Human action recognition is an important area of research in computer vision. Its applications include surveillance systems, patient monitoring, human-computer interaction, just to name a few. Numerous techniques have been developed to solve this problem in 2D and 3D spaces. However 3D imaging gained a lot of interest nowadays. In this paper we propose a novel view-independent action recognition algorithm based on fusion between a global feature and a graph based feature. We used the motion history of skeleton volumes; we compute a skeleton for each volume and a motion history for each action

Nowadays, an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication. However, because user-generated content is unregulated, it may contain offensive content such as fake news, insults, and harassment phrases. The identification of fake news and rumors and their dissemination on social media has become a critical requirement. They have adverse effects on users, businesses, enterprises, and even political regimes and governments. State of the art has tackled the English language for news and

Neural networks have become the main building block on revolutionizing the field of artificial intelligence aided applications. With the wide availability of data and the increasing capacity of computing resources, they triggered a new era of state-of-the-art results in diverse directions. However, building neural network models is domain-specific, and figuring out the best architecture and hyper-parameters in each problem is still an art. In practice, it is a highly iterative process that is very time-consuming, requires substantial computing resources, and needs deep knowledge and solid

This paper presents improved automatic moving target detection and tracking framework that is suitable for UAV imagery. The framework is comprised of motion compensation phase to detect moving targets from a moving camera, target state estimation with Kalman filter, and overlap-rate-based data association. Finally, P-N learning is used to maintain target appearance by utilizing novel structural constraints to select positive and negative samples, where data association decisions are used as positive (P) constraints. After learning target appearance, a cascaded classifier is employed to detect

New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health management of ADAS’ components needs special improvements. Since software contribution in ADAS’ development is increasing significantly, remote diagnosis and maintenance for ADAS become more important. Furthermore, it is highly recommended to predict the remaining useful life (RUL) for the prognosis of ADAS’ safety critical components; e.g. (Ultrasonic, Cameras, Radar, LIDAR). This paper

Motion and depth provide critical information in autonomous driving and they are commonly used for generic object detection. In this paper, we leverage them for improving semantic segmentation. Depth cues can be useful for detecting road as it lies below the horizon line. There is also a strong structural similarity for different instances of different objects including buildings and trees. Motion cues are useful as the scene is highly dynamic with moving objects including vehicles and pedestrians. This work utilizes geometric information modelled by depth maps and motion cues represented by

Reputation systems are an emerging area of research in ad-hoc networks. They have been introduced as a security solution for nodes' misbehaving problem. A reputation system should cope with any kind of misbehavior. It enables honest nodes to make fair decisions about their neighbors. This may encourage nodes to behave well and cooperate in order to avoid being penalized or isolated. In this paper, we propose a new reputation system for Misbehavior Detection And Control in ad hoc Networks (MDAC). It aims to overcome some of the unsolved issues of other reputation systems, and it is customizable

Recently, digital forensics analysis got a great attention in IT security. This is especially after cyber incidents are getting new form of organized crime which introduced Advanced Persistent Threats (APT), and hacking Kill Chain definitions. The threat intense rises when it is affecting the healthcare organization where it will be life-threatening. Handling such incidents is a great challenge for handlers to uncover the attack steps. With various sources of evidential data that require analysis, one analysis technique can be more beneficial than another, comparing to the time and resources

Wireless Sensor Networks (WSNs) consist of a collection of cheap, easy to deploy Sensor nodes arranged together to fulfill a specific purpose (monitoring, tracking...etc.). A WSN network is composed of a Base Station (BS) and collection of sensors. There are a lot of approaches for the network construction. Amongst them is the hierarchical structure, where the network is divided into clusters and the node inside this cluster communicates with BS through a chosen leader called Cluster Head (CH). In this paper, we present cluster-Head election algorithms for WSNs. We will discuss the operations

With the widespread use of mobile applications in daily life, it has become crucial for software companies to develop the applications for the most popular platforms like Android and iOS. Using a native development is time consuming and costly. Cross-platform mobile development like Xamarin and React native emerged as a solution to the mentioned problem of native development for the time and cost. Meanwhile it requires the developers to learn a new language. Other tools are converting the mobile apps of specific platform to the corresponding platform, but most of them still lack the mobile