Timetabling manually is a complex activity that is time-consuming and stressful because a high number of high and soft constraints need to be satisfied; there is therefore the need to automate the timetabling process. Timetabling automating involves combinatorial optimization, time-based planning, realizing highly constrained problems and addressing multi-dimensional complexities. Researchers h...

Neste texto, escrevo sobre meu percurso na pós-graduação, que coincidiu com mutações minha identidade de gênero. Como não binárie, passei e passo por situações violentas me deparo frequentemente impedimentos estruturais organização dos programas pós-graduação. Meu método é o fala a partir da própria experiência, trazendo para diálogo autoras feminismos estudos gênero versam fundamentos continge...

Clustering is the division of data into groups of similar objects. In clustering, some details are disregarded in exchange for data simplification. Clustering can be viewed as a data modeling technique that provides for concise summaries of the data. Clustering is therefore related to many disciplines and plays an important role in a broad range of applications. The applications of clustering u...

We present two algorithms for exact and ap­ proximate inference in causal networks. The first algorithm, dynamic conditioning, is a re­ finement of cutset conditioning that has lin­ ear complexity on some networks for which cutset conditioning is exponential. The sec­ ond algorithm, B-conditioning, is an algo­ rithm for approximate inference that allows one to trade-off the quality of approxima...

It is well known that conditional indepen­ dence can be used to factorize a joint prob­ ability into a multiplication of conditional probabilities. This paper proposes a con­ structive definition of intercausal indepen­ dence, which can be used to further factorize a conditional probability. An inference algo­ rithm is developed, which makes use of both conditional independence and intercausal ...