876 research outputs found

    DataProVe: Fully Automated Conformance Verification Between Data Protection Policies and System Architectures

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    Privacy and data protection by design are relevant parts of the General Data Protection Regulation (GDPR), in which businesses and organisations are encouraged to implement measures at an early stage of the system design phase to fulfil data protection requirements. This paper addresses the policy and system architecture design and propose two variants of privacy policy language and architecture description language, respectively, for specifying and verifying data protection and privacy requirements. In addition, we develop a fully automated algorithm based on logic, for verifying three types of conformance relations (privacy, data protection, and functional conformance) between a policy and an architecture specified in our languages’ variants. Compared to related works, this approach supports a more systematic and fine-grained analysis of the privacy, data protection, and functional properties of a system. Our theoretical methods are then implemented as a software tool called DataProVe and its feasibility is demonstrated based on the centralised and decentralised approaches of COVID-19 contact tracing applications

    DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation

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    Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation conditions such as Longitudinal MS Lesion Segmentation Challenge (ISBI Challenge). However state-of-the-art approaches trained to perform well on highly-controlled datasets fail to generalize on clinical data from unseen datasets. Instead of proposing another improvement of the segmentation accuracy, we propose a novel method robust to domain shift and performing well on unseen datasets, called DeepLesionBrain (DLB). This generalization property results from three main contributions. First, DLB is based on a large group of compact 3D CNNs. This spatially distributed strategy ensures a robust prediction despite the risk of generalization failure of some individual networks. Second, DLB includes a new image quality data augmentation to reduce dependency to training data specificity (e.g., acquisition protocol). Finally, to learn a more generalizable representation of MS lesions, we propose a hierarchical specialization learning (HSL). HSL is performed by pre-training a generic network over the whole brain, before using its weights as initialization to locally specialized networks. By this end, DLB learns both generic features extracted at global image level and specific features extracted at local image level. DLB generalization was validated in cross-dataset experiments on MSSEG'16, ISBI challenge, and in-house datasets. During experiments, DLB showed higher segmentation accuracy, better segmentation consistency and greater generalization performance compared to state-of-the-art methods. Therefore, DLB offers a robust framework well-suited for clinical practice

    Measurement of the flavour composition of dijet events in pp collisions at root s=7 TeV with the ATLAS detector

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    This paper describes a measurement of the flavour composition of dijet events produced in pp collisions at √s=7 TeV using the ATLAS detector. The measurement uses the full 2010 data sample, corresponding to an integrated luminosity of 39¬†pb‚ąí1. Six possible combinations of light, charm and bottom jets are identified in the dijet events, where the jet flavour is defined by the presence of bottom, charm or solely light flavour hadrons in the jet. Kinematic variables, based on the properties of displaced decay vertices and optimised for jet flavour identification, are used in a multidimensional template fit to measure the fractions of these dijet flavour states as functions of the leading jet transverse momentum in the range 40¬†GeV to 500¬†GeV and jet rapidity |y|<2.1. The fit results agree with the predictions of leading- and next-to-leading-order calculations, with the exception of the dijet fraction composed of bottom and light flavour jets, which is underestimated by all models at large transverse jet momenta. The ability to identify jets containing two b-hadrons, originating from e.g. gluon splitting, is demonstrated. The difference between bottom jet production rates in leading and subleading jets is consistent with the next-to-leading-order predictions

    Measurement of Ōá c1 and Ōá c2 production with s‚ąö = 7 TeV pp collisions at ATLAS