Publications

Max-tree computation on GPUs

Abstract

In Mathematical Morphology, the max-tree is a region-based representation that encodes the inclusion relationship of the threshold sets of an image. This tree has been proven useful in numerous image processing applications. For the last decade, works have been led to improve the building time of this structure; mixing algorithmic optimizations, parallel and distributed computing. Nevertheless, there is still no algorithm that takes benefit from the computing power of the massively parallel architectures. In this work, we propose the first GPU algorithm to compute the max-tree. The proposed approach leads to significant speed-ups, and is up to one order of magnitude faster than the current State-of-the-Art parallel CPU algorithms. This work paves the way for a max-tree integration in image processing GPU pipelines and real-time image processing based on Mathematical Morphology. It is also a foundation for porting other image representations from Mathematical Morphology on GPUs.

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Anomaly detection on static and dynamic graphs using graph convolutional neural networks

Abstract

Anomalies represent rare observations that vary significantly from others. Anomaly detection intended to discover these rare observations has the power to prevent detrimental events, such as financial fraud, network intrusion, and social spam. However, conventional anomaly detection methods cannot handle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.) [1]. Thanks to the rise of deep learning in solving these limitations, graph anomaly detection with deep learning has obtained an increasing attention from many scientists recently. However, while deep learning can capture unseen patterns of multi-dimensional Euclidean data, there is a huge number of applications where data are represented in the form of graphs. Graphs have been used to represent the structural relational information, which raises the graph anomaly detection problem - identifying anomalous graph objects (i.e., vertex, edges, sub-graphs, and change detection). These graphs can be constructed as a static graph, or a dynamic graph based on the availability of timestamp. Recent years have observed a huge efforts on static graphs, among which Graph Convolutional Network (GCN) has appeared as a useful class of models. A challenge today is to detect anomalies with dynamic structures. In this chapter, we aim at providing methods used for detecting anomalies in static and dynamic graphs using graph analysis, graph embedding, and graph convolutional neural networks. For static graphs we categorize these methods according to plain and attribute static graphs. For dynamic graphs we categorize existing methods according to the type of anomalies that they can detect. Moreover, we focus on the challenges in this research area and discuss the strengths and weaknesses of various methods in each category. Finally, we provide open challenges for graph anomaly detection using graph convolutional neural networks on dynamic graphs.

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ETAP: Experimental typesetting algorithms platform

Abstract

We present the early development stages of ETAP, a platform for experimenting with typesetting algorithms. The purpose of this platform is twofold: while its primary objective is to provide building blocks for quickly and easily designing and testing new algorithms (or variations on existing ones), it can also be used as an interactive, real time demonstrator for many features of digital typography, such as kerning, hyphenation, or ligaturing.

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New security protocols for offline point-of-sale machines

Abstract

EMV (Europay MasterCard Visa) is the protocol implemented to secure the communication, between a client’s payment device and a Point-of-Sale machine, during a contact or an NFC (Near Field Communication) purchase transaction. In several studies, researchers have analyzed the operation of this protocol in order to verify its safety: unfortunately, they have identified two security vulnerabilities that lead to multiple attacks and dangerous risks threatening both clients and merchants. In this paper, we are interested in proposing new security solutions that aim to overcome the two dangerous EMV vulnerabilities. Our solutions address the case of Point-of-Sale machines that do not have access to the banking network and are therefore in the “offline” connectivity mode. We verify the accuracy of our proposals by using the Scyther security verification tool.

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GenIDA, une base de données participative internationale permettant de mieux connaître l’histoire naturelle et les comorbidités des formes génétiques de troubles neurodéveloppementaux

Abstract

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How to boost close-range remote sensing courses using a serious game: Uncover in a fun way the complexity and transversality of multi-domain field acquisitions

Abstract

Close-range remote sensing, and more particularly, its acquisition part that is linked to field robotics, is at the crossroads of many scientific and engineering fields. Thus, it takes time for students to acquire the solid foundations needed before practicing on real systems. Therefore, we are interested in a means that allow students without prerequisites to quickly appropriate the fundamentals of this interdisciplinary field. For this, we adapted a haggle game to the close-range remote sensing theme. In this article, we explain the mechanics that serve our educational purposes. We have used it, so far, for four academic years with hundreds of students. The experience was assessed through quality surveys and quizzes to calculate success indicators. The results show that the serious game is well appreciated by the students. It allows them to better structure information and acquire a good global vision of multi-domain acquisition and data processing in close-range remote sensing. The students are also more involved in the rest of the lessons; all of this helps to facilitate their learning of the theoretical parts. Thus, we were able to shorten the time before moving on to real practice by replacing three lesson sessions with one serious game session, with an increase in mastering fundamental skills. The designed serious game can be useful for close-range remote sensing teachers looking for an effective starting lesson. In addition, teachers from other technical fields can draw inspiration from the creation mechanisms described in this article to create their own adapted version. Such a serious game is also a good asset for selecting promising students in a recruitment context.

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Practical applications of the Alternating Cycle Decomposition

Abstract

In 2021, Casares, Colcombet, and Fijalkow introduced the Alternating Cycle Decomposition (ACD) to study properties and transformations of Muller automata. We present the first practical implementation of the ACD in two different tools, Owl and Spot, and adapt it to the framework of Emerson-Lei automata, i.e., $\omega$-automata whose acceptance conditions are defined by Boolean formulas. The ACD provides a transformation of Emerson-Lei automata into parity automata with strong optimality guarantees: the resulting parity automaton is minimal among those automata that can be obtained by duplication of states. Our empirical results show that this transformation is usable in practice. Further, we show how the ACD can generalize many other specialized constructions such as deciding typeness of automata and degeneralization of generalized Büchi automata, providing a framework of practical algorithms for $\omega$-automata.

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Découverte de sous-groupes de prédictions interprétables pour le triage d’incidents

Abstract

The need for predictive maintenance comes with an increasing number of incidents, where it is imperative to quickly decide which service to contact for corrective actions. Several predictive models have been designed to automate this process, but the efficient models are opaque (say, black boxes). Many approaches have been proposed to locally explain each prediction of such models. However, providing an explanation for every result is not conceivable when it comes to a large number of daily predictions to analyze. In this article we propose a method based on Subgroup Discovery in order to (1) group together objects that share similar explanations and (2) provide a description that characterises each subgroup

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Hate speech and toxic comment detection using transformers

Abstract

Hate speech and toxic comment detection on social media has proven to be an essential issue for content moderation. This paper displays a comparison between different Transformer models for Hate Speech detection such as Hate BERT, a BERT-based model, RoBERTa and BERTweet which is a RoBERTa based model. These Transformer models are tested on Jibes&Delight 2021 reddit dataset using the same training and testing conditions. Multiple approaches are detailed in this paper considering feature extraction and data augmentation. The paper concludes that our RoBERTa st4-aug model trained with data augmentation outperforms simple RoBERTa and HateBERT models.

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QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation — Analysis of ranking scores and benchmarking results

Abstract

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.

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