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BEGIN:VEVENT
DTSTART;VALUE=DATE:20240701
DTEND;VALUE=DATE:20240707
DTSTAMP:20260516T122715
CREATED:20240422T084046Z
LAST-MODIFIED:20240422T084046Z
UID:26050-1719792000-1720310399@www.loria.fr
SUMMARY:IJCAR 2024 : International Joint Conference on Automated Reasoning
DESCRIPTION:IJCAR 2024 will be organized in Nancy\, France by the Inria research center at University of Lorraine as an in-person conference. It will take place at IDMC from July 1 to July 6\, 2024. \nImportant Dates \n\nAbstract submission: January 29\, 2024\nPaper submission: February 5\, 2024 (AoE)\nRebuttal: March 10-12\, 2024\nNotification: March 28\, 2024\nCamera-ready version: April 28\, 2024\nCo-located events: July 1-2\, 2024\nConference\, including CASC: July 3-6\, 2024\n\n  \nThe CADE ATP System Competition CASC and the Termination Competition will take place during IJCAR. This year we are planning a special session to celebrate 30 years of CASC. \nThe 2024 edition of the SAT/SMT/AR summer school will take place in Nancy during the week preceding IJCAR 2024. \n\nEvent website
URL:https://www.loria.fr/event/ijcar-2024-international-joint-conference-on-automated-reasoning/
LOCATION:IDMC\, 13 Rue Michel Ney\, Nancy\, France
CATEGORIES:Conférence
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20240704T133000
DTEND;TZID=Europe/Paris:20240704T163000
DTSTAMP:20260516T122715
CREATED:20240527T131417Z
LAST-MODIFIED:20240527T131417Z
UID:26330-1720099800-1720110600@www.loria.fr
SUMMARY:PhD Defense: Abdelkarim Elassam (Tangram)
DESCRIPTION:Abdelkarim Elassam (Tangram) will defend his thesis\, entitled « Learning-based vanishing point detection and its application to large-baseline image registration »\, on Thursday\, July 4 at 1:30 p.m.\, in room A008. \nAbstract\n\nThis thesis examines the detection of vanishing points and the horizon line and their application to visual localization tasks in urban environments. The thesis proposes new deep learning methods to overcome the limitations of existing approaches to vanishing point detection. The first key contribution introduces a novel approach for horizon line and vanishing point detection. Unlike most existing methods\, our method directly infers both the HL and an unlimited number of horizontal VPs\, even those extending beyond the image frame. The second key contribution of this thesis is a structure-enhanced vanishing point detector. This method utilizes a multi-task learning framework to estimate multiple horizontal vanishing points from a single image. It goes beyond simple vanishing point detection by generating masks that identify vertical planar structures corresponding to each vanishing point\, providing valuable scene layout information. Experimental results demonstrate that our method outperforms traditional line-based methods and modern deep learning-based methods. The thesis then explores the use of vanishing points for image matching and registration\, particularly in cases where images are captured from vastly different viewpoints. Despite continuous progress in feature extractors and descriptors\, these methods often fail in the presence of significant scale or viewpoint variations. The proposed methods address this challenge by incorporating vanishing points and scene structures. One major challenge in using vanishing points for registration is establishing reliable correspondences\, especially in large-scale scenarios. This work addresses this challenge by proposing a vanishing point matching method aided by the detection of masks of vertical scene structures corresponding to these vanishing points. To our knowledge\, this is the first implementation of a method for vanishing point matching that exploits image content rather than just detected segments. This vanishing point correspondence facilitates the estimation of the camera’s relative rotation\, particularly in large-scale scenarios. Additionally\, incorporating information from scene structures enables more reliable keypoint correspondence within these structures. Consequently\, the method facilitates the estimation of relative translation\, which is itself constrained by the rotation derived from the vanishing points. The quality of rotation can sometimes be impacted by the imprecision of detected vanishing points. Therefore\, we propose a vanishing point-guided image matching method that is much less sensitive to the accuracy of vanishing point detection.