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New technology foresight method based on intelligent knowledge management

Frontiers of Engineering management 2020年第2期7卷 238-247页

作者： Lingling ZHANG Siting HUANG School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Key Laboratory of Big Data Mining and Knowledge ManagementChinese Academy of SciencesBeijing 100190China School of Economics and Management University of Chinese Academy

The increasing importance of technology foresight has simultaneously raised the significance of methods that determine crucial areas and ***,qualitative and quantitative methods have *** former involve high costs and many limitations,while the latter lack expert *** knowledge management emphasizes human–machine integration,which combines the advantages of expert experience and data ***,we proposed a new technology foresight method based on intelligent knowledge *** method constructs a technological online platform to increase the number of participating experts.A secondary mining is performed on the results of patent analysis and ***,forward-looking,innovative,and disruptive areas and relevant experts must be discovered through the following comprehensive process:Topic acquisition→topic delivery→topic monitoring→topic guidance→topic reclamation→topic sorting→topic evolution→topic conforming→expert recommendation.

关键词： technology foresight intelligent knowledge management technological online platform

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Suppress content shift: better diffusion features via off-the-shelf generation techniques 24

Suppress content shift: better diffusion features via off-th...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Zhiyong Yang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at https://***/Darkbblue/diffusion-content-shift.

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Evaluation of China’s Digital Economy: A Case Study using Entropy and TOPSIS

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Procedia Computer Science 2024年 242卷 1256-1262页

作者： Junjie Liu Boya Wang Jiayu Xue Yi Qu Yong Shi Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 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 Faculty of Science and Engineering University of Nottingham Ningbo China Ningbo Zhejiang 315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

Recently, digital economy in social science has become increasingly significant. A well-constructed evaluation structure could generally and promptly describe and reveal its overall social digitalization development. Building on existent researches, this paper improves the evaluation system, which comprises four first-level indicators and thirty-one second-level indicators. Notably, this paper innovatively incorporates higher education into digital innovation of the evaluation. The Entropy method is utilized to assign weights to various indicators in an objective way, with employing the TOPSIS approach to prioritize the development of digital economy in places based on the closeness to the ideal solution. By analysing related data related from 31 provinces in China between 2017 to 2021, this paper greatly finds: Firstly, the development of digital economy vary significantly across provinces, with most provinces at a relatively poor level, indicating considerable potential for overall growth; Secondly, Guangdong, Jiangsu, Zhejiang, and Shandong consistently ranked in the top four from 2017 to 2021, reflecting their specific development attributes and their emphasis on digital economy; Thirdly, significant disparities exist both between regions and among provinces. Consequently, this paper suggests establishing a comprehensive national digital economy development plan, formulating region-specific digital economy policies tailored to local conditions, and encouraging leading provinces to actively share experiences with other provinces.

关键词： Digital economy Multi-source information Evaluation TOPSIS

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Product assortment and online sales in community group-buying channel: Evidence from a convenience store chain

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JOURNAL OF RETAILING AND CONSUMER SERVICES 2024年 79卷

作者： Tian, Xin Jiang, Hailiang Zhao, Xuan Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Wilfrid Laurier Univ Lazaridis Sch Business & Econ Waterloo ON Canada

Although community group -buying (CGB) has been adopted worldwide since its emergence in China, empirical research examining how retailers' assortment strategies affect sales performance and consumer behavior in CGB channels is scarce. We explore the impact of three product assortment characteristics-assortment width, perishability, and recommended diversity-on CGB sales and consumer behavior. Our assessment of CGB sales relies on sales revenue per group -buying for each store as a primary metric;the average item quantity per order (average basket size) and average item price in all orders (average item price) are utilized as fundamental indicators for evaluating consumer behavior. This study employs panel fixed effects models to analyze operational CGB data. Our results show that assortment width, perishability, and recommended diversity have significantly positive impacts on CGB sales. Furthermore, assortment width positively and significantly influences both the average basket size and average item price. The perishability of an assortment negatively affects the former but positively impacts the latter. Conversely, the recommended diversity of assortments positively impacts the average basket size but has a negative impact on the average item price. Additionally, holidays can moderate the impact of product assortments on CGB sales and consumer behavior.

