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Traditional keyword based search is found to have some limitations. Such as word sense ambiguity, and the query intent ambiguity which can hurt the precision. Semantic search uses the contextual meaning of terms in addition to the semantic matching techniques in order to overcome these limitations. This paper introduces a query expansion approach using an ontology built from Wikipedia pages in addition to other thesaurus to improve search accuracy for Arabic language. Our approach outperformed the traditional keyword based approach in terms of both F-score and NDCG measures.

Wireless Sensor Networks (WSNs) are exposed to many security attacks, and it can be easily compromised. One of the main reasons for these vulnerabilities is the deployment nature, where sensor nodes are deployed without physical guarding duty. That makes the network susceptible to physical attacks. The communication nature between sensor nodes is another reason, where intruders can easily send/receive information if they are located in the network communication range. In this paper, most of the possible WSN attacks are discussed, different security services expected in WSN are explained, and trust-based solutions proposed in the literature are listed. Moreover, the state-of-the-art of the attacks’ mitigation and avoidance techniques are presented. Besides, this paper is enriched with a new classification of the WSNs attacks regarding attacks’ characteristics. It will be beneficial to researchers in the field of WSNs security if they can distinguish between different attacks that have common characteristics.

This paper presents a fast and simple method for human action recognition. The proposed technique relies on detecting interest points using SIFT (scale invariant feature transform) from each frame of the video. A fine-tuning step is used here to limit the number of interesting points according to the amount of details. Then the popular approach Bag of Video Words is applied with a new normalization technique. This normalization technique remarkably improves the results. Finally a multi class linear Support Vector Machine (SVM) is utilized for classification. Experiments were conducted on the KTH and Weizmann datasets. The results demonstrate that our approach outperforms most existing methods, achieving accuracy of 97.89% for KTH and 96.66% for Weizmann.

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Wireless Sensor Networks (WSNs) are one of the most important technologies in the fields of wireless networking today. WSNs have a vast amount of applications including sensors embedded in the outer surface of pipeline or mounted along the supporting structure of bridges, robotics and health care. In this paper, we study the issues of linear sensor placement to monitor oil pipelines. We address the problem of optimal number of sensors to be deployed given initial energy of each sensor node and message buffering limitations. The objectives of the deployment process are: 1) maximizing the coverage of the pipe, 2) producing a connected network, and 3) prolonging the overall network lifetime. The paper utilizes two of the evolutionary algorithms to solve the deployment problem which are Genetic Algorithms (GA) and Ant Colony Optimization (ACO). Extensive set of experiments are performed for performance evaluation.

Thermal imaging is simply the technique of using the heat given off by an object to produce an image of it or locate it. New thermal imaging frameworks for detection, segmentation and unique feature extraction and similarity measurements for human physiological biometrics recognition have been introduced in literature. The research investigates specialized algorithms that would use the individual's heat signature for human detection, crowd counting and applications that take benefits of this new technology. The highly accurate results obtained by the algorithms presented clearly demonstrate the ability of the thermal infrared systems to extend in application to other thermal imaging based systems.

In this paper we present CBC (context based clearing), a procedure for solving the niching problem. CBC is a clearing technique governed by the amount of heterogeneity in a subpopulation as measured by the standard deviation. CBC was tested using the M7 function, a massively multimodal deceptive optimization function typically used for testing the efficiency of finding global optima in a search space. The results are compared with a standard clearing procedure. Results show that CBC reaches global optima several generations earlier than in the standard clearing procedure. In this work the target was to test the effectiveness of context information in controlling clearing. A subpopulation includes a fixed number of candidates rather than a fixed radius. Each subpopulation is then cleared either totally or partially according to the heterogeneity of its candidates. This automatically regulates the radius size of the area cleared around the pivot of the subpopulation.