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

作者： Zhang, Linzi Xue, Jiayu Shang, Mengyu Qi, Zhiquan School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 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

Credit insurance is a new loan guarantee contract which can effectively stimulate domestic demand and promote consumption. According to different modes of payment, individual credit loan guarantees mainly include credit insurance and guarantee insurance. In this paper, we apply the Complete Information Game to analyze the strategies of the insured subject and the risk subject under different scenarios of personal loan guarantee, and obtain the influencing factors of each decision-making subject in supply chain finance. The conclusion shows that the credit status of individual loan applicants greatly affects the decision-making of supply chain financial entities such as banks. Based on this, this paper puts forward corresponding suggestions for the credit insurance business management. © 2022 The Authors. Published by Elsevier B.V.

关键词： Costs

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A Conditional Deep Framework for Automatic Layout Generation

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IEEE ACCESS 2022年 10卷 86092-86100页

作者： Shi, Yong Shang, Mengyu Qi, Zhiquan Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China

Automatic layout generation, which means making computers enjoy creativity, is difficult yet exciting work. Up to now, how to generate reasonable and visually appealing layouts remains a complex challenge. In this paper, we propose a novel layout generation model based on Conditional Generative Adversarial Networks (L-CGAN), which can generate layouts simply and efficiently by positioning, scaling, and flipping the given primitives. To break the bottleneck of limitation of the fixed input size of Generative Adversarial Networks, we develop a pre-processing algorithm to enable the model to generate layouts with an unrestricted number of input elements. Moreover, a graph-constraint module is proposed to guide layout optimization. We demonstrate the competitive performance of our designs in diverse data domains such as handwriting digit layout generation (MNIST Layouts), scene layout generation (AbstractScene-Layouts), and document layout generation (PubLayNet).

关键词： Layout Image analysis Generative adversarial networks Deep learning data models Image synthesis Transformers Deep learning layout generation conditional generative adversarial networks abstract scene layout

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Fuzzy-Based Concept Learning Method: Exploiting data With Fuzzy Conceptual Clustering

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第1期52卷 582-593页

作者： Mi, Yunlong Shi, Yong Li, Jinhai Liu, Wenqi Yan, Mengyu Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Kunming Univ Sci & Technol Data Sci Res Ctr Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Sci Kunming 650500 Yunnan Peoples R China

Concepts have been adopted in concept-cognitive learning (CCL) and conceptual clustering for concept classification and concept discovery. However, the standard CCL algorithms are incapable of tackling continuous data directly, and some standard conceptual clustering methods mainly focus on the attribute information, ignoring the object information that is also important to improve clustering analysis and concept classification ability. Therefore, in this article, we present a novel concept learning method, called the fuzzy-based concept learning model (FCLM), to address these two issues by exploiting concept hierarchical relations in concept lattices. More specifically, we first show some new related notions for FCLM based on a regular fuzzy formal decision context;among these notions, the object-oriented and attribute-oriented fuzzy concept similarities are used to achieve the concept similarity measure in concept lattices. Moreover, a novel fuzzy concept learning framework is designed, and its corresponding learning algorithms are developed. Finally, we conduct some experiments on various real-world datasets to demonstrate that the proposed method can achieve the state-of-the-art classification performance among similarity-based learning methods. In addition, we further verify the effectiveness of our method in concept discovery on the MNIST dataset.

关键词： Learning systems Task analysis Object oriented modeling Fuzzy sets Standards Lattices Cognition Concept lattices concept learning fuzzy concept learning fuzzy conceptual clustering granular computing (GrC)

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A Review on the Construction of Business Intelligence System Based on Unstructured Image data 8

A Review on the Construction of Business Intelligence System...

