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A Novel High-Efficiency Transaction Verification Scheme for Blockchain systems

computer Modeling in engineering & sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

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Gauze:enabling communication-friendly block synchronization with cuckoo filter

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Frontiers of computer science 2023年第3期17卷 113-123页

作者： Xiaoqiang DING Liushun ZHAO Lailong LUO Junjie XIE Deke GUO Jinxi LI College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Computer Science and Technology Xidian UniversityXi’an 710071China Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China Institute of Systems Engineering AMSPLABeijing 100141China

Block synchronization is an essential component of blockchain ***,blockchain systems tend to send all the transactions from one node to another for ***,such a method may lead to an extremely high network bandwidth overhead and significant transmission *** is crucial to speed up such a block synchronization process and save bandwidth consumption.A feasible solution is to reduce the amount of data transmission in the block synchronization process between any pair of ***,existing methods based on the Bloom filter or its variants still suffer from multiple roundtrips of communications and significant synchronization *** this paper,we propose a novel protocol named Gauze for fast block *** utilizes the Cuckoo filter(CF)to discern the transactions in the receiver’s mempool and the block to verify,providing an efficient solution to the problem of set reconciliation in the P2P(Peer-to-Peer Network)*** up to two rounds of exchanging and querying the CFs,the sending node can acknowledge whether the transactions in a block are contained by the receiver’s mempool or *** on this message,the sender only needs to transfer the missed transactions to the receiver,which speeds up the block synchronization and saves precious bandwidth *** evaluation results show that Gauze outperforms existing methods in terms of the average processing latency(about lower than Graphene)and the total synchronization space cost(about lower than Compact Blocks)in different scenarios.

关键词： block synchronization cuckoo filter probabilistic data structure

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Points of interest in the city of Barcelos in Portugal through augmented reality

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 40-48页

作者： Pereira, Miguel Silva, João Carlos Pinheiro, Marisa Carvalho, Sandro Santos, Gilberto Polytechnic Institute of Cávado and Ave Barcelos Portugal 2Ai – Applied Artificial Intelligence Laboratory Barcelos Portugal INESC TEC - Institute for Systems and Computer Engineering Porto Portugal LIACC - Laboratory of Artificial Intelligence and Computer Science Porto Portugal

Barcelos is a historic city in Portugal with many tourist attractions, attracting more and more visitors who come to the city with the aim of exploring it. The main objective of this article is to boost tourism in the city of Barcelos, specifically highlighting tourist, historical and leisure spots, based on the development of a mobile application using augmented reality technologies and geolocation. This application intends to allow the users to know historical points of interest in Barcelos, as well as interact with a certain point. The results of this investigation were evaluated by testing the application by end users, with the aim of identifying whether the application meets their needs, in particular the promotion of tourist and historical points. © 2023

关键词： Augmented reality

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Decision making framework for heterogeneous QoS information: an application to cloud service selection

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2915-2934页

作者： Tiwari, Rohit Kumar Kumar, Rakesh Baranwal, Gaurav Buyya, Rajkumar Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur273010 India Department of Computer Science BHU Varanasi India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne Australia

In recent times, appropriate decision-making in challenging and critical situations has been very well supported by multicriteria decision-making (MCDM) methods. The technique for order of preference by similarity to ideal solution (TOPSIS) is the most widely used MCDM method for solving decision problems. However, it restricts decision-makers to use only one type of Quality of Service (QoS) information, and it suffers from the rank reversal problem. Restriction to only one type of QoS makes the decision problems more challenging, as it restricts the decision-makers freedom. Further, the rank reversal problem makes the decision result unreliable. To address these issues of TOPSIS, we have proposed a reliable rank reversal robust modular TOPSIS (RMo-TOPSIS). RMo-TOPSIS allows crisp, interval, fuzzy, intuitionistic and neutrosophic fuzzy QoS metrics. It does not suffer from the rank reversal problem. Cloud computing provides computing services on-demand basis without involving maintenance by its users. The availability of many cloud service providers and their services makes cloud service selection a challenging problem. To validate RMo-TOPSIS, we select the cloud service selection consisting of different types of QoS metrics as an application. Experiments on cloud service selection show consistency and accuracy in results obtained by RMo-TOPSIS and its robustness against rank reversal. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision making

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Prior-Boosted GRL: Microarchitecture Design Space Exploration via Graph Representation Learning

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 1141-1154页

作者： Wu, Zheng Shen, Jinyi Yi, Xiaoling Shang, Li Yang, Fan Zeng, Xuan Fudan University State Key Laboratory of Integrated Circuits and Systems The School of Microelectronics Shanghai200433 China Fudan University State Key Laboratory of Integrated Circuits and Systems Shanghai200433 China KU Leuven Department of Electrical Engineering Microelectronics Circuits and Systems Leuven3000 Belgium Fudan University State Key Laboratory of Integrated Circuits and Systems The School of Computer Science Shanghai200433 China

