References of "Bouvry, Pascal 50001021"
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See detailBayesian optimization to enhance coverage performance of a swarm of UAV with chaotic dynamics
Kieffer, Emmanuel UL; Rosalie, Martin UL; Danoy, Grégoire UL et al

Scientific Conference (2018, February 26)

We introduce the optimization of CACOC through Bayesian Optimization. CACOC is based on a chaotic system, i.e. Rossler system whose behavior can be modified by tuning the α parameter. In order to evaluate ... [more ▼]

We introduce the optimization of CACOC through Bayesian Optimization. CACOC is based on a chaotic system, i.e. Rossler system whose behavior can be modified by tuning the α parameter. In order to evaluate the performance of CACOC for different value of α, the coverage metric has to be evaluated after simulation. The latter is time-consuming. Therefore, a surrogate-based optimization, i.e. Bayesian Optimization has been privilegied to tackle this issue. An analysis of the chaotic system with the obtained α value has been performed to compare the periodic orbits and their associated patterns. Numerical results show that the best α value avoid a waste of time in periodic region of the bifurcation diagram. Future works will focus on more complex chaotic system as well as new application domain of the optimized CACOC approach. [less ▲]

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See detailProceedings - 2017 ILILAS Distinguished Lectures
Bouvry, Pascal UL; Bisdorff, Raymond; Schommer, Christoph UL et al

Report (2018)

The Proceedings summarizes the 12 lectures that have taken place within the ILIAS Dinstguished Lecture series 2017. It contains a brief abstract of the talks as well as some additional information about ... [more ▼]

The Proceedings summarizes the 12 lectures that have taken place within the ILIAS Dinstguished Lecture series 2017. It contains a brief abstract of the talks as well as some additional information about each speaker. [less ▲]

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See detailPRESENCE: Toward a Novel Approach for Performance Evaluation of Mobile Cloud SaaS Web Services
Ibrahim, Abdallah Ali Zainelabden Abdallah UL; Varrette, Sébastien UL; Bouvry, Pascal UL

in Proc. of the 32nd IEEE Intl. Conf. on Information Networking (ICOIN 2018) (2018, January)

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See detailVisualizing the Template of a Chaotic Attractor
Olszewski, Maya Alexandra UL; Meder, Jeff Alphonse Antoine UL; Kieffer, Emmanuel UL et al

in 26th International Symposium on Graph Drawing and Network Visualization (GD 2018) (2018)

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See detailChaos-enhanced mobility models for multilevel swarms of UAVs
Rosalie, Martin UL; Danoy, Grégoire UL; Chaumette, Serge et al

in Swarm and Evolutionary Computation (2018)

The number of civilian and military applications using Unmanned Aerial Vehicles (UAVs) has increased during the last years and the forecasts for upcoming years are exponential. One of the current major ... [more ▼]

The number of civilian and military applications using Unmanned Aerial Vehicles (UAVs) has increased during the last years and the forecasts for upcoming years are exponential. One of the current major challenges consist in considering UAVs as autonomous swarms to address some limitations of single UAV usage such as autonomy, range of operation and resilience. In this article we propose novel mobility models for multi-level swarms of collaborating UAVs used for the coverage of a given area. These mobility models generate unpredictable trajectories using a chaotic solution of a dynamical system. We detail how the chaotic properties are used to structure the exploration of an unknown area and enhance the exploration part of an Ant Colony Optimization method. Empirical evidence of the improvement of the coverage efficiency obtained by our mobility models is provided via simulation. It clearly outperforms state-of-the-art approaches. [less ▲]

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See detailClustering approaches for visual knowledge exploration in molecular interaction networks.
Ostaszewski, Marek UL; Kieffer, Emmanuel UL; Danoy, Gregoire et al

in BMC bioinformatics (2018), 19(1), 308

BACKGROUND: Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and ... [more ▼]

