Publications

A Kleene theorem for higher-dimensional automata

Abstract

We prove a Kleene theorem for higher-dimensional automata (HDAs). It states that the languages they recognise are precisely the rational subsumption-closed sets of interval pomsets. The rational operations include a gluing composition, for which we equip pomsets with interfaces. For our proof, we introduce HDAs with interfaces as presheaves over labelled precube categories and use tools inspired by algebraic topology, such as cylinders and (co)fibrations. HDAs are a general model of non-interleaving concurrency, which subsumes many other models in this field. Interval orders are used as models for concurrent or distributed systems where events extend in time. Our tools and techniques may therefore yield templates for Kleene theorems in various models and applications.

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A machine learning based approach for the detection of sybil attacks in c-ITS

Abstract

The intrusion detection systems are vital for the sustainability of Cooperative Intelligent Transportation Systems (C-ITS) and the detection of sybil attacks are particularly challenging. In this work, we propose a novel approach for the detection of sybil attacks in C-ITS environments. We provide an evaluation of our approach using extensive simulations that rely on real traces, showing our detection approach?s effectiveness.

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Architectures vs the Ports tree: A losing battle?

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Comparing use-cases of tree-fold vs fold-left, how to fold and color a map

Abstract

In this article we examine some consequences of computation order of two different conceptual implementations of the fold function. We explore a set of performance- and accuracy-based experiments on two implementations of this function. In particular, we contrast the traditional fold-left implementation with another approach we refer to as tree-fold. It is often implicitly supposed that the binary operation in question has constant complexity. We explore several application areas which diverge from that assumption: rational arithmetic, floating-point arithmetic, and Binary Decisions Diagram construction. These are binary operations which degrade in performance as the fold iteration progresses. We show that these types of binary operations are good candidates for tree-fold.

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Label-efficient self-supervised speaker verification with information maximization and contrastive learning

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Using subgroup discovery to relate odor pleasantness and intensity to peripheral nervous system reactions

Abstract

Activation of the autonomic nervous system is a primary characteristic of human hedonic responses to sensory stimuli. For smells, general tendencies of physiological reactions have been described using classical statistics. However, these physiological variations are generally not quantified precisely; each psychophysiological parameter has very often been studied separately and individual variability was not systematically considered. The current study presents an innovative approach based on data mining, whose goal is to extract knowledge from a dataset. This approach uses a subgroup discovery algorithm which allows extraction of rules that apply to as many olfactory stimuli and individuals as possible. These rules are described by intervals on a set of physiological attributes. Results allowed both quantifying how each physiological parameter relates to odor pleasantness and perceived intensity but also describing the participation of each individual to these rules. This approach can be applied to other fields of affective sciences characterized by complex and heterogeneous datasets.

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What does my GNN really capture? On exploring internal GNN representations

Abstract

GNNs are efficient for classifying graphs but their internal workings is opaque which limits their field of application. Existing methods for explaining GNN focus on disclosing the relationships between input graphs and the model’s decision. In contrary, the method we propose isolates internal features, hidden in the network layers, which are automatically identified by the GNN to classify graphs. We show that this method makes it possible to know the parts of the input graphs used by GNN with much less bias than the SOTA methods and therefore to provide confidence in the decision process.

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On robustness for the Skolem and positivity problems

Abstract

The Skolem problem is a long-standing open problem in linear dynamical systems: can a linear recurrence sequence (LRS) ever reach 0 from a given initial configuration? Similarly, the positivity problem asks whether the LRS stays positive from an initial configuration. Deciding Skolem (or positivity) has been open for half a century: The best known decidability results are for LRS with special properties (e.g., low order recurrences). On the other hand, these problems are much easier for “uninitialized” variants, where the initial configuration is not fixed but can vary arbitrarily: checking if there is an initial configuration from which the LRS stays positive can be decided by polynomial time algorithms (Tiwari in 2004, Braverman in 2006).

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A hybrid optimization tool for active magnetic regenerator

Abstract

Active Magnetic Regenerator (AMR) refrigeration is an innovate technology, which can reduce energy consumption and the depletion of the ozone layer. However, to develop a commercially applicable design of the AMR model is still an issue, because of the difficulty to find a configuration of the AMR parameters, which are suitable for various applications needs. In this work, we focus on the optimization method for finding a common parameters of the AMR model in two application modes: a magnetic refrigeration system and a thermo-magnetic generator. This paper proposes a robust optimisation tool, which ensures the scalability with respect to the number of objectives and allows to easily set up different optimisation experiments. A tool validation is presented. It is expected that this tool can help to make a qualitative jump in the development of AMR refrigeration.

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PhishGNN: A phishing website detection framework using graph neural networks

Abstract

Because of the importance of the web in our daily lives, phishing attacks have been causing a significant damage to both individuals and organizations. Indeed, phishing attacks are today among the most widespread and serious threats to the web and its users. The main approaches deployed against such attacks are blacklists. However, the latter represents numerous drawbacks. In this paper, we introduce PhishGNN, a Deep Learning framework based on Graph Neural Networks, which leverages and uses the hyperlink graph structure of web- sites along with different other hand-designed features. The performance results obtained, demonstrate that PhishGNN outperforms state of the art results with a 99.7% prediction accuracy.

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