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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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GA-Net: Gated Attention Mechanism Based Global Refinement network for Image Inpainting 29

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Zhao, Shengrong Liang, Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images. © 2023 IEEE.

关键词： Gated convolution Image inpainting Underwater images

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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A Study on a Reduced Indicator System for the network Security Situational Assessment Model

A Study on a Reduced Indicator System for the Network Securi...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Zhang, Hongbin Bai, Yikang Zhao, Dongmei Liu, Bin Xu, Ying Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China

ISBN: (纸本)9798350327380

It is a challenge to monitor the network security state in a large-scale network environment because the selection of situation assessment indicators is overrun by massive observational information. To address this issue, this paper proposes a network security situational assessment model based on a reduced indicator system. The model categorises the characteristics of network assets and threats into endogenous situational factors and exogenous situational factors. By applying the multi-label extremely random tree algorithm to select the importance of network features, a reduced indicator system with independence and strong comprehensiveness is established. Additionally, the weights of the reduced indicator system are calculated using an improved analytical hierarchy process, which yields the evaluation results for network security situations. The experimental results show that the model improves the accuracy and timeliness of the situation assessment model, and the method can reflect the network situation in real time. © 2023 IEEE.

关键词： network security

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Multi-action self-healing coatings with simultaneous recovery of corrosion resistance and adhesion strength

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Journal of Materials Science & technology 2022年第6期101卷 18-27页

作者： Chenhao Ren Yao Huang Wenkui Hao Dawei Zhang Xiejing Luo Lingwei Ma Jinke Wang Thee Chowwanonthapunya Chaofang Dong Xiaogang Li Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China State Key Laboratory of Advanced Power Transmission Technology(Global Energy Interconnection Research Institute Co.Ltd.) Beijing 100083China Faculty of International Maritime Studies Kasetsart UniversitySriracha Campus199 TungsuklaSrirachaChonburi 20230Thailand BRI Southeast Asia Network for Corrosion and Protection(MOE) Shunde Graduate School of University of Science and Technology BeijingFoshan 528300China

In this study, a new self-healing strategy that can simultaneously recover the corrosion resistance and the adhesion strength of coatings was introduced. The coating was based on a shape memory epoxy resin containing ethylene vinyl acetate(EVA) microspheres loaded with Ce(NO_(3))_(3)inhibitors, and was cured at a relatively high temperature to facilitate the fusion of adjacent microspheres for a strengthened self-healing effect. The electrochemical impedance spectroscopy(EIS) and scanning electrochemical microscopy(SECM) results demonstrated that the shape memory effect of epoxy matrix, the filling of molten EVA microspheres as well as the release of Ce(NO_(3))_(3)inhibitors contributed synergistically to suppress the corrosion reaction at the coating damage. After healing, the low frequency impedance modulus of the coatings containing Ce(NO_(3))_(3)-EVA microspheres was three orders of magnitude higher than that of the blank epoxy coating. The adhesion strength of the coatings containing Ce(NO_(3))_(3)-EVA microspheres on the metal substrate was also largely repaired thanks to the strong bonding effect of the EVA microspheres.

关键词： Self-healing coating Corrosion protection Adhesion strength Shape memory effect Bonding effect

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6G-Enabled Edge AI for Metaverse:Challenges, Methods,and Future Research Directions

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Journal of Communications and Information networks 2022年第2期7卷 107-121页

作者： Luyi Chang Zhe Zhang Pei Li Shan Xi Wei Guo Yukang Shen Zehui Xiong Jiawen Kang Dusit Niyato Xiuquan Qiao Yi Wu School of Data Science and Technology Heilongjiang UniversityHarbin 150080China Institute for Cryptology and Network Security Heilongjiang UniversityHarbin 150080China SenseTime Group Limited Shenzhen 518000China Pillar of Information Systems Technology and Design Singapore University of Technology and Design.Singapore 487372Singapore School of Automation.Guangdong University of Technology and Center for Intelligenl Batch Manufacturing based on IoT TechnologyGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798.Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications.Beijing 100876China

Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,*** and more next-generation wireless network smart service applications are changing our way of life and improving our quality of *** the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality ***,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and *** this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and ***,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in *** we summarize technical challenges that these architectures face in Metaverse and the existing ***,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital ***,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.

关键词： edge artificial intelligence artificial intelli-gence 6G metaverse federated learning

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An Anomaly Event Detection Method Based on GNN Algorithm for Multi-data Sources 3

An Anomaly Event Detection Method Based on GNN Algorithm for...

