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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Ren, Hao Luo, Zeyu Jing, Fengshi Zhang, Xinyue He, Han Yu, Yonghao Zhao, Dawei City University of Macau Faculty of Data Science China Minzu University of China Beijing China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

Lung diseases such as pneumonia and tuberculosis present significant global health challenges, leading to millions of deaths annually. Chest X-ray imaging is crucial for diagnosing these conditions, but interpretation often relies on specialized radiologists, creating barriers in resource-limited areas. This study introduces a novel framework integrating topological data analysis (TDA), deep learning with ResNet-50, and contrastive learning to enhance the accuracy and computational efficiency of chest X-ray analysis. TDA captures global topological structures, while ResNet-50 extracts detailed local features. Contrastive learning then optimizes the feature space, enhancing the model's discriminative power and generalization capabilities. Comprehensive evaluations on multiple datasets demonstrate the framework's robustness and effectiveness, showing substantial improvements in diagnostic performance and computational efficiency. These findings suggest that the proposed approach is well-suited for real-time clinical applications, especially in resource-limited settings. Future work will focus on further optimizing the model and exploring its application to other medical imaging modalities, broadening its clinical utility. © 2024 IEEE.

关键词： Contrastive Learning

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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A Short-Term Electric Load Forecasting Method Based on the Fusion of Global and Local Features 9

A Short-Term Electric Load Forecasting Method Based on the F...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Gu, Haozhan Tian, Hangwei Wang, Jingchun Li, Yumei Shandong University Of Arts Academy of Fine Arts China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

Accurate short-term electric load forecasting is crucial for ensuring the stability and efficiency of the power grid. This study proposes an innovative dual-branch electric load forecasting model that enhances the accuracy of predictions by processing global and local features in parallel. Global features are extracted through the Savitzky-Golay filter and a linear model to capture the overall trends of the load data. Local features are identified using the Convolutional Self-Attention Module, which integrates the local perception capabilities of Convolutional neural network with the self-attention mechanism of Transformers. By employing a Weighted Feature Fusion network, we effectively integrate global and local information to enhance the performance of load forecasting. Experimental results demonstrate that the proposed model significantly outperforms existing models in predictive performance. The model presented in this study offers a new perspective on electric load forecasting and has the potential to become an effective tool for power system planning and scheduling. © 2024 IEEE.

关键词： Deep learning Feature fusion Load prediction Time series data analysis

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LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks 24

LampMark: Proactive Deepfake Detection via Training-Free Lan...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Tianyi Huang, Mengxiao Cheng, Harry Zhang, Xiao Shen, Zhiqi Nanyang Technological University Singapore Singapore Qilu University of Technology Jinan China Shandong University Qingdao China NTU-WeBank Research Centre on Fintech Nanyang Technological University Singapore Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798400706868

Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algorithms that have been attempted to thwart malicious Deepfake attacks, they mostly struggle with the generalizability challenge when confronted with hyper-realistic synthetic facial images. To tackle the problem, this paper proposes a proactive Deepfake detection approach by introducing a novel training-free landmark perceptual watermark, LampMark for short. We first analyze the structure-sensitive characteristics of Deepfake manipulations and devise a secure and confidential transformation pipeline from the structural representations, i.e. facial landmarks, to binary landmark perceptual watermarks. Subsequently, we present an end-to-end watermarking framework that imperceptibly and robustly embeds and extracts watermarks concerning the images to be protected. Relying on promising watermark recovery accuracies, Deepfake detection is accomplished by assessing the consistency between the content-matched landmark perceptual watermark and the robustly recovered watermark of the suspect image. Experimental results demonstrate the superior performance of our approach in watermark recovery and Deepfake detection compared to state-of-the-art methods across in-dataset, cross-dataset, and cross-manipulation scenarios. © 2024 ACM.

关键词： Watermarking

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Research on Electric Micro-Meteorological Monitoring and Early Warning System 2

Research on Electric Micro-Meteorological Monitoring and Ear...

