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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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JGC-IAGCL: Fusing joint graph convolution and intent-aware graph contrastive learning for explainable recommendation

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Information Fusion 2025年 123卷

作者： Yang, Zhi Lin, Chuan Qin, Yongbin Huang, Ruizhang Chen, Yanping Qin, Jiwei State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou550025 China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang Guizhou550025 China College of Information Science and Engineering Xinjiang University Xinjiang Uygur Autonomous Region Urumqi830046 China

Graph contrastive learning (GCL) enhances recommendation accuracy by leveraging self-supervised features to refine node representations from large-scale unlabeled data. Traditional GCL-based recommendation models typically construct contrastive views via graph augmentation (e.g., stochastic node/edge dropout) or embedding-space perturbation, aiming to maximize representation consistency. However, these methods struggle to effectively model and interpret user preferences and consumption intents, limiting explainability and recommendation performance. To address these challenges, we propose JGC-IAGCL (Joint Graph Convolution and Intent-Aware Graph Contrastive Learning), an explainable recommendation framework. JGC-IAGCL integrates joint graph convolution to capture implicit user preferences and employs intent-aware graph contrastive learning to extract explicit user intents from user–item interactions. By fusing these features, our method generates evenly distributed, intent-propensity-aware user/item representations. Theoretical analysis shows that JGC-IAGCL mitigates popularity bias while enhancing the exposure of long-tail items. Extensive experiments on four highly sparse public datasets validate its effectiveness, demonstrating superior recommendation accuracy and improved interpretability. © 2025

关键词： Self-supervised learning

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A Heuristic Sampling Method for Maintaining the Probability Distribution

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Journal of Computer Science & technology 2021年第4期36卷 896-909页

作者： Jiao-Yun Yang Jun-Da Wang Yi-Fang Zhang Wen-Juan Cheng Lian Li Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei 230601China National Smart EldeTca/re International Science and Technology Coopemtion Base Hefei University of Technology Hefei 230601China School of Computer Science and Information Engirwering Hefei University of TechnologyHefei 230601China School of Mathematics Hefei University of TechnologyHefei 230601China

Sampling is a fundamental method for generating data *** many data analysis methods are developed based on probability distributions,maintaining distributions when sampling can help to ensure good data analysis ***,sampling a minimum subset while maintaining probability distributions is still a *** this paper,we decompose a joint probability distribution into a product of conditional probabilities based on Bayesian networks and use the chi-square test to formulate a sampling problem that requires that the sampled subset pass the distribution test to ensure the ***,a heuristic sampling algorithm is proposed to generate the required subset by designing two scoring functions:one based on the chi-square test and the other based on likelihood *** on four types of datasets with a size of 60000 show that when the significant difference level,a,is set to 0.05,the algorithm can exclude 99.9%,99.0%,93.1%and 96.7%of the samples based on their Bayesian networks-ASIA,ALARM,HEPAR2,and ANDES,*** subsets of the same size are sampled,the subset generated by our algorithm passes all the distribution tests and the average distribution difference is approximately 0.03;by contrast,the subsets generated by random sampling pass only 83.8%of the tests,and the average distribution difference is approximately 0.24.

关键词： Bayesian network chi-square test sampling probability distribution

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A Fine-Grained Anomaly Detection Method Fusing Isolation Forest and Knowledge Graph Reasoning 19th

A Fine-Grained Anomaly Detection Method Fusing Isolation For...

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19th International Conference on Web Information Systems and applications, WISA 2022

作者： Xu, Jie Zhou, Jiantao College of Computer Science Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China

ISBN: (纸本)9783031203084

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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A Collusion Attack on Identity-Based Public Auditing Scheme via Blockchain 15th

A Collusion Attack on Identity-Based Public Auditing Scheme ...

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15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information technology, applications and Tools, FITAT 2019

作者： Zou, Xing Deng, Xiaoting Wu, Tsu-Yang Chen, Chien-Ming Shenzhen China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China China and National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou China

ISBN: (纸本)9789811397134

With cloud storage systems, users can access and update outsourced data remotely. Owing to the accompanying growth of the importance of data integrity, a great deal of attention has been focused on public auditing schemes. Identity-based public auditing (IBPA) scheme allows a third-party auditor (TPA) to verify the integrity of the outsourced data on behalf of users. However, malicious TPAs might collude with cloud servers and forge audit data to deceive users. In this paper, we first review the architecture of a traditional IBPA scheme and a novel IBPA scheme which try to solve the above problem via blockchain. Then, we analyze two main limitations in this newly proposed public auditing scheme against malicious auditors and illustrate our collusion attack on this IBPA scheme. Finally, we offer some suggestions to overcome the disadvantages and help to create a more trustworthy blockchain-based public auditing scheme. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of Computer Science & technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Fabrication of 1D nanometer tungsten trioxide under different solvent system

Fabrication of 1D nanometer tungsten trioxide under differen...

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Chinese Materials Conference, 2018

作者： Zhao, Linyan Shen, Yuegang Fan, Youshu Ma, Liwen Xi, Xiaoli National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China College of Material Science and Engineering Key Laboratory of Advanced Functional Materials Education Ministry of China Beijing University of Technology Beijing100124 China

ISBN: (纸本)9783035713848

As a cheap and stable transition metal oxide, the tungsten trioxide (WO3) has received extensive attentions due to superior physical and chemical properties and could be used in electronic devices, lithium-ion batteries, gas sensors, dye sensitized solar cells, catalysts. In this study, the well-designed 1D architecture of nanowires and nanorods was successfully synthesized via a simple and facile solvethermal method with no template or additives. It was found that both solvent type and concentration of W raw material could affect the size and morphology of WO3 significantly in a regular way. Different products showed distinct photocatalytic activities during the processing of degradation methylene blue under visible light. And finally the underlying reasons for the different photocatalytic activities were discussed. © 2019 Trans Tech Publications, Switzerland.

关键词： Tungsten compounds

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Optical vortices 30 years on:OAM manipulation from topological charge to multiple singularities

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Light(Science & applications) 2019年第1期8卷 339-367页

作者： Yijie Shen Xuejiao Wang Zhenwei Xie Changjun Min Xing Fu Qiang Liu Mali Gong Xiaocong Yuan Key Laboratory of Photonic Control Technology(Tsinghua University) Ministry of Education100084 BeijingChina State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentTsinghua University100084 BeijingChina National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data(NELPSRPC) China Academy of Electronics and Information Technology of CETCChina Electronic Technology Group Corporation100041 BeijingChina Nanophotonics Research Center Shenzhen University518060 ShenzhenChina

Thirty years ago,Coullet et *** that a special optical field exists in laser cavities bearing some analogy with the superfluid *** then,optical vortices have been widely studied,inspired by the hydrodynamics sharing similar *** to a fluid vortex with a central flow singularity,an optical vortex beam has a phase singularity with a certain topological charge,giving rise to a hollow intensity *** a beam with helical phase fronts and orbital angular momentum reveals a subtle connection between macroscopic physical optics and microscopic quantum *** amazing properties provide a new understanding of a wide range of optical and physical phenomena,including twisting photons,spin–orbital interactions,Bose-Einstein condensates,etc.,while the associated technologies for manipulating optical vortices have become increasingly tunable and ***,owing to these salient properties and optical manipulation technologies,tunable vortex beams have engendered tremendous advanced applications such as optical tweezers,high-order quantum entanglement,and nonlinear *** article reviews the recent progress in tunable vortex technologies along with their advanced applications.

关键词： tunable optics charge

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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