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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Zhou, Liangzhu Zhuo, Xingrui Wu, Gongqing Zhang, Zan Bao, Xianyu Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data of Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438582

Crowdsourcing is a cheap and popular method to solve problems that are difficult for computers to handle. Due to the differences in ability among workers on crowdsourcing platforms, existing research use aggregation strategies to deal with the labels of different workers to improve the utility of crowdsourcing data. However, most of these studies are based on probabilistic graphical models, which have problems such as difficulty in setting initial parameters. This paper proposes a novel crowdsourcing method Truth Inference based on Graph Embedding (TIGE) for single-choice questions, the method draws on the idea of graph autoencoder, constructs feature vectors for each crowdsourcing task, embeds the relationship between crowdsourcing tasks and workers in graphs, then uses graph neural networks to convert crowdsourcing problems into graph node prediction problems. The feature vectors are continuously optimized in the convolutional layer to obtain the final result. Compared with the six state-of-the-art algorithms on real-world datasets, our method has significant advantages in accuracy and F1-score. © 2021 IEEE.

关键词： Graph neural networks

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Information propagation in online social networks based on user behavior

Information propagation in online social networks based on u...

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3rd International Conference on Information Computing and Applications, ICICA 2012

作者： Li, Niu Han, Xiaoting Key Laboratory of Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing China School of Information Resource Management Renmin University of China Beijing China School of Economics and Management Beihang University Beijing China

ISBN: (纸本)9783642340611

Along with the development of Internet and Web2.0, online social networks (OSNs) are becoming an important information propagation platform. Therefore, it is of great significance to study the information propagation rules in OSNs. An information propagation model named IP-OSN is proposed in this paper, and some simulation experiments are carried out to investigate the mechanism of information propagation. From the experimental results, we can see that along with the information propagation, the number of known nodes increases and reaches its maximum, then keep an unchanging status. Moreover, from the user behavior aspect, we find that different user behavior in OSNs causes different information propagation results, the more users who are willing to diffuse information, the more scope the information can propagate and the faster the information diffuses. Findings in this paper are meaningful for theory of information propagation and complex networks. © 2012 Springer-Verlag.

关键词： Social networking (online)

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A frequency and two-hop configuration checking-driven local search algorithm for the minimum weakly connected dominating set problem

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Neural Computing and Applications 2024年第22期36卷 13833-13852页

作者： Li, Ruizhi He, Jintao Lin, Cuisong Liu, Ying Hu, Shuli Yin, Minghao Jilin University of Finance and Economics Changchun130117 China Northeast Normal University Changchun130117 China Business Big Data Research Center of Jilin Province Changchun130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

The minimum weakly connected dominating set problem is a typical NP-hard problem with a wide range of applications. To solve this problem, we propose a frequency property and two-hop configuration checking strategy-driven local search algorithm (FCC2LS). In this algorithm, we first propose a lock-vertex-based initial solution construction procedure. This procedure guarantees that certain vertices, which must be included in the optimal solution, are added to the solution. Second, we propose a two-hop configuration checking strategy and a frequency property. The two-hop configuration checking strategy is a new variant of the original configuration checking strategy, which prohibits vertices with unchanged configuration from being added to the candidate solution. The frequency property is used to record the number of times for each vertex is added to the solution, which can increase the diversity of selected vertices. Third, we combine two scoring functions, Dscore and Nscore, with the above strategies and propose effective vertex selection methods to help the algorithm select suitable vertices to add into or remove from the candidate solutions. Finally, we evaluate the proposed algorithm FCC2LS with four state-of-the-art algorithms on four groups of benchmark instances. Experimental results show that our algorithm performs better on four classical benchmark instances. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Combinatorial optimization

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MTSC-GE: A Novel Graph based Method for Multivariate Time Series Clustering 12

MTSC-GE: A Novel Graph based Method for Multivariate Time Se...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Yang, Ze Tai, Changyang Wu, Gongqing Zhang, Zan Bao, Xianyu Key Laboratory of Knowledge Engineering With Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438582

Few clustering methods show good performance on multivariate time series (MTS) data. Traditional methods rely too much on similarity measures and perform poorly on the MTS data with complex structures. This paper proposes an MTS clustering algorithm based on graph embedding called MTSC-GE to improve the performance of MTS clustering. MTSC-GE can map MTS samples to the feature representations in a low-dimensional space and then cluster them. While mining the information of the samples themselves, MTSC-GE builds the whole time series data into a graph, paying attention to the connections between samples from an overall perspective and discovering the local structural feature of MTS data. The proposed MTSC-G E consists of three stages. The first stage builds a graph using the original dataset, where each of the MTS samples is regarded as a node in the graph. The second stage uses the graph embedding technique to obtain a new representation of each node. Finally, MTSC-G E uses the K - Means algorithm to cluster based on the newly obtained representation. We compare MTSC-GE with six state-of-the-art benchmark methods on five public datasets, experimental results show that MTSC-GE has achieved good performance. © 2021 IEEE.

