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arXiv 2022年

作者： Gao, Xiang Tian, Yingjie Qi, Zhiquan School of Computer Science and Technology University of Chinese Academy of Sciences China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences China

We propose an end-to-end-trainable feature augmentation module built for image classification that extracts and exploits multi-view local features to boost model performance. Different from using global average pooling (GAP) to extract vectorized features from only the global view, we propose to sample and ensemble diverse multi-view local features to improve model robustness. To sample class-representative local features, we incorporate a simple auxiliary classifier head (comprising only one 1×1 convolutional layer) which efficiently and adaptively attends to class-discriminative local regions of feature maps via our proposed AdaCAM (Adaptive Class Activation Mapping). Extensive experiments demonstrate consistent and noticeable performance gains achieved by our multi-view feature augmentation module. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Optimization Strategies for Client Drift in Federated Learning: A review 9th

Optimization Strategies for Client Drift in Federated Learni...

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

作者： Shi, Yong Zhang, Yuanying Xiao, Yang Niu, Lingfeng 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 College of Information Sciences and Technology University of Nebraska at Omaha NE68182 United States School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100190 China Faculty of Information Technology Beijing University of Technology Beijing100124 China

With the development of AI technology, there is an increasing awareness and concern about data privacy, and training data is becoming more and more fragmented. To make better use of this distributed data while ensuring privacy, federated learning is proposed, which can federate multiple distributed devices to train a machine learning model and does not exchange the raw training data. A key challenge in federated learning is that the distribution of data across clients is heterogeneous, which leads to client drift, that is, local model updates toward local optimal solutions, resulting in performance degradation of the global model. To address this problem, many federated learning algorithms have been proposed. In this paper, we provide a review of existing federated learning optimization strategies. In our opinion, the existing optimization strategies for client drift can be roughly classified into the following two categories: model aggregation and model training. We introduce these two categories separately. Finally, we present our perspectives on the future development. © 2022 The Author(s).

关键词： data privacy

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The Development of Green Olympic Games: A Comparison of CO2Emission Reduction Strategies between PyeongChang 2018 and Beijing 2022 9th

The Development of Green Olympic Games: A Comparison of CO2E...

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

作者： Ding, Xuesong Jiang, Yilin Yang, Banban Su, Jingchun Chen, Liming Li, Na School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Chinese Academy of Fiscal Sciences Ministry of Finance Beijing100142 China Division of Sports Science and Physical Education Tsinghua University Beijing100084 China Division of Sports Science and Physical Education Tangshan Polytechnic College Tangshan063299 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 Sciences Beijing100190 China

The Olympic Games are striking a balance between economic and ecological benefits. Taking the 5 ice event venues of the 2018 PyeongChang Winter Olympic Games as the research object, the CO2 emissions under the 2018 PyeongChang Low-carbon scenario and 2022 Beijing Low-carbon scenario are compared. The empirical results show that, in terms of venue operation, the Beijing strategies can reduce CO2 emissions by 715.7 t compared with the PyeongChang strategies. In terms of the refrigerant escape, Beijing's adoption of CO2 refrigerant can reduce the emission of CO2 by 1202.2 t compared with the R404 refrigerant used in PyeongChang. This paper confirms the progress of the Green Winter Olympic Games, and may provide implications for the future host countries as well as the cities which in the green transformation. © 2022 The Author(s).

关键词： Refrigerants

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Price change prediction of Ultra high frequency financial data based on temporal convolutional network

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Procedia Computer Science 2022年 199卷 1177-1183页

作者： Wei Dai Yuan An Wen Long School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 P.R. China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 P.R. China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 P.R. China

Through in-depth analysis of Ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are proposed in this paper. Firstly, we classify the price changes into several categories. Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. Furthermore, attention mechanism is added into the TCN architecture to model the time-varying distribution for stock price change data. Empirical research on constituent stocks of Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model and the TCN (attention) framework have a better overall performance than GARCH family models and the long short-term memory (LSTM) framework model for the description of the dynamic process of the UHF stock price change sequence. In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese stock market.

关键词： Temporal convolutional network Ultra high frequency Financial prediction

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Research on the Rules of Scientists’ knowledge Sharing based on Interdisciplinary Measurement

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Procedia Computer Science 2022年 199卷 657-664页

作者： Yong Shi Yunong Wang 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 Science and Technology University of Nebraska at Omaha NE 68182 USA

With the development of modern science, the adjustment of knowledge structure and the deepening of knowledge, the mode of single discipline research has been gradually broken, and interdisciplinary research has become a necessary way. All the information science and library science articles collected in the core collection of web of science from 1982 to 2020 are used for research and analysis. Through the research of interdisciplinary measurement index, this paper improves the existing interdisciplinary measurement index, uses Excel, Python and other tools to carry out data mining, cleaning, processing, analysis, and determines the interdisciplinary literature data defined in this paper. Finally, with vosviewer and other visual tools, this paper uses the citation network of bibliometrics, keywords co-occurrence and other ways to summarize interdisciplinary science’s law of knowledge sharing and provides suggestions and directions for future research.

