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802 results on '"CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)"'

1. Do You Believe Your (Social Media) Data? A Personal Story on Location Data Biases, Errors, and Plausibility as Well as Their Visualization

Author: Tobias Isenberg, Zujany Salazar, Rafael Blanco, Catherine Plaisant, Analysis and Visualization (AVIZ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Télécom SudParis (TSP), University of Maryland [Baltimore], CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: iNaturalist, Data obfuscation, [SCCO.COMP]Cognitive science/Computer science, 020207 software engineering, 02 engineering and technology, Data error, Citizen science, Computer Graphics and Computer-Aided Design, Social media data, Bias, Signal Processing, Computer Graphics, 0202 electrical engineering, electronic engineering, information engineering, Humans, Data bias, Panoramio, Computer Vision and Pattern Recognition, Flickr, Social Media, Data plausibility, Software
Abstract: We present a case study on a journey about a personal data collection of carnivorous plant species habitats, and the resulting scientific exploration of location data biases, data errors, location hiding, and data plausibility. While initially driven by personal interest, our work led to the analysis and development of various means for visualizing threats to insight from geo-tagged social media data. In the course of this endeavor we analyzed local and global geographic distributions and their inaccuracies. We also contribute Motion Plausibility Profiles-a new means for visualizing how believable a specific contributor's location data is or if it was likely manipulated. We then compared our own repurposed social media dataset with data from a dedicated citizen science project. Compared to biases and errors in the literature on traditional citizen science data, with our visualizations we could also identify some new types or show new aspects for known ones. Moreover, we demonstrate several types of errors and biases for repurposed social media data. Please note that people with color impairments may consider our alternative paper version.
Published: 2022

2. Black-Box Optimization Revisited: Improving Algorithm Selection Wizards Through Massive Benchmarking

Author: Laurent Meunier, Paco Wong, Jeremy Rapin, Antoine Moreau, Olivier Teytaud, Carola Doerr, Baptiste Roziere, Herilalaina Rakotoarison, Facebook AI Research [Paris] (FAIR), Facebook, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), The Chinese University of Hong Kong [Hong Kong], Université Clermont Auvergne (UCA), SIGMA Clermont (SIGMA Clermont), Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Recherche Opérationnelle (RO), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Optimization problem, Computer science, 02 engineering and technology, Overfitting, Machine learning, computer.software_genre, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Theoretical Computer Science, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Black box, 0202 electrical engineering, electronic engineering, information engineering, business.industry, Black-Box Optimization, Suite, Benchmarking, Wizard, Range (mathematics), Computational Theory and Mathematics, Benchmark (computing), 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Software
Abstract: International audience; Existing studies in black-box optimization for machine learning suffer from low generalizability, caused by a typically selective choice of problem instances used for training and testing different optimization algorithms. Among other issues, this practice promotes overfitting and poor-performing user guidelines. To address this shortcoming, we propose in this work a benchmark suite, OptimSuite, which covers a broad range of black-box optimization problems, ranging from academic benchmarks to real-world applications, from discrete over numerical to mixed-integer problems, from small to very large-scale problems, from noisy over dynamic to static problems, etc. We demonstrate the advantages of such a broad collection by deriving from it Automated Black Box Optimizer (ABBO), a general-purpose algorithm selection wizard. Using three different types of algorithm selection techniques, ABBO achieves competitive performance on all benchmark suites. It significantly outperforms previous state of the art on some of them, including YABBOB and LSGO. ABBO relies on many high-quality base components. Its excellent performance is obtained without any task-specific parametrization. The OptimSuite benchmark collection, the ABBO wizard and its base solvers have all been merged into the open-source Nevergrad platform, where they are available for reproducible research.
Published: 2022

3. Cultural transmission of reproductive success impacts genomic diversity, coalescent tree topologies, and demographic inferences

Author: Jérémy Guez, Guillaume Achaz, François Bienvenu, Jean Cury, Bruno Toupance, Évelyne Heyer, Flora Jay, Frédéric Austerlitz, Éco-Anthropologie (EA), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), BioInformatique (BioInfo), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Evolution et ingénierie de systèmes dynamiques (SEED (UMR-S 1284/U 1284)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Bioinformatique (LRI) (BioInfo - LRI), Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ANR-20-CE45-0010,RoDAPoG,Apprentissage profond robuste pour la génomique des populations(2020), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), Jay, Flora, and Apprentissage profond robuste pour la génomique des populations - - RoDAPoG2020 - ANR-20-CE45-0010 - AAPG2020 - VALID
Subjects: coalescent tree shape, Genetics, population genetics, genetic diversity, [SDV.BID]Life Sciences [q-bio]/Biodiversity, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], demographic inference, imbalanced topology, cultural process, [SDV.BID] Life Sciences [q-bio]/Biodiversity, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: Cultural transmission of reproductive success has been observed in many human populations as well as other animals. Cultural transmission of reproductive success consists of a positive correlation of nongenetic origin between the progeny size of parents and children. This correlation can result from various factors, such as the social influence of parents on their children, the increase of children’s survival through allocare from uncles and aunts, or the transmission of resources. Here, we study the evolution of genomic diversity over time under cultural transmission of reproductive success. Cultural transmission of reproductive success has a threefold impact on population genetics: (1) the effective population size decreases when cultural transmission of reproductive success starts, mimicking a population contraction, and increases back to its original value when cultural transmission of reproductive success stops; (2) coalescent tree topologies are distorted under cultural transmission of reproductive success, with higher imbalance and a higher number of polytomies; and (3) branch lengths are reduced nonhomogenously, with a higher impact on older branches. Under long-lasting cultural transmission of reproductive success, the effective population size stabilizes but the distortion of tree topology and the nonhomogenous branch length reduction remain, yielding U-shaped site frequency spectra under a constant population size. We show that this yields a bias in site frequency spectra-based demographic inference. Considering that cultural transmission of reproductive success was detected in numerous human and animal populations worldwide, one should be cautious because inferring population past histories from genomic data can be biased by this cultural process.
Published: 2023

4. MiningVis: Visual Analytics of the Bitcoin Mining Economy

Author: Nicolas Heulot, Nicolas Soulié, Petra Isenberg, Natkamon Tovanich, IRT SystemX, Analysis and Visualization (AVIZ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Département Droit, Economie et Finances (IMT-BS - DEFI), Télécom Ecole de Management (TEM)-Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom Business School (IMT-BS), Institut Mines-Télécom [Paris] (IMT), Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) (LITEM), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay-Institut Mines-Télécom Business School (IMT-BS), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), LITEM-NPR, IRT SystemX (IRT SystemX), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Département Droit, Economie et Finances (DEFI), and Institut Mines-Télécom [Paris] (IMT)-Télécom Ecole de Management (TEM)-Institut Mines-Télécom Business School (IMT-BS)
Subjects: Value (ethics), Visual analytics, Cryptocurrency, Computer science, 02 engineering and technology, Pool hopping, User studies, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Order (exchange), Component (UML), 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Practical implications, Bitcoin mining, 020207 software engineering, [SHS.ECO]Humanities and Social Sciences/Economics and Finance, Computer Graphics and Computer-Aided Design, Data science, 8. Economic growth, Signal Processing, Research questions, Computer Vision and Pattern Recognition, Mining pools, Software, Bitcoin
Abstract: International audience; We present a visual analytics tool, MiningVis, to explore the long-term historical evolution and dynamics of the Bitcoin mining ecosystem. Bitcoin is a cryptocurrency that attracts much attention but remains difficult to understand. Particularly important to the success, stability, and security of Bitcoin is a component of the system called "mining.'' Miners are responsible for validating transactions and are incentivized to participate by the promise of a monetary reward. Mining pools have emerged as collectives of miners that ensure a more stable and predictable income. MiningVis aims to help analysts understand the evolution and dynamics of the Bitcoin mining ecosystem, including mining market statistics, multi-measure mining pool rankings, and pool hopping behavior. Each of these features can be compared to external data concerning pool characteristics and Bitcoin news. In order to assess the value of MiningVis, we conducted online interviews and insight-based user studies with Bitcoin miners. We describe research questions tackled and insights made by our participants and illustrate practical implications for visual analytics systems for Bitcoin mining.
Published: 2022

5. Co-Designers Not Troublemakers

Author: Lilly Arstad Helmersen, Viktor Gustafsson, Wendy E. Mackay, Extreme Situated Interaction (EX-SITU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Virtual Tabletop Role-Playing Games, Co-design, Computer Networks and Communications, Computer science, [INFO.INFO-CE]Computer Science [cs]/Computational Engineering, Finance, and Science [cs.CE], 05 social sciences, ComputingMilieux_PERSONALCOMPUTING, 050801 communication & media studies, Adventure, Object (philosophy), Narrative inquiry, Persistence, Human-Computer Interaction, 0508 media and communications, Human–computer interaction, 0501 psychology and cognitive sciences, Narrative, Affect (linguistics), Interactive Digital Narratives, Content (Freudian dream analysis), 050107 human factors, Social Sciences (miscellaneous), Analysis method
Abstract: International audience; Players in Massively Multiplayer Online Role-Playing Games (MMORPGs) generate long-standing histories with their characters, but cannot express or see traces of their adventures in the game worlds. We are interested in designing game systems where players shape and contribute their own narratives as game content. Study one designed and playtested three Virtual Tabletop Role-Playing Game (VTTRPG) prototypes where we found that structured, graphical representations of players' traces support co-design and narrative analysis. We also identified four categories of traces: environment, build, memory and object. We introduce Play Traces, a novel analysis method for representing and co-designing with players and their narratives. A structured observation including Play Traces studied 17 players over 16 four-hour sessions in the third VTTRPG prototype. We found that players successfully (and enjoyably) co-designed novel narratives. We identified three themes for how traces can affect and support players in shaping new interactive narratives. We present four design implications describing how player-created narratives in MMORPGs should first Reveal & Pull Attention from other players, Invite & Push further exploration, Guide & Assist toward endings, and optionally Show & Hide traces. Finally, we discuss how treating players as co-designers offers a promising approach for developing the next generation of MMORPGs. CCS Concepts: • Human-centered computing → Human computer interaction (HCI); • Information systems → Massively multiplayer online games.
Published: 2021

