Thwarting Sybil Attackers in Reputation-based Scheme in Mobile Ad hoc Networks

Routing in mobile ad hoc networks is performed in a distributed fashion where each node acts as host and router, such that it forwards incoming packets for others without relying on a dedicated router. Nodes are mostly resource constraint and the users are usually inclined to conserve their resources and exhibit selfish behaviour by not contributing in the routing process. The trust and reputation models have been proposed to motivate selfish nodes for cooperation in the packet forwarding process. Nodes having bad trust or reputation are detected and secluded from the network, eventually. However, due to the lack of proper identity management and use of non-persistent identities in ad hoc networks, malicious nodes can pose various threats to these methods. For example, a malicious node can discard the bad reputed identity and enter into the system with another identity afresh, called whitewashing. Similarly, a malicious node may create more than one identity, called Sybil attack, for self-promotion, defame other nodes, and broadcast fake recommendations in the network. These identity-based attacks disrupt the overall detection of the reputation systems. In this paper, we propose a reputation-based scheme that detects selfish nodes and deters identity attacks. We address the issue in such a way that, for normal selfish nodes, it will become no longer advantageous to carry out a whitewash. Sybil attackers are also discouraged (i.e., on a single battery, they may create fewer identities). We design and analyse our rationale via game theory and evaluate our proposed reputation system using NS-2 simulator. The results obtained from the simulation demonstrate that our proposed technique considerably diminishes the throughput and utility of selfish nodes with a single identity and selfish nodes with multiple identities when compared to the benchmark scheme

Carcinome métaplasique du sein : à propos d’un cas

Le carcinome métaplasique du sein est une tumeur maligne primaire rare et agressive, représentant 0,2-5% de tous les cancers du sein. Cette tumeur maligne est caractérisée par la présence histologique d'au moins deux types cellulaires, généralement des composants épithéliaux et mésenchymateux. Nous rapportons le cas d’une patiente qui présentait un carcinome métaplasique du sein droit. A travers ce cas et une revue de la littérature, les caractéristiques anatomo-cliniques, radiologiques, thérapeutiques et évolutives seront discutées.Les variantes métaplasiques sont agressives, chimiorésistantes et ont une forte propension à métastaser ainsi qu’un risque de récidive locale plus élevé, rendant ainsi leur pronostic plus sombre que les carcinomes du sein non métaplasiques c'est-à-dire, les carcinomes canalaires invasifs. Même si les carcinomes métaplasiques du sein sont traités de la même manière que les carcinomes invasifs, il n’en reste pas moins que leur prise en charge reste difficile et qu’il n’existe pas de standard thérapeutique. Le principal traitement reste la chirurgi

Translating the foundational model of anatomy into french using knowledge-based and lexical methods

<p>Abstract</p> <p>Background</p> <p>The Foundational Model of Anatomy (FMA) is the reference ontology regarding human anatomy. FMA vocabulary was integrated into the Health Multi Terminological Portal (HMTP) developed by CISMeF based on the CISMeF Information System which also includes 26 other terminologies and controlled vocabularies, mainly in French. However, FMA is primarily in English. In this context, the translation of FMA English terms into French could also be useful for searching and indexing French anatomy resources. Various studies have investigated automatic methods to assist the translation of medical terminologies or create multilingual medical vocabularies. The goal of this study was to facilitate the translation of FMA vocabulary into French.</p> <p>Methods</p> <p>We compare two types of approaches to translate the FMA terms into French. The first one is UMLS-based on the conceptual information of the UMLS metathesaurus. The second method is lexically-based on several Natural Language Processing (NLP) tools.</p> <p>Results</p> <p>The UMLS-based approach produced a translation of 3,661 FMA terms into French whereas the lexical approach produced a translation of 3,129 FMA terms into French. A qualitative evaluation was made on 100 FMA terms translated by each method. For the UMLS-based approach, among the 100 translations, 52% were manually rated as "very good" and only 7% translations as "bad". For the lexical approach, among the 100 translations, 47% were rated as "very good" and 20% translations as "bad".</p> <p>Conclusions</p> <p>Overall, a low rate of translations were demonstrated by the two methods. The two approaches permitted us to semi-automatically translate 3,776 FMA terms from English into French, this was to added to the existing 10,844 French FMA terms in the HMTP (4,436 FMA French terms and 6,408 FMA terms manually translated).</p

Additional file 1: of Searching for rare diseases in PubMed: a blind comparison of Orphanet expert query and query based on terminological knowledge

Contains all the evaluated citation, their metadata and their final evaluation. As authors are French, this file is in French, however, the entire work was performed in English. Column 1 contains the unique disease ID, column 2 the disease name, column 3 the query, column 4 the MeSH level, column 5 the citation PubMed ID, column 6 the final answer for relevance (“Does the article directly concern the disease?”), column 7 the year of publication, column 8 the journal title, column 9 a link toward the citation, column 10 and 11 the two evaluators, column 12 the adjudicator, if any. (XLSX 62 kb