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*  [https://www.enib.fr/~chevaill/documents/master/siia_bibl/Safety_and_Usability_of_Speech_Interfaces_for_In-V.pdf Safety and Usability of Speech Interfaces for In-Vehicle Tasks while Driving: A Brief Literature Review], Adriana Barón and Paul Green, Tec/. Report, The University of Michigan Transportation Research Institute, 2006.
*  [https://www.enib.fr/~chevaill/documents/master/siia_bibl/Safety_and_Usability_of_Speech_Interfaces_for_In-V.pdf Safety and Usability of Speech Interfaces for In-Vehicle Tasks while Driving: A Brief Literature Review], Adriana Barón and Paul Green, Tec/. Report, The University of Michigan Transportation Research Institute, 2006.


== Sujets d'étude 2021-2022 ==
= Sujets d'étude 2022-2023 =


=== Programmation de systèmes domotiques par les utilisateurs finaux ===
=== How collaboration in mixed reality can benefit from the use of heterogeneous devices? ===


* Enseignant : Eric Maisel (maisel@enib.fr), Lab-STICC, ENIB
* Enseignants : [mailto:cedric.fleury@imt-atlantique.fr Cédric Fleury] et [mailto:etienne.peillard@imt-atlantique.fr Etienne Peillard]
* Sujet en lien avec un stage : non.
* Sujet en lien avec un stage : oui ([[Stages|Perception of Shared Spaces in Collaborative Augmented Reality]])
* '''stage non attribué'''


Le développement de la programmation événementielle, basée sur des règles trigger-action, contribue au développement des systèmes domotiques en permettant la mise en relation entre capteurs (de température, de luminosité, de présence, ...) et effecteurs (ampoules, radiateurs, stores, ...). L'utilisation de ces systèmes ayant pour objectif d'améliorer la sécurité et le confort dans les bâtiments mais également de permettre d'en réduire l'emprunte écologique. La personnalisation de ces systèmes est nécessaire de façon à les adapter aux différents contextes tant architecturaux que technologiques, environnementaux et culturels. A court et moyen terme cette adaptation passe encore par une programmation de ces systèmes domotiques par les utilisateurs finaux (tout un chacun chez soi, le personnel soignant voire les patients dans les hôpitaux, les salariés dans les bureaux, ...). Cette tâche de programmation est plus complexe qu'il n'y parait et suppose le développement d'assistants logiciels de façon à la rendre accessible.
The massive development of display technologies brings a wide range of new devices, such as mobile phones, AR/VR headsets and large displays, available to the general public. These devices offer many opportunities for co-located and remote collaboration on physical and digital content. Some can handle groups of co-located users [10, 13], while others enable remote users to connect in various situations [3, 6, 11, 14]. For example, some previous systems allow users to use a mobile device to interact with a co-located partner wearing a VR headset [4, 7]. Other systems enable users in VR to guide a remote collaborator using an AR headset [1, 8, 9, 12].
[1] H. Bai, P. Sasikumar, J. Yang, and M. Billinghurst. "A User Study on Mixed Reality Remote
Collaboration with Eye Gaze and Hand Gesture Sharing". Proceedings of the CHI Conference on
Human Factors in Computing Systems (CHI’20), 2020.


L'objet de cette étude bibliographique est d'une part de présenter en quoi consiste cette programmation trigger-action à partir d'articles académiques et d'autre part d'esquisser un panorama des différentes approches envisageables afin  d'en faciliter l'utilisation par les utilisateurs finaux.
[2] H. H. Clark, and S. E. Brennan. "Grounding in communication". In: L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). American Psychological Association. 1991.


B. Ur, E. McManus, M. Pak Yong Ho, M. L. Littman, "Practical trigger-action programming in the smart home", Proc. of the SIGCHI Conference on Human Factors in Computing Systems, pp 802-812, CHI'14, Avril 2014, Toronto, Canada.
[3] C. Fleury, T. Duval, V. Gouranton, A. Steed. "Evaluation of Remote Collaborative Manipulation for Scientific Data Analysis", ACM Symposium on Virtual Reality Software and Technology (VRST’12), 2012.


B. Ur, M. Pak Yong Ho, S. Brawner, J. Lee, S. Menniken, N. Picard, D. Schulze, M. L. Littman, "Trigger-Action Programming in the Wild : An Analysis of 200.000 IFTTT Recipes" in Proc. of the 2016 CHI Conference on Human Factors in Computing Systems, pp-3227-3231, CHI'16, Mai 2016, San Jose, USA.
[4] J. Gugenheimer, E. Stemasov, J. Frommel, and E. Rukzio. "ShareVR: Enabling Co-Located Experiences for Virtual Reality between HMD and Non-HMD Users". Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17), 2017.


J. Huang, M. Cakmak, "Supporting mental model accuracy in trigger-action programming" in Proc. of the 2015 ACM International Conference on Pervasive and Ubiquitous Computing, pp 215-225, UbiComp'15, Septembre 2015,Osaka, Japon.
[5] J. Hollan and S. Stornetta. "Beyond being there". In : Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI’92), 1992.


F. Paterno, S. Alawadi, "Towards Intelligent Personalization of IoT Platforms" in Proc. of 2019 ACM Conference on Intelligent User Interfaces, IUI'19, Mars 2019, Los Angeles, USA.
[6] B. T. Kumaravel, F. Anderson, G. Fitzmaurice, B. Hartmann, and Tovi Grossman. "Loki:Facilitating Remote Instruction of Physical Tasks Using Bi-Directional Mixed-Reality Telepresence". Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '19), 2019.


