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arXiv 2017年

作者： Guo, Junliang Xu, Linli Huang, Xunpeng Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China

Recent advances in the field of network embedding have shown the low-dimensional network representation is playing a critical role in network analysis. However, most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes exibly. In this paper, we take a matrix factorization perspective of network embedding, and incorporate structure, content and label information of the network simultaneously. For structure, we validate that the matrix we construct preserves high-order proximities of the network. Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding in an unsupervised manner, i.e., without leveraging downstream classifiers. In addition, we generalize the Skip-Gram Negative Sampling model to integrate the content of the network in a matrix factorization framework. As a consequence, network embedding can be learned in a unified framework integrating network structure and node content as well as label information simultaneously. We demonstrate the efficacy of the proposed model with the tasks of semi-supervised node classification and link prediction on a variety of real-world benchmark network datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Network embeddings

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Deep Technology Tracing for High-Tech Companies

Deep Technology Tracing for High-Tech Companies

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IEEE International Conference on data Mining (ICDM)

作者： Han Wu Kun Zhang Guangyi Lv Qi Liu Runlong Yu Weihao Zhao Enhong Chen Jianhui Ma Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China

Technological change and innovation are vitally important, especially for high-tech companies. However, factors influencing their future research and development (R&D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for high-tech companies. To this end, in this paper, we develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company. Specially, DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network. For one thing, PCR and CTR aim to capture competitive relations among enterprises and collaborative relations among technologies, respectively. For another, DTT is designed for modeling dynamic interactions between companies and technologies with the above relations involved. Finally, we evaluate our DTF framework on real-world patent data, and the experimental results clearly prove that DTF can precisely help to prospect future technology emphasis of companies by exploiting hybrid factors.

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Crowdfunding dynamics tracking: A reinforcement learning approach

arXiv

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arXiv 2019年

作者： Wang, Jun Zhang, Hefu Liu, Qi Pan, Zhen Tao, Hanqing Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China

Recent years have witnessed the increasing interests in research of crowdfunding mechanism. In this area, dynamics tracking is a significant issue but is still under exploration. Existing studies either fit the fluctuations of time-series or employ regularization terms to constrain learned tendencies. However, few of them take into account the inherent decision-making process between investors and crowdfunding dynamics. To address the problem, in this paper, we propose a Trajectory-based Continuous Control for Crowdfunding (TC3) algorithm to predict the funding progress in crowdfunding. Specifically, actor-critic frameworks are employed to model the relationship between investors and campaigns, where all of the investors are viewed as an agent that could interact with the environment derived from the real dynamics of campaigns. Then, to further explore the in-depth implications of patterns (i.e., typical characters) in funding series, we propose to subdivide them into fast-growing and slow-growing ones. Moreover, for the purpose of switching from different kinds of patterns, the actor component of TC3 is extended with a structure of options, which comes to the TC3-Options. Finally, extensive experiments on the Indiegogo dataset not only demonstrate the effectiveness of our methods, but also validate our assumption that the entire pattern learned by TC3-Options is indeed the U-shaped one. Copyright © 2019, The Authors. All rights reserved.

关键词： Crowdsourcing

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A Revised Model for Sea-Surface Wind Inversion data with Joint Interpolation Optimization and Residual Network 18

A Revised Model for Sea-Surface Wind Inversion Data with Joi...

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18th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： Lin, Weihong You, Lijun Huang, Tianqiang Huang, Liqing Luo, Haifeng Xu, Chao College of Computer and Cyber Security Fujian Norm. Univ. Fujian Provincal Eng. Res. Center of Big Data Analysis and Application Digital Fujian Institute of Big Data Security Technology Fuzhou China Fujian Meteorological Information Center Fujian Key Laboratory of Severe Weather Fuzhou China

ISBN: (纸本)9798350318401

The sea-surface wind fields are pivotal for studies in both oceanography and meteorology. However, the acquisition of ocean wind data is often challenged by practical constraints, especially near coasts, leading to significant data gaps. This paper introduces a two-stage approach that combines interpolation optimization with a residual network to improve satellite-derived sea-surface wind fields data. Initially, the Kriging interpolation method is employed to fill missing regions. Subsequently, a network structure similar to ResNet-18 is tailored to revise the interpolated data using buoy anemometer observations. By establishing a correlation model between the interpolated results and ground truth through supervised training, the approach ensures the interpolated results gravitate towards the actual measurements. The efficacy of the proposed model is corroborated through experimental visual analysis and error cure assessments. © 2023 IEEE.

