the government should take active measures to ensure that the library funding, to enhance the level of network hardware, to upgrade LAN and prevention technology, to improve network control technology, network monitoring technology; to strengthen safety management concepts, to prefect the safety management system; and to improve the level of security management modernization for digital library.
Grunspan, Daniel Z.; Wiggins, Benjamin L.; Goodreau, Steven M.
Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA)…
Hampshire, John B., II; Vijaya Kumar, Bhagavatula
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.
Full Text Available Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.
Brewe, Eric; Kramer, Laird; Sawtelle, Vashti
Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.
Fetter, Sibren; Rajagopal, Kamakshi; Berlanga, Adriana; Sloep, Peter
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
and based on different, mutually complementary, principles of traffic analysis. The proposed approaches rely on machine learning algorithms (MLAs) for automated and resource-efficient identification of the patterns of malicious network traffic. We evaluated the proposed methods through extensive evaluations...
Huang, Yanping; Rao, Rajesh P N
Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.
Alfred, Mary V.
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.
D. Deichmann (Dirk)
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
Barnacle, Robyn; Mewburn, Inger
Scholars such as Kamler and Thompson argue that identity formation has a key role to play in doctoral learning, particularly the process of thesis writing. This article builds on these insights to address other sites in which scholarly identity is performed within doctoral candidature. Drawing on actor-network theory, the authors examine the role…
Computer based communication technologies, or what could be more conveniently called networking, are bringing new possibilities into teacher education in many different ways. As with distance education more generally they can facilitate flexibility in time and place of learning, but the range of
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
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.
The topic of networking the learning community with home-school links is addressed in four papers: "Internet Access via School: Expectations of Students and Parents" (Roy Crotty); "The School Library as Community Information Gateway" (Megan Perry); "Rural Access to the Internet" (Ken Eustace); and "NetDay '96:…
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…
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…
Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)
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.
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
Sicard, M.; D'Amico, G.; Comerón, A.; Mona, L.; Alados-Arboledas, L.; Amodeo, A.; Baars, H.; Baldasano, J. M.; Belegante, L.; Binietoglou, I.; Bravo-Aranda, J. A.; Fernández, A. J.; Fréville, P.; García-Vizcaíno, D.; Giunta, A.; Granados-Muñoz, M. J.; Guerrero-Rascado, J. L.; Hadjimitsis, D.; Haefele, A.; Hervo, M.; Iarlori, M.; Kokkalis, P.; Lange, D.; Mamouri, R. E.; Mattis, I.; Molero, F.; Montoux, N.; Muñoz, A.; Muñoz Porcar, C.; Navas-Guzmán, F.; Nicolae, D.; Nisantzi, A.; Papagiannopoulos, N.; Papayannis, A.; Pereira, S.; Preißler, J.; Pujadas, M.; Rizi, V.; Rocadenbosch, F.; Sellegri, K.; Simeonov, V.; Tsaknakis, G.; Wagner, F.; Pappalardo, G.
In the framework of ACTRIS (Aerosols, Clouds, and Trace Gases Research Infrastructure Network) summer 2012 measurement campaign (8 June-17 July 2012), EARLINET organized and performed a controlled exercise of feasibility to demonstrate its potential to perform operational, coordinated measurements and deliver products in near-real time. Eleven lidar stations participated in the exercise which started on 9 July 2012 at 06:00 UT and ended 72 h later on 12 July at 06:00 UT. For the first time, the single calculus chain (SCC) - the common calculus chain developed within EARLINET for the automatic evaluation of lidar data from raw signals up to the final products - was used. All stations sent in real-time measurements of a 1 h duration to the SCC server in a predefined netcdf file format. The pre-processing of the data was performed in real time by the SCC, while the optical processing was performed in near-real time after the exercise ended. 98 and 79 % of the files sent to SCC were successfully pre-processed and processed, respectively. Those percentages are quite large taking into account that no cloud screening was performed on the lidar data. The paper draws present and future SCC users' attention to the most critical parameters of the SCC product configuration and their possible optimal value but also to the limitations inherent to the raw data. The continuous use of SCC direct and derived products in heterogeneous conditions is used to demonstrate two potential applications of EARLINET infrastructure: the monitoring of a Saharan dust intrusion event and the evaluation of two dust transport models. The efforts made to define the measurements protocol and to configure properly the SCC pave the way for applying this protocol for specific applications such as the monitoring of special events, atmospheric modeling, climate research and calibration/validation activities of spaceborne observations.
