In CP literature combinatorial design problems such as sport scheduling, Steiner systems, error-correcting codes and more, are typically solved using Finite Domain (FD) models desp...
Causal Probabilistic Networks (CPNs), (a.k.a. Bayesian Networks, or Belief Networks) are well-established representations in biomedical applications such as decision support system...
Constantin F. Aliferis, Ioannis Tsamardinos, Alexa...
Defect density and defect prediction are essential for efficient resource allocation in software evolution. In an empirical study we applied data mining techniques for value seri...
—Preemptive scheduling often generates a significant runtime overhead that may increase task worst-case execution times up to 40%, with respect to a fully non preemptive executi...
Event trees are a popular technique for modelling accidents in system safety analyses. Bayesian networks are a probabilistic modelling technique representing influences between unc...