\n\nJury\n\nReviewers:\n\n\nVincent Lepetit\, Professeur des Universités – Ecole des Ponts ParisTech\n\n\nValérie Gouet-Brunet\, Directrice de Recherche – IGN – LaSTIG – Université Gustave Eiffel\n\n\nExaminers:\n\n\nAlain Pagani\, Principal Researcher – DFKI – Kaiserslautern\n\n\nPierrick Gaudry\, Directeur de Recherche – LORIA CNRS\n\n\nSupervisors:\n\n\n\n\nGilles Simon\, Professeur des Universités – Université de Lorraine\n\n\nMarie-Odile Berger\, Directrice de Recherche – Inria Nancy – Grand Est
URL:https://www.loria.fr/event/phd-defense-abdelkarim-elassam-tangram/
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20240705T090000
DTEND;TZID=Europe/Paris:20240705T110000
DTSTAMP:20260516T122715
CREATED:20240628T150932Z
LAST-MODIFIED:20240628T151312Z
UID:26478-1720170000-1720177200@www.loria.fr
SUMMARY:PhD Defense: Runbo Su (Simbiot)
DESCRIPTION:Runbo Su (Simbiot) will defend his thesis\, entitled « Trust Management in Service-Oriented Internet of Things (SO-IoT) »\, on Friday\, July 5 at 9 a.m.\, in room C005. \nAbstract\n\nUnlike Trust in Social Science\, in which interactions between humans are measured\, thanks to the integration of numerous smart devices\, Trust in IoT security focuses more on interactions between nodes. Moreover\, As IoT nodes can somehow benefit from ‘Group’/’Community’ since they form by similar interests or functionalities\, the assessment of Group-Individual and Inter-Individual Trust is also important. However\, handling limitations brought by potential threats\n(e.g.\, trust-related attack and BAR Model) and inherent vulnerability due to Trust Management (TM) architecture remains challenging. This thesis investigates Trust from three perspectives in the Service-Oriented Internet of Things (SO-IoT): Inter-Group Trust\, Group-Individual Trust\, and Inter-Individual Trust. Firstly\, a role-based dynamic model is developed to assess intra- and inter-community(group)\, enhancing service-oriented activities and addressing security issues within and between communities. A locally centralized four-phase approach is employed\, focusing on countermeasures against attacks on services within the community. Additionally\, a three-phase mechanism is devised to measure cooperativeness between communities. An implementation based on the ROS 2 system was implemented to analyze the performance of the proposed model based on the preliminary results. Secondly\, to address misbehavior in SO-IoT in terms of Inter-Individual trust\, a Stochastic Bayesian Game (SBG) is introduced. This game theoretical model considers the heterogeneity of IoT nodes and complex behavioral schemes of service providers\, encouraging cooperation and penalizing malicious strategical actions. Lastly\, the work of assessing the Trust of V2X messages in IoV demonstrates the possibility of implementing Trust Management in a concrete IoT environment.\n\nKeywords: Trust Management\, IoT security\, Trust modeling\, misbehavior detection\, Service-Oriented IoT\n\nJury\n\nReviewers:\n\n\nValeria Loscrì\, Inria Lille-Nord Est\n\n\nAbdelmadjid Bouabdallah\, Université de technologie de Compiègne\n\n\nExaminers:\n\n\nClaudia-Lavinia Ignat\, Inria-Nancy Grand Est\, Université de Lorraine\n\n\nAnna Maria Mandalari\, University College London\n\n\n\nInvités:\n\n\n\n\nYe-Qiong Song\, Université de Lorraine\n\n\nRiahi Arbia\, Cylab\, CISS\, Royal Military Academy\, Belgium\n\n\n\nSupervisors:\n\n\n\n\nEnrico Natalizio\, Loria\, Université de Lorraine; Technology Innovation Institute (TII)\, UAE\n\n\nPascal Moyal\, IECL\, Université de Lorraine
URL:https://www.loria.fr/event/phd-defense-runbo-su-simbiot/
LOCATION:C005
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240709
