Parametric analysis is a powerful tool for designing modern embedded systems, because it permits to explore the space of design parameters, and to check the robustness of the system with respect to variations of some uncontrollable variable. In this paper, we address the problem of parametric schedulability analysis of distributed real-time systems scheduled by fixed priority. In particular, we propose two different approaches to parametric analysis: the first one is a novel technique based on classical schedulability analysis, whereas the second approach is based on model checking of Parametric Timed Automata (PTA). The proposed analytic method extends existing sensitivity analysis for single processors to the case of a distributed system, supporting preemptive and non-preemptive scheduling, jitters and unconstrained deadlines. Parametric Timed Automata are used to model all possible behaviours of a distributed system, and therefore it is a necessary and sufficient analysis. Both techniques have been implemented in two software tools, and they have been compared with classical holistic analysis on two meaningful test cases. The results show that the analytic method provides results similar to classical holistic analysis in a very efficient way, whereas the PTA approach is slower but covers the entire space of solutions.
Also based on PTA is the approach proposed by @cite_18 . Their work is based on the Inverse Method @cite_6 and it is very general because it permits to perform analysis on any system parameter. However, this generality can be paid in terms of complexity.
@cite_18 @cite_6
1917164756 26908971
In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.