关键词： Community group -buying (CGB) Product assortment Perishable products Recommended products

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An Innovative Double-Frontier Approach to Measure Sustainability Efficiency Based on an Energy Use and Operations management Perspective

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ENERGIES 2024年第16期17卷 3972页

作者： Zhang, Linyan Xu, Chunhao Zhang, Jian Lei, Bingyin Xie, Anke Shen, Ning Li, Yujie Gao, Kaiye Beijing Informat Sci & Technol Univ Sch Econ & Management Beijing 100192 Peoples R China Beijing Key Lab Green Dev Decis Based Big Data Beijing 100192 Peoples R China Yunnan Key Lab Blockchain Applicat Technol Kunming 650233 Peoples R China Beihang Univ Yunnan Innovat Inst Kunming 650233 Peoples R China Res Ctr Knowledge Management Beijing 100192 Peoples R China Beijing Forestry Univ Sch Econ & Management Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Econ & Management Beijing 100083 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hong Kong 999077 Peoples R China

China's economic development has achieved great success in recent years, but the problems of energy scarcity and environmental pollution have become increasingly serious. To enhance the reliability and efficiency between energy, the environment and the economy, sustainable development is an inevitable choice. In the context of measuring sustainability efficiency, a network data envelopment analysis model is proposed to formulate the two-stage process of energy use and operations management. A double frontier is derived to optimize the available energy for sustainable development. Due to nonlinearity, previous linear methods are not directly applicable to identify the double frontier and calculate stage efficiencies for inefficient decision-making units. To address this problem, this study develops the primal-dual relationship between multiplicative and envelopment network models based on the Lagrange duality principle of parametric linear programming. The newly developed approach is used to evaluate the sustainability efficiency of 30 administrative regions in China. The results show that insufficient sustainability efficiency is a systemic problem. Different regions should take different measures to conserve energy and reduce pollutant emissions for sustainable development. To increase sustainability efficiency, regions should support energy-saving and emission-reducing technologies in production processes and strengthen their capacity for technological innovation. Compared with energy use efficiency, operations management efficiency in China has a wider range of changes. During the operations management stage, there is not much difference between the capacity and quantity of each region. Based on benchmark regions at the efficiency frontier, there is an opportunity to improve operations management in the near future. Blockchain technology can effectively improve energy allocation efficiency.

关键词： sustainability efficiency double frontier energy use operations management blockchain technology

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Does Air Pollution Impact Fiscal Sustainability? Evidence from Chinese Cities

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ENERGIES 2021年第21期14卷 7247-7247页

作者： Gao, Ge Li, Xiuting Liu, Xiaoting Dong, Jichang Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Fiscal sustainability is an issue of great concern for governments globally and air pollution control has become an important factor affecting fiscal sustainability. This study aims to examine the impact of air pollution on fiscal sustainability in the short and long run. We conducted an empirical analysis based on air pollution and local government debt data on China's prefecture-level cities in 2014-2019, using regression discontinuity design (RDD) and a panel data model. The results show that air pollution reduces the debt burden of governments in the short run. However, in the long run, addressing the negative impacts of air pollution adds to the debt burden of local governments, hindering fiscal sustainability. Fiscal freedom and the level of public services significantly moderate the negative impact of air pollution on fiscal sustainability. A higher level of fiscal freedom generally indicates a greater incentive for local governments to raise pollutant emission standards, strengthen the construction of green infrastructure, and subsidize green enterprises. Furthermore, a higher level of public services reflects better infrastructure and higher levels of investment in environmental protection, which help to reduce the negative impact of air pollution. The governments are suggested to take measures to effectively control air pollution, so as to enhance fiscal stability in the long run.

关键词： fiscal sustainability public finance sustainability air pollution sustainable development

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Robust Obstacle Detection and Recognition for Driver Assistance Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2020年第4期21卷 1560-1571页

作者： Leng, Jiaxu Liu, Ying Du, Dawei Zhang, Tianlin Quan, Pei Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101400 Peoples R China Chinese Acad Sci Data Min & High Performance Comp Lab Beijing 101400 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

This paper proposes a robust obstacle detection and recognition method for driver assistance systems. Unlike existing methods, our method aims to detect and recognize obstacles on the road rather than all the obstacles in the view. The proposed method involves two stages aiming at an increased quality of the results. The first stage is to locate the positions of obstacles on the road. In order to accurately locate the on-road obstacles, we propose an obstacle detection method based on the U-V disparity map generated from a stereo vision system. The proposed U-V disparity algorithm makes use of the V-disparity map that provides a good representation of the geometric content of the road region to extract the road features, and then detects the on-road obstacles using our proposed realistic U-disparity map that eliminates the foreshortening effects caused by the perspective projection of pinhole imaging. The proposed realistic U-disparity map greatly improves the detection accuracy of the distant obstacles compared with the conventional U-disparity map. Second, the detection results of our proposed U-V disparity algorithm are put into a context-aware Faster-RCNN that combines the interior and contextual features to improve the recognition accuracy of small and occluded obstacles. Specifically, we propose a context-aware module and apply it into the architecture of Faster-RCNN. The experimental results on two public datasets show that our proposed method achieves state-of-the-art performance under various driving conditions.