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8th International Conference on Information Technology and Quantitative management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Zhang, Linzi Qi, Zhiquan Meng, Fan Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Peking Univ Dept Infomat Management Beijing 100871 Peoples R China

Business Intelligence (BI) has been an emerging significant issue in modern government and corporate functions, which attracts the attention of both researchers and merchants. Using the raw data from customers, BI provides effective decision-making knowledge for various cooperate governance operations with the method of data Warehouse and data mining. As the explosive growth of data volume, it has evolved into using unstructured data including image and text in business intelligence system, from the initial descriptive analysis of computational statements. This paper will review the methods of storing and analyzing the unstructured image data used in the construction of BI system, including traditional image processing and novel deep learning methods. In each method, the specific algorithms and application scenarios will be summarized. Together, we will discuss the possible innovative progress of BI system construction and its future trends. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Business intelligence Unstructured image data big data fusion

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Model diagnostics of parametric Tobit model based on cumulative residuals

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JOURNAL OF THE KOREAN STATISTICAL SOCIETY 2021年第1期50卷 198-213页

作者： Sun, Zhihua Guo, Yuanyuan Xie, Tianfa Wang, Miaomiao Univ Chinese Acad Sci Beijing 100049 Peoples R China Key Lab Big Data Min & Knowledge Management CAS Beijing 100049 Peoples R China Beijing Univ Technol Beijing 100124 Peoples R China

In this paper, we investigate the adequate check of the parametric Tobit model. A Cramer-Von Mises type test statistic is constructed, and its asymptotic properties under the null and alternative hypotheses are rigorously studied. The method is effective for the adequacy check of parametric regression models with a scalar or multivariate covariate. Furthermore, it avoids the nonparametric smoothing of the regression function and the choice of the smoothing parameter. Simulation studies are conducted to compare the performance of the proposed test procedure and the existing methods in the literature. A real data set of income is analyzed by applying the proposed method.

关键词： Parametric Tobit model Model diagnostics Estimated empirical process Bootstrap method

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data Boundary and data Pricing Based on the Shapley Value

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IEEE ACCESS 2022年 10卷 14288-14300页

作者： Tian, Yingjie Ding, Yurong Fu, Saiji Liu, Dalian Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Beijing Union Univ Inst Math & Phys Beijing 100101 Peoples R China Beijing Union Univ Inst Fundamental & Interdisciplinary Sci Beijing 100101 Peoples R China

data pricing is to price data as asset to promote the healthy development of data share, exchange, and reuse. However, the uncertain value index and neglection of interactivity lead to information asymmetry in the transaction process. A perfect data pricing system and a well-designed data market can widely promote data transactions. We take a three-agent data market in this paper, the data owner who provides the data record, the model buyer who is interested in buying machine learning (ML) model instances, and the broker who interacts between the data owner and the model buyer. Two interaction scenarios are defined. In scenario 1, we introduce the Shapley value (SV) to measure values of data records fairly and construct a revenue optimization problem based on the sum of the SV for data boundary. The optimal solution is obtained by calculating their derivatives. In scenario 2, we utilize market research and construct a revenue maximization (RM) problem to price the ML models. Further, An Integer Linear Programming for the RM problem (RM-ILP) process is proposed to transform the RM problem into an equivalent integer linear programming (ILP) problem and solve it with the Gurobi solver. Finally, we conduct extensive experiments, which validate that the RM-ILP process can provide high revenue to the broker and high affordability to the model buyers compared to the benchmarks.

关键词： Pricing data models Computational modeling Optimization Costs Heuristic algorithms Roads data value data pricing the shapley value model pricing

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Improved incremental local outlier detection for data streams based on the landmark window model

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knowledge AND INFORMATION SYSTEMS 2021年第8期63卷 2129-2155页

作者： Li, Aihua Xu, Weijia Liu, Zhidong Shi, Yong Cent Univ Finance & Econ Sch Management Sci & Engn Beijing 102206 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Most existing algorithms of anomaly detection are suitable for static data where all data are available during detection but are incapable of handling dynamic data streams. In this study, we proposed an improved iLOF (incremental local outlier factor) algorithm based on the landmark window model, which provides an efficient method for anomaly detection in data streams and outperforms conventional methods. What is more, data windows as updating units are introduced to reduce the false alarm rate, and multiple tests are taken here to identify candidate anomalies and real anomalies. The improved iLOF shows its obvious advantage with its false positive rate. Furthermore, the proposed algorithm instantly deletes data points of identified real anomalies. We analyzed the performance of the improved algorithm and the sensitivity of certain parameters via empirical experiments using synthetic and real data sets. The experimental results demonstrate that the proposed improved algorithm achieved better performance on the higher detection rate and the lower false alarm rate compared with the original iLOF algorithm and its improvements.