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2019 2nd International Symposium on power Electronics and Control Engineering, ISPECE 2019

作者： Zhang, Zehao Yu, Zhen Xu, Xiyuan Guan, Cheng Zhang, Song Global Energy Interconnection Research Institute Co. Ltd Research Institute of Information Technology and Communication Beijing102206 China State Grid Key Laboratory of Information and Network Security China State Grid FuJian Electric Power Co. LTD Switzerland

This paper studies and analyzes the influence of micro-meteorological disasters on power system, points out the main problems in the establishment of power micro-meteorological monitoring and early warning system, puts forward the system architecture of power micro-meteorological monitoring and early warning system, which provides a reference for the establishment of power micro-meteorological monitoring and early warning system. © Published under licence by IOP Publishing Ltd.

关键词： Monitoring

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A power IoT Terminal Asset Identification Technology Suitable for Modbus Protocol

A Power IoT Terminal Asset Identification Technology Suitabl...

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2024 International Conference on Interactive Intelligent Systems and Techniques, IIST 2024

作者： Li, Yong Ma, YuanYuan Chen, Mu Wang, Chao State Grid Smart Grid Research Enstitute Co. Ltd State Grid Key Laboratory of Information & Network Security Nanjing210003 China State Grid Beijing Electric Power Research Institute Beijing100075 China

ISBN: (纸本)9798350374421

In the new power system scenario, power IoT terminals have new characteristics such as diverse hardware heterogeneity and complex subject ownership, which make it technically difficult to achieve secure access authentication based on traditional terminal hardware fingerprint recognition technology. A power IoT terminal fingerprint recognition technology suitable for the Modbus protocol is proposed to solve this problem. This method combines the characteristics of Modbus protocol packet parsing, which is a common industrial control communication protocol. Firstly, based on active packet detection technology, it realizes the survival judgment of business terminals and constructs a coarse-grained topology image of terminal production. Then, by passively monitoring and analyzing the characteristics of terminal interaction traffic behavior, a terminal behavior fingerprint recognition model is constructed based on the deep learning algorithm BiLSTM to achieve accurate terminal type discrimination, thereby establishing a power IoT terminal fingerprint recognition model. The experimental results in a distributed photovoltaic scenario show that the method has an average classification and recognition accuracy of 89.6% for common power business terminals, which can basically meet the lightweight and non-invasive business requirements of terminal fingerprint recognition in new power system scenarios. © 2024 IEEE.

关键词： Deep learning

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Research and implementation of grid based on electric distribution network composite evaluation method 6

Research and implementation of grid based on electric distri...

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6th International Conference on Intelligent computing, Communication, and Devices, ICCD 2023

作者： Qiao, Junfeng Yang, Pei Pan, Sen Shen, Xiaofeng Huang, Xingde Huang, Chenhong Gu, Hua State Grid Key Laboratory of Information and Network Security State Grid Smart Grid Research Institute Co.LTD. Jiangsu Nanjing210003 China Qingpu Power Supply Company State Grid Shanghai Electric Power Company Shanghai201799 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

As an important guide for grid development and a key link of grid construction, distribution network planning should not only adapt to the trend of intelligence and refinement in grid development and construction, but also meet the needs of users for reliable power consumption. The quality of electric distribution planning scheme exerts significant influence on the capacity of electric supply, operation level, economic measures of electric grid. Depending on the background that power supply enterprises vigorously promote the regional grid division of electric distribution at this stage, the regional grid division of electric distribution is comprehensively evaluated by establishing a scientific and effective indicator set of electric consumption management, which has important practical significance in analyzing the associated defects in the operation process of electric grid and determining the effectiveness of electric grid planning. In order to comprehensively and effectively evaluate the remote electric grid indicator set. In this article, considering the five important influencing factors of urban electric grid namely, capacity of electric supply, structure of electric grid, quality of electric supply, standard of device and investment benefit, and establishes a scientific and perfect indicator label orient to the objects and index levels involved in the evaluation and the rationality of electric consumption management. The indicator set of electric consumption management of distribution grid planning with clear hierarchy is proposed, and the definition and calculation method of the indicator are proposed. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： power quality

