References of "Bouvry, Pascal 50001021"
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See detailA Variant of Concurrent Constraint Programming on GPU
Talbot, Pierre UL; Pinel, Frederic UL; Bouvry, Pascal UL

in Proceedings of the AAAI Conference on Artificial Intelligence (2022, June), 36(4), 3830-3839

The number of cores on graphical computing units (GPUs) is reaching thousands nowadays, whereas the clock speed of processors stagnates. Unfortunately, constraint programming solvers do not take advantage ... [more ▼]

The number of cores on graphical computing units (GPUs) is reaching thousands nowadays, whereas the clock speed of processors stagnates. Unfortunately, constraint programming solvers do not take advantage yet of GPU parallelism. One reason is that constraint solvers were primarily designed within the mental frame of sequential computation. To solve this issue, we take a step back and contribute to a simple, intrinsically parallel, lock-free and formally correct programming language based on concurrent constraint programming. We then re-examine parallel constraint solving on GPUs within this formalism, and develop Turbo, a simple constraint solver entirely programmed on GPUs. Turbo validates the correctness of our approach and compares positively to a parallel CPU-based solver. [less ▲]

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See detailFrom communities to protein complexes: A local community detection algorithm on PPI networks
Dilmaghani, Saharnaz; Brust, Mathias UL; Ribeiro, Carlos H. et al

in PLoS ONE (2022), 17(1), 1-17

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See detailSuSy-EnGaD: Surveillance System Enhanced by Games of Drones
Stolfi Rosso, Daniel UL; Brust, Mathias UL; Danoy, Grégoire UL et al

in Drones (2022), 6(13),

In this article, we propose SuSy-EnGaD, a surveillance system enhanced by games of drones. We propose three different approaches to optimise a swarm of UAVs for improving intruder detection, two of them ... [more ▼]

In this article, we propose SuSy-EnGaD, a surveillance system enhanced by games of drones. We propose three different approaches to optimise a swarm of UAVs for improving intruder detection, two of them featuring a multi-objective optimisation approach, while the third approach relates to the evolutionary game theory where three different strategies based on games are proposed. We test our system on four different case studies, analyse the results presented as Pareto fronts in terms of flying time and area coverage, and compare them with the single-objective optimisation results from games. Finally, an analysis of the UAVs trajectories is performed to help understand the results achieved. [less ▲]

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See detailDeep Mining Covid-19 Literature
Sirajzade, Joshgun UL; Bouvry, Pascal UL; Schommer, Christoph UL

in Applied Informatics, 5th International Conference, ICAI 2022, Arequipa, Peru, October 27–29, 2022, Proceedings (2022)

In this paper we investigate how scientific and medical papers about Covid-19 can be effectively mined. For this purpose we use the CORD19 dataset which is a huge collection of all papers published about ... [more ▼]

In this paper we investigate how scientific and medical papers about Covid-19 can be effectively mined. For this purpose we use the CORD19 dataset which is a huge collection of all papers published about and around the SARS-CoV2 virus and the pandemic it caused. We discuss how classical text mining algorithms like Latent Semantic Analysis (LSA) or its modern version Latent Drichlet Allocation (LDA) can be used for this purpose and also touch more modern variant of these algorithms like word2vec which came with deep learning wave and show their advantages and disadvantages each. We finish the paper with showing some topic examples from the corpus and answer questions such as which topics are the most prominent for the corpus or how many percentage of the corpus is dedicated to them. We also give a discussion of how topics around RNA research in connection with Covid-19 can be examined. [less ▲]

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See detailA Framework of Hyper-Heuristics based on Q-Learning
Duflo, Gabriel UL; Danoy, Grégoire UL; Talbi, El-Ghazali UL et al

in International Conference in Optimization and Learning (OLA2022) (2022)

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See detailLearning to Optimise a Swarm of UAVs
Duflo, Gabriel UL; Danoy, Grégoire UL; Talbi, El-Ghazali UL et al

in Applied Sciences (2022), 12(19 9587),

The use of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase in interest in the past few years. Current applications mainly depend on single UAV operations, which face critical limitations such ... [more ▼]

The use of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase in interest in the past few years. Current applications mainly depend on single UAV operations, which face critical limitations such as mission range or resilience. Using several autonomous UAVs as a swarm is a promising approach to overcome these. However, designing an efficient swarm is a challenging task, since its global behaviour emerges solely from local decisions and interactions. These properties make classical multirobot design techniques not applicable, while evolutionary swarm robotics is typically limited to a single use case. This work, thus, proposes an automated swarming algorithm design approach, and more precisely, a generative hyper-heuristic relying on multi-objective reinforcement learning, that permits us to obtain not only efficient but also reusable swarming behaviours. Experimental results on a three-objective variant of the Coverage of a Connected UAV Swarm problem demonstrate that it not only permits one to generate swarming heuristics that outperform the state-of-the-art in terms of coverage by a swarm of UAVs but also provides high stability. Indeed, it is empirically demonstrated that the model trained on a certain class of instances generates heuristics and is capable of performing well on instances with a different size or swarm density. [less ▲]

