Recommender Systems (RS) often face challenges with new user and data sparsity problems. To address these issues, this paper proposes a new REcommendation Model based sOcio-enVironmEntal factors called "RE-MOVE", which takes into account socio-environmental contextual information. By reviewing state-of-the-art recommendation algorithms, the study found that combining both social and environmental data can improve recommendation quality. The paper argues that modifying the vector of characteristics of items and users can significantly enhance the model. According to the authors, no previous research has investigated matrix factorization by modeling item vector characteristics and considering social and environmental data. The proposed approach was evaluated by comparing it to methods that incorporate contextual information. The results, based on Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), demonstrate that RE-MOVE can more effectively address the challenges and improve the quality of recommendations.

The miniaturization of computers, coupled with a constant increase in computing power, led to the emergence of new sources of context information. We are facing a new paradigm, the Internet of Things (IoT). Today, this latter improves the quality of life in multiple areas. However, the heterogeneity of objects used in such environments makes their interoperability difficult. In addition, the observations produced by context providers (connected objects) are generated with different vocabularies and data formats. This heterogeneity of technologies in the IoT world makes it necessary to adopt generic solutions. Therefore, it is important to transform the raw data from these context producers into knowledge and information based on ontologies. The use of ontologies solves the challenges of heterogeneity and interoperability of IoT systems. In this paper, we propose a semantic IoT model that aims to overcome the semantic interoperability challenges introduced by the variety of objects potentially used in IoT systems. Furthermore, we enhanced this ontology with quality of context meta-data. These meta-data helps in dealing with imperfection and inconsistency of the collected IoT data.

Following technological advances carried out recently, there has been an explosion in the quantity of videos available and their accessibility. This is largely justified by the fall of the prices of acquisition and the increase of the capacity of the memory supports, which made the storage of the large document video in computer system possible. To allow an effective exploitation of the collections, it is necessary to install tools facilitating the access to the documents and handle them. In this context, we propose a multimedia retrieval approach that puts the user at the center of the retrieval process starting from a text query. The new aspects of our proposal is as follows: (i) concerning the indexation part, we propose a new approach allowing a multilevel and semantic classification of videos, (ii) regarding the retrieval part, the inclusion of query expansion mechanism helps the user to formulate the query and the relevance feedback mechanism which helps improve the results considering the user's feedback. Our contribution at the experimental level consists in the implementation of prototype VISEN. In fact the technique proposed have been integrated in system seeks by the contents to evaluate the contribution in terms of effectiveness and precision. After carrying out a set of tests on 2700 videos and 62838 images, the experimental results showed that the proposed algorithm performs well.

In this paper, we introduce a new registry that provides publishing adaptable composition Web services (CAC-WSR). Our registry allows to publish and to find composition Web services not only through the functional criterion but also through users contexts and profiles.

A Web Service is a software component allowing to expose services via Internet. It insures interactions between heterogeneous applications and systems. The explosion of the Internet users number has led to an important diversity of their profiles. Nevertheless, existing Web Services offer the same result regardless the user profile. Therefore, we are interested in defining adaptation solutions that deals with different steps on the service life cycle. In a previous work we were interested in the design step and proposed and detailed an UML (Unified Modeling Language) extension named AWS-UML (Adaptive Web Service Unified Modeling Language) that describes the different allowed users profiles at the metamodel level according to the MDA (Model Driven Architecture) principles and standards. As we modified the design of the Web Service and so the resulting implementation code, we need also to extend the service description in order to support the additional adaptation informations. Thus we introduce in the current paper an extension of the standard WSDL (Web Service Description Language) used to describe the service offered functionalities. Our proposal is named AWS-WSDL (Adaptive Web Service Web Service Description Language).