Uma linguagem de modelagem da interação para auxiliar a comunicação designer-usuário

The development of interactive systems involves several professionals and the integration between them normally uses common artifacts, such as models, that drive the development process. In the model-driven development approach, the interaction model is an artifact that includes the most of the aspects related to what and how the user can do while he/she interacting with the system. Furthermore, the interactive model may be used to identify usability problems at design time. Therefore, the central problematic addressed by this thesis is twofold. In the first place, the interaction modeling, in a perspective that helps the designer to explicit to developer, who will implement the interface, the aspcts related to the interaction process. In the second place, the anticipated identification of usability problems, that aims to reduce the application final costs. To achieve these goals, this work presents (i) the ALaDIM language, that aims to help the designer on the conception, representation and validation of his interactive message models; (ii) the ALaDIM editor, which was built using the EMF (Eclipse Modeling Framework) and its standardized technologies by OMG (Object Management Group); and (iii) the ALaDIM inspection method, which allows the anticipated identification of usability problems using ALaDIM models. ALaDIM language and editor were respectively specified and implemented using the OMG standards and they can be used in MDA (Model Driven Architecture) activities. Beyond that, we evaluated both ALaDIM language and editor using a CDN (Cognitive Dimensions of Notations) analysis. Finally, this work reports an experiment that validated the ALaDIM inspection method

Uma Análise de métodos de distriubuição de atributos em comitês de classificadores

The objective of the researches in artificial intelligence is to qualify the computer to execute functions that are performed by humans using knowledge and reasoning. This work was developed in the area of machine learning, that it s the study branch of artificial intelligence, being related to the project and development of algorithms and techniques capable to allow the computational learning. The objective of this work is analyzing a feature selection method for ensemble systems. The proposed method is inserted into the filter approach of feature selection method, it s using the variance and Spearman correlation to rank the feature and using the reward and punishment strategies to measure the feature importance for the identification of the classes. For each ensemble, several different configuration were used, which varied from hybrid (homogeneous) to non-hybrid (heterogeneous) structures of ensemble. They were submitted to five combining methods (voting, sum, sum weight, multiLayer Perceptron and naïve Bayes) which were applied in six distinct database (real and artificial). The classifiers applied during the experiments were k- nearest neighbor, multiLayer Perceptron, naïve Bayes and decision tree. Finally, the performance of ensemble was analyzed comparatively, using none feature selection method, using a filter approach (original) feature selection method and the proposed method. To do this comparison, a statistical test was applied, which demonstrate that there was a significant improvement in the precision of the ensembles