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Blockchain Technology and Information Security (ICBCTIS), International Conference on

作者： Wei Liu Ying Wang Wentao Cui Wei She Zhao Tian Henan Key Laboratory of Network Cryptography Technology Zhengzhou Key Laboratory of Blockchain and Data Intelligence School of Cyber Science and Engineering Zhengzhou University Zhengzhou China School of Cyber Science and Engineering Zhengzhou University Zhengzhou China SongShan Laboratory School of Cyber Science and Engineering Zhengzhou University Zhengzhou China Zhengzhou Key Laboratory of Blockchain and Data Intelligence School of Cyber Science and Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

Federated learning has emerged as the forefront of research in recent years. However, its distributed framework poses a risk of a single point of failure in data research. Moreover, distinguishing malicious clients in the process of realizing personalized federated learning through client similarity is a critical concern. To overcome the aforementioned problems, we recommend a blockchain-based, dual-threshold personalized federated learning approach. Blockchain is used to replace the central server in the traditional federated learning framework. And we design a double-threshold-based malicious client screening algorithm, which achieves final malicious client identification by establishing screening criteria on both the blockchain side and the client side. The experimental findings demonstrate that the suggested technique maintains good accuracy and convergence even in the presence of malicious clients, compared to the FedAvg algorithm.

关键词： Accuracy Federated learning Filtering Information security Benchmark testing Blockchains Servers Convergence

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FedDGL: Federated Dynamic Graph Learning for Temporal Evolution and data Heterogeneity 16

FedDGL: Federated Dynamic Graph Learning for Temporal Evolut...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Xie, Zaipeng Li, Likun Chen, Xiangbin Yu, Hao Huang, Qian Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China College of Artificial Intelligence and Automation Hohai University Nanjing China

Federated graph learning enhances federated learning by enabling privacy-preserving collaborative training on distributed graph data. While traditional methods are effective in managing data heterogeneity, they typically assume static graph structures, overlooking the dynamic nature of real-world graphs. Integrating federated graph learning with dynamic graph neural networks addresses this issue but often fails to retain previously acquired knowledge, limiting generalization for both global and personalized models. This paper proposes FedDGL, a novel framework designed to address temporal evolution, and data heterogeneity in federated dynamic graph learning. Unlike conventional approaches, FedDGL captures temporal dynamics through a global knowledge distillation technique and manages client heterogeneity using a global prototype-based regularization method. The framework employs contrastive learning to generate global prototypes, enhancing feature representation while utilizing a prototype similarity-based personalized aggregation strategy for effective adaptation to local and global data distributions. Experiments on multiple benchmark datasets show that FedDGL achieves significant performance improvements over state-of-the-art methods, with up to 9.02% and 8.77% gains in local and global testing, respectively, compared to FedAvg. These results highlight FedDGL’s effectiveness in improving personalized and global model performance in dynamic, heterogeneous federated graph learning scenarios. © 2024 Z. Xie, L. Li, X. Chen, H. Yu & Q. Huang.

关键词： Dynamic Graph Learning Federated Graph Learning knowledge Distillation Personalized Aggregation Prototype Learning Temporal Evolution

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Design and Implementation of an Automated APK Analysis System for Practical Forensics 13th

Design and Implementation of an Automated APK Analysis Syste...

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13th International Conference on Computer engineering and Networks, CENet 2023

作者： Li, Yicheng Liang, Guangjun Tang, keyan Teng, Yijia Ruan, Haoran Mo, Zixi Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing China Engineering Research Center of Electronic Data Forensics Analysis Nanjing China Key Laboratory of Digital Forensics Department of Public Security of Jiangsu Province Nanjing China

ISBN: (纸本)9789819992386

In order to cope with the widespread emergence of malicious APK in the application market and the increasingly rampant situation of mobile malware, as well as to meet the needs of the actual forensics work, this paper, based on the analysis of Android security mechanisms and their security problems, analyses and summarises the methodological principles, main technologies and functional flaws of existing APK automated analysis tools, and proposes an APK automated analysis system for actual forensics. Compared with traditional systems, the system is easy to operate and has a higher recognition accuracy rate. Besides, it will perform machine learning on the analysis results and calculate the similarity through the model to effectively assist the forensic work and fill the demand for APK analysis in the field of combat. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Malware

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Measuring drug similarity using drug–drug interactions

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Quantitative Biology 2024年第2期12卷 164-172页

作者： Ji Lv Guixia Liu Yuan Ju Houhou Huang Ying Sun College of Computer Science and Technology Jilin UniversityChangchunChina Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina Sichuan University Library Sichuan UniversityChengduChina College of Chemistry Jilin UniversityChangchunChina Department of Respiratory Medicine The First Hospital of Jilin UniversityChangchunChina