Biggs, John B.
A common thread in contemporary research in student learning refers to the ways in which students go about learning. A theory of learning is presented that accentuates the interaction between the person and the situation. Research evidence implies a form of meta-cognition called meta-learning, the awareness of students of their own learning…
Kyvik, Svein; Reymert, Ingvild
The purpose of this paper is to give a macro-picture of collaboration in research groups and networks across all academic fields in Norwegian research universities, and to examine the relative importance of membership in groups and networks for individual publication output. To our knowledge, this is a new approach, which may provide valuable information on collaborative patterns in a particular national system, but of clear relevance to other national university systems. At the system level, conducting research in groups and networks are equally important, but there are large differences between academic fields. The research group is clearly most important in the field of medicine and health, while undertaking research in an international network is most important in the natural sciences. Membership in a research group and active participation in international networks are likely to enhance publication productivity and the quality of research.
This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.
Lu, Peng; Cong, Xiao; Bi, Fangyan; Zhou, Dongdai
With the rapid development of network technology, informal learning based on online become the main way for college students to learn a variety of subject knowledge. The favor to the SNS community of students and the characteristics of SNS itself provide a good opportunity for the informal learning of college students. This research first analyzes the related research of the informal learning and SNS, next, discusses the characteristics of informal learning and theoretical basis. Then, it proposed an informal learning model of college students based on SNS according to the support role of SNS to the informal learning of students. Finally, according to the theoretical model and the principles proposed in this study, using the Elgg and related tools which is the open source SNS program to achieve the informal learning community. This research is trying to overcome issues such as the lack of social realism, interactivity, resource transfer mode in the current network informal learning communities, so as to provide a new way of informal learning for college students.
Winarno, Sri; Muthu, Kalaiarasi Sonai; Ling, Lew Sook
This study presents students' feedback and learning impact on design and development of a multimedia learning in Direct Problem-Based Learning approach (mDPBL) for Computer Networks in Dian Nuswantoro University, Indonesia. This study examined the usefulness, contents and navigation of the multimedia learning as well as learning impacts towards…
AUTHOR|(INSPIRE)INSPIRE-00218873; The ATLAS collaboration; Toler, Wesley; Vamosi, Ralf; Bogado Garcia, Joaquin Ignacio
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...
Hayakawa, Takashi; Aoyagi, Toshio
Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.
Thompson, Jennifer Jo; Conaway, Evan; Dolan, Erin L.
Recent calls for reform in undergraduate biology education have emphasized integrating research experiences into the learning experiences of all undergraduates. Contemporary science research increasingly demands collaboration across disciplines and institutions to investigate complex research questions, providing new contexts and models for involving undergraduates in research. In this study, we examined the experiences of undergraduates participating in a multi-institution and interdisciplinary biology research network. Unlike the traditional apprenticeship model of research, in which a student participates in research under the guidance of a single faculty member, students participating in networked research have the opportunity to develop relationships with additional faculty and students working in other areas of the project, at their own and at other institutions. We examined how students in this network develop social ties and to what extent a networked research experience affords opportunities for students to develop social, cultural, and human capital. Most studies of undergraduate involvement in science research have focused on documenting student outcomes rather than elucidating how students gain access to research experiences or how elements of research participation lead to desired student outcomes. By taking a qualitative approach framed by capital theories, we have identified ways that undergraduates utilize and further develop various forms of capital important for success in science research. In our study of the first 16 months of a biology research network, we found that undergraduates drew upon a combination of human, cultural, and social capital to gain access to the network. Within their immediate research groups, students built multidimensional social ties with faculty, peers, and others, yielding social capital that can be drawn upon for information, resources, and support. They reported developing cultural capital in the form of learning to
Many people with learning disabilities are frequently excluded from active involvement in research and, as a result, along with researchers, have questioned research processes. These discussions have influenced how research is undertaken by, and with, people who have learning disabilities. Learning disability research is now increasingly framed as inclusive. This article explores the development of inclusive learning disability research by tracing its background and influences, identifying key characteristics and highlighting some of the challenges in its application. It demonstrates how inclusive research can give people with learning disabilities a voice that will help to inform practice.