A. Mattioli, F. Paterno, "A Visual Environment for End-User Creation of IoT Customization Rule with Recommendation Support" in Proc. of the International Conference on Advanced Visual Interfaces, pp 1-5, AVI'20, Septembre 2020, Salerno, Italie.
[7] B. T. Kumaravel, C. Nguyen, S. DiVerdi, and B. Hartmann. "TransceiVR: Bridging Asymmetrical Communication Between VR Users and External Collaborators". Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '20), 2020.


F. Corno, L. De Russis, A. Monge Roffarello, "TAPrec : Supporting the Composition of Trigger-Action Rules Through Dynamic Recommendations" in Proc. of the 25th Conference on Intelligent User Interfaces, pp 579-588, IUI'20, Mars 2020, Cagliari, Itale.
[8] M. Le Chénéchal, T. Duval, J. Royan, V. Gouranton, and B. Arnaldi. “Vishnu: Virtual Immersive Support for HelpiNg Users - An Interaction Paradigm for Remote Collaborative Maintenance in Mixed Reality”. Proceedings of 3DCVE 2016 (IEEE VR 2016 International Workshop on 3D Collaborative Virtual Environments). 2016.


F. Corno, L. De Russis, A. Monge Roffarello, "A Semantic Web Approach to Simplifying Trigger-Action Programming in the IoT", in Computer, vol 50, Issue 11, pp 18-24, 2017.
[9] M. Le Chénéchal, T. Duval, V. Gouranton, J. Royan, and B. Arnaldi. “The Stretchable Arms for Collaborative Remote Guiding”. Proceedings of ICAT-EGVE 2015, Eurographics. 2015.


A.-M. Vainio, M. Valtonen, J. Vanhala, "Proactive Fuzzy Control and Adaptation Methods for Smart Homes" in IEEE Intelligent Systems, vol 23 issue 2, pp 42-49, 2008.
[10] C. Liu, O. Chapuis, M. Beaudouin-Lafon, and E. Lecolinet. “Shared Interaction on a Wall-Sized Display in a Data Manipulation Task.” In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. CHI ’16.


A.-M. Vainio, M. Valtonen, J. Vanhala, "Learning and adaptive fuzzy control system for smart home", in Developing Ambient Intelligence, pp 28-47, Springer.
[11] P. Mohr, S. Mori, T. Langlotz, B. H. Thomas, D. Schmalstieg, and D. Kalkofen. "Mixed Reality Light Fields for Interactive Remote Assistance". Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’20), 2020.


=== Systèmes de recommandation sérendipitifs ===
[12] O. Oda, C. Elvezio, M. Sukan, S. Feiner, and B. Tversky. "Virtual Replicas for Remote Assistance in Virtual and Augmented Reality". Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (UIST '15), 2015.


* Enseignant•e•(s) : Eric Maisel (maisel@enib.fr), Lab-STICC, ENIB
[13] Y. Okuya, O. Gladin, N. Ladévèze, C. Fleury, P. Bourdot. "Investigating Collaborative Exploration of Design Alternatives on a Wall-Sized Display", ACM Conference on Human Factors in Computing Systems (CHI’20), 2020.
* sujet en lien avec un stage : non.


Les systèmes de recommandation sont utilisés pour proposer à un utilisateur particulier confronté à un problème donné un ensemble de solutions pertinentes. Ils sont d'autant plus nécessaires que la quantité d'informations accessibles ne cesse d'augmenter et de dépasser la quantité d'information qu'un être humain peut traiter.
[14] H. Xia, S. Herscher, K. Perlin, and D. Wigdor. "Spacetime: Enabling Fluid Individual and Collaborative Editing in Virtual Reality". Proceedings of the ACM Symposium on User Interface Software and Technology (UIST ’18), 2018.


Il s'agira dans ce travail de se focaliser sur un problème particulier : celui des bulles informationnelles. L'apparition de ces bulles est directement lié à la nature des algorithmes de recommandation : ceux-ci calculent leurs propositions en tenant compte des choix précédents de l'utilisateur ou de ceux des autres utilisateurs dans la mesure où ceux-ci sont similaires à l'utilisateur considéré. Dans les deux cas les recommandations faites à cet utilisateur restent limitées et n'évoluent que peu.
=== How telepresence systems can support collaborative dynamics in large interactive spaces? ===


La sérendipité - une des cibles de cette étude bibliographique -  est la propriété que satisfont les systèmes de recherche d'information quand ils sont capables de proposer des solutions qui sont à la fois pertinentes pour l'utilisateur et auxquelles cet utilisateur ne s'attendait pas. Cette propriété est une des solutions aux bulles informationnelles.
* Enseignant : [mailto:cedric.fleury@imt-atlantique.fr Cédric Fleury
 
* Sujet en lien avec un stage : non.
Cette étude bibliographique a pour objectif d'une part de rappeler ce que sont les systèmes de recommandation, en particulier les systèmes de recommandation basés sur les connaissances et d'autre part de présenter les différentes approches permettant de mettre en oeuvre des systèmes de recommandation sérendipitifs. Il faudra également s'intéresser à la manière dont ces systèmes peuvent être évalués.


Videoconferencing and telepresence have long been a way to enhance communication among remote users. They improve turn-taking, mutual understanding, and negotiation of common ground by supporting non-verbal cues such as eye-gaze direction, facial expressions, gestures, and body langue [3, 6, 10]. They are also an effective solution to avoid the "Uncanny Valley" effect [7] that can be encountered when using avatars.


