8,178 research outputs found

    Hidden-Markov Program Algebra with iteration

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    We use Hidden Markov Models to motivate a quantitative compositional semantics for noninterference-based security with iteration, including a refinement- or "implements" relation that compares two programs with respect to their information leakage; and we propose a program algebra for source-level reasoning about such programs, in particular as a means of establishing that an "implementation" program leaks no more than its "specification" program. This joins two themes: we extend our earlier work, having iteration but only qualitative, by making it quantitative; and we extend our earlier quantitative work by including iteration. We advocate stepwise refinement and source-level program algebra, both as conceptual reasoning tools and as targets for automated assistance. A selection of algebraic laws is given to support this view in the case of quantitative noninterference; and it is demonstrated on a simple iterated password-guessing attack

    Extraction and Classification of Diving Clips from Continuous Video Footage

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    Due to recent advances in technology, the recording and analysis of video data has become an increasingly common component of athlete training programmes. Today it is incredibly easy and affordable to set up a fixed camera and record athletes in a wide range of sports, such as diving, gymnastics, golf, tennis, etc. However, the manual analysis of the obtained footage is a time-consuming task which involves isolating actions of interest and categorizing them using domain-specific knowledge. In order to automate this kind of task, three challenging sub-problems are often encountered: 1) temporally cropping events/actions of interest from continuous video; 2) tracking the object of interest; and 3) classifying the events/actions of interest. Most previous work has focused on solving just one of the above sub-problems in isolation. In contrast, this paper provides a complete solution to the overall action monitoring task in the context of a challenging real-world exemplar. Specifically, we address the problem of diving classification. This is a challenging problem since the person (diver) of interest typically occupies fewer than 1% of the pixels in each frame. The model is required to learn the temporal boundaries of a dive, even though other divers and bystanders may be in view. Finally, the model must be sensitive to subtle changes in body pose over a large number of frames to determine the classification code. We provide effective solutions to each of the sub-problems which combine to provide a highly functional solution to the task as a whole. The techniques proposed can be easily generalized to video footage recorded from other sports.Comment: To appear at CVsports 201

    Multi-Purpose Designs in Lightweight Cryptography

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    The purpose of this thesis is to explore a number of techniques used in lightweight cryptography design and their applications in the hardware designs of two lightweight permutations called sLiSCP and sLiSCP-light. Most of current methods in lightweight cryptography are optimized around one functionality and is only useful for applications that require their specific design. We aimed to provide a design that can provide multiple functionalities. In this thesis, we focus and show the hash function and authenticated encryption of our design. We implemented two lightweight permutations designs of sLiSCP and sLiSCP-light in VHDL. During the verification of sLiSCP cipher, we discovered additional area that could be saved if we tweaked the design slightly. This would lead us to consider the design of sLiSCP-light which helps dramatically reduce area. Results of our designs of sLiSCP and sLiSCP-light satisfied the lightweight requirements, including hardware area, power, and throughput, for applications such as passive RFID tags. Lastly, we did tests on the randomness of Simeck and Simon Feistel structures. We wanted to observe the pseudorandom nature of structures similar to Simeck and Simon so we performed exhaustive tests on small instances of these structures to trace any trends in their behavior. We confirmed that Simon and Simeck were very consistent and provided acceptable pseudorandom results. For larger sizes, we expect similar results from Simon and Simeck

    A face recognition system for assistive robots

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    Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga

    Selective hydrogenation of levulinic acid using Ru/C catalysts prepared by sol-immobilsation

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    A 1% Ru/C catalyst prepared by the sol immobilization method showed a high yield of γ-valerolactone from levulinic acid. We performed an optimization of the catalyst by varying the preparation variables involved in the sol immobilization method and detremined that the ratio of PVA, NaBH4 to Ru and heat treatment conditions play a crucial role in the synthesis of active and selective catalysts. By varying these parameters we have identified the optimum conditions for catalyst preparation by providing well dispersed nanoparticles of RuOx on the carbon support that are reducible under low reaction temperature and in turn gave an enhanced catalytic activity. In contrast to a catalyst prepared without using a PVA stabiliser, the use of a small amount PVA (PVA/Ru = 0.1) provided active nanoparticles, by controlling the steric size of the Ru nanoparticles. An optimum amount of NaBH4 was required in order to provide the reducible Ru species on the surface of catalyst and further increase in NaBH4 was found to cause a decline in activity that was related to the kinetics of nanoparticle formation during catalyst preparation. A variation of heat treatment temperature showed a corresponding decrease in catalytic activity linked with the sintering and an increase in particle size
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