Conferences related to Online Learning Systems

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2014 IEEE Frontiers in Education Conference (FIE)

The Frontiers in Education (FIE)Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2014 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2013 IEEE Frontiers in Education Conference (FIE)

    The Frontiers in Education (FIE)Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2013 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2012 IEEE Frontiers in Education Conference (FIE)

    The Frontiers in Education (FIE)Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2012 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2011 Frontiers in Education Conference (FIE)

    The Frontiers in Education (FIE) Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2011 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2010 IEEE Frontiers in Education Conference (FIE)

    (FIE) Conference is a major international conference devoted to improvements in computer science, engineering, and technology (CSET) education. FIE 2008 continues a long tradition of disseminating educational research results and innovative practices in CSET education. It is an ideal forum for sharing ideas, learning about developments in CSET education, and interacting with colleagues.

  • 2009 IEEE Frontiers in Education Conference (FIE)

    FIE is a major international conference devoted to improvements in computer science, engineering and technology (CSET) education. FIE continues a loong tradition of disseminating educational research results and innovative practices in CSET education. It is an ideal forum for sharing ideas, learning about developments in CSET education, and interacting with colleagues.

  • 2008 IEEE Frontiers in Education Conference (FIE)

  • 2007 IEEE Frontiers in Education Conference (FIE)

    Globalization has dramatically changed engineering. Global engineering teams design products for global markets. Knowledge has no borders in a world where information flow is digitalized and sent worldwide in seconds. A core requirement of engineering globalization is an understanding of how the different cultures of the global marketplace shape product development, mult-national engineering teams, and consumer expectations. Engineering education must address this issue.

  • 2006 IEEE Frontiers in Education Conference (FIE)

  • 2005 IEEE Frontiers in Education Conference (FIE)


2013 8th International Forum on Strategic Technology (IFOST)

Strategic technologies including advanced materials, applied engineering sciences, information technologies, mechanical engineering, and so on.

  • 2012 7th International Forum on Strategic Technology (IFOST)

    International Forum on Strategic Technology is an annual academic conference and technical forum for researchers, engineers, industry representatives and policy planners.

  • 2011 6th International Forum on Strategic Technology (IFOST)

    1. Advanced Materials / Nano Technology 2. Renewable Energy / Smart Grid 3. Information Technology 4. E-vehicle / Green Car 5. Mechatronics 6. Others

  • 2010 International Forum on Strategic Technology (IFOST)

    a. e-Vehicle / Green Car b. New Materials c. Renewable Energy d. Smart Grid e. Bio-/Chemio-/Nano- Technology f. Other Strategic Technology

  • 2008 3rd International Forum on Strategic Technology (IFOST)

    New materials and technologies Nanotechnologies Information technologies Mechatronics and Automation Power engineering and resource-saving Environmental protection and conservancy

  • 2007 International Forum on Strategic Technology (IFOST)

    The International Forum organized under the main topic Power Engineering - Ecology will be carried out through the following sections: 1. Power Engineering 2. Mining Production and Metallurgy 3. Civil Engineering and Geo-Engineering 4. Information and Communication Technology 5. Ecosphere and Industrial Ecology


2013 IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)

GEFS2013 will provide an opportunity to meet researchers working on the topic, make new contacts and exchange ideas. The GEFS series of workshops are an important part of the activities of the Evolutionary Fuzzy Systems Task Force of the Fuzzy System Technical Committee (IEEE Computational Intelligence Society).

  • 2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)

    Genetic and evolutionary fuzzy systems meld the approximate reasoning method of fuzzy systems with the adaptation capabilities of evolutionary algorithms. The objective of GEFS2010 is to facilitate the promotion of novel problems, research, results, and future directions in the latter growing area.

  • 2008 3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS)

    One of the most prominent approaches to hybridize fuzzy systems with learning and adaptation methods has resulted in the emergence of genetic and evolving fuzzy systems, which combine the approximate reasoning method of fuzzy systems with the adaptation capabilities of evolutionary algorithms. Fuzzy systems have demonstrated the ability to formalize in a computationally efficient manner the approximate reasoning typical of humans.


2011 11th International Conference on Hybrid Intelligent Systems (HIS 2011)

The objectives of HIS 2011 are: to increase the awareness of the research community of the broad spectrum of hybrid techniques, to bring together AI researchers from around the world to present their cutting-edge results, to discuss the current trends in HIS research, to develop a collective vision of future opportunities, to establish international collaborative opportunities, and as a result to advance the state of the art of the field.