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A Detection Method of WLAN security Mechanisms Based on MAC Frame Resolution

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Wuhan University Journal of Natural Sciences 2017年第2期22卷 93-102页

作者： LI June YUAN Kai ZHOU Liang HAN Lifang LI Ling WANG Zhihao LIU Yinbin HUANG Wenbin Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education Wuhan 430072HubeiChina School of Computer Wuhan UniversityWuhan 430072HubeiChina School of Electrical Engineering Wuhan UniversityWuhan 430072HubeiChina China Electric Power Research Institute Beijing 100192China

security mechanism detection is not only an important content of vulnerabilities evaluation but also the foundation of key strength test for wireless local area network （WLAN）. This paper analyzes the specifications of WLAN security mechanisms and points out the defects in design of security mechanisms detection algorithm based on the standards. By capturing and analyzing a large number of Beacon frames from different vendor＇s access points （APs）, we summarize the relevant fields and information elements in a Beacon frame, and present their values or status when an AP is set to every specific security mechanism. A detection algorithm of WLAN security mechanisms is proposed based on the experimental study result and the pseudo code of a reference implementation for the algorithm is designed. The validity of the algorithm is illustrated by examples, which shows it can detect every WLAN security mechanism accurately.

关键词： IEEE 802.11 security mechanism detection algorithm MAC frame resolution vulnerabilities evaluation

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Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare 18th

Genetic-A* Algorithm-Based Routing for Continuous-Flow Micr...

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18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Huang, Huichang Yang, Zhongliao Zhong, Jiayuan Xu, Li Dong, Chen Bao, Ruishen College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

In the field of intelligent digital healthcare, Continuous-flow microfluidic biochip (CFMB) has become a research direction of widespread concern. CFMB integrates a large number of microvalves and large-scale microchannel networks into a single chip, enabling efficient execution of various biochemical protocols. However, as the scale of the chip increases, the routing task for CFMB becomes increasingly complex, and traditional manual routing is no longer sufficient to meet the requirements. Therefore, this paper proposes an automatic routing framework for CFMB based on Genetic algorithm (GA) and A* algorithms. Specifically, we adopt a two-stage A* algorithm to design the routing between modules, using the routing results obtained from the A* algorithm as the basis for evaluating the quality of solutions in the GA algorithm. Then, the GA algorithm is used to search for the optimal approximate solution in the solution space. Experimental results show that this method can reduce routing length and minimize routing crossings, thereby improving the parallel transmission speed of reagents on CFMB. This approach provides a feasible solution for large-scale automated routing of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic algorithms

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Research on Static Reverse Analysis Technology for security Detection of power Industrial Control Software 3

Research on Static Reverse Analysis Technology for Security ...

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3rd International Conference on Intelligent power and Systems, ICIPS 2023

作者： Chen, Jinshan Yu, Sihang Xie, Xuanxuan Cheng, Yu Tang, Zhijun Deng, Chaoping Li, June State Grid Fujian Electric Power Research Institute Fujian Fuzhou350000 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Hubei Wuhan430072 China

ISBN: (纸本)9798350328691

In the power industrial control software, defects of software and possible pre-implanted backdoors and malicious codes are important factors that threaten the security of the power industrial control system. The complete and accurate extraction of function call relationship is the basis of software security detection based on function call graph. Therefore, based on analyzing function call sequence of abnormal behavior of electric power industrial control software, a method of software static reverse analysis is proposed. The method identified the function call relationship optimized by the compiler to be a jmp instruction by searching the call/jmp instructions. In order to extract a simplified key function call relationship, a key function list was obtained by performing keyword fuzzy matching. Experiments show that this method can accurately and comprehensively obtain the function call relation, the simplified key function call relationship can improve the efficiency of reverse analysis and provide a better support for software defects, backdoors and malicious codes detection. ©2023 IEEE.

关键词： Industrial research

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