Combination therapy is a promising approach to address the challenge of antimicrobial resistance,and computational models have been proposed for predicting drug–drug *** existing models rely on drug similarity measures based on characteristics such as chemical structure and the mechanism of *** this study,we focus on the network structure itself and propose a drug similarity measure based on drug–drug interaction *** explore the potential applications of this measure by combining it with unsupervised learning and semi-supervised learning *** unsupervised learning,drugs can be grouped based on their interactions,leading to almost monochromatic group–group *** addition,drugs within the same group tend to have similar mechanisms of action(MoA).In semi-supervised learning,the similarity measure can be utilized to construct affinity matrices,enabling the prediction of unknown drug–drug *** method exceeds existing approaches in terms of ***,our experiments demonstrate the effectiveness and practicability of the proposed similarity *** the one hand,when combined with clustering algorithms,it can be used for functional annotation of compounds with unknown *** the other hand,when combined with semi-supervised graph learning,it enables the prediction of unknown drug–drug interactions.

关键词： drug similarity drug–drug interactions drug combinations synergy effect clustering semi-supervised learning

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A Police UAV System Based on Target Recognition 13th

A Police UAV System Based on Target Recognition

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13th International Conference on Computer engineering and Networks, CENet 2023

作者： Qiu, Zhenyu Liang, Guangjun Du, Hongtian Yan, Bohan Peng, Jiahao Ju, Zexin Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing China Engineering Research Center of Electronic Data Forensics Analysis Nanjing China Key Laboratory of Digital Forensics Department of Public Security of Jiangsu Province Nanjing China

ISBN: (纸本)9789819992386

The As a kind of aircraft without carrying operators, unmanned aerial vehicle (UAV) has been rapidly developed in the civil field. It is widely used in police, urban management, agriculture, geology, meteorology, electric power, disaster relief and other industries. However, the current police UAV still has shortcomings in specific target recognition and tracking. Therefore, the police UAV equipped with target recognition technology is designed in this project to enhance its performance. The project also investigates the materials and structure of police UAV, and focuses on target recognition systems which use sensors and algorithms to identify, classify and track targets, providing support for autonomous navigation and surveillance of UAV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Python

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Theoretical analyses of solute concentration-gradient driven water flow and solute concentration variation in solution-saturated semi-permeable materials

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Science China(Technological Sciences) 2025年第5期68卷 243-253页

作者： Yao LIU Chongbin ZHAO Bruce HOBBS Alison ORD Xiangtao ZHANG School of Tourism Data Guilin Tourism UniversityGuilin 541006China Guangxi Key Laboratory of Culture and Tourism Smart Technology Guilin Tourism UniversityGuilin 541006China Computational Geosciences Research Centre Central South UniversityChangsha 410083China School of Earth and Environment the University of Western AustraliaCrawleyWA 6009Australia Department of Hydraulic Engineering Tsinghua UniversityBeijing 100084China

Based on the commonly-used definition of the semi-permeable material(SPM),in which the solvent(such as water)can pass through but the solute cannot move freely,a novel constitutive equation is presented,in this paper,to theoretically express the intrinsic constitutive relationship between the porosity variation rate with time and the solute concentration variation rate with *** this theoretical finding,a mathematical model is established to describe the solute concentrationgradient driven water flow and solute concentration variation problem in solution-saturated semi-permeable materials(SSSPMs).In particular,the solute concentration variation in the considered problem can be mathematically described as a linear homogeneous second-order partial differential equation with a variable coefficient in the front of the time term.A special mathematical transform is presented and used to solve this equation,so that the analytical solution for the solute concentration variation in the considered problem has been derived in a purely mathematical *** derived analytical solution is then used to provide some theoretical understanding of solute concentration variations in the SSSPM layer.

关键词： water flow concentration gradient semi-permeable material theoretical analyses analytical solution mathematical proof

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Spectroscopy and molecule opacity investigation on excited states of SiS

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Chinese Physics B 2024年第5期33卷 381-387页

作者：李瑞吕浩男桑纪群刘晓华梁桂颖吴勇 College of Teacher Education Qiqihar UniversityQiqihar 161006China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Department of Physics College of ScienceQiqihar UniversityQiqihar 161006China School of Data Science and Artificial Intelligence Jilin Engineering Normal UniversityChangchun 130052China HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100084China

The SiS molecule,which plays a significant role in space,has attracted a great deal of attention for many *** to complex interactions among its low-lying electronic states,precise information regarding the molecular structure of SiS is *** obtain accurate information about the structure of its excited states,the high-precision multireference configuration interaction(MRCI)method has been *** method is used to calculate the potential energy curves(PECs)of the 18Λ–S states corresponding to the lowest dissociation limit of *** core–valence correlation effect,Davidson’s correction and the scalar relativistic effect are also included to guarantee the precision of the MRCI *** on the calculated PECs,the spectroscopic constants of quasi-bound and bound electronic states are calculated and they are in accordance with previous experimental *** transition dipole moments(TDMs)and dipole moments(DMs)are determined by the MRCI *** addition,the abrupt variations of the DMs for the 1^(5)Σ^(+)and 2^(5)Σ^(+)states at the avoided crossing point are attributed to the variation of the electronic *** opacity of SiS at a pressure of 100 atms is presented across a series of *** increasing temperature,the expanding population of excited states blurs the band boundaries.

