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DTSTART;TZID=Europe/Paris:20210201T140000
DTEND;TZID=Europe/Paris:20210201T160000
DTSTAMP:20210118T085751Z
CREATED:20210118T085751Z
LAST-MODIFIED:20210118T085751Z
UID:11494-1612188000-1612195200@www.loria.fr
SUMMARY:PhD defense: Itsaka Rakotonirina
DESCRIPTION:Itsaka Rakotonirina\, PhD student in the Pesto Team\, will defend his thesis on Monday\, February 1st at 2pm. \n \nTitle: Symbolic verification of cryptographic protocols\, theory and practice \nThe defence will be english. You can also find his dissertation here. \nAbstract: \nThis thesis studies the analysis of cryptographic protocols. They are sequences of instructions permitting to interact with a recipient remotely while protecting the sensitive content of the communication from a potential malicious third party. Classical cases where the confidentiality and the integrity of the communication are critical are\, among others\, online payments and medical-service booking\, or electronic voting. \nWe study notions of security defined technically by observational equivalences (which includes among others confidentiality\, anonymity or non-traceability). We designed a program\, DeepSec\, which\, from the description of a protocol for a fixed number of participants\, verifies in a fully-automated way whether the protocol offers a security guarantee of this type. We demonstrate the ability of this tool to analyse complex attack scenarios through several examples\, optimisations\, and a detailed study of the complexity of the underlying problem. \nJury: \nDavid Basin (ETH Zurich) – reviewer\, president \nTamara Rezk (Inria Sophia Antipolis) – reviewer \nMyrto Arapinis (University of Edinburgh) \nVincent Cheval (Inria Nancy) – co-advisor \nThomas Jensen (Inria Rennes) \nSteve Kremer (Inria Nancy) – advisor \n\n\n\nCatuscia Palamidessi (Inria Saclay)
URL:https://www.loria.fr/event/phd-defense-itsaka-rakotonirina/
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20210211T090000
DTEND;TZID=Europe/Paris:20210211T120000
DTSTAMP:20210211T150748Z
CREATED:20210211T150748Z
LAST-MODIFIED:20210211T150748Z
UID:11510-1613034000-1613044800@www.loria.fr
SUMMARY:PhD defense: Daniel El Ouraoui
DESCRIPTION:Daniel El Ouraoui\, doctorant dans l’équipe Mosel-Veridis\, soutiendra sa thèse intitulée « Méthodes pour le raisonnement d’ordre supérieur dans SMT »\, effectuée sous la direction de Jasmin Blanchette\, Pascal Fontaine et Stephan Merz\, le 11 février à 9h. \n \nRésumé :\n \nLa vérification formelle de programmes informatiques ou de systèmes dits\ncritiques tels que dans le transport\, l’énergie\, etc\, est essentielle pour\ngarantir le bon fonctionnement de ces systèmes. Les méthodes de vérification\nemployées s’appuient très fortement sur des procédés mathématiques et logiques\npermettant de raisonner de manière formelle sur le comportement de ces systèmes.\nCes procédés définissent généralement les comportements sous forme de grands\nensembles de contraintes logiques. L’approche par satisfaisabilité est une\nméthode largement utilisée pour vérifier ces contraintes et est un exemple de\ncas\, où les solveurs SMT (satisfaisabilité modulo théories) sont\nfortement sollicités \nGénéralement\, les solveurs SMT ne gèrent que la logique de premier ordre et ils \nne peuvent généralement pas effectuer de preuves par induction. C’est regrettable\, car la\nplupart des outils de vérification interactifs\, qui utilisent les solveurs SMT\,\nutilisent des langages d’ordre supérieur. \nL’objectif de cette thèse dans sa globalité est d’offrir des solutions pour\naméliorer les interactions entre solveur automatique et assistant de preuves. En\nparticulier nous répondons à deux problématiques importantes permettant\nd’améliorer les usages de solveurs SMT au sein des assistants de preuves. Notre\npremière contribution permet de réduire l’écart entre solveur et assistant de\npreuve en proposant une architecture adaptée pour la logique d’ordre supérieur.