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IEEE International Conference on Big data

作者： Zhaoxi Li Jun He Hongyan Liu Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China School of Economics and Management Tsinghua g University Beijing China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Multivariate time series forecasting is very important for many applications. Many studies have been conducted for accurate and interpretable prediction methods. However, existing methods either cannot take both times series and covariates into consideration, lacking of interpretability, or ignore global trends across multivariate time series. In this paper, we aim to solve these issues. To this end, we propose a new model named TEDGE for accurate and interpretable time series prediction. In this model, we extract global trends hidden across multivariate times series to improve prediction accuracy. Meanwhile, we utilize a deep recurrent model with attention mechanism to find long-and short-term sequential patterns hidden in individual time series with interpretability. We conduct experiments on several datasets to evaluate the proposed models performance. Results demonstrate the superior performance of our proposed model.

关键词： Conferences Time series analysis Predictive models Big data Market research data models Forecasting

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Pyramid scheme in stock market: A kind of financial market simulation

arXiv

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arXiv 2021年

作者： Shi, Yong Li, Bo Du, Guangle School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Wenzhou Institute University of Chinese Academy of Sciences WenzhouZhejiang325001 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China

Artificial stock market simulation based on agent is an important means to study financial market. Based on the assumption that the investors are composed of a main fund, small trend and contrarian investors characterized by four parameters, we simulate and research a kind of financial phenomenon with the characteristics of pyramid schemes. Our simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors, the small investors’ parameters of taking profit and stopping loss, the order size of the main fund and the strategies adopted by the main fund. Our work are helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets, design trading rules for regulators and develop trading strategies for investors. © 2021, CC BY-NC-ND.

关键词： Earnings

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Automatic Pointer Meter Reading Based on Machine Vision 4

Automatic Pointer Meter Reading Based on Machine Vision

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4th IEEE International Conference on Image, Vision and Computing, ICIVC 2019

作者： Hung, Mao-Hsiung Hsieh, Chaur-Heh Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China Yango University College of Information Engineering Fuzhou China

ISBN: (纸本)9781728123257

Pointer meters in power systems are wildly used. Although smart meters gradually replace pointer meters to conveniently acquire measurement, digitalization of some special meters have not been available. The paper presents a new method based on machine vision to detect pointer meter reading automatically. Two phases of training and testing are proposed in this method. In the training phase, a template of meter feature points on a training image is extracted. In the testing phase, the template matching performs on a testing image to find feature points. Then, an elliptic arc fitting based on the feature points to generate a 1-D signal. Finally, the 1-D signal is used to locate the pointer and the reading result is obtained. Differing with existing methods, the proposed method performs effectively and efficiently automatic meter reading without using Hough transformation. © 2019 IEEE.

关键词： Computer vision

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Study of the Degree of Automation of High-Tech Smart Factory: Taiwan 12" Semiconductor Factory Case Study 6th

Study of the Degree of Automation of High-Tech Smart Factory...

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Chu, Kai-Chun Chang, Kuo-Chi Chang, Fu-Hsiang Wang, Hsiao-Chuan Lin, Yuh-Chung Hsu, Tsui-Lien Trong-The, Nguyen Sung, Tien-Wen School of Management Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Information Science and Engineering Fujian University of Technology Fuzhou China Department of Tourism Shih-Hsin University Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia Department of Business Administration Group of Strategic Management from National Central University Taoyuan Taiwan

ISBN: (纸本)9783030697167

The globalization of Taiwan's corporate industries is subject to competition and threats at two critical levels. Taiwan's factories could use Industry 4.0 to enhance their competitiveness. This listing Taiwanese semiconductor manufacturing factory Hsinchu A, B conducts industrial maturity assessment analysis of automation equipment. This study shows that the existing equipment automation in Factory A is 1.20, the ideal is 2.74, the average is 1.65, and the proportion of artificial is 80%, which is much higher than the industry average of 42%. The actual value of equipment automation in Factory B is 2.96, and the ideal is 2.76. The standard is 1.67, fully automatic, accounted for 100%, much higher than the industry average of 15%. The automation of the equipment evaluation project shows the average value of the leading industry self-evaluation. Finally, although this equipment's operation has been relatively smooth, we must pay attention to the industry's future trends and promptly improve it. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Industry 4.0

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Indoor Sound Source Localization Algorithm Based on BP Neural Network

Indoor Sound Source Localization Algorithm Based on BP Neura...