A. Ameen, "Knowledge based Recommendation System in Semantic Web - A Survey" in International Journal of Computer Applications, Vol. 182, No 43, Mars 2019 .
However, such systems are often limited to basic setups in which each user must seat in front of a computer equipped with a camera. Other systems, such as Multiview [9] or MMSpace [8], handle groups, but still only group-to-group conversations are possible. This leads to awkward situations in which colleagues in the same building stay in their office to attend a videoconference meeting instead of attending together, or participants are forced to have side conversations via chat. More recent work investigates dynamic setups that allow users to move into the system and interact with share content. t-Rooms [5] displays remote users on circular screens around a tabletop. CamRay [1] handles video communication between two users interacting on remote wall-sized displays. GazeLens [4] integrates a remote user in a group collaboration around physical artifacts on a table. Nevertheless, such systems do not support different moments in the collaboration, such as tightly coupled and loose collaboration, subgroup collaboration, spontaneous or side discussions. Supporting such dynamics in collaboration is a major challenge for the next telepresence systems.


Y. Du, S. Ranwez, N. Sutton-Charani, V. Ranwez, "Apport des ontologies aux systèmes de recommandation : état de l'art et perspective", In Proc. of 30es Journées Francophones d'Ingéniérie des Connaissances, IC 2019, AFIA, Juillet 2019, Toulouse, France, pp 64-77 .
[1]  I. Avellino, C. Fleury, W. Mackay and M. Beaudouin-Lafon. “CamRay: Camera Arrays Support Remote Collaboration on Wall-Sized Displays”. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI’17). 2017.


Y. Wang, N. Stash, L. Aroyo, L. Hollink, G. Scheiber, "Using Semantic Relations for Content-based Recommender Systems in Cultural heritage", in Proc. of Worshop on Ontology Patterns 2009  in  ISWC workshop, 2009.
[3]  E. A. Isaacs and J. C. Tang. “What Video Can and Can’t Do for Collaboration: A Case Study.” Proceedings of the ACM International Conference on Multimedia (MULTIMEDIA’93). 1993.


D. Kotlov, S Wang, J. Veijalaien, "A survey of serendipity in recommender system", Knowledge-based Systems, vol 111, pp 180-192, November 2016 .
[4]  K.-D. Le, I. Avellino, C. Fleury, M. Fjeld, A. Kunz. “GazeLens: Guiding Attention to Improve Gaze Interpretation in Hub-Satellite Collaboration”. Proceedings of the Conference on Human- Computer Interaction (INTERACT’19). 2019.


D Kotlov, J. Veijalain, S. Wang, "Challenges of Serendipity in Recommender Systems", in Proc. of the 12th International Conference on Web Information Systems and Technologies, WEBIST 2016, Vol 2 pp 251-256 .
[5]  P. K. Luff, N. Yamashita, H. Kuzuoka, and C. Heath. “Flexible Ecologies And Incongruent Locations.” Proceedings of the Conf. on Human Factors in Computing Systems. (CHI ’15). 1995.


N.I.Y. Saat, S.A.M. Noah, M. Mohd, "Towards Serendipity for Content-Based Recommender Systems", International Journal on Advanced Science Engineering Information Technology, Vol. 8, No 4-2, pp 1762-1769, 2018 .
[6]  A. F. Monk and C. Gale. “A Look Is Worth a Thousand Words: Full Gaze Awareness in Video- Mediated Conversation.” In: Discourse Processes 33.3, 2002, pp. 257–278.


L. Iaquinta, M.de Gemmis, P. Lops, G. Semeraro, M. Filannino, P. Molino, "Introducing Serendipity in a Content-based Recommender System", in Proc. of the IEEE Eighth International Conference on Hybrid Intelligent Systems, Barcelone, Espagne.
[7]  M. Mori, K. F. MacDorman and N. Kageki, “The Uncanny Valley [From the Field]”, IEEE Robotics & Automation Magazine, vol. 19, no. 2, pp. 98-100, 2012.


E. E. Toms, "Serendipitous Information Retrieval", in Proc. of DELOS, Workshop : Information Seeking, Searching and Quering in Digital Libraries, pp 17-20, 2000 .
[8]  K. Otsuka, “MMSpace: Kinetically-augmented telepresence for small group-to-group conversations”. Proceedings of 2016 IEEE Virtual Reality (VR’16). 2016.


L. McGinty, B. Smyth (2003) On the Role of Diversity in Conversational Recommender Systems. In: Ashley K.D., Bridge D.G. (eds) Case-Based Reasoning Research and Development. ICCBR 2003. Lecture Notes in Computer Science, vol 2689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45006-8_23
[9]  A. Sellen, B. Buxton, and J. Arnott. “Using Spatial Cues to Improve Videoconferencing.” Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI ’92). 1992.


T. Dorjmaa, T. Shin, "Evaluating the Quality of Recommendation System by Using Serendipity Measure", in Journal of Intelligent Systems, Vol 25, No 4, pp 89-103, Décembre 2019.
[10]  E. S. Veinott, J. Olson, G. M. Olson, and X. Fu. “Video Helps Remote Work: Speakers Who Need to Negotiate Common Ground Benefit from Seeing Each Other.” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’99). 1999.


M. Kaminskas, D. Bridge, "Measuring Surprise in Recommender Systems", in Proc. of Workshop on Recommender Systems Evaluation : Dimensions and Design (REDD 2014), held in conjunction with RecSys 2014, Octobre 2014, Silicon Valley, USA.
=== How to represent the physical space surrounding users in remote AR collaboration? ===


A. S. Nugroho, I. Ardiyanto, T. B. Adji, "User Curiosity Factor in Determining Serendipity of Recommender System", in proc of the International Journal of Innovative Technology and Exploring Engineering IJITEE, Vol 5, No 3, Septembre 2021.
* Enseignants : [mailto:cedric.fleury@imt-atlantique.fr Cédric Fleury] et [mailto:thierry.duval@imt-atlantique.fr Thierry Duval]
* Sujet en lien avec un stage : oui ([[Stages|Hybrid Collaborative across Heterogeneous Devices]])
* '''stage non attribué'''