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See detailA RNN-Based Hyper-Heuristic for Combinatorial Problems
Kieffer, Emmanuel UL; Duflo, Gabriel UL; Danoy, Grégoire UL et al

in A RNN-Based Hyper-Heuristic for Combinatorial Problems (2022)

Designing efficient heuristics is a laborious and tedious task that generally requires a full understanding and knowledge of a given optimization problem. Hyper-heuristics have been mainly introduced to ... [more ▼]

Designing efficient heuristics is a laborious and tedious task that generally requires a full understanding and knowledge of a given optimization problem. Hyper-heuristics have been mainly introduced to tackle this issue and are mostly relying on Genetic Programming and its variants. Many attempts in the literature have shown that an automatic training mechanism for heuristic learning is possible and can challenge human-based heuristics in terms of gap to optimality. In this work, we introduce a novel approach based on a recent work on Deep Symbolic Regression. We demonstrate that scoring functions can be trained using Recurrent Neural Networks to tackle a well-know combinatorial problem, i.e., the Multi-dimensional Knapsack. Experiments have been conducted on instances from the OR-Library and results show that the proposed modus operandi is an alternative and promising approach to human- based heuristics and classical heuristic generation approaches. [less ▲]

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See detailMetaheuristics-based Exploration Strategies for Multi-Objective Reinforcement Learning
Felten, Florian UL; Danoy, Grégoire; Talbi, El-Ghazali UL et al

in Proceedings of the 14th International Conference on Agents and Artificial Intelligence (2022)

The fields of Reinforcement Learning (RL) and Optimization aim at finding an optimal solution to a problem, characterized by an objective function. The exploration-exploitation dilemma (EED) is a well ... [more ▼]

The fields of Reinforcement Learning (RL) and Optimization aim at finding an optimal solution to a problem, characterized by an objective function. The exploration-exploitation dilemma (EED) is a well known subject in those fields. Indeed, a consequent amount of literature has already been proposed on the subject and shown it is a non-negligible topic to consider to achieve good performances. Yet, many problems in real life involve the optimization of multiple objectives. Multi-Policy Multi-Objective Reinforcement Learning (MPMORL) offers a way to learn various optimised behaviours for the agent in such problems. This work introduces a modular framework for the learning phase of such algorithms, allowing to ease the study of the EED in Inner- Loop MPMORL algorithms. We present three new exploration strategies inspired from the metaheuristics domain. To assess the performance of our methods on various environments, we use a classical benchmark - the Deep Sea Treasure (DST) - as well as propose a harder version of it. Our experiments show all of the proposed strategies outperform the current state-of-the-art ε-greedy based methods on the studied benchmarks. [less ▲]

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See detailProtection of Personal Data in High Performance Computing Platform for Scientific Research Purposes
Paseri, Ludovica; Varrette, Sébastien UL; Bouvry, Pascal UL

in Proc. of the EU Annual Privacy Forum (APF) 2021 (2021, June)

The Open Science projects are also aimed at strongly encouraging the use of Cloud technologies and High Performance Computing (HPC), for the benefit of European researchers and universities. The emerging ... [more ▼]

The Open Science projects are also aimed at strongly encouraging the use of Cloud technologies and High Performance Computing (HPC), for the benefit of European researchers and universities. The emerging paradigm of Open Science enables an easier access to expert knowledge and material; however, it also raises some challenges regarding the protection of personal data, considering that part of the research data are personal data thus subjected to the EU’s General Data Protection Regulation (GDPR). This paper investigates the concept of scientific research in the field of data protection, with regard both to the European (GDPR) and national (Luxembourg Data Protection Law) legal framework for the compliance of the HPC technology. Therefore, it focuses on a case study, the HPC platform of the University of Luxembourg (ULHPC), to pinpoint the major data protection issues arising from the processing activities through HPC from the perspective of the HPC platform operators. Our study illustrates where the most problematic aspects of compliance lie. In this regard, possible solutions are also suggested, which mainly revolve around (1) standardisation of procedures; (2) cooperation at institutional level; (3) identification of guidelines for common challenges. This research is aimed to support legal researchers in the field of data protection, in order to help deepen the understanding of HPC technology’s challenges and universities and research centres holding an HPC platform for research purposes, which have to address the same issues. [less ▲]

