Privacy and Security of Big Data in AI Systems:A Research and Standards Perspective

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

A Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms

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

IEEE Workshop on Parallel / Distributed Combinatorics and Optimization (PDCO 2020)

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

Tackling Large-Scale and Combinatorial Bi-Level Problems With a Genetic Programming Hyper-Heuristic

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

Design Challenges of Trustworthy Artificial Intelligence Learning Systems

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

Autonomous Flight of Unmanned Aerial Vehicles Using Evolutionary Algorithms

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 ▲]

Automatic Software Tuning of Parallel Programs for Energy-Aware Executions

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 ▲]

A Multilayer Low-Altitude Airspace Model for UAV Traffic Management

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 ▲]

Link Definition ameliorating Community Detection in Collaboration Networks

in Frontiers in Big Data (2019), 2

Tackling Large-Scale and Combinatorial Bi-level Problems with a Genetic Programming Hyper-heuristic

in IEEE Transactions on Evolutionary Computation (2019)

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is a NP-hard problem. In this work, we tackle large-scale and combinatorial bi-level problems using GP ... [more ▼]

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is a NP-hard problem. In this work, we tackle large-scale and combinatorial bi-level problems using GP Hyper-heuristics, i.e., an approach that permits to train heuristics like a machine learning model. Our contribution aims at targeting the intensive and complex lower-level optimizations that occur when solving a large-scale and combinatorial bi-level problem. For this purpose, we consider hyper-heuristics through heuristic generation. Using a GP hyper-heuristic approach, we train greedy heuristics in order to make them more reliable when encountering unseen lower-level instances that could be generated during bi-level optimization. To validate our approach referred to as GA+AGH, we tackle instances from the Bi-level Cloud Pricing Optimization Problem (BCPOP) that model the trading interactions between a cloud service provider and cloud service customers. Numerical results demonstrate the abilities of the trained heuristics to cope with the inherent nested structure that makes bi-level optimization problems so hard. Furthermore, it has been shown that training heuristics for lower-level optimization permits to outperform human-based heuristics and metaheuristics which constitute an excellent outcome for bi-level optimization. [less ▲]