A. Menk, L. Sebastia, R. Ferreira, "Curumin, A serendipitous Recommender System based on Human Curiosity" in Procedia Computer Science 112 (2017), pp 484-493.  
Augmented Reality (AR) is becoming a very popular technology to support remote collaboration, as it enables users to share virtual content with distant collaborators. However, sharing the physical spaces surrounding users is still a major challenge. Each user involved in an AR collaborative situation enters the shared environment with a part of its own environment [4, 9]. For example, this space can be shared in several ways for two remote users [3]: (i) in an equitable mode (i.e., half from user 1 and half from user 2) [5], (ii) in a host-guest situation where the host imposes the shape of the augmented environment to the guest [7, 8], or (iii) in a mixed environment specifically designed for the collaborative task [6]. Whatever the configuration, the question of how users perceive and use this shared environment arises [2]


X Niu, F. Abbas, M. L. Maher, K. Grace, "Surprise Me If You Can : Serendipity in Health Information", Proc of the 2018 CHI Conference on Human Factors in Computing SystemsCHI 2018, CHI 2018, pp 1-12, Avril 2018, Montréal, Canada.
[1] H. H. Clark, and S. E. Brennan. "Grounding in communication". In: L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). American Psychological Association. 1991.


=== Étude des techniques de prédiction en Machine Learning pour lutter contre l’échec et le décrochage scolaire ===
[2] S. R. Fussell, R. E. Kraut, and J. Siegel. “Coordination of communication: effects of shared visual context on collaborative work”. Proceedings of the 2000 ACM conference on Computer supported cooperative work (CSCW '00). 2000.


* Enseignante : Fahima DJELIL
[3] B. T. Kumaravel, F. Anderson, G. Fitzmaurice, B. Hartmann, and Tovi Grossman. "Loki:Facilitating Remote Instruction of Physical Tasks Using Bi-Directional Mixed-Reality Telepresence". Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '19), 2019.
* Sujet en lien avec un stage : non.


[[Media:BIBL-2021_Djelil_SujetBiblioPredictionDecrochageScolaire.pdf|Présentation du sujet]]
[4] P. Ladwig and C. Geiger. “A Literature Review on Collaboration in Mixed Reality”. International Conference on Remote Engineering and Virtual Instrumentation (REV). 2018.


=== Algorithmes de comportements auto-organisés pour des essaims de drones ===
[5] N. H. Lehment, D. Merget and G. Rigoll. "Creating automatically aligned consensus realities for AR videoconferencing". IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2014.
L'étude des "Robot swarms", ou essaims de robots, porte sur les systèmes comprenant de nombreux robots - ou drones - volants, roulants, etc.  
qui se coordonnent de façon autonome, à partir de règles de contrôle locales basées sur les perceptions du robot et son état actuel, à la manière d'agents dans les SMA.
Les algorithmes de comportement que suivent les robots se basent souvent sur des principes d'auto-organisation inspirés de la biologie ou de la physique : mouvement coordonné, répartition de tâches, trouver le plus court chemin …


On s'intéresse ici particulièrement aux comportements qui mènent les robots à s'auto-organiser dans l'espace. Ces comportements sont au nombre de cinq : le flocking, l'agrégation, la couverture de zone, et la formation d'un pattern ou d'une chaine entre 2 points. Il est demandé dans ce travail de faire un état de l'art des différents algorithmes de comportement qui existent pour chacun de ces comportements. Le travail attendu doit être le plus exhaustif possible, et proposer une classification de ces algorithmes selon les critères les plus appropriés.  
[6] T. Mahmood, W. Fulmer, N. Mungoli, J. Huang and A. Lu. "Improving Information Sharing and Collaborative Analysis for Remote GeoSpatial Visualization Using Mixed Reality". IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2019.


Références :
[7] O. Oda, C. Elvezio, M. Sukan, S. Feiner, and B. Tversky. "Virtual Replicas for Remote Assistance in Virtual and Augmented Reality". Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (UIST '15), 2015.
* Schranz M, Umlauft M, Sende M and Elmenreich W. Swarm Robotic Behaviors and Current Applications. Frontiers in Robotics and AI, vol. 7, n°136, 2020


* M. Brambilla, E. Ferrante, M. Birattari et M. Dorigo. Swarm robotics: a review from the swarm engineering perspective, Swarm Intelligence, vol. 7, n°11, pp. 1-41, 2013.  
[8] S. Orts-Escolano, C. Rhemann, S. Fanello, W. Chang, A. Kowdle, Y. Degtyarev, and al. “Holoportation: Virtual 3D Teleportation in Real-time”. Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST '16). 2016.


* Krishnan, Vishaal and Sonia Martínez. Distributed Control for Spatial Self-Organization of Multi-agent Swarms. SIAM J. Control. Optim. vol. 56, pp. 3642-3667, 2018.
[9] M. Sereno, X. Wang, L. Besancon, M. J. Mcguffin and T. Isenberg, "Collaborative Work in Augmented Reality: A Survey". IEEE Transactions on Visualization and Computer Graphics. 2020.


* Trianni V., Campo A. Fundamental Collective Behaviors in Swarm Robotics. In: Kacprzyk J., Pedrycz W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. 2015.