关键词： Interpolation

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Rumor Detection with Hierarchical Recurrent Convolutional Neural Network 8th

Rumor Detection with Hierarchical Recurrent Convolutional Ne...

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8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

作者： Lin, Xiang Liao, Xiangwen Xu, Tong Pian, Wenjing Wong, Kam-Fai College of Mathematics and Computer Science Fuzhou University Fuzhou China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei China The Chinese University of Hong Kong Sha Tin Hong Kong

ISBN: (纸本)9783030322359

Automatic rumor detection for events on online social media has attracted considerable attention in recent years. Usually, the events on social media are divided into several time segments, and for each segment, corresponding text will be converted as vectors for various neural network models to detect rumors. During this process, however, only sentence-level embedding has been considered, while the contextual information at the word level has been largely ignored. To address that issue, in this paper, we propose a novel rumor detection method based on a hierarchical recurrent convolutional neural network, which integrates contextual information for rumor detection. Specifically, with dividing events on social media into time segments, recurrent convolution neural network is adapted to learn the contextual representation information. Along this line, a bidirectional GRU network with attention mechanism is integrated to learn the time period information via combining event feature vectors. Experiments on real-world data sets validate that our solution could outperform several state-of-the-art methods. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

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User Rating Prediction Based on Trust-Driven Probabilistic Matrix Factorization

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Ruan Jian Xue Bao/Journal of Software 2018年第12期29卷 3747-3763页

作者： Du, Dong-Fang Xu, Tong Lu, Ya-Nan Guan, Chu Liu, Qi Chen, En-Hong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei230027 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China

The development of Internet has brought convenience to the public, but also troubles users in making choices among enormous data. Thus, recommender systems based on user understanding are urgently in need. Different from the traditional techniques that usually focus on individual users, the social-based recommender systems perform better with integrating social influence modeling to achieve more accurate user profiling. However, current works usually generalize influence in simple mode, while deep discussions on intrinsic mechanism have been largely ignored. To solve this problem, this paper studies the social influence within users who affects both rating and user attributes, and then proposes a novel trust-driven PMF (TPMF) algorithm to merge these two mechanisms. Furthermore, to deal with the task that different user should have personalized parameters, the study clusters users according to rating correlation and then maps them to corresponding weights, thereby achieving the personalized selection of users' model parameters. Comprehensive experiments on open data sets validate that TPMF and its derivation algorithm can effectively predict users' rating compared with several state of the art baselines, which demonstrates the capability of the presented influence mechanism and technical framework. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Recommender systems

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EarthquakeAware: Visual Analytics for Understanding Human Impacts of Earthquakes from Social Media data

EarthquakeAware: Visual Analytics for Understanding Human Im...

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IEEE Symposium on Visual Analytics Science and Technology (VAST)

作者： Shuai Chen Sihang Li Liwenhan Xie Yi Zhong Yun Han Xiaoru Yuan Key Laboratory of Machine Perception Ministry of Education School of EECS Peking University National Engineering Laboratory for Big Data Analysis and Application Peking University

In this paper, we propose EathquakeAware, a visual analytics system for social media visualization. Our system supports analyzing geo-tagged messages from different perspectives, including location, keyword, account. It can summarize the evolution patterns of topics and reveal the discussing trend of different keywords to help evaluate situations in different regions. We take VAST Challenge 2019 Mini Challenge 3 as case studies to demonstrate its effectiveness in a fictitious analysis scenario.

