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arXiv 2017年

作者： Feng, Xiangnan Wei, Wei Wang, Jiannan Shi, Ying Zheng, Zhiming School of Mathematics and Systems Science Beihang University Beijing China Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

When analyzing the statistical and topological characteristics of complex networks, an effective and convenient way is to compute the centralities for recognizing influential and significant nodes or structures, yet most of them are restricted to local environment or some specific configurations. In this paper we propose a new centrality for nodes based on the von Neumann entropy, which allows us to investigate the importance of nodes in the view of spectrum eigenvalues distribution. By presenting the performances of this centrality with network examples in reality, it is shown that the von Neumann entropy node centrality is an excellent index for selecting crucial nodes as well as classical ones. Then to lower down the computational complexity, an approximation calculation to this centrality is given which only depends on its first and second neighbors. Furthermore, in the optimal spreader problem and reducing average clustering coefficients, this entropy centrality presents excellent efficiency and unveil topological structure features of networks accurately. The entropy centrality could reduce the scales of giant connected components fastly in Erdös-Rényi and scale-free networks, and break down the cluster structures efficiently in random geometric graphs. This new methodology reveals the node importance in the perspective of spectrum, which provides a new insight into networks research and performs great potentials to discover essential structural features in networks. Copyright © 2017, The Authors. All rights reserved.

关键词： Complex networks

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Incentivizing the Biased Requesters: Truthful Task Assignment Mechanisms in Crowdsourcing

Incentivizing the Biased Requesters: Truthful Task Assignmen...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Jia Xu Hui Li Yanxu Li Dejun Yang Tao Li Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing Jiangsu China School of Computer Nanjing University of Posts and Telecommunications Nanjing Jiangsu China Department of Computer Science Colorado School of Mines Golden CO USA

ISBN: (纸本)9781509066001

Crowdsourcing has become an effective tool to utilize human intelligence to perform tasks that are challenging for machines. In the integrated crowdsourcing systems, the requesters are non- monopolistic and may show preferences over the workers. We are the first to design the incentive mechanisms, which consider the issue of stimulating the biased requesters in the competing crowdsourcing market. In this paper, we explore truthful task assignment mechanisms to maximize the total value of accomplished tasks for this new scenario. We present three models of crowdsourcing, which take the preferences of the requesters and the workload constraints of the workers into consideration. We design a task assignment mechanism, which follows the matching approach to solve the Valuation Maximizing Assignment (VMA) problem for each of the three models. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed assignment mechanisms achieve computational efficiency, workload feasibility, preference (universal) truthfulness and constant approximation.

关键词： Crowdsourcing Computational modeling Cost accounting Mobile communication Aggregates Mechanical factors Knowledge engineering

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Software Defined Networking Based On-Demand Routing Protocol in Vehicle Ad Hoc Networks 12

Software Defined Networking Based On-Demand Routing Protocol...

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12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Dong, Baihong Wu, Weigang Yang, Zhiwei Li, Junjie School of Data and Computer Science Sun Yat-Sen University China Guangdong Key Lab of Big Data Analysis and Processing China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9781509056965

This paper comes up with a SDN based On-Demand Routing Protocol, SVAO, which separates data forwarding layer and network control layer, as in SDN, to enhance the data transmission efficiency within VANETs. The Roadside Service Unit plays the role of Local Controller and is in charge of selecting vehicles to forward packet within a road segment. All the vehicles state in the road. Correspondingly, a two-level design is used. Global Level is distributed and adopts a ranked query scheme to collect vehicle information and determine the road segments along which a message should be forwarded. And the Local Level is in charge of selecting forwarding vehicles in each road segment determined by the global level. We compare SVAO with popular ad-hoc network routing protocols, including OLSR, DSR, DSDV, and DB via simulations. We consider the impact of vehicle density, speed on data transmission rate and average packet delay. The simulation results show that in case of large network scales or high vehicle speed, SVAO performs better than the others. © 2016 IEEE.

