. As organizations in both the private and the public sectors routinely exchange such information, individ.uals have no way of knowing if the information is inaccurate, obsolete, or otherwise inappropriate. The foundation is being laid for a dossier society, in which computers could be used to infer individuals

-aware applications. In this paper, we address these three problems in turn. We first define context, identify categories of contextual information, and characterize context-aware application behavior. Though the full impact of context-aware computing requires understanding very subtle and high-level notions

formulations of standard choice theoretic principles. I identify informational asymmetries under which apparently anomalous behaviors, namely the compromise effect and choice overload, arise as market equilibria. Firms respond to consumers’ contextual inference; in case of the compromise effect, a firm may

et al., 2004) from aligned tree-string pairs, and present two main extensions of their approach: first, instead of merely computing a single derivation that minimally explains a sentence pair, we construct a large number of derivations that include contextually richer rules, and account for multiple

is potentially available (e.g., what is known, the available inference procedures), this part is what we call context (of reasoning); however (ii) there is compatibility among the reasoning performed in different contexts. We validate our semantics by formalizing two important forms of contextual reasoning

: the information implicit in a firm’s decision to o¤er a particular set of goods allows uninformed consumers to infer from the product line which good is optimal for them. Consequently, these consumers rationally violate naïve formulations of standard choice theoretic principles. I identify informational

of others and depart from the standard model of the rational economic agent in other ways. If people display bounded rationality when it comes to maximizing utility, then their choices do not necessarily reflect their "true" preferences, and an exclusive reliance on choices to infer what people

We show how high-level scene properties can be inferred from classification of low-level image features, specifically for the indoor-outdoor scene retrieval problem. We systematically studied the features: (1) histograms in the Ohta color space (2) multiresolution, simultaneous autoregressive model

Ontologies are a key component for building open and dynamic distributed pervasive computing systems in which agents and devices share contextual information. We describe our use of the Web Ontology Language OWL and other tools for building the foundation ontology for the Context Broker

is learned by assembling graph fragments in an additive model. The connections between individual pixels are not very informative, but by using dense graphs, we can pool information from large regions of the image; dense models also support efficient inference. We show how contextual information from other