A Bayesian network is an appropriate tool to deal with the uncertainty that is typical of real-life applications. Bayesian network arcs represent statistical dependence between dif...
We study the extension of applicability of system-level testing techniques to the construction of a consistent model of (legacy) systems under test, which are seen as black boxes....
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of the...
In this paper, we study face hallucination or synthesizing a high-resolution face image from a low-resolution input, with the help of a large collection of other highresolution fa...
This paper deals with a class of Prolog programs, called context-free term transformations (CFT). We present a polynomial time algorithm to identify a subclass of CFT, whose progr...