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

作者： Pan, Shengjie Peng, Shuyang Wan, Jiayu Qu, Yi Zhejiang Mercantile Exchange Co. Ltd. Zhoushan City316000 China School of Economics and Management Southeast University Nanjing211189 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 School of Economics and Management Nanjing Forestry University Nanjing210037 China

This paper focuses on the regulation effect of money market interest rates from monetary policy tools. By employing Time-Varying Parameter Stochastic Volatility Vector Autoregressive model, we comparatively analyze the effects of the Standing Lending Facility (SLF) and the 7-Day Reverse Repurchase Agreements in the Open Market (RR7D) on the 7-Day Repo Rate of Deposit-taking Financial Institutions (DR007). It is found that SLF can marginally improve DR007 in the short term but can effectively lower it in the medium to long term by acting as the ceiling of the interest rate corridor. RR7D can improve DR007 with a stronger impact, compared to the SLF. The effectiveness of these monetary policy tools remains relatively stable under different socioeconomic backgrounds, achieving the intended objectives of the central bank. Finally, the study proposes suggestions, close integration of the interest rate corridor and open market operations in monetary policy regulation, and encourages the use of SLFs to support the high-quality development of the national economy. © 2024 The Authors.

关键词： Stochastic systems

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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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Study on autonomous ship enhancement by optimizing the object detecting algorithms 5

Study on autonomous ship enhancement by optimizing the objec...

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5th International Conference on Maritime Autonomous Surface Ships, ICMASS 2023

作者： Jiang, Xue Zhang, Xinhui Yang, Kun Zhao, Penghui School of Naval Architecture & Maritime Zhejiang Ocean University Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

The accuracy and efficiency of object detection is the key technology to spread autonomous driving. In order to address issues such as missed and false detection in traditional target detection algorithms, deep learning technology is used to optimize the ship target detection model as well as the attention mechanisms in this article. Specifically, this paper proposes an improved model based the traditional YOLOv5s algorithm, and a self-created ship dataset is also provided. To refine ship target detection, various attention mechanisms were implemented and compared, including CBAM, ECA, GAM, SimAM, and SK-Net. Through comparative analysis of the effects of these mechanisms, GAM was identified as the optimal choice. The experimental results indicate that the mean average precision (mAP) of detecting ship targets was augmented by 1.0% following the incorporation of GAM. The YOLOv5sGAM model is therefore deemed as an effective target detection approach in enhancing the safety of ship autopilot, with potential applications in the future. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Object detection

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Identifying Scientific and Technical“Unicorns”

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Journal of data and Information Science 2021年第2期6卷 96-115页

作者： Lucy L.Xu Miao Qi Fred Y.Ye Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information ManagementNanjing UniversityNanjing 210023China University Library Nantong UniversityNantong 226019China

Purpose:Using the metaphor of"unicorn,"we identify the scientific papers and technical patents characterized by the informetric feature of very high citations in the first ten years after publishing,which may provide a new pattern to understand very high impact works in science and ***/methodology/approach:When we set CT as the total citations of papers or patents in the first ten years after publication,with CT≥5,000 for scientific"unicorn"and CT≥500 for technical"unicorn,"we have an absolute standard for identifying scientific and technical"unicorn"***:We identify 165 scientific"unicorns"in 14,301,875 WoS papers and 224 technical"unicorns"in 13,728,950 DII patents during 2001–*** 50%of"unicorns"belong to biomedicine,in which selected cases are individually *** rare"unicorns"increase following linear model,the fitting data show 95%confidence with the RMSE of scientific"unicorn"is 0.2127 while the RMSE of technical"unicorn"is *** limitations:A"unicorn"is a pure quantitative consideration without concerning its quality,and"potential unicorns"as CT≤5,000 for papers and CT≤500 for patents are left in future *** implications:Scientific and technical"unicorns"provide a new pattern to understand high-impact works in science and ***"unicorn"pattern supplies a concise approach to identify very high-impact scientific papers and technical ***/value:The"unicorn"pattern supplies a concise approach to identify very high impact scientific papers and technical patents.

关键词： Unicorn Scientific paper Technical patent Citation analysis Patent analysis

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Large Language Model for Table Processing: A Survey

arXiv

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arXiv 2024年

作者： Lu, Weizheng Zhang, Jing Fan, Ju Fu, Zihao Chen, Yueguo Du, Xiaoyong School of Information Renmin University of China Beijing100872 China Key Laboratory of Data Engineering and Knowledge Engineering Beijing100872 China WPS Office Kingsoft Co. Zhuhai519080 China

Tables, typically two-dimensional and structured to store large amounts of data, are essential in daily activities like database queries, spreadsheet manipulations, web table question answering, and image table information extraction. Automating these table-centric tasks with Large Language Models (LLMs) or Visual Language Models (VLMs) offers significant public benefits, garnering interest from academia and industry. This survey provides a comprehensive overview of table-related tasks, examining both user scenarios and technical aspects. It covers traditional tasks like table question answering as well as emerging fields such as spreadsheet manipulation and table data analysis. We summarize the training techniques for LLMs and VLMs tailored for table processing. Additionally, we discuss prompt engineering, particularly the use of LLM-powered agents, for various table-related tasks. Finally, we highlight several challenges, including diverse user input when serving and slow thinking using chain-of-thought. © 2024, CC BY.

