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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Zhiqiang Zhang Yongqiang Jiang Qian Gao Zhipeng Wang Key Laboratory of Computing Power Network and information Security Ministry of Eaucation Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Jinan China

Existing multimodal recommendation research still faces some challenges, such as not being able to fully mine the implicit relevance information of neighbor nodes, and the unreasonable weight allocation to imbalanced nodes. To address the aforementioned challenges, this paper introduces a new multimodal recommendation model called NS-MAR+. Specifically, the model firstly constructs a neighbor similarity graph convolutional network to capture the implicit relevance information and reasonably assigns the attention weights through the graph attention mechanism. Secondly, the model introduces a modal alignment and fusion mechanism by using a multilayer perceptron (MLP) to map image and text features into a shared space for comparison and fusion. In addition, the model constructs a user co-interaction graph and an item semantic graph based on the original information and performs graph convolution operations to enhance the preference information of users and items, and to better capture the interactions and internal features between users and items. Finally, MLP is employed to aggregate user and item representations and predict personalized recommendation rankings. To validate the experiment's efficacy, this paper compares with several leading multimodal recommendation models on public datasets with a performance improvement of 1% to 3%. The experimental outcomes indicate that the model in this paper has good superiority and accuracy in multimodal recommendation tasks.

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Improvement of Low-Contrast Objective Detecting Capability for YOLOv5 Based on Receptive Field Enhancement and Redundant Feature Reuse

Improvement of Low-Contrast Objective Detecting Capability f...

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International Joint Conference on Neural networks (IJCNN)

作者： Qingyu Xu Jiguo Yu Anming Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

YOLOv5s is a classic deep learning target detection framework with balanced speed and performance in recognition, which is widely used in industrial defect inspections. YOLOv5s relies on the residual structure in the C3 module for feature extraction, but due to its simple structure and the small, fixed convolution kernel, it is difficult to accurately and completely extract target features when applied to the detection of defects with inconspicuous features and irregular shapes, which results in a decrease in recognition accuracy. To solve this problem, this paper proposes an improved YOLO architecture based on receptive field enhancement and redundant feature reuse. Firstly, the method designs a redundant feature reuse module (RFRM) to replace the residual structure in the C3 module for feature extraction. This module can deepen the network with a smaller cost and enhance the network’s ability to extract features. Furthermore, the feature reuse process allows the network to deepen while retaining detailed information, enabling the network to classify and localize more accurately. Secondly, a simplified convolutional block attention module (SCBAM) is designed and embedded into the neck of the detection network. This module directs the detection network to focus more on inconspicuous defect features and reduce interference from background noise in the feature map, thereby improving the accuracy of defect classification and localization. Finally, a receptive field enhancement module (RFEM) is designed and inserted between the neck and head of the detection network. This module adds only a small computational cost to expand the receptive field of the network, enabling it to further extract and comb irregular features from the neck in a comprehensive manner, making the features reaching the detection head more complete. Applying the above improvements to defect detection, the experimental results show that the improved YOLO architecture based on receptive field enhancem

关键词： YOLO Head Accuracy Target recognition Shape Computer architecture Feature extraction

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Thinking the Importance of Patient’s Chief Complaint in TCM Syndrome Differentiation

Thinking the Importance of Patient’s Chief Complaint in TCM...

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International Conference on Computer Supported Cooperative Work in Design

作者： Zhizhuo Zhao Xueping Peng Yong Li Hao Wu Weiyu Zhang Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Australia Jinan ChangQing District People’s Hospital School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapeutic approach with widespread application worldwide. The unique diagnostic methods of TCM often require a comprehensive analysis of patient information, much of which is contained in clinical text. Numerous studies have demonstrated the effectiveness of natural language processing (NLP) techniques in TCM disease classification. Therefore, this paper focuses on the task of TCM syndrome differentiation, proposing a novel matching score calculation method and a new label attention calculation method to assist the model in focusing on the relationship between TCM syndrome and disease symptom. Specifically, we enhance the model’s attention to the uniqueness of the relationship between TCM syndrome and symptom by introducing a finer-grained token-level matching score. Simultaneously, we improve the model’s attention to the generality of the relationship between TCM syndrome and symptom through a more global label attention mechanism. Additionally, we observe a severe long-tail problem in the dataset. To alleviate this issue, we propose the use of focal loss to help the model pay more attention to challenging samples. Extensive experiments on the TCM-SD dataset indicate that our approach significantly outperforms state-of-the-art baselines 1 .