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Molecular property prediction: A multilevel quantum interactions modeling perspective

arXiv

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arXiv 2019年

作者： Lu, Chengqiang Liu, Qi Wang, Chao Huang, Zhenya Lin, Peize He, Lixin Anhui Province Key Lab. of Big Data Analysis and Application University of SandT of China Key Laboratory of Quantum Information University of SandT of China

Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functional theory (DFT) in physics are proved to be time-consuming for predicting large number of molecules. Recently, the machine learning methods, which consider much rule-based information, have also shown potentials for this issue. However, the complex inherent quantum interactions of molecules are still largely underexplored by existing solutions. In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction. Specifically, we represent each molecule as a graph to preserve its internal structure. Moreover, the well-designed hierarchical graph neural network directly extracts features from the conformation and spatial information followed by the multilevel interactions. As a consequence, the multilevel overall representations can be utilized to make the prediction. Extensive experiments on both datasets of equilibrium and off-equilibrium molecules demonstrate the effectiveness of our model. Furthermore, the detailed results also prove that MGCN is generalizable and transferable for the prediction. Copyright © 2019, The Authors. All rights reserved.

关键词： Molecules

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Using Multidisciplinary analysis to Develop Adaptation Options against Extreme Coastal Floods

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International Journal of Disaster Risk Science 2022年第4期13卷 577-591页

作者： Xinmeng Shan Jun Wang Jiahong Wen Hengzhi Hu Lei Wang Jie Yin Mengya Li Key Laboratory of Geographic Information Science Ministry of EducationEast China Normal UniversityShanghai 200241China School of Geographic Sciences East China Normal UniversityShanghai 200241China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural ResourcesShanghai 200241China School of Environmental and Geographical Sciences Shanghai Normal UniversityShanghai 200234China Department of Hospitality Management Shanghai Business SchoolShanghai 200235China Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70810USA

Long-term flood risk adaptation and decision making are complex because the future is full of deep *** and robustness can be used to deal with future *** study developed an integrated modeling framework that extends previous studies to the spatial domain to assess the future flood risks and the cost and benefit of three adaptation measures for four types of buildings in *** options analysis(ROA)and dynamic adaptive policy pathways(DAPP)were integrated to develop a dynamic adaptation pathway and identify robust adaptation *** results show that:(1)Sea level rise and land subsidence will significantly exacerbate the flood risks in Shanghai;(2)Among the three flood control measures,wet-floodproofing has the best economic performance in terms of both the net present value and the benefit/cost ratio,followed by dry-floodproofing,and elevation;(3)Dryfloodproofing can be used at the beginning of the future period(2030–2100),and it can be replaced by wet-floodproofing in 2035–2042;the elevation measure also shows good performance at the beginning of implementation,but its performance will decline after 2041–2045;(4)The combined strategy of dry-and wet-floodproofing in 2044–2046and a hybrid strategy combining the three measures should be the optimal solution for reducing the flood risks in 2047–*** methodology developed in this study can provide insights for coastal cities to formulate cost-effective and feasible adaptation strategies in a deeply uncertain future.

关键词： Adaptation options Building-level measures Climate change Coastal floods Cost-benefit analysis Shanghai

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Noise Effect on the 2D Stochastic Burgers Equation

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Acta Mathematica Sinica,English Series 2024年第9期40卷 2065-2090页

作者： Zhao DONG Jiang Lun WU Guo Li ZHOU RCSDS Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190P.R.China Beijing Normal University-Hong Kong Baptist University United International College Zhuhai 519087P.R.China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science(IRADS) UICZhuhai 519087P.R.China School of Statistics and Mathematics Chongqing University Key Laboratory of Nonlinear Analysis and Its Applications(Chongqing University) Ministry of EducationChongqing 400044P.R.China

By comprehensive utilizing of the geometry structure of 2D Burgers equation and the stochastic noise,we find the decay properties of the solution to the stochastic 2D Burgers equation with Dirichlet boundary ***,the e... 详细信息

关键词： 2D stochastic Burgers equation regularity

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