Auswahl der wissenschaftlichen Literatur zum Thema „Vérification de l'auteur“
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Zeitschriftenartikel zum Thema "Vérification de l'auteur"
Gauthier, Yvon. „Vérité et vérification en logique mathématique et dans les théories physiques“. Articles 9, Nr. 1 (09.01.2007): 135–45. http://dx.doi.org/10.7202/203186ar.
Der volle Inhalt der QuelleTremblay, Guy. „La taxation provinciale indirecte par voie de licences“. Les Cahiers de droit 38, Nr. 3 (12.04.2005): 677–90. http://dx.doi.org/10.7202/043457ar.
Der volle Inhalt der QuelleMoline, Estelle. „La meilleure façon de marcher. Compléments de manière et propriétés sémantiques du prédicat verbal : l’exemple de marcher“. Scolia 27, Nr. 1 (2013): 97–117. http://dx.doi.org/10.3406/scoli.2013.1156.
Der volle Inhalt der QuelleJean, Bruno. „Idéologies et professionnalisation : le cas des agronomes“. Notes de recherche 19, Nr. 2 (12.04.2005): 251–60. http://dx.doi.org/10.7202/055790ar.
Der volle Inhalt der QuelleHalley, Paule. „La vérification environnementale : réflexions sur l'émergence des modes d'autorégulation“. Les Cahiers de droit 40, Nr. 3 (12.04.2005): 621–43. http://dx.doi.org/10.7202/043563ar.
Der volle Inhalt der QuelleBruneau, Monik. „L’évaluation des apprentissages en danse : une utopie?“ Articles 19, Nr. 4 (10.10.2007): 695–713. http://dx.doi.org/10.7202/031673ar.
Der volle Inhalt der QuelleDissertationen zum Thema "Vérification de l'auteur"
Hammoud, Khodor. „Trust in online data : privacy in text, and semantic-based author verification in micro-messages“. Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5203.
Der volle Inhalt der QuelleMany Problems surround the spread and use of data on social media. There is a need to promote trust on social platforms, regarding the sharing and consumption of data. Data online is mostly in textual form which poses challenges for automation solutions because of the richness of natural language. In addition, the use of micro-messages as the main means of communication on social media makes the problem much more challenging because of the scarceness of features to analyze per body of text. Our experiments show that data anonymity solutions cannot preserve user anonymity without sacrificing data quality. In addition, in the field of author verification, which is the problem of determining if a body of text was written by a specific person or not, given a set of documents known to be authored by them, we found a lack of research working with micro-messages. We also noticed that the state-of-the-art does not take text semantics into consideration, making them vulnerable to impersonation attacks. Motivated by these findings, we devote this thesis to tackle the tasks of (1) identifying the current problems with user data anonymity in text, and provide an initial novel semantic-based approach to tackle this problem, (2) study author verification in micro-messages and identify the challenges in this field, and develop a novel semantics-based approach to solve these challenges, and (3) study the effect of including semantics in handling manipulation attacks, and the temporal effect of data, where the authors might have changing opinions over time. The first part of the thesis focuses on user anonymity in textual data, with the aim to anonymize personal information from online user data for safe data analysis without compromising users’ privacy. We present an initial novel semantic-based approach, which can be customized to balance between preserving data quality and maximizing user anonymity depending on the application at hand. In the second part, we study author verification in micro-messages on social media. We confirm the lack of research in author verification on micro-messages, and we show that the state-of-the-art, which primarily handles long and medium-sized texts, does not perform well when applied on micro-messages. Then we present a semantics-based novel approach which uses word embeddings and sentiment analysis to collect the author’s opinion history to determine the correctness of the claim of authorship, and show its competitive performance on micro-messages. We use these results in the third part of the thesis to further improve upon our approach. We construct a dataset consisting of the tweets of the 88 most followed twitter influencers. We use it to show that the state-of-the-art is not able to handle impersonation attacks, where the content of a tweet is altered, changing the message behind the tweet, while the writing pattern is preserved. On the other hand, since our approach is aware of the text’s semantics, it is able to detect text manipulations with an accuracy above 90%. And in the fourth part of the thesis, we analyze the temporal effect of data on our approach for author verification. We study the change of authors’ opinions over time, and how to accommodate for that in our approach. We study trends of sentiments of an author per a specific topic over a period of time, and predict false authorship claims depending on what timeframe does the claim of authorship fall in