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Revisiting Multiple Pattern Matching Algorithms for Multi-Core Architecture

Journal of Computer Science & technology 2011年第5期26卷 866-874页

作者：谭光明刘萍卜东波刘燕兵 Key Laboratory of Computer System and Architecture Institute of Computing TechnologyChinese Academy of Sciences Key Laboratory of Network Technology Institute of Computing TechnologyChinese Academy of Sciences

Due to the huge size of patterns to be searched,multiple pattern searching remains a challenge to several newly-arising applications like network intrusion *** this paper,we present an attempt to design efficient multiple pattern searching algorithms on multi-core *** observe an important feature which indicates that the multiple pattern matching time mainly depends on the number and minimal length of *** multi-core algorithm proposed in this paper leverages this feature to decompose pattern set so that the parallel execution time is *** formulate the problem as an optimal decomposition and scheduling of a pattern set,then propose a heuristic algorithm,which takes advantage of dynamic programming and greedy algorithmic techniques,to solve the optimization *** results suggest that our decomposition approach can increase the searching speed by more than 200% on a 4-core AMD Barcelona system.

关键词： parallel algorithm multi-core multiple pattern matching

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Efficient Module-Lattice-based Certificateless Online/Offline Signcryption Scheme for Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Bai, Ye He, Debiao Yang, Zhichao Luo, Min Peng, Cong Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 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 Jinan250014 China Naval University of Engineering Department of Information Security Wuhan430000 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

The Internet of Medical Things (IoMT) has achieved remote diagnosis and real-time health monitoring through intelligent sensor devices and Internet of Things (IoT) technology, providing great convenience for analyzing medical conditions between doctors and patients. However, sensitive information such as patient medical data may face security challenges such as data leakage and abuse during transmission in the IoMT. To ensure the confidentiality and unforgeability of medical data transmission, scholars have proposed many cryptographic schemes. With the development of quantum computers, schemes based on traditional cryptographic primitives have become insecure. Existing cryptographic schemes for IoMT cannot simultaneously meet the security requirements of high communication performance, low computational overhead, and resistance to quantum attacks. Therefore, we propose an efficient module-lattice-based certificateless online/offline signcryption (MLCLOOSC) scheme resistant to quantum attacks while meeting the confidentiality and unforgeability requirements under Type I and Type II attacks. Compared with five recent CLOOSC schemes, theoretical analysis, and experimental test results show that the proposed scheme outperforms the other five schemes regarding computational and communication costs and security. Therefore, our scheme is more suitable for application in IoMT scenarios. © 2014 IEEE.

关键词： Quantum computers

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Non-IID Free Federated Learning with Fuzzy Optimization for Consumer Electronics systems

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Jun Wu, Di Zeng, Shan Zhao, Yao Qu, Youyang Yu, Shui School of Information Technology Deakin University MelbourneVIC3125 Australia School of Mathematics Physics and Computing University of Southern Queensland ToowoombaQLD4350 Australia School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China School of Computing Technologies RMIT University MelbourneVIC3000 Australia Qilu University of Technology Shandong Academy of Sciences Jinan250101 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250102 China School of Computer Science University of Technology Sydney SydneyNSW2007 Australia

—Consumer electronics suffer from data surges which lead to data privacy and integrity issues. Federated Learning (FL) keeps data with local devices which enhances privacy, and efficient device collaboration by processing data locally without transmitting sensitive information to centralized servers. However, data heterogeneity within practical FL settings poses a formidable challenge, such as client model drift, significantly impacting the convergence speed of the global model and leading to a substantial communication overhead. To address these issues arising from data heterogeneity, we propose a novel FL framework based on latent dependency optimization with fuzzy-based client selection (FCS), named FedLaDO. Specifically, in the consumer electronic client-side training, we introduce two new loss regularizers: a positive latent dependency loss and a perceptive knowledge co-distillation loss. These regularizers are integrated into the client training objective function to enhance the client models’ reinforcement learning over its distinctive training data while preserving the global knowledge inherited from the prior global model. For consumer electronic server-side model aggregation, we propose an FCS mechanism to filter out underperforming and overperforming clients and introduce a novel inverse dependency aggregation algorithm to effectively integrate drifted client models. The efficacy of our approach is validated through extensive experiments on diverse real-world datasets, showcasing its superior performance in achieving higher model convergence accuracy compared to state-of-the-art FL baseline methods. © 2025 IEEE. All rights reserved,

