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Personalized Privacy-Preserving Routing Mechanism Design in Payment Channel Network

Journal of Computer Science & Technology 2024年第6期39卷 1380-1400页

作者： Peng-Cheng Zhao Li-Jie Xu Jia Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China

Payment Channel Network(PCN)provides the off-chain settlement of *** is one of the most promising solutions to solve the scalability issue of the *** routing techniques in PCN have been ***,both incentive attack and privacy protection have not been considered in existing *** this paper,we present an auction-based system model for PCN routing using the Laplace differential privacy *** formulate the cost optimization problem to minimize the path cost under the constraints of the Hashed Time-Lock Contract(HTLC)tolerance and the channel *** propose an approximation algorithm to find the top K shortest paths constrained by the HTLC tolerance and the channel capacity,i.e.,top K-restricted shortest ***,we design the probability comparison function to find the path with the largest probability of having the lowest path cost among the top K-restricted shortest paths as the final ***,we apply the binary search to calculate the transaction fee of each *** both theoretical analysis and extensive simulations,we demonstrate that the proposed routing mechanism can guarantee the truthfulness and individual rationality with the probabilities of 1/2 and 1/4,*** can also ensure the differential privacy of the *** experiments on the real-world datasets demonstrate that the privacy leakage of the proposed mechanism is 73.21%lower than that of the unified privacy protection mechanism with only 13.2%more path cost compared with the algorithm without privacy protection on average.

关键词： blockchain payment channel network routing mechanism differential privacy personalized privacy-preserving

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A Dual-Port Millimeter-Wave Frequency Reconfigurable Array Antenna

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Progress in Electromagnetics Research C 2025年 154卷 221-228页

作者： Gao, Mingming Yuan, Hang Nan, Jingchang Niu, Hongliang Ge, Chang Liaoning Technical University China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications China

With the advancement of millimeter wave communication technology, reconfigurable antennas have garnered significant attention due to their adaptability. However, their radiation gain and sidelobe suppression performance are often constrained by factors such as diode package size and array scale. To address these challenges, this paper proposes a three-state frequency reconfigurable array antenna with high gain and low sidelobe characteristics, specifically designed to meet the demands of millimeter-wave communication. By optimizing the feed network and radiating element design, the proposed antenna achieves enhanced gain and improved sidelobe suppression. The design employs a dual-port feeding architecture that integrates a Taylor non-uniform amplitude distribution with a series-parallel hybrid feed network. This configuration ensures phase consistency while minimizing the number of diodes to just four, significantly reducing insertion loss and structural complexity. The antenna prototype is fabricated using standard printed circuit board (PCB) technology, with overall dimensions of 60.4 × 63 × 0.508 mm3. Measurement results indicate that the antenna exhibits an impedance bandwidth spanning from 27.5 GHz to 28.5 GHz and from 34.5 GHz to 35.5 GHz. The corresponding peak gains are 19.69 dBi and 19.51 dBi, with the sidelobe levels of 18.93 dB and 18.03 dB, respectively. The proposed antenna demonstrates excellent radiation characteristics and significantly enhances radiation efficiency. With its simple structure, dual-band radiation capability, high gain, and low sidelobe levels, this antenna is highly suitable for millimeter-wave wireless communication systems. It offers a high-performance solution for multi-band communication in 5G/6G networks. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Printed circuit boards

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Helicity-Controlled Spin Hall Angle in 2D Altermagnets with Rashba Spin-Orbit Coupling

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Chinese Physics Letters 2025年第1期42卷 134-140页

作者： Weiwei Chen Longhai Zeng W.Zhu Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of SciencesChina Jiliang UniversityHangzhou 310018China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of ScienceChina Jiliang UniversityHangzhou 310018China School of Science Westlake UniversityHangzhou 310024China

We investigated the efficiency of charge-to-spin conversion in two-dimensional Rashba altermagnets,a class of materials that combines the characteristics of both ferromagnets and *** quantum linear response theory,we quantified the longitudinal and spin Hall conductivities in this system and demonstrated a substantial enhancement in the spin Hall angle below the band crossing point through the dual effects of relativistic spin–orbit interaction and nonrelativistic altermagnetic exchange ***,the results showed that the skew scattering and intrinsic mechanisms arising from Fermi sea states are almost negligible in this system,in contrast to conventional ferromagnetic Rashba *** findings not only elucidate the spin dynamics in Rashba altermagnets but also pave the way for developing novel strategies for manipulating charge-to-spin conversion via sophisticated control of noncollinear and collinear out-of-plane spin textures.

