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WVCC: Weighted virtual congestion control for datacenter networks

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WVCC: Weighted virtual congestion control for datacenter net...

ACM SIGCOMM 2017 Conference

作者： Dong, Jiaqing Wang, Yi Tian, Chen Jin, Bo Yin, Hao Zhang, Gong Department of Computer Science Tsinghua University China Future Network Theory Lab Huawei China State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9781450350570

Enforcing virtualized congestion control is a new trend for datacenter networks. Virtual Congestion Control (VCC) can also enforce differentiated Quality-of-Service (QoS) for flows. However, current flow differentiation algorithms only provide qualitative rather than quantitative bandwidth allocation. Weighted bandwidth allocation is critical to enforce administrator policy. In this work, we propose Weighted Virtual Congestion Control (WVCC) enforcement for datacenter networks. It is a novel per-flow differentiation mechanism capable of proportionally allocating bandwidth among flows. © 2017 ACM.

关键词： Virtualization

Spatial sparse subspace clustering for compressive spectral imaging

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arXiv 2019年

作者： Zhu, Jianchen Zhang, Tong Zhao, Shengjie Hinojosa, Carlos Liu, Zengli Arce, Gonzalo R. Key Laboratory of Embedded System and Service Computing Ministry of Education School of Software Engineering Tongji University Shanghai201804 China School of Electronic and Information Engineering Tongji University Shanghai201804 China Department of System Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia Institute of information engineering and automation Computer Center Kunming University of Science and Technology Kunming650500 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

This paper aims at developing a clustering approach with spectral images directly from CASSI compressive measurements. The proposed clustering method first assumes that compressed measurements lie in the union of multiple low-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is an unsupervised method that assigns compressed measurements to their respective subspaces. In addition, a 3D spatial regularizer is added into the SSC problem, thus taking full advantages of the spatial information contained in spectral images. The performance of the proposed spectral image clustering approach is improved by taking optimal CASSI measurements obtained when optimal coded apertures are used in CASSI system. Simulation with one real dataset illustrates the accuracy of the proposed spectral image clustering approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

Versatile Routing and Self-Certifying Features Support for Secure Mobility in eXpressive Internet Architecture

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China Communications 2017年第4期14卷 73-88页

作者： Hongwei Meng Zhong Chen Jianbin Hu Chuck Song Cong Tang School of Electronics Engineering and Computer Science Peking University Beijing 100871 China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing 100871China China Academy of Electronics and Information Technology Beijing 100041 China Department of Computer Science Carnegie Mellon University Pittsburgh 15213 USA

Integrating mobility and security in the network layer has become a key factor for Future Internet Architecture(FIA). This paper proposes a secure mobility support mechanism in e Xpressive Internet Architecture(XIA),a new FIA currently under development as part of the US National science Foundation's(NSF) program. Utilizing the natural features of ID/locator decoupling and versatile routing in XIA, a general mechanism to support host mobility is proposed. Exploiting the self-certifying identifier, a secure binding update protocol to overcome the potential threats introduced by the proposed mobility support mechanism is also given. We demonstrate that our design in XIA outperforms IP based solutions in terms of efficiency and flexibility. We also outline our initial design to illustrate one derivative benefit of an evolvable architecture:mobility support customizability with no sacrifice of architectural generality.

关键词： future Internet architecture expressive Internet architecture mobility security self-certifying

