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An Unmanned Aerial Vehicle (UAV) network specifies a novel type of Mobile Ad hoc Network (MANET) in which drones serve as nodes and facilitate the retransmission of messages to their final destinations. Aside from its military application, it has recently begun to seep into the civilian sector. Similar to MANET and vehicular ad hoc networks, Flying Ad hoc Networks (FANET) are a subset of ad hoc networks. An FANET is different because it is founded on UAVs. Due to the characteristics of this sort of network, which is defined by a highly changing topology in a 3D environment, we must employ an adjusted configuration method to ensure good routing performance. Therefore, to deal with this problem, a technique that responds to any change in topology by always finding the best route is required. In this work, we propose a new protocol based on the hybrid optimization of the 2-opt heuristic and Honey Badger Algorithm (HBA), called HB-AODV. In order to locate its prey, a badger must move slowly and continuously while using scent markers and mouse-digging skills to catch it. In other words, the most efficient routes in terms of the number of hops are identified. Several simulations were conducted via the 3D version of Network Simulator (NS-2) on different deployment strategies. In comparison to AODV, DSDV, and AntHocNet, the obtained results demonstrated the proposed scheme’s good performance in terms of quality of service metrics.

Many projects fail each year simply because a risk has been misjudged, ignored or unidentified. An essential motivation for analyzing the risk of a project is to inform managers in order to reduce the risk, and therefore the loss of the project. Risk analysis can help identify the best actions that would reduce the risk and assess by how much. In the last decades, the Fuzzy Cognitive Map emerged as a powerful tool for modeling and supervising dynamic interactions in complex systems. There is two ways to construct them, the first way by experts of domain and the second way by learning method based on the historical of data. In this paper, we develop a new learning fuzzy cognitive maps based on a reinforcement learning algorithm so called Q-learning and we propose here a new formulation of kosko causality principle. This connection between fuzzy cognitive maps and reinforcement learning allows us to choose based on the historical of data learning process the best and the most important connections between concepts. In this work, we illustrate the effectiveness of the proposed approach by modeling and studying the analysis of project risk management as an economic decision support system.

[EN] When a shift in writing style is noticed in a document, doubts arise about its originality. Based on this clue to plagiarism, the intrinsic approach to plagiarism detection identifies the stolen passages by analysing the writing style of the suspicious document without comparing it to textual resources that may serve as sources for the plagiarist. Character n-grams are recognised as a successful approach to modelling text for writing style analysis. Although prior studies have investigated the best practice of using character n-grams in authorship attribution and other problems, there is still a need for such investigations in the context of intrinsic plagiarism detection. Moreover, it has been assumed in previous works that the ways of using character n-grams in authorship attribution remain the same for intrinsic plagiarism detection. In this paper, we study the effect of character n-grams frequency and length on the performance of intrinsic plagiarism detection. Our experiments utilise two state-of-the-art methods and five large document collections of PAN labs written in English and Arabic. We demonstrate empirically that the low- and the high-frequency n-grams are not equally relevant for intrinsic plagiarism detection, but their performance depends on the way they are exploited., We are very grateful to the anonymous reviewers for their insightful suggestions and constructive comments that greatly improved the paper. This work has been partially supported by the Ecole Superieure de Comptabilite et de Finances de Constantine. The work of Paolo Rosso has been partially funded by the SomEMBED TIN2015-71147-C2-1-P research project (MINECO/FEDER). The work of Salim Chikhi has been partially funded by CNEPRU/DGRSDT/B*07120140018 research project.

International audience; Stock market is a complex system composed from heterogeneous traderswith highly non-linear interactions from which emerge a phenomenon of specula-tive bubble. To understand the role of heterogeneous behaviors of traders andinteractions between them in the emergence of bubbles, we propose an agent-basedmodel of double auction market, with asynchronous time management, where tra-ders act asynchronously and take different times to make decisions. The market is populated by heterogeneous traders. In addition to fundamentalist, noise, and technical (chartist) traders, we propose a hybrid trader, which can switch between technical (chartist) and fundamentalist strategies integrating panicking behavior. We find that when market is populated by a majority of hybrid traders, we observequite realistic bubble formation characterized by a boom phase when hybrid tradersswitch to technical behavior, followed by a relatively shorter burst phase whenhybrid traders return to fundamentalist strategy and change to panicked state. The aim is to design agents which act asynchronously, with simple behaviors, but complex enough to produce realistic price dynamics, which provide a basis fordeveloping agents with sophisticated decision-making processes.