6. Professional Differences: A Comparative Study of Visualization Task Performance and Spatial Ability Across Disciplines

Author: Kyle Wm. Hall, Christopher Collins, Anastasia Bezerianos, Danielle Albers Szafir, Anthony Kouroupis, Temple University [Philadelphia], Pennsylvania Commonwealth System of Higher Education (PCSHE), University of Ontario Institute of Technology (UOIT), University of Western Ontario (UWO), Interacting with Large Data (ILDA), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of Colorado [Boulder], We acknowledge the support of NSF #1764089 & 2046725 and NSERC[RGPIN-2015-03916]. We thank Dr. So Yoon Yoon (University ofCincinnati) for providing the revised PSVT:R materials., Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Computer science, Spatial ability, media_common.quotation_subject, Computer Science - Human-Computer Interaction, 02 engineering and technology, perception, discipline, Human-Computer Interaction (cs.HC), law.invention, H.5.2, Data visualization, Computer Science - Graphics, H.5.0, law, Perception, 0202 electrical engineering, electronic engineering, information engineering, domain-specific, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], visualization, media_common, spatial ability, business.industry, Perspective (graphical), Pie chart, 020207 software engineering, Cognition, Computer Graphics and Computer-Aided Design, Graphics (cs.GR), Visualization, I.3.0, task performance, Signal Processing, Task analysis, Computer Vision and Pattern Recognition, business, empirical evaluation, Software, Cognitive psychology
Abstract: Problem-driven visualization work is rooted in deeply understanding the data, actors, processes, and workflows of a target domain. However, an individual's personality traits and cognitive abilities may also influence visualization use. Diverse user needs and abilities raise natural questions for specificity in visualization design: Could individuals from different domains exhibit performance differences when using visualizations? Are any systematic variations related to their cognitive abilities? This study bridges domain-specific perspectives on visualization design with those provided by cognition and perception. We measure variations in visualization task performance across chemistry, computer science, and education, and relate these differences to variations in spatial ability. We conducted an online study with over 60 domain experts consisting of tasks related to pie charts, isocontour plots, and 3D scatterplots, and grounded by a well-documented spatial ability test. Task performance (correctness) varied with profession across more complex visualizations, but not pie charts, a comparatively common visualization. We found that correctness correlates with spatial ability, and the professions differ in terms of spatial ability. These results indicate that domains differ not only in the specifics of their data and tasks, but also in terms of how effectively their constituent members engage with visualizations and their cognitive traits. Analyzing participants' confidence and strategy comments suggests that focusing on performance neglects important nuances, such as differing approaches to engage with even common visualizations and potential skill transference. Our findings offer a fresh perspective on discipline-specific visualization with recommendations to help guide visualization design that celebrates the uniqueness of the disciplines and individuals we seek to serve., The paper has been accepted to IEEE VIS 2021, and will appear in IEEE TVCG. 11 pages with 9 figures
Published: 2022
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7. Sur la transférabilité interlinguistique des modèles prototypiques multilingues à travers les tâches NLU

Author: Christophe Servan, Sophie Rosset, Oralie Cattan, Information, Langue Ecrite et Signée (ILES), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), QWANT RESEARCH (QR), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and QWANT
Subjects: FOS: Computer and information sciences, Computer Science - Computation and Language, Meta learning (computer science), Artificial neural network, business.industry, Computer science, Deep learning, 68T50, I.2.7, Natural language understanding, Context (language use), NLU, computer.software_genre, Language acquisition, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO]Computer Science [cs], Language model, Artificial intelligence, Transfer of learning, business, Computation and Language (cs.CL), computer, Natural language processing, Meta Learning
Abstract: Supervised deep learning-based approaches have been applied to task-oriented dialog and have proven to be effective for limited domain and language applications when a sufficient number of training examples are available. In practice, these approaches suffer from the drawbacks of domain-driven design and under-resourced languages. Domain and language models are supposed to grow and change as the problem space evolves. On one hand, research on transfer learning has demonstrated the cross-lingual ability of multilingual Transformers-based models to learn semantically rich representations. On the other, in addition to the above approaches, meta-learning have enabled the development of task and language learning algorithms capable of far generalization. Through this context, this article proposes to investigate the cross-lingual transferability of using synergistically few-shot learning with prototypical neural networks and multilingual Transformers-based models. Experiments in natural language understanding tasks on MultiATIS++ corpus shows that our approach substantially improves the observed transfer learning performances between the low and the high resource languages. More generally our approach confirms that the meaningful latent space learned in a given language can be can be generalized to unseen and under-resourced ones using meta-learning., Accepted to the ACL workshop METANLP 2021
Published: 2022

8. Exploring the Role of Artifacts in Collective Dance Re-staging

Author: Jean-Philippe Rivière, Sarah Fdili Alaoui, Baptiste Caramiaux, Wendy E. Mackay, Extreme Situated Interaction (EX-SITU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), This work was partially supported by European Research Council (ERC) grants N° 321135 'CREATIV: Creating Co-Adaptive Human-ComputerPartnerships', the Agence National de la Recherche (ANR) grants N° ANR-18-CE33-0002 'ELEMENT: Enabling Learnability in Embodied Movement Interaction', and the Diagonale de Paris-Saclay for their involvement., ANR-18-CE33-0002,ELEMENT,Stimuler l'Apprentissage de Mouvements dans les Interactions Humain-Machine(2018), European Project: 321135,CREATIV, CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Vocabulary, Qualitative Methods, Dance, Computer Networks and Communications, Process (engineering), media_common.quotation_subject, 02 engineering and technology, Task (project management), Visual arts, Segmentation, Common knowledge, 0202 electrical engineering, electronic engineering, information engineering, Learning, Longitudinal Study, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], 050107 human factors, media_common, Digital artifact, 05 social sciences, Perspective (graphical), 020207 software engineering, Human-Computer Interaction, Artifacts, Psychology, Social Sciences (miscellaneous), Qualitative research
Abstract: International audience; Preparing a new dance performance involves more than learning individual steps. We are interested in understanding how dancers collaborate as they rehearse a new dance piece, with a particular emphasis on how they use physical and digital artifacts to support this process. We conducted a 12-month longitudinal observational study with a dance company that re-staged a dance piece, taken from the contemporary repertoire and unknown to the dancers. The study focused on the role that artifacts play in shaping the learning of a dance piece. We showed how dancers produced an ecology of artifacts with the aim of analyzing the choreographic ideas behind the dance and sharing them with other learners. We showed that sharing these artifacts was challenging because they are idiosyncratic and embody their creator perspective and vocabulary. We then illustrated how dancers overcome this challenge by compiling artifacts and distributing the learning task among the group in order to create a common knowledge of the piece which improves the learning process. We conclude with design opportunities for technologies supporting long-term dance learning processes.
Published: 2021

9. Expressive Authoring of Node-Link Diagrams With Graphies

Author: Emmanuel Pietriga, Caroline Appert, Hugo Romat, Interacting with Large Data (ILDA), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), TecKnowMetrix, Entreprise privée, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Bar chart, Computer science, Multivariate Networks, 02 engineering and technology, Index Terms-Expressive Design, law.invention, Vector graphics, Data visualization, law, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], 050107 human factors, User-centered design, Communication design, business.industry, [INFO.INFO-WB]Computer Science [cs]/Web, 05 social sciences, 020207 software engineering, Node-Link Diagram, Data structure, Computer Graphics and Computer-Aided Design, Visualization, Signal Processing, Design process, Computer Vision and Pattern Recognition, business, Software
Abstract: International audience; Expressive design environments enable visualization designers not only to specify chart types and visual mappings, but also to customize individual graphical marks, as they would in a vector graphics drawing tool. Prior work has mainly investigated how to support the expressive design of a wide range of charts generated from tabular data: bar charts, scatterplots, maps, etc. We focus here on an expressive design environment for node-link diagrams generated from multivariate networks. Such data structures raise specific challenges and opportunities in terms of visual design and interactive authoring. We discuss those specificities and describe the user-centered design process that led to Graphies, a prototype environment for expressive node-link diagram authoring. We then report on a study in which participants successfully reproduced several expressive designs, and created their own designs as well.
Published: 2021

10. Revisiting Multi-Domain Machine Translation

Author: Minh Quang Pham, Josep Maria Crego, François Yvon, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Sud - Paris 11 (UP11)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE), Traitement du Langage Parlé (TLP ), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), SYSTRAN, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Neural Machine Translation, Linguistics and Language, Domain adaptation, Machine translation, domain adaptation, Computer science, Communication, 02 engineering and technology, Multi-domain MT, computer.software_genre, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Computer Science Applications, Large sample, Human-Computer Interaction, Multi domain, Machine Translation, Work (electrical), Artificial Intelligence, Human–computer interaction, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 020201 artificial intelligence & image processing, Transfer of learning, computer
Abstract: International audience; When building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from unexpected domains in testing. This multi-domain scenario has attracted a lot of recent work, that fall under the general umbrella of transfer learning. In this study, we revisit multi-domain machine translation, with the aim to formulate the motivations for developing such systems and the associated expectations with respect to performance. Our experiments with a large sample of multi-domain systems show that most of these expectations are hardly met and suggest that further work is needed to better analyze the current behaviour of multi-domain systems and to make them fully hold their promises.
Published: 2021

11. Starting a new treebank? Go SUD! Theoretical and practical benefits of the Surface-Syntactic distributional approach

Author: Gerdes, Kim, Guillaume, Bruno, Kahane, Sylvain, Perrier, Guy, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Traitement du Langage Parlé (TLP ), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Semantic Analysis of Natural Language (SEMAGRAMME), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Modèles, Dynamiques, Corpus (MoDyCo), Université Paris Nanterre (UPN)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Guillaume, Bruno
Subjects: [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [SHS.LANGUE]Humanities and Social Sciences/Linguistics, [SHS.LANGUE] Humanities and Social Sciences/Linguistics, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Abstract: International audience; The paper brings to the fore some advantages to first develop a new treebank in Surface-Syntactic Universal Dependencies (SUD) annotation scheme, even if the goal is to obtain a UD treebank. Theoretical benefits of SUD are presented, as well as UD-compatible SUD innovations. The twoway UD , SUD conversion is explained, as well as the possibility to customize the conversion for a given language. The paper concludes by a practical guide for the development of a SUD treebank.
Published: 2022