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International Conference on Communication Technology (ICCT)

作者： Lan Wang Kun Zhang Chong Shen Chai Wang Xixi Fu State Key Laboratory of Marine Resources Utilization in South China Sea Hainan University Haikou Hainan China College of Information and Communication Engineering Hainan University Haikou Hainan China Data Mining and Monitoring and Early Warning Technology of Island Tourism Resources of Key Laboratory of the Ministry of Culture and Tourism of the People's Republic of China Hainan Tropical Ocean University Sanya Hainan China

ISBN: (纸本)9781665432078

Sound source localization is an important part of the perception of things around. Sound source localization can overcome the shortcomings of visual localization, and can also locate the invisible place. The application of sound source localization in indoor is the latest trend. Starting from the application of deep learning to indoor sound source localization, this paper focuses on the analysis and research of BP neural network applied to indoor sound source localization algorithm. In this paper, an off-line sampling scheme is used to construct the network structure with 7 neurons hidden in the layer, and the BP algorithm of LevenBerg-Marquardt is used as the training function, this algorithm can solve the traditional algorithm through the study of the physical properties of sound, set up the corresponding equation, and then solve, the process is complex, to solve the difficult problem. The simulation results show that the algorithm can be implemented in 100 square meters of the house, through sampling 400 sets of data for machine training, positioning error can be controlled in a few centimeters effect.

关键词： Location awareness Training Meters Visualization Simulation Neurons Position measurement

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Analysis of the spatial and temporal evolution of PM2.5 pollution in China during COVID -19 epidemic

Analysis of the spatial and temporal evolution of PM2.5 poll...

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Big data, Artificial Intelligence and Internet of Things engineering (ICBAIE), International Conference on

作者： ChuanLin Li Fenghua Huang Digital China Research Institute(Fujian) Key Laboratory of Spatial Data Mining& Information Sharing of Ministry of Education Fuzhou University Fuzhou China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University Fuzhou China

ISBN: (数字)9781728164991

ISBN: (纸本)9781728165004

Environmental health has recently become more and more important in the discussion of international health issues. Due to the impact of haze pollution on human health, ecological environment and climate change, it has become the first problem to be solved in the process of protecting environmental health. PM2.5 is the main cause of haze pollution. Based on the observation data of PM2.5 concentration in 368 cities in China from February to march in 2020, this paper uses the statistical model of spatial data to reveal the spatial and temporal pattern distribution of PM2.5 in China during the period of severe outbreak and spread (February 3 to March 29) of 8 weeks and 56 days. The analysis results show that 1) during the epidemic period, the average concentration of PM2.5 in China ranged from 32.876 to 42.173, which generally showed a trend of rapid decrease and then rebound and continued decline after the outbreak of the epidemic. 2) in terms of space, the concentration of PM2.5 is the highest in the central region, higher in the north than in the south, and higher in the eastern bohai rim region and northern China than in the western qinghai-tibet plateau and Himalayan mountain belt. The high-value areas were mainly distributed in the north China plain, the beijing-tianjin-hebei region, urumqi and hotan regions of xinjiang. 3) the hot spot analysis shows that the hot spots of pollution distribution are the beijing-tianjin-hebei region and the north China plain. The cold spots are the pearl river delta and the yunnan-guizhou region. 4) spatial autocorrelation analysis shows that the average weekly concentration of PM2.5 shows a strong local spatial autocorrelation.

关键词： Indexes Big data Artificial intelligence Internet of Things Monitoring Reactive power Correlation

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Lymph node metastasis mapping and prognostic analysis of early gastric cancer

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Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract 2025年第7期29卷 102082页

作者： Wenqian Ma Honghai Guo Peigang Yang Jinchen He Tongkun Li Haotian Wu Jiaxiang Wu Jiaxuan Yang Yuan Tian Shuo Guo Gongning Wang Hongcai Wang Qi Wang Limian Er Ping'an Ding Qun Zhao The Third Department of Surgery the Fourth Hospital of Hebei Medical University Shijiazhuang 050011 China Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer Shijiazhuang 050011 China Big data analysis and mining application for precise diagnosis and treatment of gastric cancer Hebei Provincial Engineering Research Center Shijiazhuang 050011 China Department of Endoscopy The Fourth Hospital of Hebei Medical University Shijiazhuang 050011 China. Big data analysis and mining application for precise diagnosis and treatment of gastric cancer Hebei Provincial Engineering Research Center Shijiazhuang 050011 China. Big data analysis and mining application for precise diagnosis and treatment of gastric cancer Hebei Provincial Engineering Research Center Shijiazhuang 050011 China. Electronic address: ding_ping_an@***. Big data analysis and mining application for precise diagnosis and treatment of gastric cancer Hebei Provincial Engineering Research Center Shijiazhuang 050011 China. Electronic address: zhaoqun@***.

