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Digitizing Highways:Unleashing Potential Through data Element Circulation for Safety,Cost Efficiency,and Environmental Impact Optimization

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Journal of Systems Science and Information 2025年第1期13卷 23-60页

作者： Yanyan JIA Wen LONG Yingjie TIAN School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

As the era of large-scale highway maintenance arrives,the maintenance strategies have transitioned to a holistic approach that prioritizes safety,economic feasibility,and environmental *** research introduces a multi-objective optimization model for highway maintenance that incorporates the interplay of decision-maker preferences across three key objectives:Highway safety performance,maintenance engineering cost,and carbon *** study employs a large-sample data analysis on a subset of the Lianhuo Highway network,which includes 2,842 pavement *** approach mitigates the impact of outliers,ensuring a substantial data buffer that fortifies the model’s capacity for generalization and bolsters its *** findings reveal a Pareto-optimal relationship among the three scrutinized variables.A particularly noteworthy observation is the M-shaped trajectory of carbon emissions,which initially rise,then decline,and ultimately rebound,contingent upon the selected maintenance ***,an examination of the relationship between maintenance costs and safety performance discloses a trend of diminishing marginal returns,illustrating that the incremental gains in safety performance attenuate as maintenance investment escalates.

关键词： data element circulation highway maintenance multi-objective optimization

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research on Pricing of data Based on Bi-level Programming Model

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Annals of data Science 2024年第4期11卷 1391-1419页

作者： Ding, Yurong Tian, Yingjie 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 MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing100190 China

Effective value measurement and pricing methods can greatly promote the healthy development of data sharing, exchange and reuse. However, the uncertainty of data value and neglect of interactivity lead to information asymmetry in the transaction process. A perfect pricing system and well-designed data trading market (hereafter called data market) can widely promote data transactions. We take the three-agents data market as an example to construct a sound data trading process. The data owner who provides data records, the model buyer who is interested in buying machine learning (ML) model instances, and the data broker who interacts between the data owner and the model buyer. Based on the characteristics of data market, like truthfulness, revenue maximization, version control, fairness and non-arbitrage, we propose a data pricing methods based on different model versions. Firstly, we utilize market research and construct a revenue maximization (RM) problem to price the different versions of ML models and solve it with the RM-ILP process. However, the RM model based on market research has two major problems: one is that the model buyer has no incentive to tell the truth, that is, the model buyer will lie in the market research to obtain a lower model price;the other is that it asks the data broker to release version menu in advance, resulting in an inefficient operation of the data market. In view of the defects of the RM transaction model, we propose a model buyers behavior analysis, establish the revenue maximization function based on different data versions to establish a bi-level linear programming model. We further add the incentive compatibility constraint and the individual rationality constraint, taking the utility of the model buyer and the revenue of the data broker into account. This reflects the consumer driven model in the data transaction mode. Finally, the RM-BLP process is proposed to transform RM problem into an equivalent single-level integer program

关键词： Costs

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Trade credit or credit insurance? A green supply chain finance design scheme with multi-objective programming

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Journal of Intelligent and Fuzzy Systems 2023年第2期45卷 2707-2721页

作者： Zhang, Linzi Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing China University of Birmingham Edgbaston Birmingham United Kingdom Research Center on Fictitious Economy Data Science Chinese Academy of Science Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

Classical supply chain finance (SCF) primarily focuses on the financial service among all upstream and downstream supply chain participants. Due to the continuously deteriorating of the ecological environment, an environmental-friendly SCF system is urgently needed. In this paper, we propose a novel SCF design scheme with environmental concerns, i.e., green supply chain finance (GSCF), consider the financing channels both from banks and from consumers, and design a bi-objective optimization model that depicts the trade-off between the benefit and the emission. Further, an improved normalized normal constraint (INNC) Pareto method is developed to address the optimal financing strategy of the bi-objective model. We then conduct a numerical case of a Taiwanese steel firm to verify the effectiveness and efficiency of our method. Results show that our model provides a portfolio of optimal solutions on Pareto frontier which can be applied as an effective decision support system when designing a GSCF. Furthermore, the sensitivity analysis also presents the impact of environmental investment cost, technological ratio of companies and the interest rate of trade credit on the optimal configuration of the GSCF. © 2023 - IOS Press. All rights reserved.

关键词： Multiobjective optimization

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Analysis on the Effect of Government Investment on Private Investment in Western China——Take Province A as an Example

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Journal of Systems Science and Information 2023年第3期11卷 297-313页

作者： Ming CHEN Yingjie TIAN School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

Since China began reforming and opening up its economy,and especially since the launch of development projects in western China,province A has attracted an increasing amount of investment,which is the main driving force for provincial economic ***,this study uses a state space model to examine how government investment has affected economic growth in province A in western China,and explains whether there is a crowding-in effect or a crowding-out effect of local government investment on private *** findings indicate that both government and private investments have a positive,stimulating influence on economic growth in province A,with the latter being more impactful than the *** and non-productive investments have different effects on province A’s economic *** the perspective of the trajectory of government investment elasticity,the elasticity of government and private investments in province A presents a very large spatio-temporal *** is,from 1994 to 2009,government investment in province A had a crowding-in effect on private investment,but from 2010 to 2017,a crowding-out effect was observed.