\nLa seconde contribution permet d’améliorer les capacités de raisonnement des\nsolveurs SMT pour les quantificateurs. Pour les deux approches développées nous\napportons un ensemble d’évaluation sur des problèmes extraits pour la grande\nmajorité de problèmes de formalisation. Les résultats obtenus lors de ces\névaluations sont encourageants et montrent que les techniques développées dans\ncette thèse peuvent apporter de bonnes améliorations pour les solveurs SMT. \nCe doctorat s’est effectué dans le cadre du projet ERC porté par Jasmin Blanchette\n(Matryoshka)\, un projet qui vise à concevoir des\nprouveurs automatiques utiles pour la vérification interactive\, et réduire\nl’écart entre les prouveurs interactifs et solveurs automatiques. L’un des\nobjectifs concrets du projet est d’étendre les capacités de raisonnement des\nsolveurs SMT vers l’ordre supérieur. \n\n\nMembres du jury :\n \nRapporteurs :\nMme Micaela MAYERO \, Maître de conférences\, IUT de Villetaneuse – Université Sorbonne Paris Nord – FRANCE\nM. Yakoub SALHI \, Professeur\, Université d’Artois – FRANCE\nExaminateurs :\nM. David DÉHARBE\, Professeur\, CLEARSY Aix-en-Provence – FRANCE\n\nMme Catherine DUBOIS\, Professeur\, Ecole Nationale Supérieure d’Informatique pour l’Industrie et l’Entreprise – FRANCE\nMme Chantal KELLER\, Maître de conférences\, LRI\, Université Paris-Saclay – FRANCE\n\n\nEncadrants :\nM. Jasmin BLANCHETTE\, Professeur associé\, Université libre d’Amsterdam – PAYS-BAS\nM. Pascal FONTAINE\, Professeur\, Université de Liège – BELGIQUE\nM. Stephan MERZ\, DR2\, Inria Nancy – Grand Est – FRANCE
URL:https://www.loria.fr/event/phd-defense-daniel-el-ouraoui/
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20210222T160000
DTEND;TZID=Europe/Paris:20210222T173000
DTSTAMP:20210218T085445Z
CREATED:20210218T085445Z
LAST-MODIFIED:20210218T085445Z
UID:11544-1614009600-1614015000@www.loria.fr
SUMMARY:PhD defense: Sylvain Cecchetto
DESCRIPTION:Sylvain Cecchetto (Carbone) will defend his PhD on Monday\, February 22nd at 4pm. \nHis thesis is entitled « Data flow analysis to build control flow graph of obfuscated codes »\, his presentation will be held in French.\n\n \nAbstract:\nThe increase in cyber attacks around the world makes malicious code analysis a priority research area. This software uses various protection methods\, also known as obfuscations\, to bypass antivirus software and slow down the analysis process. In this context\, this thesis provides a solution to build the Control Float Graph (CFG) of obfuscated binary code. We developed the BOA platform (Basic blOck Analysis) which performs a static analysis of a protected binary code. For this\, we have defined a semantics based on the BINSEC tool to which we have added continuations. These allow on one hand to control the self-modifications\, and on the other hand to simulate the operating system to handle system calls and interruptions. The static analysis is done by symbolically executing the binary code and calculating the values of the system states using SMT solvers. Thus\, we perform a data flow analysis to build the CFG by calculating the transfer addresses. Finally\, loop handling is performed by transforming a CFG into a pushdown automaton. BOA is able to compute dynamic jump addresses\, to detect opaque predicates\, to compute return addresses on a stack even if they have been falsified\, to manage interrupt handler falsifications\, to rebuild import tables on the fly\, and finally\, to manage self-modifications. We validated the BOA correction using the Tigress code obfuscator. Then\, we tested BOA on 35 known packers and showed that in 30 cases\, BOA was able to completely or partially rebuild the initially protected binary. Finally\, we detected the opaque predicates protecting XTunnel\, a malware used during the 2016 U.S. elections\, and we partially unpacked a sample of the Emotet Trojan\, which on 14/10/2020 was detected by only 7 antivirus programs out of the 63 offered by VirusTotal This work contributes to the development of tools for static analysis of malicious code. In contrast to dynamic methods\, this solution allows an analysis without executing the binary\, which offers a double advantage : on the one hand\, a static approach is easier to deploy\, and on the other hand\, since the malicious code is not executed\, it cannot warn its author.Keywords: Malware\, Obfuscation\, Data flow\, Symbolic execution\, Control flow graph.Jury members:Referes:Valérie Viet Triem Tong – CentraleSupélec Rennes José Fernandez – Polytechnique MontréalExaminers:Nadia Tawbi – Université LavalSarah Zennou – AirbusStephan Merz – Université de LorraineGuest: \nSébastien Bardin – CEA LIST\nColas Le Guernic – Verimag\nSupervisors: \nJean-Yves Marion – Université de Lorraine\nGuillaume Bonfante – Université de Lorraine