With the rapid advent of using various devices like smart phones, vehicles etc, the connection of these devices with the help of Internet connectivity has emerged to IoT paradigm. The interconnection of smart objects under the various real world constraints like communication technologies, network scalability, node mobility, energy consumption etc, is a big challenge and requires designing new robust, adaptive, dynamic, and configurable routing protocols. With the arrival of the 5G and the future Internet, the latency time will be extremely reduced, this motivated us to propose a new protocol that entrust internet to transport a large part of control and data traffic of the network. In this paper, a dynamic multi-sink routing protocol (DMS-RP) is proposed for self-organizing multi-hop WSNs, iMANETs and IoT, it uses a sink-based auto-clustering of the network without using a dedicated clustering algorithm and it allows a multipoint-to-multipoint communication for local areas as well as for distant separated areas. Three multi-hop forwarding techniques of the proposed protocol are studied: B A S I C , M U L T I − P A R E N T and M U L T I − P A R E N T + S N R . We have implemented the proposed protocol as a new module for NS-3 simulator. The simulation results show that the proposed protocol achieves a largely better performance in terms of packet delivery, network delay, overhead, energy and network lifetime when compared with one of common multi-hop self-organizing routing protocols (AODV). Furthermore, the protocol analysis shows its high efficiency regarding scalability for static as well as for mobile networks where the studied performances are not significantly affected by the network size which makes it a suitable routing protocol for large-scale network architectures.

An ear biometric system based on artificial bees for ear image contrast enhancement is proposed.In the feature extraction stage, the scale invariant feature transform is used.The proposed approach was tested on three databases of ear biometrics: IIT Delhi, USTB 1 and USTB 2.The obtained results proved the superiority of the proposed in two out of three test databases. Ear recognition is a new biometric technology that competes with well-known biometric modalities such as fingerprint, face and iris. However, this modality suffers from common image acquisition problems, such as change in illumination, poor contrast, noise and pose variation. Using a 3D ear models reduce rotation, scale variation and translation-related problems, but they are computationally expensive. This paper presents a new architecture of ear biometrics that aims at solving the acquisition problems of 2D ear images. The proposed system uses a new ear image contrast enhancement approach based on the gray-level mapping technique, and uses an artificial bee colony (ABC) algorithm as an optimizer. This technique permits getting better-contrasted 2D ear images. In the feature extraction stage, the scale invariant feature transform (SIFT) is used. For the matching phase, the Euclidean distance is adopted. The proposed approach was tested on three reference ear image databases: IIT Delhi, USTB 1 and USTB 2, and compared with traditional ear image contrast enhancement approaches, histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE). The obtained results show that the proposed approach outperforms traditional ear image contrast enhancement techniques, and increases the amount of detail in the ear image, and consequently improves the recognition rate.

To ensure good network performance, a routing protocol for MANETs must change its routing policy online to account for changes in network conditions and to deal with routing information imprecision.The main focus of this paper is on the use of fuzzy logic and reinforcement learning.A dynamic membership function is defined to enhance the adaptivity of legacy fuzzy logic systems.A fuzzy extension of a reinforcement learning based routing protocol for MANETs is presented.Dynamic fuzzy logic is more appropriate than reinforcement learning for adaptive energy aware routing in MANETs. In this paper, a dynamic fuzzy energy state based AODV (DFES-AODV) routing protocol for Mobile Ad-hoc NETworks (MANETs) is presented. In DFES-AODV route discovery phase, each node uses a Mamdani fuzzy logic system (FLS) to decide its Route REQuests (RREQs) forwarding probability. The FLS inputs are residual battery level and energy drain rate of mobile node. Unlike previous related-works, membership function of residual energy input is made dynamic. Also, a zero-order Takagi Sugeno FLS with the same inputs is used as a means of generalization for state-space in SARSA-AODV a reinforcement learning based energy-aware routing protocol. The simulation study confirms that using a dynamic fuzzy system ensures more energy efficiency in comparison to its static counterpart. Moreover, DFES-AODV exhibits similar performance to SARSA-AODV and its fuzzy extension FSARSA-AODV. Therefore, the use of dynamic fuzzy logic for adaptive routing in MANETs is recommended.

Numerous studies have analyzed the performances of routing protocols in mobile Ad-hoc networks (MANETs); most of these studies vary at most one or two parameters in experiments and do not study the interactions among these parameters. Furthermore, efficient mathematical modeling of the performances has not been investigated; such models can be useful for performance analysis, optimization, and prediction. This study aims to show the effectiveness of the response surface methodology (RSM) on the performance analysis of routing protocols in MANETs and establish a relationship between the influential parameters and these performances through mathematical modeling. Given that routing performances usually do not follow a linear pattern according to the parameters; mathematical models of factorial designs are not suitable for establishing a valid and reliable relationship between performances and parameters. Therefore, a Box–Behnken design, which is an RSM technique and provides quadratic mathematical models, is used in this study to establish a relationship. The obtained models are statistically analyzed; the models show that the studied performances accurately follow a quadratic evolution. These models provide invaluable information and can be useful in analyzing, optimizing, and predicting performances for mobile Ad-hoc routing protocols.