关键词： province A in western China government investment private investment state space model

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Comparative research on multi-source heterogeneous data fusion technologies 11

Comparative research on multi-source heterogeneous data fusi...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Long, Wen Gao, Jing Guo, Man School of Economics and Management University of Chinese Academy of Sciences Beijing100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing100190 China

Both structured market data and unstructured financial news data can significantly affect stock price fluctuations. Therefore, relying only on a single data source for stock price trend prediction may produce information bias. This article combines these two types of heterogeneous data and systematically compares the effectiveness of different data fusion technologies to establish the optimal data integration strategy, thereby improving the accuracy of stock price prediction. Specifically, this paper innovatively proposes a stock price trend prediction model named MVL-SVM. The model successfully combines multi-perspective learning with support vector machine, and realizes the effective fusion of stock market data and financial news data. It is worth mentioning that the prediction accuracy of MVL-SVM model using direct fusion method is more than 30% higher than that of index modeling method, which indicates that direct fusion method can fully learn effective information from multiple information sources, thus significantly improving the prediction effect. © 2024 The Authors.

关键词： data accuracy

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Measuring China's Real Estate Financial Innovation from the Perspective of Government,Enterprises and the Public:Index Compilation and Its Spatial-Temporal Characteristics Analysis

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Journal of Systems Science and Information 2023年第1期11卷 1-34页

作者： Jichang DONG Lijun YIN Xiaoting LIU Xiuting LI School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China China Construction Second Engineering Bureau Co.Ltd Beijing 100070China

In recent years,China has witnessed the rapid development in housing finance,and there have emerged constantly real estate finance innovations;however,there exists no relevant index for measuring the innovations of China's real estate *** on the perspectives of the governments,enterprises and the public,this paper constructs the"innovation index of real estate finance"on a quarterly basis from 2009 to 2019,with the method of empowerment which combines the subjective method(analytic hierarchy process)and the objective one(range coefficient method).It clearly and concretely depicts the innovations in housing finance and the related temporal-spatial characteristics in China since the outbreak of the financial crisis in *** index covers 30 provinces,autonomous regions and municipalities directly under the central government,and analyzes its temporal and spatial *** findings show that there exist a strong spatial autocorrelation and a big regional difference in innovations.

关键词： governments enterprises public real estate finance innovation

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation 38

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmenta...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024 Neural information processing systems foundation. All rights reserved.

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Volatility and risk contagion of international stock market in the context of COVID-19 10

Volatility and risk contagion of international stock market ...

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Long, Wen Zeng, Yuhan Guo, Man School of Economics and Management University of Chinese Academy of Sciences Beijing100090 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

In order to reveal the impact of the outbreak of COVID-19 epidemic on risk contagion in the international stock market, this paper establishes an ARMA-GARCH-Copula model based on the R-Vine structure to empirically analyze the stock index data of 19 important countries(regions) around the world, and uses complex network to display the risk contagion path. This research shows that the risk contagion correlation of the global stock market has significantly improved due to the impact of COVID-19;The correlation of risk transmission among European countries is significantly higher than that of other regions, reflecting a closer integration connection between EU countries;Finally, unlike previous literature, this article finds that stock market of Hong Kong in China has become an important node in the international stock market, playing an important role in the risk network before and after the epidemic. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： COVID-19

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research on Liquidity Measurement and Influencing Factors of China Carbon Emission Allocation Trading Market 10

Research on Liquidity Measurement and Influencing Factors of...

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Shi, Yong Peng, Shuyang Yu, Anqi Guo, Kun 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

Considering multi-dimensional concepts of liquidity, we introduces two classical liquidity measures to quantify the liquidity of China carbon emission allowance(CCEA) trading markets, and analyze their trading activities. Then monthly unbalanced panel was constructed based on daily trading data to further study on the influencing factors of CCEA market. We find that: 1) No matter what method we take, the Hubei pilot is the most liquid in the context of overall running periods, and the national carbon market and Guangdong pilot are also well performed compared to the other markets. 2)The foundation of national generalized market could bring about noticeable changes on the liquidity of carbon market, and Guangdong market which is one of the 8 carbon market pilots become more developed and liquid, while no significant variation in the rest of 7 pilots. 3) The emission control enterprises of each pilot have no inborn incentive to trade proactively, therefore transactions are concentrated on the performance cycle. 4)Regression results show that the price at the end of each month, trading volumes aggregated in each month, trading frequency of each month are all positively correlated with the liquidity, and return volatility of each month, carbon market fragmentation are negatively related to the liquidity. Finally we put forward some suggestion and policy recommendation rested on the above qualitative and quantitative analysis. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Emission control

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A dynamic key node learning algorithm in evolving financial network: case study during the period of coronavirus 9th

A dynamic key node learning algorithm in evolving financial ...

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Lu, Doudou Deng, Wei Xu, Yongyin Shi, Yong College of Big Data Statistics Guizhou University of Finance and Economics Guizhou550025 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Finding an influential set of nodes in the interconnected network, whose activities would maximally influence the development of overall network, is a fundamental topic in finance, biology, social science, epidemic control and other scenarios with respect to network science. In recent years, some works are proposed to find key nodes via network connectivity measures, these studies assume a static environment, and besides, key nodes are calculated through pairwise connectivity, the number of connected components and other measures from the perspective of graph theory. However, these studies leave out of consideration on the dynamic evolving environment and causal relationships in the network. In this paper, we introduce a key node learning algorithm in an evolving financial network. The intuition of the algorithm is to model the causality of financial entities over the time, and the metrics for cascading networks are proposed to grade the entities in the network. In the experiment, the worldwide financial activities before and during the period of coronavirus are analyzed. The proposed metrics can effectively capture the influential entities and quantitatively describe these influences on the whole financial system. © 2022 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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