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A Temporal Knowledge Graph Embedding Model Based on Variable Translation

Tsinghua Science and Technology 2024年第5期29卷 1554-1565页

作者： Yadan Han Guangquan Lu Shichao Zhang Liang Zhang Cuifang Zou Guoqiu Wen Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilin 541004China and also with Guangxi Key Lab of Multi-Source Information Mining and SecurityGuangxi Normal UniversityGuilin 541004China

Knowledge representation learning(KRL)aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous *** is popularly used in knowledge graph completion(or link prediction)***-based knowledge representation learning methods perform well in knowledge graph completion(KGC).However,the translation principles adopted by these methods are too strict and cannot model complex entities and relationships(i.e.,N-1,1-N,and N-N)***,these traditional translation principles are primarily used in static knowledge graphs and overlook the temporal properties of triplet ***,we propose a temporal knowledge graph embedding model based on variable translation(TKGE-VT).The model proposes a new variable translation principle,which enables flexible transformation between entities and relationship ***,this paper considers the temporal properties of both entities and relationships and applies the proposed principle of variable translation to temporal knowledge *** conduct link prediction and triplet classification experiments on four benchmark datasets:WN11,WN18,FB13,and *** model outperforms baseline models on multiple evaluation metrics according to the experimental results.

关键词： knowledge graph knowledge graph completion variable translation temporal properties link prediction triplet classification

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Class Activation Regularization-Based Facial Emotion Recognition Network and its Application in Students’ Emotional Engagement Assessment

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IEEE Transactions on Affective Computing 2025年第2期16卷 1044-1055页

作者： Xu, Luhui Gan, Yanling Jin, Yi Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541001 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541001 China

Students’ emotional engagement in the classroom can be evaluated through their facial emotions. However, facial emotion recognition remains challenging due to the uncertainty problem, which is routinely caused by factors such as low resolution, intra-class variability, and inter-class similarity. To deal with this problem, we propose a class activation regularization network, which includes two main modules: class activation dropping module (CADM) and hard sample mining module (HSMM). Our key idea is to perform consistency learning between the original images and class-uncertain regions, thereby enabling models to perceive uncertainty of emotional semantics. In CADM, the class-specific attention is obtained using class activation mapping. Then, the attention is partly discarded by imitating dropout mechanism to obtain class-uncertain regions. In HSMM, the hard samples with high uncertainties are identified based on the class-uncertain regions and confidence thresholds. For the hard samples, regularization loss is applied to align the probability distributions between the original images and the class-uncertain regions, thereby improving the network’s awareness of emotional semantics and its ability to handle uncertainty. Extensive experiments on three datasets demonstrate the competitiveness of the proposed method. In addition, our method can be deployed in the classroom for students’ emotional engagement assessment. © 2010-2012 IEEE.

关键词： Students

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Efficient multi-scale community search method based on spectral graph wavelet

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Frontiers of Computer Science 2023年第5期17卷 89-101页

作者： Cairui YAN Huifang MA Qingqing LI Fanyi YANG Zhixin LI College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541004China

Community search is an important problem in network analysis,which has attracted much attention in recent *** a query-oriented variant of community detection problem,community search starts with some given nodes,pays more attention to local network structures,and gets personalized resultant communities *** existing community search method typically returns a single target community containing query nodes by *** is a strict requirement and does not allow much *** many realworld applications,however,query nodes are expected to be located in multiple communities with different *** address this limitation of existing methods,an efficient spectral-based multi-Scale Community Search method(MSCS)is proposed,which can simultaneously identify the multi-scale target local communities to which query node *** MSCS,each node is equipped with a graph Fourier multiplier *** access of the graph Fourier multiplier operator helps nodes to obtain feature representations at various community *** addition,an efficient algorithm is proposed for avoiding the large number of matrix operations due to spectral *** experimental evaluations on a variety of real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

关键词： community search multi-scale spectral wavelets

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Aspect-level sentiment analysis based on semantic heterogeneous graph convolutional network

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Frontiers of Computer Science 2023年第6期17卷 87-99页

作者： Yufei ZENG Zhixin LI Zhenbin CHEN Huifang MA Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541004China College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China

The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis(ABSA).However,the accuracy of the dependency parser cannot be determined,which may keep aspect words away from its related opinion words in a dependency ***,few models incorporate external affective knowledge for *** on this,we propose a novel architecture to tackle the above two limitations,while fills up the gap in applying heterogeneous graphs convolution network to ***,we employ affective knowledge as an sentiment node to augment the representation of ***,linking sentiment node which have different attributes with word node through a specific edge to form a heterogeneous graph based on dependency ***,we design a multi-level semantic heterogeneous graph convolution network(Semantic-HGCN)to encode the heterogeneous graph for sentiment *** experiments are conducted on the datasets SemEval 2014 Task 4,SemEval 2015 task 12,SemEval 2016 task 5 and ACL 14 *** experimental results show that our method achieves the state-of-the-art performance.

