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13th IEEE Conference on Visual Analytics Science and Technology, VAST 2018

作者： He, Huan Zheng, Oinghua Dong, Bo Key Laboratory of Intelligent Networks and Network Security Ministry of Education Xi'An Jiaotong University China School of Electronic and Information Engineering Xi'An Jiaotong University China School of Continuing Education Xi'An Jiaotong University China National Engineering Lab for Big Data Analytics Xi'An Jiaotong University China

ISBN: (纸本)9781538668610

Online Distance Education (ODE) provides massive course videos of various specialties for students across the country to learn professional knowledge anytime and anywhere. Analyzing the utilization of these videos from user log data can help academics better understand the learning process of students, evaluate the quality of service provided by regional learning centers, and improve the quality of program curriculum in the future. However, due to the lack of comparable indicators, it is a great challenge to discover the utilization patterns of massive videos and analyze the learning process of large-scale student population from learning log data. In this paper, we introduce a visual analytics system, called VUSphere, to explore the video utilization from multiple perspectives with two proposed indicators. This system offers three coordinated views: A spherical layout overview to depict the overall utilization distribution of videos, courses, and students;a detailed statistics view with four panels to present video utilization statistics of each element from multiple perspectives;and a comparison view to examine the differences in individual elements. Based on the real dataset from our ODE school, several patterns related to video utilization and enrollment are found in the case study with our domain experts. © 2018 IEEE.

关键词： Students

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Efficient trajectory data privacy protection scheme based on laplace's differential privacy

Efficient trajectory data privacy protection scheme based on...

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作者： Gu, Ke Yang, Lihao Liu, Yongzhi Yin, Bo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Information Science and Engineering Central South University Changsha410083 China

Now many applications of trajectory (location) data have facilitated people's daily life. However, publishing trajectory data may divulge individual sensitive information so as to influence people's normal life. On the other hand, if we cannot mine and share trajectory data information, trajectory data will lose its value to serve our society. Currently, because the records of trajectory data are discrete in database, some existing privacy protection schemes are difficult to protect trajectory data. In this paper, we propose a trajectory data privacy protection scheme based on Laplace's differential privacy mechanism. In the proposed scheme, the algorithm first selects the protected points from the user's trajectory data;secondly, the algorithm builds the polygons according to the protected points and the adjacent and high frequent accessed points selected from the accessed point database, then the algorithm calculates the polygon centroids;finally, the noises are added to the polygon centroids by the Laplace's differential privacy method, and the new polygon centroids are used to replace the protected points, and then the algorithm constructs and issues the new trajectory data. The experiments show that the running time of the proposed algorithms is fast, the privacy protection of the scheme is effective and the data usability of the scheme is higher. © 2018 Slovene Society Informatika. All rights reserved.

关键词： Trajectories

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Inequality-Type Zhang Equivalency Originating from Neural Dynamics for Time-Varying Problems Solving

Inequality-Type Zhang Equivalency Originating from Neural Dy...

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IEEE International Conference on Mechatronics and Automation

作者： Yunong Zhang Liangjie Ming Min Yang Binbin Qiu Zhonghua Li School of Data and Computer Science Sun Yat-sen University Guangzhou P. R. China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen P. R. China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou P. R. China School of Electronics and Information Technology Sun Yat-sen University Guangzhou P. R. China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou P. R. China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In this paper, a concept of Zhang equivalency (ZE) is presented, and its type of inequality to inequality (simply termed, inequality type) is further discussed. By applying Zhang neural dynamics method, we thus propose the order-1, order2, and order-3 ZE general formulas of such an inequality type, respectively. Then, the inequalities in four different situations are investigated, i.e., the robot situation, matrix situation (including matrix-vector situation), state-control situation, and state situation. We propose the corresponding order-1, order-2, and order-3 ZE formulas to prepare for solving corresponding time-varying inequality-related problems.

关键词： Mechatronics Automation Conferences Linear matrix inequalities Problem-solving Robots

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Fully-convolutional intensive feature flow neural network for text recognition

arXiv

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arXiv 2019年

作者： Zhang, Zhao Tang, Zemin Zhang, Zheng Wang, Yang Qin, Jie Wang, Meng School of Computer Science and Technology Soochow University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer and Information Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable;2) the traditional convolution operation optimizes slowly and the hierarchical features from different layers are not fully utilized. In this work, we address these problems by developing a novel deep network model called Fully-Convolutional Intensive Feature Flow Neural Network (IntensiveNet). Specifically, we design a further dense block called intensive block to extract the feature information, where the original inputs and two dense blocks are connected tightly. To encode data appropriately, we present the concepts of dense fusion block and further dense fusion operations for our new intensive block. By adding short connections to different layers, the feature flow and coupling between layers are enhanced. We also replace the traditional convolution by depthwise separable convolution to make the operation efficient. To prevent important feature information being lost to a certain extent, we use a convolution operation with stride 2 to replace the original pooling operation in the customary transition layers. The recognition results on large-scale Chinese string and MNIST datasets show that our IntensiveNet can deliver enhanced recognition results, compared with other related deep models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Pan-genome and Haplotype Map of Cassava Cultivars and Wild Ancestors Provide Insights into its Adaptive Evolution and Domestication