关键词： Question answering

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Stock index prediction using global market indices: A Granger causality-based graph representation learning method 10

Stock index prediction using global market indices: A Grange...

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

作者： Wang, Yunong Wu, Jie Shi, Yong 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 Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Global stock markets are increasingly connected and may experience strongly correlated stock price fluctuations due to external shocks, cooperation, and competition. In this paper, we propose a Granger causality-based graph representation learning method to improve the accuracy of stock index prediction. In the first stage, a Granger causality test is performed using the historical data of the target stock market and other global stock markets to select the neighboring stock markets with Granger causality and get the relational weight of each neighboring stock market. Then, the temporal weight of trading days is obtained based on the constructed VAR model coefficients. Subsequently, the daily graph embedding of the target stock index is learned by graph convolution operation for the prediction of the next day's closing price. The method developed in this paper experimented on four well-known Chinese stock indices, and it outperformed the benchmark model used in this paper. © 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： Financial markets

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Novelty detection technology for Alzheimer's disease based on autoencoder feature extractor and MCD classification 7

Novelty detection technology for Alzheimer's disease based o...

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Real-time Photonic Measurements, data Management, and Processing VII 2023

作者： Zhang, Shuheng Zhang, Yan Pan, Jie Lin, Xintian Ding, Xuemei Yang, Hongqin Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education Provincial Key Laboratory for Photonics Technology Fujian Normal University Fuzhou350117 China Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fujian Normal University Fuzhou350117 China Xiamen Jingyi zhikang Technology Co. Ltd. Xiamen361000 China

ISBN: (纸本)9781510667938

Alzheimer's disease (AD) is a neurodegenerative disorder that affects the life quality of millions of people worldwide. To diagnose new cases in a timely manner, we propose a new novelty detection technique that combines Autoencoder and Minimum Covariance Determinant (MCD). The technique consists of two steps: first, we use an Autoencoder to extract low-dimensional and discriminative features from the publicly available ADNI dataset, where we only train the Autoencoder with normal data, making the abnormal data more distinguishable in the feature space;second, based on the features of normal data, we use MCD to construct a decision boundary, and judge the degree of abnormality by the distance of the test point to the boundary. Compared with traditional methods without using Autoencoder, our technique has significant advantages in terms of accuracy and sensitivity, and can effectively deal with data imbalance problem. Experimental results show that our method can efficiently detect novel AD cases, and has a wide range of application prospects. © 2023 SPIE.

关键词： Neurodegenerative diseases

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arXiv

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arXiv 2025年

作者： Li, Yangning Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Niyato, Dusit College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore

An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long-short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. Simulation results show that the proposed OMRP improves network lifetime by 17% compared to benchmark routing protocols, while the SoftPPO-LSTM method for CB node selection achieves an 8.3% increase in throughput over benchmark algorithms. The results also reveal that the combined OMRP with the SoftPPO- Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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

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Procedia Computer Science 2023年 221卷 1448-1457页

作者： Yong Shi Shuyang Peng Anqi Yu Kun Guo 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

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.

关键词： CEA,Carbon market,Liquidity,Measurement,Influencing factors

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FOSS: A Self-Learned Doctor for Query Optimizer

FOSS: A Self-Learned Doctor for Query Optimizer

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International Conference on data engineering

作者： Kai Zhong Luming Sun Tao Ji Cuiping Li Hong Chen Renmin University of China China MOE Key Laboratory of Data Engineering and Knowledge Engineering China Shanghai Yunxi Technology Co. Ltd. Engineering Research Center of Database and Business Intelligence MOE China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or limited plan search space. To address these challenges, we introduce FOSS, a novel framework for query optimization based on deep reinforcement learning. FOSS initiates optimization from the original plan generated by a traditional optimizer and incrementally refines suboptimal nodes of the plan through a sequence of actions. Additionally, we devise an asymmetric advantage model to evaluate the advantage between two plans. We integrate it with a traditional optimizer to form a simulated environment. Leveraging this simulated environment, FOSS can bootstrap itself to rapidly generate a large amount of high-quality simulated experiences. FOSS then learns from these experiences to improve its optimization capability. We evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack Overflow. The experimental results demonstrate that FOSS outperforms the state-of-the-art methods in terms of latency performance. Compared to PostgreSQL, FOSS achieves speedup ranging from 1.15x to 8.33x in total latency across different benchmarks.

关键词： Training Query processing Medical services Benchmark testing Deep reinforcement learning data engineering Distance measurement

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