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10th International Conference on Information technology and Quantitative Management, ITQM 2023

作者： Qu, Yi Xue, Xiaodong 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 College of Information Science and Technology University of Nebraska OmahaNE68182 United States

In the routine production and operation of nuclear power plants (NPPs), safety management is extremely important, and consequently, a fault detection system yielding high accuracy could provide effective decision support and prevent the accidents to the maximum extent. Most of existing studies are based on the analysis of time-series data, while researches oriented to the implementation of graph data mining are barely seen. In this paper, we propose an improved fault detection method to address this task from the perspective of graph representation learning. By calculating the similarity between indicators, the multivariate temporal sequences of the running characteristics of NPPs have been formulated as a group of graphs with corresponding labels, on which the graph convolutional network (GCN) has been introduced to detect fault samples in the way of graph classification. Compared to conventional supervised learning classifiers, our proposed methodology performs more superior identification of major faults, and the sensitivity regarding significant parameter or settings in the model design has been analyzed to investigate their optimal arrangements. The findings in this article not only present the promising prospects of introducing graph learning techniques into safety management, also indicate that various kinds of digital signals could be reformulated into graph dataset based on which the original task could be executed more effectively in the way of graph representation learning. © 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： Nuclear power plants

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A Brief Survey on Graph Anomaly Detection 11

A Brief Survey on Graph Anomaly Detection

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11th International Conference on Information technology and Quantitative Management, ITQM 2024

作者： Song, Chengxi Niu, Lingfeng Lei, Minglong School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Economic and Management University of Chinese Academy of Sciences Beijing100190 China Faculty of Information Technology Beijing University of Technology Beijing100124 China CAS Research Center on Fictitious Economy & Data Science University of Chinese Academy of Sciences Beijing100190 China

Graph anomaly detection (GAD) has been extensively studied in recent years. GAD aims to detect nodes, edges, and subgraphs that exhibit characteristics and distributions different from those of the majority of graph data. With the advancement of deep learning, many researchers have applied machine learning to address anomaly detection at various scales. In this paper, we classify GAD methods into detector-based and classifier-based approaches and provide a brief introduction and summary of relevant articles from the past three years. Finally, we analyze the challenges and future development directions in the field of GAD. © 2024 The Authors.

关键词： Adversarial machine learning

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D4M: dataset Distillation via Disentangled Diffusion Model

D4M: Dataset Distillation via Disentangled Diffusion Model

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Conference on Computer Vision and Pattern Recognition (CVPR)

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

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

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 (D 4 M), an efficient framework for dataset distillation. Compared to architecture-dependent methods, D 4 M 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.

关键词： Training Computer vision Computational modeling Prototypes Computer architecture Diffusion models Pattern recognition

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Unified Image Harmonization with Region Augmented Attention Normalization

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Annals of data Science 2024年第5期11卷 1865-1886页

作者： Hou, Junjie Zhang, Yuqi Su, Duo Sino-Danish College University of Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Zhongguancun East Rd Beijing100190 China

The image harmonization task endeavors to adjust foreground information within an image synthesis process to achieve visual consistency by leveraging background information. In academic research, this task conventionally involves the utilization of simple synthesized images and matching masks as inputs. However, obtaining precise masks for image harmonization in practical applications poses a significant challenge, thereby creating a notable disparity between research findings and real-world applicability. To mitigate this disparity, we propose a redefinition of the image harmonization task as "Unified Image Harmonization," where the input comprises only a single image, thereby enhancing its applicability in real-world scenarios. To address this challenge, we have developed a novel framework. Within this framework, we initially employ inharmonious region localization to detect the mask, which is subsequently utilized for harmonization tasks. The pivotal aspect of the harmonization process lies in normalization, which is accountable for information transfer. Nonetheless, the current background-to-foreground information transfer and guidance mechanisms are limited by single-layer guidance, thereby constraining their effectiveness. To overcome this limitation, we introduce Region Augmented Attention Normalization (RA2N), which enhances the attention mechanism for foreground feature alignment, consequently leading to improved alignment and transfer capabilities. Through qualitative and quantitative comparisons on the iHarmony4 dataset, our model exhibits exceptional performance not only in unified image harmonization but also in conventional image harmonization tasks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Image reconstruction

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Latest Technologies on dataset Distillation: A Survey 11

Latest Technologies on Dataset Distillation: A Survey

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11th International Conference on Information technology and Quantitative Management, ITQM 2024

作者： Li, Muyang Qu, Yi 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 Computer Science and Technology University of 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

dataset distillation refers to the process of constructing a smaller dataset based on a larger dataset, so that the training model with the smaller dataset can obtain similar results to the larger dataset. In this paper, we first briefly review the basic methods and some recent advances in this field from the perspectives of meta-learning framework and data matching framework, including the backpropagation through time(BPTT), kernel ridge regression(KRR), decoupled techniques, gradient matching(DC), trajectory matching(MTT) and distribution matching(DM). The results of the plug-and-play approach in recent years are then presented. Finally, we discuss possible future developments from the perspectives of data distillation algorithms on large datasets, the relationship between compress ratio and information contained, more tasks and data modalities, and the use of generative models to improve the distillation results of datasets. © 2024 The Authors.

