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Nowadays, special attention is directed to trust issues in the Decentralized Online Social Network (DOSN). In a distributed system for social networking, interactions and collaborations can be unreliable because some users resort to malicious behaviors in order to increase their trust values in the network to be chosen later by others, and launch trust-related attacks. In this unreliable situation, users will not be able to estimate the trustworthiness of the received social services' list of recommendations. Hence, a trust management model becomes a necessity in order to overcome its trust-related attacks and to recommend trustworthy social services. In this respect, we propose a new trust management model that helps prevent trust-related attacks in order to ensure a reliable environment. Towards this end, our suggested model implements a new technology, called blockchain. Based on the studied trust-related attacks, we intend to add logical security to blockchain since this technology takes into account only the physical security. Evaluation values show the effectiveness of our model. [ABSTRACT FROM AUTHOR]

Nowadays, Learning Management Systems (LMS) play an intrinsic role in education. They gather traces about the learner (course view, wiki view, quiz attempt, etc.) in event logs. These logs offer the opportunity to provide dashboards and analysis on learners. There are several techniques that analyze event logs for different purposes (adaptation, recommendation, performance detection, etc.). Within this framework, our central focus is upon Educational Process Mining technique which generates process models for improving learning resource recommendation. We set forward an architecture leading to discover process models and recommend to the learner not only learning resource but also process models, each of which is relative to a specific learning resource. These models exert a certain influence on the result of learning resource recommendation. One of the reason that endows our work with an original aspect is that it automatically analyses event logs based on multi-features extracted from the learner's profiles. However, the state of the art works require a manual analysis step based on learning results uniquely. We evaluated the discovered process models grounded on the event logs of Moodle LMS. These event logs contain 42,438 traces of 100 students who learned a course over one semester. Results corroborate the good performance of our work. [ABSTRACT FROM AUTHOR]

The adaptive educational systems include several solutions for providing personalized access to the learning process. The learner's profile constitutes the key element of these solutions. Therefore, an educational system represents the learner's profile by its own syntax, semantic and structure. Each system can have incomplete or partial learner's data. As a consequence, there is a strong need to exchange the learner's profiles between different systems to enhance and enrich the learner's knowledge. However, the data exchange between the learner's profiles implies interoperability problems. In our work, we are interested in the evolving learner's profiles interoperability problem. In this context, we propose an architecture allowing the data exchange of the learner's profile in educational cross-systems in order to improve the adaptation navigation. This architecture is automatically adapted to the learner's profiles that evolve over time. These latter are syntactically, semantically and structurally heterogeneous. The evaluation values show the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The access to heterogeneous data through their metadata needs a matching process of the metadata schemas. This process identifies the correspondence relations called “Mappings” between the schemas to identify a global schema. This latter allows a uniform access to heterogeneous data. In this context, several works are proposed. However, the obtained mappings and the global schema are identified regardless of the user's profile. Thus, the queries results are the same for any user despite the various profiles. In this paper, we present a matching process that (i) deals with the metadata schema heterogeneities and (ii) considers the users’ profile. [ABSTRACT FROM AUTHOR]

Abstract: Several systems such as adaptive systems, etc. provide responses to the user by taking into account, among other, his profile. After each user-system interaction, new information should be added to the user profile content. By the time and after several updating operations, the profile can become overloaded and the removal of irrelevant content is necessary. In this paper, we tackle the profile overloading problem. We propose a new method based on co-training algorithm for detecting and removing irrelevant elements. Our method is automatically adapted to the content of any profile and allows us to obtain the most generic classifier to each one. An experimental study by qualitative and comparative evaluations shows that the proposed method can detect and remove irrelevant profile content effectively. [Copyright &y& Elsevier]