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Sparse Multiple Instance Learning for Elderly People Balance Ability

Procedia Computer Science 2022年 199卷 621-628页

作者： Yuqi Zhang Duo Su Xiaoxi Zhao Yingjie Tian School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China

Falling occurs when a person’s body (excluding their feet) accidentally touches the ground, resulting in severe injury for the elderly. As a response, determining old people’s balance characteristics and assisting them in preventing falls is important. We proposed the sparse SyMIL with SCAD to find the important factors that may cause an elderly person to fall, which can yield realistic and accurate results. This work can also be used in future research about falling prevention and balance supervision.

关键词： Multiple Instance Learning Feature Selection Elderly People Balance Ability

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The neural network methods for solving Traveling Salesman Problem

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Procedia Computer Science 2022年 199卷 681-686页

作者： Yong Shi Yuanying Zhang School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China College of Information Sciences and Technology University of Nebraska at Omaha NE 68182 USA School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100190 China

Traveling Salesman Problem(TSP) is a main attention issue at present. Neural network can be used to solve combinatorial optimization problems. In recent years, there have existed many neural network methods for solving TSP, which has made a big step forward for solving combinatorial optimization problems. This paper reviews the neural network methods for solving TSP in recent years, including Hopfield neural network, graph neural network and neural network with reinforcement learning. Using neural network to solve TSP can effectively improve the accuracy of the approximate solution. Finally, we put forward the prospect of solving TSP in the future.

关键词： Traveling Salesman Problem Neural network Hopfield neural network Graph neural network Reinforcement learning

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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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A knowledge graph constructed for job-related crimes

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Procedia Computer Science 2022年 199卷 540-547页

作者： Yong Shi Wenlu An Jiayu Xue Yi Qu School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 CHINA Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 CHINA Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 CHINA College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA People’s Procuratorate of Shanghai Pudong New District Shanghai 200135 CHINA School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100190 China

Since the 18th National Congress, the Party Central Committee has greatly enhanced its efforts to "fight corruption and advocate honesty". While achieving excellent results, the rising number of crime cases has also brought great challenges to the work of the prosecution organs. At the same time, the traditional relational database has become more and more disadvantageous in dealing with large-scale, unstructured and complex dynamic data, and can no longer meet the actual needs. In order to further improve the efficiency and quality of prosecutors’ work, alleviate the problem of "too many cases and too few people", and promote the construction of "smart prosecution", this paper uses knowledge extraction and other methods to identify the key entities and relationships in the job-related crimes indictments of the Shanghai Pudong New District Procuratorate. Then using Neo4j graph database to store the extracted information and build a knowledge graph for job-related crime cases, which realizes the visualization and deep mining of job-related crime, providing a new way and tool for prosecution work as well as prosecution personnel to analyze and handle job-related crime cases.

关键词： big data in prosecution knowledge graph job-related crimes Neo4j

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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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Penetration Effect of Informatization on Technological Progress in Logistics Industry: Empirical Evidence from Inter-provincial Panel data in China

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Procedia Computer Science 2022年 214卷 1249-1255页

作者： Yaling Gong Xin Tian Zuoliang Jiang Yamin Jiao School of Economics and Management Yuzhang Normal University Nanchang 330103 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Shanghai HEADING Information Engineering Co. Ltd. Shanghai 201112 Shanghai Winjoin Information Technology Co. Ltd. Shanghai 200126

This paper investigates pervasiveness effect of information technology on the technological progress of logistics in China. Using panel data of 30-regions in China during 2008 to 2020, we propose both static and dynamic panel data models, and find both resource level and spreading rate of information technology have negative influences on technological progress of logistics. The resource level of information technology has a short-term effect on technological progress of logistics, whereas the spreading rate of information technology has a long-term effect. Moreover, the result reveals that the discordant problem of information construction of logistics between resource level and spreading rate cause the negative influence of information technology.