关键词： SiS opacity excited state spectroscopic constant configuration interaction

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Estimation of Forest Aboveground Biomass Using multi-temporal Quad-Polarimetric PALSAR-2 SAR data By Model-Free Decomposition approach in planted forest

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13519-13537页

作者： Peng, Cong Fang, Chao Long, Jiangping Zhang, Tingchen Zheng, Huanna Ye, Zilin Central South University of Forestry and Technology Research Center of Forestry Remote Sensing & Information Engineering Changsha410004 China Co. Ltd. Hunan Branch Changsha410004 China Hunan Key Laboratory of Forestry Remote Sensing Big Data and Security Changsha410004 China National Forestry and Grassland Administration Key Laboratory of Forest Resources Management and Testing in Southern China Changsha410004 China

Remote sensing technology has emerged as a promising approach for indirectly mapping forest aboveground biomass (AGB) over large areas with limited ground measurements. Polarimetric synthetic aperture radar (PolSAR) images exhibit significant potential in accurately mapping AGB in mountainous forests due to their excellent penetration capability. However, the accuracy and reliability of mapping forest AGB are often constrained by the quality of features and uncertain atmospheric conditions. Therefore, it is crucial to obtain relevant features closely associated with forest AGB. In this study, to overcome the disadvantages of common polarization decomposition methods, model-free decomposition methods were firstly applied to map forest AGB by extracting decomposition features from L-band quad-polarimetric PALSAR-2 images. Additionally, various data combination methods used to integrate the multi-temporal quad-polarimetric SAR images were also applied to investigate the combined effects of the datasets from different acquisition dates and evaluate their capability in reducing errors caused by uncertain atmospheric conditions. Ultimately, a forward feature selection method was employed along with four regression models to effectively map forest AGB. The results demonstrated that the model-free decomposition method exhibits less sensitivity towards changes in window size compared to other methods. Furthermore, there is a significant improvement observed in the sensitivity between forest AGB and features extracted through model-free decompositions compared to common polarization decomposition methods;some features also demonstrated superior capabilities in capturing forest structure information related to AGB. Moreover, given the model-based or model-free decomposition methods, using the combined datasets from multi-temporal SAR images led to a substantial increase in determination coefficient (R2) and a great decrease of relative root mean square error (rRMSE) of mapping

关键词： Mean square error

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A Collaborative Heterogeneous Graph Neural Network for Personalized News Recommendation

A Collaborative Heterogeneous Graph Neural Network for Perso...

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

作者： Shi, Chenglong Geng, Haibin Jiang, Wenfeng Li, Jinbao Wu, Chao Liu, Song 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)9781665410205

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models. © 2024 IEEE.

关键词： Graph neural networks

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Combined Particle Filter and Its Application on Human Pose Estimation 20th

Combined Particle Filter and Its Application on Human Pose...

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20th International Forum on Digital TV and Wireless Multimedia Communications, IFTC 2023

作者： Liu, Xinyang Ye, Long Yang, Yinghao School of Information and Communication Engineering Communication University of China Beijing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China School of Data Science and Media Intelligence Communication University of China Beijing China

ISBN: (纸本)9789819736225

Time series analysis has a wide range of applications in various domains. When addressing this task, tracking and identification are usually solved as two separate problems. However, it introduces a lot of computational redundancy and makes it difficult to guarantee synchronization and real-time performance. We propose a joint problem combining recognition and tracking and use particle filtering to solve the state estimation problem. However, the general particle filter cannot accurately estimate the state of the system in the time series state estimation task, because the system is time-varying and the prediction model of the particle filter is fixed. To address this issue, we assume that the system transition space is a set of finite prediction modes, and then propose a new Combined Particle Filter (CPF) framework that jointly achieves prediction mode recognition and state tracking. In the CPF, the prediction mode is regarded as a variable to be estimated, along with the system state variables. As a result, the resampled particle state set forms the global estimation of the system state, while the resampled mode variables indicate the optimal transition mode of the current frame. We construct an evaluation index system and conduct several evaluation tests to demonstrate the excellent performance of the CPF. Finally, we apply the CPF to the articulated human pose estimation task and obtain satisfactory results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： State estimation

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