Enforcing Full Arc Consistency in Asynchronous Forward Bounding Algorithm

The AFB BJ+ DAC* is the latest variant of asynchronous forward bounding algorithms used to solve Distributed Constraint Optimization Problems (DCOPs). It uses Directional Arc Consistency (DAC*) to remove, from domains of a given DCOP, values that do not belong to its optimal solution. However, in some cases, DAC∗ does not remove all suboptimal values, which causes more unnecessary research to reach the optimal solution. In this paper, to clear more and more suboptimal values from a DCOP, we use a higher level of DAC* called Full Directional Arc Consistency (FDAC*). This level is based on reapplying AC* several times, which gives the possibility of making more deletions and thus quickly reaching the optimal solution. Experiments on some benchmarks show that the new algorithm, AFB BJ+ FDAC*, is better in terms of communication load and computation effort

Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach

Defining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, “Bat-Cluster (BC).” This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values

Étude de la densité électronique des phases paramagnétique et antiferromagnétique des monosulfures NiS, CoS et FeS de structure hexagonale dans l'approximation de la densité locale de spin

Not availableNous avons étudié la densité de l'état fondamental des monosulfures FeS, Cos et NiS dans les phases antiferromagnétique et paramagnétique en utilisant les équations de Kohn-Sham. Ces équations sont représentées dans le formalisme des combinaisons linéaires d'orbitales atomiques (CLOA). Elles sont résolues de façon autocohérente dans le cadre de l'approximation de la densité locale de spin (LSDA), de l'approximation du gradient généralisé (GGA) et de la correction LSDA+ U ou U est l'interaction de Coulomb intra-site. Nous avons utilisé la structure hexagonale idéale de NiAs pour calculer la densité d'états électronique des monosulfures dans les deux phases magnétiques. L'organisation des états électronique doit expliquer la transition semi-conducteur-métal de FeS et NiS et le caractère métallique de CoS à toutes les températures. Dans l'approche LSDA nous avons précisé l'origine de la bande interdite de NiS en évaluant les influences respectives du champ cristallin, de l'énergie d'échange-corrélation et des paramètres du réseau. Nous avons montré que la diminution du paramètre c avait une action prédominante sur la suppression de la bande interdite. Pour FeS la comparaison entre la densité d'états calculée et les spectres de photoémission et de BIS permet l'identification des structures présentés dans les spectres expérimentaux. L'existence d'une bande interdite a pu être montré grâce à l'approximation LSDA + U. Le calcul de la densité d'états de CoS réalisé avec l'approximation LSDA montre que le composé est métallique en dessous et au dessus de la température de Néel. La simple LSDA est apte à décrire les propriétés électriques de FeS, CoS et NiS. Alors que la correction GGA n'apporte pas de modification significative aux résultats LSDA, l'approximation LSDA+U améliore la largeur de la bande interdite et la valeur du moment de spin pour FeS et Ni

Disparity estimation using Graph cuts for road applications

This paper proposes a new edge based stereo matching approach for road applications. The new approach consists in matching the edge points extracted from the input stereo images using temporal constraints. At the current frame, we propose to estimate a disparity range for each image line based on the disparity map of its preceding one. The stereo images are divided into multiple parts according to the estimated disparity ranges. The optimal solution of each part is independently approximated via the state-of-the-art energy minimization approach Graph cuts. The disparity search space at each image part is very small compared to the global one, which improves the results and reduces the execution time. Furthermore, as a similarity criterion between corresponding edge points, we propose a new cost function based on the intensity, the gradient magnitude and gradient orientation. The proposed method has been tested on virtual stereo images, and it has been compared to a recently proposed method and the results are satisfactory