关键词： Pervasiveness effect Information technology Technological progress Logistics industry

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AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

arXiv

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arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

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Three IQs of AI systems and their testing methods

Three IQs of AI systems and their testing methods

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3rd Asian Conference on Artificial Intelligence technology, ACAIT 2019

作者： Liu, Feng Liu, Ying Shi, Yong 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 School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

The rapid development of artificial intelligence (AI) has brought the AI threat theory as well as the problem about how to evaluate the intelligence level of intelligent products. Both need to find a quantitative method to evaluate the intelligence level of intelligence systems, including human intelligence. Based on the standard intelligence system and the extended Von Neumann architecture, this study proposes General intelligence quotient (IQ), Service IQ and Value IQ evaluation methods for intelligence systems, depending on different evaluation purposes. Among them, the General IQ of intelligence systems is to answer the question of whether 'the artificial intelligence can surpass the human intelligence', which is reflected in putting the intelligence systems on an equal status and conducting the unified evaluation. The Service IQ and Value IQ of intelligence systems are used to answer the question of 'how the intelligent products can better serve the human', reflecting the intelligence and required cost of each intelligence system as a product in the process of serving human. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Testing

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Who knows more? The role of structural hole spanners in accurate information identification on social media

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Pacific-Basin Finance Journal 2025年 93卷

作者： Man Guo Wen Long Yanqiang Zhong Wei Zhang School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 PR China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 PR China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 PR China Department of Information Systems and Management Engineering Southern University of Science and Technology Shenzhen 518055 PR China

Based on structural hole theory, this study explores the differences in the ability of Chinese social media users with different characteristics to provide accurate information. Using over 20 million interactive data from 2.19 million users, we construct a social network and identify structural hole spanners and ordinary users. As key players in a network, structural hole spanners are typically located at the intersections of different groups or information sources. They differ from ordinary users in the way they gather and disseminate information. The empirical results indicate that the accuracy of market judgements by structural hole spanners is at least 12.38 % higher than that of ordinary users. Strategy simulations show that using information from structural hole spanners to make investment decisions can achieve a 116 % cumulative return during the backtesting period, nearly four times that of ordinary users. This information advantage occurs primarily because opinion distance and information uniqueness play significant roles in improving information accuracy. This study not only helps deepen understanding of information dissemination characteristics among heterogeneous users on social media but also provides empirical support for the application of structural hole theory in finance, with important theoretical and practical implications.

关键词： Accurate information Social media Stock market Structural hole spanners

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Fine-grained attribute-aware analysis for person re-identification

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Procedia Computer Science 2022年 199卷 276-283页

作者： Kunlong Bai Saiji Fu Linrui Yang Dalian Liu School of Computer Science & Technology University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing 100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 China Institute of Mathematics and Physics Beijing Union University Beijing 100101 China Institute of Fundamental and Interdisciplinary Sciences Beijing Union University Beijing 100101 China

How and where to learn the discriminative feature has always been a critical issue of person re-identification (re-ID). Most of the previous methods focus on extracting global representation from overall identity image and lack of high-level semantic attribute information. Based on the considerations above, we focus on looking for a way to make people’s attribute play a role in person re-identification task. In this paper, a novel multitask-like network, namely, Attribute-Identity Complementary Network (AICNet), is designed. It contains two branches to learn the identity and attribute feature separately, and a reciprocal interaction process is added to enrich the discrimination of the resulting feature. In addition, in order to better extract high-level semantic attribute information, multi-scale features are combined in the process of attribute extraction. With the help of this attribute-identity complementary strategy, the generated feature can be guided to learn most distinctive attribute feature that is most relevant to identity feature. The present paper carries out extensive experiments on two large-scale datasets, including Market-1501, and DukeMTMC-reID, after which it is found that our attribute-identity complementary framework significantly outperforms the baseline model and achieves competitive performance compared with the state-of-the-art person re-ID methods.

关键词： Person re-identification Attribute recognition Multi-task learning Deep neural network

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