关键词： Interdisciplinarity Disciplinary Similarity knowledge Sharing

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Liquidity Commonality Effect in Stock Market and Risk Correlation from the Perspective of Complex Network

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Procedia Computer Science 2022年 199卷 725-732页

作者： Haoyuan Feng Jie Wu Kun Guo 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 Academy of Mathematics and Systems Sciences Chinese Academy of Sciences Beijing 100190 China

This paper uses a complex network method to study the liquidity changes and liquidity commonality effects of the Chinese stock market in the two crises of 2015 and 2020. Liquidity indicator and liquidity commonality indicator-the average weighted degree are calculated to revel the risk characteristic of the two crisis. By comparing the two crisis, main conclusions are obtained: Highly leveraged transactions and excess macro-liquidity may lead to the good liquidity condition and the stronger commonality effect in 2015; Besides, the average weighted degree better reflects the systemic risks in a way of revealing the liquidity risk faced by the system.

关键词： Liquidity liquidity commonality systemic risk stock market

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D4M: dataset Distillation via Disentangled Diffusion Model

arXiv

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arXiv 2024年

作者： Su, Duo Hou, Junjie Gao, Weizhi Tian, Yingjie Tang, Bowen School of Computer Science and Technology UCAS United Kingdom Sino-Danish College UCAS United Kingdom Department of Computer Science NCSU United States School of Economics and Management UCAS United Kingdom Research Center on Fictitious Economy and Data Science CAS China Key Laboratory of Big Data Mining and Knowledge Management CAS China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS United Kingdom Institute of Computing Technology CAS China

dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this, we introduce dataset Distillation via Disentangled Diffusion Model (D4M), an efficient framework for dataset distillation. Compared to architecture-dependent methods, D4M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile, eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments, D4M demonstrates superior performance and robust generalization, surpassing the SOTA methods across most aspects. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Robustness and orthogonality: Time series forecasting via wavelets

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Information Sciences 2025年 717卷

作者： Tian, Yingjie Zhao, Siyu Zhang, Xingyu School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 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 National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing100190 China

Time series forecasting (TSF) is an important issue that spans various fields in daily life. With advancements in deep learning, numerous TSF methods have been developed leveraging both time and frequency domain characteristics of time series data. However, existing TSF methods primarily concentrate on data characteristics, yet which key property truly enhances model performance has not been thoroughly explored. Especially, for frequency domain-based methods, the reliance on stringent assumptions like the Linear Time-Invariant (LTI) system limits their applicability to non-stationary time series data. To address these issues, this paper introduces a novel TSF method called Robust Orthogonal decomposition via Wavelets (ROW). ROW employs wavelet transform to remove reliance on LTI assumptions and can capture local characteristics. Furthermore, this paper provides robustness analysis of ROW, i.e., the impact of orthogonal wavelet basis functions on model robustness. Through two examples, we illustrate that the robustness constant of ROW has a tighter upper bound under orthogonal wavelet basis, which indicates that ROW is more robust under perturbations. Experimental results also validate the effectiveness and robustness of ROW compared to existing methods. © 2025 Elsevier Inc.

关键词： Wavelet decomposition

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Credit and guarantee insurance: A consistent pricing approach in supply chain finance

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Procedia Computer Science 2022年 214卷 1256-1261页

作者： Linzi Zhang Jiayu Xue Mengyu Shang Zhiquan Qi 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 School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 101408 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.

关键词： Credit guarantee insurance supply chain finance pricing criterion perfect information game

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Sparse and Semi-Attention Guided Faults Diagnosis Approach for Distributed Online Services

SSRN

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SSRN 2023年

作者： Shi, Yong Zhang, Linzi School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100049 China Research Center on Fictitious Economy & Data Science Chinese Academy of Science Beijing100190 China

The booming popularity and indispensability of online service in daily life claims a higher requirement to its reliability and stability. Therefore, Key Performance Indicators (KPIs) of these systems, e.g., CPU utilization, network traffic, and average response time, are then recorded chronologically as multivariate time series for anomaly detection and diagnosis. Due to the complex patterns and unusual faults embedded in KPIs, a collection of unsupervised reconstruction approaches have been designed to learn representations from normal samples and pinpoint anomalous behavior in real- time sequences. However, under such a challenging setting, advanced reconstruction networks may fall into identity shortcut, where anomalous samples also can be well recovered, and hence escape from anomaly detection. To tackle this obstacle, in this paper, we propose a lightweight end-to-end fault diagnosis framework based on sequences reconstruction, called Sparse Semi-Attention (SS- Attention) model, which could quickly and accurately reconstruct the normal KPIs while spot the anomalous ones. Extensive empirical studies on three public online service datasets demonstrate the SS-Attention model can outperform state-of-the-art benchmark methods in fault detection and localization, achieving an average 95.91% F1-Score with an approx. 34.48% economy of training time. The ablation studies also endorse the contributions of each main components. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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