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Analyzing human behaviors during emergency situations contributes to build efficient emergency management plans. Indeed, research shows that emotions have a major influence on human behavior particularly to respond to highly emotive events such as those happening in emergency situations. Therefore, it is recognized that they are necessary to produce human-like behaviors in artificial agents. In this...
Information overload is an increasing challenge for the enterprise knowledge worker. Traditional information retrieval, i.e. Search-based approaches for knowledge management in the enterprise are under strain because users do not have the time to search, often they are not even aware that material relevant to they current needs exists. Neither do they have the time to track the various external news...
Unlike traditional recommender systems, which make recommendations only by using the relation between users and items, a context-aware recommender system makes recommendations by incorporating available contextual information into the recommendation process. One problem of context-aware approaches is that it is required techniques to extract such additional information in an automatic manner. In this...
Context-aware recommender systems (CARS) are extensions of traditional recommenders that also take into account contextual condition of a user to whom a recommendation is made. The recommendation problem is, however, still focused on recommending a set of items to a target user. In this paper, we consider the problem of recommending to a user the appropriate contexts in which an item should be selected...
The execution of a multiagent-based simulation (MABS) model necessitates a scheduler that synchronizes the agents execution and simulates the simultaneity of their behaviors. In the majority of MABS frameworks the scheduler activates the agents who compute their context to decide the action to execute. This context computation process is time-consuming and is one of the barriers to increased use of...
In this investigation, we propose a novel approach to document stream classification using both online topic model and partially labelled documents. Although we may have several features for the classification, it seems natural that these features may vary dynamically depending upon the contents of stream. This is because they depend heavily on each theme within one class while we should follow dynamic...
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