BACKGROUND: Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually. RESULTS: We investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach together with an evolutionary algorithm for informative combination of distance metrics. We compare the enrichment of the obtained clusters between the solutions and with expert knowledge. We calculate the number of Gene and Disease Ontology terms discovered by different solutions as a measure of cluster quality. Our results show that combining distance metrics can improve clustering accuracy, based on the comparison with expert-provided clusters. Also, the performance of specific combinations of distance functions depends on the clustering depth (number of clusters). By employing bi-level optimization approach we evaluated relative importance of distance functions and we found that indeed the order by which they are combined affects clustering performance. Next, with the enrichment analysis of clustering results we found that both hierarchical and bi-level clustering schemes discovered more Gene and Disease Ontology terms than expert-provided clusters for the same knowledge repository. Moreover, bi-level clustering found more enriched terms than the best hierarchical clustering solution for three distinct distance metric combinations in three different instances of disease maps. CONCLUSIONS: In this work we examined the impact of different distance functions on clustering of a visual biomedical knowledge repository. We found that combining distance functions may be beneficial for clustering, and improve exploration of such repositories. We proposed bi-level optimization to evaluate the importance of order by which the distance functions are combined. Both combination and order of these functions affected clustering quality and knowledge recognition in the considered benchmarks. We propose that multiple dimensions can be utilized simultaneously for visual knowledge exploration. [less ▲]

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See detailDetecting Target-Area Link-Flooding DDoS Attacks using Traffic Analysis and Supervised Learning
Rezazad, Mostafa; Brust, Matthias R. UL; Akbari, Mohammad et al

in Advances in Intelligent Systems and Computing (2018)

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See detailElliptic Curves Discrete Logarithm Problem over a Finite Field Fp and p-adic Approximations
Leprévost, Franck UL; Bernard, Nicolas UL; Bouvry, Pascal UL

in Proceedings of the 3rd International Conference on Applications in Information Technology (ICAIT-2018) (2018)

These notes summarize some computations conducted around the Elliptic Curves Discrete Logarithm Problem (ECDLP) over a finite field Fp.

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See detailLightweight Key Agreement for Wireless Sensor Networks
Mesit, Jaruwan; Brust, Matthias R. UL; Bouvry, Pascal UL

in 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) (2018)

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See detailA Degenerate Agglomerative Hierarchical Clustering Algorithm for Community Detection
Fiscarelli, Antonio Maria UL; Beliakov, Aleksandr UL; Konchenko, Stanislav UL et al

in Nguyen, Ngoc Thanh; Hoang, Duong Hung; Hong, Tzung-Pei (Eds.) et al Intelligent Information and Database Systems (2018)

Community detection consists of grouping related vertices that usually show high intra-cluster connectivity and low inter-cluster connectivity. This is an important feature that many networks exhibit and ... [more ▼]

Community detection consists of grouping related vertices that usually show high intra-cluster connectivity and low inter-cluster connectivity. This is an important feature that many networks exhibit and detecting such communities can be challenging, especially when they are densely connected. The method we propose is a degenerate agglomerative hierarchical clustering algorithm (DAHCA) that aims at finding a community structure in networks. We tested this method using common classes of graph benchmarks and compared it to some state-of-the-art community detection algorithms. [less ▲]

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See detailSingle and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters
Curi, María Eugenia; Carozzi, Lucía; Massobrio, Renzo et al

in Bioinspired Optimization Methods and Their Applications (2018)

This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the ... [more ▼]

This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the evolutionary search as simple as possible in order to scale up for solving large instances. The experimental evaluation is performed considering a set of real problem instances, including a real-life problem of analyzing biomedical information in the Parkinson's disease map project. The main results demonstrate that the proposed evolutionary approaches are able to compute accurate trade-off solutions and efficiently handle the problem instance involving biomedical information. [less ▲]

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See detailRapidRMSD: Rapid determination of RMSDs corresponding to motions of flexible molecules
Neveu, Emilie; Popov, Petr; Hoffmann, Alexandre et al

in Bioinformatics (2018)

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See detailChaotic Traversal (CHAT): Very Large Graphs Traversal Using Chaotic Dynamics
Changaival, Boonyarit UL; Rosalie, Martin UL; Danoy, Grégoire UL et al

in International Journal of Bifurcation and Chaos (2017), 27(14), 1750215

Graph Traversal algorithms can find their applications in various fields such as routing problems, natural language processing or even database querying. The exploration can be considered as a first ... [more ▼]

