Evolving a Deep Neural Network Training Time Estimator

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 ▲]

Reducing overfitting and improving generalization in training convolutional neural network under limited sample sizes in image recognition

in 5th International Conference on Information Technology, Bangsaen 21-22 October 2020 (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 ▲]

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)

Bayesian optimisation to select Rössler system parameters used in Chaotic Ant Colony Optimisation for Coverage

in Journal of Computational Science (2020), 41

Competitive Evolution of a UAV Swarm for Improving Intruder Detection Rates

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

Emerging Inter-Swarm Collaboration for Surveillance Using Pheromones and Evolutionary Techniques

in Sensors (2020), 20(9), 2566

Internet of Unmanned Aerial Vehicles—A Multilayer Low-Altitude Airspace Model for Distributed UAV Traffic Management

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 ▲]

Technical report on data protection and privacy in smart ICT: Internet of Things: Gap analysis between scientific research and technical standardisation: Gap analysis Internet of Things

in Technical report on data protection and privacy in smart ICT (2019), 1

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

With the emergence of new digital trends like the Internet of Things (IoT), more industry actors and technical committees pursue research in utilizing such technologies as they promise better and optimized management, improved energy efficiency and better quality living by facilitating a magnitude of value-added services. However, as communication, sensing and actuation become increasingly sophisticated, such promising data-driven IoT systems generate, process, and exchange larger amounts of data, some of which is privacy-sensitive and security-critical. The sustained increase in number of connected devices, catalyzed by IoT, affirms the importance of addressing data protection, privacy and security challenges, as indices of trust, to achieve market acceptance. This consequently, emphasizes the need of a solid technical and regulatory foundation to ensure trustworthiness within the IoT ecosystem. The goal of this study is to first introduce the concept of trustworthiness in IoT with its main pillars, data protection, privacy and security, and then analyze developments in research and standardization for each of these. The study presents a gap analysis on data protection, privacy and security between research and standardization, throughout which the use case of Unmanned Aerial Vehicles (UAVs) is referred to, as a promising value-added service example of mobile IoT devices. The study concludes with suggestions for future research and standardization in order to address the identified gaps. [less ▲]

Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities

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 ▲]