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DedupChain: A Secure Blockchain-Enabled Storage System With Deduplication for Zero-Trust Network

IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2070-2086页

作者： Qi, Saiyu Wang, Qiuhao Wei, Wei Yang, Xu Zhao, Hongguang Liu, Yuhao Qi, Yong Xi’an Jiaotong University School of Computer Science and Technology Xi’an710049 China Xi’an University of Technology School of Computer Science and Engineering Xi’an710049 China Northwestern Polytechnical University School of Electronics and Information Xi’an710072 China Alibaba Group Hangzhou311121 China

Permissioned blockchain is a promising methodology to build zero-trust storage foundation with trusted data storage and sharing for the zero-trust network. However, the inherent full-backup feature of the permissioned blockchain poses potential data privacy risks and substantial storage costs, hindering its usage as a storage medium. These issues necessitate the usage of secure data deduplication technology to mitigate them. Unfortunately, current secure data deduplication schemes are predominantly designed with centralized cloud servers in mind and are not suitable for distributed blockchain systems. The reason is that the full backup feature of the permissioned blockchain renders a wide attack surface to offline brute-force and frequency analysis attacks. In response, we propose DedupChain, a secure blockchain-enabled storage system with deduplication for zero-trust networks. DedupChain employs a trusted execution environment (i.e., Inter SGX enclave) in conjunction with Oblivious RAM (ORAM) to offer a novel security guarantee named oblivious data deduplication, which empowers DedupChain with the ability to defend offline brute-force and frequency analysis attacks. DedupChain also proposes several novel techniques to address the security and efficiency issues raised by the SGX enclave. We implemented a system prototype of DedupChain and evaluated its performance metrics. Our experimental results show that DedupChain exhibits satisfactory operational delays, throughput, and storage overhead. Security analysis shows that DedupChain is robust enough to withstand several types of attacks. To the best of our knowledge, we are the first to apply secure data deduplication techniques to address data privacy and storage cost issues raised by permissioned blockchain when used as a zero-trust storage medium. © 1983-2012 IEEE.

关键词： Blockchain

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An LSTM-based hybrid proximal policy optimization spectrum access algorithm in vehicular network

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International Journal of Intelligent Computing and Cybernetics 2024年第3期17卷 486-502页

作者： Lin Kang Junjie Chen Jie Wang Yaqi Wei School of Electronic and Information Engineering Taiyuan University of Science and TechnologyTaiyuanChina

Purpose:In order to meet the different quality of service(QoS)requirements of vehicle-to-infrastructure(V2I)and multiple vehicle-to-vehicle(V2V)links in vehicle networks,an efficient V2V spectrum access mechanism is proposed in this ***/methodology/approach:A long-short-term-memory-based multi-agent hybrid proximal policy optimization(LSTM-H-PPO)algorithm is proposed,through which the distributed spectrum access and continuous power control of V2V link are ***:Simulation results show that compared with the baseline algorithm,the proposed algorithm has significant advantages in terms of total system capacity,payload delivery success rate of V2V link and convergence ***/value:The LSTM layer uses the time sequence information to estimate the accurate system state,which ensures the choice of V2V spectrum access based on local observation *** hybrid PPO framework shares training parameters among agents which speeds up the entire training *** proposed algorithm adopts the mode of centralized training and distributed execution,so that the agent can achieve the optimal spectrum access based on local observation information with less signaling overhead.

关键词： V2X Spectrum access Deep reinforcement learning PPO

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"Survey and analysis of blockchain technologies with respect to: properties, algorithms, architecture, models, evolution and framework"

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Multimedia Tools and Applications 2025年第14期84卷 13571-13615页

作者： Tiwari, Nikita Ranjan, Prakash Biswal, Pradeep Kumar Barde, Chetan Sinha, Neha Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India

