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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 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 detailCommunity Detection in Complex Networks: A Survey on Local Approaches
Esmaeilzadeh Dilmaghani, Saharnaz UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

Scientific Conference (2021)

Early approaches of community detection algorithms often depend on the network’s global structure with a time complexity correlated to the network size. Local algorithms emerged as a more efficient ... [more ▼]

Early approaches of community detection algorithms often depend on the network’s global structure with a time complexity correlated to the network size. Local algorithms emerged as a more efficient solution to deal with large-scale networks with millions to billions of nodes. This methodology has shifted the attention from global structure towards the local level to deal with a network using only a portion of nodes. Investigating the state-of-the-art, we notice the absence of a standard definition of locality between community detection algorithms. Different goals have been explored under the local terminology of community detection approaches that can be misunderstood. This paper probes existing contributions to extract the scopes where an algorithm performs locally. Our purpose is to interpret the concept of locality in community detection algorithms. We propose a locality exploration scheme to investigate the concept of locality at each stage of an existing community detection workflow. We summarized terminologies concerning the locality in the state-of-the-art community detection approaches. In some cases, we observe how different terms are used for the same concept. We demonstrate the applicability of our algorithm by providing a review of some algorithms using our proposed scheme. Our review highlights a research gap in community detection algorithms and initiates new research topics in this domain. [less ▲]

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See detailA Q-Learning Based Hyper-Heuristic for Generating Efficient UAV Swarming Behaviours
Duflo, Gabriel UL; Danoy, Grégoire UL; Talbi, El-Ghazali UL et al

in Intelligent Information and Database Systems - 13th Asian Conference ACIIDS 2021, Phuket, Thailand, April 7-10, 2021, Proceedings (2021)

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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 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 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 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

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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 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 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 detailInnovation Networks from Inter-organizational Research Collaborations
Esmaeilzadeh Dilmaghani, Saharnaz UL; Piyatumrong, Apivadee UL; Danoy, Grégoire UL et al

in Heuristics for Optimization and Learning (2020)

We consider the problem of automatizing network generation from inter-organizational research collaboration data. The resulting networks promise to obtain crucial advanced insights. In this paper, we ... [more ▼]

We consider the problem of automatizing network generation from inter-organizational research collaboration data. The resulting networks promise to obtain crucial advanced insights. In this paper, we propose a method to convert relational data to a set of networks using a single parameter, called Linkage Threshold (LT). To analyze the impact of the LT-value, we apply standard network metrics such as network density and centrality measures on each network produced. The feasibility and impact of our approach are demonstrated by using a real-world collaboration data set from an established research institution. We show how the produced network layers can reveal insights and patterns by presenting a correlation matrix. [less ▲]

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See detailAutomating the Design of Efficient Distributed Behaviours for a Swarm of UAVs
Duflo, Gabriel UL; Danoy, Grégoire UL; Talbi, El-Ghazali UL et al

in IEEE Symposium Series on Computational Intelligence, Canberra 1-4 December 2020 (2020, December)

The usage of Unmanned Aerial Vehicles (UAVs) is gradually gaining momentum for commercial applications. The vast majority considers a single UAV, which comes with several constraints such as its range of ... [more ▼]

The usage of Unmanned Aerial Vehicles (UAVs) is gradually gaining momentum for commercial applications. The vast majority considers a single UAV, which comes with several constraints such as its range of operations or the number of sensors it can carry. Using multiple autonomous UAVs simultaneously as a swarm makes it possible to overcome these limitations. However, manually designing complex emerging behaviours like swarming is a difficult and tedious task especially for such distributed systems which performance is hardly predictable. This article therefore proposes to automate the design of UAV swarming behaviours by defining a multi-objective optimisation problem, so called Coverage of a Connected-UAV Swarm (CCUS), and designing a Q-Learning based Hyper-Heuristic (QLHH) for generating distributed CCUS heuristics. Experimental results demonstrate the capacity of QLHH to generate efficient heuristics for any instance from a given class. [less ▲]

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See detailLocal Community Detection Algorithm with Self-defining Source Nodes
Esmaeilzadeh Dilmaghani, Saharnaz UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Complex Networks & Their Applications IX (2020, September 01)

Surprising insights in community structures of complex networks have raised tremendous interest in developing various kinds of community detection algorithms. Considering the growing size of existing ... [more ▼]

Surprising insights in community structures of complex networks have raised tremendous interest in developing various kinds of community detection algorithms. Considering the growing size of existing networks, local community detection methods have gained attention in contrast to global methods that impose a top-down view of global network information. Current local community detection algorithms are mainly aimed to discover local communities around a given node. Besides, their performance is influenced by the quality of the source node. In this paper, we propose a community detection algorithm that outputs all the communities of a network benefiting from a set of local principles and a self-defining source node selection. Each node in our algorithm progressively adjusts its community label based on an even more restrictive level of locality, considering its neighbours local information solely. Our algorithm offers a computational complexity of linear order with respect to the network size. Experiments on both artificial and real networks show that our algorithm gains moreover networks with weak community structures compared to networks with strong community structures. Additionally, we provide experiments to demonstrate the ability of the self-defining source node of our algorithm by implementing various source node selection methods from the literature. [less ▲]

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See detailPerformance Analysis of Distributed and Scalable Deep Learning
Mahon, S.; Varrette, Sébastien UL; Plugaru, Valentin UL et al

in 20th IEEE/ACM Intl. Symp. on Cluster, Cloud and Internet Computing (CCGrid'20) (2020, May)

With renewed global interest for Artificial Intelligence (AI) methods, the past decade has seen a myriad of new programming models and tools that enable better and faster Machine Learning (ML). More ... [more ▼]

With renewed global interest for Artificial Intelligence (AI) methods, the past decade has seen a myriad of new programming models and tools that enable better and faster Machine Learning (ML). More recently, a subset of ML known as Deep Learning (DL) raised an increased interest due to its inherent ability to tackle efficiently novel cognitive computing applications. DL allows computational models that are composed of multiple processing layers to learn in an automated way representations of data with multiple levels of abstraction, and can deliver higher predictive accuracy when trained on larger data sets. Based on Artificial Neural Networks (ANN), DL is now at the core of state of the art voice recognition systems (which enable easy control over e.g. Internet-of- Things (IoT) smart home appliances for instance), self-driving car engine, online recommendation systems. The ecosystem of DL frameworks is fast evolving, as well as the DL architectures that are shown to perform well on specialized tasks and to exploit GPU accelerators. For this reason, the frequent performance evaluation of the DL ecosystem is re- quired, especially since the advent of novel distributed training frameworks such as Horovod allowing for scalable training across multiple computing resources. In this paper, the scalability evaluation of the reference DL frameworks (Tensorflow, Keras, MXNet, and PyTorch) is performed over up-to-date High Performance Comput- ing (HPC) resources to compare the efficiency of differ- ent implementations across several hardware architectures (CPU and GPU). Experimental results demonstrate that the DistributedDataParallel features in the Pytorch library seem to be the most efficient framework for distributing the training process across many devices, allowing to reach a throughput speedup of 10.11 when using 12 NVidia Tesla V100 GPUs when training Resnet44 on the CIFAR10 dataset. [less ▲]

Detailed reference viewed: 159 (14 UL)