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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20181206T100000
DTEND;TZID=Europe/Paris:20181206T120000
DTSTAMP:20181204T143414Z
CREATED:20181204T143414Z
LAST-MODIFIED:20181204T143414Z
UID:6292-1544090400-1544097600@www.loria.fr
SUMMARY:PhD Defense : Pierre Kimmel
DESCRIPTION:Pierre Kimmel (Types team) will defend his thesis on Thursday\, December 6th at 10am in room B013. \nHis presentation is entitled « Modal extensions of resource logics : expressiveness and calculi ». \n \nThe jury members are  : \n\nNicolas Olivetti\, Professeur Université Aix-Marseille\, LSIS\, Marseille\nSerenella Cerrito\, Professeur Université Evry Val d’Essonne\, IBISC\, Evry\nDavid Pym\, Professeur University College London\, Londres\nHans van Ditmarsch\, Directeur de recherche CNRS\, LORIA\, Nancy\nDidier Galmiche\, Professeur Université de Lorraine\, LORIA\, Nancy\nDominique Larchey-Wendling\, Chargé de recherche CNRS\, LORIA\, Nancy\n\nAbstract : \nThe design of new logical formalisms is at the heart of several problems in formal methods. Those formalisms must respond to requirements both concerning modelling (they must be able to describe certain systems) and computing (they must provide complete and sound calculus methods). In this context\, we look at resource logics\, and in particular BI and BBI logics\, that deal with the separation and sharing of resources and have led to several separation logics whose applications to software verification have been widely developped recently.\n\nWe propose in this thesis\, starting from BI and BBI logics\, to study some modal and epistemic separation logics by focusing on their modelling capacities and their expresiveness\, as well as on the new proof calculi for those logics.\nA first study deals with the modelling of dynamic resource properties through new logic LTBI\, which is a temporal separation logic\, based on BI logic and temporal modalities. This logic notably offers interesting perspectives intemporal branching modelling\, allowing for instance to characterize multi-thread processes.\nA complementary study concerns the modelling of access by agents to properties under the conditions of\nposessing some resources\, through a new logic ERL\, which is an epistemic separation logic\, based on BBI logic and epistemic modalities. This logic allows many modellings of access control systems.\nIn order to extend the expressivity of such separation logics\, like BBI logic and its variants\, a study on the\ninternalization of resources symbols in the logic’s syntax has been developed through the new logics HRL and\nHBBI (hybrid version of BBI). Internalization allows both the extension of the expressivity of logics and the\naxiomatisation of BBI logic and some of its variants.\nIn addition to the conception of those logics\, the study of their semantics and their modelling capacities\, a part\nof this thesis is dedicated to the definition of proof calculi\, here tableaux calculi\, for those new logics\, as well as\ntheir proofs of soundness and completeness.
URL:https://www.loria.fr/event/phd-defense-pierre-kimmel/
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20181212T100000
DTEND;TZID=Europe/Paris:20181212T120000
DTSTAMP:20181128T133309Z
CREATED:20181128T133309Z
LAST-MODIFIED:20181128T133309Z
UID:6270-1544608800-1544616000@www.loria.fr
SUMMARY:HDR : Bernadetta Addis
DESCRIPTION:Bernadetta Addis will defend her « Habilitation à diriger des recherches » on Wednesday\, December 12th at 10 am in room A008\, with a presentation entitled « A journey through optimization:from global to discrete optimization and back ». \n \nJury/Commitee : \nPr. Jean-Charles BILLAUT (Université de Tours)\nPr. Bernard FORTZ (Université Libre de Bruxelles) – reviewer\nPr. Martine LABBÉ (Université Libre de Bruxelles)\nPr. Ammar OULAMARA (Université de Lorraine)\nPr. Laura PALAGI (Sapienza Università di Roma) – reviewer\nPr. Alain QUILLIOT (Université Clermont Auvergne) – reviewer \nSummary:\nMy contributions (and also my research project) are divided along two axes: one dedicated to global optimization and the other to discrete optimization problems resulting from ICT  (Information and Communication Technology) applications.\nThe common elements between these two axes are represented by: the design of mathematical programming models to solve applied problems (green-networking\, placement and routing of virtualized network functions\, optimal design of spatial trajectories\, gas separation processes by membranes\, etc.)\, the analysis of underlying mathematical structures and their use in the design of effective optimization algorithms (heuristic and exact) for large scale problems.
