Era um sonho desde criança : a representação social da docência para os professores do município de Queimadas-PB

The objective of this work was to access and understand the teaching social representation (MOSCOVICI, 2005) for the teachers of the children education and fundamental education at Queimadas city, Paraíba. We assume that one representation that allows the teacher to name its profession and to act on it, is a derivative of regularities that are expressed by means of a habitus (BOURDIEU, 1983a), generative matrix of perception and action. This teaching habitus is originated from the experiences and the trajectories of social and professional life of the group. Therefore, from some variables, we tried to access the profile of the group of teachers studied and to get closer of their life style to understand their profession choice and the teaching social representation for this group. In this research, it was used four data sources: a) the questionnaires of characterization; b) the questionnaires of practices and meanings; c) the experience reports and; d) the interviews in depth. The analysis of the data collected was done by means of the simple statistics (frequency), the intersection of variables through cross tables and, the thematic analysis of the contents. The results show that there is a lightly homogeneous group in terms of its social origin and its life style, moreover, they conduct to an overlap between this origin/style and the professional choice. On the other hand, the teacher representation is multi-dimensional such that, all dimensions intercept and articulate with each other to provide a concise teacher representation. They are four dimensions: love and care, help and donation, teaching and learning and, sacrifice and hope. The elements of the teacher representation are substantiated in the schemes of perception and appreciation of the group, in the regularities and life experiences in the context of religion, family, gender and profession. In these regularities we find the elements that comprise the teaching habitus which drives perception and action, representation and daily practice of these teachers. The teaching social representation is still perceived as a threshold for the professional identity of the group of teachers considered. We also observed that there are signs of changes in the practices used by these professionals since they graduate from the course of pedagogy. However, it is not possible to say that these changes are isolated or they lead to a transformation in the teaching habitus or the teaching social representation

Aplicação de técnicas de aprendizado de máquina no reconhecimento de classes estruturais de proteínas

Nowadays, classifying proteins in structural classes, which concerns the inference of patterns in their 3D conformation, is one of the most important open problems in Molecular Biology. The main reason for this is that the function of a protein is intrinsically related to its spatial conformation. However, such conformations are very difficult to be obtained experimentally in laboratory. Thus, this problem has drawn the attention of many researchers in Bioinformatics. Considering the great difference between the number of protein sequences already known and the number of three-dimensional structures determined experimentally, the demand of automated techniques for structural classification of proteins is very high. In this context, computational tools, especially Machine Learning (ML) techniques, have become essential to deal with this problem. In this work, ML techniques are used in the recognition of protein structural classes: Decision Trees, k-Nearest Neighbor, Naive Bayes, Support Vector Machine and Neural Networks. These methods have been chosen because they represent different paradigms of learning and have been widely used in the Bioinfornmatics literature. Aiming to obtain an improvment in the performance of these techniques (individual classifiers), homogeneous (Bagging and Boosting) and heterogeneous (Voting, Stacking and StackingC) multiclassification systems are used. Moreover, since the protein database used in this work presents the problem of imbalanced classes, artificial techniques for class balance (Undersampling Random, Tomek Links, CNN, NCL and OSS) are used to minimize such a problem. In order to evaluate the ML methods, a cross-validation procedure is applied, where the accuracy of the classifiers is measured using the mean of classification error rate, on independent test sets. These means are compared, two by two, by the hypothesis test aiming to evaluate if there is, statistically, a significant difference between them. With respect to the results obtained with the individual classifiers, Support Vector Machine presented the best accuracy. In terms of the multi-classification systems (homogeneous and heterogeneous), they showed, in general, a superior or similar performance when compared to the one achieved by the individual classifiers used - especially Boosting with Decision Tree and the StackingC with Linear Regression as meta classifier. The Voting method, despite of its simplicity, has shown to be adequate for solving the problem presented in this work. The techniques for class balance, on the other hand, have not produced a significant improvement in the global classification error. Nevertheless, the use of such techniques did improve the classification error for the minority class. In this context, the NCL technique has shown to be more appropriated