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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Xiaolong Xu Kun Wang Yan Deng Tao Li School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Graduate School Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

In accordance with the researchers, students, and faculty' need to use the digital library for efficient literature investigation and knowledge learning, this paper proposes REPDL, a research-oriented e-learning platform based on digital library. REPDL includes the structural literature organization and reading route planning mechanism based on knowledge map, the personalized research progress management, the key information marker & sharing mechanism, and the Fine-grained multi-dimensional cooperative research network and multi-level communication mechanism. The design of REPDL is in accordance with the constructivism-based learning theory. The main target is to further integrate the massive literature resources and the communication channels of digital library, comprehensively use various advanced technologies such as data mining, artificial intelligence, machine learning, visualization technology and social networking, in order to provide the researcher-oriented professional knowledge architecture and the hierarchical literature navigation services to high-level learners, reduce the learners' cognitive load, and effectively prevent disorientation during learning. With REPDL, especially with the automatically personalized manage research progress and the key information marker interface, the literature reading can be more efficient and more satisfy personalized requirements of learners. The researcher cooperative communication network and positioning function of REPDL ensures that learners at different research phases can conduct clearer knowledge communications on multiple levels.

关键词： Conferences Education Knowledge engineering Libraries

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Image representation based on multi-features

Image representation based on multi-features

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Xianzhong Long Lei Chen Qun Li College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu China School of Computer Science & Technology Nanjing University of Posts and Telecommunications Nanjing China

As one kind of popular application in computer vision, image clustering has attracted many attentions. Some machine learning algorithms have been widely employed, such as K-Means, Non-negative Matrix Factorization (NMF), Graph regularized Non-negative Matrix Factorization (GNMF) and Locally Consistent Concept Factorization (LCCF). These methods possess respective strength and weakness. The common problem existing in these clustering algorithms is that they only use one kind of feature. However, different kinds of features complement each other and can be used to improve performance results. In this paper, in order to make use of the complementarity between different features, we propose an image representation method based on multi-features. Clustering results on several benchmark image data sets show that the proposed scheme outperforms some classical methods.

关键词： Clustering algorithms Image representation Feature extraction Optimization Symmetric matrices Machine learning algorithms Signal processing algorithms

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A Low-Area Overhead Secure Scan Architecture Resisting Scan-Based Attacks for Crypto Chips

A Low-Area Overhead Secure Scan Architecture Resisting Scan-...

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2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017

作者： Weizheng, Wang Jincheng, Wang Shuo, Cai Peng, Liu Tieqiao, Liu College of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha Hunan410114 China College of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China Information Science and Engineering Department Hunan University Changsha410082 China Management School Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9789881404732

Scan design is a widely used design-for-testability (DFT) technique that improves the controllability and observability of integrated circuits (ICs) resulting in the facilitation of the testing. However, it can also be used to access secret information of crypto chips, and thus threaten dramatically the security of the cipher keys. In this paper, we propose a secure scan DFT architecture to thwart scan-based side-channel attacks. This architecture provides the scan chain reset mechanism, and thus can prevent these attacks based on mode switching. Meanwhile, the secret key is isolated from scan chains of an advanced encryption standard (AES) design in the test mode. Therefore, it can also halt the test-mode-only scan attacks. The proposed secure scan DFT technique ensures the security without compromising the testability of original chip. Most important of all, the secure scan test is implemented with extremely low hardware overhead.

关键词： Design for testability

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Corrigendum to “Manifold Adaptive Kernelized Low-Rank Representation for Semisupervised Image Classification”

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Complexity 2018年第1期2018卷

作者： Yong Peng Wanzeng Kong Feiwei Qin Feiping Nie School of Computer Science Hangzhou Dianzi University Hangzhou 310018 *** Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 *** Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an 710072 ***

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Qian, Jin Chang, Yu-Hui Lv, Ping Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou213015 China Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9781509003891

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets. © 2016 IEEE.

关键词： Decision tables

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Network-coding-based video transmission via WiFi direct in local mobile networks

Network-coding-based video transmission via WiFi direct in l...

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Lei Wang Qing Wang Zhongwei Sun Jun Yin Lijie Xu Yuwang Yang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing P.R. China School of Computer Nanjing University of Posts and Telecommunications Nanjing P.R. China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing P.R. China

It will incur high expense by sharing large-scale files (such as video) with others through commercial 4G networks. When the users are located at the same area, the traffic cost could be saved via the technology of WiFi direct. In this paper, a network-coding-based video transmission scheme is proposed for such scenario. In the scheme, when a source user needs to transmit a video to others, it equally splits the original file into multiple segments. When a neighbour connects to it and requests a segment, the user will linearly generate a new segment with all the segments it buffers, and then send the re-encoded segment to other neighbours. After the neighbours receive all or part of the segments, they could become a new source and send data to their own neighbours through the technology of WiFi direct. The results show that compared with the traditional scheme, the transmission scheme based on network coding could be used to complete transmission at a higher rate.