The author, who has had previous experience as a nurse researcher, has been engaged in helping nurse lecturers to undertake evaluation research studies into innovations in their teaching, learning and assessment methods. In order to undertake this work successfully, it was important to move from thinking like a nurse researcher to thinking like an educational researcher and developing the role of the nursing lecturer as researcher of their teaching. This article explores the difference between evaluation and evaluation research and argues for the need to use educational research methods when undertaking evaluation research into innovations in teaching, learning and assessment. A new model for educational evaluation research is presented together with two case examples of the model in use. The model has been tested on over 30 research studies into innovations in teaching, learning and assessment over the past 8 years. Copyright © 2015 Elsevier Ltd. All rights reserved.
Koush, Yury; Meskaldji, Djalel-E; Pichon, Swann; Rey, Gwladys; Rieger, Sebastian W; Linden, David E J; Van De Ville, Dimitri; Vuilleumier, Patrik; Scharnowski, Frank
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: firstname.lastname@example.org.
Garbutt, Ruth; Tattersall, John; Dunn, Jo; Boycott-Garnett, Rachel
This is an article that talks about our research about sex and relationships for people with learning disabilities. It talks about how people with learning disabilities have been fully involved in the research. (Contains 2 footnotes.)
Murray, Darrel L.
This article reviews recent research studies and experiences relating the learning theories of Ausubel to biology instruction. Also some suggestions are made for future research on the learning of biology. (MR)
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.
Full Text Available Urban Rail Transit is an important part of the public transit, it is necessary to carry out the corresponding network function analysis. Previous studies mainly about network performance analysis of a single city rail transit, lacking of horizontal comparison between the multi-city, it is difficult to find inner unity of different Urban Rail Transit network functions. Taking into account the Urban Rail Transit network is a typical complex networks, so this paper proposes the application of complex network theory to research the homogeneity of Urban Rail Transit network performance. This paper selects rail networks of Beijing, Shanghai, Guangzhou as calculation case, gave them a complex network mapping through the L, P Space method and had a static topological analysis using complex network theory, Network characteristics in three cities were calculated and analyzed form node degree distribution and node connection preference. Finally, this paper studied the network efficiency changes of Urban Rail Transit system under different attack mode. The results showed that, although rail transport network size, model construction and construction planning of the three cities are different, but their network performance in many aspects showed high homogeneity.
Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L; Schulz, Andreas L; Ohl, Frank W; Lommerzheim, Marcel; Neumann, Heiko
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
Fowler, Zoe; Stanley, Grant; Murray, Jean; Jones, Marion; McNamara, Olwen
This article focuses on a virtual research environment (VRE) and how it facilitated the networking of teacher educators participating in an Economic and Social Research Council-funded research capacity-building project. Using the theoretical lenses of situated learning and socio-cultural approaches to literacy, participants' ways of engaging with…
Sep 12, 2012 ... The growth of networked technologies has created new opportunities for advancing human ... The I&N research awardee will ideally explore research questions centred ... such as engineering or computer/information science.
Casquero, Oskar; Ovelar, Ramón; Romo, Jesús; Benito, Manuel; Alberdi, Mikel
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…
Casey, Gail; Evans, Terry
This paper deploys notions of emergence, connections, and designs for learning to conceptualize high school students' interactions when using online social media as a learning environment. It makes links to chaos and complexity theories and to fractal patterns as it reports on a part of the first author's action research study, conducted while she…
Ponulak, Filip; Kasinski, Andrzej
The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.
Bannan, Brenda; Cook, John; Pachler, Norbert
The purpose of this paper is to begin to examine how the intersection of mobile learning and design research prompts the reconceptualization of research and design individually as well as their integration appropriate for current, complex learning environments. To fully conceptualize and reconceptualize design research in mobile learning, the…
Dubois, Cathy; Long, Lori
E-learning researchers face considerable challenges in creating meaningful and generalizable studies due to the complex nature of this dynamic training medium. Our experience in conducting workplace e-learning research led us to create this guide for planning research on e-learning. We share the unanticipated complications we encountered in our…
Hadfield, Mark; Jopling, Michael
This article explores how recent research in education has applied different aspects of "network" theory to the study of school leadership. Constructs from different network theories are often used because of their perceived potential to clarify two perennial issues in leadership research. The first is the relative importance of formal and…
Molema, A.M.; van der Zwet, Arno
In the spring of 2017, the Research Network on Regional Economic and Policy History organised its inaugural workshop in London. The network aims to stimulate research in relation to regional economic development and planning challenges, by exploring the importance of historical approaches and
Networking and Information Technology Research and Development, Executive Office of the President — This paper documents the findings of the March 12-14, 2001 Workshop on New Visions for Large-Scale Networks: Research and Applications. The workshops objectives were...