关键词： Attention mechanisms Federated learning Focusing Natural language processing Task analysis Medical diagnostic imaging Diseases

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An Efficient Lightweight Satellite Image Classification Model with Improved MobileNetV3

An Efficient Lightweight Satellite Image Classification Mode...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Xiaoteng Yang Lei Liu Xifei Song Jie Feng Qingqi Pei Xiaoming Yuan Jianqiao Li School of Telecommunication Engineering Xidian University Xi'an China Guangzhou Institute of Technology Xidian University Guangzhou China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an Shaanxi China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an China Northeastern University Qinhuangdao Branch Campus Qinhuangdao China China Academy of Space Technology-Xi'an Xi'an China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

In view of the huge satellite images, which will consume a large amount of resources if they are completely transmitted to the ground for processing, an improved lightweight on-orbit image classification model is proposed in this study. By pushing the image processing task to the satellite, real-time processing is achieved and the ground communication burden is significantly reduced. The model is based on MobileNetV3,which utilises depth-separable convolution and inverted residual linear structure to maintain the accuracy of the model while keeping the computational efficiency. The integrated channel attention mechanism and spatial pyramid pooling structure further enhance the model's classification accuracy and multi-scale sensing capability. Experiments demonstrate that the accuracy of the model is improved by 1.09% and 1.84% on the two datasets while keeping the model parameters consistent, which validates the effectiveness and accuracy of the method in this paper. This innovative lightweight model provides an efficient and feasible solution for satellite in-orbit image classification tasks.

关键词： Accuracy Satellites Computational modeling Image processing Real-time systems Satellite images Sensors

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A Comprehensive Survey on Edge Data Integrity Verification: Fundamentals and Future Trends

arXiv

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arXiv 2022年

作者： Zhao, Yao Qu, Youyang Xiang, Yong Uddin, Md Palash Peng, Dezhong Gao, Longxiang School of Information Technology Deakin University Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China College of Computer Science Sichuan University China

Recent advances in edge computing (EC) have pushed cloud-based data caching services to edge, however, such emerging edge storage comes with numerous challenging and unique security issues. One of them is the problem of edge data integrity verification (EDIV) which coordinates multiple participants (e.g., data owners and edge nodes) to inspect whether data cached on edge is authentic. To date, various solutions have been proposed to address the EDIV problem, while there is no systematic review. Thus, we offer a comprehensive survey for the first time, aiming to show current research status, open problems, and potentially promising insights for readers to further investigate this under-explored field. Specifically, we begin by stating the significance of the EDIV problem, the integrity verification difference between data cached on cloud and edge, and three typical system models with corresponding inspection processes. To thoroughly assess prior research efforts, we synthesize a universal criteria framework that an effective verification approach should satisfy. On top of it, a schematic development timeline is developed to reveal the research advance on EDIV in a sequential manner, followed by a detailed review of the existing EDIV solutions. Finally, we highlight intriguing research challenges and possible directions for future work, along with a discussion on how forthcoming technology, e.g., machine learning and context-aware security, can augment security in EC. Given our findings, some major observations are: there is a noticeable trend to equip EDIV solutions with various functions and diversify study scenarios;completing EDIV within two types of participants (i.e., data owner and edge nodes) is garnering escalating interest among researchers;although the majority of existing methods rely on cryptography, emerging technology is being explored to handle the EDIV problem. Copyright © 2022, The Authors. All rights reserved.

关键词： Data integrity

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Research on Virtual 3D Remote Sensing Technology of power Grid Station Based on 2D Vision 22

Research on Virtual 3D Remote Sensing Technology of Power Gr...

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Proceedings of the 5th International Conference on information Technologies and Electrical Engineering

作者： Hai Yu Shiyang Tang Lin Peng Zhen Yu Kun Qian School of Automation Southeast University China and Power Grid Digitization Technology Research Institute State Grid Smart Grid Research Institute China Power Grid Digitization Technology Research Institute State Grid Smart Grid Research Institute State Grid Smart Grid Research Institute China and State Grid Key Laboratory of Information & Network Security China Power Grid Digitization Technology Research Institute State Grid Smart Grid Research Institutean Electric Power Company Limited Power Supply Service Center(Metrology Center) State Grid Smart Grid Research Institute China and State Grid Key Laboratory of Information & Network Security China School of Automation Southeast University China

ISBN: (纸本)9781450396806

Aiming at the production safety management and control problems of substations, the digital twin 3D volume of substations is constructed based on 3D twinning technology, and the 2D/3D space fusion positioning perception technology based on existing multi-band surveillance cameras is studied. The automatic safety risk monitoring algorithm of common monitoring equipment, such as the automatic safety risk monitoring algorithm, realizes the high-precision perception analysis of the three-dimensional space of personnel/equipment in the substation, and can be applied to the safety management and control of personnel, equipment and perimeter of the substation, and improves the safety management level of the power grid.

关键词： 2D and 3D fusion Digital twin Visual positioning Spatial transformation analysis Plant station monitoring

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network Channel Protection Technology for Grid 5G Business 6

Network Channel Protection Technology for Grid 5G Business

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6th International Conference on power and Renewable Energy, ICPRE 2021

作者： Gao, Xianzhou Zhang, Xiaojian Gu, Zhimin Yao, Qigui Global Energy Interconnection Research Institute Co. Ltd. State Grid Key Laboratory of Information Network Security Nanjing China State Grid Jiangsu Electric Power CO. LTD. Research Institute Nanjing China

ISBN: (纸本)9781665449618

This paper introduces the network slicing technology adopted by 5G to meet diverse vertical grid industry applications. Introduced the core content network channel isolation technology of network channel security, including the isolation realization of network slicing in the access network, bearer network and core network. Combining the application scenarios of network slicing in the power industry, we have analyzed the isolation realization of network slicing applied to the power industry and network slicing of other industries and the isolation realization of network slicing corresponding to different businesses in the power industry. Finally, we provide an implementation case of network channel isolation for power grid business. © 2021 IEEE.

