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Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies 1

24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

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Research on Penetration Strategy of UAV 2020

Research on Penetration Strategy of UAV

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Daojun Han Hongzhen Xiang Haolin Chen Yi Xie Institute of Data and Knowledge Engineering Henan University Kaifeng PR China School of Computer and Information Engineering Henan University Kaifeng PR China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng PR China

ISBN: (纸本)9781450387828

Taking a military operation as an example, this paper makes an in-depth study of what tactics a single UAV should adopt when facing the interception of multiple enemy UAV groups. According to the analysis of the impact diagram, the aerial range of the UAV standoff defines a threat area, that is, when the UAV arrives at the threat area, it needs to adopt a strategy for penetration, or it will be intercepted. The strategy adopted by UAV in the threat area is analyzed, and the decision control method is made.

关键词： Decision control Threat area Influence chart analysis UAV penetration

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Discovering the relationship between the number of film review topics and box office with NLP techniques

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Procedia Computer Science 2024年 242卷 640-646页

作者： Bo Li Wei Dai Shang Liu Yong Shi School of Finance and Economics Wuhan City Vocational College No. 10 Yezhihu West Road Hongshan District Wuhan 430064 China PKU-WUHAN Institute for Artificial Intelligence No. 770 Gaoxin Avenue Hongshan District Wuhan 430070 China School of Economics Shanghai University 99 Shangda Road BaoShan District Shanghai 200444 China School of Economics and Management University of Chinese Academy of Sciences No. 3 Zhongguancun South Yitiao Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing 100190 China

Numerous movie reviews on the internet can be used to analyze audience experiences of movies and provide inspiration for the operation and production of movies. In this work, we integrate and match the review data on Douban and the box office data on the website ***, and utilize the topic model in natural language processing to determine the number of each movie review topics, then establish an regression equation to analyze the relationship between the number of topics and movie box office. Our analysis shows a significant positive correlation between film box office and the number of movie review topics, and we have provided an explanation for this. Finally, we also analyzed the practical application value of this work.

关键词： Film Reviews Topic Model Sentiment Analysis Latent Dirichlet Allocation (LDA) Biterm Topic Model (BTM)

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An Empirical Analysis for Forecasting Stock Index based on LSTM Neural Network 21

An Empirical Analysis for Forecasting Stock Index based on L...

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5th International Conference on Electronic Information Technology and Computer engineering, EITCE 2021

作者： Wang, Chia-Hung Wu, Xiaojing Chen, Yu-Tin College of Computer Science and Mathematics Fujian Univ. of Technology and Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou City China College of Electronics Electrical Engineering and Physics Fujian University of Technology Fuzhou City China HengZen Technology Company Ltd. Taipei City Taiwan

ISBN: (纸本)9781450384322

In this paper, we study a stock price forecasting model based on a trained neural network for the Chinese stock market. We propose a Long Short-Term Memory (LSTM) network model to predict the closing price on a targeted day. The transaction data of four representative stock indices are investigated in the empirical analysis, including Shanghai Composite Index (stock code 000001), Shenzhen Composite Index (stock code 399001), CSI 300 Index (stock code 399300) and SSE 50 Index (stock code 000016). To predict the closing price for the next period, five important financial characteristics of the transaction data are selected as the input features, such as the opening price, historical closing price, trading volume, highest price, and lowest price. In addition, the proposed LSTM model is compared with the traditional Recurrent Neural Network (RNN) model on four performance measures. In numerical results, we indicate the applicability of the proposed method through the empirical analysis of those four stock indexes. Our works could contribute to an effective guidance in practical trading analysis for investors when making rational investments in the stock markets. © 2021 ACM.

关键词： Long short-term memory

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Impact of China Railway Express on Urban Exports: Evidence from Prefectural-Level City Economic data

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Procedia Computer Science 2022年 214卷 1222-1227页

作者： Bochao Yan Xin Tian Shiyong Deng Junfeng Wu Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China China Railway Fifth Survey and Design Institute Group Co. Ltd. Beijing 102600 China Shanghai WinJoin Information Technology Co. Ltd. Shanghai 200126 China

As a key project of infrastructure connectivity in the “Five-Pronged Approach” of strategic construction “The Belt and Road”, the China Railway Express, has significantly improved the level of trade level in countries along the railway. This study uses an empirical model to correlate the container export data of China Railway Express from June 2020 to June 2021 with the export value and finds that the trade volume of China Railway Express is positively correlated with the export value of cities. The relationship between the operation of the China Railway Express and the export trade of cities that enjoy the rail service is rigorously explained through the actual data of the operation of the China Railway Express provided by the enterprises. The empirical results further prove the positive impact of the China Railway Express on cities that enjoy the rail service.