关键词： heterogeneous graph convolution network multi-head attention network aspect-based sentiment analysis deep learning affective knowledge

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Attribute subspace-guided multi-scale community detection

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Neural Computing and Applications 2024年第22期36卷 13975-13988页

作者： Yan, Cairui Ma, Huifang Tang, Yuechen Li, Zhixin College of Computer Science and Engineering Northwest Normal University Gansu Lanzhou730070 China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guangxi Guilin541004 China

Community detection is designed to divide a network into multiple subnetworks (communities) with high cohesiveness, which has attracted wide attention in graph analysis. Attributes are typically served as auxiliary side information to improve the quality of community detection. In spite of their effectiveness, they suffer from two limitations: (1) existing methods usually return a single partition of the network by default, which is a critical requirement and not allowing much flexibility;(2) existing approach just globally assigns the same attribute weights to each community. We believe that community detection should be approached from the perspective of attribute subspace with different dimensional correlations. Toward this end, a novel attribute subspace-guided multi-scale community detection method (ASMS) is proposed, which can identify multi-scale communities with personalized subspaces. Specifically, ASMS can output multiple network divisions of different scales, and each subdivision has a distinctive attribute subspace that is used to reveal the inner meaning of that community formation. In particular, we devise three operators to infer the attribute subspaces. Abundant experimental results indicate that ASMS outperforms the existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Population dynamics

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Incorporating metapath interaction on heterogeneous information network for social recommendation

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Frontiers of Computer Science 2024年第1期18卷 33-48页

作者： Yanbin JIANG Huifang MA Xiaohui ZHANG Zhixin LI Liang CHANG College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541001China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph *** existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths,they have the following major *** works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths,which are not expressive enough to capture more complicated dependency relationships involved on the ***,the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately *** tackle these limitations,we propose a novel social recommendation model MPISR,which models MetaPath Interaction for Social Recommendation on heterogeneous information ***,our model first learns the initial node representation through a pretraining module,and then identifies potential social friends and item relations based on their similarity to construct a unified *** then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different *** experiments on five real datasets demonstrate the effectiveness of our method.

关键词： heterogeneous information network social reco-mmender system metapath interaction attention mechanism

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Robust Hashing with Deep Features and Meixner Moments for Image Copy Detection

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ACM Transactions on multimedia Computing, Communications and Applications 2024年第12期20卷 1-23页

作者： Yu, Mengzhu Tang, Zhenjun Liang, Xiaoping Zhang, Xianquan Li, Zhixin Zhang, Xinpeng Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science Fudan University Shanghai China

Copy detection is a key task of image copyright protection. Most robust hashing schemes do not make satisfied performance of image copy detection yet. To address this, a robust hashing scheme with deep features and Meixner moments is proposed for image copy detection. In the proposed hashing, global deep features are extracted by applying tensor Singular Value Decomposition (t-SVD) to the three-order tensor constructed in the DWT domain of the feature maps calculated by the pre-trained VGG16. Since the feature maps in the DWT domain are slightly disturbed by digital operations, the constructed three-order tensor is stable and thus the desirable robustness is guaranteed. Moreover, since t-SVD can decompose a three-order tensor into multiple low-dimensional matrices reflecting intrinsic structure, the global deep feature calculation from the low-dimensional matrices can provide good discrimination. Local features are calculated by the block-based Meixner moments. As the Meixner moments are resistant to geometric transformation and can efficiently discriminate various images, the use of the block-based Meixner moments can make discriminative and robust local features. Hash is ultimately determined by quantifying and combining global deep features and local features. The results of extensive experiments on open image datasets demonstrate that the proposed robust hashing outperforms some state-of-the-art robust hashing schemes in terms of classification and copy detection performances. © 2024 Copyright held by the owner/author(s).