关键词： 5G mobile communication systems

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Design and implementation of an intelligent penetration security assessment system based on Graph Neural network (GNN) technology

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Journal of Cyber security Technology 2025年

作者： Xu, Chuanmao Du, Jinran Lai, Boyu Wang, Hong Zheng, Hongliang Dai, Tao Liang, Zhihong Yang, Yiwei Digitalization and Information Security Center CSG Electric Power Research Institute Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

To enhance security assessment and intelligence in penetration testing, this study employs a Graph Neural network (GNN) to model network states in complex environments and dynamic decision-making scenarios. Integrating an improved Deep Q-network (DQN), the intelligent agent’s threat path discovery is optimized and evaluated. Using the NASim platform to simulate network environments, multi-layer graph convolutional networks extract features, while pre-trained models and residual connections enhance performance. The model’s effectiveness is assessed based on cumulative reward values and operation steps, testing action space optimization and convergence. Experimental results show that the improved DQN significantly boosts efficiency, reducing steps per round from 35.5 to 12.75 after 800 training rounds. Decoupling techniques shrink the action space to 12.6% of its original size. In vulnerability detection, the method achieves 68.13% accuracy, a 6.05% improvement over conventional pre-trained models, and outperforms others in F1 score and recall. This study enables intelligent penetration detection and assessment by constructing a comprehensive network behavior graph, allowing the system to learn and recognize complex attack patterns effectively. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Graph neural networks

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Credible Identity Authentication Mechanism of Electric Internet of Things Based on Blockchain 10th

Credible Identity Authentication Mechanism of Electric Inter...

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10th International Conference on Computer Engineering and networks, CENet 2020

作者： Wang, Liming Huang, Xiuli Chen, Lei Fan, Jie Zhang, Ming State Grid Jiangsu Electric Power Co. Ltd. Nanjing211106 China State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute Co. Ltd. Beijing102200 China

ISBN: (纸本)9789811584619

In order to solve the problems of low authentication efficiency and easy tampering of authentication data in the electric IoT system, this paper proposes the electric IoT based on blockchain. First, the authentication model based on blockchain is proposed, and four key issues are analyzed, such as data security storage, user biometrics extraction, power terminal equipment feature extraction, and authentication key generation, then technical solutions are proposed. Secondly, based on the authentication model, the power terminal registration process, user registration process, and authentication process are proposed. The performance analysis of the authentication mechanism is carried out from four aspects: availability, integrity, privacy and efficiency. The analysis results show that the authentication mechanism proposed in this paper has good performance. Finally, the application scenarios and precautions of the authentication mechanism are analyzed. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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power Grid Disaster Loss information Fusion Analysis and Prediction Technology 5

Power Grid Disaster Loss Information Fusion Analysis and Pre...

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5th Annual International Conference on Data Science and Business Analytics, ICDSBA 2021

作者： Xu, Min Yang, Jianhua Yang, Jian Peng, Lin Tang, Shiyang Li, Nige Hou, Zhansheng He, Zhimin Wang, Gang Wang, He Bao, Xingchuan Yu, Hai Zhu, Liang Zhang, Zehao Li, Jing Gu, Tianxiong Yang, Yang Ye, Dailiang Global Energy Interconnection Research Institute Jiangsu Nanjing210003 China State Grid ZheJiang Electric Power Co. LTD Hangzhou Zhejiang310000 China State Grid Key Laboratory of Information Network Security Nanjing210003 China

ISBN: (纸本)9781665445900

After the natural disaster attacks the power grid, a large number of alarm information will flow into the control center, especially when the power grid has complex faults or the automatic device acts abnormally. Because there are some uncertain factors in protection, circuit breaker misoperation, rejection and channel reasons, a kind of disaster assessment method that can realize uncertain information is of great significance. Although the traditional evaluation algorithm based on rough set can deal with uncertain information, it can only deal with discrete data effectively. In practice, there are continuous data and most of them are fuzzy, and the boundary between concepts is not very clear. Fuzzy set can overcome this disadvantage. This paper creatively proposes a method based on intuitionistic fuzzy rough set to evaluate power grid disasters timely and accurately. In the evaluation, attribute filtering and data cleaning are used to reduce the redundancy of disaster attributes;Through value filtering, the evaluation rules are extracted, the disaster level is determined through matching, and adjusted and updated with the development of the situation, so as to provide a scientific basis for emergency decision-making. © 2021 IEEE.

关键词： Disasters

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