Drivers’ behaviour modelling for virtual worlds

In this paper we present a study that looks at modelling drivers’ behaviour with a view to contribute to the problem of road rage. The approach we adopt is based on agent technology, particularly multi-agent systems. Each driver is represented by a software agent. A virtual environment is used to simulate drivers’ behaviour, thus enabling us to observe the conditions leading to road rage. The simulated model is then used to suggest possible ways of alleviating this societal problem. Our agents are equipped with an emotional module which will make their behaviours more human-like. For this, we propose a computational emotion model based on the OCC model and probabilistic cognitive maps. The key influencing factors that are included in the model are personality, emotions and some social/personal attributes

On data selection for the energy efficiency of neural networks: Towards a new solution based on a dynamic selectivity ratio

International audienceIn this paper, we address the energy efficiency of neural networks training through data selection techniques. We first study the impact of a random data selection approach that renews the selected examples periodically during training. We find that random selection should be considered as a serious option as it allows high energy gains with small accuracy losses. Unexpectedly, it even outperforms a more elaborate approach in some cases. Our study of the random approach conducted us to observe that low selectivity ratios allow important energy savings, but also cause a significant accuracy decrease. To mitigate the effect of such ratios on the prediction quality, we propose to use a dynamic selectivity ratio with a decreasing schedule, that can be integrated to any selection approach. Our first results show that using such a schedule provides around 60% energy gains on the CIFAR-10 dataset with less than 1% accuracy decrease. It also improves the convergence when compared to a fixed ratio

ARTIFICIAL FINANCIAL MARKET. Risk Analysis Approach

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Classical Mechanics Optimization for image segmentation

International Conference on Swarm Intelligence Based Optimization: Theoretical Advances and Real World Application (ICSIBO’2016), UHA, Mulhouse, France, 13-14 June 2016In this work, we focus on image segmentation by simulating the natural phenomenon of the bodies moving through space. For this, a subset of image pixels is regularly selected as planets and the rest as satellites. The attraction force is defined by Newton’s third law (gravitational interaction) according to the distance and color similarity. In the first phase of the algorithm, we seek an equilibrium state of the earth-moon system in order to achieve the second phase, in which we search an equilibrium state of the earth-apple system. As a result of these two phases, bodies in space are constructed; they represent segments in the image. The objective of this simulation is to find and then extract the multiple segments from an image

Solving the Unrelated Parallel Machine Scheduling Problem with Setups Using Late Acceptance Hill Climbing

International audienceWe propose a Late Acceptance Hill-Climbing (LAHC) approach to solve the unrelated parallel machine scheduling problem with sequence and machine-dependent setup times. LAHC is an iterative list-based single-parameter metaheuristic that exploits information from one iteration to another to decide whether the new candidate solution is accepted. A dynamic job insertion heuristic is used to generate initial solutions. Three local search operators (job swap between different machines, job swap within the same machine and job insertion from one machine to another) are used to improve solutions. A Variable Neighborhood Descent (VND) method is proposed to improve the candidate solution and accelerate the convergence of the LAHC. To the best of our knowledge, this is the first application of LAHC to parallel machine scheduling problems. We evaluate and compare the proposed algorithm against the best methods from the literature. Having a single parameter which makes it simpler than all existing approaches, the proposed method outperforms existing methods on most of the tested benchmark instances

Optimized multi‐biometric enhancement analysis

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Threats models on biometri systems: A comparative study

Several biometric threats systems models have been proposed to facilitate the design, implementation and validation techniques for securing these systems. Some models classify threats by type of attacks, others by specific attacks and other by using vulnerabilities and threat agent. Each model proposes a vision and a different approach to identify these threats. For example, to design security techniques for wireless biometric card, one should identify all threats facing this kind of device. In this paper, a comparative study and synthesis to help choose the most fitting model, depending on the security problems addressed, is given

Chaff-Points Generation Method Based on Adaptive Genetic Algorithms and Non-Fixed Size Squares Boundaries for Resource Constrained Devices

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Multibiometrics Enhancement Using Quality Measurement in Score Level Fusion

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