  • 2010 10th International Conference on Hybrid Intelligent Systems (HIS 2010)

    Hybridization of intelligent systems is a promising research field of computational intelligence focusing on synergistic combinations of multiple approaches to develop the next generation of intelligent systems. A fundamental stimulus to the investigations of Hybrid Intelligent Systems (HIS) is the awareness that combined approaches will be necessary if the remaining tough problems in Artificial Intelligence are to be solved. Join the feast of Hybrid Computational Intelligence researchers! Neural c

  • 2009 9th International Conference on Hybrid Intelligent Systems (HIS 2009)

    Hybridization of intelligent systems is a promising research field of computational intelligence focusing on synergistic combinations of multiple approaches to develop the next generation of intelligent systems. The objectives of HIS 2009 are to bring world-wide AI researchers together to present their cutting-edge results, to develop a collective vision of future opportunities, to establish international collaborative opportunities, and as a result to advance the state of the art in AI fields.

  • 2008 8th International Conference on Hybrid Intelligent Systems (HIS 2008)

    Hybridization provides the leverage to deal with complexity and performance challenges imposed on intelligent systems and their physical embodiment. The conference goals are to provide a forum for advanced methods from neural computing, machine learning, fuzzy logic, evolutionary algorithms and related techniques and their combination to efficient systems.

  • 2007 7th International Conference on Hybrid Intelligent Systems (HIS 2007)

    Hybridization provides the leverage to deal with complexity and performance challenges imposed on intelligent systems and their physical embodiment. The conference goals are to provide a forum for advanced methods from neural computing, machine learning, fuzzy logic, evolutionary algorithms, and related techniques and their combination to efficient systems. In particular, approaches to intelligent system design as well as dynamic aspects of system evolution is a major focus of the conference.



Periodicals related to Online Learning Systems

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Education, IEEE Transactions on

Educational methods, technology, and programs; history of technology; impact of evolving research on education.


Intelligent Transportation Systems, IEEE Transactions on

The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical ...


Learning Technologies, IEEE Transactions on

The IEEE Transactions on Learning Technologies publishes archival research papers and critical survey papers on technology advances in online learning systems; intelligent tutors; educational software applications and games; simulation systems for education and training; collaborative learning tools, devices and interfaces for learning; interactive techniques for learning; tools for formative and summative assessment; ontologies for learning systems; standards and web services ...


Systems Journal, IEEE

This publication provides a systems-level, focused forum for application-oriented manuscripts that address complex systems and system-of-systems of national and global significance.


Systems, Man, and Cybernetics, Part B, IEEE Transactions on

The scope of the IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or between machines, humans, and organizations. The scope of Part B includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, ...



Most published Xplore authors for Online Learning Systems

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Xplore Articles related to Online Learning Systems

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Work-in-progress: Integrating a remote laboratory system in an online learning environment

Leonardo Favario; Enrico Masala 2016 IEEE Global Engineering Education Conference (EDUCON), 2016

The possibility to include practical training sessions in online learning systems is important to increase the value of engineering courses. This paper presents a work in progress focusing on integrating a remote laboratory system in an existing online learning environment so that, through the use of a simple web browser, students can develop both theoretical and practical skills at the ...


Instantaneous anomaly detection in online learning fuzzy systems

Werner Brockmann; Nils Rosemann Genetic and Evolving Systems, 2008. GEFS 2008. 3rd International Workshop on, 2008

In the field of self-optimizing automation systems, incremental local learning is an important technique. But especially in case of closed loop coupling, learnt anomalies may have a negative influence on the entire future of the evolving system. In the worst case, this may result in unstable or chaotic system behavior. Thus it is crucial to detect anomalies in online learning ...


A P2P Replication-Aware Approach for Content Distribution in E-Learning Systems

Á ngel Navarro-Estepa; Fatos Xhafa; Santi Caball&#x00E9 Complex, Intelligent and Software Intensive Systems (CISIS), 2012 Sixth International Conference on, 2012

Among different emerging disruptive technologies, P2P computing has shown its usefulness in designing decentralized and scalable online learning systems. One important feature of P2P systems explored in this context is that of direct peer-to-peer communication. It has been shown in several recent research works that the direct communication between peers increases the interaction among peers and eventually the social and ...


Supporting Scenario-Based Online Learning with P2P Group-Based Systems

Fatos Xhafa; Leonard Barolli; Santi Caballe; Raul Fernandez Network-Based Information Systems (NBiS), 2010 13th International Conference on, 2010

Recently there has been an increasing interest in exploring P2P technologies for developing groupware tools to support online learning activity. Among the many features that make P2P technology an important alternative to develop online learning systems is its fully decentralized nature as well as the possibility to customize the learning environments to the needs of groups of learners. In fact, ...


Extending TAM for online learning systems: An intrinsic motivation perspective

Sheng Zhang; Jue Zhao; Weiwei Tan Tsinghua Science and Technology, 2008

To get a better understanding of user behavior towards online learning systems, the technology acceptance model (TAM) was extended to include an intrinsic motivational factor. An online survey posted on a campus BBS was conducted to collect research data with a total of 121 usable responses. The results support the motivational model and show that the explained variance of online ...


