Zayed University

About: Zayed University is a education organization based out in Abu Dhabi, United Arab Emirates. It is known for research contribution in the topics: Web service & Computer science. The organization has 1030 authors who have published 3346 publications receiving 42546 citations.

Context-based personalization of Web services composition and provisioning

Abstract: This work presents an approach that aims at personalizing Web services composition and provisioning using context. Composition addresses the situation of a user's request that cannot be satisfied by any available service, and thus requires the combination of several Web services. Provisioning focuses on the deployment of Web services according to users' preferences. A Web service is an accessible application that other applications and humans can discover and trigger. Context is the information that characterizes the interactions between humans, applications, and the surrounding environment. Web services are subject to personalization if there is a need of accommodating users' preferences during service performance and outcome delivery. To be able to track personalization in terms of what happened, what is happening, and what might happen three types of context are devised, and they are referred to as user-, Web service-, and resource-context.

Help-Seeking Attitudes of United Arab Emirates Students Examining Loss of Face, Stigma, and Self-Disclosure

Abstract: The psychological help-seeking patterns of college students in the United Arab Emirates (UAE) have only recently begun to be examined. Initial suggestions indicate that the majority of Emirati students treat help seeking from counselors as a last resort, which may be linked to aspects of Emirati culture including feared loss of societal face, stigma associated with seeking help, and discouragement of self-disclosure to individuals outside of the family. The relationship among fear of losing face, stigma, self-disclosure expectations (i.e., risks and benefits), and help-seeking attitudes was examined using structural equation modeling with 407 Emirati college students. Loss of face and stigma were related to self-disclosure expectations, which in turn were related to help-seeking attitudes. Gender differences were also examined with results indicating significant mean differences across all variables, as well as across two paths of the structural model. These findings are discussed within the cultural cont...

SONAR: Automatic Detection of Cyber Security Events over the Twitter Stream

Abstract: Everyday, security experts face a growing number of security events that affecting people well-being, their information systems and sometimes the critical infrastructure. The sooner they can detect and understand these threats, the more they can mitigate and forensically investigate them. Therefore, they need to have a situation awareness of the existing security events and their possible effects. However, given the large number of events, it can be difficult for security analysts and researchers to handle this flow of information in an adequate manner and answer the following questions in near-real time: what are the current security events? How long do they last? In this paper, we will try to answer these issues by leveraging social networks that contain a massive amount of valuable information on many topics. However, because of the very high volume, extracting meaningful information can be challenging. For this reason, we propose SONAR: an automatic, self-learned framework that can detect, geolocate and categorize cyber security events in near-real time over the Twitter stream. SONAR is based on a taxonomy of cyber security events and a set of seed keywords describing type of events that we want to follow in order to start detecting events. Using these seed keywords, it automatically discovers new relevant keywords such as malware names to enhance the range of detection while staying in the same domain. Using a custom taxonomy describing all type of cyber threats, we demonstrate the capabilities of SONAR on a dataset of approximately 47.8 million tweets related to cyber security in the last 9 months. SONAR could efficiently and effectively detect, categorize and monitor cyber security related events before getting on the security news, and it could automatically discover new security terminologies with their event. Additionally, SONAR is highly scalable and customizable by design; therefore we could adapt SONAR framework for virtually any type of events that experts are interested in.