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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

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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...

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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

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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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Constructing PLC Binary Program Model for Detection Purposes

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Journal of Physics: Conference Series 2018年第2期1087卷

作者： Tianyou Chang Qiang Wei Yangyang Geng Hongwei Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450001 China The people's bank of china center branch of Zhoukou city Zhoukou Henan 466000 China

Programmable Logic Controller (PLC) programs are vulnerable to tampering attacks with addition of malware, which can substantially cause severe physical destructions. In order to solve the problems, We propose a static analysis method of Binary PLC programs for the detection of PLC malwares. Firstly, disassemble the binary program into STL program. Secondly, constructed the control flow graph of STL program according to the characteristics of STL language. Thirdly, generate the program execution paths according to traverse the control flow graph and analysis loop paths. Finally the PLC program output and input mapping relations are obtained based on the program execution paths. The experiment shows that the proposed method has achieved the analysis of PLC programs and get each path predicate and the mapping relation between output and input.

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Global Pinning Synchronization with PI Controller in General Complex Directed Networks

Global Pinning Synchronization with PI Controller in General...

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第37届中国控制会议

作者： Peng Liu Haibo Gu Jinhu Lü Yu Kang Department of Automation University of Science and Technology of China LSC Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environmentand Beijing Advanced Innovation Center for Big Data and Brain Machine IntelligenceBeihang University

Global pinning synchronization in complex networks has been extensively studied in the last decades due to its potential *** question is how to design an available controller to achieve global synchronization when the network is *** we all know,the classical PI controller is most widely and successfully used in engineering systems with nonlinear ***,the global synchronization with PI controller in general complex directed networks still remain *** this paper we will theoretically show that the global synchronization of some directed networks can also be ensured by designing an appropriate PI controller and pioneering some selected nodes,then a simple numerical example will also be given to verify our main results.

关键词： Directed network Pining synchronization PI controller Lyapunov method

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Distributed PI Control for Synchronization in Directed Strongly Connected Complex Dynamical Networks

Distributed PI Control for Synchronization in Directed Stron...

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第37届中国控制会议

作者： Haibo Gu Peng Liu Xiong Wang Jinhu L LSC Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Department of Automation University of Science and Technology of China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University

Synchronization of directed complex networks has been extensively studied in the previous two decades due to its potential applications in the real world practices. To reveal and analyze the inherent mechanism of global synchronization in the complex networks with directed topologies, this paper attempts to developing a novel synchronization approach by using algebraic connectivity and PI control protocols. First, we studied the distributed PI control for synchronization in directed strongly connected networks and obtained some sufficient conditions to guarantee the networks reach global ***, a numerical example is given to verify the effectiveness of the proposed theoretical results.

关键词： Synchronization distributed PI control strongly connected network network control systems

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Overdue Prediction of Bank Loans Based on Deep Neural Network

Overdue Prediction of Bank Loans Based on Deep Neural Networ...

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2017 International Symposium on Computer Science and Artificial Intelligence（ISCSAI 2017）

作者： Li Xin Sun Guozi Li Huakang Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Collaborative Innovation Center for Economics crime investigation and prevention technology State Key Laboratory of Mathematical Engineering and Advanced Computing

With the development of information technology, the application of big data in financial aspects becomes more and more deepening. However, in the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, are not benefit from the data *** paper proposes to use DNN algorithm to forecast the risk of user loan based on the difficulties of current overdue prediction and the excellent learning ability of DNN. This article uses user basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. Firstly, this paper record bank records according to the transaction type, respectively, to generate income and spending data. Secondly, to sum the user browsing behavior also, and to record the average of credit card bill. In addition, in order to reduce the effect of eigenvalue size on the result, all characteristics are standardized. Finally, users who lack user information are discarded and the above fields are spliced. The spliced fields are the basic input for DNN. From the experimental results, DNN algorithm inc rease over 6% prediction than kNN, Bayes algorithm.

关键词： Overdue Prediction Bank Loans DNN

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Nonabelian magnonics in antiferromagnets

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Physical Review B 2018年第13期98卷 134450-134450页

作者： Matthew W. Daniels Ran Cheng Weichao Yu (余伟超) Jiang Xiao (萧江) Di Xiao Department of Physics Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Department of Physics and State Key Laboratory of Surface Physics Fudan University Shanghai 200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 China Institute for Nanoelectronics Devices and Quantum Computing Fudan University Shanghai 200433 China

We present a semiclassical formalism for antiferromagnetic (AFM) magnonics which promotes the central ingredient of spin wave chirality, encoded in a quantity called magnonic isospin, to a first-class citizen of the theory. We use this formalism to unify results of interest from the field under a single chirality-centric formulation. Our main result is that the isospin is governed by unitary time evolution, through a Hamiltonian projected down from the full spin wave dynamics. Because isospin is SU(2) valued, its dynamics on the Bloch sphere are precisely rotations, which, in general, do not commute. Consequently, the induced group of operations on AFM spin waves is nonabelian. This is a paradigmatic departure from ferromagnetic magnonics, which operates purely within the abelian group generated by spin wave phase and amplitude. Our investigation of this nonabelian magnonics in AFM insulators focuses on studying several simple gate operations, and offering in broad strokes a program of study for interesting new logic families in antiferromagnetic spin wave systems.

关键词： Antiferromagnetism Geometric & topological phases Hall effect Magnetic texture Magnons Spin current Spin waves Spintronics 1-dimensional spin chains 2-dimensional systems Antiferromagnets Magnetic insulators Nanoribbon Bloch wave theory Classical spin chains Discrete symmetries in condensed matter Domain walls Heisenberg model Landau-Lifschitz-Gilbert equation Landau-Lifshitz model Methods in transport Non-Abelian models

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