关键词： Federated learning

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Satisfaction-Aware Incentive Scheme for Federated Learning in Industrial Metaverse: DRL-Based Stackbelberg Game Approach

arXiv

引用

arXiv 2025年

作者： Li, Xiaohuan Qin, Shaowen Tang, Xin Kang, Jiawen Ye, Jin Zhao, Zhonghua Niyato, Dusit Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Nanning530001 China School of Automation Guangdong University of Technology Guangzhou510006 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530000 China School of Computer and Electronic Information Guangxi University Nanning530000 China College of Computing and Data Science Nanyang Technological University Singapore

Industrial Metaverse leverages the Industrial Internet of Things (IIoT) to integrate data from diverse devices, employing federated learning and meta-computing to train models in a distributed manner while ensuring data privacy. Achieving an immersive experience for industrial Metaverse necessitates maintaining a balance between model quality and training latency. Consequently, a primary challenge in federated learning tasks is optimizing overall system performance by balancing model quality and training latency. This paper designs a satisfaction function that accounts for data size, Age of Information (AoI), and training latency. Additionally, the satisfaction function is incorporated into the utility functions to incentivize node participation in model training. We model the utility functions of servers and nodes as a two-stage Stackelberg game and employ a deep reinforcement learning approach to learn the Stackelberg equilibrium. This approach ensures balanced rewards and enhances the applicability of the incentive scheme for industrial Metaverse. Simulation results demonstrate that, under the same budget constraints, the proposed incentive scheme improves at least 23.7% utility compared to existing schemes without compromising model accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition

arXiv

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arXiv 2025年

作者： Wang, Changwei Chen, Shunpeng Song, Yukun Xu, Rongtao Zhang, Zherui Zhang, Jiguang Yang, Haoran Zhang, Yu Fu, Kexue Du, Shide Xu, Zhiwei Gao, Longxiang Guo, Li Xu, Shibiao 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 School of Artificial Intelligence Beijing University of Posts and Telecommunications China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China MAIS Institute of Automation Chinese Academy of Sciences China Tongji University China College of Computer and Data Science Fuzhou University China Shandong University China

Visual Place Recognition (VPR) is aimed at predicting the location of a query image by referencing a database of geotagged images. For VPR task, often fewer discriminative local regions in an image produce important effects while mundane background regions do not contribute or even cause perceptual aliasing because of easy overlap. However, existing methods lack precisely modeling and full exploitation of these discriminative regions. In this paper, we propose the Focus on Local (FoL) approach to stimulate the performance of image retrieval and re-ranking in VPR simultaneously by mining and exploiting reliable discriminative local regions in images and introducing pseudo-correlation supervision. First, we design two losses, Extraction-Aggregation Spatial Alignment Loss (SAL) and Foreground-Background Contrast Enhancement Loss (CEL), to explicitly model reliable discriminative local regions and use them to guide the generation of global representations and efficient re-ranking. Second, we introduce a weakly-supervised local feature training strategy based on pseudo-correspondences obtained from aggregating global features to alleviate the lack of local correspondences ground truth for the VPR task. Third, we suggest an efficient re-ranking pipeline that is efficiently and precisely based on discriminative region guidance. Finally, experimental results show that our FoL achieves the state-of-the-art on multiple VPR benchmarks in both image retrieval and re-ranking stages and also significantly outperforms existing two-stage VPR methods in terms of computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Photointerpretation

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A Machine Learning-Based Model Infers the Sea Surface Velocity of Surface Water and Ocean Topography (SWOT)

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Geophysical Research Letters 2025年第9期52卷

作者： Zhou, Shuyi Dong, Jihai Li, Hong Xu, Guangjun Xu, Fanghua Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China School of Marine Sciences Nanjing University of Information Science and Technology Nanjing China Tianjin Key Laboratory for Marine Environmental Research and Service School of Marine Science and Technology Tianjin University Tianjin China College of Electronic and Information Engineering Guangdong Ocean University Zhanjiang China

High-resolution sea surface velocity (SSV) is crucial for advancing our understanding of ocean sub-mesoscale processes, energy cascades, etc. The recently launched Surface Water and Ocean Topography (SWOT) satellite measures sea surface height with a sub-mesoscale resolved resolution. Based on geostrophic balance, the so-called geostrophic velocity in SWOT is estimated. Although the SWOT-derived velocity is not true geostrophic velocity as it does not consider the separation of balanced and unbalanced motions, it offers valuable insights into both geostrophic and ageostrophic velocities. Here we propose a machine learning-based model to infer the SSV using SWOT and drifter data. The result demonstrates the error between the geostrophic velocities from SWOT and the total velocities from the drifter are reduced by about 50%. Furthermore, the kinetic energy of inferred velocities aligns more closely with reanalysis data, particularly at low latitudes. This study thus presents a promising approach for inferring global SSV using SWOT data. © 2025. The Author(s).