Full Text Available If one thinks about accessing and reusing qualitative data with an international and interdisciplinary perspective, this topic also contains organisational and networking tasks beyond the field of qualitative archiving in the narrow sense—some of them necessarily relying on the Internet and its tools. I had the chance to gain experiences within international networking while editing the online journal FQS and I would like to summarise some aspects, hopefully helpful also for the planned networking of qualitative archives within INQUADA. So let me first shortly introduce FQS—its origin and its current state—, and afterwards I will stress some opportunities and also some challenges, FQS and similar networking projects confront. URN: urn:nbn:de:0114-fqs0003346
Sep 7, 2016 ... For example, open data has great potential for driving innovation. ... of marginalized groups, such as women and youth, to new technologies. ... the new opportunities and challenges arising from emerging networked societies ...
Apr 25, 2016 ... These may include what are called teams, alliances, partnerships, exchanges, joint ... IDRC has always recognized the importance of networks in supporting ... A comprehensive strategic evaluation, launched in 2004, began ...
If one thinks about accessing and reusing qualitative data with an international and interdisciplinary perspective, this topic also contains organisational and networking tasks beyond the field of qualitative archiving in the narrow sense—some of them necessarily relying on the Internet and its tools. I had the chance to gain experiences within international networking while editing the online journal FQS and I would like to summarise some aspects, hopefully helpful also for the planned netwo...
Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing
Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure.
Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming
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.
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...
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
Nalepa, Gislaine M.; Ávila, Bráulio C.; Enembreck, Fabrício; Scalabrin, Edson E.
This paper presents an intelligent offer policy in a negotiation environment, in which each agent involved learns the preferences of its opponent in order to improve its own performance. Each agent must also be able to detect drifts in the opponent's preferences so as to quickly adjust itself to their new offer policy. For this purpose, two simple learning techniques were first evaluated: (i) based on instances (IB3) and (ii) based on Bayesian Networks. Additionally, as its known that in theory group learning produces better results than individual/single learning, the efficiency of IB3 and Bayesian classifier groups were also analyzed. Finally, each decision model was evaluated in moments of concept drift, being the drift gradual, moderate or abrupt. Results showed that both groups of classifiers were able to effectively detect drifts in the opponent's preferences.
Protopopescu, V.; Rao, N.S.V.
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.
Full Text Available Picasso is a free open-source (Eclipse Public License web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks. Picasso ships with occlusion maps and saliency maps, two visualizations which help reveal issues that evaluation metrics like loss and accuracy might hide: for example, learning a proxy classification task. Picasso works with the Tensorflow deep learning framework, and Keras (when the model can be loaded into the Tensorflow backend. Picasso can be used with minimal configuration by deep learning researchers and engineers alike across various neural network architectures. Adding new visualizations is simple: the user can specify their visualization code and HTML template separately from the application code.
CERN. Geneva; Monga, Inder
The Energy Sciences Network (ESnet) is a high-performance, unclassified national network built to support scientific research. Funded by the U.S. Department of Energy’s Office of Science (SC) and managed by Lawrence Berkeley National Laboratory, ESnet provides services to more than 40 DOE research sites, including the entire National Laboratory system, its supercomputing facilities, and its major scientific instruments. ESnet also connects to 140 research and commercial networks, permitting DOE-funded scientists to productively collaborate with partners around the world. ESnet Division Director (Interim) Inder Monga and ESnet Networking Engineer David Mitchell will present current ESnet projects and research activities which help support the HEP community. ESnet helps support the CERN community by providing 100Gbps trans-Atlantic network transport for the LHCONE and LHCOPN services. ESnet is also actively engaged in researching connectivity to cloud computing resources for HEP workflows a...