=== Interactions avec essaim de drones  ===
=== Techniques for localized data representation in Augmented Reality ===
* Enseignants : [mailto:etienne.peillard@imt-atlantique.fr Etienne Peillard] et [mailto:Aymeric.Henard@univ-brest.fr Aymeric Henard]
* Sujet en lien avec un stage : oui ([[Stages|Visualisation immersive et localisée de données en Réalité Augmentée]])
* '''stage attribué'''


* Enseignant•e•(s) : Jérémy Rivière
ugmented reality allows for the superimposition of virtual elements in a real-world environment that can be associated with it. It enables, for example, the display of temperature data in a room to visually identify cold spots, or the display of robot speed and trajectory to understand their movement. However, the display possibilities are twofold: the data to be displayed can be of various types (discrete/continuous, 1D/2D/3D/see 4D), and there are numerous ways to display them. Furthermore, due to augmented reality's limitations, some techniques may not be adapted or may cause display issues, particularly when the visualizations become distant or overlapping. This research topic aims to review all of the techniques that allow data to be displayed in AR in a co-localized manner, identifying their benefits and drawbacks as detailed in the scientific literature.
* Lien avec le stage Robotique en essaims et Systèmes Multi-Agents.


Il s'agira dans la bibliographie de faire une veille technologique des différents dispositifs de tracking existants, puis de comparer et d'évaluer ceux qui sont utilisés en particulier dans des travaux de recherche sur les essaims de robots (Mona et autres).
[1] Olshannikova, Ekaterina ; Ometov, Aleksandr ; Koucheryavy, Yevgeni ; Olsson, Thomas: Visualizing Big Data with augmented and virtual reality: challenges and research agenda. In: Journal of Big Data Bd. 2, SpringerOpen (2015), Nr. 1, S. 1–27


=== Techniques et usages possibles de la réalité virtuelle pour l’empathie environnementale  ===
[2] Hedley, Nicholas R. ; Billinghurst, Mark ; Postner, Lori ; May, Richard ; Kato, Hirokazu: Explorations in the use of augmented reality for geographic visualization. In: Presence: Teleoperators and Virtual Environments Bd. 11 (2002), Nr. 2, S. 119–133


Teacher: Anne-Gwenn Bosser (ENIB) Lab-STICC - COMMEDIA.
[3] Olshannikova, Ekaterina ; Ometov, Aleksandr ; Koucheryavy, Yevgeni: Towards big data visualization for augmented reality. In: Proceedings - 16th IEEE Conference on Business Informatics, CBI 2014 Bd. 2, Institute of Electrical and Electronics Engineers Inc. (2014), S. 33–37 — ISBN 9781479957781
Subject related to an internship.


=== Génération automatique d’humour ou de jeux de mots ===
[4] Miranda, Brunelli P. ; Queiroz, Vinicius F. ; Araújo, Tiago D.O. ; Santos, Carlos G.R. ; Meiguins, Bianchi S.: A low-cost multi-user augmented reality application for data visualization. In: Multimedia Tools and Applications Bd. 81, Springer (2022), Nr. 11, S. 14773–14801


Teacher: Anne-Gwenn Bosser(ENIB) Lab-STICC - COMMEDIA.
[5] Martins, Nuno Cid ; Marques, Bernardo ; Alves, João ; Araújo, Tiago ; Dias, Paulo ; Santos, Beatriz Sousa: Augmented reality situated visualization in decision-making. In: Multimedia Tools and Applications Bd. 81, Springer (2022), Nr. 11, S. 14749–14772
Subject not related to an internship


=== Principes et mise en oeuvre des architectures de Machine Learning de type « Transformers » ===
=== Principes et mise en oeuvre des architectures de Machine Learning de type « Transformers » ===


* Teacher: Pierre De Loor, ENIB, Lab-STICC - COMMEDIA.
* Enseignant : [mailto:pierre.deloor@enib.fr Pierre De Loor]
* Subject not related to an internship
* Sujet en lien avec un stage : non.


[[Media:BIBL-2021_Deloor_SujetBiblioTransformer.pdf|Subject summary]]
[[Media:BIBL-2021_Deloor_SujetBiblioTransformer.pdf|Subject summary]]


