Abstract. Network Intrusion Detection Systems (NIDS) aim at preventing network attacks and unauthorised remote use of computers. More accurately, depending on the kind of attack it...
Bayesian networks are indispensable for determining the probability of events which are influenced by various components. Bayesian probabilities encode degrees of belief about ce...
Macro programming a distributed system, such as a sensor network, is the ability to specify application tasks at a global level while relying on compiler-like software to translat...
Genetic linkage analysis is a challenging application which requires Bayesian networks consisting of thousands of vertices. Consequently, computing the likelihood of data, which i...
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Functional verification is widely acknowledged as the bottleneck in the hardware design cycle. This paper addresses one of the main challenges of simulation based verification (or...
It was recently proposed the use of Bayesian networks for object tracking. Bayesian networks allow to model the interaction among detected trajectories, in order to obtain a relia...
Arnaldo J. Abrantes, Jorge S. Marques, Pedro Mende...
Probabilistic graphical models such as Bayesian Networks have been increasingly applied to many computer vision problems. Accuracy of inferences in such models depends on the quali...
The ambiguity inherent in a localized analysis of events from video can be resolved by exploiting constraints between events and examining only feasible global explanations. We sho...
Dima Damen (University of Leeds), David Hogg (Univ...