References of "Bouvry, Pascal 50001021"
     in
Bookmark and Share    
Full Text
Peer Reviewed
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: 99 (4 UL)
Full Text
Peer Reviewed
See detailPrivacy and Security of Big Data in AI Systems:A Research and Standards Perspective
Esmaeilzadeh Dilmaghani, Saharnaz UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in 2019 IEEE International Conference on Big Data (Big Data), 9-12 December 2019 (2020, February 24)

Detailed reference viewed: 125 (20 UL)
Full Text
Peer Reviewed
See detailEvolving a Deep Neural Network Training Time Estimator
Pinel, Frédéric UL; Yin, Jian-xiong; Hundt, Christian UL et al

in Communications in Computer and Information Science (2020, February)

We present a procedure for the design of a Deep Neural Net- work (DNN) that estimates the execution time for training a deep neural network per batch on GPU accelerators. The estimator is destined to be ... [more ▼]

We present a procedure for the design of a Deep Neural Net- work (DNN) that estimates the execution time for training a deep neural network per batch on GPU accelerators. The estimator is destined to be embedded in the scheduler of a shared GPU infrastructure, capable of providing estimated training times for a wide range of network architectures, when the user submits a training job. To this end, a very short and simple representation for a given DNN is chosen. In order to compensate for the limited degree of description of the basic network representation, a novel co-evolutionary approach is taken to fit the estimator. The training set for the estimator, i.e. DNNs, is evolved by an evolutionary algorithm that optimizes the accuracy of the estimator. In the process, the genetic algorithm evolves DNNs, generates Python-Keras programs and projects them onto the simple representation. The genetic operators are dynamic, they change with the estimator’s accuracy in order to balance accuracy with generalization. Results show that despite the low degree of information in the representation and the simple initial design for the predictor, co-evolving the training set performs better than near random generated population of DNNs. [less ▲]

Detailed reference viewed: 56 (2 UL)
Full Text
Peer Reviewed
See detailAutonomous Flight of Unmanned Aerial Vehicles Using Evolutionary Algorithms
Gaudín, Américo; Madruga, Gabriel; Rodríguez, Carlos et al

in High Performance Computing (2020)

This article explores the application of evolutionary algorithms and agent-oriented programming to solve the problem of searching and monitoring objectives through a fleet of unmanned aerial vehicles. The ... [more ▼]

This article explores the application of evolutionary algorithms and agent-oriented programming to solve the problem of searching and monitoring objectives through a fleet of unmanned aerial vehicles. The subproblem of static off-line planning is studied to find initial flight plans for each vehicle in the fleet, using evolutionary algorithms to achieve compromise values between the size of the explored area, the proximity of the vehicles, and the monitoring of points of interest defined in the area. The results obtained in the experimental analysis on representative instances of the surveillance problem indicate that the proposed techniques are capable of computing effective flight plans. [less ▲]

Detailed reference viewed: 90 (13 UL)
Full Text
Peer Reviewed
See detailCompetitive Evolution of a UAV Swarm for Improving Intruder Detection Rates
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in 2020 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020, New Orleans, LA, USA, May 18-22, 2020 (2020)

Detailed reference viewed: 84 (4 UL)
Full Text
Peer Reviewed
See detailA Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in IEEE 17th Annual Consumer Communications & Networking Conference CCNC 2020, Las Vegas, NV, USA, January 10-13, 2020 (2020)

Detailed reference viewed: 26 (4 UL)
Full Text
Peer Reviewed
See detailOptimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Optimization and Learning - Third International Conference, OLA 2020, Cádiz, Spain, February 17-19, 2020, Proceedings (2020)

Detailed reference viewed: 62 (6 UL)
Full Text
Peer Reviewed
See detailTackling Large-Scale and Combinatorial Bi-Level Problems With a Genetic Programming Hyper-Heuristic
Kieffer, Emmanuel UL; Danoy, Grégoire UL; Brust, Matthias R. UL et al

in IEEE Transactions on Evolutionary Computation (2020), 24(1), 44--56

Detailed reference viewed: 25 (3 UL)
Full Text
Peer Reviewed
See detailDesign Challenges of Trustworthy Artificial Intelligence Learning Systems
Brust, Matthias R. UL; Bouvry, Pascal UL; Danoy, Grégoire UL et al

in Intelligent Information and Database Systems - 12th Asian Conference ACIIDS 2020, Phuket, Thailand, March 23-26, 2020, Companion Proceedings (2020)