关键词： Routing protocols

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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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Metric All-k-Nearest-Neighbor Search

Metric All-k-Nearest-Neighbor Search

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作者： Chen, Lu Gao, Yunjun Chen, Gang Zhang, Haida College of Computer Science Zhejiang University 38 Zheda Road Hangzhou310027 China Key Lab of Big Data Intelligent Computing of Zhejiang Province Zhejiang University Hangzhou China

An all-k-nearest-neighbor (AkNN) query finds from a given object set O, k nearest neighbors for each object in a specified query set Q. This operation is common in many applications such as GIS, data mining, and image analysis. Although it has received much attention in the Euclidean space, there is little prior work on the metric space. In this paper, we study the problem of AkNN retrieval in metric spaces, termed metric AkNN (MAkNN) search, and propose efficient algorithms for supporting MAk NN queries with arbitrary k value. Our methods utilize dynamic disk-based metric indexes (e.g., M-tree), employ a series of pruning rules, take advantage of grouping, reuse, pre-processing, and progressive pruning techniques, require no detailed representations of objects, and can be applied as long as the distance metric satisfies the triangle inequality. In addition, we extend our approaches to tackle metric self-AkNN (MSAkNN) search, a natural variation of MAkNN queries, where the query set Q is identical to the object set O. Extensive experiments using both real and synthetic data sets demonstrate, compared with state-of-the-art euclidean Ak NN, MAkNN, and MSAkNN algorithms, the performance of our proposed algorithms and the effectiveness of our presented techniques. © 2015 IEEE.

关键词： Nearest neighbor search

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System Identification and Prediction of Dynamic System and Microwave Thermal Process Using a Recurrent Fuzzy Quantum Neural Network

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IOP Conference Series: Materials Science and Engineering 2018年第1期466卷

作者： Chaoyin Li Qingyu Xiong Kai Wang Yang Yu Tong Liu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Automation Chongqing University Chongqing 400044 China School of Big Data & Software Chongqing University Chongqing 401331 China

Microwave heating is a time-varying, non-linear process. Mechanism modeling of the microwave thermal process is extremely difficult because of the complex microwave heating environment. This paper presents a recurrent fuzzy quantum neural network with full feedbacks (RFQNN) for prediction and identification of dynamic systems and the actual microwave heating process. In the RFQNN, a quantum neural network is introduced to the consequent part of the fuzzy rules to improve the mapping ability and the identification precision. All of the rules are generated and learned online through a simultaneous structure and parameter learning. During the structure learning, an online clustering algorithm combined with Mahalanobis distance elimination algorithm perform effectively in generating or removing fuzzy rules. And then a gradient descent algorithm is introduced to update the parameters during the parameter learning process. And finally, we test the RFQNN by dynamic plants and the microwave thermal process. The results show that it performs well in dynamic system processing compared with other recurrent fuzzy neural networks.

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Survey of key technologies on millimeter-wave CMOS integrated circuits

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IOP Conference Series: Materials Science and Engineering 2018年第1期351卷

作者： Fei Yu Lei Gao Lixiang Li Shuo Cai Wei Wang Chunhua Wang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation & School of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410114 China College of Information Science and Engineering Hunan University Changsha 410082 China

In order to provide guidance for the development of high performance millimeter-wave complementary metal oxide semiconductor (MMW-CMOS) integrated circuits (IC), this paper provides a survey of key technologies on MMW-CMOS IC. Technical background of MMW wireless communications is described. Then the recent development of the critical technologies of the MMW-CMOS IC are introduced in detail and compared. A summarization is given, and the development prospects on MMW-CMOS IC are also discussed.

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VideoGIS data retrieval based on multi-feature fusion

VideoGIS data retrieval based on multi-feature fusion

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Haihong Dai Bin Hu Qian Cui Zhiqiang Zou College of Computer Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Geographic Science Nanjing Normal University Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu China Key Laboratory of Virtual Geographic Environment (Nanjing Normal University) Ministry of Education Nanjing China

With the rapid development of smart cities and the advance of city safety and defense demands, the question that how to accurately discover and retrieve the data that users request from VideoGIS data faces a series of bottleneck problems. VideoGIS data retrieval is one of the important ways to solve above problems. In order to accelerate the rate of feature matching and improve the efficiency of the video retrieval, a new method of VideoGIS data retrieval based on multi-feature fusion is proposed in this paper. The method firstly use video frame difference based on Euclidean distance to extract the key frames under the spatial and temporal sampling of the video. On the basis of this, the global features (e.g. color, shape, texture) are fused by different weighted coefficients, then the feature vector, as the video multi-feature fusion representation, can be constructed by fusing the global features and local features. Based on the multi-feature fusion, correlation between video features is made full use. Compared with the method of single feature and two-feature fusion, the experimental results indicate that the proposed retrieval method has better retrieval effect.

关键词： Feature extraction Image color analysis data mining Correlation Euclidean distance Shape Streaming media

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REPDL: Research-oriented e-learning platform based on digital library

REPDL: Research-oriented e-learning platform based on digita...

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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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