=== L'incarnation sensorimotrice d'un agent virtuel chez un humain en RV ===
=== Self-learning agents having intrinsic motivation to learn ===
* Teacher: Pierre Chevaillier (ENIB) Lab-STICC COMMEDIA
* Study not related to an internship
Intrinsic motivation drives artificial agents, such as robots, to discover novel ’states’ by exploring their environment. The exploration is not motivated by any explicit task-oriented goal, unless the one to learn. In other words, intrinsic motivation is for an agent the quest of novelty, which suppose for the agent to intrinsically curious. The concept has been used for self-learning agents using the principle of reinforcement learning. It leads the agent to learn new skills which may become useful to get some rewards in the future. This heuristic is interesting in situations where the agent would only get very sparse extrinsic rewards from its successful actions.
The above paragraph is dense in concepts which must be explained and formally defined: intrinsic motivation, novelty, curiosity-driven learning, etc. This study aims at presenting the scientific motivations behind this approach, the main principles of curiosity-driven learning and intrinsic motivation and their recent applications in reinforcement learning. The benefits of this approach should be clearly stated.
** Aubret, A., Matignon, L., and Hassas, S. (2019). A survey on intrinsic motivation in reinforcement learning. ArXiv.
** Nguyen, S. M., Duminy, N., Manoury, A., Duhaut, D., and Buche, C. (2021). Robots learn increas- ingly complex tasks with intrinsic motivation and automatic curriculum learning. Ku ̈nstliche Intelligenz, 35:81–90.
** Oudeyer, P.-Y. (2018). The New Science of Curiosity, chapter Computational Theories of Curiosity- Driven Learning, pages 43–72. Psychology of Emotions, Motivations and Actions. Nova Science Publisher.
** Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T. (2017). Curiosity-driven exploration by self-supervised prediction. In Precup, D. and Teh, Y. W., editors, Proceedings of the 34th In- ternational Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 2778–2787. PMLR.
=== How naive agents can learn a representation of space from their sensorimotor experience? ===
* Teacher: Pierre Chevaillier (ENIB) Lab-STICC COMMEDIA
* Study not related to an internship
According to the Sensorimotor Contingency Theory (SCT), the acquisition of space knowledge results from the interaction between perception and body movements. This theory is based on Poincar ́e’s assumption that our body naturally compensates a change in our perception by a movement and thus ’embodies’ a representation of space. The (hidden) relationship between the agent’s sensations (sensory inputs) and its body movements (motor outputs) depends on the characteristics of its surrounding space, as the agent can experience it, that also depends on its own capabilities (perception and action).
It is an ambitious scientific track to try to implement such an embodied cognition in an artificial agent. Up to now it has been experimented by simulation on very simple setups, having in mind applications to robotics (Le Clec’H et al., 2016). Other authors have tried to put the SCT in action and to characterize what a space representation could be (Terekhov and O’Regan, 2016) and what kind of spacial properties could be learned (Laflaquière et al., 2018; Laflaquière, 2020).
This study shall briefly introduce the Sensorimotor Contingency Theory and present the principles and the recent results in the learning of space representation by a naive robotic agent.
Références
** Laflaquière, A., O’Regan, J. K., Gas, B., and Terekhov, A. V. (2018). Discovering space – grounding spatial topology and metric regularity in a naive agent’s sensorimotor experience. Neural Network, 105:371–392.
**Laflaquière, A. (2020). Emergence of spatial coordinates via exploration. arXiv:2010.15469v1 [cs.LG]. 4 pages, 2 figures, BabyMind Workshop at NeurIPS 2020.
** Le Clec’H, G., Gas, B., and O’Regan, J. K. (2016). Acquisition of a space representation by a naive agent from sensorimotor invariance and proprioceptive compensation. International Journal of Advanced Robotic Systems, 13(6):172988141667513.
** Terekhov, A. V. and O’Regan, J. K. (2016). Space as an invention of active agents. Frontiers in Robotics and AI, 3.
=== Recommandations de visualisation pour la cybersécurité ===
* Teacher: Nicolas Delcombel, IMT Atlantique, Lab-STICC - INUIT.
* Study not related to an internship
[[Media:BIBL_2021_Delcombel_VisuCyber.pdf|Présentation de l'étude]]
=== Méthodes de segmentation sémantique de nuages de points  ===
* Teacher: Cédric Buche (CNRS) IRL CROSSING
* Study related to an internship (see 'page des stages')
=== Étude de l'impact des techniques d'interaction sur la perception en Réalité Augmentée ===
* Teacher: Etienne Paillard (IMT Atlantique), Lab-STICC INUIT.
* Study related to an internship (see page des stages).
=== IHM pour l'exploration de données temporelles ===
* Teacher: Olivier Augereau (ENIB, Lab-STICC COMMEDIA)
* Subject related to an internship
=== Communication non verbale en environnement virtuel ===
* Teachers: Olivier Augereau (ENIB, Lab-STICC COMMEDIA), Antoine Dellavalle
* Subject related to an internship (see page des stages)
=== Caractérisation Affective Automatique d’une Expérience Immersive ===
* Teachers: Anne-Gwenn Bosser (ENIB), Olivier Augereau (ENIB), Lab-STICC COMMEDIA
* Subject related to an internship (see 'page des stages')
=== Digital commensability in VR ===
* Teachers: Elisabetta Bevacqua (ENIB), Gireg Desmeulles (ENIB), Lab-STICC - COMMEDIA
* Subject related to an internship (see 'page des stages')


=== Reconnaissance automatique d’activités humaines ===
=== Modèle du comportement réactif du regard pour un agent virtuel inoccupé, basé sur signaux visuels et acoustiques ===


* Teacher: Alexis Nédélec (ENIB) Lab-STICC - INUIT
* Enseignants : [mailto:elisabetta.bevacqua@enib.fr Elisabetta Bevacqua] et [mailto:desmeulles@enib.fr Gireg Desmeulles]
* Subject related to an internship.
* Sujet en lien avec un stage : oui ([[Stages]])
* Student : Vincent FER.
* '''stage non attribué'''

Version du 21 novembre 2022 à 14:32


Méthode de travail et objectifs

Affectations sujets

Here is a shared table that present sthe different subjects of study: list of studies.

Students are invited to indicate their choice(s) in this table.

Indications générales

Voici quelques indications pour la rédaction de l'étude bibliographique et sa restitution orale : instructions biblio (révision nov. 2020).

Documents à étudier

Comme toute technique d'ingénierie, ou toute démarche scientifique, la réalisation d'une étude bibliographique, appelée aussi revue de littérature (littérature review), doit être réalisée de manière méthodique et apporter des éléments pour en apprécier la justesse et la pertinence. Même si les motivations pour réaliser un tel exercice peuvent être diverses, dans ses grandes lignes, la méthodologie reste la même.

Voici quelques documents à lire avant et pendant la réalisation de votre étude.

Sujets d'étude 2022-2023

How collaboration in mixed reality can benefit from the use of heterogeneous devices?

The massive development of display technologies brings a wide range of new devices, such as mobile phones, AR/VR headsets and large displays, available to the general public. These devices offer many opportunities for co-located and remote collaboration on physical and digital content. Some can handle groups of co-located users [10, 13], while others enable remote users to connect in various situations [3, 6, 11, 14]. For example, some previous systems allow users to use a mobile device to interact with a co-located partner wearing a VR headset [4, 7]. Other systems enable users in VR to guide a remote collaborator using an AR headset [1, 8, 9, 12]. [1] H. Bai, P. Sasikumar, J. Yang, and M. Billinghurst. "A User Study on Mixed Reality Remote Collaboration with Eye Gaze and Hand Gesture Sharing". Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’20), 2020.

