Abstract Recommender systems in e-learning are powerful tools to find suitable educational material during the learning experience. By including contextual information derived from the use of ubiquitous learning environments, the possibility of incorporating proactivity to the recommendation process has arisen to enhance the traditional user requestresponse pattern. In this article we present methods to generate proactive recommendations to e-learning systems users... Recommender systems in e-learning are powerful tools to find suitable educational material during the learning experience. By including contextual information derived from the use of ubiquitous learning environments, the possibility of incorporating proactivity to the recommendation process has arisen to enhance the traditional user requestresponse pattern. In this article we present methods to generate proactive recommendations to e-learning systems users when the situation is appropriate without being needed their explicit request. As a result, interesting learning objects can be recommended attending to the user’s needs in every situation based on location and user context information. The impact of this proactive recommendations generated have been evaluated among teachers and scientists in a real elearning social network called Virtual Science Hub related to the GLOBAL excursion European project. Outcomes indicate that the methods proposed are valid to generate such kind of recommendations in e-learning scenarios. The results also show that the users’ perceived appropriateness of having proactive recommendations is high in general, although few situations have proved to be inappropriate by users in such educational scenario. In addition to this, details about the proactive user interfaces designed as a consequence of the previous results are provided. Read More Read Less

Abstract The IMSLD specification had emerged in 2003, with the aim of allowing learning scenarios design with regards to good and successful pedagogical guidelines. Since that time, many research works had been carried out to provide authoring tools and/or LMSs that implement it. So the IMSLD success does not only depend on its own principals but it greatly depends on tools... The IMSLD specification had emerged in 2003, with the aim of allowing learning scenarios design with regards to good and successful pedagogical guidelines. Since that time, many research works had been carried out to provide authoring tools and/or LMSs that implement it. So the IMSLD success does not only depend on its own principals but it greatly depends on tools implementing it. However some authoring tools and LMSs (which are considered as LD execution environments) lack some software quality characteristics as interoperability, flexibility adaptability, graphical interface usability and so on. But with the SOA oriented LMSs, the learning process should be considered as a business process where learning services are orchestrated to provide to learners the best learning experiences either individually or collaboratively. In this paper, we present a novel approach and a graphical tool based both on the IMSLD specification and on MDA (Model Driven Architecture) transformations to Business Process Modeling Ontology (BPMO) notation. Read More Read Less

Abstract Safety critical applications, such as explosion prediction, require continuous and reliable operation of Wireless sensor networks (WSNs). However, validating that a WSN system will function correctly at run time is a hard problem. This is due to the numerous faults one may encounter in the resource-constrained nature of sensor platforms together with the unreliability of the wireless links networks. A... Safety critical applications, such as explosion prediction, require continuous and reliable operation of Wireless sensor networks (WSNs). However, validating that a WSN system will function correctly at run time is a hard problem. This is due to the numerous faults one may encounter in the resource-constrained nature of sensor platforms together with the unreliability of the wireless links networks. A holistic fault tolerance approach that addresses all fault issues does not exist. Existing fault tolerance work most likely misses some potential causes of system failures. In this paper, we propose an integrated fault tolerance framework (IFTF) that reduces the false negative by combining a network diagnosis service (component/element level monitoring) with an application testing service (system level monitoring). Thanks to these two complementary services, the maintenance operations will be more efficient leading to a more dependable WSN. From the design view, IFTF offers to the application many tunable parameters that make it suitable for various application needs. Simulation results show that the IFTF reduces the false negative rate of application level failures to 60% with an increase of 4% in power consumption (communication overhead) compared to using solely network diagnosis solutions. Read More Read Less

Abstract Principal Components Analysis (PCA) has been intensively studied and is widely applied in industrial process monitoring. The main purpose of using PCA is the dimensionality reduction by extraction of the feature space that still contain the most information in the original data set. Despite its success in this field, the most important obstacle faced is the sensitivity to noise, also... Principal Components Analysis (PCA) has been intensively studied and is widely applied in industrial process monitoring. The main purpose of using PCA is the dimensionality reduction by extraction of the feature space that still contain the most information in the original data set. Despite its success in this field, the most important obstacle faced is the sensitivity to noise, also the fact that the majority of collected data from industrial processes are normally contaminated by noise makes it unreliable in some cases. To overcome these limitations, several strategies have been used. One of these has been interested to combine the robustness theory with PCA method, such theory sonsists in robustifying the existing algorithms against noise or outliers. Fuzzy Robust Principal Components Analysis (FRPCA) is one of the results for such combination that acheive better result compared with the classical method. In this work the RFPCA method is used and compared with the classical one to monitoring a biological nitrogen removal process. The obtained results demonstrate the performances superiority of this method compared with the conventional one. Read More Read Less