DTEND;VALUE=DATE:20240710
DTSTAMP:20260516T122715
CREATED:20240425T121054Z
LAST-MODIFIED:20240425T121310Z
UID:26107-1720483200-1720569599@www.loria.fr
SUMMARY:NCSB : Nancy Computational Structural Biology meeting
DESCRIPTION:NCSB is an annual meeting where scientists working on computational and structural biology come together in a friendly environment and share their recent projects. It is also an opportunity for younger scientists to bring posters and discuss their work. This meeting takes place in Nancy\, the cradle of the “Art Nouveau”. \n\nGo to the event website\nRead the program
URL:https://www.loria.fr/event/ncsb-2024/
LOCATION:Loria (Amphi Gilles Kahn)
CATEGORIES:Séminaire
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20240710T100000
DTEND;TZID=Europe/Paris:20240710T110000
DTSTAMP:20260516T122715
CREATED:20240628T092202Z
LAST-MODIFIED:20240628T092202Z
UID:26468-1720605600-1720609200@www.loria.fr
SUMMARY:Data Science for Cybersecurity: An Overview Focused on Networking\, a talk by Michele Nogueira
DESCRIPTION:Michele Nogueira\, Associate Professor at the Department of Computer Science at the Federal University of Minas Gerais will present a seminar entitled « Data Science for Cybersecurity: An Overview Focused on Networking ». It will take place at 10 am\, in room A008. \nAbstract: Cyberattacks persistently pose threats to valuable data\, resulting in time and resource wastage\, and damaging the reputation of companies and institutions worldwide. Even prominent organizations have experienced the severe consequences of cyberattacks\, impacting not only themselves but also their customers\, collaborators\, and society at large. Ransomware such as WannaCry continues to pose a significant threat\, encrypting data and demanding ransom payments in Bitcoin cryptocurrency. Furthermore\, sophisticated versions of Distributed Denial-of-Service (DDoS) attacks\, originating from various Internet-connected devices like IP cameras\, residential gateways\, and baby monitors\, disrupt major Internet platforms and services\, affecting users globally. These examples underscore the increasing power and sophistication of cyberattacks\, highlighting the evolving nature of cybersecurity. In the era of ubiquitous systems\, with an estimated 75 billion connected devices or “smart things” expected by 2025\, the urgency to address these issues is apparent. However\, academia’s role in combating the sophistication of cyberattacks remains a crucial aspect. Can academia effectively anticipate attackers’ next moves to safeguard information assets? How can academia harness the data generated on networks to develop security intelligence and preemptively prevent attacks? This presentation aims to initiate a discussion surrounding these questions\, providing an overview of the related research conducted by Dr. Nogueira’s research team\, along with future directions in these areas. \nBio: Michele Nogueira is an Associate Professor in the Computer Science Department at the Federal University of Minas Gerais (UFMG)\, Brazil. She received her doctorate in Computer Science from the University Pierre et Marie Curie – Sorbonne Université\, France. She was on sabbatical leave at Carnegie Mellon University\, USA (2016-2017). Her research interests include wireless networks\, security\, and dependability. She has worked on providing resilience to self-organized\, cognitive\, and wireless networks through adaptive and opportunistic approaches. Dr. Nogueira was one of the pioneers in addressing survivability issues in self-organized wireless networks\, being the work “A Survey of Survivability in Mobile Ad Hoc Networks” one of her prominent scientific contributions. She has received Academic Scholarships from the Brazilian Government in her undergraduate and graduate years\, and international grants such as the ACM SIGCOMM Geodiversity program. She has served as Associate Technical Editor for the IEEE Communications Magazine. She served as chair for the IEEE ComSoc Internet Technical Committee and is an ACM and IEEE Senior Member.
