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

作者： Yu, Anqi Peng, Shuyang Li, Yizhi Guo, Kun 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 Dornslife School of Arts and Science University of Southern California Los AngelesCA90007 United States Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

As global climate change intensifies, carbon emissions trading has become an important tool for countries to address climate change. China, as the world's largest carbon emitter, has been conducting carbon trading pilots since 2011, and officially launched a national carbon market in 2017. The construction of China's carbon market is crucial for China and global efforts to address climate change. Market efficiency is a key indicator of its success. This article aims to measure and analyze the effectiveness of China's carbon market, and to explore policy measures to enhance its effectiveness. It selected data from effective trading days and conducted an exploratory study on the effectiveness of China's pilot carbon markets. To analyze the effectiveness of the carbon market, we combined the efficient market hypothesis theory with the fractal market hypothesis theory, adopted the variance ratio test method, and the rescaled range analysis method to conduct a comprehensive analysis of the Chinese carbon market, while also accounting for the reasons behind their computational inconsistency. As our research shows that China's pilot carbon market has not reached the weak efficiency level, we have provided some suggestions based on the current operational status of China's carbon market. © 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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Generative-Contrastive Graph Learning for Recommendation

arXiv

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arXiv 2023年

作者： Yang, Yonghui Wu, Zhengwei Wu, Le Zhang, Kun Hong, Richang Zhang, Zhiqiang Zhou, Jun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China Ant Group China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

By treating users’ interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering (CF) based recommendation. Recently, researchers have introduced Graph Contrastive Learning (GCL) techniques into CF to alleviate the sparse supervision issue, which first constructs contrastive views by data augmentations and then provides self-supervised signals by maximizing the mutual information between contrastive views. Despite the effectiveness, we argue that current GCL-based recommendation models are still limited as current data augmentation techniques, either structure augmentation or feature augmentation. First, structure augmentation randomly dropout nodes or edges, which is easy to destroy the intrinsic nature of the user-item graph. Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph. To tackle the above limitations, we propose a novel Variational Graph Generative-Contrastive Learning (VGCL) framework for recommendation. Specifically, we leverage variational graph reconstruction to estimate a Gaussian distribution of each node, then generate multiple contrastive views through multiple samplings from the estimated distributions, which builds a bridge between generative and contrastive learning. The generated contrastive views can well reconstruct the input graph without information distortion. Besides, the estimated variances are tailored to each node, which regulates the scale of contrastive loss for each node on optimization. Considering the similarity of the estimated distributions, we propose a cluster-aware twofold contrastive learning, a node-level to encourage consistency of a node’s contrastive views and a cluster-level to encourage consistency of nodes in a cluster. Finally, extensive experimental results on three public datasets clearly demonstrate the effectiveness of the proposed model. Copyright © 2023, The Authors.

关键词： Collaborative filtering

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LBFL: A Lightweight Blockchain-based Federated Learning Framework with Proof-of-Contribution Committee Consensus

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IEEE Transactions on big data 2024年

作者： Qiao, Shaojie Jiang, Yuhe Han, Nan Hua, Wei Lin, Yufeng Min, Shengjie Wu, Xindong Chengdu University of Information Technology School of Software Engineering Chengdu610225 China Sichuan University NMPA Key Laboratory of Human Cosmetic Evaluation and Big Data West China Hospital Chengdu610041 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230009 China

Blockchain technology makes it possible to design robust decentralized federated learning (FL). Minimizing the communication cost and storage consumption incurred is one of the essential challenges. In addition, maintaining the security and privacy of big data raises to be a difficult problem. Aiming to tackle these challenges, this paper presents LBFL (a Lightweight Blockchain-based FL framework) that offers three novel features. First, it employs a new committee consensus mechanism called Proof-of-Contribution, which is used to avoid the selection latency from the competition of miners and alleviate the congestion in cross-validation of parameters in an asynchronous fashion. Second, LBFL employs a role-adaptive incentive mechanism to estimate devices' workloads and identify malicious nodes effectively. Third, to cope with the excessive storage overheads incurred in full-replication, LBFL applies a new storage partition mechanism that distributes triple redundant chunks in ReedSolomon coding (RSC) evenly to participating devices with high fault tolerance and recovery efficiency. To evaluate LBFL, empirical studies are performed on the famous MNIST dataset and LBFL is compared with the state-of-the-art FL frameworks. The results demonstrate that LBFL can reduce evaluation latency and storage consumption by 69.2% and 72.1%, respectively, and the learning efficiency of LBFL is higher than the state-ofthe-art methods. In particular, important findings are obtained: the proposed role-adaptive incentive mechanism can properly identify malicious devices and switch the roles of legitimate devices to achieve good decentralization © 2015 IEEE.

