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| Web service composition (WSC) offers a range of solutions for rapid creation of complex applications by facilitating the composition of already existing concrete web services. One critical challenge in WSC is the dynamic selection of concrete services to ... In this paper, a new approach based on the concept of combined controllability and observability is proposed to quantify the interaction among the inputs and outputs of both stable and unstable linear multivariable systems. The proposed approach computes ... Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists ... Lightness illusions, such as the seemingly opposing effects of brightness contrast and assimilation, are characterized by visually perceived intensity images that differ from physical reality. Traditional hypotheses from signal processing community ... There has been a renewed interest at the Internet Engineering Task Force (IETF) in using Less-than-Best Effort (LBE) methods for background applications. IETF recently published a RFC for Low Extra Delay Background Transport (LEDBAT), a congestion ... Catch-up TV has revolutionised the watching habits, as it provides users the opportunity to watch programs at their preferred time and place. With the increasing offer of TV content, it is evident that there is a need for personalised recommendation ... In 2007, Bessiere et al. have proposed a framework for learning constraint networks via membership queries, that is, by asking the user to classify total assignments of the variables as positive or negative. In this paper we consider the case where the ... We describe an approach to computing upper bounds on the lengths of solutions to reachability problems in transition systems. It is based on a decomposition of state-variable dependency graphs (causal graphs). Our approach is able to find prac- ... Bayesian approaches to preference learning using Gaussian Processes (GPs) are attractive due to their ability to explicitly model uncertainty in users' latent utility functions; unfortunately existing techniques have cubic time complexity in the number of... |