12. Who bears the burden of a pandemic? COVID-19 and the transfer of risk to digital platform workers

Author: Tubaro, Paola, Casilli, Antonio, TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut interdisciplinaire de l’innovation (I3, une unité mixte de recherche CNRS (UMR 9217)), École polytechnique (X)-Télécom ParisTech-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Département Sciences Economiques et Sociales (SES), Télécom ParisTech, Sociologie Information-Communication Design (SID), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Télécom ParisTech-MINES ParisTech - École nationale supérieure des mines de Paris, Institut Polytechnique de Paris (IP Paris), Institut interdisciplinaire d'anthropologie du contemporain (IIAC), École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), ANR-19-CE10-0012,HUSH,La chaîne d'approvisionnement humaine derrière les technologies intelligentes(2019), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut interdisciplinaire de l’innovation de Telecom Paris (I3 SES), Télécom ParisTech-Institut interdisciplinaire de l’innovation (I3), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Télécom ParisTech-Institut interdisciplinaire de l’innovation (I3), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Télécom ParisTech-MINES ParisTech - École nationale supérieure des mines de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
Subjects: Cultural Studies, Platforms, digital labor, [SHS.SOCIO]Humanities and Social Sciences/Sociology, Sociology and Political Science, Social Psychology, 05 social sciences, General Social Sciences, COVID-19, [SHS.ECO]Humanities and Social Sciences/Economics and Finance, Education, work-from-home, 8. Economic growth, 0502 economics and business, 050207 economics, 050203 business & management, risk
Abstract: International audience; In this paper, we analyze the recessionary effects of the COVID-19 pandemic on digital platform workers. The crisis has been described as a great work-from-home experiment, with platform ecosystems positing as its most advanced form. Our analysis differentiates the direct (health) and indirect (economic) risks incurred by workers, to critically assess the portrayal of platforms as buffers against crisis-induced layoffs. We submit that platform-mediated labor may eventually increase precarity, without necessarily reducing health risks for workers. Our argument is based on a comparison of the three main categories of platform work – “on-demand labor” (gigs such as delivery and transportation), “online labor” (tasks performed remotely, such as data annotation) and “social networking labor” (content generation and moderation). We discuss the strategies that platforms deploy to transfer risk from clients onto workers, thus deepening existing power imbalances between them. These results question the problematic equivalence between work-from-home and platform labor. Instead of attaining the advantages of the former in terms of direct and indirect risk mitigation, an increasing number of platformized jobs drift toward high economic and insuppressible health risks.
Published: 2022

13. Point Specification in Collaborative Visualization for 3D Scalar Fields Using Augmented Reality

Author: Mickael Sereno, Lonni Besançon, Tobias Isenberg, Analysis and Visualization (AVIZ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Linköpings universitet, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Human-Computer Interaction, 3D visualization, 3D point specification, [INFO]Computer Science [cs], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], CSCW, Computer Graphics and Computer-Aided Design, Software, AR
Abstract: International audience; We compared three techniques to specify 3D positions for collaborative augmented reality (AR) visualization. AR Head-Mounted Displays allow multiple users to share the same physical space, while keeping seamless social interactions. Interactions being key parts of exploratory visualization tasks, we adapted from the Virtual Reality literature three distinct techniques to specify points in 3D space, such as for placing annotations for which they cannot rely on existing data objects. We evaluated these techniques on their accuracy and speed, the user's subjective workload and preferences, as well as their co-presence, mutual understanding, and behavior in collaborative tasks. Our results suggest that all the three techniques provide good mutual understanding and co-presence among collaborators. They differ, however, in the way users behave, their accuracy, and their speed.
Published: 2022

14. Stability and optimal forcing analysis of a wind turbine wake: Comparison with POD

Author: Pietro De Palma, Stefania Cherubini, Stefano Leonardi, Giovanni De Cillis, Onofrio Semeraro, Department of Mechanics, Mathematics and Management (DMMM), Politecnico di Bari, Euro-Mediterranean Center on Climate Change (CMCC), Department of Mechanical Engineering, University of Texas at Dallas, Texas, USA, DAtascience, trAnsition, Fluid instability, contrOl, Turbulence (DATAFLOT), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Mécanique-Energétique (M.-E.), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Mécanique-Energétique (M.-E.), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Bi-local stability analysis, 020209 energy, Large Eddy Simulation (LES), Resolvent analysis, 02 engineering and technology, Wake, 01 natural sciences, 7. Clean energy, Stability (probability), Turbine, 010305 fluids & plasmas, law.invention, Large eddy simulation (LES), Proper orthogonal decomposition (POD), Wind turbine wake, Physics::Fluid Dynamics, [SPI]Engineering Sciences [physics], law, Stability theory, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Wavenumber, Mean flow, Physics, Renewable Energy, Sustainability and the Environment, Rotor (electric), Turbulence, Mechanics, Proper Orthogonal Decomposition (POD)
Abstract: International audience; Understanding the dynamics and generation of coherent structures in wind-turbine wakes is crucial for efficiency improvement of wind farms, which will most probably represent one of the main renewable power generation sources in 2050. In this paper, we investigate the origin of such coherent structures by performing modal and non-modal stability analysis of the mean flow downstream of a wind-turbine rotor. The database consists of large-eddysimulation results. Bi-local linear-stability and optimal-forcing analyses are performed at several wake's cross-sections. Most unstable perturbations are compared with the most energetic coherent structures recovered by the proper orthogonal decomposition (POD) analysis, showing a good agreement close to the rotor. Further downstream, these modes are overtaken by others with wavenumbers departing from those of the main POD modes. However, optimal-forcing analysis shows that asymptotically stable modes can be amplified by more than one order of magnitude via quasi-resonance mechanisms, bypassing the growth of the most unstable modes in the far wake. This suggests that the most energetic structures are originated by modal instabilities, which trigger quasiresonance mechanisms in the far wake, determining the emergence of specific frequencies in the turbulent flow. These findings are crucial for designing efficient control systems to optimize wind farm performance.
Published: 2022

15. Collaborative Work in Augmented Reality: A Survey

Author: Lonni Besançon, Tobias Isenberg, Mickael Sereno, Michael J. McGuffin, Xiyao Wang, Analysis and Visualization (AVIZ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Linköpings universitet, Ecole de technologie supérieure, Université du Québec, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Computer-Supported Cooperative Work, 02 engineering and technology, Space (commercial competition), Field (computer science), Domain (software engineering), Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, Computer Graphics, [INFO]Computer Science [cs], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Modalities, Augmented Reality, Product design, Immersive Analytics, 020207 software engineering, Computer Graphics and Computer-Aided Design, Visualization, Virtual and Augmented Reality, Research Design, Introductory and Survey, Signal Processing, Computer-supported cooperative work, Augmented reality, Computer Vision and Pattern Recognition, Software
Abstract: International audience; In Augmented Reality (AR), users perceive virtual content anchored in the real world. It is used in medicine, education, games, navigation, maintenance, product design, and visualization, in both single-user and multiuser scenarios. Multiuser AR has received limited attention from researchers, even though AR has been in development for more than two decades. We present the state of existing work at the intersection of AR and Computer-Supported Collaborative Work (AR-CSCW), by combining a systematic survey approach with an exploratory, opportunistic literature search. We categorize 65 papers along the dimensions of space, time, role symmetry (whether the roles of users are symmetric), technology symmetry (whether the hardware platforms of users are symmetric), and output and input modalities. We derive design considerations for collaborative AR environments, and identify under-explored research topics. These include the use of heterogeneous hardware considerations and 3D data exploration research areas. This survey is useful for newcomers to the field, readers interested in an overview of CSCW in AR applications, and domain experts seeking up-to-date information.
Published: 2022

16. Atlas linguistique sonore de la Gallo-Romania : focus sur le wallon

Author: Boula de Mareüil, Philippe, Mahin, Lucien, Traitement du Langage Parlé (TLP ), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Stéphanie Noirard, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SHS.LANGUE]Humanities and Social Sciences/Linguistics, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2022

17. OmniPrint: A Configurable Printed Character Synthesizer

Author: Sun, Haozhe, Tu, Wei-Wei, Guyon, Isabelle, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay, Centre National de la Recherche Scientifique (CNRS), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), 4Paradigm, Chalearn, ANR-19-CHIA-0022,HUMANIA,Intelligence Artificielle pour Tous(2019), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, [STAT.AP]Statistics [stat]/Applications [stat.AP], Machine Leanring, Optical Character Recognition, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [SCCO.COMP]Cognitive science/Computer science, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: We introduce OmniPrint, a synthetic data generator of isolated printed characters, geared toward machine learning research. It draws inspiration from famous datasets such as MNIST, SVHN and Omniglot, but offers the capability of generating a wide variety of printed characters from various languages, fonts and styles, with customized distortions. We include 935 fonts from 27 scripts and many types of distortions. As a proof of concept, we show various use cases, including an example of meta-learning dataset designed for the upcoming MetaDL NeurIPS 2021 competition. OmniPrint is available at https://github.com/SunHaozhe/OmniPrint., Accepted at 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks. https://openreview.net/forum?id=R07XwJPmgpl
Published: 2022