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The LHCb Upgrade I

arXiv 2023年

作者： Aaij, R. Abdelmotteleb, A.S.W. Beteta, C. Abellan Abudinén, F. Achard, C. Ackernley, T. Adeva, B. Adinolfi, M. Adlarson, P. Afsharnia, H. Agapopoulou, C. Aidala, C.A. Ajaltouni, Z. Akar, S. Akiba, K. Albicocco, P. Albrecht, J. Alessio, F. Alexander, M. Albero, A. Alfonso Aliouche, Z. Cartelle, P. Alvarez Amalric, R. Amato, S. Amey, J.L. Amhis, Y. An, L. Anderlini, L. Andersson, M. Andreani, A. Andreianov, A. Andreotti, M. Andreou, D. Andrews, J.E. Anelli, M. Anjam, A. Ao, D. Archilli, F. Arnaud, K. Artamonov, A. Artuso, M. Ashby, J. Aslanides, E. Atzeni, M. Audurier, B. Rocha, D. Ayres Perea, I.B. Bachiller Bachmann, S. Bachmayer, M. Back, J.J. Bailly-Reyre, A. Rodriguez, P. Baladron Balagura, V. Balbi, G. Baldini, W. Balla, A. Baltazar, M. Band, H. de Souza Leite, J. Baptista Barbetti, M. Barclay, P. Barlow, R.J. Barsuk, S. Barter, W. Bartolini, M. Baryshnikov, F. Basels, J.M. Bassi, G. Baszczyk, M. Lopes, J.C. Batista Batsukh, B. Battig, A. Bay, A. Beck, A. Becker, M. Bedeschi, F. Bediaga, I.B. Beigbeder-Beau, C. Beiter, A. Belin, S. Bellee, V. Belous, K. Belov, I. Belyaev, I. Benane, G. Bencivenni, G. Benettoni, M. Ben-Haim, E. Berezhnoy, A. Bernard, F. Bernet, R. Andres, S. Bernet Berninghoff, D. Bernstein, H.C. Bertella, C. Bertolin, A. Betancourt, C. Betti, F. Bezshyiko, Ia. Bezshyyko, O. Bhasin, S. Bhom, J. Bian, L. Bieker, M.S. Biesuz, N.V. Billoir, P. Biolchini, A. Birch, M. Bishop, F.C.R. Bitadze, A. Blago, M.P. Blake, T. Blanc, F. Blank, J.E. Blusk, S. Bobulska, D. Bochin, B. Boelhauve, J.A. Garcia, O. Boente Boettcher, T. Bogdanova, G. Boiaryntseva, I. Boldyrev, A. Bolognani, C.S. Bolzonella, R. Bondar, N. Booth, M.J. Borgato, F. Borghi, S. Borsato, M. Borsuk, J.T. Boterenbrood, H. Bouchiba, S.A. Bowcock, T.J.V. Boyaryntsev, A. Boyer, A. Bozzi, C. Bradley, M.J. Braun, S. Rodriguez, A. Brea Bregliozzi, G. Bridges, K. Briere, M.M.J. Brock, M. Brodski, M. Brodzicka, J. Gonzalo, A. Brossa Brown, C. Brown, J. Brummitt, A.J. Brundu, D. Brunetti, L. Buda, L. Rio de Janeiro Brazil Rio de Janeiro Brazil Center for High Energy Physics Tsinghua University Beijing China Beijing China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Particle Physics Central China Normal University Hubei Wuhan China Université Savoie Mont Blanc CNRS IN2P3-LAPP Annecy France Université Clermont Auvergne CNRS IN2P3 LPC Clermont-Ferrand France Aix Marseille Univ CNRS IN2P3 CPPM Marseille France Université Paris-Saclay CNRS IN2P3 IJCLab Orsay France Laboratoire Leprince-Ringuet CNRS IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano Milano Italy INFN Sezione di Milano-Bicocca Milano Italy INFN Sezione di Cagliari Monserrato Italy Università degli Studi di Padova Università e INFN Padova Italy INFN Sezione di Pisa Pisa Italy INFN Sezione di Roma La Sapienza Roma Italy INFN Sezione di Roma Tor Vergata Roma Italy Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Nikhef National Institute for Subatomic Physics VU University Amsterdam Amsterdam Netherlands AGH - University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Henryk Niewodniczanski Institute of Nuclear Physics Poli

The LHCb upgrade represents a major change of the experiment. The detectors have been almost completely renewed to allow running at an instantaneous luminosity five times larger than that of the previous running periods. Readout of all detectors into an all-software trigger is central to the new design, facilitating the reconstruction of events at the maximum LHC interaction rate, and their selection in real time. The experiment’s tracking system has been completely upgraded with a new pixel vertex detector, a silicon tracker upstream of the dipole magnet and three scintillating fibre tracking stations downstream of the magnet. The whole photon detection system of the RICH detectors has been renewed and the readout electronics of the calorimeter and muon systems have been fully overhauled. The first stage of the all-software trigger is implemented on a GPU farm. The output of the trigger provides a combination of totally reconstructed physics objects, such as tracks and vertices, ready for final analysis, and of entire events which need further offline reprocessing. This scheme required a complete revision of the computing model and rewriting of the experiment’s software. © 2023, CC BY.

关键词： Silicon detectors

Robust frequent directions with application in online learning

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2019年第1期20卷

作者： Luo Luo Cheng Chen Zhihua Zhang Wu-Jun Li Tong Zhang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University Beijing China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University Nanjing China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms.