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Molecular plant 2025年

作者： Zhiqiang Xia Zhenglin Du Xincheng Zhou Sirong Jiang Tingting Zhu Le Wang Fei Chen Luiz Carvalho Meiling Zou Luis Augusto Becerra López-Lavalle Xiaofei Zhang Liangye Xu Zhenyu Wang Meili Chen Xin Guo Shujuan Wang Mengtao Li Yuanchao Li Haiyan Wang Shisheng Liu Yuting Bao Long Zhao Chenji Zhang Jianjia Xiao Fengguang Guo Xu Shen Haozheng Li Cheng Lu Fei Qiao Hernan Ceballos Huabing Yan Xiaochun Qin Ling Ma Huaifang Zhang Shuang He Wenming Zhao Yinglang Wan Yinhua Chen Dongyi Huang Kaimian Li Bin Liu Ming Peng Weixiong Zhang Birger Lindberg Møller Xin Chen Ming-Cheng Luo Jingfa Xiao Wenquan Wang Hainan University Haikou 570228 China National Key Laboratory of Biotechnology and Breeding of Tropical Crops. National Genomics Data Center Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation Beijing 100101 China. Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571101 China. Department of Plant Sciences University of California Davis California 95616 USA. Brazilian Enterprise for Agricultural Research (EMBRAPA) Genetic Resources and Biotechnology Brasilia 70770 Brazil. International Center for Tropical Agriculture (CIAT) Cali 6713 Colombia. Institute of Nanfan & Seed Industry Guangdong Academy of Sciences Guangzhou 510316 China. State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources/College of Life Science and Technology Guangxi University Nanning 530004 China. The Academy of Agriculture and Forestry Sciences Qinghai University Xining 810016 China. Tropical Crop Genetic Resources Institute Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571700 China. Economic Crop Institute Guangxi Academy of Agricultural Sciences Nanning China. School of Biological Science and Technology University of JINAN Jinan China. Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571101 China Department of Health Technology and Informatics Department of Computing The Hong Kong Polytechnic University Hong Kong China. Plant Biochemistry Laboratory Department of Plant and Environmental Sciences University of Copenhagen DK-1871 Frederiksberg C Denmark. Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571101 China. Electronic address: xiaojingfa@***. National Genomics Data Center Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation Beijing 100101

Cassava is an important, resilient tropical crop that produces large starchy storage roots and high biomass. But how did cassava´s remarkable environmental adaptability and key economic traits evolve from its wild species? We constructed nearly T2T genomes and their haplotype forms for the cultivar AM560, wild ancestors FLA4047 and W14, a graphic pan-genome of 30 representatives with 1.15 Gb, and built a clarified evolutionary tree of 486 accessions. A comparison of structural variations (SVs) and single-nucleotide variations (SNVs) between the ancestors and cultivated cassavas reveals predominant expansions and contractions of genes and gene families, which are mainly driven by transposons. Significant selective sweeping occurred in 122 footprints of genomes and affects 1,519 domesticated genes. We identify selective mutations in MeCSK and MeFNR2 that could promote photoreactions associated with MeNADP-ME in C photosynthesis in modern cassava. Co-evolution of retard floral primordia and initiation of storage roots may arise from MeCOL5 variants with altered bindings to MeFT1, MeFT2, and MeTFL2. Mutations of MeMATE1 and MeGTR occur in sweet cassava, and MeAHL19 has evolved to regulate the biosynthesis, transport, and endogenous remobilization of cyanogenic glucosides in cassava. These extensive genomic and gene resources provided here and the findings of the evolutionary mechanisms responsible for beneficial traits in modern cultivars represent a stepping-stone in future breeding of cassava.

关键词： Cassava Evolution and Domestication Genome Pan-genome Selective sweeping Structural Variations

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Weighted Feature Pyramid Networks for Object Detection

Weighted Feature Pyramid Networks for Object Detection

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IEEE International Conference on big data and Cloud computing (BdCloud)

作者： Xiaohan Li Taotao Lai Shuaiyu Wang Quan Chen Changcai Yang Riqing Chen Jinxun Lin Fu Zheng Digital Fujian Institute of Big Data for Agriculture and Forestry College of Computer and information Sciences Fujian Agriculture and Forestry University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Fu Jian Shu Bo Xun Information Technology Co. Ltd

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Feature Pyramid Networks (FPN) is a popular feature extraction. However, FPN and its variants do not investigate the influence of resolution information and semantic information in the object detection. Thus, FPN and its variants cannot detect some objects on challenging images. In this paper, based on FPN, we propose to use gaussian kernel function to assign different weight values to semantic information and resolution information for different images in the object detection. The proposed method, is called a Weighted Feature Pyramid Network (WFPN), and shows significant improvement over the traditional feature pyramids in several applications. Using WFPN in Faster R-CNN system, the proposed method achieves better performance on the PASCAL detection benchmark.