关键词： interaction magnets Rashba

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Learning Counterfactual Explanation of Graph Neural Networks via Generative Flow Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4607-4619页

作者： He, Kangjia Liu, Li Zhang, Youmin Wang, Ye Liu, Qun Wang, Guoyin Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Chongqing400065 China

Counterfactual subgraphs explain graph neural networks (GNNs) by answering the question: 'How would the prediction change if a certain subgraph were absent in the input instance?' The differentiable proxy adjacency matrix is prevalent in current counterfactual subgraph discovery studies due to its ability to avoid exhaustive edge searching. However, a prediction gap exists when feeding the proxy matrix with continuous values and the thresholded discrete adjacency matrix to GNNs, compromising the optimization of the subgraph generator. Furthermore, the end-to-end learning schema adopted in the subgraph generator limits the diversity of counterfactual subgraphs. To this end, we propose CF-GFNExplainer, a flow-based approach for learning counterfactual subgraphs. CF-GFNExplainer employs a policy network with a discrete edge removal schema to construct counterfactual subgraph generation trajectories. Additionally, we introduce a loss function designed to guide CF-GFNExplainer's optimization. The discrete adjacency matrix generated in each trajectory eliminates the prediction gap, enhancing the validity of the learned subgraphs. Furthermore, the multitrajectories sampling strategy adopted in CF-GFNExplainer results in diverse counterfactual subgraphs. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of the proposed method in terms of validity and diversity. © 2020 IEEE.

关键词： Trajectories

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A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations

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Computers, Materials & Continua 2025年第4期83卷 1195-1218页

作者： Wenchang Yu Xiaoqin Ma Zheqing Zhang Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and TechnologyChizhou247000China

Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy *** primary issue stems from these methods’undue reliance on all *** overcome these challenges,we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection ***,we construct a robust fuzzy relation by introducing a truncation ***,based on this fuzzy relation,we propose the concept of cross-similarity,which emphasizes the sample-to-sample similarity relations that uniquely determine feature importance,rather than considering all such relations *** studying the manifestations and properties of cross-similarity across different fuzzy granularities,we propose a forward greedy feature selection algorithm that leverages cross-similarity as the foundation for information *** algorithm significantly reduces the time complexity from O(m2n2)to O(mn2).Experimental findings reveal that the average runtime of five state-of-the-art comparison algorithms is roughly 3.7 times longer than our algorithm,while our algorithm achieves an average accuracy that surpasses those of the five comparison algorithms by approximately 3.52%.This underscores the effectiveness of our *** paper paves the way for applying feature selection algorithms grounded in fuzzy rough sets to large-scale gene datasets.

关键词： Fuzzy rough sets feature selection cross-similarity fuzzy relations

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Underwater target recognition based on adaptive multi-feature fusion network

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Multimedia Tools and Applications 2025年第10期84卷 7297-7317页

作者： Pan, Xiaoying Sun, Jia Feng, TianHao Lei, MingZhu Wang, Hao Zhang, WuXia Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an710121 China School of Computer Science and Technology Xi’an University of Post & Telecommunications Xi’an710121 China

Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics research. In recent years, deep learning has been widely used in underwater recognition due to its powerful feature learning capabilities. However, most deep learning-based underwater target recognition methods use only the time-frequency domain features of signals, ignoring the frequency domain image features of signals. Additionally, they only use simple summation or splicing strategies to fuse and discriminate the extracted features, which leads to limited improvement in the accuracy of underwater target recognition. To address these issues, this paper proposes an underwater target recognition method based on an adaptive multi-feature fusion network. The method consists of a data pre-processing module, a multi-dimensional feature extraction module, and an adaptive multi-feature fusion module. In the multi-dimensional feature extraction module, the long short time memory network (LSTM) and the one-dimensional convolutional neural network (1DCNN) are used to extract the time-frequency features of the underwater signal. Furthermore, the two-dimensional convolutional neural network (2DCNN) is used to extract the image features in the frequency domain. The adaptive multi-feature fusion module uses an attention mechanism for adaptive weighted fusion to make full use of the learned features. The effectiveness of the proposed method is validated on the ShipEar dataset, and the recognition accuracy reaches 94.92%, which is higher than other existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A Novel Competition-Based Coordination Model With Dynamic Feedback for Multi-Robot Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2029-2031页