关键词： Obstacle location obstacle recognition U-V disparity map neural network road extraction

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A novel method based on deep learning for aligned fingerprints matching

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APPLIED INTELLIGENCE 2020年第2期50卷 397-416页

作者： Liu, Yonghong Zhou, Baicun Han, Congying Guo, Tiande Qin, Jin Univ Chinese Acad Sci Beijing Peoples R China Univ Chinese Acad Sci Operat Res & Cybernet Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

In this study, a novel method based on deep learning for aligned fingerprints matching is proposed. According to the characteristics of fingerprint images, a convolutional network, Finger ConvNet, is designed. In addition, a new joint supervision signal is used to train Finger ConvNet to obtain deep features. Experimental studies are performed on public fingerprint datasets, the ID Card fingerprint dataset and the Ten-Finger Fingerprint Card fingerprint dataset. Furthermore, four performance indicators, the false matching rate (FMR), false non-matching rate (FNMR), equal error rate (EER) and receiver operating characteristic (ROC) curve, are measured. The experimental results demonstrate the effectiveness of the proposed method, which achieved a competitive effect in comparison with conventional fingerprint matching algorithms in fingerprint verification tasks using the FVC2000, FVC2002, and FVC2004 datasets. Moreover, the matching speed of the proposed method was almost 5 times faster than the fastest conventional fingerprint matching algorithms. In addition, it can be used as a fast matching method to filter out many templates with low scores by setting a threshold according to the matching scores and thus accelerate the process in identification tasks.

关键词： Fingerprint matching Deep learning Finger ConvNet Fast matching method

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How Do Climate Factors Affect Household Portfolio Choices? Empirical Evidence from China's Household Survey

SSRN

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SSRN 2021年

作者： Zhao, Wen Li, Xiuting Liu, Xiaoting Guo, Sijia Dong, Zuoji School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

This paper empirically investigates the impacts of climate factors, including temperature and precipitation, on household portfolio choices based on the panel data of 2,110 households continuously observed from the China Family Panel Studies (CFPS) for 2010, 2012, 2014 and 2016. The results show that climate factors significantly affect household portfolio choices mainly through channels including income, asset prices, health and risk attitudes. Furthermore, the results also show that climate factors moderate the effects of income, asset prices and investors’ confidence on household portfolio choices. This research contributes to the literature by introducing theories regarding behavioral finance and climate economics into the research on household portfolio choices, which provides insights into the impact mechanisms of climate factors on household behaviors and enriches the studies of behavioral investment decision making and environmental economics. © 2021, The Authors. All rights reserved.

关键词： Investments

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Multi-step ahead prediction of carbon price movement using time-series privileged information

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期255卷

作者： Tian, Yingjie Wen, Haonan Fu, Saiji Univ Chinese Acad Sci Sch Econ & Management 80 Zhongguancun East Rd Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci 80 Zhongguancun East Rd Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge management 80 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci MOE Social Sci Lab Digital Econ Forecasts & Policy 3 Zhongguancun South St 1 Beijing 100190 Peoples R China Beijing Univ Posts & Telecommun Sch Econ & Management 10 Xitucheng Rd Beijing 100876 Peoples R China

Multi-step ahead carbon price forecasting is crucial for making informed decisions in production, operations, and investments. However, many forecasting methods fail to incorporate intermediate time series data due to its unavailability during testing. This study leverages such data within the Learning using Privileged Information (LUPI) framework. Under the guidance of two prevalent mechanisms in LUPI, we develop two forecasting models-a privileged information-enhanced Least Squares Support Vector Machine (LSSVM+) and a Gated Recurrent Unit (GRU) network equipped with generalized distillation. Both methods are rigorously assessed using European Union Allowance (EUA) carbon future prices in both univariate and multivariate time series modeling scenarios. The outcomes emphasize the superiority of LUPI-based methods in boosting multi-step carbon price forecasting, leading to more strategic decisions in carbon markets.

关键词： Carbon price Multi-step ahead prediction Privileged information Least Squares Support Vector Machines Gated Recurrent Unit

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