Negative Selection Algorithm (NSA) is one of the widely used techniques for Intrusion Detection Systems (IDS) designing. In this paper, the proposed is an IDS based on a new model of NSA namely HNSA-IDSA (Hybrid NSA for Intrusion Detection System Adaptation). The proposed system can detect unknown attacks; moreover can be adapted automatically when new profiles' changes of the system are detected. To determine the efficiency of the proposed approach, the standard KDD99 dataset was used for performing experiments. The obtained results show that the authors' mechanism outperforms some literature techniques providing variant important properties as high detection rate, low false positive, adaptability and new attacks detection.

Multiple sequence alignment (MSA) is one of the major problems that can be encountered in the bioinformatics field. MSA consists in aligning a set of biological sequences to extract the similarities between them. Unfortunately, this problem has been shown to be NP-hard. In this article, a new algorithm was proposed to deal with this problem; it is based on a quantum-inspired cuckoo search algorithm. The other feature of the proposed approach is the use of a randomized progressive alignment method based on a hybrid global/local pairwise algorithm to construct the initial population. The results obtained by this hybridization are very encouraging and show the feasibility and effectiveness of the proposed solution.

International audience; Agent-based artificial stock markets attracted much attention over the last years, and many models have been proposed. However, among them, few models take into account the social interactions and mimicking behaviour of traders, while the economic literature describes investors on financial markets as influenced by decisions of their peers and explains that this mimicking behaviour has a decisive impact on price dynamics and market stability. In this paper we propose a continuous double auction model of financial market, populated by heterogeneous traders who interact through a social network of influence. Traders use different investment strategies, namely: fundamentalists who make a decisions based on the fundamental value of assets; hybrids who are initially fundamentalists, but switch to a speculative strategy when they detect an uptrend in prices; noise traders who don’t have sufficient information to take rational decisions, and finally mimetic traders who imitate the decisions of their mentors on the interactions network. An experimental design is performed to show the feasibility and utility of the proposed model.

RNA secondary structures prediction is one of the main issues in bioinformatics. It seeks to elucidate structural conserved regions within a set of RNA sequences. Unfortunately, finding an accurate conserved structure is a very hard task to do. Within the present study, the prediction problem is considered as a multiobjective optimization process in which the structural conservation and the sensitivity of the multiple alignment are optimized. The proposed method called GRASPMORSA is based on an aggregate function and GRASP procedure. The initial solutions are obtained by using a random progressive local/ global algorithm, and then they are refined by an iterative realignment. Experiments within a large scale of data have shown the efficacy and effectiveness of the proposed method and its capacity to reach good quality solutions.

Mobile Ad hoc network (MANET) is an autonomous system of mobile hosts (nodes) connected by wireless link forming a temporary network without the aid of any established infrastructure or centralized administration. Typical applications of MANETs are: emergency and rescue operations, disaster relief efforts, military operations and exploration mission where cellular infrastructure is unavailable. The main problem of mobile ad hoc networks is to design routing protocols allowing for communication between the hosts. The dynamic nature of ad hoc networks makes this problem especially challenging. Communication in MANET is multi-hop due to limited transmission range; this decentralized operation relies on the cooperative participations of all nodes. MANETs are considered as complex system characterized by high dynamic topology, local interactions, auto-organization and emergence. Modeling and simulation are very important in the design and development of distributed interacting system because of their particular stochastic nature. This article seeks to use agent-based tools for modeling ad hoc network. We focus on Netlogo, an important tool in the modeling and simulation domain of complex system. We have successfully implemented distributed Dijkstra's shortest path algorithm to solve the routing problem. Obtained Results show the quick convergence of Dijkstra's Algorithm to shortest paths relating a source node with all accessible destinations.

In this paper, we combine a probabilistic neural method with radial-bias functions in order to construct the lithofacies of the wells DF01, DF02 and DF03 situated in the Triassic province of Algeria (Sahara). The determination of lithofacies is a crucial problem in reservoir characterization. Our objective is to facilitate the experts' work in the geological domain and allow them to obtain quickly the structure and nature of the land around the drilling. This study intends to design a tool that helps automatic deduction from numerical data. We use a probabilistic formalism to enhance the classification process initiated by a self-organized map procedure. Our system gives the lithofacies, from well-log data, of the reservoir wells concerned in a way that is easy to read by a geology expert who identifies the potential for oil production at a given source and so forms the basis for estimating the financial returns and economic benefits.