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Educational Resources on Online Learning Systems

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eLearning

Work-in-progress: Integrating a remote laboratory system in an online learning environment

Leonardo Favario; Enrico Masala 2016 IEEE Global Engineering Education Conference (EDUCON), 2016

The possibility to include practical training sessions in online learning systems is important to increase the value of engineering courses. This paper presents a work in progress focusing on integrating a remote laboratory system in an existing online learning environment so that, through the use of a simple web browser, students can develop both theoretical and practical skills at the ...


Instantaneous anomaly detection in online learning fuzzy systems

Werner Brockmann; Nils Rosemann Genetic and Evolving Systems, 2008. GEFS 2008. 3rd International Workshop on, 2008

In the field of self-optimizing automation systems, incremental local learning is an important technique. But especially in case of closed loop coupling, learnt anomalies may have a negative influence on the entire future of the evolving system. In the worst case, this may result in unstable or chaotic system behavior. Thus it is crucial to detect anomalies in online learning ...


A P2P Replication-Aware Approach for Content Distribution in E-Learning Systems

Á ngel Navarro-Estepa; Fatos Xhafa; Santi Caball&#x00E9 Complex, Intelligent and Software Intensive Systems (CISIS), 2012 Sixth International Conference on, 2012

Among different emerging disruptive technologies, P2P computing has shown its usefulness in designing decentralized and scalable online learning systems. One important feature of P2P systems explored in this context is that of direct peer-to-peer communication. It has been shown in several recent research works that the direct communication between peers increases the interaction among peers and eventually the social and ...


Supporting Scenario-Based Online Learning with P2P Group-Based Systems

Fatos Xhafa; Leonard Barolli; Santi Caballe; Raul Fernandez Network-Based Information Systems (NBiS), 2010 13th International Conference on, 2010

Recently there has been an increasing interest in exploring P2P technologies for developing groupware tools to support online learning activity. Among the many features that make P2P technology an important alternative to develop online learning systems is its fully decentralized nature as well as the possibility to customize the learning environments to the needs of groups of learners. In fact, ...


Extending TAM for online learning systems: An intrinsic motivation perspective

Sheng Zhang; Jue Zhao; Weiwei Tan Tsinghua Science and Technology, 2008

To get a better understanding of user behavior towards online learning systems, the technology acceptance model (TAM) was extended to include an intrinsic motivational factor. An online survey posted on a campus BBS was conducted to collect research data with a total of 121 usable responses. The results support the motivational model and show that the explained variance of online ...


More eLearning Resources

IEEE.tv Videos

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IEEE-USA E-Books

  • Analysis

    Complex communicating computer systems -- computers connected by data networks and in constant communication with their environments -- do not always behave as expected. This book introduces behavioral modeling, a rigorous approach to behavioral specification and verification of concurrent and distributed systems. It is among the very few techniques capable of modeling systems interaction at a level of abstraction sufficient for the interaction to be understood and analyzed. Offering both a mathematically grounded theory and real-world applications, the book is suitable for classroom use and as a reference for system architects. The book covers the foundation of behavioral modeling using process algebra, transition systems, abstract data types, and modal logics. Exercises and examples augment the theoretical discussion. The book introduces a modeling language, mCRL2, that enables concise descriptions of even the most intricate distributed algorithms and protocols. Using behavioral xioms and such proof methods as confluence, cones, and foci, readers will learn how to prove such algorithms equal to their specifications. Specifications in mCRL2 can be simulated, visualized, or verified against their requirements. An extensive mCRL2 toolset for mechanically verifying the requirements is freely available online; this toolset has been successfully used to design and analyze industrial software that ranges from healthcare applications to particle accelerators at CERN. Appendixes offer material on equations and notation as well as exercise solutions.

  • Index

    Complex communicating computer systems -- computers connected by data networks and in constant communication with their environments -- do not always behave as expected. This book introduces behavioral modeling, a rigorous approach to behavioral specification and verification of concurrent and distributed systems. It is among the very few techniques capable of modeling systems interaction at a level of abstraction sufficient for the interaction to be understood and analyzed. Offering both a mathematically grounded theory and real-world applications, the book is suitable for classroom use and as a reference for system architects. The book covers the foundation of behavioral modeling using process algebra, transition systems, abstract data types, and modal logics. Exercises and examples augment the theoretical discussion. The book introduces a modeling language, mCRL2, that enables concise descriptions of even the most intricate distributed algorithms and protocols. Using behavioral xioms and such proof methods as confluence, cones, and foci, readers will learn how to prove such algorithms equal to their specifications. Specifications in mCRL2 can be simulated, visualized, or verified against their requirements. An extensive mCRL2 toolset for mechanically verifying the requirements is freely available online; this toolset has been successfully used to design and analyze industrial software that ranges from healthcare applications to particle accelerators at CERN. Appendixes offer material on equations and notation as well as exercise solutions.

