1,225 research outputs found

    The effect of time on gait recognition performance

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    Many studies have shown that it is possible to recognize people by the way they walk. However, there are a number of covariate factors that affect recognition performance. The time between capturing the gallery and the probe has been reported to affect recognition the most. To date, no study has shown the isolated effect of time, irrespective of other covariates. Here we present the first principled study that examines the effect of elapsed time on gait recognition. Using empirical evidence we show for the first time that elapsed time does not affect recognition significantly in the short to medium term. By controlling the clothing worn by the subjects and the environment, a Correct Classification Rate (CCR) of 95% has been achieved over 9 months, on a dataset of 2280 gait samples. Our results show that gait can be used as a reliable biometric over time and at a distance. We have created a new multimodal temporal database to enable the research community to investigate various gait and face covariates. We have also investigated the effect of different type of clothes, variations in speed and footwear on the recognition performance. We have demonstrated that clothing drastically affects performance regardless of elapsed time and significantly more than any of the other covariates that we have considered here. The research then suggests a move towards developing appearance invariant recognition algorithms. Thi

    HIL: designing an exokernel for the data center

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    We propose a new Exokernel-like layer to allow mutually untrusting physically deployed services to efficiently share the resources of a data center. We believe that such a layer offers not only efficiency gains, but may also enable new economic models, new applications, and new security-sensitive uses. A prototype (currently in active use) demonstrates that the proposed layer is viable, and can support a variety of existing provisioning tools and use cases.Partial support for this work was provided by the MassTech Collaborative Research Matching Grant Program, National Science Foundation awards 1347525 and 1149232 as well as the several commercial partners of the Massachusetts Open Cloud who may be found at http://www.massopencloud.or

    A data augmentation methodology for training machine/deep learning gait recognition algorithms

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    There are several confounding factors that can reduce the accuracy of gait recognition systems. These factors can reduce the distinctiveness, or alter the features used to characterise gait; they include variations in clothing, lighting, pose and environment, such as the walking surface. Full invariance to all confounding factors is challenging in the absence of high-quality labelled training data. We introduce a simulation-based methodology and a subject-specific dataset which can be used for generating synthetic video frames and sequences for data augmentation. With this methodology, we generated a multi-modal dataset. In addition, we supply simulation files that provide the ability to simultaneously sample from several confounding variables. The basis of the data is real motion capture data of subjects walking and running on a treadmill at different speeds. Results from gait recognition experiments suggest that information about the identity of subjects is retained within synthetically generated examples. The dataset and methodology allow studies into fully-invariant identity recognition spanning a far greater number of observation conditions than would otherwise be possible

    Understanding young people's transitions in university halls through space and time

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    This article contributes to the theoretical discussion about young people's transitions through space and time. Space and time are complex overarching concepts that have creative potential in deepening understanding of transition. The focus of this research is young people's experiences of communal living in university halls. It is argued that particular space-time concepts draw attention to different facets of experience and in combination deepen the understanding of young people's individual and collective transitions. The focus of the article is the uses of the space-time concepts 'routine', 'representation', 'rhythm' and 'ritual' to research young people's experiences. The article draws on research findings from two studies in the North of England. © 2010 SAGE Publications

    OSHI - Open Source Hybrid IP/SDN networking (and its emulation on Mininet and on distributed SDN testbeds)

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    The introduction of SDN in IP backbones requires the coexistence of regular IP forwarding and SDN based forwarding. The former is typically applied to best effort Internet traffic, the latter can be used for different types of advanced services (VPNs, Virtual Leased Lines, Traffic Engineering...). In this paper we first introduce the architecture and the services of an "hybrid" IP/SDN networking scenario. Then we describe the design and implementation of an Open Source Hybrid IP/SDN (OSHI) node. It combines Quagga for OSPF routing and Open vSwitch for OpenFlow based switching on Linux. The availability of tools for experimental validation and performance evaluation of SDN solutions is fundamental for the evolution of SDN. We provide a set of open source tools that allow to facilitate the design of hybrid IP/SDN experimental networks, their deployment on Mininet or on distributed SDN research testbeds and their test. Finally, using the provided tools, we evaluate key performance aspects of the proposed solutions. The OSHI development and test environment is available in a VirtualBox VM image that can be downloaded.Comment: Final version (Last updated August, 2014

    IRMA via SDN: Intrusion Response and Monitoring Appliance via Software-Defined Networking

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    Recent approaches to network intrusion prevention systems (NIPSs) use software-defined networking (SDN) to take advantage of dynamic network reconfigurability and programmability, but issues remain with system component modularity, network size scalability, and response latency. We present IRMA, a novel SDN-based NIPS for enterprise networks, as a network appliance that captures data traffic, checks for intrusions, issues alerts, and responds to alerts by automatically reconfiguring network flows via the SDN control plane. With a composable, modular, and parallelizable service design, we show improved throughput and less than 100 ms average latency between alert detection and response.Roy J. Carver FellowshipOpe
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