Graph Traversal algorithms can find their applications in various fields such as routing problems, natural language processing or even database querying. The exploration can be considered as a first stepping stone into knowledge extraction from the graph which is now a popular topic. Classical solutions such as Breadth First Search (BFS) and Depth First Search (DFS) require huge amounts of memory for exploring very large graphs. In this research, we present a novel memoryless graph traversal algorithm, Chaotic Traversal (CHAT) which integrates chaotic dynamics to traverse large unknown graphs via the Lozi map and the Rössler system. To compare various dynamics effects on our algorithm, we present an original way to perform the exploration of a parameter space using a bifurcation diagram with respect to the topological structure of attractors. The resulting algorithm is an efficient and nonresource demanding algorithm, and is therefore very suitable for partial traversal of very large and/or unknown environment graphs. CHAT performance using Lozi map is proven superior than the, commonly known, Random Walk, in terms of number of nodes visited (coverage percentage) and computation time where the environment is unknown and memory usage is restricted. [less ▲]

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See detailIntelligent Gaming for Mobile Crowd-Sensing Participants to Acquire Trustworthy Big Data in the Internet of Things
Pouryazdan, Maryam; Fiandrino, Claudio; Kantarci, Burak et al

in IEEE Access (2017), 5

In mobile crowd-sensing systems, the value of crowd-sensed big data can be increased by incentivizing the users appropriately. Since data acquisition is participatory, crowd-sensing systems face the ... [more ▼]

In mobile crowd-sensing systems, the value of crowd-sensed big data can be increased by incentivizing the users appropriately. Since data acquisition is participatory, crowd-sensing systems face the challenge of data trustworthiness and truthfulness assurance in the presence of adversaries whose motivation can be either manipulating sensed data or collaborating unfaithfully with the motivation of maximizing their income. This paper proposes a game theoretic methodology to ensure trustworthiness in user recruitment in mobile crowd-sensing systems. The proposed methodology is a platform-centric framework that consists of three phases: user recruitment, collaborative decision making on trust scores, and badge rewarding. In the proposed framework, users are incentivized by running sub-game perfect equilibrium and gami cation techniques. Through simulations, we showthat approximately 50% and a minimum of 15% improvement can be achieved by the proposed methodology in terms of platform and user utility, respectively, when compared with fully distributed and user-centric trustworthy crowd-sensing. [less ▲]

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See detailCoverage optimization with connectivity preservation for UAV swarms applying chaotic dynamics
Rosalie, Martin UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in IEEE International Conference on Autonomic Computing (ICAC), Columbus 17-21 July 2017 (2017, August 11)

Cooperative usage of multiple UAVs as a swarm can deliver high-quality surveillance performance. However, the communication capabilities within the UAV swarm must be maintained for local data propagation ... [more ▼]

Cooperative usage of multiple UAVs as a swarm can deliver high-quality surveillance performance. However, the communication capabilities within the UAV swarm must be maintained for local data propagation to swarm members in favor of achieving an efficient global behavior. In this paper, we address the problem of optimizing two adversary criteria for such a UAV swarm: (a) maximizing the area coverage, while (b) preserving network connectivity. Our approach, called CACOC², solves the problem with a novel chaotic ant colony optimization approach, which combines an Ant Colony Optimization approach (ACO) with a chaotic dynamical system. CACOC² employs swarming behavior to obtain UAV clustering that result in maximized area coverage and preserved network connectivity. We show by extensive simulations how the size of the UAV swarm influences the coverage and connectivity. A metrics comparison chart shows the correlation of coverage and connectivity metrics. [less ▲]

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See detailArea exploration with a swarm of UAVs combining deterministic Chaotic Ant Colony Mobility with position MPC
Rosalie, Martin UL; Dentler, Jan Eric UL; Danoy, Grégoire UL et al

in 2017 International Conference on Unmanned Aircraft Systems (ICUAS) (2017, July 27)

The recent advances in Unmanned Aerial Vehicles (UAVs) technology permit to develop new usages for them. One of the current challenges is to operate UAVs as an autonomous swarm. In this domain we already ... [more ▼]