URL:https://www.loria.fr/event/hdr-bernadetta-addis/
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20181214T100000
DTEND;TZID=Europe/Paris:20181214T120000
DTSTAMP:20181211T101044Z
CREATED:20181211T101044Z
LAST-MODIFIED:20181211T101044Z
UID:6303-1544781600-1544788800@www.loria.fr
SUMMARY:PhD defense : Konstantinos Chatzilygeroudis
DESCRIPTION:Konstantinos Chatzilygeroudis will defend his thesis on Friday\, December 14th at 10am in room A008. \nHis presentation is entitled « Micro-Data Reinforcement Learning for Adaptive Robots ». \n \nThe jury members are: \n\nPierre-Yves OUDEYER\, Directeur de recherche\, Inria Bordeaux Sud-Ouest\, France\nYiannis DEMIRIS\, Professor\, Imperial College London\, UK\nVerena V. HAFNER\, Professor\, Humboldt-Universität zu Berlin\, Germany\nAlain DUTECH\, Chargé de recherche\, Inria\, CNRS\, Université de Lorraine\, France\nAude BILLARD\, Professor\, EPFL\, Switzerland\n\nSupervisor: \n\nJean-Baptiste MOURET\, Directeur de recherche\, Inria\, CNRS\, Université de Lorraine\, France\n\nAbstract: \nRobots have to face the real world\, in which trying something might take seconds\, hours\, or even days. Unfortunately\, the current state-of-the-art reinforcement learning algorithms (e.g.\, deep reinforcement learning) require big interaction times to find effective policies. In this thesis\, we explored approaches that tackle the challenge of learning by trial-and-error in a few minutes on physical robots. We call this challenge “micro-data reinforcement learning”. \nIn our first contribution\, we introduced a novel learning algorithm called “Reset-free Trial-and-Error” that allows complex robots to quickly recover from unknown circumstances (e.g.\, damages or different terrain) while completing their tasks and taking the environment into account; in particular\, a physical damaged hexapod robot recovered most of its locomotion abilities in an environment with obstacles\, and without any human intervention. \nIn our second contribution\, we introduced a novel model-based reinforcement learning algorithm\, called Black-DROPS that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes)\, (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics\, and (3) is as fast (or faster) than analytical approaches when several cores are available. We additionally proposed Multi-DEX\, a model-based policy search approach\, that takes inspiration from novelty-based ideas and effectively solved several sparse reward scenarios. \nIn our third contribution\, we introduced a new model learning procedure in Black-DROPS (we call it GP-MI) that leverages parameterized black-box priors to scale up to high-dimensional systems; for instance\, it found high-performing walking policies for a physical damaged hexapod robot (48D state and 18D action space) in less than 1 minute of interaction time. \nFinally\, in the last part of the thesis\, we explored a few ideas on how to incorporate safety constraints\, robustness and leverage multiple priors in Bayesian optimization in order to tackle the micro-data reinforcement learning challenge. \nThroughout this thesis\, our goal was to design algorithms that work on physical robots\, and not only in simulation. Consequently\, all the proposed approaches have been evaluated on at least one physical robot. Overall\, this thesis aimed at providing methods and algorithms that will allow physical robots to be more autonomous and be able to learn in a handful of trials.