Wang, Kui; Fu, Xiufen
Based on the actual data of urban transport in Guangzhou, 19,150 bus stations in Guangzhou (as of 2014) are selected as nodes. Based on the theory of complex network, the network model of Guangzhou urban transport is constructed. By analyzing the degree centrality index, betweenness centrality index and closeness centrality index of nodes in the network, the level of centrality of each node in the network is studied. From a different point of view to determine the hub node of Guangzhou urban transport network, corresponding to the city's key sites and major transfer sites. The reliability of the network is determined by the stability of some key nodes (transport hub station). The research of network node centralization can provide a theoretical basis for the rational allocation of urban transport network sites and public transport system planning.
Gyanendra Prasad Joshi
Full Text Available A cognitive radio wireless sensor network is one of the candidate areas where cognitive techniques can be used for opportunistic spectrum access. Research in this area is still in its infancy, but it is progressing rapidly. The aim of this study is to classify the existing literature of this fast emerging application area of cognitive radio wireless sensor networks, highlight the key research that has already been undertaken, and indicate open problems. This paper describes the advantages of cognitive radio wireless sensor networks, the difference between ad hoc cognitive radio networks, wireless sensor networks, and cognitive radio wireless sensor networks, potential application areas of cognitive radio wireless sensor networks, challenges and research trend in cognitive radio wireless sensor networks. The sensing schemes suited for cognitive radio wireless sensor networks scenarios are discussed with an emphasis on cooperation and spectrum access methods that ensure the availability of the required QoS. Finally, this paper lists several open research challenges aimed at drawing the attention of the readers toward the important issues that need to be addressed before the vision of completely autonomous cognitive radio wireless sensor networks can be realized.
Joshi, Gyanendra Prasad; Nam, Seung Yeob; Kim, Sung Won
A cognitive radio wireless sensor network is one of the candidate areas where cognitive techniques can be used for opportunistic spectrum access. Research in this area is still in its infancy, but it is progressing rapidly. The aim of this study is to classify the existing literature of this fast emerging application area of cognitive radio wireless sensor networks, highlight the key research that has already been undertaken, and indicate open problems. This paper describes the advantages of cognitive radio wireless sensor networks, the difference between ad hoc cognitive radio networks, wireless sensor networks, and cognitive radio wireless sensor networks, potential application areas of cognitive radio wireless sensor networks, challenges and research trend in cognitive radio wireless sensor networks. The sensing schemes suited for cognitive radio wireless sensor networks scenarios are discussed with an emphasis on cooperation and spectrum access methods that ensure the availability of the required QoS. Finally, this paper lists several open research challenges aimed at drawing the attention of the readers toward the important issues that need to be addressed before the vision of completely autonomous cognitive radio wireless sensor networks can be realized.
Joshi, Gyanendra Prasad; Nam, Seung Yeob; Kim, Sung Won
A cognitive radio wireless sensor network is one of the candidate areas where cognitive techniques can be used for opportunistic spectrum access. Research in this area is still in its infancy, but it is progressing rapidly. The aim of this study is to classify the existing literature of this fast emerging application area of cognitive radio wireless sensor networks, highlight the key research that has already been undertaken, and indicate open problems. This paper describes the advantages of cognitive radio wireless sensor networks, the difference between ad hoc cognitive radio networks, wireless sensor networks, and cognitive radio wireless sensor networks, potential application areas of cognitive radio wireless sensor networks, challenges and research trend in cognitive radio wireless sensor networks. The sensing schemes suited for cognitive radio wireless sensor networks scenarios are discussed with an emphasis on cooperation and spectrum access methods that ensure the availability of the required QoS. Finally, this paper lists several open research challenges aimed at drawing the attention of the readers toward the important issues that need to be addressed before the vision of completely autonomous cognitive radio wireless sensor networks can be realized. PMID:23974152
text production, but discusses an individual dictionary for a particular function. It is shown that in a general context of learning accounting and its relevant LSP with a view to writing or translating financial reporting texts, the modern theory of dictionary functions provides a good theoretical...... 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......-lexicographic environment, i.e. what happens outside the dictionary when users write or translate texts, and relate these findings to the lexicographic environment represented by the theoretical basis and the dictionary itself. Nielsen (2006) gives a preliminary discussion of monolingual accounting dictionaries for EFL...