The recent advances in Unmanned Aerial Vehicles (UAVs) technology permit to develop new usages for them. One of the current challenges is to operate UAVs as an autonomous swarm. In this domain we already proposed a new mobility model using Ant Colony Algorithms combined with chaotic dynamics (CACOC) to enhance the coverage of an area by a swarm of UAVs. In this paper we propose to consider this mobility model as waypoints for real UAVs. A control model of the UAVs is deployed to test the efficiency of the coverage of an area by the swarm. We have tested our approach in a realistic robotics simulator (V-Rep) which is connected with ROS. We compare the performance in terms of coverage using several metrics to ensure that this mobility model is efficient for real UAVs. [less ▲]

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See detailMeasuring data locality ratio in virtual MapReduce cluster using WorkflowSim
Wangsom, Peerasak; Lavangnananda, Kittichai; Bouvry, Pascal UL

in Proceedings of the 14th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2017 14th (2017, July)

The data locality is significant factor which has a direct impact on the performance of MapReduce framework. Several previous works have proposed alternative scheduling algorithms for improving the ... [more ▼]

The data locality is significant factor which has a direct impact on the performance of MapReduce framework. Several previous works have proposed alternative scheduling algorithms for improving the performance by increasing data locality. Nevertheless, their studies had focused the data locality on physical MapReduce cluster. As more and more deployment of MapReduce cluster have been on virtual environment, a more suitable evaluation of MapReduce cluster may be necessary. This study adopts a simulation based approach. Five scheduling algorithms were used for the simulation. WorkflowSim is extended by inclusion of three implemented modules to assess the new performance measure called `data locality ratio'. Comparison of their results reveals interesting findings. The proposed implementation can be used to assess `data locality ratio' and allows users prior to efficiently select and tune scheduler and system configurations suitable for an environment prior to its actual physical MapReduce deployment. [less ▲]

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See detailASIMUT project: Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques
Bouvry, Pascal UL; Chaumette, Serge; Danoy, Grégoire UL et al

in DroNet'17 Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications (2017, June 23)

This document summarizes the activities and results of the ASIMUT project (Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques) carried out by the consortium ... [more ▼]

This document summarizes the activities and results of the ASIMUT project (Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques) carried out by the consortium composed of Thales, Fraunhofer IOSB, Fly-n-Sense, University of Bordeaux and University of Luxembourg. Funded by the European Defence Agency (EDA), the objectives of the ASIMUT project are to design, implement and validate algorithms that will allow the efficient usage of autonomous swarms of Unmanned Aerial Vehicles (UAVs) for surveillance missions. [less ▲]

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See detailA Scalable Parallel Cooperative Coevolutionary PSO Algorithm for Multi-objective Optimization
Atashpendar, Arash UL; Dorronsoro, Bernabé; Danoy, Grégoire UL et al

in Journal of Parallel & Distributed Computing (2017)

We present a parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm. The algorithm, called CCSMPSO, is the first ... [more ▼]

We present a parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm. The algorithm, called CCSMPSO, is the first multi-objective cooperative coevolutionary algorithm based on PSO in the literature. SMPSO adopts a strategy for limiting the velocity of the particles that prevents them from having erratic movements. This characteristic provides the algorithm with a high degree of reliability. In order to demonstrate the effectiveness of CCSMPSO, we compare our work with the original SMPSO and three different state-of-the-art multi-objective CC metaheuristics, namely CCNSGA-II, CCSPEA2 and CCMOCell, along with their original sequential counterparts. Our experiments indicate that our proposed solution, CCSMPSO, offers significant computational speedups, a higher convergence speed and better or comparable results in terms of solution quality, when evaluated against three other CC algorithms and four state-of-the-art optimizers (namely SMPSO, NSGA-II, SPEA2, and MOCell), respectively. We then provide a scalability analysis, which consists of two studies. First, we analyze how the algorithms scale when varying the problem size, i.e., the number of variables. Second, we analyze their scalability in terms of parallelization, i.e., the impact of using more computational cores on the quality of solutions and on the execution time of the algorithms. Three different criteria are used for making the comparisons, namely the quality of the resulting approximation sets, average computational time and the convergence speed to the Pareto front. [less ▲]

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