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IEEE International Conference on Communications (ICC)

作者： Zheng Lian Mang Su Anmin Fu Huaqun Wang Chunyi Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing PR China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Hybrid cloud has both the strong computing power of public cloud and easy control of private cloud. It provides users with robust services and convenience, meanwhile faces numerous security challenges. How to implement the effective access control is one of them, the purpose of which is deploying policy in private cloud to protect the ciphertext in public cloud. Furthermore, it becomes more and more difficult to describe the access control policy, which is suitable for multi-factor and dynamic updating. Considering the issues above, we propose a proxy re-encryption (PRE) scheme for complicated access control factors description in hybrid cloud. Firstly, we build the system model combining PRE with access control in hybrid cloud. Secondly, we design the algorithm for our scheme including the key construction with multi-factor and its weight, which achieve the target of dynamic updating. Finally, we analyze the security of this scheme by the mathematical method and performance by theory, experiment and comparisons with some other works. Our scheme has made the deployment of access control in hybrid cloud more reliable and scalable.

关键词： Cloud computing Access control Cryptography XML Reliability Privacy

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Point Cloud Compression Method Based on Curvature Grading and Inter-point Compression for Industrial Products

Point Cloud Compression Method Based on Curvature Grading an...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Lei Wang Wei Huang Yiping Chen Ran Chen School of Computer Nanjing University of Posts and Telecommunications Nanjing P. R. China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing P. R. China

The traditional point cloud compression methods fail to meet the compression requirements of complex surface point cloud files in the industrial field. This paper proposes a point cloud compression method suitable for industrial products with defective and complex surfaces, which compresses the defect and non-defect parts separately using different compression approaches. A curvature-based compression method is designed for the defect points, and an inter-point compression method is developed for the non-defect part, achieving compression of both components of the point cloud. The experimental results show the advantages of the proposed compression method through a comparative analysis of time efficiency, compression ratio, and reconstruction accuracy for industrial point cloud compression.

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TFS-index-based Multi-keyword Ranked Search Scheme Over Cloud Encrypted data

TFS-index-based Multi-keyword Ranked Search Scheme Over Clou...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yinfu Deng Hua Dai Yuanlong Liu Zhangchen Li Geng Yang Xun Yi Nanjing University of Post and Telecommunication Nanjing China Jiangsu Security and Intelligent Processing Lab of Big Data Nanjing University of Post and Telecommunication Nanjing China Royal Melbourne Institute of Technology University Melbourne Australia

Traditional searchable encryption schemes for clouds are generally based on TF-IDF vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It will lead to substantial computational cost of inner product, and thus slows the search speed. In this paper, we propose a two-layer fast search index-based multi-keyword ranked search scheme (TFSRS) to address this problem. In the proposed TFSRS scheme, a keyword clustering-based equal-length dictionary partition (KCEDP) strategy is adopted to compress the document and search vectors, which benefits the process of inner product. Based on the strategy, a novel KCEDP-based vector space model (KCEDP-VSM) is proposed, and based on which, a two-layer fast search index (TFS-index) is presented. By using the TFS-index, secure inner product and symmetric encryption, the efficient multi-keyword ranked search scheme over encrypted cloud data is proposed. Experimental results show the better performance of the proposed schemes in search efficiency.

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Path Planning based on Reinforcement Learning with Improved APF model for Synergistic Multi-UAVs

Path Planning based on Reinforcement Learning with Improved ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qun Ding Xiaolong Xu Wenming Gui Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Jinling Institute of Technology Nanjing China

As the emerging technology of Unmanned Aerial Vehicles (UAVs) becomes mature, UAVs are widely used in environmental monitoring, communication and other fields. In view of this, this paper analyzed the task of synergistic multi-UAVs exploration of unknown environments, and proposed a path planning method for them based on reinforcement learning. Firstly, the path planning task of the UAVs was divided into two parts: the path travel strategy module and the information exploration strategy module. Models of the two modules were based on the Deep Deterministic Policy Gradient algorithm (DDPG), and an improved Artificial Potential Field (APF) force traction mechanism was introduced in the path travel strategy module. Its aim was to assist in guiding the generation of UAV flight path trajectories. Also it could enhance the learning capability of the model. The path travel strategy module would generate the complete flight path of the whole cluster in a distributed manner. A series of temporary target points provided by the information exploration strategy module helped. In maps with 21.5%, 25.3% and 29.6% of obstacles, multi-UAVs could achieve 84.2%, 76.7% and 69.9% of environmental exploration by the designed method. Compared with the APF method, the A star method and the Breath First Search (BFS) method, the proposed method is not only able to plan feasible paths in a more complex map model, but also the curvature of the planned paths is smoother, thus achieving the goal of reducing the energy cost of UAVs.

