We propose a new static approach to Role-Based Access Control (RBAC) policy enforcement. The static approach we advocate includes a new design methodology, for applications involving RBAC, which integrates the security requirements into the systems architecture. We apply this new approach to policies restricting calls to methods in Java applications. We present a language to express RBAC policies on calls to methods in Java, a set of design patterns which Java programs must adhere to for the policy to be enforced statically, and a description of the checks made by our static verifier for static enforcement.
Multicasting is a fundamental networking primitive utilized by numerous applications. This also holds true for cognitive radio networks (CRNs) which have been proposed as a solution to the problems that emanate from the static non-adaptive features of classical wireless networks. A prime application of CRNs is dynamic spectrum access (DSA), which improves the efficiency of spectrum allocation by allowing a secondary network, comprising of secondary users (SUs), to share spectrum licensed to a primary licensed networks comprising of primary users (PUs). Multicasting in CRNs is a challenging problem due to the dynamic nature of spectrum opportunities available to the SUs. Various approaches, including those based in optimization theory, network coding, algorithms, have been proposed for performing efficient multicast in CRNs. In this paper, we provide a self-contained tutorial on algorithms and techniques useful for solving the multicast problem, and then provide a comprehensive survey of protocols that have been proposed for multicasting in CRNs. We conclude this paper by identifying open research questions and future research directions.
Statistical error Correction technique is the most accurate and widely used approach today, but for a language like Sindhi which is a low resourced language the trained corporas are not available, so the statistical techniques are not possible at all. Instead a useful alternative would be to exploit various spelling error trends in Sindhi by using a Rule based approach. For designing such technique an essential prerequisite would be to study the various error patterns in a language. This pa per presents various studies of spelling error trends and their types in Sindhi Language. The research shows that the error trends common to all languages are also encountered in Sindhi but their do exist some error patters that are catered specifically to a Sindhi language.
Extreme mass ratio inspirals (EMRIs) are thought to be one of the most exciting gravitational wave sources to be detected with LISA. Due to their complicated nature and weak amplitudes the detection and parameter estimation of such sources is a challenging task. In this paper we present a statistical methodology based on Bayesian inference in which the estimation of parameters is carried out by advanced Markov chain Monte Carlo (MCMC) algorithms such as parallel tempering MCMC. We analysed high and medium mass EMRI systems that fall well inside the low frequency range of LISA. In the context of the Mock LISA Data Challenges, our investigation and results are also the first instance in which a fully Markovian algorithm is applied for EMRI searches. Results show that our algorithm worked well in recovering EMRI signals from different (simulated) LISA data sets having single and multiple EMRI sources and holds great promise for posterior computation under more realistic conditions. The search and estimation methods presented in this paper are general in their nature, and can be applied in any other scenario such as AdLIGO, AdVIRGO and Einstein Telescope with their respective response functions.
Context-awareness is an essential requirement for pervasive computing applications, which enables them to adapt and perform tasks based on context. One of the adaptive features of context-awareness is contextual reconfiguration. Contextual reconfiguration involves discovering remote service(s) based on context and binding them to the application components to realize new behaviors, which may be needed to satisfy user needs or to enrich user experience. One of the steps in the reconfiguration process involves a remote lookup to discover the service(s) based on context. This remote lookup process provides the largest contribution to reconfiguration time and this is due to fact that the remote calls are much slower than local calls. Consequently, it affects system performance. In pervasive computing applications, this may turn out to be undesirable in terms of user experience. Moreover, other distributed applications using the network may be affected as every remote method call decreases the amount of bandwidth available on the network. Various systems provide reconfiguration support and offer high-level reconfiguration directives to develop adaptive context-aware applications, but do not address this performance bottleneck. We address this issue and implement seamless caching of virtual stubs within our PCRA1 for improved performance. In this paper we present and describe our transparent caching support and also provide its performance evaluation.
