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Improved locality-sensitive hashing method for the approximate nearest neighbor problem

Chinese Physics B 2014年第8期23卷 217-225页

作者：陆颖华马廷淮钟水明曹杰王新 Abdullah Al-Dhelaane Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology School of Computer & Software Nanjing University of Information Science & Technology School of Economic & Management Nanjing University of Information Science & Technology Huafeng Meteorological Media Group Computer Science Department College of Computer and Information Science King Saud University

In recent years, the nearest neighbor search （NNS） problem has been widely used in various interesting applications. Locality-sensitive hashing （LSH）, a popular algorithm for the approximate nearest neighbor problem, is proved to be an efficient method to solve the NNS problem in the high-dimensional and large-scale databases. Based on the scheme of p-stable LSH, this paper introduces a novel improvement algorithm called randomness-based locality-sensitive hashing （RLSH） based on p-stable LSH. Our proposed algorithm modifies the query strategy that it randomly selects a certain hash table to project the query point instead of mapping the query point into all hash tables in the period of the nearest neighbor query and reconstructs the candidate points for finding the nearest neighbors. This improvement strategy ensures that RLSH spends less time searching for the nearest neighbors than the p-stable LSH algorithm to keep a high recall. Besides, this strategy is proved to promote the diversity of the candidate points even with fewer hash tables. Experiments are executed on the synthetic dataset and open dataset. The results show that our method can cost less time consumption and less space requirements than the p-stable LSH while balancing the same recall.

关键词： approximate nearest neighbor problem locality-sensitive hashing

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Announcing the Genome Atlas of Bamboo and Rattan (GABR) project: promoting research in evolution and in economically and ecologically beneficial plants

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GigaScience 2017年第7期6卷 1-7页

作者： Hansheng Zhao Shancen Zhao Benhua Fei Huan Liu Huanming Yang Honghai Dai Dan Wang Wei Jin Feng Tang Qiang Gao Hang Xun Yuwei Wang Lianghua Qi Xianghua Yue Shuyan Lin Lianfeng Gu Lubin Li Tiansheng Zhu Qiang Wei Zhen Su Tarmeze Bin Wanoup Ariffin Wan Daniel A Ofori George Mbeva Muthike Yigardu Mulatu Mengesha Roberto Magno de Castro E Silva Antonio Ludovico Beraldo Zhimin Gao Xin Liu Zehui Jiang State Forestry Administration Key Open Laboratory on the Science and Technology of Bamboo and Rattan International Center for Bamboo and Rattan Futongdong Rd WangJing Chaoyang District 100102 Beijing China. BGI-Shenzhen Main Building Beishan Industrial Zone Yantian District Shenzhen 518083 Guangdong China. International Network for Bamboo and Rattan Futongdong Rd Wangjing Chaoyang District 100102 Beijing China. Hainan Sanya National Positioning and Monitoring Station for Ecosystem of Bamboo and Rattan Associated Forest Xincheng Rd Tianya District Sanya 572000 Hainan China. Research Institute of Tropical Forest Plants International Center for Bamboo and Rattan Xincheng Rd Tianya District Sanya 572000 Hainan China. Anhui Taiping Experimental Station International Center for Bamboo and Rattan Cuiwei Rd Huangshan District Huangshan 245716 Anhui China. Nanjing Forestry University Bamboo Research Institute Longpan Rd Xuanwu District Nanjing 210037 Jiangsu China. Basic Forestry and Proteomics Research Center Haixia Institute of Science and Technology Fujian Agriculture and Forestry University Shangxiadian Rd Cangshan District Fuzhou 350002 Fujian China. Research Institute of Forestry Chinese Academy of Forestry Xiangshan Rd Haidian District 100091 Beijing China. School of Computer Science Fudan University Handan Rd Yangpu District 200433 Shanghai China. Co-Innovation Center for Sustainable Forestry in Southern China Nanjing Forestry University Longpan Rd Xuanwu District Nanjing 210037 Jiangsu China. Bamboo Research Institute Nanjing Forestry University Longpan Rd Xuanwu District Nanjing 210037 Jiangsu China. State Key Laboratory of Plant Physiology and Biochemistry College of Biological Sciences China Agricultural University Yuanmingyuan W Rd Haidian District 100193 Beijing China. Forest Products Division Forest Research Institute Malaysia Jalan Frim Kepong Kuala Lumpur 52109 Selangor Malaysia. Forestry Research Institute of Ghana KNUST Ku

