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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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Adaptive load shedding for data stream using PI feedback controller

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Journal of Computational Information Systems 2007年第5期3卷 1827-1832页

作者： Huang, Chongzheng Cai, Shaojiang Xie, Zhijun Zhang, Yuanyu Chen, Hong School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering China School of Electronics and Information Engineering Xi'an Jiaotong University Xi'an 710000 China

Load shedding has been widely used in data stream management systems (DSMSs) to keep DSMSs running steadily. One key problem in load shedding is determining how much system load to shed. Existing works tend to adapt coarse algorithm (CA) to solve this problem. In this paper, we present an adaptive PI controller-based load shedding framework for data stream. The main contribution of this paper is our use of feedback control theory to design the load shedding scheme. In contrast to the existing approaches, we firstly apply system identification to establish a dynamic model to describe DSMS, which enables us analyze DSMS quantitatively. Then, based on the model, we use the Root Locus method to design the PI controller with proven performance guarantees. The adaptive framework has been implemented by modifying Borealis system. Theoretic analysis and experimental results demonstrate that our approach is robust even when system load changes frequently. Comparing to existing strategies, our approach also achieves significantly better performance.

关键词： Feedback control

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An Effective Automatic XML Query Expansion Method Based on Local Co-occurrence Model for Pseudo-Relevance Feedback

An Effective Automatic XML Query Expansion Method Based on L...

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2012 Third International Conference on Theoretical and Mathematical Foundations of Computer Science(ICTMF 2012)

作者： Minjuan Zhong School of Information Technology Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Pseudo-relevance feedback has been perceived as an effective solution for automatic query ***,a recent study has shown that traditional pseudo-relevance feedback may bring into topic drift and hence be harmful to the retrieval *** this paper,using the idea of local analysis,an effective XML query expansion method,is presented,which utilizes the local word co-occurrence information in good pseudo-relevance document collection and the structural semantics of XML to select most appropriate expansion *** results on INEX 2005 IEEE-CS collection show that the proposed expansion method offers better retrieval performance,compared with original query.

关键词： Query Expansion,Pseudo-Relevance Feedback,Information Retrieval,Local Word Co-occurrence

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A RFID Mixed Coding Scheme Based on Huffman Algorithm

A RFID Mixed Coding Scheme Based on Huffman Algorithm

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International Conference on Management of e-Commerce and e-Government, ICMECG

作者： Jiang Jian Liao Guoqiong Wang Hao Wan Qizhi School of Information Technology Jiangxi university of Finance and Economics China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang Nanchang China

In RFID application systems with multiple packaging layers, labeling packaging relationship of objects in different packaging layers by encoding methods is a important technology field. Prefix-based labeling scheme is a method by which the packaging relationship of objects amounts to testing whether one object is an prefix of the other, whereas the region numbering scheme is a method by which an ancestor query amount to an interval containment test on the labels. This paper first presents a mixed-encoding scheme based on Huffman algorithm, then propose a variant of this encoding scheme in which new "virtual nodes" are involved in order to improve the stability of encoding. Our experiments prove that the mixed-based encoding scheme is effective due to the enhancement of update performance and query efficiency.

关键词： Encoding Labeling Radiofrequency identification XML Algorithm design and analysis Testing

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A dominating set based approach to identify effective leader group of social network

A dominating set based approach to identify effective leader...

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19th International Computing and Combinatorics Conference, COCOON 2013

作者： Kim, Donghyun Li, Deying Asgari, Omid Li, Yingshu Tokuta, Alade O. Dept. of Mathematics and Computer Science North Carolina Central University Durham NC 27707 United States Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Renmin 100872 China Dept. of Computer Science Georgia State University Atlanta GA 30303 United States

ISBN: (纸本)9783642387678

Very recently, the study of social networks has received a huge attention since we can learn and understand many hidden properties of our society. This paper investigates the potential of social network analysis to select an effective leadership group of a society. Based on our real life observation, we establish three essential requirements for an effective leadership group, namely Influenceability, Partisanship, and Bipartisanship. Then, we formulate the problem of finding a minimum size leader group satisfying the three requirements as the minimum connected k-core dominating set problem (MCkCDSP), and show its NP-hardness. In addition, we introduce an extension of MCkCDSP, namely MCkCDSP-C, which assumes the society has a number of communities and requires at least one representative from each community should be in the leadership. At last, we propose an approximation algorithm for a subclass of MCkCDSP with k = 2, and show an α-approximation algorithm of MCkCDSP can be used to obtain an α-approximation algorithm of MCkCDSP-C. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Approximation algorithms

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Does web news media have opinions? Evidence from real estate market prediction 18

Does web news media have opinions? Evidence from real estate...