Capacitated Vehicle Routing Problem (CVRP) is among transportation problems that are of the foremost concerns in logistics. Ensuring an effective product distribution over a large distribution network while reducing the required costs represents the scope of the present work. A synergic and interactive environment of parallel meta-heuristics is developed using a generalized island model to deal with large instances of CVRP. In the proposed model, cooperative meta-heuristics, namely genetic algorithms (GA) and ant colony optimization algorithms (ACO), are organized into archipelagoes. They communicate synchronously, globally and locally by exchanging solutions. In order to handle properly the migration of solutions, either between archipelagoes or between islands within the same archipelago, appropriate selection and replacement policies are adopted. Furthermore, the proposed approach uses other new features including a new binary solution representation and different optimization process (i.e GA, ACO) on each island. To prove the efficiency of the present work, tests over the well-known set of benchmarks, comparative studies and experimental analysis have been conducted. 23 02

The nature has become a main inspiration source of scientists for developing new intelligent systems and techniques. Nature-inspired meta-heuristics is a kind of algorithms that imitate the social behaviour of some biological species. The bat algorithm (BA) is a new bio-inspired algorithm recently introduced by Yang (2010a). It is an optimisation method that is based on the echolocation behaviour of microbats. Firstly, the BA has been proposed for continuous problems. In this paper, we propose a discrete binary bat algorithm (BinBA) for solving the optimisation problems in binary space. The proposed algorithm is based on the sigmoid function used by Kennedy and Eberhart in 1997 for their binary particle swarm optimisation algorithm. The BinBA was tested on hard instances of the multidimensional knapsack problem. The obtained results are very promising compared to other bio-inspired algorithms 6 2

Evolutionary algorithms (EAs) are a range of problem-solving techniques based on mechanisms inspired by biological evolution. Nowadays, EAs have proven their ability and effectiveness to solve combinatorial problems. However, these methods require a considerable time of calculation. To overcome this problem, several parallelization strategies have been proposed in the literature. In this paper, we present a new parallel agent-based EC framework for solving numerical optimization problems in order to optimize computation time and solutions quality 6 4

This paper presents a novel GRASP algorithm based on a new randomized heuristic for solving the capacitated vehicle routing problem, which characterized by using a fleet of homogenous vehicle capacity that will start from one depot, to serve a number of customers with demands that are less than the vehicle capacity. The proposed method is based on a new constructive heuristic and a simulated annealing procedure as an improvement phase. The new constructive heuristic uses four steps to generate feasible initial solutions, and the simulated annealing enhances these solutions found to reach the optimal one. We tested our algorithm on two sets of benchmark instances and the obtained results are very encouraging.

In the Natural Immune Systems NIS, adaptive and emergent behaviors result from the behaviors of each cell and their interactions with other cells and environment. Modeling and Simulating NIS requires aggregating these cognitive interactions between the individual cells and the environment. In last years the Fuzzy Cognitive Maps (FCM) has been shown to be a convenient tool for modeling, controlling and simulating complex systems. In this paper, a new type of learning fuzzy cognitive maps (LFCM) have been proposed as an extension of traditional FCM for modeling complex adaptive system is described. Our approach is summarized in two major ideas: The first one is to increase the reinforcement learning capabilities of the FCM by using an adaptation of Q-learning technique and the second one is to foster diversity of concept's states within the FCM by adopting an IF-THEN rule based system. Through modeling and simulating response of natural immune system, we show the effectiveness of the proposed approach in modeling CASs.

The multi-objective travelling salesman problem (MOTSP) is a well-known combinatorial optimisation problem that belongs to the class of NP-hard problems. The MOTSP plays an important role in computing theory and in many real life applications. Unfortunately, finding efficient MOTSP solutions is still a challenging problem. That is why several methods were proposed to deal with this problem. This paper proposes a new multi-objective chemical reaction optimisation (MOCRO) algorithm to solve MOTSP. To maintain the diversity of the solutions, MOCRO uses a non-dominated sorting procedure proposed in NSGA2. Two new variants of MOCRO are proposed to deal the MOTSP. The first is MOCRO based on amount of domination (MOCRO-Dom), while the second is MOCRO based on weighted sum (MOCRO-WS). The experimental results have shown the superior performance of MOCRO-Dom and MOCRO-WS compared to NSGA2. Moreover, a comparative study between MOCRO-Dom and MOCRO-WS and the impact of the major parameters on the performance has been investigated.

International audience; We propose a feature fusion method for crowd counting. By image feature extraction and texture feature analysis methods, data obtained from multiple sources are used to count the crowd. We count people in high density static images. Most of the existed people counting methods only work in small areas, such as office corridors, parks, subways and so on. Our method uses only static images to estimate the count in high density images (hundreds or even thousands of people), for example, large concerts, National Day parade. At this scale, we can't rely on only one set of features for counting estimation. Therefore, we use multiple sources of information, namely, HOG and LBP. These sources provide separate estimates and other combinations of statistical measurements. Using the support vector machine (SVM) classification technique, and regression analysis, we count the crowd with high density. The method gives good results in crowded scenes.