Publications

A systemic mapping of methods and tools for performance analysis of data streaming with containerized microservices architecture

By S. Ris, Jean Araujo, David Beserra

2023-06-01

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

Abstract With the Internet of Things (IoT) growth and customer expectations, the importance of data streaming and streaming processing has increased. Data Streaming refers to the concept where data is processed and transmitted continuously and in real-time without necessarily being stored in a physical location. Personal health monitors and home security systems are examples of data streaming sources. This paper presents a systematic mapping study of the performance analysis of Data Streaming systems in the context of Containerization and Microservices.

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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 de 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.

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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.

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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.

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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.

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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.

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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

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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.

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An experience report on the optimization of the product configuration system of Renault

By Hao Xu, Souheib Baarir, Tewfik Ziadi, Siham Essodaigui, Yves Bossu, Lom Messan Hillah

2023-04-03

In Proceedings of the 26th international conference on engineering of complex computer systems (ICECCS’23)

Abstract The problem of configuring a variability model is widespread in many different domains. A leading automobile manufacturer has developed its technology internally to model vehicle diversity. This technology relies on the approach known as knowledge compilation to explore the configurations space. However, the growing variability and complexity of the vehicles’ range hardens the space representation problem and impacts performance requirements. This paper tackles these issues by exploiting symmetries that represent isomorphic parts in the configurations space.

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Optimization of the product configuration system of renault

By Hao Xu, Souheib Baarir, Tewfik Ziadi, Siham Essodaigui, Yves Bossu, Lom Messan Hillah

2023-04-03

In Proceedings of the 38th ACM/SIGAPP symposium on applied computing (SAC’23)

Abstract The problem of configuring a variability model is widespread in many different domains. Renault has developed its technology internally to model vehicle diversity. This technology relies on the approach known as knowledge compilation to explore the configurations space. However, the growing variability and complexity of the vehicles’ range hardens the space representation problem and impacts performance requirements. This paper tackles these issues by exploiting symmetries that represent isomorphic parts in the configurations space.

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