The design space exploration (DSE) of contemporary microprocessors faces a significant challenge of high-computational cost. In this context, we introduce Prior-boosted graph representation learning (GRL), a novel framework for the DSE of the microarchitectures the microprocessors underpinned by graph embeddings. Using GRL, Prior-boosted GRL constructs a compact and continuous vector space for design representation. This framework is further boosted by an efficient sampling algorithm informed by prior knowledge, which is instrumental in generating a superior set of initial designs to accelerate the exploration process. A well-designed ensemble surrogate model is combined with the multiobjective Bayesian optimization to explore the design space holistically within this graph-embedding domain. Rigorous experimental evaluations conducted on the RISC-V Berkeley-Out-of-Order Machine (BOOM) platform demonstrate that Prior-boosted GRL substantially surpasses preceding methods, achieving a 107.79% enhancement in Pareto front quality compared to the state-of-the-art DSE algorithm. It also outstrips manual designs on performance, power, and area metrics. As of this writing, Prior-boosted GRL holds the first place in the ICCAD 2022 CAD Contest evaluation platform. © 2024 IEEE.

关键词： Graph embeddings

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Flexible, Super-stable, and Multifunctional Capacitive Pressure Sensors with Tilted Micropillar Array by Inclined Laser Ablation and Demolding

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IEEE Sensors Journal 2025年第6期25卷 9367-9374页

作者： Xu, Tianlong Li, Kaixin Zhou, Yihao Zhang, Dawei Sheng, Bin College of Optical Electrical and Computer Engineering Unversity of Shanghai for Science and Technology Shanghai 200093 China. Shanghai Key Laboratory of Modern Optical Systems Engineering Research Center of Optical Instruments and System Shanghai 200093 China

Flexible capacitive pressure sensors have garnered considerable interest across diverse applications, including medical monitoring, electronic skin, and robotic tactile systems, owing to their straightforward fabrication process, low energy consumption, and robust stability. In this paper, we introduce a new inclined laser ablation (ILA) combined with demolding technique for the fabrication of flexible capacitive sensors featuring a microstructured dielectric layer composed of a tilted micropillar array (TMA). The design parameters of TMA were systematically optimized, including the tilt angle of the micropillars, array gap, aspect ratio, and bottom film thickness. The resulting capacitive pressure sensor, equipped with 75° tilted micropillar arrays (aspect ratio of 5:1), demonstrates exceptional sensitivity (1.12 kPa-1) within the low-pressure range (0-2 kPa), rapid response and recovery times, a low pressure detection limit of 0.25 Pa, and transient response stability factor as high as 243.9. The proposed capacitive pressure sensor was validated for the detection and recognition of human motions when attached to various body parts. Additionally, the sensors also demonstrated significant potential for diverse applications, including tactile sensing, and Morse code communication. © 2025 IEEE.

关键词： Aspect ratio

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Additive Noise Model Structure Learning Based on Spatial Coordinates

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3858-3871页

作者： Yang, Jing Lu, Ting Zhu, Youjie Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China

Discovering causal relationships from a large amount of observational data is an important research direction in data mining. To address the challenges of discovering and constructing causal networks on nonlinear and high-dimensional data, this article proposes a new structural learning algorithm called spatial coordinates based (SCB). The SCB algorithm effectively discovers causal networks from large scale nonlinear data. In this article, we make three main contributions. Firstly, based on the Hilbert-Schmidt independence criterion (HSIC), we propose new independence test coefficients: spatial coordinate (SC) coefficient and conditional spatial coordinate (CSC) coefficient. We also prove that the statistical distribution of the CSC coefficient follows a standard normal distribution. Secondly, using the statistical distribution of the CSC coefficient, we redefine the correlation of variables and combine it with local learning to propose the SCB algorithm. Finally, we demonstrate the effectiveness of the algorithm through experiments on data generated from various nonlinear function dependencies. Compared to existing algorithms, the causal network models constructed using the SCB algorithm exhibit significant improvements in accuracy and time performance. The effectiveness of the proposed method is further validated on real power plant data. © 2020 IEEE.

关键词： Additive noise

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Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

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Machine Intelligence Research 2024年第6期21卷 1077-1091页

作者： Yu Xie Yanfeng Liang Chao Wen A.K.Qin Maoguo Gong School of Computer and Information Technology Shanxi UniversityTaiyuan030000China Key Laboratory of Collaborative Intelligence Systems of Ministry of Education Xidian UniversityXi’an710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyMelbourne3000Australia

Graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic *** import-antly,real-world graph data are often scattered in different *** by these observations,this article presents federated collaborative graph neural networks for few-shot graph classification,termed *** its owned graph examples,each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learn-ing model that can generalize to novel categories not seen while *** each branch,initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggrega-tion layers for few-shot graph classification,which can reduce over-fitting while learning with scarce labeled graph ***,multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effect-ively tackle the graph data island *** experimental results on few-shot graph classification benchmarks demonstrate the ef-fectiveness and superiority of our proposed framework.

关键词： Graph neural networks federated learning collaborative learning few-shot learning graph classification

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