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Information Leakage Problem in High-Capacity Quantum Secure Communication with Authentication Using Einstein-Podolsky-Rosen Pairs

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Chinese Physics Letters 2016年第7期33卷 14-15页

作者：刘志昊陈汉武刘文杰 School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration(Ministry of Education) Southeast University Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information TechnologyUniversity of Technology Sydney School of Computer and Software Nanjing University of Information Science & Technology

The information leakage problem often exists in bidirectional quantum secure direct communication or quantum dialogue. In this work, we find that this problem also exists in the one-way quantum secure communication protocol [Chin. Phys. Lett. 32 （2015） 050301]. Specifically, the first bit of every four-bit message block is leaked out without awareness. A way to improve the information leakage problem is given.

关键词： in or of is that with

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Query from Sketch: A Common Subgraph Correspondence Mining Framework

Query from Sketch: A Common Subgraph Correspondence Mining F...

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IEEE International Conference on Multimedia Big Data (BigMM)

作者： Lingheng Zhu Wu Liu Lingyang Chu Peiye Liu Xiaoyan Gu Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China School of Computing Science Simon Fraser University Vancouver Canada Information of Information Engineering Chinese Academic of Science Beijing China

ISBN: (纸本)9781509065509

We investigate a subgraph mining framework, that can connect similar entities according to their structure and attribute similarities. We take one mapping between two related points chosen from the query and target graph as one vertex in the correspondence graph and decide the weight of the edge based on the similarity score. In this way, we transform the problem to a dense subgraph discovery problem. To adapt this method to large scale, we choose the candidate group by some effective pruning methods. We also add some techniques to make our method more flexible to fit uncertain user sketched input. We investigate how changes to certain parameters in the algorithm can influence the results. By integrating all these adjustments into the framework, we can provide a method that exhibits both accuracy and flexibility in many situations with a degree of generality. Experiments on both certain and uncertain query graphs can give satisfactory and informative results.

关键词： Data mining Uncertainty Pattern matching Keyword search Search problems Computer vision Multimedia communication

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A new chaotic feature for EEG classification based seizure diagnosis

A new chaotic feature for EEG classification based seizure d...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Su Yang Anqin Zhang Jiulong Zhang Weishan Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China School of Computer Science Xi'an University of Technology China Department of Software Engineering China University of Petroleum Qingdao China

ISBN: (纸本)9781509041183

Seeking effective measures to characterize the chaotic patterns of EEG signals for seizure diagnosis is a long-term endeavor in the literature. We propose to count the number of zero-crossing (ZC) points on Poincare surface as a feature when the time series of interest is embedded into the reconstructed state space. The experiments show that Poincare surface can act as a platform to observe the chaotic patterns of EEG signals and the ZC feature on Poincare surface is a promising pattern descriptor to discriminate different categories of EEG signals. When used alone for EEG classification, the ZC feature achieves 100%, 99.27%, and 94.68% accuracy in 2-class, 3-class, and 5-class classification on a widely used benchmark.

关键词： Nonlinear time series analysis feature extraction pattern recognition EEG signal classification Electroencephalography Feature extraction Pattern recognition time series analysis Seizures signal classification signals Phyla state space

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Downhole-Friction-Estimation-Based Rotary Speed Control for Drillstring System with Stick-Slip Vibrations

Downhole-Friction-Estimation-Based Rotary Speed Control for ...

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Asian Control Conference

作者： Chengda Lu Min Wu Xin Chen Weihua Cao Chao Gan Jinhua She School of Software and Electrical Engineering Swinburne University of Technology Melbourne Vic Australia School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (纸本)9781509015740;9781509015733

In a drilling process, stick-slip vibrations are one of the main resources resulting in unstable motions or drillstring failures. In this paper, combined with a LuGre bit-rock interaction, the drillstring system is modeled as lumped parameters to capture the main features of stick-slip vibrations. Then, a novel control configuration involving five modules is developed in order to track a desired speed while suppressing the vibration, where the equivalent-input-disturbance method is skilfully employed to suppress such vibrations. In such five modules, the state observer is combined with the friction estimator to obtain the equivalent friction torques, which are corresponding to downhole frictions, on the surface control platform. In addition, the internal model and the state feedback are utilized to get the speed tracking torques so as to guarantee the desired tracking precision. Based on the dual conversion and two quadratic performances, the closed-loop stability is guaranteed by appropriate design of the controller and observer's gain matrices. Simulation is conducted to compare our method with an LQR method, showing our method's superiority and effectiveness.

关键词： Friction Vibrations Observers Torque State feedback

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Locality preserving projections for grassmann manifold

arXiv

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arXiv 2017年

作者： Wang, Boyue Hu, Yongli Gao, Junbin Sun, Yanfeng Chen, Haoran Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology Beijing University of Technology Discipline of Business Analytics University of Sydney Business School University of Sydney Faculty of Electronic Information and Electrical Engineering Dalian University of Technology

Learning on Grassmann manifold has become pop ular in many computer vision tasks, with the strong capability to extract discriminative information fo imagesets and videos. However, such learning al gorithms particularly on high-dimensional Grass mann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Pre serving Projections (LPP) criterion. LPP is a com monly used dimensionality reduction algorithm for vector-valued data, aiming to preserve local structure of data in the dimension-reduced space. The strategy is to construct a mapping from higher dimensional Grassmann manifold into the one in a relative low-dimensional with more discriminative capability. The proposed method can be optimized as a basic eigenvalue problem. The performance of our proposed method is assessed on several classification and clustering tasks and the experimental results show its clear advantages over other Grassmann based algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Nuclear norm regularized coding with local position-patch and nonlocal similarity for face hallucination