关键词： data assimilation

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Image Orientation Estimation Based On Deep Learning - A Survey 11

Image Orientation Estimation Based On Deep Learning - A Surv...

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11th International Conference on Information technology and Quantitative Management, ITQM 2024

作者： Xu, Ruijie Shi, Yong Qi, Zhiquan School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China College of Information Science and Technology University of Nebraska Omaha68182 United States

In the realm of visual tasks, the estimation of orientation plays a pivotal role yet often remains an understudied aspect in the gamut of image analysis research. This paper endeavors to address this deficiency by conducting a comprehensive review of methods for orientation estimation within 2D imaging contexts. We aim to provide researchers with an exhaustive overview and detailed analysis of the techniques utilized for orientation estimation, thereby enhancing their understanding of this complex field. We introduce an organized classification of orientation estimation methods, segmented into approaches based on classification algorithms and those based on regression models. This classification not only sheds light on the distinct methodologies but also scrutinizes their underlying assumptions, practical applications. Additionally, the study identifies and underscores emerging research opportunities that hold the potential to enrich the discourse and technical advancements in orientation estimation, thereby encouraging deeper academic investigation and technological development. © 2024 The Authors.

关键词： Classification-based Orientation Estimation Regression-based Orientation Estimation

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Structure Aware Loss for Deep Unsupervised Industrial Image Registration 24

Structure Aware Loss for Deep Unsupervised Industrial Image ...

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24th IEEE International Conference on data Mining Workshops, ICDMW 2024

作者： Shi, Yong Lyu, Mengjin Qi, Zhiquan Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science Beijing100190 China Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing100190 China University of Nebraska at Omaha College of Information Science and Technology OmahaNE 68182 United States University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100190 China University of Chinese Academy of Sciences School of Economics and Management Beijing100190 China

ISBN: (纸本)9798331530631

Image registration is extensively utilized in industrial applications. However, specific challenges inherent to these environments - such as low texture, significant displacement, and poor lighting conditions - complicate the registration process. This paper introduces a structure-aware loss module tailored to the unique characteristics of industrial images. This module promotes the network's focus on salient regions while emphasizing structural features. Additionally, it is designed as a plug-in component, facilitating integration with any loss function and homography estimation method. We develop an unsupervised registration network and create an industrial dataset to evaluate the module's effectiveness. A series of experiments demonstrate that our structure-aware module significantly enhances registration performance. © 2024 IEEE.

关键词： Image registration

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Impact of the China Railway Express on Employment Structure - Evidence from Urban Economic data 11

Impact of the China Railway Express on Employment Structure ...

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11th International Conference on Information technology and Quantitative Management, ITQM 2024

作者： Yan, Bochao Jiang, Hailiang Wu, Junfeng Tian, Xin China Agricultural University Beijing100083 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China 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 Shanghai WinJoin Information Technology Co. Ltd. Shanghai200126 China

As the main international trade channel of China's "westward development", the opening of China Railway Express has significantly improved the trade facilitation of countries along the route. This paper investigates the impact of the opening of China Railway Express sites on the employment choices of individuals by using the difference in difference method based on the economic panel data of China's prefecture-level cities and the data on the opening of China Railway Express during the period of 2007~2019, which are matched by the correlation. The results of the study show that the opening of the China Railway Express increases the probability of individuals within the radiation area to enter the private sector for employment. The empirical results provide a new explanatory mechanism for the opening of the China Railway Express on economic growth, which has certain theoretical and policy value. © 2024 The Authors.

关键词： Railroads

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Review and research frontier analysis of low carbon economy - Based on bibliometric methods 10

Review and research frontier analysis of low carbon economy ...

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10th International Conference on Information technology and Quantitative Management, ITQM 2023

作者： Yao, Tongsheng Shi, Yong Chen, Ming School of Economics and Management University of Chinese Academy 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 School of Management and Economics Beijing Institute of Technology Beijing100081 China

research in the carbon field has experienced rapid growth in recent years and has attracted widespread academic attention. This paper conducts a comprehensive analysis of the carbon field using bibliometric methods. By collecting a large number of academic publications related to carbon and utilizing bibliometric indicators, We performed statistical and descriptive analyses on the development trends, research areas, research institutions, and authors in the field of carbon economy literature. Additionally, we employed co-citation analysis and keyword co-occurrence analysis to identify the current research focuses in the carbon field and explore the temporal changes in research hotspots. Ultimately, we analyzed the future development trends of the carbon field, providing valuable insights for scholars to conduct further research in this field. These research findings are of great significance in promoting the development and application of the carbon economy field. © 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： Carbon

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A Brief Survey of Distribution Robust Graph Neural Networks 11

A Brief Survey of Distribution Robust Graph Neural Networks

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11th International Conference on Information technology and Quantitative Management, ITQM 2024

作者： Zheng, Lei Quan, Pei Shi, Yong Niu, Lingfeng The School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100190 China The College of Economics and Management Beijing University of Technology Beijing100124 China The School of Economics and Management University of Chinese Academy of Sciences Beijing1001090 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

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. © 2024 The Authors.

关键词： Distribution generalization Graph neural networks Shift robust

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