URL:https://www.loria.fr/event/data-science-for-cybersecurity-an-overview-focused-on-networking-a-talk-by-michele-nogueira/
LOCATION:A008
CATEGORIES:Séminaire
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20240710T140000
DTEND;TZID=Europe/Paris:20240710T160000
DTSTAMP:20260516T122715
CREATED:20240612T153126Z
LAST-MODIFIED:20240612T153126Z
UID:26419-1720620000-1720627200@www.loria.fr
SUMMARY:PhD Defense: Alaaeddine Chaoub (Synalp)
DESCRIPTION:Alaaeddine Chaoub (Synalp) will defend his thesis\, entitled « Deep learning representations for prognostics and health management »\, on Wednesday\, July 10 at 2 p.m.\, in Amphi 7\, Bâtiment Victor Grignard. \nAbstract\nThis thesis contributes to the application of Deep Learning (DL) in Remaining useful life (RUL) prediction of industrial equipment\, addressing significant challenges in this field. Our research is driven by the need to develop DL architectures that mitigate performance degradation under various operating conditions\, to improve model interpretability\, and to address data scarcity by leveraging external (un)labeled data. We structured our work into two principal parts. In the first part\, we explore architectures capable of handling data variability resulting from different operating conditions\, without manual feature engineering. This led us to propose an MLP-LSTM-MLP architecture. By employing an MLP at the first stage\, we were able to normalize this variability\, thus improving performances under such settings. Furthermore\, To enhance interpretability\, we proposed to replaced the first-stage MLP stage with a Gated mixture of experts (GMoE) system\, enabling interpretable decomposition based on operating conditions. The second part of the thesis addresses the issue of data scarcity\, a widely recognized challenge in the Prognostics and health management (PHM) field. Through the introduction of adapters\, i.e. task-specific layers that address the challenge of handling multiple input/output structures\, we proposed an auxiliary training approach that leverages external labeled data\, presenting a method that surpasses traditional techniques found in the literature. Moreover\, to utilize external unlabeled data in auxiliary training\, We proposed a meta-learning approach to automatically derive auxiliary objectives from these data by pseudo-labeling them in an end-task aware manner. The goal of this part was to leverage broader spectrum of available data to improve RUL prediction performances. In reflecting upon our work\, we acknowledge the limitations of the proposed approaches and suggest both immediate and long-term directions for future research. These include tackling the challenges of processing long sequence data\, further improving model interpretability\, addressing data scarcity with more advanced training methodologies\, and exploring the potential of federated learning and large language models in industrial settings.\nJury\n\nReviewers:\n\nEmmanuel Ramasso – Associate professor HDR – Institut FEMTO-ST\nCéline Hudelot – Professor – Centralesupelec University of Paris-Saclay\n\nExaminers:\n\nBernardetta Addis – Professor – Université de Lorraine\nBirgit Vogel-Heuser – Professor – Technische Universität München\nRaphaël Couturier – Professor – Université de Franche-Comté\n\nSupervisors:\n\n\n\nChristophe Cerisara – Researcher HDR (CR) – Université de Lorraine\, LORIA\nAlexandre Voisin – Associate professor HDR – Université de Lorraine\, CRAN
URL:https://www.loria.fr/event/phd-defense-alaaeddine-chaoub-synalp/
LOCATION:Amphi 7\, Bâtiment Victor Grignard
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20240710T140000
DTEND;TZID=Europe/Paris:20240710T160000
DTSTAMP:20260516T122715
CREATED:20240630T202622Z
LAST-MODIFIED:20240710T081214Z
UID:26417-1720620000-1720627200@www.loria.fr
SUMMARY:PhD Defense: Diego Amaya (Capsid)
