2,244 research outputs found

    A Survey of ARX-based Symmetric-key Primitives

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    Addition Rotation XOR is suitable for fast implementation symmetric –key primitives, such as stream and block ciphers. This paper presents a review of several block and stream ciphers based on ARX construction followed by the discussion on the security analysis of symmetric key primitives where the best attack for every cipher was carried out. We benchmark the implementation on software and hardware according to the evaluation metrics. Therefore, this paper aims at providing a reference for a better selection of ARX design strategy

    Actors that Unify Threads and Events

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    There is an impedance mismatch between message-passing concurrency and virtual machines, such as the JVM. VMs usually map their threads to heavyweight OS processes. Without a lightweight process abstraction, users are often forced to write parts of concurrent applications in an event-driven style which obscures control flow, and increases the burden on the programmer. In this paper we show how thread-based and event-based programming can be unified under a single actor abstraction. Using advanced abstraction mechanisms of the Scala programming language, we implemented our approach on unmodified JVMs. Our programming model integrates well with the threading model of the underlying VM

    Hypersparse Neural Network Analysis of Large-Scale Internet Traffic

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    The Internet is transforming our society, necessitating a quantitative understanding of Internet traffic. Our team collects and curates the largest publicly available Internet traffic data containing 50 billion packets. Utilizing a novel hypersparse neural network analysis of "video" streams of this traffic using 10,000 processors in the MIT SuperCloud reveals a new phenomena: the importance of otherwise unseen leaf nodes and isolated links in Internet traffic. Our neural network approach further shows that a two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide variety of source/destination statistics on moving sample windows ranging from 100,000 to 100,000,000 packets over collections that span years and continents. The inferred model parameters distinguish different network streams and the model leaf parameter strongly correlates with the fraction of the traffic in different underlying network topologies. The hypersparse neural network pipeline is highly adaptable and different network statistics and training models can be incorporated with simple changes to the image filter functions.Comment: 11 pages, 10 figures, 3 tables, 60 citations; to appear in IEEE High Performance Extreme Computing (HPEC) 201

    Type IIA Orientifolds on General Supersymmetric Z_N Orbifolds

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    We construct Type IIA orientifolds for general supersymmetric Z_N orbifolds. In particular, we provide the methods to deal with the non-factorisable six-dimensional tori for the cases Z7, Z8, Z8', Z12 and Z12'. As an application of these methods we explicitly construct many new orientifold models.Comment: 48 pages, LaTeX, 14 figures, refs. added, closed string spectra slightly change

    Knowledge-based vision for space station object motion detection, recognition, and tracking

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    Computer vision, especially color image analysis and understanding, has much to offer in the area of the automation of Space Station tasks such as construction, satellite servicing, rendezvous and proximity operations, inspection, experiment monitoring, data management and training. Knowledge-based techniques improve the performance of vision algorithms for unstructured environments because of their ability to deal with imprecise a priori information or inaccurately estimated feature data and still produce useful results. Conventional techniques using statistical and purely model-based approaches lack flexibility in dealing with the variabilities anticipated in the unstructured viewing environment of space. Algorithms developed under NASA sponsorship for Space Station applications to demonstrate the value of a hypothesized architecture for a Video Image Processor (VIP) are presented. Approaches to the enhancement of the performance of these algorithms with knowledge-based techniques and the potential for deployment of highly-parallel multi-processor systems for these algorithms are discussed
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