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Since their beginning, Massive Open Online Courses (MOOC) have known great success and have managed to establish themselves with significant enrollment rates. However, this success was quickly disrupted by the drop‐out phenomenon observed in the majority of MOOCs, which reaches 90% in some courses. Studying and understanding this phenomenon, and consequently determining the relevance of the efforts made to develop MOOCs, has led several researchers to propose predictive models of learners at risk of dropping out. On one hand, these models have been made relying on machine learning and the massive data generated by learners' navigation. On the other hand, these models only provide weekly predictions and do not give clear visibility about the overall course progress. We present in this paper a framework based on the recurrent neural networks' strengths which uses generator and predictor modules. Our framework allows not only the prediction of dropouts but also the generation of each learners' behaviors during the whole course since its first week. Besides, an OLAP analytical module proved great support for MOOC moderators to report on the learners' behavior at‐risk to target their interventions and guide their support. [ABSTRACT FROM AUTHOR]

Artificial technologies are rapidly becoming one of the most powerful and popular technologies for solving complicated problems involving distributed systems. Nevertheless, their potential for application to advanced artificial transportation systems has not been sufficiently explored. This paper presents a traffic optimization system based on agent technology and fuzzy logic that aims to manage road traffic, prioritize emergency vehicles, and promote collective modes of transport in smart cities. This approach aims to optimize traffic light control at a signalized intersection by acting on the length and order of traffic light phases in order to favor priority flows and fluidize traffic at an isolated intersection and for the whole multi-intersection network, through both inter- and intra-intersection collaboration and coordination. Regulation and prioritization decisions are made on real-time monitoring through cooperation, communication, and coordination between decentralized agents. The performance of the proposed system is investigated by implementing it in the AnyLogic simulator, using a section of the road network that contains priority links. The results indicate that our system can significantly increase the efficiency of the traffic regulation system. [ABSTRACT FROM AUTHOR]

There is a significantly increasing demand for monitoring systems for elderly people in the health-care sector. As the aging population increases, patient privacy violations and the cost of elderly assistance have driven the research community toward computer vision and image processing to design and deploy new systems for monitoring the elderly in the authors' society and turning their living houses into smart environments. By exploiting recent advances and the low cost of three-dimensional (3D) depth sensors such as Microsoft Kinect, the authors propose a new skeleton-based approach to describe the spatio-temporal aspects of a human activity sequence, using the Minkowski and cosine distances between the 3D joints. We trained and validated their approach on the Microsoft MSR 3D Action and MSR Daily Activity 3D datasets using the Extremely Randomised Trees algorithm. The results are very promising, demonstrating that the trained model can be used to build a monitoring system for the elderly using open-source libraries and a low-cost depth sensor. [ABSTRACT FROM AUTHOR]

The content-based retrieval systems for 3D models on the Web become necessary since digital databases of 3D objects are growing. In this paper, we propose a new method to describe 3D models. This method is based on 3D discrete cosine transform which is applied for the voxelized 3D model. The discrete cosine transform is widely used for 2D image compress ion and it shows its performance for the JPEG compression algorithm. The proposed descriptor is implemented in our 3D search engine, tested using the princeton shape benchmark database, tested for noise and decimation; and compared to other 3D descriptors known in the literature. [ABSTRACT FROM AUTHOR]