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3rd ACM International Symposium on Blockchain and Secure Critical Infrastructure, BSCI 2021

作者： Ji, Yipeng Wang, Jingyi Li, Shaoning Li, Yangyang Lin, Shenwen Li, Xiong School of Computer Science and Engineering Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Beijing China Beijing China Beijing Biwei Network Technology Co. Ltd Beijing China Beijing Xindatek Technology Co. Ltd Beijing China

ISBN: (纸本)9781450384001

Anomaly event detection is crucial for critical infrastructure security(transportation system, social-ecological sector, insurance service, government sector etc.) due to its ability to reveal and address the potential cyber-Threats in advance by analysing the data(messages, microblogs, logs etc.) from digital systems and networks. However, the convenience and applicability of smart devices and the maturity of connected technology make the social anomaly events data multi-source and dynamic, which result in the inadaptability for multi-source data detection and thus affect the critical infrastructure security. To effectively address the proposed problems, in this paper, we design a novel anomaly detection method based on multi-source data. First, we leverage spectral clustering algorithm for feature extraction and fusion of multiple data sources. Second, by harnessing the power of deep graph neural network(Deep-GNN), we perform a fine-gained anomaly social event detection, revealing the threatening events and guarantee the critical infrastructure security. Experimental results demonstrate that our framework outperforms other baseline anomaly event detection methods and shows high tracking accuracy, strong robustness and stability. © 2021 ACM.

关键词： Critical infrastructures

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Efficient privacy-preserving user authentication scheme with forward secrecy for industry 4.0

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Science China(Information Sciences) 2022年第1期65卷 192-206页

作者： Chenyu WANG Ding WANG Guoai XU Debiao HE School of Cyber Security Beijing University of Posts and Telecommunications State Key Laboratory of Cryptology College of Cyber Science Nankai University Tianjin Key Laboratory of Network and Data Security Technology Nankai University School of Cyber Science and Engineering Wuhan University

Industry 4.0, which combines information technology, network and industrial production, is expected to have a tremendous impact on our daily lives. In such a complex and security-critical system with resource-constrained sensor nodes, the design of a secure user authentication scheme for preventing real-time data from unauthorized access is full of challenges, and the main crux lies in how to realize the important property of forward secrecy. Existing schemes either fail to achieve forward secrecy or achieve forward secrecy with high computation cost on sensor nodes. Besides, they often fail to conform to the development trend of industry 4.0 systems where a cloud center is necessary to help intelligent decision-making and alleviate computation and storage pressure. Therefore, in this paper, we propose an efficient privacy-preserving user authentication scheme with forward secrecy for industry 4.0, and formally prove its security in the random oracle model. Compared with previous schemes, it has three advantages:(1) all eleven state-of-the-art criteria are achieved;(2) its computation cost on sensor nodes is comparable to those insecure schemes that employ only symmetric cryptographic algorithms, and is superior to those that also use asymmetric cryptographic algorithms;(3) it takes the advantage of the computation and storage capabilities of the cloud center to achieve user anonymity and the resistance to offline dictionary attack without performing any asymmetric cryptographic algorithms on gateways. Our computation cost on gateways is the smallest among all state-of-the-art relevant schemes for comparison.

关键词： industry 4.0 wireless sensor networks password authentication forward secrecy offline dictionary attack

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Siamese network Visual Tracking Algorithm Based on GCT Attention and Dual-Template Update 5

Siamese Network Visual Tracking Algorithm Based on GCT Atten...

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5th International Conference on Natural Language Processing, ICNLP 2023

作者： Ma, Sugang Sun, Siwei Pu, Lei Yang, Xiaobao Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Rocket Force Engineering University School of Operational Support Xi'an China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China

ISBN: (纸本)9798350302219

To address the problem of insufficient representational capability and lack of online update of the Fully-convolutional Siamese network (SiamFC) tracker in complex scenes, this paper proposes a siamese network visual tracking algorithm based on GCT attention and dual-template update mechanism. First, the feature extraction network is constructed by replacing AlexNet with the VGG16 network and SoftPool is used to replace the maximum pooling layer. Secondly, the attention module is added after the backbone network to enhance the network's ability to extract object features. Finally, a dual-template update mechanism is designed for response map fusion. Average Peak-to-Correlation Energy (APCE) is used to determine whether to update the dynamic templates, effectively improving the tracking robustness. The proposed algorithm is trained on the Got-10k dataset and tested on the OTB2015 and VOT2018 datasets. The experimental results show that, compared with SiamFC, the success rate and accuracy reach 0.663 and 0.891 on the OTB2015, which improve respectively 7.6% and 11.9%;On the VOT2018 dataset, the tracking accuracy, robustness and EAO are improved respectively by 2.9%, 29% and 14%. The proposed algorithm achieves high tracking accuracy in complex scenes and the tracking speed reaches 52.6 Fps, which meets the real-time tracking requirements. © 2023 IEEE.

关键词： Complex networks

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A Ji-shaped Chinese-character millimeter-wave antenna for 5G communications 4

A Ji-shaped Chinese-character millimeter-wave antenna for 5G...

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4th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2021

作者： Yang, Baifa Huang, Haixuan Feng, Botao Deng, Li College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781665432030

A Ji-shaped Chinese-character patch antenna is proposed for future communications. The proposed antenna is comprised of two dielectric layers. Ji-shaped patch is etched on the upper surface of the top layer, whereas a feeding network is designed in the other layer. Furthermore, the conformal Jishaped antenna is designed to be placed on the surface of the cylindrical objects. Simulated results demonstrate that a gain of 7.9±0.3 dBi over the whole operating frequency band (24.75-27.5 GHz) can be obtained. © 2021 IEEE

关键词： Substrate integrated waveguides

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