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Digital Signal Processing 2017年 64卷 107-120页

作者： Guangwei Gao Jian Yang Zhihui Lai Pu Huang Jielin Jiang Hao Gao Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing 210023 PR China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou 350121 PR China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 PR China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 PR China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 PR China

Learning-based face hallucination methods have received much attention and progress in past few decades. Specially, position-patch based approaches have been proposed to replace the probabilistic graph-based or manifold learning-based ones. As opposed to the existing patch based methods, where the input image patch matrix is converted into vectors before combination coefficients calculation, in this paper, we propose to directly use the image matrix based regression model for combination coefficients computation to preserve the essential structural information of the input patch matrix. For each input low-resolution (LR) patch matrix, its combination coefficients over the training image patch matrices at the same position can be computed. Then the corresponding high-resolution (HR) patch matrix can be obtained with the LR training patches replaced by the corresponding HR ones. The nonlocal self-similarities are finally utilized to further improve the hallucination performance. Various experimental results on standard face databases indicate that our proposed method outperforms some state-of-the-art algorithms in terms of both visual quantity and objective metrics.

关键词： Face hallucination Position-patch Matrix based regression Nuclear norm Nonlocal similarity

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Product image search with deep attribute mining and re-ranking 17th

Product image search with deep attribute mining and re-ranki...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Zhou, Xin Zhang, Yuqi Bai, Xiuxiu Zhu, Jihua Zhu, Li Qian, Xueming School of Software Engineering Xi’an Jiaotong University Xi’an710049 China The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China

ISBN: (纸本)9783319488950

With the high-growing of e-commerce, more and more users have changed to buy from websites rather than in stores. To deal with mass products, the traditional text-based product search has become incompetent to meet use’s requirement. In this paper, we explore deep learning with convolutional neural networks (CNN) to resolve query’s classification, and propose an efficient approach for product image search. For a query image, we first train a CNN model of a large database containing various product images to discriminate the query’s category. Then we search similar products from the established category and utilize these visual results to parse the query with attribute. Finally we use the extracted attribute tags to finish the textual re-ranking and obtain the most relevant retrieved product list. Experimental evaluation shows that our approach significantly outperforms state of art in product image search. © Springer International Publishing AG 2016.

关键词： Query processing

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Deep neural networks for free-hand sketch recognition 17th

Deep neural networks for free-hand sketch recognition

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Zhang, Yuqi Zhang, Yuting Qian, Xueming The School of Software Engineering Xi’an Jiaotong University Xi’an710049 China The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China

ISBN: (纸本)9783319488950

The paper presents a deep Convolutional Neural Network (CNN) framework for free-hand sketch recognition. One of the main challenges in free-hand sketch recognition is to increase the recognition accuracy on sketches drawn by different people. To overcome this problem, we use deep Convolutional Neural Networks (CNNs) that have dominated top results in the field of image recognition. And we use the contours of natural images for training, because sketches drawn by different people may be very different and databases of the sketch images for training are very limited. We propose a CNN training on contours that performs well on sketch recognition over different databases of the sketch images. And we make some adjustments to the contours for training and reach higher recognition accuracy. Experimental results show the effectiveness of the proposed approach. © Springer International Publishing AG 2016.

关键词： Convolution

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Modeling Self-Adaptive software Systems with Learning Petri Nets

Modeling Self-Adaptive Software Systems with Learning Petri ...

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作者： Ding, Zuohua Zhou, Yuan Zhou, Mengchu Laboratory of Intelligent Computing and Software Engineering Zhejiang Sci-Tech University Hangzhou310018 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NewarkNJ07102 United States

Traditional models unable to model adaptive software systems since they deal with fixed requirements only, but cannot handle the behaviors that change at runtime in response to environmental changes. In this paper, an adaptive Petri net (APN) is proposed to model a self-adaptive software system. It is an extension of hybrid Petri nets by embedding a neural network algorithm into them at some special transitions. The proposed net has the following advantages: 1) it can model a runtime environment;2) the components in the model can collaborate to make adaption decisions while the system is running;and 3) the computation is done at the local component, while the adaption is for the whole system. We illustrate the proposed APN by modeling a manufacturing system. © 2015 IEEE.

关键词： Petri nets

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H∞ Consensus Control for Uncertain Stochastic Multi-Agent Systems:A Sliding Mode Approach 35

H∞ Consensus Control for Uncertain Stochastic Multi-Agent S...

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第35届中国控制会议

作者： ZHAO Lin JIA Yingmin YU Jinpeng WANG Na DU Junping School of Automation and Electrical Engineering Qingdao University The Seventh Research Division Beihang University (BUAA) Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and TechnologyBeijing University of Posts and Telecommunications

ISBN: (纸本)9781509009107

This paper studies the consensus control problem of stochastic multi-agent systems subject to unknown state nonlinearities and external disturbances.A novel distributed integral-type sliding surface is constructed for each agent,and a sufficient condition based on linear matrix inequalities（LMIs） is given,which can guarantee the system states are mean-square consensus with H disturbance attenuation index on sliding ***,a distributed adaptive sliding mode control（SMC） law with updated law is proposed such that the sliding surface is *** example is included to show the effectiveness of the proposed method.

关键词： Multi-agent systems Stochastic systems H_∞ Sliding mode control External disturbances

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