14 resultados para mobile social learning network

em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"


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Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

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Vehicular networks ensure that the information received from any vehicle is promptly and correctly propagated to nearby vehicles, to prevent accidents. A crucial point is how to trust the information transmitted, when the neighboring vehicles are rapidly changing and moving in and out of range. Current trust management schemes for vehicular networks establish trust by voting on the decision received by several nodes, which might not be required for practical scenarios. It might just be enough to check the validity of incoming information. Due to the ephemeral nature of vehicular networks, reputation schemes for mobile ad hoc networks (MANETs) cannot be applied to vehicular ad hoc networks (VANET). We point out several limitations of trust management schemes for VANET. In particular, we identify the problem of information cascading and oversampling, which commonly arise in social networks. Oversampling is a situation in which a node observing two or more nodes, takes into consideration both their opinions equally without knowing that they might have influenced each other in decision making. We show that simple voting for decision making, leads to oversampling and gives incorrect results. We propose an algorithm to overcome this problem in VANET. This is the first paper which discusses the concept of cascading effect and oversampling effects to ad hoc networks. © 2011 IEEE.

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Pós-graduação em Ciências Sociais - FFC

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Pós-graduação em Educação - FFC

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Introduction: Health indicators tend to be altered due to the participation of people in social networks. Objective: To find out ideas of individuals belonging to Portuguese speaking communities in Toronto, Canada, about the possibility of creating a social support network for women experiencing breast cancer. Method: Nineteen participants of the present ethnographic and critical study answered to questions, providing their opinions regarding to the social support network and its positive and negative aspects. Also, the participants suggested other possible individuals who could participate and help in the creation of such network. Discussions were transcribed, analysed and coded using qualitative software called Atlas ti 6.0. Results: The main components for the creation of the social support network were: the demystification of breast cancer and its prevention, emphasis in health education, dissemination of the need of volunteers and a direct social support to those women. The positive aspects were the participation of oldest women as social leaders and the utilization of schools and religious institutions for publicity. Negative aspects that were perceived as barriers are: the belief that breast cancer is a disease lived by women, the lack of knowledge about its cure and rehabilitation, as well as a collective sensitiveness to it. Also, about the participation of community leaders, the suggestions were: diplomats, priests and pastors, schools directors and communication entrepreneurs. Conclusion: The creation of the social support network should consider the cultural sensitiveness and the inner diversity of the consulted Portuguese speaking communities. Due to the insufficient number of Angolan participants to sustain a major analysis, a special recommendation was that Angolan social leaders and professionals should be invited to design the structure of such network according to their specific cultural traits.

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The intensification of the production system in the poultry industry and the vertical integration of the poultry agribusiness have brought profound changes in the physical and social environment of domestic fowls in comparison to their ancestors and have modified the expression of aggression and submission. The present review has covered the studies focusing on the different aspects linked to aggressiveness in the genus Gallus. The evaluated studies have shown that aggressiveness and subordination are complex behavioral expressions that involve genetic differences between breeds, strains and individuals, and differences in the cerebral development during growth, in the hormonal metabolism, in the rearing conditions of individuals, including feed restriction, density, housing type (litter or cage), influence of the opposite sex during the growth period, existence of hostile stimuli (pain and frustration), ability to recognize individuals and social learning. The utilization of fighting birds as experimental material in the study of mechanisms that have influence on the manifestation of aggressiveness in the genus Gallus might comparatively help to elucidate important biological aspects of such behavior.

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The Harris-Todaro model of the rural-urban migration process is revisited under an agent-based approach. The migration of the workers is interpreted as a process of social learning by imitation, formalized by a computational model. By simulating this model, we observe a transitional dynamics with continuous growth of the urban fraction of overall population toward an equilibrium. Such an equilibrium is characterized by stabilization of rural-urban expected wages differential (generalized Harris-Todaro equilibrium condition), urban concentration and urban unemployment. These classic results obtained originally by Harris and Todaro are emergent properties of our model.

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Pós-graduação em Enfermagem (mestrado profissional) - FMB

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Pós-graduação em Psicologia do Desenvolvimento e Aprendizagem - FC

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Enfermagem (mestrado profissional) - FMB

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Modern society is characterized by being in evolution finds, technology strides and every day becomes the setting in which we live. At school, however, we found no features of these changes, the years pass and the methodological tools remain unchanged. In this paper we analyze an object produced by the Learning Network Interactive Virtual Education - RIVED to discuss whether it covers the content and answers to the questions proposed by the National Curricular and Curricular Proposal of the State of Sao Paulo, and may be inserted in the educational environment, both to perform a qualitative analysis, focusing on information retrieval. The results showed that the Learning Objects: Abundance, has most of the proposals relating to the subject of study, prepared by the NCP and the Proposed Structure for the State of Sao Paulo, so it is valid for teachers to use this tool trying that with a new approach, we can give students a better understanding of chemical concepts

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Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.