关键词： Incremental local outlier factor algorithm Landmark window model Anomaly detection data streams

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Graph Influence Network

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第10期53卷 6146-6159页

作者： Shi, Yong Quan, Pei Xiao, Yang Lei, Minglong Niu, Lingfeng Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing Peoples R China

Due to the extraordinary abilities in extracting complex patterns, graph neural networks (GNNs) have demonstrated strong performances and received increasing attention in recent years. Despite their prominent achievements, recent GNNs do not pay enough attention to discriminate nodes when determining the information sources. Some of them select information sources from all or part of neighbors without distinction, and others merely distinguish nodes according to either graph structures or node features. To solve this problem, we propose the concept of the Influence Set and design a novel general GNN framework called the graph influence network (GINN), which discriminates neighbors by evaluating their influences on targets. In GINN, both topological structures and node features of the graph are utilized to find the most influential nodes. More specifically, given a target node, we first construct its influence set from the corresponding neighbors based on the local graph structure. To this aim, the pairwise influence comparison relations are extracted from the paths and a HodgeRank-based algorithm with analytical expression is devised to estimate the neighbors' structure influences. Then, after determining the influence set, the feature influences of nodes in the set are measured by the attention mechanism, and some task-irrelevant ones are further dislodged. Finally, only neighbor nodes that have high accessibility in structure and strong task relevance in features are chosen as the information sources. Extensive experiments on several datasets demonstrate that our model achieves state-of-the-art performances over several baselines and prove the effectiveness of discriminating neighbors in graph representation learning.

关键词： Task analysis Representation learning Graph neural networks Feature extraction data mining Convolution Social networking (online) Graph neural networks (GNNs) graph representation learning HodgeRank influence ranking influence set neighbors discrimination

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Analysis on the Transmission of International Oil Price to China's Price Index--Based on Granger Causality Network 8

Analysis on the Transmission of International Oil Price to C...

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8th International Conference on Information Technology and Quantitative management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Wang, Yijing Wang, Xiuzhenzi Guo, Kun Univ Chinese Acad Sci Sch Econ & Management Beijing 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 Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China

This paper uses complex network theory and Granger causality method to construct Granger Causality Network among price indices (PIGCN), specifically based on crude oil price and China's domestic price index from a systematic perspective and provides a statistical physical method to analyze conduction fluctuations. This paper selects four typical price indices in China, including 36 sub price indices as samples for empirical research. Through the analysis of the PIGCN structure, we have analyzed the volatility transmission path of the international crude oil price and Chinese domestic price index, and further revealed the dynamic relationship among the price indices. Also, we have put forward some policy recommendations to better monitor the spread of price fluctuation risks. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Granger causality network Price transmission International crude oil price Price indices

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A Brief Survey of Distribution Robust Graph Neural Networks

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Procedia Computer Science 2024年 242卷 1281-1286页

作者： Lei Zheng Pei Quan Yong Shi Lingfeng Niu The School of Mathematical Sciences 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 The College of Economics and Management Beijing University of Technology Beijing 100124 China The School of Economics and Management University of Chinese Academy of Sciences Beijing 1001090 China

Graph neural network is a powerful tool for solving various graph tasks, such as node classification and graph classification. However, there is increasing evidence suggesting that it is sensitive to distribution shifts and its performance is reduced by at least 10% when distribution shifts happen. In the last three years, to solve the wide existing distribution shifts, some distribution robust strategies have been designed specifically for graph neural networks. It is also one of the popular research topics in graph neural networks in recent years. Therefore, in this paper, we divide these strategies into three categories depending on the specific problems they solve: distribution shift for semi-supervised node classification, distribution shift for unsupervised node classification, and out-of-distribution shift for graph classification. Furthermore, we discuss and summarize the strategies for each type of problem. After investigating these strategies, we put forward future development direction.

关键词： Graph neural networks Shift robust Distribution generalization

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