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Driver fatigue is still a principal cause of traffic accidents. While many ways allowing fatigue detection, a diversity of obstacles such as head position, luminosity, and facial expressions make it a very challenging problem. In this paper, we propose a hybrid approach using deep learning techniques to detect driver drowsiness by combining between structural and global classification methods. The structural method tracks eyes, eyebrows, and mouth movements to assess blink and yawning, for this purpose we calculate eye-opening and mouth-opening ratios relative to their width. Five parameters are extracted LEM (left eye movement), REM (right eye movement), LEB M (left eyebrow movement), REBM (right eyebrow movement), and MM (mouth movement), whereas the global method is based on Convolutional Neural Network (CNN) to describe the whole face. Eight-layer pre-trained Alexnet network is used to extract features and make classification of each frame. To do video classification, the five structural parameters, along with the global classification decision, are combined into a single vector to be input into Long-Short-Term Memory (LSTM) networks that is an improved version of Recurrent Networks. LSTM decision score is determined after running 150 steps, providing information about driver state Extensive Experiments are performed on a Driver Drowsiness Detection Dataset that contains subjects of different ethnicities. The experimental results show that the proposed method with the combined features improves drowsiness detection significantly as well as outperforms the state-of-the-art models in terms of drowsiness scores. [ABSTRACT FROM AUTHOR]

Summary: With the advent of Web 2.0, numerous collaborative annotation systems have been developed in an effort to enable distant users to annotate the same multimedia resources such as texts, audio, images, and videos. However, the existing systems do not support the semantic aspect of the data available on the Web and ignore the convergence aspect when executing concurrent annotations. Based on the technologies of Semantic Web, this article presents a new conflict‐free replicated data type called OAC‐Set, which extends Open Annotation Collaboration data model to enable concurrent annotations while guaranteeing convergence, causality, and intention preservation criteria. The experimental results show that our approach is efficient and effective. [ABSTRACT FROM AUTHOR]

In this study, a collaborative annotation approach based on Linked Data technology (CAALDT) is proposed for improving students’ learning. An experiment was conducted to evaluate the effectiveness of the proposed approach by comparing the learning performance of the students who learned with CAALDT and those who learned with the conventional collaborative annotation approach. From the experimental results, it was found that the proposed approach improved not only the students’ learning achievements, but also their learning motivation and attitudes. Moreover, it was found that the CAALDT approach provided more challenging learning opportunities that enabled students to review the annotations of their peers or teacher as well as to expose their annotations as Linked Data, therefore contributing to the expansion of the learning contents. [ABSTRACT FROM AUTHOR]