URL:https://www.loria.fr/event/phd-defense-sylvain-cecchetto/
LOCATION:Teams
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20210226T090000
DTEND;TZID=Europe/Paris:20210226T103000
DTSTAMP:20210225T122432Z
CREATED:20210225T112147Z
LAST-MODIFIED:20210225T122432Z
UID:11617-1614330000-1614335400@www.loria.fr
SUMMARY:PhD defense : Pierre-Edouard Osche
DESCRIPTION:Pierre-Edouard Osche (Kiwi) will defend his thesis on Friday\, 26th February at 9 am. \nHis thesis is entitled “Sequence-based recommendations in a multidimensional space » and supervised by Anne Boyer and Sylvain Castagnos. \nAbstract:\nRecommender systems are a fundamental research topic at the intersection of several major disciplines such as machine learning\, human-computer interaction and cognitive sciences. They also constitute an ambitious application framework for the community of researchers in Artificial Intelligence by their great complexity and the numerous constraints they generate. \nThe purpose of these systems is to improve the interaction between the general audience and the systems of search and access to information. It has become difficult to identify the most relevant items in the context of big data. The goal is thus to assist users in their explorations (whether in a virtual or physical environment)\, but also to propose items that may interest them but that they would not consult spontaneously. \nCurrent systems have largely proven their added value and are based on various machine learning techniques (numerical or symbolic\, supervised or not\, etc.) [Castagnos\, 2008]. Nevertheless\, they still suffer from limitations when making recommendations of sequences (recommending items in a specific order may depend on requirements\, progressiveness\, context\, time constraints\, etc.). Some models\, such as the DANCE model [Castagnos\, 2015]\, integrate this temporal dimension by following in real time the evolution in diversity of resources consulted by users to better understand the exploration context. In [Bonnin\, 2010]\, the author also proposes a temporal model capable of detecting frequent consultation patterns in a history of consultations\, in order to provide a priori resource recommendations related to the same context. Nevertheless\, while temporal and spatial modeling have been made possible [Zheng\, 2015]\, state-of-the-art models that focus on sequence recommendations or on the overall quality of the sequence are still too rare. \nIn the framework of this thesis\, we will focus on defining a new formalism and a methodological framework allowing : (1) the definition of human factors leading to decision making and user satisfaction; (2) the construction of a generic and multi-criteria model (physical or temporal constraints\, diversity\, progressiveness\, etc.)\, integrating these human factors in order to recommend relevant resources in a coherent sequence; (3) a holistic evaluation of user satisfaction with its recommendation path. The evaluation of recommendations\, all domains included\, is currently done recommendation by recommendation with each evaluation metric taken independently (accuracy\, diversity\, novelty\, coverage\, …). Thus\, we expect a more comprehensive evaluation framework\, measuring the progressiveness and the completeness of the path. \nSuch a multi-criteria recommendation model has many application frameworks. As an example\, it can be used in the context of online music listening with the recommendation of adaptive playlists (recommendation of music sequences to change the atmosphere in a place such as a bar\, to raise or lower the emotion felt by the audience progressively\, or to adapt to the complementary/similar/different expectations of a group). It can also be useful to adapt the recommendation