关键词： Submarine geophysics

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Impact of the Next-Nearest-Neighbor Interaction on Traffic Flow of Highway with Slopes

引用

Communications in Theoretical Physics 2012年第10期58卷 590-598页

作者：李志鹏周盈 The Key Laboratory of Embedded System and Service Computing supported by Ministry of Education Tongji University

In this paper,we study the motion course of traffic flow on the slopes of a highway by applying a microscopic traffic model,which takes into account the next-nearest-neighbor interaction in an intelligent transportation system *** common gradients of the highway,which are sag terrain,uphill terrain,and downhill terrain on a single-lane roadway,are selected to clarify the impact on the traffic flow by the next-nearest-neighbor interaction in relative *** obtain the current-density relation for traffic flow on the sag,the uphill and the downhill under the next-nearest-neighbor interaction *** is observed that the current saturates when the density is greater than a critical value and the current decreases when the density is greater than another critical *** the density falls into the intermediate range between the two critical densities it is also found that the oscillatory jam,easily leads to traffic accidents,often appears in the downhill stage,and the next-nearest-neighbor interaction in relative velocity has a strong suppressing effect on this kind of dangerous congestion.A theoretical analysis is also presented to explain this important conclusion.

关键词： traffic flow traffic jam next-nearest-neighbor interaction

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Behavior Consistency Computation for Workflow Nets With Unknown Correspondence

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 281-291页

作者： Mimi Wang Guanjun Liu Peihai Zhao Chungang Yan Changjun Jiang Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China IEEE

Consistency degree calculation is established on the basis of known correspondence, but in real life, the correspondence is generally unknown, so how to calculate consistency of two models under unknown correspondence has become a problem. For this condition, we should analyze unknown correspondence due to the influence of different *** this paper we obtain the relations of transitions based on event relations using branching processes, and build a behavioral matrix of relations. Based on the permutation of behavioral matrix, we express different correspondences, and define a new formula to compute the maximal consistency degree of two workflow nets. Additionally, this paper utilizes an example to show these definitions, computation as well as the advantages.

关键词： Behavioral matrix branching process consistency degree unknown correspondence workflow net(WF-nets)

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

引用

IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Neuron Circuit Design and Signal Integrity Analysis of a Large-scale Memristor-based Crossbar Array

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IEEE Transactions on Components, Packaging and Manufacturing technology 2025年

作者： Li, Yan Fang, Lidan Li, Da Zhang, Ling Zhou, Haomiao Wang, Jiawei Liu, En-Xiao Li, Er-Ping China Jiliang University Key Laboratory of Electro-magnetic Wave Information Technology and Metrology of Zhejiang Province College of Information Engineering Hangzhou310018 China China Jiliang University Modern Science and Technology College Yiwu322000 China Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart System Hangzhou310027 China Urbana–Champaign Institute Zhejiang University Zhejiang University–the University of Illinois Haining314400 China A*STAR Institute of High Performance Computing 1 Fusionpolis Way Singapore Singapore

This paper addresses the limitations of standard chips in processing large-scale datasets by leveraging neuromorphic architectures, particularly spiking neural networks (SNNs), to simulate the pulsed signals of biological brains for enhanced power efficiency and performance. Initially, we developed a CMOS circuit based on the Integrate-and-Fire (IF) neuron model, which generates spike signals with the spatiotemporal dynamics typical of neurons. This approach provides a novel perspective for neuromorphic chip design. Next, we introduce the Partial Element Equivalent Circuit (PEEC) method to establish a unit circuit model for the memristor crossbar array, accounting for all resistance-inductance-capacitance (RLC) parasitic couplings. This model enables precise signal integrity (SI) analysis, offering deeper insights into the signal transmission mechanisms within memristor crossbar arrays and serving as a foundation for optimizing neuromorphic chip performance. Building upon this model, we designed a 24×24 crossbar array hardware prototype, experimentally validating the reliability and feasibility of the circuit. Finally, we perform SI analysis to demonstrate that the performance of the memristor crossbar array can be enhanced by optimizing the rise time, adjusting the memristor’s resistance state, and adding inductance at the output. These findings provide both a theoretical foundation and a technical pathway for improving the performance of neuromorphic chips in practical applications. © 2025 IEEE. All rights reserved.

关键词： Memristors

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