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Relayer Aggregation Using Chainless Multi-Layer Consensus

IEEE Access 2025年 13卷 70928-70946页

作者： Yanagihara, Takaaki Fujihara, Akihiro Suwa University of Science Department of Applied Information Engineering Chino392-0292 Japan Chiba Institute of Technology Department of Information and Communication Systems Engineering Narashino275-0016 Japan

The increasing adoption of blockchain technology has underscored the need for cross-chain systems that enable seamless communication among multiple BC networks. Achieving cross-chain interoperability, which ensures secure and efficient data storage and transfer across BCs, remains a critical technical challenge. Among existing solutions, the Inter-Blockchain Communication (IBC) protocol within the Cosmos ecosystem is a prominent framework that facilitates cross-chain communication using light clients and continuous monitoring. However, IBC faces limitations due to its reliance on the consensus algorithms and block generation intervals of participating blockchains. Cross-chain transactions are processed sequentially, requiring approval at each stage, which reduces efficiency. Furthermore, the increasing number of relayers introduces scalability and operational challenges. To address these issues, this study proposes a novel framework called Relayer Aggregation (RA), which aims to enhance cross-chain communication by employing a chainless multi-layer consensus mechanism. RA enables parallel transaction processing to improve performance and scalability. Experimental nodes were developed, and comparative performance evaluations of RA and IBC were conducted to validate the proposed approach. The results demonstrate that RA significantly reduces the number of required relayer nodes and enhances processing efficiency through parallelization. By overcoming the sequential processing limitations of IBC, RA offers a scalable and efficient solution for cross-chain interoperability. This study contributes to advancing blockchain ecosystems by addressing key bottlenecks in cross-chain systems and providing a foundation for future optimization in distributed environments. © 2013 IEEE.

关键词： Blockchain

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UAV-Assisted Zero Knowledge Model Proof for Generative AI: A Multiagent Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第10期12卷 13441-13454页

作者： Hao, Min Shang, Chen Wang, Siming Jiang, Wenchao Nie, Jiangtian Information Systems Technology and Design Singapore University of Technology and Design 487372 Singapore School of Computer Science and Engineering Nanyang Technological University 638788 Singapore

As more users seek generative AI (GAI) models to enhance work efficiency, GAI and Model-as-a-Service will drive transformative changes and upgrades across all industries. However, when users utilize GAI models provided by the service provider, they cannot be certain that the model’s quality matches the provider’s claims. Considering the need to protect intellectual property, the service provider will not disclose model details for user verification. To this end, we take the Internet of Vehicles as research background, proposing a zero knowledge model proof architecture based on UAVs. We also introduce a multiagent reinforcement learning algorithm to optimize the verification process. In specific, we first propose a verification scheme for the key operations of generative adversarial networks based on noninteractive zero knowledge proof. The zero knowledge proof architecture ensures that model parameters cannot be stolen during the verification process. After that, we propose an Age of Verification (AoV) metric to ensure the timeliness and freshness of zero knowledge proof. We also construct a tradeoff optimization problem between the energy consumption of UAV as a verifier and the AoV of edge servers as service providers, and transform the problem based on Lyapunov optimization theory. Following that, we propose an enhanced multiagent proximal policy optimization algorithm to enable the collaborative verification of edge servers by multiple UAVs. The algorithm simulation results demonstrate that the reward value of our proposed algorithm is over 10% higher than that of the standard algorithm, with a faster and more stable overall convergence speed. Additionally, the zero knowledge proof performance test results indicate that the verification delay in our proposed architecture is less than 500 ms during the verification phase, meeting practical requirements. © 2014 IEEE.

关键词： Generative adversarial networks

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An enhanced framework for smart automated evaluations of answer scripts using NLP and deep learning methods

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Multimedia Tools and Applications 2025年第11期84卷 8491-8513页

作者： G, Mohanraj R.K, Nadesh M, Marimuthu V, Sathiyapriya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Knowledge Institute of Technology Salem637504 India

The manual process of evaluating answer scripts is strenuous. Evaluators use the answer key to assess the answers in the answer scripts. Advancements in technology and the introduction of new learning paradigms need automation of the evaluation process. This work aims to develop an enhanced novel hybrid framework that can evaluate answer scripts and automatically assign marks for different type of questions based on keywords, grammar, symbols, special keywords, and the given factors. First, the proposed system uses Optical Character Recognition (OCR) to convert image answer scripts into an editable text format. Second, the sentence transformers, the Natural Language Processing (NLP) technique flips the answer script and answers key texts into word embedding vectors. To find similarity measures, these vectors are matched using BERT encoding, spearmanś rank-order correlation, and fuzzy search. At last, the proposed model is trained using Deep Columnar Convolutional Neural Network (DCCNN) in the third step with MINST and Kaggle handwritten mathematical symbols and tested with the segmented mathematical equations to find the similarity. The performance of proposed model is measured using precision, recall, accuracy, and F1-score, and its gives highest accuracy of 93% and 95% when compared to the existing methodologies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Optical character recognition

