Quentin Laporte Chabasse et Victorien Elvinger at 4th IEEE International Conference on Collaboration and Internet Computing
Quentin Laporte Chabasse and Victorien Elvinger are at the 4th IEEE International Conference on Collaboration and Internet Computing to present our work
Link-Sign Prediction in Dynamic Signed Directed Social Networks
Quang-Vinh Dang and Claudia-Lavinia Ignat (Universite ́de Lorraine)
24th International Conference on Collaboration and Technology
Costa de Caparica, Portugal — September 5-7, 2018
From group to large scale trustworthy distributed collaborative systems
Seminar by Siavash Atarodi
From cognitive psychology to Trust Theory
Presentation by Chahrazed Labba : Adaptive Deployment of Multi-Agent Systems on Cloud Environments – 16/3/2017
Adaptive Deployment of Multi-Agent Systems on Cloud Environments
Multi-agent systems (MAS) are highly dynamic and require powerful computational resources to be properly executed. However, such resources are not available for all organizations due to many reasons such as budget constraints. Thus to support more flexibility and enjoy greater scalability, it is common today to outsource totally or partially various types of applications to the cloud environments. The main challenge in this regard is to ensure an optimal allocation of cloud resources over-time. For MAS, this challenge is deepened due to irregular workload progress and intensive communications between the agents, which may result in high computing and data transfer costs. Our research work contributes to the deployment of MAS on various cloud environments. It proposes a generic pre-deployment method to ensure a cost-efficient adaptive deployment of agent-based systems on various cloud infrastructures. Firstly, we propose a conceptual framework and its operationalization for recommending partitioning algorithms to efficiently distribute MAS on cloud resources. The framework supports the MAS designers in: (i) determining the MAS types based on a set of defined recurrent criteria; (ii) analysing the appropriateness of the partitioning algorithms based on the types of both MAS and target cloud environment; and (iii) providing guidelines to develop new distributed systems. Secondly, we propose a set of new partitioning algorithms that support the deployment of different MAS types on various cloud environments. The algorithms are made up from the combinations of extended versions of two basic graph-based partitioning algorithms, which are Fiduccia-Mattheyses (FM) and Pairwise Movement Fiduccia-Mattheyses algorithms (PMFM). Unlike the existing partitioning algorithms for MAS, the new algorithms support the cloud specifications, allow allocating low cost resources that fulfil the system requirements, and maintain reduced communications costs. Thirdly, our research work provides a prediction process that estimates and optimizes the deployment costs. The process allows monitoring the changes in the MAS requirements in terms of computational resources and invoking, once required, the set of candidate algorithms to enhance the quality of the partitioning. It uses a cost-deployment model to compare the obtained deployment solutions and then recommend the suitable one. To validate our approach, we develop two case studies including the deployment of a crisis evacuation MAS on a hybrid cloud as well as the deployment of a dynamic business process on a cloud federation. Experimental results highlight the efficiency of our approach and show that deployment costs are sensitive to many factors such as the initial deployment, the used partitioning algorithm and the cloud configuration settings.
Presentation by Beatrice Linot – Trust in Computer-Supported Crisis Management Communication 9/2/2018
Title: Trust in Computer-Supported Crisis Management Communication
Speaker: Béatrice Linot, Laboratoire INTERPSY (Psychologie des
Interactions et des Relations Inter-subjective – EA 4432), Université de
Communication issues arise in sociotechnical systems despite functioning
communication equipment, generally attributed to the absence of
information sharing. Computer scientists envision a giant virtual
display accessible to all, but little thought has gone into the
principles for selecting, formatting and organizing content to make it
useful. I argue that a model of communication, in which trust is a
central construct, is key to the design of computer-supported crisis
management communication. Drawing on documentation of the Deepwater
Horizon Accident in 2010, I distinguish between data and information,
illustrate the role of trust in both the data interpretation problem,
and the exchange of information between workers with different
responsibilities. To assure that information is trustworthy, successful
computer support for crisis management communication will need to
situate information in the context of prior action and the ongoing plan,
provide information pedigree to establish its validity and manage the
potential decay of information validity over time.
– Wolfgang Prinz Professeur, RWTH Aachen, Allemagne
– Sihem Amer-Yahia Directeur de Recherche, LIG-CNRS, Grenoble
– Isabelle Chrisment Professeur, Université de Lorraine, LORIA
– Lionel Brunie Professeur, Insa Lyon
– Francois Charoy Professeur, Université de Lorraine, LORIA
– Claudia Ignat Chargée de Recherche, Inria Nancy-Grand Est, LORIA
Large-scale collaborative systems wherein a large number of users collaborate to perform a shared task attract a lot of attention from both academic and industry. Trust is an important factor for the success of a large-scale collaboration. It is difficult for end-users to manually assess the trust level of each partner in this collaboration. We study the trust assessment problem and aim to design a computational trust model for collaborative systems.
We focused on three research questions.
1. What is the effect of deploying a trust model and showing trust scores of partners to users? We designed and organized a user-experiment based on trust game, a well-known money-exchange lab-control protocol, wherein we introduced user trust scores. Our comprehensive analysis on user behavior proved that: (i) showing trust score to users encourages collaboration between them significantly at a similar level with showing nick- name, and (ii) users follow the trust score in decision-making. The results suggest that a trust model can be deployed in collaborative systems to assist users.
2. How to calculate trust score between users that experienced a collaboration? We designed a trust model for repeated trust game that computes user trust scores based on their past behavior. We validated our trust model against: (i) simulated data, (ii) human opinion, and (iii) real-world experimental data. We extended our trust model to Wikipedia based on user contributions to the quality of the edited Wikipedia articles. We proposed three machine learning approaches to assess the quality of Wikipedia articles: the first one based on random forest with manually-designed features while the other two ones based on deep learning methods.
3. How to predict trust relation between users that did not interact in the past? Given a network in which the links represent the trust/distrust relations between users, we aim to predict future relations. We proposed an algorithm that takes into account the established time information of the links in the network to predict future user trust/distrust relationships. Our algorithm outperforms state-of-the-art approaches on real-world signed directed social network datasets.
Keywords: collaboration, trust, game theory, machine learning
Amina Ahmed Nacer presented her paper at the EDOC 2017 Conference
A Metric for Evaluating the Privacy level of a Business Process Know-How in a Multi-Cloud Deployment
Guillaume Rosinosky presented his paper Efficient Migration-Aware Algorithms for Elastic BPMaaS at the BPM Conference