关键词： Federated learning

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基于数据挖掘技术的税务风险检测方法评述

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Engineering 2024年第3期34卷 43-59页

作者： Qinghua Zheng Yiming Xu Huixiang Liu Bin Shi Jiaxiang Wang Bo Dong School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an 710049China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong UniversityXi’an 710049China School of Distance Education Xi’an Jiaotong UniversityXi’an 710049China

Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair *** recent years,tax risk detection,driven by information technology such as data mining and artificial intelligence,has received extensive *** promote the high-quality development of tax risk detection methods,this paper provides the first comprehensive overview and summary of existing tax risk detection methods *** specifi-cally,it first discusses the causes and negative impacts of tax risk behaviors,along with the development of tax risk *** then focuses on data-mining-based tax risk detection methods utilized around the *** on the different principles employed by the algorithms,existing risk detection methods can be divided into two categories:relationship-based and non-relationship-based.A total of 14 risk detection methods are identified,and each method is thoroughly explored and ***,four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed,including the difficulty of integrating and using fiscal and tax fragmented knowledge,unexplainable risk detection results,the high cost of risk detection algorithms,and the reliance of existing algorithms on labeled *** investigating these issues,it is concluded that knowledge-guided and datadriven big data knowledge engineering will be the development trend in the field of tax risk in the future;that is,the gradual transition of tax risk detection from informatization to intelligence is the future development direction.

关键词： Tax risk detection data mining knowledge guide Informatization Intellectualization

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Improving Session Search by Modeling Multi-Granularity Historical Query Change 22

Improving Session Search by Modeling Multi-Granularity Histo...

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15th ACM International Conference on Web Search and data Mining (WSDM)

作者： Zuo, Xiaochen Dou, Zhicheng Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Key Lab Data Engn & Knowledge Engn MOE Beijing Peoples R China

ISBN: (纸本)9781450391320

In session search, it's important to utilize historical interactions between users and the search engines to improve document retrieval. However, not all historical information contributes to document ranking. Users often express their preferences in the process of modifying the previous query, which can help us catch useful information in the historical interactions. Inspired by it, we propose to model historical query change to improve document ranking performance. Especially, we characterize multi-granularity query change between each pair of adjacent queries at both term level and semantic level. For term level query change, we calculate three types of term weights, including the retained term weights, added term weights and removed term weights. Then we perform term-based interaction between the candidate document and historical queries based on the term weights. For semantic level query change, we calculate an overall representation of user intent by integrating the representations of each historical query obtained by different types of term weights. Then we adopt representation-based matching between this representation and the candidate document. To improve the effect of query change modeling, we introduce query change classification as an auxiliary task. Experimental results on AOL and TianGong-ST search logs show that our model outperforms most existing models for session search.

关键词： Session Search Query Change Document Ranking

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Frontiers of Artificial Intelligent and Quantitative management: Preface for ITQM 2024

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Procedia Computer Science 2024年 242卷 1-8页

作者： Filip, Florin Gheorghe Shi, Yong Pocatilu, Paul Ciurea, Cristian-Eugen Li, Jianping Tien, James M. Berg, Daniel Romanian Academy Information Science and Technology Section Bucharest Romania Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab 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 College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Bucharest University of Economic Studies Bucharest Romania College of Engineering University of Miami Coral GablesFL33124 United States

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Friend Relation Recognization Algorithm Based on The Campus Card Consumption 22

Friend Relation Recognization Algorithm Based on The Campus ...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Haopeng Zhang Jinbo Yu Mengyu Li Yuchen Zhang Yulong Ling Xiao Zhang Key Laboratory of Big Data Storage and Management Northwestern Polytechnical University China

ISBN: (纸本)9781450397056

College students live alone without their parents and bear the influence of academics, life, personality, family, and other factors alone, which leads to the phenomenon of isolation and autism in some students. If this situation is not detected and resolved in time, it may cause serious consequences. This paper uses the consumption data of students to analyze the students' friendship situation. First, it examines the consumption data of the students' campus all-in-one cards and observes the consumption behaviors of the students from the three aspects of consumption time, consumption location, and consumption frequency. It is found that the more overlapping the trajectories of the consumption locations among students, the more likely there is a friendship between students. On this basis, this paper proposes a student friend discovery model, which further explores the social relationship between students from the perspective of multiple colleges, and can find both friend relationships and lonely students. The experimental results show that the excavated social relationships align with the actual situation.