[2] H. H. Clark, and S. E. Brennan. "Grounding in communication". In: L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). American Psychological Association. 1991.

[3] C. Fleury, T. Duval, V. Gouranton, A. Steed. "Evaluation of Remote Collaborative Manipulation for Scientific Data Analysis", ACM Symposium on Virtual Reality Software and Technology (VRST’12), 2012.

[4] J. Gugenheimer, E. Stemasov, J. Frommel, and E. Rukzio. "ShareVR: Enabling Co-Located Experiences for Virtual Reality between HMD and Non-HMD Users". Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17), 2017.

[5] J. Hollan and S. Stornetta. "Beyond being there". In : Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI’92), 1992.

[6] B. T. Kumaravel, F. Anderson, G. Fitzmaurice, B. Hartmann, and Tovi Grossman. "Loki:Facilitating Remote Instruction of Physical Tasks Using Bi-Directional Mixed-Reality Telepresence". Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '19), 2019.

[7] B. T. Kumaravel, C. Nguyen, S. DiVerdi, and B. Hartmann. "TransceiVR: Bridging Asymmetrical Communication Between VR Users and External Collaborators". Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '20), 2020.

[8] M. Le Chénéchal, T. Duval, J. Royan, V. Gouranton, and B. Arnaldi. “Vishnu: Virtual Immersive Support for HelpiNg Users - An Interaction Paradigm for Remote Collaborative Maintenance in Mixed Reality”. Proceedings of 3DCVE 2016 (IEEE VR 2016 International Workshop on 3D Collaborative Virtual Environments). 2016.

[9] M. Le Chénéchal, T. Duval, V. Gouranton, J. Royan, and B. Arnaldi. “The Stretchable Arms for Collaborative Remote Guiding”. Proceedings of ICAT-EGVE 2015, Eurographics. 2015.

[10] C. Liu, O. Chapuis, M. Beaudouin-Lafon, and E. Lecolinet. “Shared Interaction on a Wall-Sized Display in a Data Manipulation Task.” In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. CHI ’16.

[11] P. Mohr, S. Mori, T. Langlotz, B. H. Thomas, D. Schmalstieg, and D. Kalkofen. "Mixed Reality Light Fields for Interactive Remote Assistance". Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’20), 2020.

[12] O. Oda, C. Elvezio, M. Sukan, S. Feiner, and B. Tversky. "Virtual Replicas for Remote Assistance in Virtual and Augmented Reality". Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (UIST '15), 2015.

[13] Y. Okuya, O. Gladin, N. Ladévèze, C. Fleury, P. Bourdot. "Investigating Collaborative Exploration of Design Alternatives on a Wall-Sized Display", ACM Conference on Human Factors in Computing Systems (CHI’20), 2020.

[14] H. Xia, S. Herscher, K. Perlin, and D. Wigdor. "Spacetime: Enabling Fluid Individual and Collaborative Editing in Virtual Reality". Proceedings of the ACM Symposium on User Interface Software and Technology (UIST ’18), 2018.

How telepresence systems can support collaborative dynamics in large interactive spaces?

Videoconferencing and telepresence have long been a way to enhance communication among remote users. They improve turn-taking, mutual understanding, and negotiation of common ground by supporting non-verbal cues such as eye-gaze direction, facial expressions, gestures, and body langue [3, 6, 10]. They are also an effective solution to avoid the "Uncanny Valley" effect [7] that can be encountered when using avatars.

However, such systems are often limited to basic setups in which each user must seat in front of a computer equipped with a camera. Other systems, such as Multiview [9] or MMSpace [8], handle groups, but still only group-to-group conversations are possible. This leads to awkward situations in which colleagues in the same building stay in their office to attend a videoconference meeting instead of attending together, or participants are forced to have side conversations via chat. More recent work investigates dynamic setups that allow users to move into the system and interact with share content. t-Rooms [5] displays remote users on circular screens around a tabletop. CamRay [1] handles video communication between two users interacting on remote wall-sized displays. GazeLens [4] integrates a remote user in a group collaboration around physical artifacts on a table. Nevertheless, such systems do not support different moments in the collaboration, such as tightly coupled and loose collaboration, subgroup collaboration, spontaneous or side discussions. Supporting such dynamics in collaboration is a major challenge for the next telepresence systems.

[1] I. Avellino, C. Fleury, W. Mackay and M. Beaudouin-Lafon. “CamRay: Camera Arrays Support Remote Collaboration on Wall-Sized Displays”. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI’17). 2017.

[3] E. A. Isaacs and J. C. Tang. “What Video Can and Can’t Do for Collaboration: A Case Study.” Proceedings of the ACM International Conference on Multimedia (MULTIMEDIA’93). 1993.

[4] K.-D. Le, I. Avellino, C. Fleury, M. Fjeld, A. Kunz. “GazeLens: Guiding Attention to Improve Gaze Interpretation in Hub-Satellite Collaboration”. Proceedings of the Conference on Human- Computer Interaction (INTERACT’19). 2019.

[5] P. K. Luff, N. Yamashita, H. Kuzuoka, and C. Heath. “Flexible Ecologies And Incongruent Locations.” Proceedings of the Conf. on Human Factors in Computing Systems. (CHI ’15). 1995.

[6] A. F. Monk and C. Gale. “A Look Is Worth a Thousand Words: Full Gaze Awareness in Video- Mediated Conversation.” In: Discourse Processes 33.3, 2002, pp. 257–278.

[7] M. Mori, K. F. MacDorman and N. Kageki, “The Uncanny Valley [From the Field]”, IEEE Robotics & Automation Magazine, vol. 19, no. 2, pp. 98-100, 2012.