18. Gender in 30 Years of IEEE Visualization

Author: Natkamon Tovanich, Petra Isenberg, Pierre Dragicevic, IRT SystemX (IRT SystemX), Analysis and Visualization (AVIZ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), IRT SystemX, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Matching (statistics), Computer science, media_common.quotation_subject, Scientometry, 02 engineering and technology, Proxy (climate), Representation (politics), 5. Gender equality, 0202 electrical engineering, electronic engineering, information engineering, Computer Graphics, Humans, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], media_common, Visualization, Diversity, Descriptive statistics, Gender, 020207 software engineering, Gender studies, Exploratory analysis, Computer Graphics and Computer-Aided Design, Collaboration, Signal Processing, Publication, Female, Computer Vision and Pattern Recognition, [SHS.GENRE]Humanities and Social Sciences/Gender studies, Software, Diversity (politics)
Abstract: We present an exploratory analysis of gender representation among the authors, committee members, and award winners at the IEEE Visualization (VIS) conference over the last 30 years. Our goal is to provide descriptive data on which diversity discussions and efforts in the community can build. We look in particular at the gender of VIS authors as a proxy for the community at large. We consider measures of overall gender representation among authors, differences in careers, positions in author lists, and collaborations. We found that the proportion of female authors has increased from 9% in the first five years to 22% in the last five years of the conference. Over the years, we found the same representation of women in program committees and slightly more women in organizing committees. Women are less likely to appear in the last author position, but more in the middle positions. In terms of collaboration patterns, female authors tend to collaborate more than expected with other women in the community. All non-gender related data is available on https://osf.io/ydfj4/ and the gender-author matching can be accessed through https://nyu.databrary.org/volume/1301.
Published: 2022

19. Compressible solver for two-phase flows with sharp interface and capillary effects preserving accuracy in the low Mach regime

Author: Ziqiang Zou, Christian Tenaud, Samuel Kokh, Nicolas Grenier, Edouard Audit, Maison de la Simulation (MDLS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), COuplages Multiphysiques Et Transferts (COMET), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Mécanique-Energétique (M.-E.), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay, CEA- Saclay (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire d'Énergétique Moléculaire et Macroscopique, Combustion (EM2C), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Mécanique-Energétique (M.-E.), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Service de Thermo-hydraulique et de Mécanique des Fluides (STMF), Département de Modélisation des Systèmes et Structures (DM2S), CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay
Subjects: [PHYS.PHYS.PHYS-FLU-DYN]Physics [physics]/Physics [physics]/Fluid Dynamics [physics.flu-dyn], Physics and Astronomy (miscellaneous), Truncation error (numerical integration), Level Set, compressible approach, 010103 numerical & computational mathematics, truncation error analysis, 01 natural sciences, Two-phase flow, [SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], Physics::Fluid Dynamics, symbols.namesake, Speed of sound, sharp interface, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], [PHYS.MECA.MEFL]Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], low Mach correction, 0101 mathematics, ComputingMilieux_MISCELLANEOUS, Physics, Numerical Analysis, Finite volume method, Applied Mathematics, Numerical analysis, Mechanics, Solver, Dissipation, Computer Science Applications, 010101 applied mathematics, Computational Mathematics, Mach number, Modeling and Simulation, Compressibility, symbols
Abstract: We present a sharp interface approach for compressible two-phase flows that preserves its accuracy in the low Mach regime. The interface between both fluids is captured by a Level Set function via the Ghost Fluid method. In the low Mach regime, classic Finite Volume compressible solvers lose accuracy on quadrangle or hexahedral meshes and a low Mach correction is then necessary to reduce the numerical dissipation. The sharp interface that separates both fluids, may induce an important jump in the medium properties that induces additional challenges for the design of a numerical scheme. Indeed, the well-known low Mach fixes in the literature could lead to significant truncation errors when the flow involves large density and sound speed ratios between both phases. To preserve the accuracy of the numerical method, we propose a specific low Mach fix for this interface problem that yields a uniform truncation error with respect to the Mach number. Numerical results show significant evidances of the efficiency and accuracy of the proposed low Mach correction.
Published: 2022

20. Perception! Immersion! Empowerment! Superpowers as Inspiration for Visualization

Author: Petra Isenberg, Bon Adriel Aseniero, Sheelagh Carpendale, Yvonne Jansen, Wesley Willett, Lora Oehlberg, Pierre Dragicevic, University of Calgary, Autodesk Reasearch [Toronto], Autodesk Reasearch, Department of Computer Science (Simon Fraser University), Simon Fraser University (SFU.ca), Analysis and Visualization (AVIZ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR-19-CE33-0012,EMBER,Visualisations situées pour l'analyse de données personnelles(2019), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Subjects: FOS: Computer and information sciences, Computer science, Vision, media_common.quotation_subject, Computer Science - Human-Computer Interaction, 02 engineering and technology, Comics, Human-Computer Interaction (cs.HC), Cognition, Intersection, Situated visualization, Perception, Immersion, 0202 electrical engineering, electronic engineering, information engineering, Computer Graphics, Fiction, Humans, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Set (psychology), Empowerment, media_common, Visualization, Cognitive science, superpowers, business.industry, 020207 software engineering, Computer Graphics and Computer-Aided Design, Variety (cybernetics), Signal Processing, Computer Vision and Pattern Recognition, business, Software
Abstract: We explore how the lens of fictional superpowers can help characterize how visualizations empower people and provide inspiration for new visualization systems. Researchers and practitioners often tout visualizations' ability to "make the invisible visible" and to "enhance cognitive abilities." Meanwhile superhero comics and other modern fiction often depict characters with similarly fantastic abilities that allow them to see and interpret the world in ways that transcend traditional human perception. We investigate the intersection of these domains, and show how the language of superpowers can be used to characterize existing visualization systems and suggest opportunities for new and empowering ones. We introduce two frameworks: The first characterizes seven underlying mechanisms that form the basis for a variety of visual superpowers portrayed in fiction. The second identifies seven ways in which visualization tools and interfaces can instill a sense of empowerment in the people who use them. Building on these observations, we illustrate a diverse set of "visualization superpowers" and highlight opportunities for the visualization community to create new systems and interactions that empower new experiences with data., 11 pages, 13 figures. To appear at IEEE VIS 2021. Supplemental material and illustrations are available at https://osf.io/8yhfz
Published: 2022

21. The influence of incoming turbulence on the dynamic modes of an NREL-5MW wind turbine wake

Author: Stefania Cherubini, Giovanni De Cillis, Onofrio Semeraro, Stefano Leonardi, Pietro De Palma, Department of Mechanics, Mathematics and Management (DMMM), Politecnico di Bari, Euro-Mediterranean Center on Climate Change (CMCC), DAtascience, trAnsition, Fluid instability, contrOl, Turbulence (DATAFLOT), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Mécanique-Energétique (M.-E.), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of Texas at Dallas [Richardson] (UT Dallas), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Mécanique-Energétique (M.-E.), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: NREL 5MW wind turbine, 020209 energy, Flow (psychology), 02 engineering and technology, Inflow, Wake, 01 natural sciences, 7. Clean energy, Turbine, 010305 fluids & plasmas, [SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], Physics::Fluid Dynamics, DMD modes, Turbulent precursor simulation, Uniform or turbulent inflow, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Dynamic mode decomposition, Physics, Renewable Energy, Sustainability and the Environment, Turbulence, Mechanics, Vortex, Boundary layer, [SPI.MECA.STRU]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Structural mechanics [physics.class-ph], Physics::Space Physics
Abstract: International audience; Knowledge of the dynamics of wind turbine wakes and its dependence on the incoming boundary layer is fundamental to optimize and control the power production of wind farms. This work aims at investigating the effect of inflow turbulence on the wake of the NREL-5MW wind turbine. Sparsity-Promoting Dynamic Mode Decomposition (SP-DMD) is performed on snapshots extracted from large-eddy simulations of the turbine wake, for detecting the most dynamically-relevant flow structures in the presence or absence of inflow turbulence. We demonstrate that inflow turbulence generated by a precursor simulation radically changes the most dynamically-relevant flow structures. For the laminar-inflow case the DMD modes selected by the SP algorithm have high wavenumbers and are spatially localized. When turbulence is added at the inflow, these high-frequency modes are superseded by low-frequency modes lying in the frequency range of the wake meandering and filling the whole domain, mostly corresponding to those dynamically relevant for the precursor simulation. These results show that, in the presence of inflow turbulence, coherent structures linked to endogenous mechanisms such as tip and root vortices loose their dynamical relevance in favour of those exogenously excited by turbulence, indicating that low-dimensional models of turbine wakes should take into account atmospheric turbulence.
Published: 2022

22. Multiscale Unfolding: Illustratively Visualizing the Whole Genome at a Glance

Author: Halladjian, Sarkis, Kouřil, David, Miao, Haichao, Gröller, Eduard, Viola, Ivan, Isenberg, Tobias, Analysis and Visualization (AVIZ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institute of Visual Computing & Human-Centered Technology, Vienna University of Technology (TU Wien), Institute of Computer Graphics and Algorithms, Visual Computing Center, King Abdullah University of Science and Technology, Part of this work was funded under the ILLUSTRARE grant by both the Austrian Science Fund (FWF): I 2953-N31 and the French National Research Agency (ANR): ANR-16-CE91-0011-01. The research was further supported by funding from King Abdullah University of Science and Technology (KAUST), under award number BAS/1/1680-01-01 and by funding from the ILLVISATION grant by WWTF (VRG11-010)., ANR-16-CE91-0011,ILLUSTRARE,Integrative Visual Abstraction of Molecular Data(2016), Analysis and Visualization (AViz), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Interaction avec l'Humain (IaH), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec, Technical University of Vienna [Vienna] (TU WIEN), King Abdullah University of Science and Technology (KAUST), Part of this work was funded under the ILLUSTRARE grant by both the Austrian Science Fund (FWF): I 2953-N31 and the French National Research Agency (ANR): ANR-16-CE91-0011-01. The research was further supported by funding from King Abdullah University of Science and Technology(KAUST), under award number BAS/1/1680-01-01 and by funding from the ILLVISATION grant by WWTF (VRG11-010)., Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Multiscale visualization, visual abstraction, [SCCO.COMP]Cognitive science/Computer science, spatially-controlled scale transition, DNA, illustrative visualization, genome
Abstract: International audience; We present Multiscale Unfolding, an interactive technique for illustratively visualizing multiple hierarchical scales of DNA in a single view, showing the genome at different scales and demonstrating how one scale spatially folds into the next. The DNA's extremely long sequential structure---arranged differently on several distinct scale levels---is often lost in traditional 3D depictions, mainly due to its multiple levels of dense spatial packing and the resulting occlusion. Furthermore, interactive exploration of this complex structure is cumbersome, requiring visibility management like cutaways. In contrast to existing temporally controlled multiscale data exploration, we allow viewers to always see and interact with any of the involved scales. For this purpose we separate the depiction into constant-scale and scale transition zones. Constant-scale zones maintain a single-scale representation, while still linearly unfolding the DNA. Inspired by illustration, scale transition zones connect adjacent constant-scale zones via level unfolding, scaling, and transparency. We thus represent the spatial structure of the whole DNA macro-molecule, maintain its local organizational characteristics, linearize its higher-level organization, and use spatially controlled, understandable interpolation between neighboring scales. We also contribute interaction techniques that provide viewers with a coarse-to-fine control for navigating within our all-scales-in-one-view representations and visual aids to illustrate the size differences. Overall, Multiscale Unfolding allows viewers to grasp the DNA's structural composition from chromosomes to the atoms, with increasing levels of "unfoldedness," and can be applied in data-driven illustration and communication.
Published: 2022

