A inclusão de pessoas com deficiência na rede regular de educação profissional

Pós-graduação em Educação - FFC

Necropsia cosmética em aves

Necropsy is a post-mortem criterious exam that helps in clinical diagnosis and consists in external and internal analysis of body cavities and organs of a corpse in order to determine the cause of death or to verify the extension and the nature of lesions caused by diseases. Considering the bonds between owners and their pets many veterinarians seems to visualize necropsy as a difficult task. This fact has gained great importance in avian Veterinary Medicine since bird owners have both love and economic bonds. Necropsy in often neglected in Veterinary Medicine although being a very important practice for the professional learning and also for reaching a reliable diagnosis of certain diseases. For this reason, this article aims to describe the procedure for performing avian cosmetic necropsy, which keeps the external characteristics of animals and increases the acceptance of owners by not disfiguring the corpse.

Análise de adequações curriculares no ensino fundamental: subsídios para programas de pesquisa colaborativa na formação de professores

Pós-graduação em Psicologia do Desenvolvimento e Aprendizagem - FC

Educação para o trabalho: a Escola Profissional de Franca (1924-1970)

Pós-graduação em História - FCHS

Alfabetização científica nos anos iniciais: necessidades formativas e aprendizagens profissionais da docência no contexto dos sistemas de avaliação em larga escala

Pós-graduação em Educação para a Ciência - FC

Ensino de Ciências e os sistemas de avaliação em larga escala na educação básica: processos formativos e aprendizagens profissionais da docência

Pós-graduação em Educação para a Ciência - FC

Ser professora iniciante na educação infantil: aprendizagens e desenvolvimento profissional em contexto de enfrentamentos e superações de dilemas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A formação de professores de matemática no contexto da resolução de problemas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estrutura de conteúdos educativos para TV Digital: um modelo de referência

This paper proposes a model of educative content structuring for interactive digital television programs. Its intent was to provide references for collaborative production processes and content organization in learning networks associated with university and educative television broadcasting services. The model defines terms, actors, events, environments, as well as content categories, classes and attributes, indicating criteria for their synchronic or asynchronic association in a dynamic television schedule. The model is presented in both descriptive and visual formats, with the use of conceptual maps. The results indicate that interactive use of digital television in education requires systematic content models covering communitarian participation in both media production and distribution processes, in order to enhance learning instruments beyond vertical, hierarchical and centralized communication sustained by traditional broadcast channels.

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

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

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Aquatic weed automatic classification using machine learning techniques

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

Comparative study between RBF and radial-PPS neural networks

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.