URL:https://www.loria.fr/event/phd-defense-konstantinos-chatzilygeroudis/
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20181217T103000
DTEND;TZID=Europe/Paris:20181217T123000
DTSTAMP:20181213T111900Z
CREATED:20181213T111900Z
LAST-MODIFIED:20181213T111900Z
UID:6309-1545042600-1545049800@www.loria.fr
SUMMARY:PhD Defense : Daishi Kondo
DESCRIPTION:Daishi Kondo will defend his thesis on Monday\, December 17th at 10.30am in room C005. \nHis presentation is entitled « Preventing information leakage in NDN with name and flow filters ».  \nThe jury members are:\n\nReporter:\nMiki Yamamoto\, Professor at Kansai University\nToru Hasegawa\, Professor at Osaka University\n\nExaminer:\nGiovanna Carofiglio\, Distinguished Engineer / Senior Director at Cisco Systems\nHouda Labiod\, Professor at Telecom ParisTech\nIsabelle Chrisment\, Professor at University of Lorraine\nSylvain Contassot-Vivier\, Professor at University of Lorraine\n\nInvited:\nHideki Tode\, Professor at Osaka Prefecture University\nTohru Asami\, CEO at ATR\n\nSupervisor:\nOlivier Perrin\, Professor at University of Lorraine\nThomas Silverston\, Associate Professor at Shibaura Institute of Technology\n\nAbstract:\n\nIn recent years\, Named Data Networking (NDN) has emerged as one of the most promising future networking architectures. To be adopted at Internet scale\, NDN needs to resolve the inherent issues of the current Internet. Since information leakage from an enterprise is one of the big issues even in the Internet and it is very crucial to assess the risk before replacing the Internet with NDN completely\, this thesis investigates whether a new security threat causing the information leakage can happen in NDN. Assuming that (i) a computer is located in the enterprise network that is based on an NDN architecture\, (ii) the computer has been already compromised by suspicious media such as a malicious email\, and (iii) the company installs a firewall connected to the NDN-based future Internet\, this thesis focuses on a situation that the compromised computer (i.e.\, malware) attempts to send leaked data to the outside attacker.\n\nNDN is basically a « pull »-based architecture and there are only two kinds of packets: Interest and Data\, which are a request and a response packet\, respectively. In order to retrieve content\, a consumer first sends the Interest to NDN network and then obtains the corresponding Data from the producer or the intermediate NDN node. In other words\, they cannot send a Data unless they receive the Interest packet. Therefore\, as one of the naive methods to mitigate information leakage through a Data\, an enterprise network firewall can carefully inspect a Data to publish\, and produce it instead of the inside employee in the network (i.e.\, a whitelist). In this case\, all the publicly-accessible content is on the firewall.\n\nHowever\, the firewall cannot manage a naming policy on the outside content and NDN forwarding nodes do not verify whether the name really exists. That causes a risk of information leakage through an Interest by malware’s hiding information such as customer information in the Interest name and sending it toward the outside attacker. The malware can pretend to access outside content\, so that it is quite difficult for the firewall to detect the information leakage attack. This thesis argues that the information leakage attack through an Interest in NDN should be one of the essential security attacks at protocol level and it is important to develop the detection method of this attack.\n\nThe contributions of this thesis are fivefold. Firstly\, this thesis proposes an information leakage attack through a Data and through an Interest in NDN. This thesis investigates the one through an Interest deeply\, and\, as a more advanced attack for the attacker to hide the malicious activity\, this thesis proposes a steganography-embedded Interest name to perform information leakage efficiently. To the best of author’s knowledge\, this is the first research about the information leakage attack in NDN.\n\nSecondly\, in order to address the information leakage attack\, this thesis proposes an NDN firewall which monitors and processes the NDN traffic coming from the consumers with the whitelist and blacklist. To design the firewall\, this thesis focuses on two requirements: (i) designing an NDN firewall independent from NDN Forwarding Daemon (NFD)\, which deicides how to forward an Interest\, and (ii) performing a fast lookup of the names or name prefixes in the whitelist and blacklist. By utilizing a cuckoo filter\, which is a probabilistic filter\, the proposed NDN firewall provides Interest packet filtering based on the names or name prefixes in the lists that can be updated on the fly. While satisfying the requirements and providing the functions\, the firewall implementation achieves high performance. Specifically\, the throughput degradation with the firewall is only from 0.912% to 2.34%\, which will be acceptable in an enterprise network.