23. An Introduction to Mobile Data Visualization

Author: Petra Isenberg, Christopher Collins, Lonni Besançon, Tim Dwyer, Tobias Isenberg, Ricardo Langner, Christian Tominski, Bongshin Lee, Charles Perin, Technische Universität Dresden = Dresden University of Technology (TU Dresden), Linköping University (LIU), University of Ontario Institute of Technology (UOIT), Monash University [Melbourne], Analysis and Visualization (AVIZ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Microsoft Research [Redmond], Microsoft Corporation [Redmond, Wash.], University of Victoria [Canada] (UVIC), University of Rostock, CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Multimedia, Computer science, Mobile broadband, [SCCO.COMP]Cognitive science/Computer science, computer.software_genre, computer, ComputingMilieux_MISCELLANEOUS, Visualization
Abstract: International audience
Published: 2021

24. A corpus-based study of the distribution of word-final schwa in Standard French and what it teaches us about its phonological status

Author: Mathilde Hutin, Adèle Jatteau, Ioana Vasilescu, Lori Lamel, Yaru Wu, Martine Adda-Decker, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Traitement du Langage Parlé (TLP ), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Savoirs, Textes, Langage (STL) - UMR 8163 (STL), Université de Lille-Centre National de la Recherche Scientifique (CNRS), LPP - Laboratoire de Phonétique et Phonologie - UMR 7018 (LPP), Université Sorbonne Nouvelle - Paris 3-Centre National de la Recherche Scientifique (CNRS), Université de Caen Normandie - UFR Humanités et Sciences Sociales (UNICAEN UFR HSS), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU), Laboratoire Interdisciplinaire des Sciences du Numérique [LISN], Savoirs, Textes, Langage (STL) - UMR 8163 [STL], LPP - Laboratoire de Phonétique et Phonologie - UMR 7018 [LPP], CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Standard French, Standard french, Pronunciation variation, automatic alignment, schwa, P1-1091, large corpora, [SCCO.LING]Cognitive science/Linguistics, epenthesis, Large corpora, Automatic alignment, pronunciation variation, PC1-5498, Schwa, [SHS.LANGUE]Humanities and Social Sciences/Linguistics, Philology. Linguistics, Epenthesis, ComputingMilieux_MISCELLANEOUS, Romanic languages
Abstract: What is commonly considered as an epenthetic vowel can actually refer to at least two different realities: phonological epenthesis or phonetic excrescence. French schwa, noted [ә], is a vowel alternating with zero and limited to unstressed syllables that can appear word-internally or word-finally. This paper presents an extensive description of the distribution of word-final schwa in Standard French in order to shed light on its nature: is it an intrusive vowel or a full epenthetic vowel? To that extent, three large corpora of French containing more than 110 hours of speech were used to establish the presence of word-final schwa as a function of sociolinguistics, orthography, phonotactics and phonetics. Our conclusions are that word-final schwa is impacted by speech style, gender, orthography, phonotactics (i.e., the number of adjacent consonants and their sonority profile), and the phonological properties of the codas. However, speech rate does not impact word-final schwa realization. The specific results lead us to suggest that word-final schwa in Standard French shares similarities with intrusive vowels but ultimately behaves like a legit epenthetic vowel.
Published: 2021

25. Franco-German position paper on 'Speeding up industrial AI and trustworthiness'

Author: chiaroni, julien, Zillner, Sonja, Bertels, Natalie, Bezombes, Patrick, Bonhomme, Yannick, Amadou-Boubacar, Habiboulaye, Cantat, Loic, Cattaneo, Gabriella, Cordesse, Lionel, Curry, Edward, Doufene, Abdelkrim, Escorihuela, Emmanuelle, Robles, Ana García, Gómez, Jon Ander, Hahn, Thomas, Jost-Dummer, Stefan, Jurie, Frederic, Mann, Zoltan, Mattioli, Juliette, Metzger, Andreas, Nicolas, Yves, Perrotton, Xavier, Petkovic, Milan, Romao, Artur, Scerri, Simon, Scholz, Maike, Schöning, Harald, Schoenauer, Marc, Sghiouer, Kaoutar, Stevens, Richard, Södergard, Caj, Tardieu, Hubert, Terrier, François, Tordjman, Eric, Tran, Jean-Michel, Vink, Henk-Jan, Walsh, Ray, Zissis, Dimitris, Secrétariat général pour l'investissement, IRT SystemX, Air Liquide, Centre de Recherche Claude-Delorme, Paris-Saclay, France., The Irish Software Engineering Research Centre (LERO), Safran Tech, Universität Duisburg-Essen = University of Duisburg-Essen [Essen], THALES [France], University of Duisbourg-Essen, Valeo Driving Assistance Domain, VALEO, Eindhoven University of Technology [Eindhoven] (TU/e), Fraunhofer Institute for Intelligent Analysis and Information Systems (Fraunhofer IAIS), Fraunhofer (Fraunhofer-Gesellschaft), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris Descartes – Institut de psychologie (UPD5 Psychologie), Université Paris Descartes - Paris 5 (UPD5), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), IRT SystemX (IRT SystemX), Universität Duisburg-Essen [Essen], THALES, CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), and Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], GeneralLiterature_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: The full benefit from using AI to generate value for businesses, societal wellbeing and the environment is still to be fully realised. To lower adoption barriers of Industrial AI, challenges on multiple levels (technical complexity, trustworthiness, industrialisation, data frameworks and infrastructures, etc.) need to be addressed to benefit from its full socio economic potential for economy, society and welfare. It is now time to foster the development of "industrial and trustworthy AI" and nurture European innovation and sovereignty ambitions and benefit society and leading European industries. In this position paper, a comprehensive industrial and trustworthy AI framework that clusters the priority area for AI research, innovation and deployment is introduced. It covers tools and methodologies that support the design, test, validation, verification, and maintainability of AI based functions and systems and addresses the development of AI based process and systems to demonstrate its integration into new products and services. Conformity assessment schemes, balancing innovation, business and European perspectives, are considered to connect risk management, functional andtrustworthiness requirements to industrial processes. In addition adequate standards supporting industrial AI and trustworthiness will play a central role. Implementing the industrial and trustworthy AI framework will require resources beyond the means of any European private stakeholders. Therefore, strong support from ecosystems, governments, and Europe might not be an option but necessary.
Published: 2021

26. Aircraft Numerical 'Twin': A Time Series Regression Competition

Author: Pavao, Adrien, Guyon, Isabelle, Stéphane, Nachar, Lebeau, Fabrice, Ghienne, Martin, Platon, Ludovic, Barbagelata, Tristan, Escamilla, Pierre, Mzali, Sana, Liao, Meng, Lassonde, Sylvain, Braun, Antonin, Amor, Slim, Cucu-Grosjean, Liliana, Wehaiba, Marwan, Bar-Hen, Avner, Gogonel, Adriana, Cheikh, Alaeddine, Duda, Marc, Laugel, Julien, Marauri, Mathieu, Souissi, Mhamed, Lecerf, Théo, Elion, Mehdi, Tabti, Sonia, Budynek, Julien, Le Bouteiller, Pauline, Penon, Antonin, Lasseri, Raphaël-David, Ripoche, Julien, Epalle, Thomas, TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), A&O (Apprentissage et Optimisation) (A&O), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Algorithmes, Apprentissage et Calcul (AAC), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Algorithmes, Apprentissage et Calcul (AAC), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Dassault Aviation, Laboratoire QUARTZ (QUARTZ ), Université Paris 8 Vincennes-Saint-Denis (UP8)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-ISAE-Supméca Institut Supérieur de Mécanique de Paris (ISAE-Supméca), ISAE-Supméca Institut Supérieur de Mécanique de Paris (ISAE-Supméca), Adapter le raisonnement pire cas à différentes criticités (KOPERNIC), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), MFG Labs, Fieldbox AI, Magic LEMP, We would like to warmly thank the Ile-de-France region for the support and funding of this project. This work was also supported by the ANR (Agence Nationale de la Recherche, National Agency for Research) un-der AI chair of excellence HUMANIA, grant number Project-ANR-19-CHIA-0022, ANR-19-CHIA-0022,HUMANIA,Intelligence Artificielle pour Tous(2019), and Institut Supérieur de Mécanique de Paris (ISAE-Supméca)
Subjects: [SPI]Engineering Sciences [physics], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Time series regression, Machine learning, Deep learning, Gradient boosted trees, ComputerApplications_COMPUTERSINOTHERSYSTEMS, Aeronautics, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; This paper presents the design and analysis of a data science competition on a problem of time series regression from aeronautics data. For the purpose of performing predictive maintenance, aviation companies seek to create aircraft "numerical twins", which are programs capable of accurately predicting strains at strategic positions in various body parts of the aircraft. Given a number of input parameters (sensor data) recorded in sequence during the flight, the competition participants had to predict output values (gauges), also recorded sequentially during test flights, but not recorded during regular flights. The competition data included hundreds of complete flights. It was a code submission competition with complete blind testing of algorithms. The results indicate that such a problem can be effectively solved with gradient boosted trees, after preprocessing and feature engineering. Deep learning methods did not prove as efficient. Index Terms-aeronautics, time series regression, machine learning, deep learning, gradient boosted trees.
Published: 2021

27. Revisiting Joint Decoding Based Multi-Talker Speech Recognition withDNN Acoustic Model

Author: Kocour, Martin, Žmolíková, Kateřina, Ondel, Lucas, Švec, Ján, Delcroix, Marc, Ochiai, Tsubasa, Burget, Lukáš, Černocký, Jan, Faculty of Information Technology [Brno] (FIT / BUT), Brno University of Technology [Brno] (BUT), Traitement du Langage Parlé (TLP ), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), NTT Corporation, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Published: 2021

28. OVERLAP-AWARE LOW-LATENCY ONLINE SPEAKER DIARIZATION BASED ON END-TO-END LOCAL SEGMENTATION

Author: Juan Manuel Coria, Hervé Bredin, Sahar Ghannay, Sophie Rosset, Information, Langue Ecrite et Signée (ILES), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ANR-16-CE92-0025,PLUMCOT,Identification non-supervisée des personnages de films et séries télévisées(2016), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Sound (cs.SD), overlapped speech detection, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Computer Science - Sound, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, low latency, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, speaker embedding, speaker diarization, [INFO]Computer Science [cs], Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: We propose to address online speaker diarization as a combination of incremental clustering and local diarization applied to a rolling buffer updated every 500ms. Every single step of the proposed pipeline is designed to take full advantage of the strong ability of a recently proposed end-to-end overlap-aware segmentation to detect and separate overlapping speakers. In particular, we propose a modified version of the statistics pooling layer (initially introduced in the x-vector architecture) to give less weight to frames where the segmentation model predicts simultaneous speakers. Furthermore, we derive cannot-link constraints from the initial segmentation step to prevent two local speakers from being wrongfully merged during the incremental clustering step. Finally, we show how the latency of the proposed approach can be adjusted between 500ms and 5s to match the requirements of a particular use case, and we provide a systematic analysis of the influence of latency on the overall performance (on AMI, DIHARD and VoxConverse)., Comment: To appear in ASRU 2021. Code available at https://github.com/juanmc2005/StreamingSpeakerDiarization/
Published: 2021

29. Can the prosody of statements and various types of questions be identified in Gallo-Romance dialects?

Author: Hansjörg Mixdorff, Philippe Boula de Mareüil, Albert Rilliard, Valentina De Iacovo, Beuth Hochschule für Technik Berlin, Beuth Hochschule für Technik, Traitement du Langage Parlé (TLP ), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Oliver Niebuhr, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: ToBI, French dialects, Fujisaki model, French dialects, speech act perception, fundamental frequency, ToBI, Fujisaki model, fundamental frequency, [SHS.LANGUE]Humanities and Social Sciences/Linguistics, speech act perception, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2021

30. Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery

Author: Lucas Ondel, Bolaji Yusuf, Lukas Burget, Murat Saraclar, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Traitement du Langage Parlé (TLP ), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Faculty of Information Technology [Brno] (FIT / BUT), Brno University of Technology [Brno] (BUT), BUSIM Speech Group, Department of Electrical and Electronic Engineering [Istanbul], Boǧaziçi üniversitesi = Boğaziçi University [Istanbul]-Boǧaziçi üniversitesi = Boğaziçi University [Istanbul], Boğaziçi University [Istanbul]-Boğaziçi University [Istanbul], Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computational Mathematics, Acoustics and Ultrasonics, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Computer Science::Sound, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Computer Science (miscellaneous), Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing), Electrical and Electronic Engineering, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Abstract: This work investigates subspace non-parametric models for the task of learning a set of acoustic units from unlabeled speech recordings. We constrain the base-measure of a Dirichlet-Process mixture with a phonetic subspace-estimated from other source languages-to build an educated prior, thereby forcing the learned acoustic units to resemble phones of known source languages. Two types of models are proposed: (i) the Subspace HMM (SHMM) which assumes that the phonetic subspace is the same for every language, (ii) the Hierarchical-Subspace HMM (H-SHMM) which relaxes this assumption and allows to have a language-specific subspace estimated on the unlabeled target data. These models are applied on 3 languages: English, Yoruba and Mboshi and they are compared with various competitive acoustic units discovery baselines. Experimental results show that both subspace models outperform other systems in terms of clustering quality and segmentation accuracy. Moreover, we observe that the H-SHMM provides results superior to the SHMM supporting the idea that language-specific priors are preferable to language-agnostic priors for acoustic unit discovery.
Published: 2021

31. Data Driven Modal Decomposition of the Wake behind an NREL-5MW Wind Turbine

Author: Pietro De Palma, Stefania Cherubini, Stefano Leonardi, Giovanni De Cillis, Onofrio Semeraro, Department of Mechanics, Mathematics and Management (DMMM), Politecnico di Bari, Euro-Mediterranean Center on Climate Change (CMCC), DAtascience, trAnsition, Fluid instability, contrOl, Turbulence (DATAFLOT), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Mécanique-Energétique (M.-E.), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Department of Mechanical Engineering, University of Texas at Dallas, Texas, USA, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Mécanique-Energétique (M.-E.), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Modal decomposition, coherent structures, Energy Engineering and Power Technology, Aerospace Engineering, wind turbine wake, Inflow, Wake, 01 natural sciences, Turbine, 010305 fluids & plasmas, Physics::Fluid Dynamics, symbols.namesake, [SPI]Engineering Sciences [physics], 0103 physical sciences, Dynamic mode decomposition, TJ1-1570, Mechanical engineering and machinery, 010306 general physics, Physics, Mechanical Engineering, NREL-5MW wind turbine, Reynolds number, Laminar flow, Mechanics, Immersed boundary method, symbols, Large eddy simulation
Abstract: The wake produced by a utility-scale wind turbine invested by a laminar, uniform inflow is analyzed by means of two different modal decompositions, the proper orthogonal decomposition (POD) and the dynamic mode decomposition (DMD), in its sparsity-promoting variant. The turbine considered is the NREL-5MW at tip-speed ratio λ=7 and a diameter-based Reynolds number of the order 108. The flow is simulated through large eddy simulation, where the forces exerted by the blades are modeled using the actuator line method, whereas tower and nacelle are modeled employing the immersed boundary method. The main flow structures identified by both modal decompositions are compared and some differences emerge that can be of great importance for the formulation of a reduced-order model. In particular, a high-frequency mode directly related to the tip vortices is found using both methods, but it is ranked differently. The other dominant modes are composed by large-scale low-frequency structures, but with different frequency content and spatial structure. The most energetic 200 POD modes account for ≈20% only of the flow kinetic energy. While using the same number of DMD modes, it is possible to reconstruct the flow field to within 80% accuracy. Despite the similarities between the set of modes, the comparison between these modal-decomposition techniques points out that an energy-based criterion such as that used in the POD may not be suitable for formulating a reduced-order model of wind turbine wakes, while the sparsity-promoting DMD appears able to perform well in reconstructing the flow field with only a few modes.
Published: 2021

32. Easy-to-use combination of POS and BERT model for domain-specific and misspelled terms

Author: Benamar, Alexandra, Bothua, Meryl, Grouin, Cyril, Vilnat, Anne, Information, Langue Ecrite et Signée (ILES), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), EDF R&D (EDF R&D), EDF (EDF), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Part-Of-Speech, Language Models, Semantic Similarity, Out-of-Vocabulary Words, Natural Language Processing
Abstract: International audience; In this paper, we present BERT-POS, a simple method for encoding syntax into BERT embeddings without retraining or finetuning data, based on Part-Of-Speech (POS). Although fine-tuning is the most popular method to apply BERT models on domain datasets, it remains expensive in terms of training time, computing resources, training data selection and retraining frequency. Our alternative works at the preprocessing level and relies on POS tagging sentences. It gives interesting results for words similarity regarding out-of-vocabulary both in terms of domain-specific words and misspellings. More specifically, the experiments were done on French language, but we believe that they would be similar on others.
Published: 2021

33. A Comment on 'Evaluation of Sampling Methods for Scatterplots'

Author: Steve Haroz, Analysis and Visualization (AVIZ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Code (cryptography), Sampling (statistics), [SCCO.COMP]Cognitive science/Computer science, Research questions, Review process, Empirical evidence, Data science
Abstract: "Evaluation of Sampling Methods for Scatterplots" claims that people perform dot-density comparisons best when using random sampling rather than other sampling approaches. This claim and other core conclusions of the article are not supported by the article’s empirical evidence. The article’s reported results and figures do not meet its own stated threshold of statistical significance, and the analyses are ill-suited for the research questions. Some of these issues are present in the article and could have been spotted by reviewers, whereas other issues were only noticeable because I requested the code and data after publication (which a reviewer would have been prohibited from demanding during the IEEE TVCG review process). A reanalysis calls into question whether any generalizable claims can be made from these results. This comment, its analysis code, and the original article’s data are freely available at http://osf.io/hsujr.
Published: 2021

34. Definitely Maybe: Hedges And Boosters in the HCI Literature

Author: Yvonne Jansen, Andy Cockburn, Lonni Besançon, Pierre Dragicevic, Linköpings universitet, Monash University [Melbourne], Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Canterbury [Christchurch], Analysis and Visualization (AVIZ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Hedges, Boosters, computer.software_genre, Data science, Scientific communication, Literature survey, Content analysis, Scripting language, Statistical inference, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Hedge (finance), Baseline (configuration management), computer
Abstract: Introduction: Hedges and boosters are terms respectively used to decrease and increase the strength of statements. They are therefore essential lexical tools in scientific communication, in particular in fields conducting human-subjects experiments such as Human-Computer Interaction (HCI). Objectives: We present an analysis of the use of hedges and boosters in the proceedings of the main HCI conference, namely CHI, between 2010 and 2018 to better understand how CHI authors report empirical findings. Methods: We only considered papers reporting a user study and focused our analysis on the sentences in the abstract that describe empirical results. We used a program to detect boosters and hedges and manually adjusted their classifications. Results: We found that CHI studies reporting inferential statistics are more likely to boost than they hedge. Conclusion: Our work intends to raise awareness within the HCI community of the importance of hedging and boosting when reporting research results. Further, our results establish a baseline of the current use of these linguistic devices in HCI, adding to the current body of linguistic research. We finally contribute tools, procedures, and materials to facilitate future analyses of hedges and boosters in scientific communication. Reproducibility: All data and scripts are available on https://osf.io/kefmr/
Published: 2021

