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Abstract. Classical information retrieval methods often lose valuable information when aggregating weights, which may diminish the discriminating power between documents. To cope with this problem, the paper presents an approach for ranking documents in IR, based on a vectorbased ordering technique already considered in fuzzy logic for multicriteria analysis purpose. Moreover, the proposed approach uses a possibilistic framework for evaluating queries to a document collection, which distinguishes between descriptors that are certainly relevant and those which are possibly relevant only. The proposal is evaluated on a standard collection that allows to compare the effectiveness of this approach with a classical one. The proposed method provides an improvement of the precision of the Mercure IR system.

International audienceEasily programming behaviors is one major issue of a large and reconfigurable deployment in the Internet of Things. Such kind of devices often requires to externalize part of their behavior such as the sensing, the data aggregation or the code offloading. Most existing context-oriented programming languages integrate in the same class or close layers the whole behavior. We propose to abstract and separate the context tracking from the decision process, and to use event-based handlers to interconnect them. We keep a very easy declarative and non-layered programming model. We illustrate by defining an extension to Golo-a JVM-based dynamic language

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