\n\nThirdly\, this thesis proposes an NDN name filter to classify a name in the Interest as legitimate or anomalous. Since NDN has not been deployed at large scale\, a dataset about NDN traffic does not exist. Assuming that it is highly possible for the future NDN naming policy to become the one naturally evolved from the current Uniform Resource Locator (URL) naming policy\, this thesis utilizes content names based on URLs collected by a web crawler. By using search engine information and applying the name dataset to an isolation forest\, this thesis builds NDN name filters. This thesis evaluates the performances of the name filters and shows that the proposed name filters can choke drastically the information leakage throughput per Interest and malware has to send 137 times more Interest packets to leak information than without using the filters.\n\nThe name filter can\, indeed\, reduce the throughput per Interest\, but to ameliorate the speed of this attack\, malware can send numerous Interests within a short period of time. Moreover\, the malware can even exploit an Interest with an explicit payload in the name (like HTTP POST message in the Internet)\, which is out of scope in the proposed name filter and can increase the information leakage throughput by adopting a longer payload. That is the limitation of the name filter. To take traffic flow to the NDN firewall from the consumer into account\, fourthly\, this thesis proposes an NDN flow monitored at an NDN firewall. At first\, this thesis introduces the concept of NDN flow and specifies it strictly\, which has not yet been standardized in NDN research. Then\, this thesis proposes a method to generate an NDN flow dataset analogically derived from the HTTP flow dataset in the current Internet because there is no dataset about NDN traffic.\n\nFifthly\, in order to deal with the drawbacks of the NDN name filter\, this thesis proposes an NDN flow filter to classify a flow as legitimate or not. Based on the generated NDN flow dataset\, this thesis builds an NDN flow filter against the information leakage attack. By applying the obtained dataset to a Support Vector Machine (SVM)\, this thesis builds an NDN flow filter against the information leakage attack\, and the performance evaluation shows that the information leakage throughput choked by the flow filter is from 1.87·10−4 to 8.08·10−3 times that of only by the name filter\, and the throughput choked by the name and flow filter under banning Interests with an explicit payload in the name reaches at most 1.72 Kbps. Thus\, the flow filter complements the name filter and greatly chokes the information leakage throughput.
URL:https://www.loria.fr/event/phd-defense-daishi-kondo/
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20181217T133000
DTEND;TZID=Europe/Paris:20181217T153000
DTSTAMP:20181213T080435Z
CREATED:20181213T080435Z
LAST-MODIFIED:20181213T080435Z
UID:6307-1545053400-1545060600@www.loria.fr
SUMMARY:PhD Defense : Oriane Dermy
DESCRIPTION:Oriane Dermy (Larsen) will defend her thesis on Monday\, December 17th at 1:30pm in room A008. \nHer presentation will be in French and is entitled « Prédiction du mouvement pour la robotique collaborative : du simple geste au mouvement corps entier ». \n \nRapporteurs : \nRachid Alami\, Directeur de recherche CNRS\, LAAS\nDaney David\, Chargé de recherche INRIA\, Bordeaux \n\nExaminateurs : \nCatherine Pelachaud\, Directeur de recherche\, CNRS\, Isir UPCM \n\nDirecteurs de thèse : \nSerena Ivaldi\, Chargée de recherche INRIA\, LORIA\nFrançois Charpillet\, Directeur de recherche INRIA\, LORIA \nRésumé :\nCette thèse se situe à l’intersection de l’apprentissage automatique et de la robotique humanoïde\, dans la thématique de l’interaction homme-robot\, et dans le domaine de la cobotique (robotique collaborative). Elle se focalise sur les interactions non verbales humain-robot\, en particulier sur l’interaction gestuelle. La prédiction de l’intention\, la compréhension et la reproduction de gestes sont donc des questions centrales de cette thèse.