作者： Bo Peng Xuerui Zhang Mingsheng Shang Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 Chongqing School University of Chinese Academy of SciencesChongqing 400714China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China

Dear Editor, Task allocation strategies are important in multi-robot systems and have been intensely investigated by researchers because they are critical in determining the performance of the system. In this letter, a novel competition-based coordination model is proposed to solve the multi-robot task allocation problem and applied to a multi-robot object tracking scenario.

关键词： robot Robot Dynamic

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Wireless Charging Scheduling for Long-term Utility Optimization

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ACM Transactions on Sensor Networks 2025年第1期21卷 1-31页

作者： Xu, Jia Chen, Wenbin Dai, Haipeng Xu, Lijie Xiao, Fu Liu, Linfeng Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Wireless power transmission has been widely used to replenish energy for wireless sensor networks, where the energy consumption rate of sensor nodes is usually time varying and indefinite. However, few works have investigated the problem of long-term charging scheduling with random variable. This article designs an optimization model for the long-term scheduling of chargers to maximize the time-averaged charging utility while ensuring its time-averaged constraints of budget and response rate. The Lyapunov optimization technique is adopted to transform the stochastic optimization problem into a deterministic optimization problem, which remains NP-hard. Thus, an approximation algorithm following greedy approach is proposed to solve the deterministic optimization problem. We further provide the theoretical analysis of feasibility and performance guarantee of the proposed scheduling algorithm. The simulation results show that our algorithm outperforms three comparison algorithms by 6.53%, 20.04%, and 19.97% in terms of time-averaged charging utility, as well as by 11.25%, 4.42%, and 3.73% in terms of time-averaged response rate on average. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Inductive power transmission

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Uncertain knowledge graph embedding:an effective method combining multi-relation and multi-path

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Frontiers of Computer Science 2024年第3期18卷 73-89页

作者： Qi LIU Qinghua ZHANG Fan ZHAO Guoyin WANG Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and TourismChongqing University of Posts and TelecommunicationsChongqing 400065China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Chongqing Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China

Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge *** research on the embedding of UKG has only recently begun,Uncertain Knowledge Graph Embedding(UKGE)model has a certain effect on solving this ***,there are still unresolved *** the one hand,when reasoning the confidence of unseen relation facts,the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information,leading to information *** the other hand,the existing UKG embedding model can only model symmetric relation facts,but the embedding problem of asymmetric relation facts has not be *** address the above issues,a Multiplex Uncertain Knowledge Graph Embedding(MUKGE)model is proposed in this ***,to combine multiple information and achieve more accurate results in confidence reasoning,the Uncertain ResourceRank(URR)reasoning algorithm is ***,the asymmetry in the UKG is *** embed asymmetric relation facts of UKG,a multi-relation embedding model is ***,experiments are carried out on different datasets via 4 tasks to verify the effectiveness of *** results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines,and it helps advance the research on UKG embedding.

关键词： knowledge representation uncertain knowledge graph multi-relation embedding uncertain reasoning

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L_(1)-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

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Computers, Materials & Continua 2024年第5期79卷 1975-1994页

作者： Chuandong Qin Yu Cao Liqun Meng School of Mathematics and Information Science North Minzu UniversityYinchuan750021China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan750021China

Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous *** tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment *** learning models have become key players in automating brain tumor *** descent methods are the mainstream algorithms for solving machine learning *** this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor ***,the smooth hinge loss is introduced to be used as the loss function of *** avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent ***,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the ***,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth *** computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of *** DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core *** to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence *** the perspective of loss reduction,DPGD converges faster than *** experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor ***-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.

关键词： Support vector machine proximal stochastic gradient descent brain tumor detection distributed computing

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