DESCRIPTION:Diego Amaya (Capsid) will defend his thesis\, entitled « Data science approach for the exploration of HLA antigenicity based on 3D structures and molecular dynamics »\, on Wednesday\, July 10 at 2 p.m.\, in room A008. \nAbstract:\n\n\nOrgan transplantation is the sole treatment for end-stage organ failure\, with 10\,810 patients on the waiting list and only 5\,493 transplants conducted in France in 2022. The primary cause of transplant loss is the recipient’s alloimmune response\, particularly the humoral response producing donor-specific antibodies (DSA) against Human Leukocyte Antigen (HLA) mismatches. The complexity of the HLA system\, with its vast genetic polymorphism\, poses challenges for defining compatibility between donor and recipient\, which currently relies on serological classifications at antigen level.\nThis thesis addresses the challenge of defining HLA antigenicity through 3D molecular data and data science. High-quality 3D structures and molecular dynamics (MD) simulations of 207 HLA antigens were generated and analyzed to understand structural differences influencing antigenicity. Complementarily\, 3D shape descriptors for comparison of dynamic protein surfaces (i.e. Zernike descriptors) were explored. Additionally an HLA Epitope Predictor (HLA-EpiCheck)  was trained on static and dynamic structural features to predict HLA antigenicity. The predictor demonstrated a high performance in identifying eplets\, showing strong agreement with experimental data. Finally\, HLA-3D-Diff was developed to visualize and compare 3D HLA structures\, aiding in the understanding of complex immunization patterns.\n\nKeywords: Organ transplantation\, molecular dynamics\, HLA antigenicity\, data science.\n\nThesis Committee:\n\nReviewers: \n\n\n\nJuliette Martin\, DR CNRS\, École Normale Supérieure Lyon\nFrédéric Cazals\, DR INRIA\, Centre Sophia Antipolis–Méditerranée\n\n\n\nExaminers: \n\n\n\nManuel Dauchez\, Professor\, University of Reims Champagne-Ardenne\nOlivier Toutirais\, Professor\, University of Caen\nMalika Smaïl-Tabbone\, MC HDR\, University of Lorraine\n\n\n\nSupervisors: \n\n\n\nMarie-Dominique Devignes\, CR CNRS (HDR)\, LORIA Nancy\nJean-Luc Taupin\, Professor\, University Paris-Cité
URL:https://www.loria.fr/event/phd-defense-diego-amaya-capsid/
LOCATION:A008
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20240711T093000
DTEND;TZID=Europe/Paris:20240711T113000
DTSTAMP:20260516T122715
CREATED:20240612T153913Z
LAST-MODIFIED:20240612T153913Z
UID:26422-1720690200-1720697400@www.loria.fr
SUMMARY:Soutenance de Marco Freire (MFX)
DESCRIPTION:Marco Freire (MFX) soutiendra sa thèse intitulée « Problèmes d’agencement sous contraintes topologiques pour la fabrication computationnelle » le 11 juillet à 9h30 en salle C005. \nRésumé\nLes problèmes d’agencement surviennent dans de nombreux domaines en ingénieurie et en informatique. Ils sont constitués d’objets\, d’un espace\, de contraintes et d’objectifs. Un agencement valide est un placement des objets dans l’espace respectant les contraintes et optimisant les objectifs. La conception de circuits électroniques et la planification architecturale sont des exemples de problèmes d’agencement. Ceux-ci impliquent souvent des contraintes topologiques portant sur les relations entre les objets\, comme des composants électroniques étant connectés ou des salles n’étant pas contigües sur un plan. Cette thèse s’intéresse aux problèmes d’agencement avec des contraintes topologiques dans la fabrication computationnelle\, plus précisément pour l’agencement de circuits électroniques et la génération de supports pour l’impression 3D. Ces technologies sont plus accessibles que jamais grâce à l’existence d’imprimantes 3D abordables et de services en ligne de fabrication de circuits. \nJury\nEncadrant \n\nSylvain LEFEBVRE\, Université de Lorraine\, CNRS\, Inria\, LORIA\n\nRapporteurs \n\nTamy BOUBEKEUR\, Adobe Research France\, Telecom Paris\nNobuyuki UMETANI\, University of Tokyo\n\nExaminatrice: \n\nMélina SKOURAS\, Université Grenoble Alpes\, Inria\, CNRS\, Grenoble INP\, LJK\n\nInvitée \n\nMarie-Odile BERGER\, Université de Lorraine\, CNRS\, Inria\, LORIA
URL:https://www.loria.fr/event/soutenance-de-marco-freire-mfx/
LOCATION:C005
CATEGORIES:Soutenance
END:VEVENT
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