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arXiv 2020年

作者： Wu, Qiong Wu, Muhong Chen, Xu Zhou, Zhi He, Kaiwen Chen, Liang School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion Department of Financial Technology Tencent Inc. Shenzhen China

—Online social networks (OSNs) are emerging as the most popular mainstream platform for content cascade diffusion. In order to provide satisfactory quality of experience (QoE) for users in OSNs, much research dedicates to proactive content placement by using the propagation pattern, user’s personal profiles and social relationships in open social network scenarios (e.g., Twitter and Weibo). In this paper, we take a new direction of popularity-aware content placement in a closed social network (e.g., WeChat Moment) where user’s privacy is highly enhanced. We propose a novel data-driven holistic deep learning framework, namely DeepCP, for joint diffusion-aware cascade prediction and autonomous content placement without utilizing users’ personal and social information. We first devise a time-window LSTM model for content popularity prediction and cascade geo-distribution estimation. Accordingly, we further propose a novel autonomous content placement mechanism CP-GAN which adopts the generative adversarial network (GAN) for agile placement decision making to reduce the content access latency and enhance users’ QoE. We conduct extensive experiments using cascade diffusion traces in WeChat Moment (WM). Evaluation results corroborate that the proposed DeepCP framework can predict the content popularity with a high accuracy, generate efficient placement decision in a real-time manner, and achieve significant content access latency reduction over existing schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Social aspects

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Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

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Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted

arXiv

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arXiv 2021年

作者： Zhao, Yining O’Dell, Samantha Yang, Xiaohan Liao, Jingyi Yang, Kexin Fumanelli, Laura Zhou, Tao Lv, Jiancheng Ajelli, Marco Liu, Quan-Hui College of Computer Science Sichuan University Chengdu China Laboratory for Computational Epidemiology and Public Health Department of Epidemiology and Biostatistics Indiana University School of Public Health BloomingtonIN United States Institute for Applied Computational Science Harvard University CambridgeMA United States Shenzhen International Graduate School Tsinghua University Shenzhen China Big Data Research Center University of Electronic Science and Technology of China Chengdu China

Contact patterns play a key role in the spread of respiratory infectious diseases in human populations. During the COVID-19 pandemic the regular contact patterns of the population has been disrupted due to social distancing both imposed by the authorities and individual choices. Here we present the results of a contact survey conducted in Chinese provinces outside Hubei in March 2020, right after lockdowns were lifted. We then leveraged the estimated mixing patterns to calibrate a model of SARS-CoV-2 transmission, which was used to estimate different metrics of COVID-19 burden by age. Study participants reported 2.3 contacts per day (IQR: 1.0-3.0) and the mean per-contact duration was 7.0 hours (IQR: 1.0-10.0). No significant differences were observed between provinces, the number of recorded contacts did not show a clear-cut trend by age, and most of the recorded contacts occurred with family members (about 78%). Our findings suggest that, despite the lockdown was no longer in place at the time of the survey, people were still heavily limiting their contacts as compared to the pre-pandemic situation. Moreover, the obtained modeling results highlight the importance of considering age-contact patterns to estimate COVID-19 *** Codes 91D30 © 2021, CC BY.

关键词： SARS

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Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Xiao, Min Zhou, Yirong Wang, Chengyan Wu, Naiming Li, Yi Gong, Yiwen Chang, Shufu Chen, Yinyin Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Institute of Artificial Intelligence Xiamen University China Human Phenome Institute Fudan University China Department of Imaging Xiamen Cardiovascular Hospital of Xiamen University School of Medicine Xiamen University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University Department of Medical Imaging Shanghai Medical School Shanghai Institute of Medical Imaging China Fujian Province Key Clinical Specialty Construction Project Medical Imaging Department Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering and Imperial-X Imperial College London United Kingdom Department of Bioengineering Imperial College London United Kingdom

Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal low-rankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses state-of-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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A hybrid approach using human posture and contour for gait recognition under body occlusion

A hybrid approach using human posture and contour for gait r...

