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To overcome increasing complexity and dynamic nature of distributed computing system, such as ubiquitous computing systems, it is critical to have computing systems that can manage themselves according to their users’ goals. Such systems are called autonomic computing systems. It is essential that such systems, especially those for critical applications, have the capability of self-protection from attacks under various situations without much human intervention or guidance. To achieve this goal, situation-aware security (SAS) needs to be considered in the development process. In this paper, a model-driven development framework for SAS in autonomic computing systems is presented. The runtime support for SAS is provided by a situation-aware middleware. The advantages of using the development framework and the situation-aware middleware to build autonomic computing systems with SAS are discussed and illustrated. Keywords: Autonomic computing, security, security policy, ubiquitous computing, model-driven development framework, situation-aware middleware. [ABSTRACT FROM AUTHOR]

PageRank is one of the most popular link analysis algorithms that have shown their effectiveness in web search. However, PageRank only consider hyperlink information. In this paper, we propose several novel ranking algorithms, which make use of both hyperlink and site structure information to measure the importance of each web page. Specifically, two kinds of methodologies are adopted to refine the PageRank algorithm: one combines hyperlink information and website structure information together by graph fusion to refine PageRank algorithm, while the other re-ranks the pages within the same site by quadratic optimization based on original PageRank values. Experiments show that both two methodologies effectively improve the retrieval performance. [ABSTRACT FROM AUTHOR]