Bamboo and rattan are widely grown for manufacturing, horticulture, and agroforestry. Bamboo and rattan production might help reduce poverty, boost economic growth, mitigate climate change, and protect the natural environment. Despite progress in research, sufficient molecular and genomic resources to study these species are lacking. We launched the Genome Atlas of Bamboo and Rattan (GABR) project, a comprehensive, coordinated international effort to accelerate understanding of bamboo and rattan genetics through genome analysis. GABR includes 2 core subprojects: Bamboo-T1K (Transcriptomes of 1000 Bamboos) and Rattan-G5 (Genomes of 5 Rattans), and several other subprojects. Here we describe the organization, directions, and status of GABR.

关键词： GABR bamboo biodiversity large-scale multi-omics rattan

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MRG-DBSCAN: An improved DBSCAN clustering method based on map reduce and grid

International Journal of Database Theory and Application

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International Journal of Database Theory and Application 2015年第2期8卷 119-128页

作者： Ma, Li Gu, Lei Li, Bo Qiao, Shouyi Wang, Jin Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Key Laboratory of Meteorological Disaster Ministry of Education Nanjing University of Information Science and Technology Nanjing China CMA Research Centre for Strategic Development Beijing China

DBSCAN is a density-based clustering algorithm. This algorithm clusters data of high density. The traditional DBSCAN clustering algorithm in finding the core object, will use this object as the center core, extends outwards continuously. At this point, the core objects growing, unprocessed objects are retained in memory, which will occupy a lot of memory and I/O overhead, algorithm efficiency is not high. In order to ensure the high efficiency of DBSCAN clustering algorithm, and reduce its memory footprint. In this paper, the original DBSCAN algorithm was improved, and the G-DBSCAN algorithm is proposed. G-DBSCAN algorithm reduces the number of query object as a starting point. Put the data into the grid, with the center point of the data in the grid to replace all the grid points as the algorithm input. The query object will be drastically reduced, thus improving the efficiency of the algorithm, reduces the memory footprint. In order to make the G-DBSCAN algorithm can adapt to large data processing, we will parallelize the G-DBSCAN algorithm, and combining it with Map Reduce framework. The results prove that G-DBSCAN and MRG-DBSCAN algorithm are feasible and effective. © 2015 SERSC.

关键词： Cluster analysis

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A density-based energy-efficient clustering heterogeneous algorithm for wireless sensor networks

International Journal of Control and Automation

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International Journal of Control and Automation 2014年第2期7卷 175-188页

作者： Xu, Zhanyang Yin, Yue Wang, Jin Kim, Jeong-Uk School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China Department of Energy Grid Sangmyung University Seoul 110-743 Korea Republic of

Clustering is an efficient method adopted in various routing algorithms for wireless sensor networks. However, most clustering algorithms are not suitable for heterogeneous networks. In this paper, we propose a Density-based Energy-efficient Clustering Heterogeneous Algorithm (DECHA). In DECHA, we define the density of a node and together with its energy condition to adjust the probability for the candidate cluster head selection dynamically. Candidate cluster heads further evaluate the energy level of its neighbors and adjust to find more proper cluster heads. Moreover, we design an intra-cluster algorithm as well as a multi-hop inter-cluster routing algorithm. Simulation results show that cluster heads are properly deployed in a heterogeneous wireless sensor network. Compared with some popular algorithms, in our DECHA, the stability period and network lifetime and prolonged and total energy consumption is prominently reduced. © 2014 SERSC.

关键词： Wireless sensor networks

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Fast Transform Unit Depth Decision Based on Quantized Coefficients for HEVC

Fast Transform Unit Depth Decision Based on Quantized Coeffi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhaoqing Pan Jianjun Lei Yun Zhang Wenjie Yan Sam Kwong Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China School of Electronic Information Engineering Tianjin University Tianjin China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer Science and Engineering Hebei University of Technology Tianjin China City University of Hong Kong Kowloon HK

ISBN: (纸本)9781479986989

The quad tree structure based Transform Unit (TU) helps high efficiency video coding to improve the coding efficiency. However, the achieved coding efficiency comes at the cost of the increased computational complexity. In this paper, based on the quantizated coefficients of the TU, we propose an early termination for the quad tree structure based TU encoding process. If the quantized coefficients of the luminance components are all zeros, the TU encoding process will be terminated. Experimental results show that the proposed method achieves about 55.13% on average TU encoding time saving, while the rate distortion performance degradation is negligible.