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See detailSwarm-based counter UAV defense system
Brust, Matthias R. UL; Danoy, Grégoire UL; Stolfi Rosso, Daniel UL et al

in Discover Internet of Things (2021), 1(1),

Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities. Thanks to their mobility, agility, and onboard sensors'customizability, UAVs ... [more ▼]

Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities. Thanks to their mobility, agility, and onboard sensors'customizability, UAVs have already demonstrated immense potential for numerous commercial applications. The UAVs expansion will come at the price of a dense, high-speed and dynamic traffic prone to UAVs going rogue or deployed with malicious intent. Counter UAV systems (C-UAS) are thus required to ensure their operations are safe. Existing C-UAS, which for the majority come from the military domain, lack scalability or induce collateral damages. This paper proposes a C-UAS able to intercept and escort intruders. It relies on an autonomous defense UAV swarm, capable of self-organizing their defense formation and to intercept the malicious UAV. This fully localized and GPS-free approach follows a modular design regarding the defense phases and it uses a newly developed balanced clustering to realize the intercept- and capture-formation. The resulting networked defense UAV swarm is resilient to communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we demonstrate the feasibility and performance of our approach. [less ▲]

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See detailComparing elementary cellular automata classifications with a convolutional neural network
Comelli, Thibaud; Pinel, Frederic UL; Bouvry, Pascal UL

in Proceedings of International Conference on Agents and Artificial Intelligence (ICAART) (2021, February 05)

Elementary cellular automata (ECA) are simple dynamic systems which display complex behaviour from simple local interactions. The complex behaviour is apparent in the two-dimensional temporal evolution of ... [more ▼]

Elementary cellular automata (ECA) are simple dynamic systems which display complex behaviour from simple local interactions. The complex behaviour is apparent in the two-dimensional temporal evolution of a cellular automata, which can be viewed as an image composed of black and white pixels. The visual patterns within these images inspired several ECA classifications, aimed at matching the automatas’ properties to observed patterns, visual or statistical. In this paper, we quantitatively compare 11 ECA classifications. In contrast to the a priori logic behind a classification, we propose an a posteriori evaluation of a classification. The evaluation employs a convolutional neural network, trained to classify each ECA to its assigned class in a classification. The prediction accuracy indicates how well the convolutional neural network is able to learn the underlying classification logic, and reflects how well this classification logic clusters patterns in the temporal evolution. Results show different prediction accuracy (yet all above 85%), three classifications are very well captured by our simple convolutional neural network (accuracy above 99%), although trained on a small extract from the temporal evolution, and with little observations (100 per ECA, evolving 513 cells). In addition, we explain an unreported ”pathological” behaviour in two ECAs. [less ▲]

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See detailA Distributed Pareto-based Path Planning Algorithm for Autonomous Unmanned Aerial Vehicles (Extended Abstract)
Samir Labib, Nader UL; Danoy, Grégoire UL; Brust, Matthias R. UL et al

Scientific Conference (2021, January 07)

Autonomous Unmanned Aerial Vehicles (UAVs) are in increasing demand thanks to their applicability in a wide range of domains. However, to fully exploit such potential, UAVs should be capable of ... [more ▼]

Autonomous Unmanned Aerial Vehicles (UAVs) are in increasing demand thanks to their applicability in a wide range of domains. However, to fully exploit such potential, UAVs should be capable of intelligently planning their collision-free paths as that impacts greatly the execution quality of their applications. While being a problem well addressed in literature, most presented solutions are either computationally complex centralised approaches or ones not suitable for the multiobjective requirements of most UAV use-cases. This extended abstract introduces ongoing research on a novel distributed Pareto path planning algorithm incorporating a dynamic multi-criteria decision matrix allowing each UAV to plan its collision-free path relying on local knowledge gained via digital stigmergy. The article presents some initial simulations results of a distributed UAV Traffic Management system (UTM) on a weighted multilayer network. [less ▲]

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See detailProximal Policy Optimisation for a Private Equity Recommitment System
Kieffer, Emmanuel UL; Pinel, Frederic UL; Meyer, Thomas et al

in Springer CCIS series (2021)

Recommitments are essential for limited partner investors to maintain a target exposure to private equity. However, recommitting to new funds is irrevocable and expose investors to cashflow uncertainty ... [more ▼]