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Robust Tracking via Uncertainty-Aware Semantic Consistency

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第4期33卷 1740-1751页

作者： Ma, Jie Lan, Xiangyuan Zhong, Bineng Li, Guorong Tang, Zhenjun Li, Xianxian Ji, Rongrong Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Xiamen Univ Sch Informat Dept Artificial Intelligence Media Analyt & Comp Lab Xiamen Peoples R China

Robust tracking has a variety of practical applications. Despite many years of progress, it is still a difficult problem due to enormous uncertainties in real-world scenes. To address this issue, we propose a robust anchor-free based tracking model with uncertainty estimation. Within the model, a new data-driven uncertainty estimation strategy is proposed to generate uncertainty-aware features with promising discriminative and descriptive power. Then, a simple yet effective pyramid-wise cross correlation operation is constructed to extract multi-scale semantic features that provide rich correlation information for uncertainty-aware estimation and thus enhances the tracking robustness. Finally, a semantic consistency checking branch is designed to further estimate uncertainty of output results from the classification and regression branches by adaptively generating semantically consistent labels. Experiments on six benchmarks (i.e., OTB100, VOT2018, VOT2020, TrackingNet, GOT-10K and LaSOT) show the competing performance of our tracker with 130 FPS.

关键词： Uncertainty Feature extraction Target tracking Semantics Correlation Estimation Adaptation models Object tracking anchor-free uncertainty estimation

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A Category-aware Multi-interest Model for Personalized Product Search 22

A Category-aware Multi-interest Model for Personalized Produ...

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31st ACM Web Conference (WWW)

作者： Liu, Jiongnan Dou, Zhicheng Zhu, Qiannan Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450390965

Product search has been an important way for people to find products on online shopping platforms. Existing approaches in personalized product search mainly embed user preferences into one single vector. However, this simple strategy easily results in sub-optimal representations, failing to model and disentangle user's multiple preferences. To overcome this problem, we proposed a category-aware multi-interest model to encode users as multiple preference embeddings to represent user-specific interests. Specifically, we also capture the category indications for each preference to indicate the distribution of categories it focuses on, which is derived from rich relations between users, products, and attributes. Based on these category indications, we develop a category attention mechanism to aggregate these various preference embeddings considering current queries and items as the user's comprehensive representation. By this means, we can use this representation to calculate matching scores of retrieved items to determine whether they meet the user's search intent. Besides, we introduce a homogenization regularization term to avoid the redundancy between user interests. Experimental results show that the proposed method significantly outperforms existing approaches.

关键词： personalized product search multi-interest knowledge graph

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RESEARCH ON data USABILITY MEASUREMENT OF SCIENTOMETRICS RESEARCH BASED ON keyWORD ANALYSIS 2

RESEARCH ON DATA USABILITY MEASUREMENT OF SCIENTOMETRICS RES...

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2nd International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2022

作者： Wang, Zongshui Zhao, Hong Zhang, Jian Beijing Information Science and Technology University Beijing Key Laboratory of Big Data Decision-Making for Green Development Beijing China University of Chinese Academy of Sciences School of Economics and Management Beijing China

ISBN: (纸本)9781510663183

In this study, we firstly combine data usability with data usability and summary the framework of scientometrics research from the perspective of data usability which includes main research fields, data sources, methods, and software. We select 1539 papers published in Scientometrics to summary the research content of scientometrics based on paper analysis, and then, develop the measurement scales based on the general process of research which consist of type of application, data source and structure, data quality, users' skill requirements, and software usage which can provide reference researchers to evaluate the difficulty of research, and also can be used to measure the value of manuscripts for editors and reviewers of scientific journals. © 2023 SPIE.

关键词： Application programs

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