Usually security protects the computer owner from attack, but sometimes the computer owner is the potential "enemy". For example, in digital rights management the owner of a computer platform is motivated to break its security to make illegal copies of protected digital content. In mobile agent applications sensitive electronic transactions are performed at untrusted hosts.

The process of verifying the correctness of a reactive system consists of establishing that the system behaves as expected. This is usually a crucial task, with key economic significance. Most often, and for hardware systems, the verification of the correct behavior of the system is ensured by model checking. The objective of this talk is to provide a brief tutorial on the verification of reactive systems by model checking.

It has recently been showed that feature selection in supervised learning can be embedded in the learning algorithm by using sparsity-promoting priors/penalties that encourage the coefficient estimates to be either significantly large or exactly zero.

Entropy measures of DNA sequences estimate their randomness or, inversely, their repeatability. L-block Shannon discrete entropy accounts for the empirical distribution of all length-L words and has convergence problems for finite sequences. A new entropy measure that extends Shannon�s formalism is proposed. Rényi�s quadratic entropy calculated with Parzen window density estimation method applied to Chaos Game Representation/Universal Sequence Map of DNA sequences constitute a novel technique to evaluate sequence global randomness without some of the former method drawbacks.

A predição da estrutura terciária das proteínas é um dos problemas centrais da bioquímica. Para além do interesse académico na resolução deste problema, existe um grande interesse por parte da indústria, nomeadamente por parte da insdústria farmacêutica. A principal abordagem a este problema passa por determinar o minímo global de uma dada função de energia. São vários os modelos matemáticos e os métodos propostos. No entanto, verifica-se que este problema de optimização é de facto NP-hard. Os modelos simplificados conduzem por sua vez a problemas desta mesma classe.

Classical algorithms for computing the similarity between two sequences use a dynamic programming matrix, and compares two strings of size n in O(n2) time. We address the challenge of computing the similarity of two strings in sub-quadratic time, for metrics which use a scoring matrix of unrestricted weights. Our algorithm applies to both local and global similarity computations. The speed-up is achieved by using the Lempel-Ziv parsing of both strings. It leads to an O(h n2 / logn) algorithm for an input on a constant-size alphabet, where h < 1 is the entropy of strings.

Genetic Programming (GP) solves complex problems by evolving populations of computer programs, using Darwinian evolution and Mendelian genetics as inspiration. Bloat is an excess of code growth caused by the genetic operators in search of better solutions, without the corresponding improvement in fitness. After a brief overview of GP, we will focus on the causes and possible solutions for bloat, including a recently published technique by the authors.

Um dos desafios mais interessantes que a Engenharia Informática enfrenta hoje em dia pode ser encarado como a resolução de uma questão básica: como extrair conhecimento de dados armazenados?

A completude dos algoritmos de descoberta de regras de associação tem como consequência a geração de um elevado número de regras. Para além da utilização de medidas de interesse para a identificação de regras potencialmente úteis, podemos recorrer a ambientes de exploração de conjuntos de regras. Nesta apresentação, para além de uma introdução geral a esta problemática descreve-se o ambiente PEAR (post processing environment for association rules), assim como possíveis extensões.

Different clustering algorithms will, in general, produce different data partitions when applied to the same data set. On this talk I address the problem of robust clustering as a problem of combining data partitions (forming a clustering ensemble) produced by multiple clusterings. I propose and analyze a voting mechanism on pair wise associations for combiningdata partitions, based on the concept of evidence accumulation. The evidence accumulation method is applied to the combination of "weak" clusterers, using the K-means to produce clustering ensembles.