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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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An ES-based model with contemporaneous and temporal aggregation for forecasting intermittent and lumpy retail demand 11

An ES-based model with contemporaneous and temporal aggregat...

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

作者： Erjiang, E. Tian, Xin Tao, Ye Sun, Huiqiang School of Management Guangxi Minzu University Nanning530006 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 Sciences Beijing100190 China Beijing Haolinju CVS Co. Ltd. Beijing100018 China Technology Development Co. Ltd. Beijing101399 China

The demand for products in the retail industry is often characterized by intermittent and volatile patterns. Specifically, at the SKU level, the demand exhibits intermittent and lumpy behavior, which presents challenges for accurate forecasting due to demand fluctuations and interval uncertainty. This study proposes an exponential smoothing (ES)-based model, called ES-C-T, that incorporates contemporaneous and temporal aggregation to forecast intermittent and lumpy retail demand. The ES-C-T model consists of three stages. Firstly, the ES model predicts the sales of each product by aggregating contemporaneously across all stores, and the resulting predictions are allocated to the SKU level using a weight vector. Secondly, the ES model forecasts the sales of each SKU by aggregating temporally within each store. Finally, the ES-C-T model derives a weight coefficient based on the predictions from the first and second stages, adjusts the initial predictions, and utilizes this as the final forecast. To validate the effectiveness of the ES-C-T model, sales data from a large Chinese convenience store chain are employed. The results demonstrate that the proposed ES-C-T model outperforms the benchmark model in terms of MAE, RMSE, and WRMSSE, effectively predicting retail sales for intermittent and lumpy demand. © 2024 The Authors.

关键词： Prediction models

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Overview of Essential Components in deep learning reference-based super resolution methods 11

Overview of Essential Components in deep learning reference-...

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

作者： Xue, Jiayu Liu, Junjie Shi, Yong School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing100049 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Reference-based super resolution (RefSR) aims to recover the lost details in a low-resolution image and generate a high-resolution result, guided by a high-resolution reference image with similar contents or textures. In contrast to the traditional single-image super-resolution, which focuses on the intrinsic properties of the single low-resolution image, the challenge of RefSR lies in matching and aggregating highly-related but misaligned reference images with low-resolution images. Several effective but complex designs have been proposed to address this challenge, which poses difficulties in implementing RefSR in real-world applications. In order to better understand the working mechanism of RefSR and design a more efficient and lightweight architecture, we provide a review about the essential components of the existing deep learning-based RefSR. We decompose and classify the common pipeline into four submodules according to the functionalities. Then, we summarize and describe the implementation details of the commonly-adopted approaches in each submodule. Finally, we discuss the challenges and promising research directions. © 2024 The Authors.

关键词： Deep learning

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Semi-Supervised Graph Neural Networks for Graph Partitioning Problem 10

Semi-Supervised Graph Neural Networks for Graph Partitioning...

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

作者： Yang, Zhengxi Shi, Ruiyang Quan, Pei Zhou, Ruizhi Niu, Lingfeng School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China CAS Research Center on Fictitious Economy and Data Science University of Chinese Academy of Sciences Beijing100190 China College of Economics and Management Beijing University of Technology Beijing100124 China Institute of Operations Research and Information Engineering Beijing University of Technology Beijing100124 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

The Graph Partitioning Problem (GPP) is a classical combinatorial optimization problem that has been extensively researched. In recent years, many methods for solving the GPP have been proposed, which can be divided into direct partitioning approaches and iterative improvement approaches. Direct partitioning approaches directly give a complete partition of the input graph. Iterative improvement approaches require further adjustment of feasible solutions based on the result of direct partitioning approaches in order to obtain a better performance. In this paper, we summarize the recent trends in algorithms and applications for GPP. In addition, we propose a graph partitioning algorithm based on semi-supervised learning in combination with graph filtering methods. © 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.

关键词： Graph neural networks

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A Brief Survey on Graph Anomaly Detection

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Procedia Computer Science 2024年 242卷 1263-1270页

作者： Chengxi Song Lingfeng Niu Minglong Lei School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China CAS Research Center on Fictitious Economy & Data Science University of Chinese Academy of Sciences Beijing 100190 China School of Economic and Management University of Chinese Academy of Sciences Beijing 100190 China Faculty of Information Technology Beijing University of Technology Beijing 100124 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 classifer-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.