Recommitments are essential for limited partner investors to maintain a target exposure to private equity. However, recommitting to new funds is irrevocable and expose investors to cashflow uncertainty and illiquidity. Maintaining a specific target allocation is therefore a tedious and critical task. Unfortunately, recommitment strategies are still manually designed and few works in the literature have endeavored to develop a recommitment system balancing opportunity cost and risk of default. Due to its strong similarities to a control system, we propose to “learn how to recommit” with Reinforcement Learning (RL) and, more specifically, using Proximal Policy Optimisation (PPO). To the best of our knowledge, this is the first attempt a RL algorithm is applied to private equity with the aim to solve the recommitment problematic. After training the RL model on simulated portfolios, the resulting recommitment policy is compared to state-of-the-art strategies. Numerical results suggest that the trained policy can achieve high target allocation while bounding the risk of being overinvested. [less ▲]

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See detailImproving Pheromone Communication for UAV Swarm Mobility Management
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in 13th International Conference on Computational Collective Intelligence (ICCCI 2021) (2021)

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See detailOptimising pheromone communication in a UAV swarm
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in GECCO '21: Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, July 10-14, 2021 (2021)

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See detailA competitive Predator–Prey approach to enhance surveillance by UAV swarms
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Applied Soft Computing (2021), 111

In this paper we present the competitive optimisation of a swarm of Unmanned Aerial Vehicles (UAV) protecting a restricted area from a number of intruders following a Predator–Prey approach. We propose a ... [more ▼]

In this paper we present the competitive optimisation of a swarm of Unmanned Aerial Vehicles (UAV) protecting a restricted area from a number of intruders following a Predator–Prey approach. We propose a Competitive Coevolutionary Genetic Algorithm (CompCGA) which optimises the parameters of the UAVs (i.e. predators) to maximise the detection of intruders, while the parameters of the intruders (i.e. preys) are optimised to maximise their intrusion success rate. Having chosen the CACOC (Chaotic Ant Colony Optimisation for Coverage) as the base mobility model for the UAVs, we propose an improved new version, where its behaviour is modified by identifying and optimising new parameters to improve the overall success rate when detecting intruders. Six case studies have been optimised using simulations by performing 30 independent runs (180 in total) of our CompCGA. Finally, we conducted a series of master tournaments (1,800,000 evaluations) using the best specimens obtained from each run and case study to test the robustness of our proposed approach against unexpected intruders. Our surveillance system improved the average percentage of intruders detected with respect to CACOC by a maximum of 126%. More than 90% of intruders were detected on average when using a swarm of 16 UAVs while CACOC’s detection rates are always under 80% in all cases. [less ▲]

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See detailEvolutionary Learning of Private Equity Recommitment Strategies
Kieffer, Emmanuel UL; Pinel, Frederic UL; Meyer, Thomas et al

in 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2021)

Achieving and maintaining high allocations to Private Equity and keeping allocations at the targeted level through recommitment strategies is a complex task which needs to be balanced against the risk of ... [more ▼]

Achieving and maintaining high allocations to Private Equity and keeping allocations at the targeted level through recommitment strategies is a complex task which needs to be balanced against the risk of becoming a defaulting investor. When looking at recommitments we are quickly faced with a combinatorial explosion of the solution space, rendering explicit enumeration impossible. As a consequence, manual management if any is becoming time-consuming and error-prone. For this reason, investors need guidance and decision aid algorithms producing reliable, robust and trustworthy recommitment strategies. In this work, we propose to generate automatically recommitment strategies based on the evolution of symbolic expressions to provide clear and understandable decision rules to Private Equity experts and investors. To the best of our knowledge, this is the first time a methodology to learn recommitment strategies using evolutionary learning is proposed. Experiments demonstrate the capacity of the proposed approach to generate efficient and robust strategies, keeping a high degree of investment while bounding the risk of being overinvested. [less ▲]

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See detailUAV-UGV-UMV Multi-Swarms for Cooperative Surveillance
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Frontiers in Robotics and AI (2021), 8

In this paper we present a surveillance system for early detection of escapers from a restricted area based on a new swarming mobility model called CROMM-MS (Chaotic Rössler Mobility Model for Multi ... [more ▼]

In this paper we present a surveillance system for early detection of escapers from a restricted area based on a new swarming mobility model called CROMM-MS (Chaotic Rössler Mobility Model for Multi-Swarms). CROMM-MS is designed for controlling the trajectories of heterogeneous multi-swarms of aerial, ground and marine unmanned vehicles with important features such as prioritising early detections and success rate. A new Competitive Coevolutionary Genetic Algorithm (CompCGA) is proposed to optimise the vehicles’ parameters and escapers’ evasion ability using a predator-prey approach. Our results show that CROMM-MS is not only viable for surveillance tasks but also that its results are competitive in regard to the state-of-the-art approaches. [less ▲]

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See detailCONSOLE: intruder detection using a UAV swarm and security rings
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Swarm Intell. (2021), 15(3), 205--235

Detailed reference viewed: 53 (5 UL)