884 resultados para CAVE, Virtual Reality Environment, 3D, Stereoscopic simulator
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The question that leads this article is What is this virtual space in the on-line mathematics education process? We focus on the question of the real and virtual as issues taken as components of cyberspace. We investigate these notions in the history of philosophy, looking to Granger to find their meaning, to enable us to understand them and fit them into the sphere of Mathematics Education. This theoretical-philosophical article, then, claims that the virtuality of cyberspace is supported by the computer screen, built by the unification of the sciences (mathematics), technology and its applications. Software and the actions taken by Internet users update the capability of these programs in a variety of characteristics and possibilities such as space-time flow interconnections as well as during the mathematics education process.
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This paper presents the work in progress of an on-demand software deployment system based on application virtualization concepts which eliminates the need of software installation and configuration on each computer. Some mechanisms were created, such as mapping of utilization of resources by the application to improve the software distribution and startup; a virtualization middleware which give all resources needed for the software execution; an asynchronous P2P transport used to optimizing distribution on the network; and off-line support where the user can execute the application even when the server is not available or when is out of the network. © Springer-Verlag Berlin Heidelberg 2010.
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This text presents some reflections about the educational and virtual processes of mathematics teachers drawing on research on the theme. Thus, in this text, considering our experiences with the development of online courses, we discuss issues such as collaboration in virtual environments, which contributes to more effective results in collaborative learning and reduces the potential of isolation of student/teacher that can occur in virtual environments. Through collaborative learning in a virtual community, students/teachers have the opportunity to practice and think in-depth about their learning experiences by sharing new ideas with the group and receiving critical and constructive feedback. Moreover, the virtuality formed by the environment of online courses supports educational spaces for teachers who teach mathematics. Thus, collaboration emerges as an essential element for construction of meanings and for sharing experiences on the practice of teaching.
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Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.
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Includes bibliography
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Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Educação para a Ciência - FC
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Educação - FCT
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)