关键词： The Belt Road China Railway Express Trade

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Research on Visualization Algorithm of Association Rules Based on Concept Lattice 2020

Research on Visualization Algorithm of Association Rules Bas...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Xiajiong Shen Lijie Bao Lei Zhang Institute of Data and Knowledge Engineering Henan University Kaifeng PR China School of Computer and Information Engineering Henan University Kaifeng PR China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng PR China

ISBN: (纸本)9781450387828

The traditional association rule representation method has no way to show the essential relationship between concepts and lacks understanding of the concept level. Concept lattice is a kind of data structure which can show the relationship between concepts, concept lattice also has unique advantages in the visualization and discovery of potential knowledge of association rules. In this paper, a visualization algorithm of association rules based on concept lattice is proposed. The algorithm visually displays the association rules extracted from concept lattice. Finally, this article combines the chronic disease record data of a certain hospital to analyze and visualize the association rules. The implementation results show that the visualization method has good effects in knowledge analysis and data display.

关键词： Visualization Concept lattice Association rules knowledge discovery

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Impact of Consumer Characteristics on IP Product Purchase: Empirical Evidence from a Chinese Chain Retailer

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Procedia Computer Science 2023年 221卷 933-940页

作者： Jiayu Tang Tingyan Wang Xin Tian Qiang Cai Research Center on Fictitious Economy and Data Science. Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing 100190 China Nuffield Department of Medicine University of Oxford South Parks Road Oxford OX1 3SY United Kingdom NIHR Oxford Biomedical Research Centre Big Data Institute University of Oxford Old Road Campus Oxford OX3 7LF United Kingdom School of Economics and Management Ningbo University of Technology Ningbo 315211 China

Developing IP product has become a common differentiation strategy for retailers to attract customers and remain competence strength. This study uses empirical model to relate consumer characteristics with IP product purchase. The results show that consumers shopping on weekends are more likely to buy IP products, and consumers who have stocking behavior are less likely to purchase IP products. Consumers who are members of the retailer also have lower probability to buy IP products. These findings provide suggestions for the retailers who want to apply differentiation strategy by introducing co-branded IP products.

关键词： IP product Weekend Stock Membership

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Emotional Neural Textures: Generating Talking-Face Videos with Continuously Controllable Emotions

SSRN

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SSRN 2024年

作者： Wan, Bin Sun, Zhiyao Lin, Matthieu Gaetan Ye, Zipeng Wen, Yu-Hui Zhang, Ruisheng School of Information Science and Engineering Lanzhou University No.222 South Tianshui Road Gansu Lanzhou730000 China Department of Computer Science and Technology Tsinghua University Tsinghua University Haidian District Beijing100084 China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University No.3 Shangyuancun Haidian District Beijing100044 China

This paper presents a real-time method for generating talking-face videos with continuously controllable emotional expressions. We base our method on the insight that emotion information primarily resides in textures, which neural textures, i.e., learned expressive feature maps, can capture. To enable continuous control of facial emotions, we introduce a novel neural network capable of generating emotional neural textures from continuous intensity emotion coding (CIEC) vectors. We employ a parametric 3D face model (3DMM) for emotional neural texture sampling and incorporate a teeth submodule to fill the mouth area cavity. Experimental results, along with a user study, demonstrate that our method surpasses three baseline approaches in producing high-quality talking-face videos with continuously controllable expressions. © 2024, The Authors. All rights reserved.

关键词： Textures

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Sampling Topic Representative Users by Integrating Node Degree and Edge Weight

Sampling Topic Representative Users by Integrating Node Degr...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Jiangwen Wang Zhijie Ban Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering College of Computer Science Inner Mongolia University Huhhot China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Finding a subset of users to statistically represent the original social network in terms of attributes has received a lot of attention recently. The existing literature had proved that the problem is NP-Hard and proposed some sampling models, but representative degrees aren't systematically studied. Taking node degree and edge weight into account, we present a topic sampling model for the whole social network in this paper. Firstly, by statistically calculating node degrees for one real dataset, we find that one node is more likely to be a representative user if its degree is high. We then give some definitions about representative degrees. Based on statistical stratified sample, we finally give a sampling model of topic representative users who are from most or all attribute groups in the network. Experimental results show that our algorithm significantly outperforms other six baseline methods and is also effective in terms of time complexity.

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Towards Feature Distribution Alignment and Diversity Enhancement for data-Free Quantization

Towards Feature Distribution Alignment and Diversity Enhance...

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IEEE International Conference on data mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Richang Hong Haijun Zhang Jicong Fan Shuicheng Yan School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Department of Computer Science Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Singapore

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance.

关键词： Degradation Training Quantization (signal) Semantics Neural networks Memory management data models

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