Detailed reference viewed: 25 (5 UL)
Full Text
Peer Reviewed
See detailNGAP: a novel hybrid metaheuristic algorithm for round-trip carsharing fleet planning
Changaival, Boonyarit UL; Danoy, Grégoire UL; Kliazovich et al

in GECCO '20: Genetic and Evolutionary Computation Conference, Companion Volume, Cancún, Mexico, July 8-12, 2020 (2020)

Detailed reference viewed: 22 (1 UL)
Full Text
Peer Reviewed
See detailBayesian optimisation to select Rössler system parameters used in Chaotic Ant Colony Optimisation for Coverage
Rosalie, Martin; Kieffer, Emmanuel UL; Brust, Matthias R. UL et al

in Journal of Computational Science (2020), 41

Detailed reference viewed: 63 (10 UL)
Full Text
Peer Reviewed
See detailAutomated design of efficient swarming behaviours: a Q-learning hyper-heuristic approach
Duflo, Gabriel UL; Danoy, Grégoire UL; Talbi, El-Ghazali UL et al

in GECCO '20: Genetic and Evolutionary Computation Conference, Companion Volume, Cancún, Mexico, July 8-12, 2020 (2020)

Detailed reference viewed: 21 (3 UL)
Full Text
Peer Reviewed
See detailAutomatic Software Tuning of Parallel Programs for Energy-Aware Executions
Varrette, Sébastien UL; Pinel, Frédéric UL; Kieffer, Emmanuel UL et al

in Proc. of 13th Intl. Conf. on Parallel Processing and Applied Mathematics (PPAM 2019) (2019, December)

For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to ... [more ▼]

For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to the type and characteristics of workload that the device is running. For this reason, this paper presents an automatic software tuning method for parallel program generation able to adapt and exploit the hardware features available on a target computing system such as an HPC facility or a cloud system in a better way than traditional compiler infrastructures. We propose a search based approach combining both exact methods and approximated heuristics evolving programs in order to find optimized configurations relying on an ever-increasing number of tunable knobs i.e., code transformation and execution options (such as the num- ber of OpenMP threads and/or the CPU frequency settings). The main objective is to outperform the configurations generated by traditional compiling infrastructures for selected KPIs i.e., performance, energy and power usage (for both for the CPU and DRAM), as well as the runtime. First experimental results tied to the local optimization phase of the proposed framework are encouraging, demonstrating between 8% and 41% improvement for all considered metrics on a reference benchmark- ing application (i.e., Linpack). This brings novel perspectives for the global optimization step currently under investigation within the presented framework, with the ambition to pave the way toward automatic tuning of energy-aware applications beyond the performance of the current state-of-the-art compiler infrastructures. [less ▲]

Detailed reference viewed: 104 (23 UL)
Full Text
Peer Reviewed
See detailInternet of Unmanned Aerial Vehicles—A Multilayer Low-Altitude Airspace Model for Distributed UAV Traffic Management
Samir Labib, Nader UL; Danoy, Grégoire UL; Musial, Jedrzej UL et al

in Sensors (2019), 19(21), 22

The rapid adoption of Internet of Things (IoT) has encouraged the integration of new connected devices such as Unmanned Aerial Vehicles (UAVs) to the ubiquitous network. UAVs promise a pragmatic solution ... [more ▼]

The rapid adoption of Internet of Things (IoT) has encouraged the integration of new connected devices such as Unmanned Aerial Vehicles (UAVs) to the ubiquitous network. UAVs promise a pragmatic solution to the limitations of existing terrestrial IoT infrastructure as well as bring new means of delivering IoT services through a wide range of applications. Owning to their potential, UAVs are expected to soon dominate the low-altitude airspace over populated cities. This introduces new research challenges such as the safe management of UAVs operation under high traffic demands. This paper proposes a novel way of structuring the uncontrolled, low-altitude airspace, with the aim of addressing the complex problem of UAV traffic management at an abstract level. The work, hence, introduces a model of the airspace as a weighted multilayer network of nodes and airways and presents a set of experimental simulation results using three UAV traffic management heuristics. [less ▲]

Detailed reference viewed: 90 (16 UL)
Full Text
Peer Reviewed
See detailA Multilayer Low-Altitude Airspace Model for UAV Traffic Management
Samir Labib, Nader UL; Danoy, Grégoire UL; Musial, Jedrzej et al

in Samir Labib, Nader; Danoy, Grégoire; Musial, Jedrzej (Eds.) et al 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet '19) (2019, November)