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International Conference on Information science and Control Engineering (ICISCE)

作者： Yue Ma Chenghao Wei Hao Long Ministry of Public Security Key Laboratory of Impression Evidence Examination and Identification Technology (National Police University of China) people’s republic of China Big Data Institute College of Computer Science and Software Engineering Shenzhen University

This paper presents a hybrid method using a Gaitset network for gait recognition. We firstly use Alphpose model to obtain key points of human posture to improve the extraction of human contour under body occlusion. The human contour map is obtained by pose2Seg model with the key points of human posture. Both human posture and body contour are used as input of the network. By using the hybrid method, it can effectively improve the accuracy and robustness of gait recognition with the upper body information under occlusion. In scenes of human walking under body occlusion, our experimental results show that the accuracy of the proposed method in self-built database can reach 84.6% accuracy.

关键词： Legged locomotion Information science Control engineering databases Feature extraction Robustness Gait recognition

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Total factor productivity analysis of industrial processes based on Malmquist model 2019

Total factor productivity analysis of industrial processes b...

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2019 International Conference on Artificial Intelligence and computer science, AICS 2019

作者： Han, Yongming Chen, Kai Ouyang, Zhi Zhang, Mingxing Wang, Yajie Geng, Zhiqiang Wei, Qin Guizhou Provincial Key Laboratory of Public Big Data Guiyang550025 China College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China Guizhou Academy of Science Guiyang550001 China

ISBN: (纸本)9781450371506

The production of industrial process is the key factor of evaluating the national industry production level. Therefore, this paper proposes total factor productivity analysis method based on Malmquist model to analyze the production efficiency statically and dynamically. Based on the input and output data of ethylene production plants in China, the total factor production index of ethylene production plants in industrial processes is decomposed into technical efficiency, technical progress, pure technical efficiency and scale efficiency through the Malmquist model based on the data envelopment analysis (DEA). Moreover, the energy efficiency of ethylene production plants can be improved. © 2019 Association for Computing Machinery.

关键词： data envelopment analysis

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Investigating 16 Open Clusters in the Kepler/K2 - Gaia DR3 field. I. Membership, Binary, and Rotation

arXiv

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arXiv 2023年

作者： Long, Liu Bi, Shaolan Zhang, Jinghua Zhang, Xianfei Zhang, Liyun Ge, Zhishuai Li, Tanda Chen, Xunzhou Li, YaGuang Ye, LiFei Sun, TianCheng Zhou, JianZhao Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China South-Western Institute for Astronomy Research Yunnan University Chenggong District Kunming650500 China College of Physics Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China Beijing Planetarium Beijing Academy of Science and Technology Beijing100044 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China School of Physics University of Sydney NSW2006 Australia Stellar Astrophysics Centre Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus CDK-8000 Denmark

Using data from the Gaia data Release 3 (Gaia DR3) and Kepler/K2, we present a catalog of 16 open clusters with ages ranging from 4 to 4000 Myr, which provides detailed information on membership, binary systems, and rotation. We assess the memberships in 5D phase space, and estimate the basic parameters of each cluster. Among the 20,160 members, there are 4,381 stars identified as binary candidates and 49 stars as blue straggler stars. The fraction of binaries vary in each cluster, and the range between 9% to 44%. We obtain the rotation periods of 5,467 members, of which 4,304 are determined in this work. To establish a benchmark for the rotation-age-color relation, we construct color-period diagrams. We find that the rotational features of binaries are similar to that of single stars, while features for binaries are more scattered in the rotation period. Moreover, the morphology of the color-period relationship is already established for Upper Scorpius at the age of 19 Myr, and some stars of varying spectral types (i.e. FG-, K-, and M-type) show different spin-down rates after the age of ∼ 110 Myr. By incorporating the effects of stalled spin-down into our analysis, we develop an empirical rotation-age-color relation, which is valid with ages between 700 - 4000 Myr and colors corresponding to a range of 0.5 BP − GRP)0 Copyright © 2023, The Authors. All rights reserved.

关键词： Color

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Global burden of human noroviruses contamination in drinking water sources and drinking water: A systematic review and meta-analysis identifying GII.4 and GII.17 as dominant genotypes

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The science of the total environment 2025年 989卷 179854页

作者： Junshan Gao Zilei Zhang Yang Jiao Yinghuan Xu Yuanling Chen Liang Xue Institute of Microbiology Guangdong Academy of Sciences State Key Laboratory of Applied Microbiology Southern China Guangdong Provincial Key Laboratory of Microbial Safety and Health National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food Guangzhou Guangdong 510070 China Key Laboratory of Big Data Technologies for Food Microbiological Safety State Administration for Market Regulation NHC Specialty Laboratory of Food Safety Risk Assessment and Standard Development Guangzhou Guangdong 510070 China. Inspection and Quarantine Technology Department Shanghai Customs College Shanghai 201204 China. Key Laboratory of Big Data Technologies for Food Microbiological Safety State Administration for Market Regulation NHC Specialty Laboratory of Food Safety Risk Assessment and Standard Development Guangzhou Guangdong 510070 China. Electronic address: xueliang@***.