关键词： data mining

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Gaussian Model-Based Fully Convolutional Networks for Multivariate Time Series Classification 12

Gaussian Model-Based Fully Convolutional Networks for Multiv...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Tai, Changyang Yang, Ze Zhang, Huicheng Wu, Gongqing Lv, Junwei Bao, Xianyu Key Laboratory of Knowledge Engineering With Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438582

Multivariate time series (MTS) classification has been regarded as one of the most challenging problems in data mining due to the difficulty in modeling the correlation of variables and samples. In addition, high-dimensional MTS modeling has a large time and space consumption. This paper proposes a novel method, Gaussian Model-based Fully Convolutional Networks (GM-FCN), to improve the performance of high-dimensional MTS classification. Each original MTS is converted into multivariate Gaussian model parameters as the input of FCN. These parameters effectively capture the correlation be-tween MTS variables and significantly reduce the data scale by aligning an MTS size to its dimension. FCN is designed to learn more in-depth features of MTS based on these parameters for modeling the correlation between samples. Thus, GM-FCN can not only model the correlation between variables, but also the correlation between samples. We compare GM-FCN with nine state-of-the-art MTS classification methods, INN-ED, INN-DTW-i, INN-DTW-D, KLD-GMC, MLP, ResNet, Encoder, MCNN, and MCDCNN, on four high-dimensional public datasets, experimen-tal results show that the accuracy of G M - FCN is significantly superior to the others. Besides, the training time of GM-FCN is dozens of times faster than FCN using the original equal-length MTS data as input. © 2021 IEEE.

关键词： data mining

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High-Dimensional Multi-label data Stream Classification With Concept Drifting Detection

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IEEE Transactions on knowledge and data engineering 2023年第8期35卷 8085-8099页

作者： Li, Peipei Zhang, Haixiang Hu, Xuegang Wu, Xindong Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Second People's Hospital of Hefei Anhui Hefei230011 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China School of Computer Science and Information Engineering Anhui Hefei230002 China

Multi-label data streams such as Web texts and images have been popular on the Web. These data present the characteristics of multiple label, high dimensionality, high volume, high velocity and especial concept drift etc. Thus, multi-label data stream classification is a very challenging and significant task especially in the handling of high-dimensional data with concept drifts. However, this challenge has received little attention from the research community. Therefore, we propose the max-relevance and min-redundancy based algorithm adaptation approach for the efficient and effective classification on multi-label data streams with high-dimensional attributes and concept drifts. In order to reduce the impact from the high-dimensional data with noisy attributes, we first refine the minimal-redundancy-maximal-relevance criterion based on mutual information to select qualified features in multi-label data streams. Second, we propose the data distribution based concept drifting detection approach to distinguish concept drifts hidden in data streams. Finally, we build an incremental ensemble classification model for efficiently classifying multi-label data streams. Extensive studies show that our approach can get optimal subsets of features while maintaining a good performance in the multi-label classification, as compared to several state-of-the-art multi-label feature selection algorithms using two efficient multi-label classification methods as base classifiers. Meanwhile, our approach is superior to three well-known multi-label data stream classification approaches in the effectiveness and efficiency. © 1989-2012 IEEE.

关键词： Classification (of information)

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Graph-Based Terminal Ranking for Sparsification of Interference Coordination Parameters in Ultra-Dense Networks 10th

Graph-Based Terminal Ranking for Sparsification of Interfere...

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10th EAI International Conference on Mobile Networks and Management, MONAMI 2020

作者： Gou, Junxiang Wang, Lusheng Lin, Hai Peng, Min Ministry of Education School of Computer Science and Information Engineering Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230601 China Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan University Wuhan430072 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230601 China

ISBN: (纸本)9783030640019

In the future mobile communication system, inter-cell interference becomes a serious problem due to the intensive deployment of cells and terminals. Traditional interference coordination schemes take long time for optimization in ultra-dense networks. Meanwhile, due to the increase of factors affecting communication and in order to better meet the communication needs of each terminal, an interference coordination scheme needs to fully consider multiple characteristic parameters of the terminal, which will further increase the scheme’s computational time. Therefore, we should compress all the data through sparsification of parameters before optimization. There are many terminal parameters, and the essence of sparsification of parameters is to rank terminals. In this paper, a graph-based terminal ranking scheme is designed. First, each terminal can be represented by its multiple parameters. Then, all terminals are used as the vertexes in the graph to form a complete weighted graph, and edge weights represent the degree of dissimilarity between terminals. A ranking of terminals is obtained by finding a minimum Hamiltonian path in the graph. Finally, the ranking of all parameter sequences is obtained according to terminals ranking, which makes the sparsity of all parameter sequences better. Simulation results show that the proposed scheme can accomplish sparsification of parameter sequences effectively, especially when the number of sequences increases. In addition, compared with the optimal coordination of traditional scheme, this scheme improves the fairness of the system while ensuring high system capacity, and dramatically reduces the computational time of interference coordination. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2020.