Over the recent years, Unmanned Aerial Vehicles' (UAVs) technology developed rapidly. In turn shedding light on a wide range of potential civil and commercial applications ranging from mapping and ... [more ▼]

Over the recent years, Unmanned Aerial Vehicles' (UAVs) technology developed rapidly. In turn shedding light on a wide range of potential civil and commercial applications ranging from mapping and surveillance, parcel delivery to more demanding ones that require UAVs to operate in heterogeneous swarms. However, with the great advantages UAVs bring, they are expected to soon dominate the shared, low-altitude airspace over populated cities, introducing multiple new research challenges in safely managing the unprecedented traffic demands. The main contribution of this work is addressing the complex problem of UAV traffic management at an abstract level by proposing a structure for the uncontrolled low-altitude airspace. The paper proposes a model of the airspace as a weighted multilayer network of nodes and airways and presents a set of experimental simulations of UAV traffic for the verification and validation of the model. Finally, the paper outlines our intended future work. [less ▲]

Detailed reference viewed: 150 (26 UL)
Full Text
Peer Reviewed
See detailTrustworthiness in IoT - A Standards Gap Analysis on Security, Data Protection and Privacy
Samir Labib, Nader UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Samir Labib, Nader; Brust, Matthias R.; Danoy, Grégoire (Eds.) et al Trustworthiness in IoT - A Standards Gap Analysis on Security, Data Protection and Privacy (2019, October)

With the emergence of new digital trends like Internet of Things (IoT), more industry actors and technical committees pursue research in utilising such technologies as they promise a better and optimised ... [more ▼]

With the emergence of new digital trends like Internet of Things (IoT), more industry actors and technical committees pursue research in utilising such technologies as they promise a better and optimised management, improved energy efficiency and a better quality living through a wide array of value-added services. However, as sensing, actuation, communication and control become increasingly more sophisticated, such promising data-driven systems generate, process, and exchange larger amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. In turn this affirms the importance of trustworthiness in IoT and emphasises the need of a solid technical and regulatory foundation. The goal of this paper is to first introduce the concept of trustworthiness in IoT, its main pillars namely, security, privacy and data protection, and then analyse the state-of-the-art in research and standardisation for each of these subareas. Throughout the paper, we develop and refer to Unmanned Aerial Vehicles (UAVs) as a promising value-added service example of mobile IoT devices. The paper then presents a thorough gap analysis and concludes with recommendations for future work. [less ▲]

Detailed reference viewed: 137 (17 UL)
Full Text
Peer Reviewed
See detailCrowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities
Capponi, Andrea UL; Vitello, Piergiorgio UL; Fiandrino, Claudio UL et al

in IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain, 2019 (2019, July)

Urban planning typically relies on experience-based solutions and traditional methodologies to face urbanization issues and investigate the complex dynamics of cities. Recently, novel data-driven ... [more ▼]

Urban planning typically relies on experience-based solutions and traditional methodologies to face urbanization issues and investigate the complex dynamics of cities. Recently, novel data-driven approaches in urban computing have emerged for researchers and companies. They aim to address historical urbanization issues by exploiting sensing data gathered by mobile devices under the so-called mobile crowdsensing (MCS) paradigm. This work shows how to exploit sensing data to improve traditionally experience-based approaches for urban decisions. In particular, we apply widely known Machine Learning (ML) techniques to achieve highly accurate results in predicting categories of local businesses (LBs) (e.g., bars, restaurants), and their attractiveness in terms of classes of temporal demands (e.g., nightlife, business hours). The performance evaluation is conducted in Luxembourg city and the city of Munich with publicly available crowdsensed datasets. The results highlight that our approach does not only achieve high accuracy, but it also unveils important hidden features of the interaction of citizens and LBs. [less ▲]

Detailed reference viewed: 215 (20 UL)
Full Text
Peer Reviewed
See detailLink Definition ameliorating Community Detection in Collaboration Networks
Esmaeilzadeh Dilmaghani, Saharnaz UL; Brust, Matthias R. UL; Piyatumrong, Apivadee et al

in Frontiers in Big Data (2019), 2

Detailed reference viewed: 88 (6 UL)
Full Text
Peer Reviewed
See detailA GP Hyper-Heuristic Approach for Generating TSP Heuristics
Duflo, Gabriel UL; Kieffer, Emmanuel UL; Brust, Matthias R. UL et al

in 33rd IEEE International Parallel & Distributed Processing Symposium (IPDPS 2019) (2019, May 20)

Detailed reference viewed: 204 (55 UL)