Human Noroviruses (HuNoVs) are the leading etiologic agents responsible for viral gastroenteritis. Drinking water (DW) serves as a significant vehicle for the transmission of HuNoVs. This study aimed to assess the occurrence of HuNoVs in drinking water sources (DWS) and DW. A systematic search spanning Web of science, EMBASE, PubMed, and Cochrane Central Register of Controlled Trials was executed up to July 15, 2024. Following rigorous screening, 76 eligible studies underwent meta-analysis with heterogeneity assessment via Stata 14.0 using a random-effects model. The results indicated that the pooled occurrence of HuNoVs was 11 % (95 % CI: 8-14), with occurrence of 15 % (95 % CI: 10-21) in DWS and 6 % (95 % CI: 3-9) in DW. Subgroup meta-analysis demonstrated that genogroup II (GII) of HuNoVs exhibited the highest contamination occurrence of 7 % (95 % CI: 4-10), with GII.4 and GII.17 being the predominant genotypes. The two continents with the highest number of studies were Asia (n = 27) and Europe (n = 19), with HuNoVs occurrence of 14 % (95 % CI: 8-22) and 15 % (95 % CI: 7-26), respectively. Furthermore, the occurrence showed no significant differences across the four seasons: in spring, the occurrence was 11 % (95 % CI: 3-23); in summer, 15 % (95 % CI: 7-25); in autumn, 11 % (95 % CI: 2-24); and in winter, 18 % (95 % CI: 10-27). These findings provided valuable epidemiological insight into the global occurrence, seasonal variation, and genotypic distribution of HuNoVs in DWS and DW, aiding policy development and public health strategies.

关键词： Drinking water Drinking water sources Human noroviruses Meta-analysis Norwalk virus Occurrence

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Private Trajectory data publication for Trajectory Classification 16th

Private Trajectory Data Publication for Trajectory Classific...

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16th Web Information Systems and Applications Conference, WISA 2019

作者： Zhu, Huaijie Yang, Xiaochun Wang, Bin Wang, Leixia Lee, Wang-Chien School of Data and Computer Science Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computer Science and Engineering Northeastern University Shenyang China The Pennsylvania State University State College United States

ISBN: (纸本)9783030309510

Trajectory classification (TC), i.e., predicting the class labels of moving objects based on their trajectories and other features, has many important real-world applications. Private trajectory data publication is to anonymize trajectory data, which can be released to the public or third parties. In this paper, we study private trajectory publication for trajectory classification (PTPTC), which not only preserves the trajectory privacy, but also guarantees high TC accuracy. We propose a private trajectory data publishing framework for TC, which constructs an anonymous trajectory set for publication and use in data services to classify the anonymous trajectories. In order to build a "good" anonymous trajectory set (i.e., to guarantee a high TC accuracy), we propose two algorithms for constructing anonymous trajectory set, namely Anonymize-POI and Anonymize-FSP. Next, we employ Support Vector Machine (SVM) classifier to classify the anonymous trajectories. Finally, the experimental results show that our proposed algorithms not only preserve the trajectory privacy, but also guarantee a high TC accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Trajectories

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Flight Path Optimization for UAVs to Provide Location Service to Ground Targets

Flight Path Optimization for UAVs to Provide Location Servic...

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IEEE Conference on Wireless Communications and Networking

作者： Youpeng Wang Xiaojun Zhu Lijie Xu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications China

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

We consider using UAVs to provide location service to ground targets. UAVs fly over the target area and broadcast their locations periodically via wireless signals. When a UAV flies along a straight line, a target will observe the strongest signal when the UAV is at the closest location, based on which it can infer its location. The problem is to minimize the flight length subject to the constraint that the transmission range of a UAV is limited and all targets in the area should be located. We formulate the problem and give an optimal flight path to cover a square with side length no greater than a certain value. In the general case when the given area cannot be covered by the square, we propose to divide UAVs into two groups, whose flight paths are either parallel or orthogonal. We conduct simulations to compare our approach with existing approaches. Results verify the superiority of our approach in terms of flight length and localization error.

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