Narjès Bellamine Ben Saoud

Complexity theory and collaboration: An agent-based simulator for a space mission design team

In this paper, we investigate how complexity theory can benefit collaboration by applying an agent-based computer simulation approach to a new form of synchronous real-time collaborative engineering design. Fieldwork was conducted with a space mission design team during their actual design sessions, to collect data on their group conversations, team interdependencies, and error monitoring and recovery practices. Based on the fieldwork analysis, an agent-based simulator was constructed. The simulation shows how error recovery and monitoring is affected by the number of small group, or sidebar, conversations, and consequent noise in the room environment. This simulation shows that it is possible to create a virtual environment with cooperating agents interacting in a dynamic environment. This simulation approach is useful for identifying the best scenarios and eliminating potential catastrophic combinations of parameters and values, where error recovery and workload in collaborative engineering design could be significantly impacted. This approach is also useful for defining strategies for integrating solutions into organizations.

Smart Homes and Sensors for Surveillance and Preventive Education at Home: Example of Obesity

(1) Background: The aim of this paper is to show that e-health tools like smart homes allow the personalization of the surveillance and preventive education of chronic patients, such as obese persons, in order to maintain a comfortable and preventive lifestyle at home. (2) Technologies and methods: Several types of sensors allow coaching the patient at home, e.g., the sensors recording the activity and monitoring the physiology of the person. All of this information serves to personalize serious games dedicated to preventive education, for example in nutrition and vision. (3) Results: We built a system of personalized preventive education at home based on serious games, derived from the feedback information they provide through a monitoring system. Therefore, it is possible to define (after clustering and personalized calibration) from the at home surveillance of chronic patients different comfort zones where their behavior can be estimated as normal or abnormal and, then, to adapt both alarm levels for surveillance and education programs for prevention, the chosen example of application being obesity.

Towards a conceptual framework to support adaptative agent-based systems partitioning

Scalability is a key issue for Multi-Agent Systems (MAS) that aim to model and simulate complex systems. Distributed infrastructures such as clusters, grids and clouds are powerful computational environments that can be effectively used to run large-scale agent-based simulations. To properly distribute an agent-based system and ensure its performance, an appropriate partitioning approach is required. Although multiple partition methods for distributed MAS exist, they remain specific to the individual requirements of a given application domain. There is no generic approach for guiding the designers and developers to select an appropriate approach for partitioning a given agent-based system. Thus a recurrent challenging task, for MAS designers and developers, is how to evaluate, select and then apply the appropriate partitioning mechanism for a given MAS. Therefore, in this paper, we present a generic conceptual framework useful to analyze existing partitioning methods. It can also be used as a basis while designing a distributed architecture of new MAS.

Management of socio-cultural knowledge using an ontology-based socio-cultural user profile in a computer-supported collaborative learning environment

Computer Supported Collaborative Learning (CSCL) is a pedagogical approach wherein learning takes place via social interaction using a computer or through the Internet. This kind of learning is characterized by the sharing and construction of knowledge among participants. This knowledge is shared through communication technologies embedded in CSCL systems. However in the context of globalization and the expansion of the internet and Information Technology (IT), communication becomes intercultural, so a complex and important dimension is added to CSCL and brings a new type of knowledge, socio-cultural knowledge, that needs to be shared and then presents new challenges. Therefore, in intercultural collaborative learning settings, we need to get and ensure better interaction, to get better learning. Better interaction is reached by promoting motivation to learn in groups, and this is obtained by enhancing user satisfaction which is achieved by the development of tailored CSCL tools to each user according to her socio-cultural background. Adapting the CSCL system to the culture of each participant will allow and facilitate group decision-making in the collaborative learning activity, as we take into account socio-cultural specificities of each learner, this latter will be more comfortable with the system and the other learners, and can collaborate with them to make decisions about the collaborative solution. So this article addresses the challenges started up by this context, which is how to consider socio-cultural specificities of each learner.

Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

Networks used in biological applications at different scales (molecule, cell and population) are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions (e.g., non-linear Wilson-Cowan system) as well as in their discrete Boolean versions (e.g., non-linear Hopfield system); in both cases, the notion of interaction graph G(J) associated to its Jacobian matrix J, and also the concepts of frustrated nodes, positive or negative circuits of G(J), kinetic energy, entropy, attractors, structural stability, etc., are relevant and useful for studying the dynamics and the robustness of these systems. We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i) attractor entropy, (ii) isochronal entropy and (iii) entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment.

Towards an Agent-Based Humanitarian Relief Inventory Management System

Natural disasters have reached unpredictable intensity around the world during the last two decades. Therefore, rapid response to the urgent relief in an efficient way is necessary for alleviation of disaster impact in the affected areas. Humanitarian Supply Chain Management plays a crucial role for disaster response management. Warehouse and inventory management is a key activity. Its effectiveness and efficiency are challenging issues during emergency response. In fact, by ensuring appropriate fast and well organized distribution of emergency relief supplies, damages would be mitigated and more lives saved. This paper draws first a literature review to better define humanitarian supply chain management and highlight inventory management characteristics and needs in a post-disasters context. An agent-based model and a simulator are developed in order to enable decision makers find efficient scenarios to respond effectively to urgent requests following a disaster. First simulation results are discussed.

Towards an Intelligent Application of Large Scale Community Detection to Support Collaboration During Emergency Management

During crisis situations, people often use social media to seek for help and to find new collaborators who can help them in emergency management. In this context, we propose an intelligent application to find and recommend potential and relevant collaborators through social media. This application is based on a large scale contextualized community detection to compose dynamic groups. To do so, we propose to reuse a new community detection algorithm that considers simultaneously the network structure (social connections) and profiles homophily (similarities). An application of the proposed solution and a comparison with another community detection algorithm evaluates its performance.

Evaluating community detection using a bi-objective optimization

Community detection consists on a partitioning networks technique into clusters (communities) with weak coupling (external connectivity) and high cohesion (internal connectivity). In order to measure the performance of the clustering, the network modularity is largely used, a metric that presents the cohesion and the coupling of communities. In this paper, a global and bi-objective function is proposed to evaluate community detection. This function combines modularity (based on structure and edges weights) and the inter-classes inertia (based on nodes weights). Then, we rely on a computational optimization technique i.e. Particle Swarm Optimization to maximize this bi-objective quality. Finally, a case study evaluates the proposed solution and illustrates practical uses.

Towards a Generic Socio-cultural Profile for Collaborative Environments

This paper focuses on socio-cultural user modeling in a social and intercultural collaborative context. we first, study and discuss several examples of user models in many research domains, we mainly focus on how these models are defined and represented. After that, we describe our generic and multidimensional socio-cultural profile, so, we define and justify its dimensions, its characteristics and its properties. We also describe the way the data to fill the model will be collected and updated. Finally, we discuss what we have presented and we give a brief plan of our future work to better address these important issues.

Adaptive deployment of service-based processes into cloud federations

Service-based processes represent compositions of software services that need to be properly executed by the resources offered by an IT infrastructure within a company. Due to the dynamic changes in their QoS requirements, service-based processes are constantly evolving and demanding new resources. To ensure agility and support more flexibility, it is common today for enterprises to outsource their service-based processes to cloud environments and recently to cloud federations. The main challenge in this regard is to ensure an optimal allocation of cloud resources to process services overtime. In fact, given the diversity of the resources within a federation and the continuous changes of the process QoS needs, the reallocation of cloud resources to process services may result in high computing costs and an increase in the communication overheads. In this paper, we propose a novel adaptive resource allocation approach which can estimate and optimize the final deployment costs. We use agent-based systems to simulate processes’ enactment. To cope with the services’ QoS changes and dynamically adapt the initial deployment, we propose an extended version of the Pairwise-Movement Fiduccia-Mattheyses (E-PMFM) partitioning algorithm. Our experimental results highlight the efficiency of E-PMFM algorithm and show that deployment costs are sensitive to the initial deployment and the used partitioning algorithm.