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A particle filter based multi-objective optimization algorithm: PFOPS

arXiv

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arXiv 2018年

作者： Liu, Bin Jin, Yaochu School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing Jiangsu210023 China Department of Computer Science University of Surrey Guildford SurreyGU2 7XH United Kingdom

This paper is concerned with a recently developed paradigm for population-based optimization, termed particle filter optimization (PFO). This paradigm is attractive in terms of coherence in theory and easiness in mathematical analysis and interpretation. Current PFO algorithms only work for single-objective optimization cases, while many real-life problems involve multiple objectives to be optimized simultaneously. To this end, we make an effort to extend the scope of application of the PFO paradigm to multi-objective optimization (MOO) cases. An idea called path sampling is adopted within the PFO scheme to balance the different objectives to be optimized. The resulting algorithm is thus termed PFO with Path Sampling (PFOPS). The validity of the presented algorithm is assessed based on three benchmark MOO experiments, in which the shapes of the Pareto fronts are convex, concave and discontinuous, respectively. Copyright © 2018, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Area Charging for Wireless Rechargeable Sensors

Area Charging for Wireless Rechargeable Sensors

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International Conference on Computer Communications and Networks (ICCCN)

作者： Haipeng Dai Xiaoyu Wang Lijie Xu Chao Dong Qian Liu Lei Meng Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing CHINA Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing CHINA Nanjing University of Aeronautics and Astronautics Nanjing CHINA

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

In this paper, we consider the problem of area charging, that is, assuming that there is a mobile charger (MC) equipped with a directional wireless charger whose charging area is in the shape of a sector, and the MC can only recharge sensors on the boundary of an Area of Interest (AOI), how to design an efficient charging scheme for the MC to recharge any sensor inside the AOI with efficient energy while its overall charging time is minimized. To address this problem, we first partition the AOI into two types of subareas, i.e., rectangle-like subareas and sector-like subareas, based on the Medial Axis of the AOI. Second, we propose rectangle-based moving strategy for the rectangle-like subareas, and sector-based rotating strategy for the sector-like subareas, respectively, for the MC. Finally, we calculate charging time of all subareas based on the above two moving strategies, and prove that our algorithm achieves a constant approximation ratio. We evaluate our algorithm by conducting extensive simulation. The results show that on average, other comparison algorithms require at least 18:70 times of charging time of that of our algorithm.

关键词： Sensors Wireless sensor networks Wireless communication Communication system security Approximation algorithms Shape security

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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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Extraction of Interactions of Genes2Genes Related to Breast Cancer

Extraction of Interactions of Genes2Genes Related to Breast ...

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International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Lejun Gong Daoyu Huang Shixing Sun Zhihong Gao Chuandi Pan Ronggen Yang Yongmin Li Geng Yang Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Zhejiang Engineering Research Center of Intelligent Medicine The First Affiliated Hospital of Wenzhou Medical University Wenzhou China College of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China

ISBN: (纸本)9781538658871

Breast cancer is the most prevalent disease to females in the worldwide. Its pathology remains unclear. Genetics factors is the ways to understand the molecular mechanism. This paper proposed a computational approach to explore the interactions of genes2genes related to breast cancer. We first defined the interactions of genes2genes, and described the representation of interactions of genes2genes. Using the experimental dataset, we implemented the proposed approach for extracting the interactions of genes2genes. Moreover, we also represented the interactions of genes2genes in two forms: relationship matrix and network visualization. By manual analysis, we extracted the interactions of top 10 genes2genes is related to breast cancer, which show the approach is promising for studying molecular mechanism related to breast cancer.

关键词： Breast cancer Pipelines data mining Manuals Genetics

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Bidirectional LSTM-CRF for biomedical named entity recognition

Bidirectional LSTM-CRF for biomedical named entity recogniti...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Xuemin Yang Zhihong Gao Yongmin Li Chuandi Pan Ronggen Yang Lejun Gong Geng Yang Jiangsu Key Laboratory of Big Data Security &Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Zhejiang Engineering Research Center of Intelligence Medicine Wenzhou China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China

ISBN: (纸本)9781538680988;9781538680971

Bio-medical entity recognition extracts significant entities, for instance cells, proteins and genes, which is an arduous task in an automatic system that mine knowledge in bioinformatics texts. In this thesis, we utilized a bidirectional long short-term memory (Bi-LSTM) combined with conditional random fields (CRFs) approach to automatically obtain word representation, obliterated the need for a marvelous number of feature engineering tasks. The consequences of this experiment represent the word representation method can effectually acquire potential semantic information. Without relying on any artificial features, the result on the test dataset obtained 76.81 % F-score. Therefore, the proposed method is expected to advance biomedical text mining in bioinformatics entity recognition.

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GNEA: A Novel GCN-based Network Embedding Algorithm for Semantic Social Network

GNEA: A Novel GCN-based Network Embedding Algorithm for Sema...

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International Symposium on System and Software Reliability (ISSSR)

作者： Jinbo Zhao Jinling He Yuanhan Du Ming Tang Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350362930

ISBN: (纸本)9798350362947

With the development of semantic social networks, social networks become more complex and their size expands rapidly, which brings significant challenges to social network analysis. Network Embedding can transform the network from a high-dimensional adjacency matrix to a low-dimensional one, which has excellent foreground in link prediction, vertex classification, and graph visualization. Nowadays, most algorithms are proposed for static networks with little consideration of the dynamic changes in the network. However, most real-world networks change over time. Directly applying static network embedding algorithms to dynamic networks will result in poor stability, flexibility, and efficiency. Because of these shortcomings, we propose a graph convolution-based dynamic network representation learning algorithm (GNEA), which combines Graph Convolutional Network (GCN) with Long Short-term Memory (LSTM) to train the time series networks. GCN is applied to extract features in the network, and LSTM is utilized to capture temporal information. GNEA can not only perform characterization learning on the network but also obtain more abundant information during the dynamic evolution of the network. GNEA is compared with several typical algorithms on the datasets Stochastic Block Model (SBM), Autonomous Systems (AS), and Digital Bibliography and Library Project (DBLP). The results prove that GNEA has good performance in MAP and precision@k.

关键词： Representation learning Accuracy Social networking (online) Heuristic algorithms Semantics Prediction algorithms Feature extraction

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