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18th Pacific Asia Conference on Information Systems, PACIS 2014

作者： Sun, Daoyuan Zhang, Ce Xu, Wei Zuo, Meiyun Zhou, Junjie Du, Yudie Department of Information Systems School of Computing National University of Singapore Singapore Singapore School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China School of Information Beijing China School of E-Commerce and Logistics Management Henan University of Economics and Law Zhengzhou China

In this paper, we propose a novel method for real estate price prediction using web new media sentiments by incorporating human searching behaivor on the web. By combining online daily news' sentiments and Google search engine query data, we construct a web news content and online search behavior-based integrated model for real estate prediction. Besides these factors, real estate price time series data are also considered into the model in order to improve the forecasting performance. Furthermore, we make a comparison between the integrated model and the baseline model without search engine query data. Experimental results indicate that the integrated model outperforms the non-integrated model, which suggests that online user searching behavior is of great value in enhancing the prediction performance. These findings imply that the proposed integrated model is effective and feasible for real estate market prediction.

关键词： Search engines

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Adaptive power allocation in cooperative OFDM transmission with three-hop DF relaying

Adaptive power allocation in cooperative OFDM transmission w...

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IET International Conference on Information and Communications Technologies (IETICT 2013)

作者： Jun Wang Xiaomeng Chai Xu Zhang Zhongshan Zhang Keping Long Institute of Advanced Network Technology and New Services (ANTS) and Beijing Key Laboratory of Knowledge Engineering for Materials Science USTB Beijing China

ISBN: (纸本)9781629931357

Adaptive power allocation subject to the total power constraint in an end-to-end cooperative orthogonal frequency Division multiplexing (OFDM) system is studied, with the optimal power allocation in terms of maximizing the sum transmission Rate being derived. The conventional two-hop system model is extended to a three-hop structure in this paper to improve the Wireless coverage. Independent and identically distributed (i.i.d.) sub-channels are assumed in each Decode-and-Forward (DF) relay without considering the effect of inter-relay interference. Relay selection is also performed in the proposed cooperative transmission, and each relay itself decides whether to forward data for the source depending on the quality of the sub-channels. The transmission bottleneck in the conventional two-hop cooperative system can be overcome effectively, especially when the source-to-destination and first-hop relay-to-destination links are both in a deep fading. The proposed theoretical analysis is validated by using simulation, and it's shown that the sum rate of the proposed algorithm is the monotonically increasing function of both the sub-channel numbers and total system power. Numerical results also prove the superiority of the proposed three-hop model over the conventional two-hop system in terms of end-to-end sum data rate.

关键词： OFDM modulation cooperative communication decode and forward communication fading channels relay networks (telecommunication)

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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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A Zero-shot Learning Method with a Multi-Modal knowledge Graph

A Zero-shot Learning Method with a Multi-Modal Knowledge Gra...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuhong Zhang Haitao Shu Chenyang Bu Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Zero-shot learning aims to recognize unseen-classes using some seen-class samples as training set. It is challenging owing to that the feature representations of unseen-class samples are unavailable. Existing methods transfer the mapping from seen-classes to unseen-classes with the correlation as a bridge, in which, the semantic representations are used to discriminate the classes. However, the unavailability of visual representations for unseen-classes and the insufficient discrimination of semantic representations make the zero-shot learning challenging. Therefore, the visual representations are learned as complements to semantic representations to construct a multi-modal knowledge graph (KG), and a zero-shot learning method based on multi-modal KG is proposed in this paper. Specially, a semantic KG is introduced to capture the correlation of classes, and with the correlation, the visual feature representations of all classes are learned. Then, the discriminative visual representations and the semantic representations are used together to construct a multi-modal KG. With the multi-modal KG, the classifier for seen-classes is transferred to unseen classes. Extensive experimental results show the effectiveness of our method.

关键词： Learning systems Representation learning Training Bridges Visualization Correlation Semantics

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Nonlinear Cross-Domain Feature Representation Learning Method Based on Dual Constraints

Nonlinear Cross-Domain Feature Representation Learning Metho...

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IEEE International Conference on Big knowledge (ICBK)

作者： Han Ding Yuhong Zhang Shuai Yang Yaojin Lin Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou China

Feature representation learning is a research focus in domain adaptation. Recently, due to the fast training speed, the marginalized Denoising Autoencoder (mDA) as a standing deep learning model has been widely utilized for feature representation learning. However, the training of mDA suffers from the lack of nonlinear relationship and does not explicitly consider the distribution discrepancy between domains. To address these problems, this paper proposes a novel method for feature representation learning, namely Nonlinear cross-domain Feature learning based Dual Constraints (NFDC), which consists of kernelization and dual constraints. Firstly, we introduce kernelization to effectively extract nonlinear relationship in feature representation learning. Secondly, we design dual constraints including Maximum Mean Discrepancy (MMD) and Manifold Regularization (MR) in order to minimize distribution discrepancy during the training process. Experimental results show that our approach is superior to several state-of-the-art methods in domain adaptation tasks.

关键词： Noise reduction Manifolds Training Adaptation models Kernel Deep learning Feature extraction

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