关键词： Tensors

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BAM_CRS: Blockchain-Based Anonymous Model for Cross-Domain Recommendation Systems

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Journal of Computer Science & Technology 2025年第2期40卷 340-358页

作者： Li-E Wang Dong-Cheng Li Peng Liu Xian-Xian Li Key Laboratory of Education Blockchain and Intelligent Technology(Ministry of Education)Guangxi Normal University Guilin 541004China Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University Guilin 541004China School of Computer Science and Engineering Guangxi Normal UniversityGuilin 541004China

The cross-domain recommendation (CDR) technique has been widely applied in recommendation systemsand can effectively relieve the data sparsity and cold start problems. However, it brings up data privacy and authenticitychallenges due to different data sources. Most existing solutions only consider data privacy, and are not robust againstparticipant poisoning attacks that affect the quality of recommendations. How to leverage the privacy and authenticity ofdata remains a key challenge. To this end, we propose a blockchain-based anonymous model for CDR systems(BAM_CRS). Under this model, we use a three-chain structure to store users, products, and the relationships between thetwo parties to guarantee privacy. This structure enables transactions to be separated from users and items to guaranteeprivacy and to establish consensus without relying on a central authority. For data authenticity, we warrant data imtamability and traceability through on-chain data collection and design a contribution-based quantification and incentive mechanism to ensure system sustainability. Additionally, a signature-based verifiable mechanism is designed to motivate nodeparticipation and further guarantee data authenticity. Experiments on real-life datasets demonstrate that our model canprotect privacy with an advantage of about 2% improvement over baselines in terms of precision and F1 metrics.

关键词： anonymous cross-domain recommendation(CDR) data authenticity privacy protection utility

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Graph neural network based approach Che upo to automatically assigning common weakness enumeration identifiers for vulnerabilities

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Cybersecurity 2024年第3期7卷 1-15页

作者： Peng Liu Wenzhe Ye Haiying Duan Xianxian Li Shuyi Zhang Chuanjian Yao Yongnan Li Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilin 541004China School of Software Beihang UniversityBeijing 100000China School of National Security People's Public Security University of ChinaBeijing 1000000China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal UniversityGuilin 541004China

Vulnerability reports are essential for improving software security since they record key information on *** a report,CWE denotes the weakness of the vulnerability and thus helps quickly understand the cause of the ***,CWE assignment is useful for categorizing newly discovered *** this paper,we propose an automatic CwE assignment method with graph neural ***,we prepare a dataset that contains 3394 real world vulnerabilities from Linux,OpenSSL,Wireshark and many other software ***,we extract state-ments with vulnerability syntax features from these vulnerabilities and use program slicing to slice them according to the categories of syntax *** top of slices,we represent these slices with graphs that characterize the data dependency and control dependency between ***,we employ the graph neural networks to learn the hidden information from these graphs and leverage the Siamese network to compute the similarity between vulnerability functions,thereby assigning CWE IDs for these *** experimental results show that the proposed method is effective compared to existing methods.

关键词： Vulnerability categorization CWE Graph representation GNN

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SIEVL-Track: Exploring Semantic information Enhancement for Visual-Language Object Tracking

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5872-5884页

作者： Li, Ning Zhong, Bineng Liang, Qihua Mo, Zhiyi Nong, Jian Song, Shuxiang Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Key Laboratory of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543002 China

With the assistance of language descriptions, Visual-Language (VL) object tracking can obtain more accurate semantic information compared to traditional Visual-Only object tracking. However, the ability of current VL trackers to obtain target semantic information has not been fully developed due to limitations such as wasted modeling capabilities and insufficient utilization of historical temporal information. On the one hand, the modeling output from Transformer shallow encoders often does not directly participate in the prediction of tracking results, resulting in a certain degree of model capability waste. On the other hand, the semantic information of historical tracking results has also not been fully utilized in the tracking process, resulting in a certain degree of lack of semantic assistance capability. Therefore, we propose a novel hierarchical multi-stage VL tracker called SIEVL-Track to enhance target semantic information. Specifically, we first design a multi-stage visual language tracking framework for modeling multi-scale semantic information in Visual-Language tracking pipeline. Secondly, we propose a selective deep and shallow semantic information fusion module (S-DSFM) that explicitly integrates shallow output features into deep output features, so to reduce the waste of modeling capabilities and obtain more high-frequency semantic information related to the target. Finally, we design a temporal cue modeling module based on linguistic classification and multi-frame historical information(MHLS-TCM), with the aim of more comprehensive utilization of historical temporal semantic information. Benefit from the above designs, our VL tracker can obtain stronger target semantic information. Competitive performance from extensive experimental results on five popular vision-language tracking benchmarks, including LaSOT, OTB99-Lang, WebUAV-3M, LaSOText and TNL2K, have demonstrated the superiority and effectiveness of our SIEVL-Track. © 1991-2012 IEEE.

关键词： Semantics

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