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This study concerns a swarm of autonomous reactive mobile robots, qualified of naïve because of their simple constitution, having the mission of gathering objects randomly distributed while respecting two contradictory objectives: maximizing quality of the emergent heap-formation and minimizing energy consumed by aforesaid robots. This problem poses two challenges: it is a multi-objective optimization problem and it is a hard problem. To solve it, one of renowned multi-objective evolutionary algorithms is used. Obtained solution, via a simulation process, unveils a close relationship between behavioral-rules and consumed energy; it represents the sought behavioral model, optimizing the grouping quality and energy consumption. Its reliability is shown by evaluating its robustness, scalability, and flexibility. Also, it is compared with a single-objective behavioral model. Results' analysis proves its high robustness, its superiority in terms of scalability and flexibility, and its longevity measured based on the activity time of the robotic system that it integrates.

With the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps. (i) image indexation step, in which we use MapReduce distributed model on Spark in order to speed up the indexation process. We also use a memory-centric distributed storage system, called Tachyon, to enhance the write operation (ii) image retrieving step which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on MapReduce model implemented under Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.

Multilevel thresholding is a widely used technique to perform image segmentation. It consists of dividing an input image into several distinct regions by finding the optimal thresholds according to...

This paper presents a teaching-learning-based optimization algorithm for discrete large-scale multi-objective problems (DLM-TLBO). Unlike the previous variants, the learning strategy used by each individual and the acquired knowledge are defined based on its level. The proposed approach is used to solve a bi-objective object clustering task (B-OCT) in a swarm robotic system, as a case study. The simple robots have as mission the gathering of a number of objects distributed randomly, while respecting two objectives: maximizing the clustering quality, and minimizing the energy consumed by these robots. The simulation results of the proposed algorithm are compared to those obtained by the well-known algorithm NSGA-II. The results show the superiority of the proposed DLM-TLBO in terms of the quality of the obtained Pareto front approximation and convergence speed.

Developments in telecommunications technology has opened up the possibility of circumventing intellectual property in the digital world, outside the legal and technical methods proposed for copyright protection, the information retrieval is considered as a computer field helping to protect these rights by proposing methods of search, analysis and comparison for protected documents. This article proposes an approach based on the distributed information retrieval allowing the detection of any unlawful manipulation for copyrighted works. In particular, we propose an architecture of a distributed information retrieval system on a peer-to-peer network as well as the specific architecture of its nodes, in addition, a double indexation (local & distributed) was proposed.

Business process model repositories are crucial in the architecture of business process management systems. These repositories facilitate the reuse of process models in the enterprise engineering projects, by offering the advanced functionalities of storage and management.This article aims to discuss strengths and weaknesses of these repositories from the perspective of the requirements in the context of a B2B process integration solution as well as their contextual and semantic interoperability.Business process models repositories should enhance the exchange of information and support more reuse of business process models, inter-organizational processes and the process fragments reuse that allow the mediation.

International audience; The polyhedral model is a well-known compiler optimization framework for the analysis and transformation of affine loop nests. We present a new method concerning a difficult geometric operation that is raised by this model: the integer affine transformation of parametric Z-polytopes. The result of such a transformation is given by a worst-case exponential union of Z-polytopes. We also propose a polynomial algorithm (for fixed dimension), to count points in arbitrary unions of a fixed number of parametric Z-polytopes. We implemented these algorithms and compared them to other existing algorithms, for a set of applications to loop nest analysis and optimization.

In this paper, we propose a novel SVD-based RGB color image watermarking, in which the watermark is assumed to be an RGB color image whose detection and extraction are performed in a blind manner. The performance of the proposed watermarking method is investigated by measuring its imperceptible and robust capabilities. For the imperceptible capability, a quantitative index, Peek Signal-to-Noise Ratio (PSNR), is employed to evaluate the difference between an original image and its corresponding watermarked image. For the robust capability, the Correlation Coefficient (CC) is also used to evaluate the similarity between the original watermark and the extracted one. Experimental results indicate that our technique is robust and produces high-quality watermarked images.

International audience; Many compiler optimization techniques depend on the ability to calculate the number of elements that satisfy certain conditions. If these conditions can be represented by linear constraints, then such problems are equivalent to counting the number of integer points in (possibly) parametric polytopes. It is well known that the enumerator of such a set can be represented by an explicit function consisting of a set of quasi-polynomials, each associated with a chamber in the parameter space. Previously, interpolation was used to obtain these quasi-polynomials, but this technique has several disadvantages. Its worst-case computation time for a single quasi-polynomial is exponential in the input size, even for fixed dimensions. The worst-case size of such a quasi-polynomial (measured in bits needed to represent the quasi-polynomial) is also exponential in the input size. Under certain conditions this technique even fails to produce a solution. Our main contribution is a novel method for calculating the required quasi-polynomials analytically. It extends an existing method, based on Barvinok's decomposition, for counting the number of integer points in a non-parametric polytope. Our technique always produces a solution and computes polynomially-sized enumerators in polynomial time (for fixed dimensions).

Many optimization techniques, including several targeted specifically at embedded systems, depend on the ability to calculate the number of elements that satisfy certain conditions. If these conditions can be represented by linear constraints, then such problems are equivalent to counting the number of integer points in (possibly) parametric polytopes. It is well known that this parametric count can be represented by a set of Ehrhart polynomials. Previously, interpolation was used to obtain these polynomials, but this technique has several disadvantages. Its worst-case computation time for a single Ehrhart polynomial is exponential in the input size, even for fixed dimensions. The worst-case size of such an Ehrhart polynomial (measured in bits needed to represent the polynomial) is also exponential in the input size. Under certain conditions this technique even fails to produce a solution. Our main contribution is a novel method for calculating Ehrhart polynomials {\em analytically}. It extends an existing method, based on Barvinok's decomposition, for counting the number of integer points in a non-parametric polytope. Our technique always produces a solution and computes polynomially-sized Ehrhart polynomials in polynomial time (for fixed dimensions). ispartof: pages:248-258 ispartof: Proceedings of the 2004 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES 2004) pages:248-258 ispartof: International Conference on Compilers, Architectures, and Synthesis for Embedded Systems (CASES 2004) location:Washington D.C. date:22 Sep - 25 Sep 2004 status: published