Using content-based addresses as a programming construct for

In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated

Most recent ad hoc network research has focused on providing routing services without considering security. In this paper, we detail security threats against ad hoc routing protocols, specifically examining AODV and DSR. In light of these threats, we identify three different environments

methods in conjunction with a notion of differential misclassification cost to produce filters Which are especially appropriate for the nuances of this task. While this may appear, at first, to be a straight-forward text classification problem, we show that by considering domain-specific features

Medical free-text queries often share the same scenario. A scenario represents a repeating task in healthcare. For example, a specific scenario is searching for treatment methods for a specific dis-ease, where “treatment ” is a term indicating the scenario. To support scenario-specific retrieval

of a specific network protocol or application. To this end, we have applied our RPGM model to two different network protocol scenarios, clustering and routing, and have evaluated network performance under different mobility patterns and for different protocol implementations. As expected, the results

simulation models have to be used. If these models are not implemented using the same technology, the composition of simulation models is an interesting approach. We developed a composition framework called mosaik, which allows to specify, compose and simulate Smart Grid scenarios based on the reuse

Knowledge-based query expansion to support

defined MSC language is a must for serious, semantically meaningful tool support for use-cases and scenarios. It is also a prerequisite to addressing what we regard as one of the central problems in behavioral specification of systems: relating scenario-based inter-object specification to state

Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed which measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain