Publications

In Proceedings of the second computer science UTM PhD symposium

Abstract Dans le domaine de la conception des médicaments, la chémoinformatique utilise des méthodes informatiques et mathématiques pour analyser des données chimiques et biologiques et essayer de trouver très en amont des molécules intéressantes. Dans notre contexte, nous transformons les molécules pour ne conserver que leurs caractéristiques pharmacophoriques (partie active de la molécule). L’objectif de ce travail est de raffiner l’activité des molécules qui seront utilisées dans le processus de conception des médicaments en des classes d’activité.

Could the topology of virtual processors affect the performance of a BSD-family OS running in a VM?

By David Beserra, Marc Espie, Jean Araujo, Léo Tomasimo, Hector Poncins, Hadrien-Samrek Lacombe, Thomas Vondracek

2023-06-01

In 18th iberian conference on information systems and technologies (CISTI’2023)

Abstract Virtual machines are an essential technology in distributed and pervasive systems. One of its configurable parameters is the topology of the virtual processing system, which can potentially impact its performance. In this work, we verify how different virtual processing topologies affect the performance of VMs running BSD OSes. We conclude that in some types of application the topology does not affect the VM performance, while in others it does, and that the performance impact also depends on the OS adopted by the VM.

CRACS: Compaction of rules in anticipatory classifier systems

By Romain Orhand, Pierre Collet, Pierre Parrend, Anne Jeannin-Girardon

2023-06-01

In Proceedings of the companion conference on genetic and evolutionary computation

Abstract Rule Compaction of populations of Learning Classifier Systems (LCS) has always been a topic of interest to get more insights into the discovered underlying patterns from the data or to remove useless classifiers from the populations. However, these techniques have neither been used nor adapted to Anticipatory Learning Classifier Systems (ALCS). ALCS differ from other LCS in that they build models of their environments from which decision policies to solve their learning tasks are learned.

Explorer les débats parlementaires français de la troisième république par leurs sujets

By Marie Puren, Aurélien Pellet

2023-06-01

In Humanistica 2023

Abstract Cet article compare trois méthodes pour explorer de grands corpus de documents historiques par leurs sujets. Nous travaillons ici sur les débats parlementaires franais de la Troisième République, qui se prêtent particulièrement bien à ce type d’analyse. Après avoir présenté le contexte de cette étude, nous exposons les résultats obtenus avec trois méthodes issues du traitement automatique des langues et appliquées sur des textes publiés entre 1876 et 1914 : l’allocation de Dirichlet latente, les plongements de mots et le Transfer Learning.

L’identification des projets de logiciel libre accessibles aux nouveaux contributeurs

By Paul Hervot, Benoı̂t Crespin

2023-06-01

In EIAH2023 : 11ème conférence sur les environnements informatiques pour l’apprentissage humain

Abstract FOSS makes an increasing amount of the public and industrial software landscape, notably for its transparency and democratic governance. However, simply publishing the source code of a software does not automatically make it accessible, and many barriers impede new contributors approaching these projects. Through a large-scale software mining of the Software Heritage archive, we test the pertinence of three signals in the identification of accessible FOSS projects for new contributors.

Linear object detection in document images using multiple object tracking

By Philippe Bernet, Joseph Chazalon, Edwin Carlinet, Alexandre Bourquelot, Élodie Puybareau

2023-06-01

In Proceedings of the international conference on document analysis and recognition (ICDAR 2023)

Abstract Linear objects convey substantial information about document structure, but are challenging to detect accurately because of degradation (curved, erased) or decoration (doubled, dashed). Many approaches can recover some vector representation, but only one closed-source technique introduced in 1994, based on Kalman filters (a particular case of Multiple Object Tracking algorithm), can perform a pixel-accurate instance segmentation of linear objects and enable to selectively remove them from the original image. We aim at re-popularizing this approach and propose: 1.

Metrics for community dynamics applied to unsupervised attacks detection

By Julien Michel, Pierre Parrend

2023-06-01

In Rencontres des jeunes chercheurs en intelligence artificielle

Abstract Attack detection in big networks has become a necessity. Yet, with the ever changing threat landscape and massive amount of data to handle, network intrusion detection systems (NIDS) end up being obsolete. Different machine-learning-based solutions have been developed to answer the detection problem for data with evolving statistical distributions. However, no approach has proved to be both scalable and robust to passing time. In this paper, we propose a scalable and unsupervised approach to detect behavioral patterns without prior knowledge on the nature of attacks.

Learning sentinel-2 reflectance dynamics for data-driven assimilation and forecasting

By Anthony Frion, Lucas Drumetz, Guillaume Tochon, Mauro Dalla Mura, Abdeldjalil Aı̈ssa El Bey

2023-05-29

In Proceedings of the 31th european signal processing conference (EUSIPCO)

Abstract Over the last few years, massive amounts of satellite multispectral and hyperspectral images covering the Earth’s surface have been made publicly available for scientific purpose, for example through the European Copernicus project. Simultaneously, the development of self-supervised learning (SSL) methods has sparked great interest in the remote sensing community, enabling to learn latent representations from unlabeled data to help treating downstream tasks for which there is few annotated examples, such as interpolation, forecasting or unmixing.

GenIDA, a participatory patient registry for genetic forms of intellectual disability provides detailed caregiver reported information on 237 individuals with Koolen-de Vries syndrome

By Florent Colin, Pauline Burger, Timothée Mazzucotelli, Axelle Strehle, Joost Kummeling, Nicole Collot, Elyette Broly, Angela Morgan, Kenneth Myers, Agnès Bloch-Zupan, Charlotte Ockeloen, Bert de Vries, Tjitske Kleefstra, David Koolen, Pierre Parrend, Jean-Louis Mandel

2023-05-01

In Genetics in Medicine Open

Abstract

Forecasting electricity prices: An optimize then predict-based approach

By Léonard Tschora, Erwan Pierre, Marc Plantevit, Céline Robardet

2023-04-10

In Advances in intelligent data analysis XXI

Abstract We are interested in electricity price forecasting at the European scale. The electricity market is ruled by price regulation mechanisms that make it possible to adjust production to demand, as electricity is difficult to store. These mechanisms ensure the highest price for producers, the lowest price for consumers and a zero energy balance by setting day-ahead prices, i.e. prices for the next 24h. Most studies have focused on learning increasingly sophisticated models to predict the next day’s 24 hourly prices for a given zone.

Clustering en chémoinformatique pour le raffinement de l’activité des molécules

Could the topology of virtual processors affect the performance of a BSD-family OS running in a VM?

CRACS: Compaction of rules in anticipatory classifier systems

Explorer les débats parlementaires français de la troisième république par leurs sujets

L’identification des projets de logiciel libre accessibles aux nouveaux contributeurs

Linear object detection in document images using multiple object tracking

Metrics for community dynamics applied to unsupervised attacks detection

Learning sentinel-2 reflectance dynamics for data-driven assimilation and forecasting

GenIDA, a participatory patient registry for genetic forms of intellectual disability provides detailed caregiver reported information on 237 individuals with Koolen-de Vries syndrome

Forecasting electricity prices: An optimize then predict-based approach

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