BACKGROUND:Many studies have focused on the analysis of risk factors for lymph node metastasis in early gastric cancer, but few studies analyze the drainage patterns of metastatic lymph nodes. METHODS:data was retrospectively analyzed from a database of gastric cancer resections from 2014-2018. The cohort included 786 pT1 patients with complete data. Outcomes evaluated were lymph node metastasis frequencies, survival analyses, and risk factors impacting prognosis. Lymph node mapping was conducted to explore the distribution pattern of metastatic nodes based on tumor location. RESULTS:The overall lymph node metastasis rate was 23.7%. Lymph node mapping showed that No. 7 (67.7%) and No. 3 (45.7%) group had the highest metastasis rates and regardless of tumor location, metastases were concentrated in perigastric lymph node groups (No. 3, 4, 5, 6, and 7), while involvement of second-tier station nodes was rare. The 5-year overall survival rate (54.8% vs 95.7%; P < 0.001) and disease-free survival rate (48.4% vs 95.7%; P < 0.001) of patients with node-positive disease were significantly worse than those of patients with node-negative disease. Multivariable Cox regression identified tumor size > 2cm (P = 0.007, < 0.001), poor differentiation (P = 0.007, < 0.001), T1b stage (all P < 0.001), lymph node metastasis (all P < 0.001), and vascular invasion (all P = 0.002, 0.016) as independent negative prognostic factors affecting 5-year OS and DFS in patients with early gastric cancer. Postoperative chemotherapy (P < 0.001, 0.019）was an independent positive prognostic factor. CONCLUSION:This retrospective study demonstrates that lymph node metastasis in early gastric cancer is widely and disorderly not depending on the location. Lymph node metastasis mainly occurs in the perigastric station, supporting a specific lymph node dissection strategy may be sufficient for some early gastric cancer. Meanwhile its prognosis is closely related to lymph node metastasis. AVAILABILITY OF DA

关键词： Early gastric cancer Lymph nodes Patterns Prognosis Risk factors

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MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results 17th

MIPI 2022 Challenge on Under-Display Camera Image Restorati...

引用

17th European Conference on Computer Vision, ECCV 2022

作者： Feng, Ruicheng Li, Chongyi Zhou, Shangchen Sun, Wenxiu Zhu, Qingpeng Jiang, Jun Yang, Qingyu Loy, Chen Change Gu, Jinwei Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Jiang, Biao Ding, Yuhan Li, WanZhang Feng, Xiaoyue Chen, Sijing Zhong, Tianheng Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang Nanyang Technological University Singapore Singapore SenseBrain San Jose United States Shanghai AI Laboratory Shanghai China SenseTime Research and Tetras.AI Beijing China University of Science and Technology of China Hefei China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology Hefei China School of Artificial Intelligence Xidian University Xi’an China School of Remote Sensing and Information Engineering Wuhan University Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Harbin Institute of Technology Harbin China University of Macau China Changsha Research Institute of Mining and Metallurgy Changsha China Tianjin University Tianjin China Computer Vision Lab University of Wurzburg Würzburg Germany Couger Inc. Tokyo Japan School of Statistics and Data Science Nankai University Tianjin China Founding Minds Software Thiruvananthapuram India Department of Electronics and Communication SRM University AP Amaravati India Fudan University Shanghai China Shenyang Aerospace University Shenyang China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783031250712

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image reconstruction

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Research on the Design and Construction of Sanya Smart Tourism Information Platform Based on Federated Migration Algorithm

Research on the Design and Construction of Sanya Smart Touri...

引用

2021 International Conference on Digital Society and Intelligent Systems, DSInS 2021

作者： Zhang, Kun Zhu, Jia Huang, Xuebin Wang, Haifeng Dina, Vaniushkina Kumushai, Kaparova Zhao, Zhe Zeng, Penghui Hainan Tropical Ocean University Key Laboratory of Island Tourism Resource Data Mining and Monitoring Ministry of Culture and Tourism Sanya China Hainan Tropical Ocean University College of Art Sanya China Hainan Tropical Ocean University College of Computer Science and Technology Sanya China Hainan Tropical Ocean University Education Center of MTA College of Tourism Sanya China Hainan Tropical Ocean University Department of Tourism Management College of Tourism Sanya China Hainan University College of Information and Communication Engineering Haikou China

ISBN: (纸本)9781665406307

The article is a series of research results of the smart tourism construction research project in Asia. Through the preliminary research, it analyzes and studies the problems existing in the smart tourism construction of Sanya City, and gives the countermeasures and measures for the smart tourism construction of Sanya City from multiple angles. Suggestions and based on the federal migration algorithm, the Sanya smart tourism information platform was designed, and the comprehensive Sanya smart tourism construction was analyzed and researched. © 2021 IEEE.

关键词： Smart city

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Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

arXiv

引用

arXiv 2020年

作者： Chen, Lei Wu, Le Hong, Richang Zhang, Kun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering HeFei University of Technology School of Computer Science and Technology University of Science and Technology of China

Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering (CF) based Recommender Systems (RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing effect with the graph convolution operation. In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data. The proposed model is a linear model and it is easy to train, scale to large datasets, and yield better efficiency and effectiveness on two real datasets. We publish the source code at https://***/newlei/LRGCCF. Copyright © 2020, The Authors. All rights reserved.

关键词： Collaborative filtering

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