[8] K. Otsuka, “MMSpace: Kinetically-augmented telepresence for small group-to-group conversations”. Proceedings of 2016 IEEE Virtual Reality (VR’16). 2016.

[9] A. Sellen, B. Buxton, and J. Arnott. “Using Spatial Cues to Improve Videoconferencing.” Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI ’92). 1992.

[10] E. S. Veinott, J. Olson, G. M. Olson, and X. Fu. “Video Helps Remote Work: Speakers Who Need to Negotiate Common Ground Benefit from Seeing Each Other.” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’99). 1999.

How to represent the physical space surrounding users in remote AR collaboration?

Augmented Reality (AR) is becoming a very popular technology to support remote collaboration, as it enables users to share virtual content with distant collaborators. However, sharing the physical spaces surrounding users is still a major challenge. Each user involved in an AR collaborative situation enters the shared environment with a part of its own environment [4, 9]. For example, this space can be shared in several ways for two remote users [3]: (i) in an equitable mode (i.e., half from user 1 and half from user 2) [5], (ii) in a host-guest situation where the host imposes the shape of the augmented environment to the guest [7, 8], or (iii) in a mixed environment specifically designed for the collaborative task [6]. Whatever the configuration, the question of how users perceive and use this shared environment arises [2]

[1] H. H. Clark, and S. E. Brennan. "Grounding in communication". In: L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). American Psychological Association. 1991.

[2] S. R. Fussell, R. E. Kraut, and J. Siegel. “Coordination of communication: effects of shared visual context on collaborative work”. Proceedings of the 2000 ACM conference on Computer supported cooperative work (CSCW '00). 2000.

[3] B. T. Kumaravel, F. Anderson, G. Fitzmaurice, B. Hartmann, and Tovi Grossman. "Loki:Facilitating Remote Instruction of Physical Tasks Using Bi-Directional Mixed-Reality Telepresence". Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '19), 2019.

[4] P. Ladwig and C. Geiger. “A Literature Review on Collaboration in Mixed Reality”. International Conference on Remote Engineering and Virtual Instrumentation (REV). 2018.

[5] N. H. Lehment, D. Merget and G. Rigoll. "Creating automatically aligned consensus realities for AR videoconferencing". IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2014.

[6] T. Mahmood, W. Fulmer, N. Mungoli, J. Huang and A. Lu. "Improving Information Sharing and Collaborative Analysis for Remote GeoSpatial Visualization Using Mixed Reality". IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2019.

[7] O. Oda, C. Elvezio, M. Sukan, S. Feiner, and B. Tversky. "Virtual Replicas for Remote Assistance in Virtual and Augmented Reality". Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (UIST '15), 2015.

[8] S. Orts-Escolano, C. Rhemann, S. Fanello, W. Chang, A. Kowdle, Y. Degtyarev, and al. “Holoportation: Virtual 3D Teleportation in Real-time”. Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST '16). 2016.

[9] M. Sereno, X. Wang, L. Besancon, M. J. Mcguffin and T. Isenberg, "Collaborative Work in Augmented Reality: A Survey". IEEE Transactions on Visualization and Computer Graphics. 2020.


Techniques for localized data representation in Augmented Reality

ugmented reality allows for the superimposition of virtual elements in a real-world environment that can be associated with it. It enables, for example, the display of temperature data in a room to visually identify cold spots, or the display of robot speed and trajectory to understand their movement. However, the display possibilities are twofold: the data to be displayed can be of various types (discrete/continuous, 1D/2D/3D/see 4D), and there are numerous ways to display them. Furthermore, due to augmented reality's limitations, some techniques may not be adapted or may cause display issues, particularly when the visualizations become distant or overlapping. This research topic aims to review all of the techniques that allow data to be displayed in AR in a co-localized manner, identifying their benefits and drawbacks as detailed in the scientific literature.

[1] Olshannikova, Ekaterina ; Ometov, Aleksandr ; Koucheryavy, Yevgeni ; Olsson, Thomas: Visualizing Big Data with augmented and virtual reality: challenges and research agenda. In: Journal of Big Data Bd. 2, SpringerOpen (2015), Nr. 1, S. 1–27

[2] Hedley, Nicholas R. ; Billinghurst, Mark ; Postner, Lori ; May, Richard ; Kato, Hirokazu: Explorations in the use of augmented reality for geographic visualization. In: Presence: Teleoperators and Virtual Environments Bd. 11 (2002), Nr. 2, S. 119–133

[3] Olshannikova, Ekaterina ; Ometov, Aleksandr ; Koucheryavy, Yevgeni: Towards big data visualization for augmented reality. In: Proceedings - 16th IEEE Conference on Business Informatics, CBI 2014 Bd. 2, Institute of Electrical and Electronics Engineers Inc. (2014), S. 33–37 — ISBN 9781479957781

[4] Miranda, Brunelli P. ; Queiroz, Vinicius F. ; Araújo, Tiago D.O. ; Santos, Carlos G.R. ; Meiguins, Bianchi S.: A low-cost multi-user augmented reality application for data visualization. In: Multimedia Tools and Applications Bd. 81, Springer (2022), Nr. 11, S. 14773–14801

[5] Martins, Nuno Cid ; Marques, Bernardo ; Alves, João ; Araújo, Tiago ; Dias, Paulo ; Santos, Beatriz Sousa: Augmented reality situated visualization in decision-making. In: Multimedia Tools and Applications Bd. 81, Springer (2022), Nr. 11, S. 14749–14772

Principes et mise en oeuvre des architectures de Machine Learning de type « Transformers »

Subject summary


Modèle du comportement réactif du regard pour un agent virtuel inoccupé, basé sur signaux visuels et acoustiques

Récupérée de « http://siia.univ-brest.fr/w/index.php?title=BIBL&oldid=1178 »