Over the next 10 years, we anticipate that personal, portable, wirelessly-networked technologies will become ubiquitous in the lives of learners — indeed, in many countries, this is already a reality. We see that rea...
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Over the next 10 years, we anticipate that personal, portable, wirelessly-networked technologies will become ubiquitous in the lives of learners — indeed, in many countries, this is already a reality. We see that ready-to-hand access creates the potential for a new phase in the evolution of technology-enhanced learning (TEL), characterized by "seamless learning spaces" and marked by continuity of the learning experience across different scenarios (or environments), and emerging from the availability of one device or more per student ("one-to-one"). One-to-one TEL has the potential to "cross the chasm" from early adopters conducting isolated design studies to adoption-based research and widespread implementation, with the help of research and evaluation that gives attention to the digital divide and other potentially negative consequences of pervasive computing. We describe technology-enhanced learning and the affordances of one-to-one computing and outline a research agenda, including the risks and challenges of reaching scale. We reflect upon how this compares with prior patterns of technology innovation and diffusion. We also introduce a community, called "G1:1," that brings together leaders of major research laboratories and one-to-one TEL projects. We share a vision of global research, inviting other research groups to collaborate in ongoing activities.
Recent work on intelligent tutoring systems has used Bayesian networks to model students' acquisition of skills. In many cases, researchers have hand-coded the parameters of the networks, arguing that the conditio...
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Web users use search engine to find useful information on the Internet. However current web search engines return answer to a query independent of specific user information need. Since web users with similar web behav...
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Some recent trends in manufacturing in particular and business in general, lead to new approaches regarding the organisation and software architecture, mainly adopting distributed solutions. Such organisations imply o...
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Some recent trends in manufacturing in particular and business in general, lead to new approaches regarding the organisation and software architecture, mainly adopting distributed solutions. Such organisations imply organisational and technological evolution through agility, distribution, decentralisation, reactivity and flexibility. New organisational and technological paradigms are needed in order to reply to the modern manufacturing systems challenges. The Multi-Agent paradigm represents one of the most promising approaches to build complex, flexible, and cost-effective scheduling systems because of its distributed and dynamic nature. Modelling the Scheduling of Manufacturing Systems by means of two technologies like Meta-Heuristics and Multi-Agent Systems seems to be an interesting way to see Industrial Systems in the future. A multi-agent based model for support dynamic scheduling in manufacturing environments is proposed.
Molecular substructure mining is currently an intensively studied research area. In this paper we present an implementation of an algorithm for finding frequent substructures in a set of molecules, which may also be u...
ISBN:
(纸本)1595932100
Molecular substructure mining is currently an intensively studied research area. In this paper we present an implementation of an algorithm for finding frequent substructures in a set of molecules, which may also be used to find substructures that discriminate well between a focus and a complement group. In addition to the basic algorithm, we discuss advanced pruning techniques, demonstrating their effectiveness with experiments on two publicly available molecular data sets, and briefly mention some other extensions. Copyright 2005 ACM.
The paper presents a general architecture for a P2P data sharing facility within a multi-agent framework, where peers as autonomous high-level nodal agents cooperate with each other to solve global tasks. A node may h...
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The paper presents a general architecture for a P2P data sharing facility within a multi-agent framework, where peers as autonomous high-level nodal agents cooperate with each other to solve global tasks. A node may have several lower level local agents including local databases and partial global ontologies. In addition there are also minder agents coordinating the activities of the peers that offer the same type of service, thus providing fault-tolerance. The ability of this architecture in data and task sharing has been demonstrated by considering query processing and directory update strategies.
Fuzzy Cognitive Maps (FCMs) can represent and reason causal knowledge with stronger semantics. And the causal knowledge widely exists in knowledge Grid. To provide information services with stronger semantics in Knowl...
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knowledge-based web information extraction methods can achieve very high precision in restricted domains;they are however slow and suffer from performance degradation beyond their specific domain. We thus plan to adap...
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knowledge-based web information extraction methods can achieve very high precision in restricted domains;they are however slow and suffer from performance degradation beyond their specific domain. We thus plan to adapt an existing XML storage and query engine to act as efficient pre-processor for such methods. The critical point of the approach is the amount of information provided as XML environment of the start-up terms/elements. For this purpose, we carried out a statistical analysis of depth distribution in the WebTREC collection.
In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear we...
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In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear weights of APWNN are trained by the self-adaptive learning rate method. On the other hand an extended Kalman filter method is used to update the nonlinear parameters such as dilation parameters and translation parameters. Additionally we demonstrate the efficiency of our proposed method through a concrete example of function approximation.
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