关键词： Approximation algorithms

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High-Quality Noise Detection for knowledge Graph Embedding with Rule-Based Triple Confidence 18th

High-Quality Noise Detection for Knowledge Graph Embedding w...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Hong, Yan Bu, Chenyang Wu, Xindong Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China

ISBN: (纸本)9783030891879

knowledge representation learning is usually used in knowledge reasoning and other related fields. Its goal is to use low-dimensional vectors to represent the entities and relations in a knowledge graph. In the process of automatic knowledge graph construction, the complexity of unstructured text and the incorrect text may make automatic construction tools unable to accurately obtain the semantic information in the text. This leads to high-quality noise with matched entity types but semantic errors. Currently knowledge representation learning methods assume that the knowledge in knowledge graphs is completely correct, and ignore the noise data generated in the process of automatic construction of knowledge graphs, resulting in errors in the vector representation of entities and relations. In order to reduce the negative impact of noise data on the construction of a representation learning model, in this study, a high-quality noise detection method with rule information is proposed. Based on the semantic association between triples in the same rule, we propose the concept of rule-based triple confidence. The calculation strategy of triple confidence is designed inspired by probabilistic soft logic (PSL). The influence of high-quality noise data in the training process of the model can be weakened by this confidence. Experiments show the effectiveness of the proposed method in dealing with high-quality noise. © 2021, Springer Nature Switzerland AG.

关键词： knowledge graph

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Fast & scalable distributed set similarity joins for big data analytics 33

Fast & scalable distributed set similarity joins for big dat...

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33rd IEEE International Conference on data engineering, ICDE 2017

作者： Rong, Chuitian Lin, Chunbin Silva, Yasin N. Wang, Jianguo Lu, Wei Du, Xiaoyong Tianjin Polytechnic University China Arizona State University United States University of California San Diego United States Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China

ISBN: (纸本)9781509065431

Set similarity join is an essential operation in big data analytics, e.g., data integration and data cleaning, that finds similar pairs from two collections of sets. To cope with the increasing scale of the data, distributed algorithms are called for to support large-scale set similarity joins. Multiple techniques have been proposed to perform similarity joins using MapReduce in recent years. These techniques, however, usually produce huge amounts of duplicates in order to perform parallel processing successfully as MapReduce is a shared-nothing framework. The large number of duplicates incurs on both large shuffle cost and unnecessary computation cost, which significantly decrease the performance. Moreover, these approaches do not provide a load balancing guarantee, which results in a skewness problem and negatively affects the scalability properties of these techniques. To address these problems, in this paper, we propose a duplicate free framework, called FS-Join, to perform set similarity joins efficiently by utilizing an innovative vertical partitioning technique. FS-Join employs three powerful filtering methods to prune dissimilar string pairs without computing their similarity scores. To further improve the performance and scalability, FS-Join integrates horizontal partitioning. Experimental results on three real datasets show that FS-Join outperforms the state-of-Theart methods by one order of magnitude on average, which demonstrates the good scalability and performance qualities of the proposed technique. © 2017 IEEE.

关键词： MapReduce

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Enable Privacy Preservation and Result Verification for Top-k Query in Two-Tiered Sensor Networks

Enable Privacy Preservation and Result Verification for Top-...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Hui Peng Xiaoying Zhang Hong Chen Yuncheng Wu Yao Wu Juru Zeng Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing China

ISBN: (纸本)9781467379533

Wireless sensor network is an important part of the Internet of Things. Preservation of privacy and integrity in wireless sensor networks is extremely urgent and challenging. This paper proposes an efficient mechanism, called PTK, for define and solve the practical and challenging problem of privacy-preserving and verifiable Top-k query processing in two-tier sensor network. PTK prevents adversaries from gaining sensitive information of both queries issued by users and data collected by sensor nodes while allows the ink to verify whether the results are valid. It offers confidentiality of queries and data by constructing a special code, and also achieves high defense capability against the background analysis attack through the implementation of obfuscation coding mechanism. Moreover, PTK provides integrity verification by the correlation among data. Finally, the experimental results show the high performance of proposed schemes in terms of privacy preservation and query efficiency.

关键词： data privacy Privacy Wireless sensor networks Protocols Query processing Cryptography data models

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