关键词： Encoding Computational complexity Transforms Video coding Video sequences Standards Bit rate

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RFID indoor localization algorithm based on adaptive self-correction

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International Journal of Smart Home 2014年第6期8卷 205-216页

作者： Gu, Yunhua Zhang, Junyong Wang, Jin Gao, Bao Du, Jie Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

With the rapid development of wireless communication and embedded system, wireless positioning systems are paid more and more attention to. Radio Frequency Identification (RFID) localization system is getting more important, due to its own advantages, such as no contact, non-line-of-sight nature, promising transmission range and cost-effectiveness. To improve the accuracy of active RFID indoor location system, some traditional RFID indoor localization systems were studied, such as LANDMARC. On this basis, an adaptive self-correction location algorithm was presented, which uses a positioning correction value to correct the positioning result. N minimum errors and position results are obtained by using adaptive K-nearest neighbor algorithm N times. The positioning correction value calculated with N minimum errors in weighted way. The sum of the positioning average value and the positioning correction value would be the final positioning results. Experimental results show that compared with adaptive K-nearest neighbor algorithm and error self-correction algorithm, the proposed method provides a higher accuracy and stability.

关键词： Indoor positioning systems

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A simplification method of terrain modeling based on spatial-autocorrelation

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous engineering 2014年第5期9卷 185-194页

作者： Gu, Yunhua Wu, Xiaoyan Wang, Jin Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China

To solve the problem of three-dimensional terrain simplification in large-scale which was based on digital elevation model (DEM) data, a terrain simplified method based on the theory of spatial autocorrelation was proposed. The slope, a key factor for expressing the terrain features, has been taken into account in this method. According to principle of regional similarities in geomorphology is related to DEM data correlation, a cluster analysis was performed on the terrain data to get the thresholds distinguish terrain features. On the basis, the gradient-based distance-weighted method was adopted to fit elevation values of the center point and generated a new terrain mesh. The results of experiments show that the method to some extent reduces the size of the data, while maintaining good terrain features and curvature characteristic. © 2014 SERSC.

关键词： Geomorphology

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International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Zhangjie Fu Jiangang Shu Jin Wang Xingming Sun School of Computer and Software & Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China

The increasing popularity of cloud computing makes the data owner to outsource sensitive data in encrypted form onto the cloud. To tackle the problem of searching over encrypted cloud data, many keyword-based searchable encryption schemes have been proposed. However, there are few document-based search schemes in this field, which are also important in real applications. In this paper, we use the simhash algorithm to realizing similarity search over encrypted document with privacy-preserving. Through our method, data users can find similar encrypted documents stored in cloud by querying a document. In addition, a trie-based index is adopted in our solution to improve search efficiency.

关键词： Indexes Fingerprint recognition Feature extraction Servers Encryption Hamming distance

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Document Attribute-Based Keyword Search over Encrypted Data

Document Attribute-Based Keyword Search over Encrypted Data

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International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Zhangjie Fu Jie Xi Jin Wang Xingming Sun School of Computer and Software & Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China

As the cloud computing popularizes, sensitive data need to be encrypted before outsourcing, which makes the traditional data utilization useless. So search over these encrypted documents is a very challenging task. This paper firstly descripts the framework of the searchable encryption, and then presents the existing keyword-based search technologies. At last, we propose a novel color-based keyword search scheme for encrypted office document. Two vectors on behalf of the keywords and the corresponding color are used. The scheme can return top-k set as correct as possible meeting user's requirements.

关键词： Keyword search Semantics Cloud computing XML Encryption Vectors

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Secure Storage of Data in Cloud Computing

Secure Storage of Data in Cloud Computing

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International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Zhangjie Fu Xinyue Cao Jin Wang Xingming Sun School of Computer and Software & Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China

Cloud storage brings convenient storage of data, at the same time there are also hidden security issues. Data storage security includes legal access to the data stored in the cloud, namely access authorization and authentication security data sharing and the encryption of stored data to ensure data confidentiality, consisting of the accessibility to effective cryptographic data and inaccessibility to the deleted cryptographic data, using tamper-proof technology to ensure the integrity of the data, as well as using tracking technology to ensure data traceability. This paper focuses on the file systems with secure data deletion. We design a file system which supports secure deletion of data. It uses CP-ABE which supports fine-grained access policy to encrypt files.

关键词： Cloud computing Encryption Access control File systems Secure storage

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