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HRMG-EA: Heterogeneous graph neural network recommendation with multi-level guidance based on enhanced-attributes

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Applied Intelligence 2025年第6期55卷 1-22页

作者： Wang, Longtao Yuan, Guiyuan Li, Chao Zhao, Yufei Duan, Hua Zeng, Qingtian College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Electronic and Information Engineering Shandong University of Science and Technology Shandong Qingdao266590 China College of Mathematics and Systems Science Shandong University of Science and Technology Shandong Qingdao266590 China

Heterogeneous Graph Neural Networks are an efficient and powerful tool for modeling graph structure data in recommendation systems. However, existing heterogeneous graph neural networks often fail to model the dependencies between user and item attribute preferences, limiting graph structure optimization and consequently reducing the accuracy of recommendations. To overcome these issues, we propose a Heterogeneous graph neural network Recommendation with Multi-level Guidance based on Enhanced-Attributes (HRMG-EA). First, we design an attribute enhanced gated network to model user-item interaction attribute scenarios and obtain enhanced-attributes by capturing complex attribute dependencies. It effectively avoids the expansion of the graph scale in attribute graph scenarios and further covers personalized attribute relationship distribution characteristics of users and items. Then, we propose a novel multi-level graph structure guidance strategy based on enhanced-attributes. It guides graph structure learning from three optimization levels, optimizing from two perspectives: explicit (heterogeneity and homogeneity) and implicit (contrast enhancement). The former can screen higher-quality heterogeneous neighbor nodes in a direct interaction environment, and filter out redundant or erroneous edges under different similar semantic interest paths to improve the quality of the neighborhood environment. The latter aligns representation embeddings of enhanced-attributes and graph structure in a latent space, explores their potential commonalities, and obtains more comprehensive, fine-grained semantic and beneficial structural information. Finally, on two real-world datasets, HRMG-EA significantly outperforms the state-of-the-art baseline algorithms in both recall and normalized discounted cumulative gain. A large number of ablation experiments and analytical verifications also verify its effectiveness. © The Author(s), under exclusive licence to Springer science+Business Med

关键词： Graph neural networks

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An autonomous dynamic recovery technology for autonomous underwater vehicles based on unmanned surface vehicles

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science China(Technological sciences) 2025年第2期68卷 315-317页

作者： Yueying WANG Yiqi SHI Guanghui WEN Huaicheng YAN School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444China Department of Systems Science School of MathematicsSoutheast UniversityNanjing 210096China School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China

Autonomous underwater vehicles(AUVs) have attracted considerable attention due to their vast potential applications, such as offshore oil exploration, underwater rescue, military reconnaissance, and marine scientific ... 详细信息

关键词： underwater autonomous battery

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Data-Driven Event-Triggered Control for Discrete-Time Neural Networks Subject to Actuator Saturation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 1003-1013页

作者： Ni, Yanyan Wang, Zhen Huang, Xia Shen, Hao Shandong University of Science and Technology College of Mathematics and Systems Science Qingdao266590 China Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao266590 China Anhui University of Technology School of Electrical and Information Engineering Maanshan243002 China

In this article, the data-driven event-triggered control is addressed for unknown discrete-time neural networks (DTNNs) under actuator saturation and external perturbation. The research problem is raised due to the following two reasons: 1) a practical system is often affected by external perturbations and it is costly to acquire an accurate system model;2) the network bandwidth and the control inputs are always constrained due to physical hardware. To handle the above issues, the methodology is to first establish a model-based stability condition under the designed saturated event-triggered controller and then to transform the model-based stability condition into a data-based stability condition relying only on the perturbation-corrupted data via the extended S-lemma. The key results are: 1) a data-based DTNNs system representation is presented by collecting perturbation-corrupted state-input data. Then, a data-based stability criterion is derived and the saturated event-triggered controller is designed without an explicit system model;2) an optimization method is presented that can maximize the estimation of attractor (EoA) and minimize the estimated domain of attraction (DoA) simultaneously. Finally, the effectiveness of the proposed approach is illustrated and some quantitative analyses are offered by two numerical examples. © 2024 IEEE.

