4,475 research outputs found

    FISH of Alu-PCR amplified YAC clones and applications in tumor cytogenetics

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    Robust Malware Detection for Internet Of (Battlefield) Things Devices Using Deep Eigenspace Learning

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    Internet of Things (IoT) in military setting generally consists of a diverse range of Internet-connected devices and nodes (e.g. medical devices to wearable combat uniforms), which are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device's Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and bening application. We also demonstrate the robustness of our proposed approach in malware detection and its sustainability against junk code insertion attacks. Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g. for evaluation of proposed malware detection approaches)

    NASA Workshop on future directions in surface modeling and grid generation

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    Given here is a summary of the paper sessions and panel discussions of the NASA Workshop on Future Directions in Surface Modeling and Grid Generation held a NASA Ames Research Center, Moffett Field, California, December 5-7, 1989. The purpose was to assess U.S. capabilities in surface modeling and grid generation and take steps to improve the focus and pace of these disciplines within NASA. The organization of the workshop centered around overviews from NASA centers and expert presentations from U.S. corporations and universities. Small discussion groups were held and summarized by group leaders. Brief overviews and a panel discussion by representatives from the DoD were held, and a NASA-only session concluded the meeting. In the NASA Program Planning Session summary there are five recommended steps for NASA to take to improve the development and application of surface modeling and grid generation

    The Impact of Simplified UNBab Mapping Function On GPS Tropospheric Delay

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    The atmospheric delay issue is widely investigated in order to minimize the positioning error due to tropospheric and ionospheric delay. The mathematical modeling on the tropospheric model mapping functions should be revised and also simplified to represent simpler mapping function models. The zenith tropospheric delay can be amplified by a coefficient factor called mapping function to form total tropospheric delay. The simplified UNBab mapping function models for both hydrostatics and non-hydrostatics can provide better understanding due to its simpler models compared to the established models. The simplified mapping functions for UNBab models for hydrostatic and non hydrostatic components are given in a form of hyperbolic rather than continued fraction form for the established models. By using linear, hyperbolic, logarithm and also regression method, the mapping function models can be simplified and at the same time can produce similar result with the original models. The calculation of tropospheric delay by using simplified UNBab models for both components does not give significant difference from the original models
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