关键词： machine learning graph Neural Networks anomaly detection

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Deep Feature Inpainting for Unsupervised Visual Anomaly Detection 10

Deep Feature Inpainting for Unsupervised Visual Anomaly Dete...

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

作者： Yang, Jie Lyu, Mengjin Qi, Zhiquan Tian, Yingjie Shi, Yong School of Mathematical Science University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management 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 College of Information Science and Technology University of Nebraska OmahaNE68182 United States School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision. In this paper, we address the problem of unsupervised visual anomaly detection from the perspective of image feature restoration. We propose deep feature inpainting (DFI) where we develop a deep learning model that can automatically inpainting the normal image patterns or features from the anomalous images by eliminating the irregular structures or patterns. By comparing the anomalous features and their reconstructed counterparts, we can identify and analyze the anomalous structures in the images. Extensive experiments have been carried out to demonstrate the effectiveness of the approach. © 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： Anomaly detection

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Deep Learning Based Image Quality Assessment: A Survey 10

Deep Learning Based Image Quality Assessment: A Survey

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

作者： Yang, Jie Lyu, Mengjin Qi, Zhiquan Shi, Yong School of Mathematical Science University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management 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 College of Information Science and Technology University of Nebraska OmahaNE68182 United States School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

Image quality assessment (IQA) is the problem of measuring the perceptual quality of images, which is crucial for many image-related applications. It is a difficult task due to the coupling of various degradation and the scarcity of annotations. To facilitate a better understanding of IQA, we survey the recent advances in deep learning based IQA methods, which have demonstrated remarkable performance and innovation in this field. We classify the IQA methods into two main groups: reference-based and reference-free methods. Reference-based methods compare query images with reference images, while reference-free methods do not. We further subdivide reference-based methods into full-reference and reduced-reference methods, depending on the amount of information they need from the reference images, and reference-free methods into single-input, pair-input, and multimodal-input methods, according to the form of input they use. The advantages and limitations of each category are analyzed and some representative examples of state-of-the-art methods are provided. We conclude our paper by highlighting some of the future directions and open challenges in deep learning based IQA. © 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： Deep learning

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Credit and guarantee insurance: A consistent pricing approach in supply chain finance 9th

Credit and guarantee insurance: A consistent pricing approac...

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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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Evaluation of China's Digital economy: A case Study using Entropy and TOPSIS 11

Evaluation of China's Digital Economy: A Case Study using En...

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

作者： Liu, Junjie Wang, Boya Xue, Jiayu Qu, Yi Shi, Yong Sino-Danish College University of Chinese Academy of Sciences Beijing100049 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 Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 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

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. © 2024 The Authors.

关键词： Digital economy Evaluation Multi-source information TOPSIS

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Exploring Freight Loading Management by Deep Learning: a case Study in Home Furnishing Industry

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Annals of data Science 2022年第2期9卷 213-228页

作者： Deng, Wei Patil, Rajvardhan Liu, Fangyao Daji, Ergu Shi, Yong College of Big Data Statistics Guizhou University of Finance and Economics Guizhou550025 China College of Computer and Information Science Arkansas Tech University RussellvilleAR68182 United States College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Research Center on Fictitious Economy and Data Science The Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Nebraska Furniture Mart OmahaNE68124 United States Division of Science Research and Technology Southwest Minzu University Chengdu610041 China

Long short-term memory (LSTM) networks as state-of-the-art Deep Learning models, have achieved remarkable results in time series forecasting. However, they are less commonly applied to the industry of logistics. This paper presents two novel LSTM networks to predict the freight loading of routing areas, and the design of a smart loading management system is introduced. While most existing works on LSTM utilize the power of its prediction very well, our study shows less accurate and instable results if only LSTM network is applied, that arise from the big variation in the dataset. Instead, two constraints are inspired in this paper. The first constraint adds a time factor node to strengthen the correlation between predicted final loading units and booked loading units as close to delivery date. And the second proposal extends to parallel training by groupwise constraint. Experiments with across 3-year records in a national wide home furnishing retail company show that constraint LSTM networks significantly improve both the accuracy and stability of the prediction. Besides, the design of a smart loading management system will be shown, in which LSTM model plays the core rule to predict loading capacity, meanwhile the rule-based system triggers different events based on loading prediction and truck size. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Long short-term memory

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