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localization that incorporates different levels of contextual interactions. We study contextual interactions at pixel, region and object level by using three different sources of context: semantic, boundary support and contextual neighborhoods. Our framework learns a single similarity metric from multiple

of apparently conflicting results concerning the roles of lexical and contextual information in sentence processing, explains differences among ambiguities in terms of ease of resolution, and provides a more unified account of language comprehension than was previously available. One of the principal goals

the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints. Publication info: Proceedings of the ACL, 2004. 1

, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

it of the context of its use. By context, we refer to any information that characterizes a situation related to the interaction between humans, applications and the surrounding environment. Context-aware applications promise richer and easier interaction, but the current state of research in this field is still far

Future computing environments will free the user from the constraints of the desktop. Applications for a mobile environment should take advantage of contextual information, suach as position, to offer greater services to the user. In his paper, we present the Cyberguide project, in which we

Context-aware computing is a mobile computing paradigm in which applications can discover and take advantage of contextual information (such as user location, time of day, nearby people and devices, and user activity). Since it was proposed about a decade ago, many researchers have studied

Context-aware technology has stimulated rigorous research into novel ways to support people in a wide range of tasks and situations. However, the effectiveness of these technologies will ultimately be dependent on the extent to which contextual interactions are understood and accounted

. 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