\nDans un premier temps\, il s’agit de faire apprendre au robot des gestes par démonstration : un utilisateur prend le robot par le bras et lui fait réaliser les gestes à apprendre et ce\, plusieurs fois. Le robot doit ensuite être capable de reproduire ces différents mouvements tout en les généralisant pour s’adapter au contexte. Pour cela\, à l’aide de ses capteurs proprioceptifs\, il interprète les signaux perçus pour comprendre le mouvement que lui fait réaliser l’utilisateur\, afin d’en générer des similaires par la suite.Dans un second temps\, le robot apprend à reconnaître l’intention de l’humain avec lequel il interagit et cela\, à partir des gestes que ce dernier initie : il s’agit ensuite pour le robot de produire les gestes adaptés à la situation et correspondant aux attentes de l’utilisateur. Cela nécessite que le robot comprenne la gestuelle de l’utilisateur. Pour cela\, différentes modalités perceptives ont été explorées. À l’aide de capteurs proprioceptifs\, le robot ressent les gestes de l’utilisateur au travers de son propre corps : il s’agit alors d’interaction physique humain-robot.À l’aide de capteurs visuels\, le robot interprète le mouvement de la tête de l’utilisateur. Enfin\, à l’aide de capteurs externes\, le robot reconnaît et prédit le mouvement corps entier de l’utilisateur. Dans ce dernier cas\, l’utilisateur porte lui-même des capteurs (vêtement X-Sens) qui transmettent sa posture au robot. De plus\, le couplage de ces modalités a été étudié. D’un point de vue méthodologique\, nous venons de voir que les questions d’apprentissage et dereconnaissance de séries temporelles (les gestes) ont été centrales dans cette thèse. Pour cela\, deux approches ont été développées. La première est fondée sur la modélisation statistique de primitives de mouvements (correspondant aux gestes) : les ProMPs. La seconde\, ajoute à la première du Deep Learning\, par l’utilisation d’auto-encodeurs\, afin de modéliser des gestes corps entier contenant beaucoup d’informations\, tout en permettant une prédiction en temps réel mou. Lors de cette thèse\, différents enjeux ont notamment été pris en compte pour la création et le développement de nos méthodes. Ces enjeux concernent : la prédiction des durées des trajectoires\, la réduction de la charge cognitive et motrice imposée à l’utilisateur\, le besoin de rapidité (temps réel mou) et de précision dans les prédictions.
URL:https://www.loria.fr/event/phd-defense-oriane-dermy/
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20181218T133000
DTEND;TZID=Europe/Paris:20181218T160000
DTSTAMP:20181214T085807Z
CREATED:20181214T085711Z
LAST-MODIFIED:20181214T085807Z
UID:6315-1545139800-1545148800@www.loria.fr
SUMMARY:PhD Defense : Maxime Compastié
DESCRIPTION:Maxime Compastié\, PhD student in Resist team will defend his thesis entitled « Software-defined security for distributed clouds » on Tuesday\, December 18th at 1:30 PM in Room B013. \nPhD Commitee:\n– Nora CUPPENS\, Full Professor\, IMT Atlantique – Reporter\n– Thierry GAYRAUD\, Full Professor\, Toulouse University – Reporter\n– Véronique LEGRAND\, Associate Professor\, Conservatoire National des Arts et Métiers – Examiner\n– Pierre-Etienne MOREAU\, Full Professor\, Lorraine University – Examiner\n– Olivier FESTOR\, Full Professor\, Lorraine University – Examiner / PhD Supervisor\n– Rémi BADONNEL\, Associate\, Lorraine University – Examiner / PhD Advisor\n– Ruan HE\, Chief Cloud Architect\, Tencent – Invited / Industrial Advisor\n– Sok-Yen LOUI\, Research Engineer\, Orange Labs – Invited / Industrial Advisor \nAbstract:\nIn this thesis\, we propose an approach for software-defined security in distributed clouds. More specifically\, we show to what extent this programmability can contribute to the protection of distributed cloud services\, through the generation of securized unikernel images. These ones are instanciated in the form of lightweight virtual machines\, whose attack surface is limited and whose security is driven by a security orchestrator. The contributions of this thesis are threefold. First\, we present a logical architecture supporting the programmability of security mechanims in a multi-cloud and multi-tenant context. It permits to align and parameterize these mechanisms for cloud services whose resources are spread over several providers and tenants. Second\, we introduce a method for generating securized unikernel images in an on-the-fly manner. This one permits to lead to specific and contrained resources\, that integrate security mechanisms as soon as the image generation phase. These ones may be built in a reactive or proactive manner\, in order to address elasticity requirements. Third\, we propose to extend the TOSCA orchestration language\, so that is is possible to generate automatically securised resources\, according to different security levels in phase with the orchestration. Finally\, we detail a prototyping and extensive series of experiments that are used to evaluate the benefits and limits of the proposed approach. \nKeywords: Security Management\, Programmability\, Distributed Cloud\, Orchestration\, Unikernel
URL:https://www.loria.fr/event/phd-defines-maxime-compastie/
CATEGORIES:Soutenance
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