Video files are one of the most proliferated data: � 1,000s CCTVs added per Month � UK alone has 4 million of public CCTVs operated by the government � 1,000s Youtube video added In the internet and viewed per day � Millions of Searches per month � Growing trends of Terabytes-Petabytes of data generated per day!

In an increasingly wired world, digital libraries are increasingly serving a worldwide audience. Even if initially designed for a local user population, a digital library can easily fi nd itself not only

The paper describes a set of patterns that extend the pattern language proposed in [Meszaros96] for improving the capacity of reactive systems. The intent of these patterns is to identify some specific causes that limit the efficiency of a distributed layered client-server system with multi-threaded servers, and to find appropriate corrective measures. The type of systems considered here is a subclass of the larger category of reactive systems, and the new patterns are dealing with their specific performance characteristics. The effects of the patterns are illustrated with performance measurements conducted on a layered client-server system.

In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (mdps) and partially observable mdps (pomdps). We then outline a novel algorithm

in five application-related domains, and found the variability to be surprisingly large. In every case two people favored the same term with probability <0.20. Simulations show how this fundamental property of language limits the success of various design methodologies for vocabulary-driven interaction

In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling

Considerable evidence indicates that domain specific knowledge in the form of schemes is the primary factor distinguishing experts from novices in problem-solving skill. Evidence that conventional problem-solving activity is not effective in schema acquisition is also accumulating. It is suggested

The University of Florida Sparse Matrix Collection is a large, widely available, and actively growing set of sparse matrices that arise in real applications. Its matrices cover a wide spectrum of problem domains, both those arising from problems with underlying 2D or 3D geometry (structural

Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be pro tably modi- ed

. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling