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International Conference on Applied System Innovation, ICASI 2015

作者： Tao, H.W. Chen, Y.X. Pang, J.M. State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China MoE Engineering Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781138028937

Due to the outstanding problems of software trustworthiness, the measurement of software trustworthiness has aroused more and more concern. However, few researches pay attention to using more rigorous approaches in the measurement of software trustworthiness and carrying out the theoretical validation of measures. Axiomatic approaches by formally defining the desirable properties of the measures for software attributes can provide precise, formal, and unambiguous terms for the quantification of software attributes. We once applied axiomatic approaches to the measured software trustworthiness, presented four properties for software trustworthiness measures and gave three measures for software trustworthiness. In this paper, we propose a new property-related user expectation to establish an improved software trustworthiness measure and validate this measure from the theory point of view by proving that it complies with the five properties presented by us. A comparative study shows that this measure is better than the popular software trustworthiness measures in terms of our properties. © 2016 Taylor & Francis Group.

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Efficient electrocatalytic reduction of CO2 by nitrogen-doped nanoporous carbon/carbon nanotube membranes - A step towards the electrochemical CO2 refinery

arXiv

引用

arXiv 2019年

作者： Wang, Hong Jia, Jia Song, Pengfei Wang, Qiang Li, Debao Min, Shixiong Qian, Chenxi Wang, Lu Li, Young Feng Ma, Chun Wu, Tom Yuan, Jiayin Antonietti, Markus Ozin, Geoffrey A. Ozin Materials Chemistry and Nanochemistry Research Group Solar Fuels Cluster Centre for Inorganic and Polymeric Nanomaterials Departments of Chemistry Chemical Engineering and Applied Chemistry and Electrical and Computing Engineering University of Toronto 80 St. George Street TorontoONM5S3H6 Canada College of Chemistry and Chemical Engineering Northwest Normal University Lanzhou730070 China State Key Laboratory of Coal Conversion Institute of Coal Chemistry Chinese Academy of Sciences Taiyuan030001 China School of Chemistry and Chemical Engineering Beifang University of Nationalities Yinchuan Ningxia China Thuwal23955-6900 Saudi Arabia Department of colloid chemistry Max Planck Institute of Colloids and Interfaces Potsdam14476 Germany Department of Chemistry and Biomolecular Science and Center for Advanced Materials Processing Clarkson University 13699 United States

The search for earth abundant, efficient and stable electrocatalysts that can enable the chemical reduction of CO2 to value-added chemicals and fuels at an industrially relevant scale, is a high priority for the development of a global network of renewable energy conversion and storage systems that can meaningfully impact greenhouse gas induced climate change. Here we introduce a straightforward, low cost, scalable and technologically relevant method to manufacture an all-carbon, electroactive, nitrogen-doped nanoporous carbon-carbon nanotube composite membrane, dubbed "HNCM/CNT". The membrane is demonstrated to function as a binder-free, high-performance electrode for the electrocatalytic reduction of CO2 to formate. The Faradaic efficiency for the production of formate is 81%. Furthermore, the robust structural and electrochemical properties of the membrane endow it with excellent long-term stability. Copyright © 2019, The Authors. All rights reserved.

关键词： Greenhouse gases

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Securely outsourcing decentralized multi-authority attribute based signature 1

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9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Sun, Jiameng Qin, Jing Ma, Jixin School of Mathematics Shandong University Jinan China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Computing and Mathematical Sciences Centre for Computer and Computational Science University of Greenwich London United Kingdom

ISBN: (数字)9783319694719

ISBN: (纸本)9783319694702

Attribute based signature (ABS) is a novel primitive of digital signature that allows the signer to endorse a piece of message with a set of certain attributes in order to preserve the privacy of the signer’s identity. There are multiple authorities that issue different secret keys for signer’s various attributes. And a central authority is usually established to manage all these attribute authorities. This brings a huge workload to compute a signature and also a threat to security and privacy if the central authority is compromised. In this paper, we present an outsourced decentralized multi-authority attribute based signature (ODMA-ABS) scheme. Compared with existing multi-authority attribute based signature schemes, the ODMA-ABS scheme achieves a stronger notion of attribute privacy and authority collusion resistance. And the workload to compute a signature is further reduced by utilizing the outsourcing technique, which makes our scheme more practical in reality. © 2017, Springer International Publishing AG.

关键词： Outsourcing

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Coexistence of Ferromagnetic and Stripe-Type Antiferromagnetic Spin Fluctuations in YFe2Ge2

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Physical Review Letters 2019年第21期122卷 217003-217003页

作者： Hongliang Wo Qisi Wang Yao Shen Xiaowen Zhang Yiqing Hao Yu Feng Shoudong Shen Zheng He Bingying Pan Wenbin Wang K. Nakajima S. Ohira-Kawamura P. Steffens M. Boehm K. Schmalzl T. R. Forrest M. Matsuda Yang Zhao J. W. Lynn Zhiping Yin Jun Zhao State Key Laboratory of Surface Physics and Department of Physics Fudan University Shanghai 200433 China Institute of Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Materials and Life Science Division J-PARC Center Tokai Ibaraki 319-1195 Japan Institut Laue-Langevin 71 Avenue des Martyrs 38042 Grenoble Cedex 9 France Forschungszentrum Jülich GmbH. Jülich Centre for Neutron Science at ILL 71 Avenue des Martyrs 38000 Grenoble France Diamond Light Source Harwell Campus Didcot OX11 0DE United Kingdom Neutron Scattering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA NIST Center for Neutron Research National Institute of Standards and Technology Gaithersburg Maryland 20899 USA Department of Materials Science and Engineering University of Maryland College Park Maryland 20742 USA Department of Physics and Center for Advanced Quantum Studies Beijing Normal University Beijing 100875 China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 China

We report neutron scattering measurements of single-crystalline YFe2Ge2 in the normal state, which has the same crystal structure as the 122 family of iron pnictide superconductors. YFe2Ge2 does not exhibit long-range magnetic order but exhibits strong spin fluctuations. Like the iron pnictides, YFe2Ge2 displays anisotropic stripe-type antiferromagnetic spin fluctuations at (π, 0, π). More interesting, however, is the observation of strong spin fluctuations at the in-plane ferromagnetic wave vector (0, 0, π). These ferromagnetic spin fluctuations are isotropic in the (H, K) plane, whose intensity exceeds that of stripe spin fluctuations. Both the ferromagnetic and stripe spin fluctuations remain gapless down to the lowest measured energies. Our results naturally explain the absence of magnetic order in YFe2Ge2 and also imply that the ferromagnetic correlations may be a key ingredient for iron-based materials.

关键词： Pairing mechanisms Spin dynamics Superconducting phase transition High-temperature superconductors Pnictides Neutron scattering

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Search for vector-like leptons coupling to first- and second-generation Standard Model leptons in pp collisions at = 13 TeV with the ATLAS detector

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Journal of High Energy Physics 2025年第5期2025卷 1-61页

作者： Aad, G. Aakvaag, E. Abbott, B. Abdelhameed, S. Abeling, K. Abicht, N. J. Abidi, S. H. Aboelela, M. Aboulhorma, A. Abramowicz, H. Abulaiti, Y. Acharya, B. S. Ackermann, A. Adam Bourdarios, C. Adamczyk, L. Addepalli, S. V. Addison, M. J. Adelman, J. Adiguzel, A. Adye, T. Affolder, A. A. Afik, Y. Agaras, M. N. Aggarwal, A. Agheorghiesei, C. Ahmadov, F. Ahuja, S. Ai, X. Aielli, G. Aikot, A. Ait Tamlihat, M. Aitbenchikh, B. Akbiyik, M. Åkesson, T. P. A. Akimov, A. V. Akiyama, D. Akolkar, N. N. Aktas, S. Alberghi, G. L. Albert, J. Albicocco, P. Albouy, G. L. Alderweireldt, S. Alegria, Z. L. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexopoulos, T. Alfonsi, F. Algren, M. Alhroob, M. Ali, B. Ali, H. M. J. Ali, S. Alibocus, S. W. Aliev, M. Alimonti, G. Alkakhi, W. Allaire, C. Allbrooke, B. M. M. Allen, J. S. Allen, J. F. Allendes Flores, C. A. Allport, P. P. Aloisio, A. Alonso, F. Alpigiani, C. Alsolami, Z. M. K. Alvarez Fernandez, A. Alves Cardoso, M. Alviggi, M. G. Aly, M. Amaral Coutinho, Y. Ambler, A. Amelung, C. Amerl, M. Ames, C. G. Amidei, D. Amini, B. Amirie, K. Amirkhanov, A. Amor Dos Santos, S. P. Amos, K. R. Amperiadou, D. An, S. Ananiev, V. Anastopoulos, C. Andeen, T. Anders, J. K. Anderson, A. C. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A. V. Annovi, A. Antel, C. Antipov, E. Antonelli, M. Anulli, F. Aoki, M. Aoki, T. Aparo, M. A. Aperio Bella, L. Appelt, C. Apyan, A. Arbiol Val, S. J. Arcangeletti, C. Arce, A. T. H. Arguin, J-F. Argyropoulos, S. Arling, J.-H. Arnaez, O. Arnold, H. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N. A. Ashby Pickering, R. A. Aslam, A. M. Assamagan, K. Astalos, R. Astrand, K. S. V. Atashi, S. Atkin, R. J. Atmani, H. Atmasiddha, P. A. Augsten, K. Auriol, A. D. Austrup, V. A. Avolio, G. Axiotis, K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Bachmann, E. Badea, A. Baer, T. M. Bagnaia, P. Bahmani, M. Bahner, D. Bai, K. Baines, J. T. Baines, L. Baker, O. K. Bakos, E. Bakshi Gupta, D. Balabram Filho, L. E. Balakrishnan, V. Balasubramanian, R. Baldin, E. M. Balek CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Department for Physics and Technology University of Bergen Bergen Norway Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America New York University Abu Dhabi Abu Dhabi United Arab Emirates II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Physics Department Brookhaven National Laboratory Upton United States of America Physics Department Southern Methodist University Dallas United States of America Faculté des sciences Université Mohammed V Rabat Morocco Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics New York University New York United States of America INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom Kirchhoff-Institut für Physik Ruprecht-Karls-Universität Heidelberg Heidelberg Germany LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow Poland SLAC National Accelerator Laboratory Stanford United States of America School of Physics and Astronomy University of Manchester Manchester United Kingdom Department of Physics Northern Illinois University DeKalb United States of America Department of Physics Istanbul University Istanbul Türkiye Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz United States of America Enrico Fermi Institute University of Chicago Chicago United States of America Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institut für Physik Universität Mainz Mainz Germany Department of Physics Alexand

A search for pair production of vector-like leptons coupling to first- and second-generation Standard Model leptons is presented. The search is based on a dataset of proton-proton collisions at $$ \sqrt{s} $$ = 13 TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 140 fb−1. Events are categorised depending on the flavour and multiplicity of leptons (electrons or muons), as well as on the scores of a deep neural network targeting particular signal topologies according to the decay modes of the vector-like leptons. In each of the signal regions, the scalar sum of the transverse momentum of the leptons and the missing transverse momentum is analysed. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No significant excess above the Standard Model background expectation is observed and limits are set at 95% confidence level on the production cross-sections of vector-like electrons and muons as a function of the vector-like lepton mass, separately for SU(2) doublet and singlet scenarios. The resulting mass lower limits are 1220 GeV (1270 GeV) and 320 GeV (400 GeV) for vector-like electrons (muons) in the doublet and singlet scenarios, respectively.

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Cyber-physical security assessment and simulation based on graph database

Cyber-physical security assessment and simulation based on g...

引用

2017 IEEE Conference on Energy Internet and Energy System Integration, EI2 2017

作者： Wang, Di Guo, Qinglai Song, Yan Gao, Kunlun Zhou, Aihua State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company Shanghai200437 China Advanced Computing and Big Data Laboratory of SGCC Global Energy Interconnection Research Institute Beijing102209 China

ISBN: (纸本)9781538614273

With the development of information technology, the operation of power grid increasingly depends upon the support of strong communication network. The power system consisting of grids infrastructure and communication equipment is a typical cyber-physical system(CPS). In recent years, power accidents that information-side failure impacts on physical operation happen at times. In order to simulate the impacts of information failure on grid operation, we develop a cyber-physical simulation tool for the secondary-voltage control of power system to implement the reasonable analysis and evaluation of the influence of different types of information failure on voltage control effect. In this paper, the graph database is introduced to construct the basic cyber-physical data model as an important part of the simulation tool, whose graphical modeling and management is friendly for users. In addition, the importance of different types of information is analyzed with the tool. It can be seen that the voltage information is more fragile and critical than the non-independent generator reactive information. © 2017 IEEE.