  • No-regret Algorithms for Online Convex Programs

    Online convex programming has recently emerged as a powerful primitive for designing machine learning algorithms. For example, OCP can be used for learning a linear classifier, dynamically rebalancing a binary search tree, finding the shortest path in a graph with unknown edge lengths, solving a structured classification problem, or finding a good strategy in an extensive- form game. Several researchers have designed no-regret algorithms for OCP. But, compared to algorithms for special cases of OCP such as learning from expert advice, these algorithms are not very numerous or flexible. In learning from expert advice, one tool which has proved particularly valuable is the correspondence between no-regret algorithms and convex potential functions: by reasoning about these potential functions, researchers have designed algorithms with a wide variety of useful guarantees such as good performance when the target hypothesis is sparse. Until now, there has been no such recipe for the more general OCP problem, and therefore no ability to tune OCP algorithms to take advantage of properties of the problem or data. In this paper we derive a new class of no-regret learning algorithms for OCP. These Lagrangian Hedging algorithms are based on a general class of potential functions, and are a direct generalization of known learning rules like weighted majority and external-regret matching. In addition to proving regret bounds, we demonstrate our algorithms learning to play one-card poker.

  • Convex Repeated Games and Fenchel Duality

    We describe an algorithmic framework for an abstract game which we term a convex repeated game. We show that various online learning and boosting algorithms can be all derived as special cases of our algorithmic framework. This unified view explains the properties of existing algorithms and also enables us to derive several new interesting algorithms. Our algorithmic framework stems from a connection that we build between the notions of regret in game theory and weak duality in convex optimization.

  • Multilayer Neural Networks and Backpropagation

    A computationally effective method for training the multilayer perceptrons is the backpropagation algorithm, which is regarded as a landmark in the development of neural network. This chapter presents two different learning methods, batch learning and online learning, on the basis of how the supervised learning of the multilayer perceptron is actually performed. The essence of backpropagation learning is to encode an input-output mapping into the synaptic weights and thresholds of a multilayer perceptron. It is hoped that the network becomes well trained so that it learns enough about the past to generalize to the future. The chapter concludes with cross-validation and generalization. Cross-validation is appealing particularly when people have to design a large neural network with good generalization as the goal in different ways. Generalization is assumed that the test data are drawn from the same population used to generate the training data.

  • Efficient Methods for Privacy Preserving Face Detection

    Bob offers a face-detection web service where clients can submit their images for analysis. Alice would very much like to use the service, but is reluctant to reveal the content of her images to Bob. Bob, for his part, is reluctant to release his face detector, as he spent a lot of time, energy and money constructing it. Secure Multi- Party computations use cryptographic tools to solve this problem without leaking any information. Unfortunately, these methods are slow to compute and we introduce a couple of machine learning techniques that allow the parties to solve the problem while leaking a controlled amount of information. The first method is an information- bottleneck variant of AdaBoost that lets Bob find a subset of features that are enough for classifying an image patch, but not enough to actually reconstruct it. The second machine learning technique is active learning that allows Alice to construct an online classifier, based on a small number of calls to Bob's face detector. She can then use her online classifier as a fast rejector before using a cryptographically secure classifier on the remaining image patches.

  • Artificial Intelligence, Document Processing, and HyperText

    Text, ConText, and HyperText presents recent developments in three related and important areas of technical communication: the design of effective documentation; the impact of new technology and research on technical writing; and the training and management of technical writers.The contributors are all authorities drawn from universities and industry who are active in defining and analyzing the role of computing in technical documentation and the role of documentation in the development of computing technology. This first synthesis of their diverse but related research provides a unique conceptualization of the field of computers and writing and documentation.The book first examines techniques for writing online documentation and the value of usability testing. It presents new research into the impact of human factors in screen design and designing online help, and looks at the impact of desktop publishing on documentation, and at visual literacy and graphic design.Artificial intelligence and documentation processing are then addressed with discussion of data acquisition, automated formatting in expert systems, and document databases; the uses of HyperText in documentation; and the future of technical writing in this new environment.Text, ConText, and HyperText concludes by examining the training and management of documentation groups: how they "learn to write" in industry, management of large-scale documentation projects and their effect on product development; and the "two cultures" of engineering and documentation.Edward Barrett is a Lecturer in the Writing Program at MIT. Text, ConText, and HyperText is included in the Information Systems series, edited by Michael Lesk.

  • References

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • Index

    The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. _ Introduction to Machine Learnin_g is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of _Introduction to Machin Learning_ reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

  • Online Stochastic Scheduling

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.



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