35. dnadna: DEEP NEURAL ARCHITECTURES FOR DNA - A DEEP LEARNING FRAMEWORK FOR POPULATION GENETIC INFERENCE

Author: Sanchez, Théophile, Madison Bray, Erik, Jobic, Pierre, Guez, Jérémy, Letournel, Anne-Catherine, Charpiat, Guillaume, Cury, Jean, Jay, Flora, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Accompagnement et Soutien aux Activités de Recherche & Développement (ASARD), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Éco-Anthropologie (EAE), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), BioInformatique (BioInfo), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD)
Subjects: [SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], deep neural networks, software, population genetics, genomic data, supervised learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination, and reusability of neural networks designed for genetic polymorphism data. dnadna defines multiple user-friendly workflows. First, users can implement new architectures and tasks, while benefiting from dnadna input/output and other utility functions, training procedure and test environment, which not only saves time but also decreases the probability of bugs. Second, implemented networks can be re-optimized based on user-specified training sets and/or tasks. Finally, users can apply pretrained networks in order to predict evolutionary history from alternative real or simulated genetic datasets, without the need of extensive knowledge in deep learning. Thanks to dnadna, newly implemented architectures and pretrained networks are easily shareable with the community for further benchmarking or applications. dnadna comes with a peer-reviewed exchangeable neural network allowing demographic inference from SNP data, that can be used directly or retrained to solve other tasks. Toy networks are also available to ease the exploration of the software, and we expect that the range of available architectures will keep expanding thanks to contributions from the community.
Published: 2021

36. Regularization of Mixture Models for Robust Principal Graph Learning

Author: Aurélien Decelle, Nabila Aghanim, Tony Bonnaire, TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut d'astrophysique spatiale (IAS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Centre National d’Études Spatiales [Paris] (CNES)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, FOS: Physical sciences, 02 engineering and technology, Minimum spanning tree, Regularization (mathematics), Machine Learning (cs.LG), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Maximum a posteriori estimation, [PHYS.COND.CM-DS-NN]Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn], Time complexity, ComputingMilieux_MISCELLANEOUS, business.industry, Applied Mathematics, Nonlinear dimensionality reduction, Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks, Mixture model, Computational Theory and Mathematics, Outlier, Graph (abstract data type), 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Algorithm, Software
Abstract: A regularized version of Mixture Models is proposed to learn a principal graph from a distribution of $D$-dimensional data points. In the particular case of manifold learning for ridge detection, we assume that the underlying manifold can be modeled as a graph structure acting like a topological prior for the Gaussian clusters turning the problem into a maximum a posteriori estimation. Parameters of the model are iteratively estimated through an Expectation-Maximization procedure making the learning of the structure computationally efficient with guaranteed convergence for any graph prior in a polynomial time. We also embed in the formalism a natural way to make the algorithm robust to outliers of the pattern and heteroscedasticity of the manifold sampling coherently with the graph structure. The method uses a graph prior given by the minimum spanning tree that we extend using random sub-samplings of the dataset to take into account cycles that can be observed in the spatial distribution., 12 pages, 6 figures
Published: 2021

37. 3D Mobile Data Visualization

Author: Tim Dwyer, Tobias Isenberg, Wolfgang Aigner, Lonni Besançon, Magdalena Boucher, Linköping University (LIU), St. Pölten University of Applied Sciences, Monash University [Melbourne], Analysis and Visualization (AVIZ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Mobile broadband, Computer graphics (images), [SCCO.COMP]Cognitive science/Computer science, ComputingMilieux_MISCELLANEOUS, Visualization
Abstract: International audience
Published: 2021

38. LISN @ WMT 2021

Author: Xu, Jitao, Abdul Rauf, Sadaf, Pham, Minh Quang, Yvon, François, Traitement du Langage Parlé (TLP ), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), SYSTRAN, and Association for Computational Linguistics
Subjects: [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Neural Machine Translation
Abstract: This paper documents an evaluation campaign.; International audience; This paper describes LISN's submissions to two shared tasks at WMT'21. For the biomedical translation task, we have developed resource-heavy systems for the English-French language pair, using both out-ofdomain and in-domain corpora. The target genre for this task (scientific abstracts) corresponds to texts that often have a standardized structure. Our systems attempt to take this structure into account using a hierarchical system of sentence-level tags. Translation systems were also prepared for the News task for the French-German language pair. The challenge was to perform unsupervised adaptation to the target domain (financial news). For this, we explored the potential of retrieval-based strategies, where sentences that are similar to test instances are used to prime the decoder.
Published: 2021

39. Screening Gender Transfer in Neural Machine Translation

Author: Guillaume Wisniewski, Lichao Zhu, Nicolas Ballier, François Yvon, Laboratoire de Linguistique Formelle (LLF - UMR7110), Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre de Linguistique Inter-langues, de Lexicologie, de Linguistique Anglaise et de Corpus (CLILLAC-ARP (URP_3967)), Université Paris Cité (UPCité), Traitement du Langage Parlé (TLP ), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Association for computational linguistics, Laboratoire de Linguistique Formelle (LLF UMR7110), Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Université de Paris (UP), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), This work was partially funded by the NeuroViz project subsidized by the Ile-de-France Region, and supported in part by the 2020 émergence research project SPECTRANS., and EMNLP
Subjects: FOS: Computer and information sciences, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Neural Machine Translation, Computer Science - Computation and Language, Probing, Gender bias, Transformer architectures, [INFO]Computer Science [cs], [SHS.LANGUE]Humanities and Social Sciences/Linguistics, Computation and Language (cs.CL)
Abstract: This paper aims at identifying the information flow in state-of-the-art machine translation systems, taking as example the transfer of gender when translating from French into English. Using a controlled set of examples, we experiment several ways to investigate how gender information circulates in a encoder-decoder architecture considering both probing techniques as well as interventions on the internal representations used in the MT system. Our results show that gender information can be found in all token representations built by the encoder and the decoder and lead us to conclude that there are multiple pathways for gender transfer., Accepted at BlackBoxNLP'2021
Published: 2021

40. Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools

Author: Bannour, Nesrine, Ghannay, Sahar, Névéol, Aurélie, Ligozat, Anne-Laure, Information, Langue Ecrite et Signée (ILES), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO]Computer Science [cs]
Abstract: International audience; Modern Natural Language Processing (NLP) makes intensive use of deep learning methods because of the accuracy they offer for a variety of applications. Due to the significant environmental impact of deep learning, costbenefit analysis including carbon footprint as well as accuracy measures has been suggested to better document the use of NLP methods for research or deployment. In this paper, we review the tools that are available to measure energy use and CO 2 emissions of NLP methods. We describe the scope of the measures provided and compare the use of six tools (carbon tracker, experiment impact tracker, green algorithms, ML CO2 impact, energy usage and cumulator) on named entity recognition experiments performed on different computational setups (local server vs. computing facility). Based on these findings, we propose actionable recommendations to accurately measure the environmental impact of NLP experiments.
Published: 2021

41. LISN @ WMT 2021

Author: Xu, Jitao, Abdul Rauf, Sadaf, Pham, Minh Quang, Yvon, François, Traitement du Langage Parlé (TLP ), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), SYSTRAN, and Association for Computational Linguistics
Subjects: [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Neural Machine Translation
Abstract: This paper documents an evaluation campaign.; International audience; This paper describes LISN's submissions to two shared tasks at WMT'21. For the biomedical translation task, we have developed resource-heavy systems for the English-French language pair, using both out-ofdomain and in-domain corpora. The target genre for this task (scientific abstracts) corresponds to texts that often have a standardized structure. Our systems attempt to take this structure into account using a hierarchical system of sentence-level tags. Translation systems were also prepared for the News task for the French-German language pair. The challenge was to perform unsupervised adaptation to the target domain (financial news). For this, we explored the potential of retrieval-based strategies, where sentences that are similar to test instances are used to prime the decoder.
Published: 2021

42. Does constituency analysis enhance domain-specific pre-trained BERT models for relation extraction?

Author: Tang, Anfu, Deléger, Louise, Bossy, Robert, Zweigenbaum, Pierre, Nédellec, Claire, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Information, Langue Ecrite et Signée (ILES), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Labex DigiCosme, ANR-11-LABX-0045,DIGICOSME,Mondes numériques: Données, programmes et architectures distribués(2011), Unité MaIAGE, INRAE, CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Artificial Intelligence, chemical-gene interactions, Quantitative Biology - Quantitative Methods, Relation Extraction, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Artificial Intelligence (cs.AI), Deep Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], constituency analysis, FOS: Biological sciences, Transformers, BERT (Bidirectional Encoder Representations from Transformers), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Computation and Language (cs.CL), Quantitative Methods (q-bio.QM)
Abstract: International audience; Recently many studies have been conducted on the topic of relation extraction. The DrugProt track at BioCreative VII provides a manually-annotated corpus for the purpose of the development and evaluation of relation extraction systems, in which interactions between chemicals and genes are studied. We describe the ensemble system that we used for our submission, which combines predictions of fine-tuned bioBERT, sciBERT and const-bioBERT models by majority voting. We specifically tested the contribution of syntactic information to relation extraction with BERT. We observed that adding constituentbased syntactic information to BERT improved precision, but decreased recall, since relations rarely seen in the train set were less likely to be predicted by BERT models in which the syntactic information is infused. Our code is available online [https://github.com/Maple177/drugprot-relation-extraction].
Published: 2021

43. O PAPEL DOS GESTOS FACIAIS NA PERCEPÇÃO DA ENTOAÇÃO DA ASSERÇÃO E DA QUESTÃO-ECO EM ÁUDIO LIMPO E DEGRADADO

Author: da Silva Miranda, Luma, de Moraes, João Antônio, Rilliard, Albert, Traitement du Langage Parlé (TLP ), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Pontifícia Universidade Católica de São Paulo, Viviane Leticia Silva Carrijo, Ulysses C. C. Diegues, Luciana Kool Modesto- Sarra, Fernando Venâncio da Costa, Viviane Klen-Alves, Gustavo Cardoso Silveira, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SHS.LANGUE]Humanities and Social Sciences/Linguistics, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2021