关键词： Feature extraction Kernel Object detection Agriculture Forestry Semantics big data

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Ship Detection Using the Surface Scattering Similarity and Scattering Power

Ship Detection Using the Surface Scattering Similarity and S...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tao Zhang Zhen Y ang Bo Mao Jian Y ang Yifang Ban Huilin Xiong Shanghai Key Lab. of Intelligent Sensing and Recognition Shanghai Jiao Tong University Shanghai China Jiangxi Science and Technology Normal University Nanchang China Jiangsu Key Laboratory of Grain Big Data Mining and Application Nanjing University of Finance & Economics Nanjing China Department of Electronic Engineering Tsinghua University Beijing China Division of Geoinformatics KTH Royal Institute of Technology Stockholm Sweden

Sea surface and ship have different backscattering mechanisms, in which surface scattering is predominant for sea surface in the low sea state case. Based on this fact, many ship detectors have been developed by suppressing the surface scattering resulted from sea surface. Actually, small ship may also have strong surface scattering sometimes. In such a case, the methods of avoiding using surface scattering features may easily miss the detection of small ships. To verify this point, in this paper, we first analyze the shortcomings of An's method which is based on surface scattering similarity and the power maximization synthesis detector (PMS), and then improve it for detecting small ships more effectively. In order to demonstrate the performance of the proposed method, AIRSAR L-Band Polarimetric SAR dataset is exploited. Comparing to other methods, the new method shows a better ship detection performance.

关键词： Marine vehicles Scattering Sea surface Covariance matrices Detectors Synthetic aperture radar Backscatter

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arXiv

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arXiv 2019年

作者： Liao, Hao Liu, Ming-Kai Mariani, Manuel Sebastian Zhou, Mingyang Wu, Xingtong National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610051 China URPP Social Networks Universität Zürich CH-8050 Switzerland

When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag strongly affects the metrics' reconstruction accuracy, depending on the network's clustering coefficient and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

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Weight Learning in Weighted ELM Classification Model Based on Genetic Algorithms

Weight Learning in Weighted ELM Classification Model Based o...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Peng Yao Xi-Zhao Wang Big Data Institute College of Computer Science and Software Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9781538652152

In cost sensitive classification problems we often suppose to have a known cost matrix in which each element represents the cost of mistakenly classifying an object from one class into another. Weighted least square, which does not equally consider individual classes and therefore assigns a different weight to each class of samples, is a typical approach to dealing with cost sensitive classification problems. Theoretically and experimentally it is confirmed that reasonable class weights will greatly improve classification ability of a learning model. Unfortunately we only know that these weights depend generally on cost matrix but very few methods can be used to specifically determine these weights according to cost matrix. This paper proposes a weighted least square (WLS) model of random weight network and then successfully uses the model in cost sensitive classification. A genetic algorithm to determine weights of different sample classes based on a cost matrix is given. Model analysis and experimental simulations are conducted. Considering the total misclassification cost as the evaluation index, a comparative study shows that our WLS model is far superior to the existing cost sensitive ELM and cost sensitive naive Bayes models.

关键词： Training Genetic algorithms data models Analytical models Indexes Support vector machines Decision trees

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A New Construction Method of Regeneration Tree for Single Node Fault Repair Mechanism in Distributed Storage System

A New Construction Method of Regeneration Tree for Single No...

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International Conference on Computational Intelligence and Security

作者： Wei Guo Yong Wang Miao Ye School of Computer Science and Information Security Guilin University of Electronic Technoloay Guilin Guangxi China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin Guangxi China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin Guangxi China School of Information and Communication Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (数字)9781728160924

ISBN: (纸本)9781728160931

Comparing with the classic redundancy policy of multi-replica technology, Erasure code is preferred due to its higher utilization rate of storage space in distributed storage system. As one kind of the important Erasure code, the traditional regeneration code with star recovery topology cost more time and more network bandwidth in the data recovery process. An efficient approach to reduce the delay time and network consumption is to construct an optimal recovery tree with the best possible bottleneck bandwidth, which is proved to be a Non-deterministic Polynomial problem. To solve this problem, this paper proposed a hybrid genetic algorithm which utilizes the designed crossover operation and mutation operation according to the problem property. A series of experiments have been conducted and the results show that our proposed method has good convergent ability and reduce the regeneration time.

关键词： Topology Bandwidth Genetic algorithms Encoding Network topology Sociology Statistics

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