L'intertextualité dans les publications scientifiques

La base de données bibliographiques de l'IEEE contient un certain nombre de duplications avérées avec indication des originaux copiés. Ce corpus est utilisé pour tester une méthode d'attribution d'auteur. La combinaison de la distance intertextuelle avec la fenêtre glissante et diverses techniques de classification permet d'identifier ces duplications avec un risque d'erreur très faible. Cette expérience montre également que plusieurs facteurs brouillent l'identité de l'auteur scientifique, notamment des collectifs de chercheurs à géométrie variable et une forte dose d'intertextualité acceptée voire recherchée

Who wrote this scientific text?

The IEEE bibliographic database contains a number of proven duplications with indication of the original paper(s) copied. This corpus is used to test a method for the detection of hidden intertextuality (commonly named "plagiarism"). The intertextual distance, combined with the sliding window and with various classification techniques, identifies these duplications with a very low risk of error. These experiments also show that several factors blur the identity of the scientific author, including variable group authorship and the high levels of intertextuality accepted, and sometimes desired, in scientific papers on the same topic

Context-Aware Recommendation Systems in Mobile Environments

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

An architectural framework for self-configuration and self-improvement at runtime

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Diffusion en directe avec du Gossip

Video streaming has become a killer application for peer-to-peer technologies. By aggregating scarce resources such as upload bandwidth, decentralized video streaming protocols make it possible to serve a video stream to huge numbers of users while requiring very limited investments from broadcasters. In this paper, we present HEAP, a novel peer-to-peer streaming protocol designed for heterogeneous scenarios. Gossip protocols have already shown their effec- tiveness in the context of live video streaming. HEAP, HEterogeneity-Aware gossip Protocol, goes beyond their applicability and performance by incorporating several novel features. First, HEAP includes a fanout-adaptation scheme that tunes the contribution of nodes to the streaming process based on their bandwidth capabilities. Second, HEAP comprises heuristics that improve reliability, as well as operation in the presence of heterogeneous network latency. We extensively evaluate HEAP on a real deployment over 200 nodes on the Grid5000 platform in a variety of settings, and assess its scalability with up to 100k simulated nodes. Our results show that HEAP significantly improves the quality of streamed videos over standard homogeneous gossip protocols, especially when the stream rate is close to the average available bandwidth.Le streaming vidéo est devenu une killer application pour les technologies pair-à- pair. En agrégeant les ressources rares telles que le debit maximale téléversement, les protocoles de diffusion vidéo décentralisée permettent servir un flux vidéo à un grand nombre d’utilisateurs tout en limitant les couts. Dans cet article, nous présentons HEAP, un nouveau protocole de streaming pair-à-pair conçu pour des réseaux hétérogènes. Les protocoles de gossip ont déjà mon- tré leur efficacité dans le contexte du streaming vidéo en direct. HEAP, em HEterogeneity-Aware Gossip Protocol, va au-delà de protocoles existantes en incorporant plusieurs caractéristiques nouvelles. Premièrement, HEAP adapte la contribution des noeuds en fonction de leurs debit maximal. Deuxièmement, HEAP inclut des heuristiques qui améliorent la fiabilité, en présence de latence de réseau hétérogène. Nous évaluons HEAP sur un déploiement réel sur 200 noeuds sur la plate-forme Grid5000 avec une variété de paramètres, et évaluons son passage à l’échelle avec jusqu’à 100k noeuds simulé. Nos résultats montrent que HEAP améliore significativement la qualité des vidéos diffusées par rapport au protocoles standard, surtout lorsque le débit est proche de la bande passante moyenne disponible