Resumo em Inglês:

Supervised statistical learning covers important models like Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we describe the idea of using the discriminant weights given by SVM and LDA separating hyperplanes to select the most discriminant features to separate sample groups. Our method, called here as Discriminant Feature Analysis (DFA), is not restricted to any particular probability density function and the number of meaningful discriminant features is not limited to the number of groups. To evaluate the discriminant features selected, two case studies have been investigated using face images and breast lesion data sets. In both case studies, our experimental results show that the DFA approach provides an intuitive interpretation of the differences between the groups, highlighting and reconstructing the most important statistical changes between the sample groups analyzed.

Resumo em Inglês:

Data abstraction is a powerful technique to overcome state explosion in model checking. For CSP Z (a formal integration of the well-known specification languages CSP and Z), current approaches can mechanically abstract infinite domains (types) as long as they are not used in communications. This work presents a compositional and systematic approach to data abstract CSP Z specifications even when communications are based on infinite domains. Therefore, we deal with a larger class of specifications than the previous techniques. Our approach requires that the domains (used in communications) being abstracted do not affect the behaviour of the system (data independence). This criteria is used to achieve an internal partitioning of the specification in such a way that complementary techniques for abstracting data types can be applied to the components of the partition. Afterwards, the partial results can be compositionally combined to abstract the entire specification. We propose an algorithm that implements the partitioning and show the application of the entire approach to a real case study.

Resumo em Inglês:

A sandwich problem for property Π asks whether there exists a sandwich graph of a given pair of graphs which has the desired property Π. Graph sandwich problems were first defined in the context of Computational Biology as natural generalizations of recognition problems. We contribute to the study of the complexity of graph sandwich problems by considering the Helly property and complementary graph classes. We obtain a graph class defined by a finite family of minimal forbidden subgraphs for which the sandwich problem is NP-complete. A graph is clique-Helly when its family of cliques satisfies the Helly property. A graph is hereditary clique-Helly when all of its induced subgraphs are clique-Helly. The clique graph of a graph is the intersection graph of the family of its cliques. The recognition problem for the class of clique graphs was a long-standing open problem that was recently solved. We show that the sandwich problems for the graph classes: clique, clique-Helly, hereditary clique-Helly, and clique-Helly nonhereditary are all NP-complete. We propose the study of the complexity of sandwich problems for complementary graph classes as a mean to further understand the sandwich problem as a generalization of the recognition problem.

Resumo em Inglês:

Cooperative manipulation refers to the simultaneous manipulation of a virtual object by multiple users in an immersive virtual environment (VE). In this work, we present techniques for cooperative manipulation based on existing single-user techniques. We discuss methods of combining simultaneous user actions, based on the separation of degrees of freedom between two users, and the awareness tools used to provide the necessary knowledge of the partner activities during the cooperative interaction process. We also present a framework for supporting the development of cooperative manipulation techniques, which are based on rules for combining single user interaction techniques. Finally, we report an evaluation of cooperative manipulation scenarios, the results indicating that, in certain situations, cooperative manipulation is more efficient and usable than singleuser manipulation.