关键词： Cyber Physical System

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Low-resolution face recognition in the wild via selective knowledge distillation

arXiv

引用

arXiv 2018年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Li, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security at University of Chinese Academy of Sciences China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine-tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15MB memory and runs at 418 faces per second on CPU and 9, 433 faces per second on GPU. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

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High-level representation sketch for video event retrieval

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Science China(Information Sciences) 2016年第7期59卷 158-172页

作者： Yu ZHANG Xiaowu CHEN Liang LIN Changqun XIA Dongqing ZOU State Key Laboratory of Virtual Reality Technology and Systems School of Compute Science and EngineeringBeihang University School of Advanced Computing Sun Yat-Sen University

Representing video events is an essential step for a wide range of visual applications. In this paper,we propose the event sketch, a high-level event representation, to depict the dynamic properties of video events composed of actions of semantic objects. We show that this representation can facilitate a novel sketch based video retrieval(SBVR) system, which has not been considered before to the best of our knowledge. In this system, users are allowed to draw the evolutions(e.g. spatiotemporal layouts and behaviors of semantic objects)on a board, and retrieve the events whose semantic objects have the similar evolutions from a database. To do this, event sketches are constructed on both the user queries and database videos, and compared under a novel graph-matching scheme based on data-driven Monta Carlo Markov chain(DDMCMC). To test our approach, we collect a novel dataset of goal events in real soccer videos, which consists actions of multiple players and shows large variability in the evolution process of the events. Experiments on this dataset and the publicly available dataset CAVIAR demonstrated the effectiveness of the proposed approach.

关键词： video retrieval event representation event sketch drawing board distance function relevance feedback DDMCMC

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A confidentiality preserving publicly verifiable computation for multivariate polynomials 15

A confidentiality preserving publicly verifiable computation...

引用

15th IEEE/ACIS International Conference on Software engineering Research, Management and Applications, SERA 2017

作者： Jiameng, Sun Binrui, Zhu Jing, Qin Jixin, Ma School of Mathematics Shandong University Jinan250100 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Centre for Computer and Computational Science School of Computing and Mathematical Sciences University of Greenwich London United Kingdom

ISBN: (纸本)9781509057566

With the development of cloud services, outsourcing computation tasks to a commercial cloud server has drawn attentions by various communities, especially in the Big Data age. Public verifiability offers a flexible functionality in real circumstance where the cloud service provider (CSP) may be untrusted or some malicious users may slander the CSP on purpose. However, sometimes the computational result is sensitive and is not willing to be exposed in the public verification phase. In this paper, we present a confidential-preserving public verifiable computation (CP-PVC) scheme for Evaluation of High Degree Polynomials. Compared with previous proposals, our scheme achieves confidentiality of computational result, while not sacrificing the property of public verifiability. We also provide the algorithm and experimental evaluation to show the efficiency of our scheme. © 2017 IEEE.

关键词： Polynomials

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NEXT: A Neural Network Framework for Next POI Recommendation

arXiv

引用

arXiv 2017年

作者： Zhang, Zhiqian Li, Chenliang Wu, Zhiyong Sun, Aixin Ye, Dengpan Luo, Xiangyang State Key Lab of Software Engineering Wuhan University 430079 China School of Computer Engineering Nanyang Technological University Singapore639798 Singapore State Key Lab of Mathematical Engineering and Advanced Computing 450001 China

The task of next POI recommendation has been studied extensively in recent years. However, developing an unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging, because of the heterogeneity nature of these information. Further, effective mechanisms to handle cold-start and endow the system with interpretability are also difficult topics. Inspired by the recent success of neural networks in many areas, in this paper, we present a simple but effective neural network framework for next POI recommendation, named NEXT. NEXT is an unified framework to learn the hidden intent regarding user’s next move, by incorporating different factors in an unified manner. Specifically, in NEXT, we incorporate meta-data information and two kinds of temporal contexts (i.e., time interval and visit time). To leverage sequential relations and geographical influence, we propose to adopt DeepWalk, a network representation learning technique, to encode such knowledge. We evaluate the effectiveness of NEXT against state-of-the-art alternatives and neural networks based solutions. Experimental results over three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI recommendation. Further experiments demonstrate the superiority of NEXT in handling cold-start. More importantly, we show that NEXT provides meaningful explanation of the dimensions in hidden intent space. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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