关键词： Stability criteria

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Toward next-generation networks: A blockchain-based approach for core network architecture and roaming identity verification

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Digital Communications and Networks 2025年第2期11卷 326-336页

作者： Yi Gong Boyuan Yu Lei Yang Fanke Meng Lei Liu Xinjue Hu Zhan Xu School of Information and Communication Engineering Beijing Information Science&Technology University Xi'an University of Posts and Telecommunications Guangzhou Institute of Technology Xidian University Intelligent Transportation Systems Research Center Wuhan University of Technology State Key Laboratory of Maritime Technology and Safety

With the evolution of next-generation communication networks, ensuring robust Core Network(CN) architecture and data security has become paramount. This paper addresses critical vulnerabilities in the architecture of CN and data security by proposing a novel framework based on blockchain technology that is specifically designed for communication networks. Traditional centralized network architectures are vulnerable to Distributed Denial of Service(DDoS) attacks, particularly in roaming scenarios where there is also a risk of private data leakage, which imposes significant operational demands. To address these issues, we introduce the Blockchain-Enhanced Core Network Architecture(BECNA) and the Secure Decentralized Identity Authentication Scheme(SDIDAS). The BECNA utilizes blockchain technology to decentralize data storage, enhancing network security, stability, and reliability by mitigating Single Points of Failure(SPoF). The SDIDAS utilizes Decentralized Identity(DID) technology to secure user identity data and streamline authentication in roaming scenarios, significantly reducing the risk of data breaches during cross-network transmissions. Our framework employs Ethereum, free5GC, Wireshark, and UERANSIM tools to create a robust, tamper-evident system model. A comprehensive security analysis confirms substantial improvements in user privacy and network security. Simulation results indicate that our approach enhances communication CNs security and reliability, while also ensuring data security.

关键词： Blockchain Core network Privacy data protection Decentralized identity Roaming identity verification

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Global Navigation Satellite System Reflectometry specular point positioning method based on reflection geometric model: Suitable for land and ocean applications

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Li, Shuaishuai He, Shanbao Cheng, Xing Wang, Feng Xie, Heng Pang, Jingjing Cai, Min Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing China Beihang University School of Electronic and Information Engineering Beijing China Beijing Information Science and Technology University School of Information and Communication Engineering Beijing China

The accurate positioning of the specular point of the spaceborne global navigation satellite system reflectometry (GNSS-R) receiver is a key step in using GNSS-R technology for geophysical parameter retrieval. As GNSS-R applications expand across diverse environments ranging from land and oceans to glaciers, traditional geometric positioning accuracy is no longer sufficient to meet the demands of these varied applications. Specifically, positioning algorithms must not only facilitate real-time spaceborne processing but also adapt to fluctuating surface conditions, all while sustaining high levels of accuracy and efficiency. In response to these demands, we propose a positioning algorithm tailored for spaceborne GNSS-R specular points across various surface environments. The proposed algorithm integrates actual surface height data by leveraging surface models for localized terrain fitting. It employs a regional grid traversal method, guided by geographic positioning criteria, to accurately search for specular points that satisfy specific conditions. Considering the impact of initial position accuracy on traversal speed, we developed an ellipsoidal reflection geometric model based on the unique properties of the ellipsoid. This model facilitates the swift, non-iterative determination of a high-precision initial position for specular points. Experimental data indicate that our proposed approach reduces the Euclidean distance error of the initial position by ~87.5% and enhances grid traversal efficiency by a factor of more than eight times. To verify the robustness of the proposed method, this paper conducted multiple experiments in both ocean and land scenarios, including case studies in the Qinghai-Tibet Plateau region. The findings highlight that the normal vector angle analysis criterion offers superior reliability in complex terrains, positioning it as the optimal criterion for the positioning of specular points. © 2025 Society of Photo-Optical Instrumentation Engi

关键词： Global positioning system

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Tri-Algo guardian ensemble approach for fake news detection in social media

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Journal of Big Data 2025年第1期12卷 1-19页

作者： Vineetha, Borra Nirmala, Munirathinam School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

The rapid spread of misinformation in the digital age highlights the need for effective fake news classification systems. This research evaluates classification methods using multiple datasets, which present distinct challenges and features. Advanced preprocessing techniques enhance data quality, while various word embedding methods capture linguistic and contextual features. To address class imbalance, oversampling and class weighting strategies are applied. An ensemble approach integrates multiple models to improve classification accuracy. Results indicate that advanced embeddings achieve superior performance, while traditional methods remain computationally efficient. Ensemble models consistently outperform individual classifiers, demonstrating their robustness and scalability. These findings emphasize the potential of deep learning techniques for real-world fake news detection. © The Author(s) 2025.

关键词： Logistic regression

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