path to the learner’s progress and the teacher’s pedagogical scenario in an e-education context. Let us also mention the tourism field where this model could integrate the spatial and temporal constraints of a physical environment (cities\, museums\, etc.). \nKeywords: Recommender systems\, Multi-agent systems\, User modeling. \nCommittee: \nRewievers:\n– Mme Sylvie Calabretto\, Professeur\, INSA de Lyon\, France\n– M. Laurent Vercouter\, Professeur\, INSA de Rouen\, France \nExaminer:\n– M. Laurent Vigneron\, Professeur\, Université de Lorraine\, France \nSupervisor:\n– M. Sylvain Castagnos\, Maître de conférences\, Université de Lorraine\, France
URL:https://www.loria.fr/event/phd-defense-pierre-edouard-osche/
LOCATION:online
CATEGORIES:Soutenance
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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20210226T140000
DTEND;TZID=Europe/Paris:20210226T153000
DTSTAMP:20210219T145022Z
CREATED:20210219T145011Z
LAST-MODIFIED:20210219T145022Z
UID:11605-1614348000-1614353400@www.loria.fr
SUMMARY:PhD defense : Anastasia Shimorina (Synalp)
DESCRIPTION:Anastasia Shimorina (Synalp) will defend her thesis on Friday\, 26th February at 2pm. \nHer thesis is entitled « Natural Language Generation: from Data Creation to Evaluation via Modelling » and supervised by Claire Gardent and Yannick Parmentier. \nAbstract:\nNatural language generation is a process of generating a natural language text from some input. This input can be texts\, documents\, images\, tables\, knowledge graphs\, databases\, dialogue acts\, meaning representations\, etc. Recent methods in natural language generation\, mostly based on neural modelling\, have yielded significant improvements in the field. Despite this recent success\, numerous issues with generation prevail\, such as faithfulness to the source\, developing multilingual models\, few-shot generation. This thesis explores several facets of natural language generation from creating training datasets and developing models to evaluating proposed methods and model outputs. \nIn this thesis\, we address the issue of multilinguality and propose possible strategies to semi-automatically translate corpora for data-to-text generation. We show that named entities constitute a major stumbling block in translation exemplified by the English-Russian translation pair. We proceed to handle rare entities in data-to-text modelling exploring two mechanisms: copying and delexicalisation. We demonstrate that rare entities strongly impact performance and that the impact of these two mechanisms greatly varies depending on how datasets are constructed. Getting back to multilinguality\, we also develop a modular approach for shallow surface realisation in several languages. Our approach splits the surface realisation task into three submodules: word ordering\, morphological inflection and contraction generation. We show\, via delexicalisation\, that the word ordering component mainly depends on syntactic information. Along with the modelling\, we also propose a framework for error analysis\, focused on word order\, for the shallow surface realisation task. The framework enables to provide linguistic insights into model performance on the sentence level and identify patterns where models underperform. Finally\, we also touch upon the subject of evaluation design while assessing automatic and human metrics\, highlighting the difference between the sentence-level and system-level type of evaluation. \nKeywords: natural language generation\, data-to-text generation\, surface realisation\, evaluation\, error analysis \nCommittee: \nReviewers:\n– Emiel Krahmer\, Full Professor\, Tilburg University\, the Netherlands\n– Kees van Deemter\, Full Professor\, Utrecht University\, the Netherlands \nExaminer:\n– Dimitra Gkatzia\, Associate Professor\, Edinburgh Napier University\, UK \nSupervisors:\n– Claire Gardent\, Directrice de recherche\, CNRS\, LORIA\, France\n– Yannick Parmentier\, Maı̂tre de conférences\, Université de Lorraine\, France
URL:https://www.loria.fr/event/phd-defense-anastasia-shimorina/
LOCATION:online
CATEGORIES:Soutenance
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