关键词： Switches Analytical models Network coding Streaming media Wireless fidelity Mobile handsets Mobile computing

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Tri-level combination for image representation 17th

Tri-level combination for image representation

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Ruiying Zhang, Chunjie Huang, Qingming School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319488899

The context of objects can provide auxiliary discrimination beyond objects. However, this effective information has not been fully explored. In this paper, we propose Tri-level Combination for Image Representation (TriCoIR) to solve the problem at three different levels: object intrinsic, strongly-related context and weakly-related context. Object intrinsic excludes external disturbances and more focuses on the objects themselves. Strongly-related context is cropped from the input image with a more loose bound to contain surrounding context. Weaklyrelated one is recovered from the image other than object for global context. First, strongly and weakly-related context are constructed from input images. Second, we make cascade transformations for more intrinsical object information, which depends on the consistency between generated global context and input images in the regions other than object. Finally, a joint representation is acquired based on these three level features. The experiments on two benchmark datasets prove the effectiveness of TriCoIR. © Springer International Publishing AG 2016.

关键词： Image representation

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Top-k Dominating Queries on Incomplete data

Top-k Dominating Queries on Incomplete Data

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作者： Miao, Xiaoye Gao, Yunjun Zheng, Baihua Chen, Gang Cui, Huiyong College of Computer Science Zhejiang University 38 Zheda Road Hangzhou310027 China Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Zhejiang University Hangzhou China School of Information Systems Singapore Management University Singapore178902 Singapore

The top-k dominating (TKD) query returns the k objects that dominate the maximum number of objects in a given dataset. It combines the advantages of skyline and top-k queries, and plays an important role in many decision support applications. Incomplete data exists in a wide spectrum of real datasets, due to device failure, privacy preservation, data loss, and so on. In this paper, for the first time, we carry out a systematic study of TKD queries on incomplete data, which involves the data having some missing dimensional value(s). We formalize this problem, and propose a suite of efficient algorithms for answering TKD queries over incomplete data. Our methods employ some novel techniques, such as upper bound score pruning, bitmap pruning, and partial score pruning, to boost query efficiency. Extensive experimental evaluation using both real and synthetic datasets demonstrates the effectiveness of our developed pruning heuristics and the performance of our presented algorithms. © 2015 IEEE.

关键词： Decision support systems

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Vehicle appearance feature recognition based on image

Vehicle appearance feature recognition based on image

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2016 6th International Conference on Machinery,Materials,Environment,Biotechnology and Computer(MMEBC 2016)

作者： Deyang Shao Chao Xu Shixian Luo Bo Feng Long Jiao Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Key Laboratory of Intelligent Computer & Signal Processing Ministry of EducationAnhui University

ISBN: (纸本)9781510828087

Now, the highway toll system still uses a single license plate recognition, this method has a problem of inaccurate identificationFor this kind of situation, this paper put forward to increase the appearance of the vehicle feature information and can improve the accuracy of recognitionIn this paper, we adopt the ORB algorithm to extract the exterior feature information of the vehicle and two-way matching、RANSAC algorithms to remove mismatching pointsAt the same time, we continue to iteration the scale parameter of the affine transformation and rotation angle at the matching point as a kind of judgment, which improves the robustness of the algorithm.

关键词： Feature information ORB RANSAC Affine transformation

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Learning robust and discriminative low-rank representations for face recognition with occlusion

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Pattern Recognition 2017年 66卷 129-143页

作者： Guangwei Gao Jian Yang Xiao-Yuan Jing Fumin Shen Wankou Yang Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Laboratory of Big Data Analysis and Control for active distribution network Nanjing University of Posts and Telecommunications Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Southeast University Nanjing China

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. Previous low-rank based methods stacked each error image into a vector and then used L 1 or L 2 norm to measure the error matrix. However, in the stacking step, the structure information of the error image can be lost. Depart from the previous methods, in this paper, we propose a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured. In order to learn more discriminative low-rank representations, we formulate our objective such that the learned representations are optimal for classification with the available supervised information and close to an ideal-code regularization term. With strong structure information preserving and discrimination capabilities, the learned robust and discriminative low-rank representation (RDLRR) works very well on face recognition problems, especially with face images corrupted by continuous occlusions. Together with a simple linear classifier, the proposed approach is shown to outperform several other state-of-the-art face recognition methods on databases with a variety of face variations.

关键词： Face recognition Low-rank matrix recovery Nuclear norm

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