44. One Source, Two Targets: Challenges and Rewards of Dual Decoding

Author: Xu, Jitao, Yvon, François, Traitement du Langage Parlé (TLP ), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Bourse régionale IDF, CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, MachineTranslation, Decoding Algorithms, Computer Science - Computation and Language, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Computation and Language (cs.CL), [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], 0105 earth and related environmental sciences
Abstract: Machine translation is generally understood as generating one target text from an input source document. In this paper, we consider a stronger requirement: to jointly generate two texts so that each output side effectively depends on the other. As we discuss, such a device serves several practical purposes, from multi-target machine translation to the generation of controlled variations of the target text. We present an analysis of possible implementations of dual decoding, and experiment with four applications. Viewing the problem from multiple angles allows us to better highlight the challenges of dual decoding and to also thoroughly analyze the benefits of generating matched, rather than independent, translations., Comment: Accepted at EMNLP 2021
Published: 2021

45. Extending Adaptive Spacing Heuristics to Multi-Skill Items

Author: Choffin, Benoît, Popineau, Fabrice, Bourda, Yolaine, Données et Connaissances Massives et Hétérogènes (LaHDAK), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), EFRAN-PARCOURS, Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [STAT.AP]Statistics [stat]/Applications [stat.AP], knowledge components, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 4. Education, ComputingMilieux_COMPUTERSANDEDUCATION, multi-skill learning items, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, adaptive spacing, skill selection heuristics, [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; Adaptive spacing algorithms are powerful tools for helping learners manage their study time efficiently. By personalizing the temporal distribution of retrieval practice of a given piece of knowledge, they improve learners' long-term memory retention compared to fixed review schedules. However, such algorithms are generally designed for the pure memorization of single items, such as vocabulary words. Yet, the spacing effect has been shown to extend to more complex knowledge, such as the practice of mathematical skills. In this article, we extend three adaptive spacing heuristics from the literature for selecting the best skill to review at any timestamp given a student's past study history. In real-world educational settings, items generally involve multiple skills at the same time. Thus, we also propose a multi-skill version for two of these heuristics: instead of selecting one single skill, they select with a greedy procedure the most promising subset of skills to review. To compare these five heuristics, we develop a synthetic experimental framework that simulates student learning and forgetting trajectories with a student model. We run multiple synthetic experiments on large cohorts of 500 simulated students and publicly release the code for these experiments. Our results highlight the strengths and weaknesses of each heuristic in terms of performance, robustness, and complexity. Finally, we find evidence that selecting the best subset of skills yields better retention compared to selecting the single best skill to review.
Published: 2021

46. Extraction of Narrative Structure from TV Series

Author: Aman Berhe, Traitement du Langage Parlé (TLP ), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay/Université Paris-Sud, LISN, CNRS, Claude Barras, Camille Guinaudeau, CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay, and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Structure narrative, Scenes Segmentation, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, Most Reportable Scenes, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Scènes remarquables, Scene Linking, [SCCO.LING]Cognitive science/Linguistics, Multimodal Fusion, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO]Cognitive science, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Segmentation en scènes, Lien entre scènes, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Scene segmentation, Narrative Structure, [INFO]Computer Science [cs], [SHS.LANGUE]Humanities and Social Sciences/Linguistics, Fusion multimodale
Abstract: In the current explosions of multimedia content, there is a need to reorganize a large collection of multimedia documents. Narratives and their structure can help in this regard, particularly, in narratively long and continuous multimedia collections, such as TV series. Current TV series are composed of complex structures involving several intertwined narratives within the same episode and this continues until the last episode of the TV series. In this thesis, we focus on extracting and understanding narrative structure of TV series considering the fusion of multimodal features and narrative elements.Narratives in TV series come in small narrative units, scenes. Hence, working on scene level is the best way to extract and understand the overall narratives. In this thesis, we have proposed a novel way of extracting and understanding narrative structure via scenes linking. To do that, we have investigated scene segmentation which is based on features extracted from neural network models that take into account the visual and textual modalities of TV series. Then, a new way of scene-linking via fuzzy clustering is investigated, at different levels of granularity. The fuzzy clustering takes into consideration the narrative elements of scenes to create the links between them. Inter and intra episode links are created to capture the progression of a narrative throughout the TV series. Next, the most reportable scenes (MRS) are detected, to spotlight the turning points of the narrative structure on the linked scenes. Deep and complex neural network models are investigated for MRS detection by taking multimodal features of scenes. Finally, a visualization and evaluation tool is proposed to display extracted narratives and evaluate them according to a third-party, human intervention.; À l'ére de l'explosion du contenu multimédia, il est nécessaire de proposer des méthodes automatiques permettant d'organiser les collections de documents multimédia. La structure narrative des collections peut aider à cet égard, en particulier dans les collections multimédia longues et continues, telles que les séries TV. Les séries TV actuelles sont composées de structures complexes impliquant plusieurs récits entrelacés au sein d'un même épisode, et ce jusqu'au dernier épisode de la série TV. Dans cette thèse, nous nous concentrons sur l'extraction et la description de la structure narrative des séries TV en considérant la fusion des caractéristiques multimodales et des éléments narratifs. La structure narrative des séries TV est constituée d'unités atomiques narratives, les scènes. Par conséquent, travailler au niveau de la scène est la meilleure façon d'extraire et de comprendre la structure narrative globale. Dans cette thèse, nous avons proposé une nouvelle façon d'extraire et de comprendre la structure narrative en reliant les scènes. Pour ce faire, nous avons étudié la segmentation des scènes en utilisant des caractéristiques extraites de modèles neuronaux qui prennent en compte les modalités visuelles et textuelles des séries TV. Ensuite, nous avons proposé une nouvelle façon de relier les scènes par le biais d'un regroupement flou (fuzzy clustering), à différents niveaux de granularité. Le fuzzy clustering prend en compte les éléments narratifs des scènes pour créer les liens entre eux. Des liens inter et intra épisodes sont créés pour capturer la progression d'un récit tout au long de la série TV. Ensuite, les scènes les plus marquantes (MRS) sont détectées, afin de mettre en évidence les points d'inflexion de la structure narrative à partir des scènes liées. Des modèles neuronaux profonds et complexes sont étudiés pour la détection des MRS à partir des caractéristiques multimodales des scènes. Enfin, un outil de visualisation et d'évaluation est proposé pour afficher les structures narratives extraites et permettre une évaluation humaine.
Published: 2021

47. ML-CI: Machine Learning Confidence Intervals for Covid-19 forecasts

Author: Lacan, Alice, Guyon, Isabelle, TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Chalearn, This work wassupported by the ANR (Agence Nationale de la Recherche, National Agency for Research) under AI chair of excellence HUMANIA, grant number ANR-19-CHIA-00222 and INRIA., ANR-19-CHIA-0022,HUMANIA,Intelligence Artificielle pour Tous(2019), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Interval forecasts, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Confidence intervals, Covid19, Epidemic forecasts, Compartmental model, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; Epidemic forecasting has always been challenging and the recent Covid-19 outbreaks emphasizes it. We introduce a novel approach to address the problem of evaluating confidence intervals (CI) of time series prediction forecasts for compartmental models, using machine learning. We evaluate our approach using real data of the Covid pandemic on 27 countries. Compartmental models were trained taking into account non pharmaceutical governmental measures. A Random Forest regressor was trained, using various engineered features, to predict the forecasting error for various horizons on synthetic data, then applied to estimate CI on real data forecasts. Our method outperforms baselines using forecast likelihood as metric.
Published: 2021

48. AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification

Author: Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash, Argonne National Laboratory [Lemont] (ANL), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ANR-19-CHIA-0022,HUMANIA,Intelligence Artificielle pour Tous(2019), Université Paris-Saclay, LISN, CNRS, and CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Neural and Evolutionary Computation, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG)
Abstract: International audience; Deep neural networks are powerful predictors for a variety of tasks. However, they do not capture uncertainty directly. Using neural network ensembles to quantify uncertainty is competitive with approaches based on Bayesian neural networks while benefiting from better computational scalability. However, building ensembles of neural networks is a challenging task because, in addition to choosing the right neural architecture or hyperparameters for each member of the ensemble, there is an added cost of training each model. We propose AutoDEUQ, an automated approach for generating an ensemble of deep neural networks. Our approach leverages joint neural architecture and hyperparameter search to generate ensembles. We use the law of total variance to decompose the predictive variance of deep ensembles into aleatoric (data) and epistemic (model) uncertainties. We show that AutoDEUQ outperforms probabilistic backpropagation, Monte Carlo dropout, deep ensemble, distribution-free ensembles, and hyper ensemble methods on a number of regression benchmarks.
Published: 2021

49. A Case Study of Using Analytic Provenance to Reconstruct User Trust in a Guided Visual Analytics System

Author: Evelyne Lutton, Nadia Boukhelifa, Anastasia Bezerianos, Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Interacting with Large Data (ILDA), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Mathématiques et Informatique Appliquées (MIA Paris-Saclay), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Interaction avec l'Humain (IaH), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Visual analytics, Provenance, Information retrieval, Computer science, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]
Abstract: International audience; In this paper, we demonstrate how analytic provenance can be exploited to re-construct user trust in a guided Visual Analytics (VA)system, and suggest that interaction log data analysis can be a valuable tool for on-line trust monitoring. Our approach explores objective trust measures that can be continuously tracked and updated during the exploration, and reflect both the confidence of the user in system suggestions, and the uncertainty of the system with regards to user goals. We argue that this approach is more suitable for guided VA systems such as ours, where user strategies, goals and even trust can evolve over time, in reaction to new system feedback and insights from the exploration. Through the analysis of log data from a past user study with twelve participants performing a guided visual analysis task, we found that the stability of user explorationstrategies is a promising factor to study trust. However, indirect metrics based on provenance, such as user evaluation counts and disagreement rates, are alone not sufficient to study trust reliably in guided VA. We conclude with open challenges and opportunities for exploiting analytic provenance to support trust monitoring in guided VA systems.
Published: 2021

50. Design of a charging infrastructure for electric vehicles under a stochastic driving range

Author: Boujelben, Mouna Kchaou, Gicquel, Céline, United Arab Emirates University (UAEU), Réseaux & Optimisation Combinatoire et Stochastique (ROCS), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2021

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