58,992 research outputs found

    Provably correct Java implementations of Spi Calculus security protocols specifications

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    Spi Calculus is an untyped high level modeling language for security protocols, used for formal protocols specification and verification. In this paper, a type system for the Spi Calculus and a translation function are formally defined, in order to formalize the refinement of a Spi Calculus specification into a Java implementation. The Java implementation generated by the translation function uses a custom Java library. Formal conditions on such library are stated, so that, if the library implementation code satisfies such conditions, then the generated Java implementation correctly simulates the Spi Calculus specification. A verified implementation of part of the custom library is further presente

    Towards a methodology for rigorous development of generic requirements patterns

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    We present work in progress on a methodology for the engineering, validation and verification of generic requirements using domain engineering and formal methods. The need to develop a generic requirement set for subsequent system instantiation is complicated by the addition of the high levels of verification demanded by safety-critical domains such as avionics. We consider the failure detection and management function for engine control systems as an application domain where product line engineering is useful. The methodology produces a generic requirement set in our, UML based, formal notation, UML-B. The formal verification both of the generic requirement set, and of a particular application, is achieved via translation to the formal specification language, B, using our U2B and ProB tools

    An overview of very high level software design methods

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    Very High Level design methods emphasize automatic transfer of requirements to formal design specifications, and/or may concentrate on automatic transformation of formal design specifications that include some semantic information of the system into machine executable form. Very high level design methods range from general domain independent methods to approaches implementable for specific applications or domains. Applying AI techniques, abstract programming methods, domain heuristics, software engineering tools, library-based programming and other methods different approaches for higher level software design are being developed. Though one finds that a given approach does not always fall exactly in any specific class, this paper provides a classification for very high level design methods including examples for each class. These methods are analyzed and compared based on their basic approaches, strengths and feasibility for future expansion toward automatic development of software systems

    Sequential Optimization for Efficient High-Quality Object Proposal Generation

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    We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5% and 16.7% on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster.Comment: Accepted by TPAM

    Sequential optimization for efficient high-quality object proposal generation

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    We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING ++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster
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