关键词： frequent directions matrix approximation online Newton algorithm online convex optimization sketching

Power Attack and Protected Implementation on Lightweight Block Cipher SKINNY

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Power Attack and Protected Implementation on Lightweight Blo...

Asia Joint Conference on Information Security (Asia JCIS)

作者： Jing Ge Yifan Xu Ruiqian Liu Enze Si Ning Shang An Wang School of Computer Science State Key Laboratory of Cryptology Beijing China School of Computer Science Beijing Institute of Technology Beijing China Network Security Squadron of Public Security Department of Henan Province Zhengzhou China Chinese Academy of Sciences China Key laboratory of network assessment technology & Institute of Information Engineering Beijing China

SKINNY is a new lightweight tweakable block cipher family, which can compete to other lightweight cipher in terms of hardware or software implementations. While its theoretical security has been widely studied, little effort has been made to analyze its implementation security such as side-channel attacks protection. In this paper, we first give correlation power attack and its experiment on SKINNY. Then, a masking scheme for software SKINNY is proposed against side-channel attacks. Its implementation shows that the protected SKINNY only costs 8.42%, 183.27%, and 90.85% of extra code, time, and RAM, respectively.

关键词： Encryption Correlation Power demand Ciphers Hamming weight

Multi-view hybrid embedding: A divide-and-conquer approach

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arXiv 2018年

作者： Xu, Jiamiao Yu, Shujian You, Xinge Leng, Mengjun Jing, Xiao-Yuan Chen, C. L. Philip School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Science University of Houston HoustonTX77204 United States State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divideand-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and interview samples respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate overwhelming advantages against the stateof-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

Quantum algorithm for visual tracking

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arXiv 2018年

作者： Yu, Chao-Hua Gao, Fei Liu, Chenghuan Huynh, Du Reynolds, Mark Wang, Jingbo State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Physics University of Western Australia Perth6009 Australia Department of Computer Science and Software Engineering University of Western Australia Perth6009 Australia

Visual tracking (VT) is the process of locating a moving object of interest in a video. It is a fundamental problem in computer vision, with various applications in human-computer interaction, security and surveillance, robot perception, traffic control, etc. In this paper, we address this problem for the first time in the quantum setting, and present a quantum algorithm for VT based on the framework proposed by Henriques et al. [IEEE Trans. Pattern Anal. Mach. Intell., 7, 583 (2015)]. Our algorithm comprises two phases: training and detection. In the training phase, in order to discriminate the object and background, the algorithm trains a ridge regression classifier in the quantum state form where the optimal fitting parameters of ridge regression are encoded in the amplitudes. In the detection phase, the classifier is then employed to generate a quantum state whose amplitudes encode the responses of all the candidate image patches. The algorithm is shown to be polylogarithmic in scaling, when the image data matrices have low condition numbers, and therefore may achieve exponential speedup over the best classical counterpart. However, only quadratic speedup can be achieved when the algorithm is applied to implement the ultimate task of Henriques’s framework, i.e., detecting the object position. We also discuss two other important applications related to VT: (1) object disappearance detection and (2) motion behavior matching, where much more significant speedup over the classical methods can be achieved. This work demonstrates the power of quantum computing in solving computer vision problems. Copyright © 2018, The Authors. All rights reserved.

关键词： Object detection

Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization

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Image and Vision Computing 2018年

作者： Tao, Wenbing Tai, Xue-Cheng School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China China Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637616 Singapore Department of Mathematics University of Bergen 5007 Bergen Norway Norway State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China China

This paper proposes an improved variational model, multiple piecewise constant with geodesic active contour (MPC-GAC) model, which generalizes the region-based active contour model by Chan and Vese, 2001 [11] and merges the edge-based active contour by Caselles et al., 1997 [7] to inherit the advantages of region-based and edge-based image segmentation models. We show that the new MPC-GAC energy functional can be iteratively minimized by graph cut algorithms with high computational efficiency compared with the level set framework. This iterative algorithm alternates between the piecewise constant functional learning and the foreground and background updating so that the energy value gradually decreases to the minimum of the energy functional. The k-means method is used to compute the piecewise constant values of the foreground and background of image. We use a graph cut method to detect and update the foreground and background. Numerical experiments show that the proposed interactive segmentation method based on the MPC-GAC model by graph cut optimization can effectively segment images with inhomogeneous objects and background. © 2011.

关键词： Image segmentation