Forced force directed placement: a new algorithm for large graph visualization

International audienceGraph Visualization is a technique that helps users to easily comprehend connected data (social networks, semantic networks, etc.) based on human perception. With the prevalence of Big Data, these graphs tend to be too large to decipher by the user’s visual abilities alone. One of the leading causes of this problem is when the nodes leave the visualization space. Many attempts have been made to optimize large graph visualization, but they all have limitations. Among these attempts, the most famous one is the Force Directed Placement Algorithm. This algorithm can provide beautiful visualizations for small to medium graphs, but when it comes to larger graphs it fails to keep some independent nodes or even subgraphs inside the visualization space. In this paper, we present an algorithm that we have named "Forced Force Directed Placement". This algorithm provides an enhancement of the classical Force Directed Placement algorithm by proposing a stronger force function. The “FForce”, as we have named it, can bring related nodes closer to each other before reaching an equilibrium position. This helped us gain more display space and that gave us the possibility to visualize larger graphs

A Multiscale Polyp Detection Approach for GI Tract Images Based on Improved DenseNet and Single-Shot Multibox Detector

Small bowel polyps exhibit variations related to color, shape, morphology, texture, and size, as well as to the presence of artifacts, irregular polyp borders, and the low illumination condition inside the gastrointestinal GI tract. Recently, researchers developed many highly accurate polyp detection models based on one-stage or two-stage object detector algorithms for wireless capsule endoscopy (WCE) and colonoscopy images. However, their implementation requires a high computational power and memory resources, thus sacrificing speed for an improvement in precision. Although the single-shot multibox detector (SSD) proves its effectiveness in many medical imaging applications, its weak detection ability for small polyp regions persists due to the lack of information complementary between features of low- and high-level layers. The aim is to consecutively reuse feature maps between layers of the original SSD network. In this paper, we propose an innovative SSD model based on a redesigned version of a dense convolutional network (DenseNet) which emphasizes multiscale pyramidal feature maps interdependence called DC-SSDNet (densely connected single-shot multibox detector). The original backbone network VGG-16 of the SSD is replaced with a modified version of DenseNet. The DenseNet-46 front stem is improved to extract highly typical characteristics and contextual information, which improves the model’s feature extraction ability. The DC-SSDNet architecture compresses unnecessary convolution layers of each dense block to reduce the CNN model complexity. Experimental results showed a remarkable improvement in the proposed DC-SSDNet to detect small polyp regions achieving an mAP of 93.96%, F1-score of 90.7%, and requiring less computational time

Mining unstructured data for a competitive intelligence system XEW

International audienceNowadays, there is a vast amount of information available in line. One of the major unsolved problems in the Competitive Intelligence (CI) is the management of unstructured data. The unstructured data such as multimedia files, documents, comments, customer support request, news, emails, reports and web pages are difficult to capture and store in the common database system. This paper will explained the main process of unstructured data, based on the web services technologies for a Competitive Intelligence System (CIS) XEW. This process could help organization to understand the significance of exploitation and transformation data in supporting decision making process

Fabrication of Thermoelectric Sensor and Cooling Devices Based on Elaborated Bismuth-Telluride Alloy Thin Films

The principal motivation of this work is the development and realization of smart cooling and sensors devices based on the elaborated and characterized semiconducting thermoelectric thin film materials. For the first time, the details design of our sensor and the principal results are published. Fabrication and characterization of Bi/Sb/Te (BST) semiconducting thin films have been successfully investigated. The best values of Seebeck coefficient (α(T)) at room temperature for Bi2Te3, and (Bi1−xSbx)2Te3 with x = 0.77 are found to be −220 µV/K and +240 µV/K, respectively. Fabrication and evaluation of performance devices are reported. 2.60°C of cooling of only one Peltier module device for an optimal current of Iopt=2.50 mA is obtained. The values of temperature measured by infrared camera, by simulation, and those measured by the integrated and external thermocouple are reported. A sensitivity of the sensors of 5 mV Torr−1 mW−1 for the pressure sensor has been found with a response time of about 600 ms

Overview of Data Visualization

International audienceData visualization can be defined as data transformation into interactive visual representations. It is very important since it allows users to have an insightful vision on a subject that might interest thems. The Big Data phenomenon has urged scientists to develop and dedicate an entire research field to data visualization since it allows the user to easily have an idea on the content provided by his different data sources, based on his visual abilities. In this paper, we will present an overview of the literature related to this topic starting by its definition then moving to its challenges and later, presenting its methods and comparing some of its most used tools