The growing realm of blockchain technology has captivated researchers and practitioners alike with its promise of decentralized, secure, and transparent transactions. This paper presents a comprehensive survey and analysis of various blockchain technologies, focusing on their fundamental properties, architectural designs, operational models, evolutionary trends, and overarching frameworks. Through an extensive review of existing literature and empirical studies, this research synthesizes the multifaceted landscape of blockchain, elucidating key concepts such as consensus mechanisms, smart contracts, scalability, interoperability, and privacy protocols. By examining the evolution of blockchain from its inception to its current state and beyond, this paper delineates the trajectory of technological advancements and identifies emerging paradigms. Furthermore, it critically evaluates the strengths and limitations of diverse blockchain architectures and models, offering insights into their applicability across different domains and use cases. Finally, this study culminates in the proposal of a comprehensive framework that encapsulates the essential elements necessary for understanding, implementing, and harnessing the transformative potential of blockchain technologies. Through this holistic analysis, this research contributes to a deeper understanding of blockchain ecosystems and provides valuable guidance for future research directions and practical implementations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Predictive analytics: Unveiling the potential of machine learning and deep learning

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第1期9卷 116-128页

作者： Kavitha, P. Shakkeera, L. School of Computer Science Engineering & Information Science Presidency University Bangalore India

Machine and deep learning methods have gained significant prominence in the healthcare industry, particularly for the prediction of cardiac diseases. The increasing prevalence of heart-related diseases underscores the necessity for proactive and accurate healthcare interventions. Machine learning, a data-driven approach, can play a crucial role in recognizing and addressing cardiovascular risks. To achieve this, researchers have utilized a range of classification techniques, such as Support Vector Machines, Random Forests, and Naive Bayes, to unravel the intricate aspects of heart disease prediction. Additionally, ensemble learning techniques, especially Stacking Technique, is employed to further enhance predictive accuracy. However, the ensemble approach has certain limitations. Therefore, confusion matrices are utilized for thorough evaluation and validation, offering better classifier performance. As research advances, prediction models aim to achieve higher accuracy and generalizability. Insights from confusion matrices can help researchers to make more robust and dependable predictions. Future research will investigate the deep learning models to detect subtle patterns in electrocardiogram data, with the aim of enabling earlier identification of cardiovascular conditions. Additionally, the integration of wearable sensor technologies holds promise for continuous risk monitoring and the development of personalized healthcare interventions. These technological advancements possess the potential to fundamentally transform the field of cardiac care, facilitating earlier disease diagnosis and substantially enhancing patient prognosis. In conclusion, the convergence of machine learning and deep learning models heralds a novel era for precision medicine, where data-driven insights empower stakeholders to tackle formidable challenges with unparalleled effectiveness. © (2025), (Society of Sytematic Innovation). All rights reserved.

关键词： Artificial Neural Network Cardiovascular Diseases Data Analysis Pattern Classification Data Mining Heart Disease Prediction Support Vector Machine

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MSNCIL: A Domain-Agnostic Class-Incremental Learning Method Tailored for Automatic Modulation Recognition

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IEEE Communications Letters 2025年第6期29卷 1456-1460页

作者： Deng, Zhiwen Luo, Chunbo Tang, Zixi Luo, Yang University of Electronic Science and Technology of China School of Information and Communication Engineering China

The emergence of new modulation types in 6G challenges the adaptability of deep learning-based automatic modulation recognition (DL-AMR) models. This letter presents multi-state neuron class-incremental learning (MSNCIL), the first domain-agnostic class-incremental learning (CIL) method for AMR. Leveraging the sparsity of wireless signal features, MSNCIL dynamically partitions a DL-AMR model into specialized sub-models, each dedicated to different modulation types. In each session, neurons are selected based on activation values, trained, frozen, and assigned state values. During inference, the session ID of a test sample is identified, which directs the corresponding neurons for recognition. Extensive experiments confirm MSNCIL's effectiveness. © 1997-2012 IEEE.

关键词： Deep learning

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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World Health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： COVID-19

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Experimental demonstration of 220-GHz terahertz signals wireless transmission over 4.6 km

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science China(information sciences) 2025年第1期68卷 386-388页

作者： Yi WEI Jianjun YU Mingxu WANG Xiongwei YANG Qiutong ZHANG Jingwen TAN Bing ZHANG Ying WU Yang HAN Peng TIAN Wen ZHOU Kaihui WANG Feng ZHAO State Key Laboratory of ASIC and System Key Laboratory for Information Science of Electromagnetic Waves (MoE)School of Information Science and Technology Fudan University School of Electronic Engineering Xi'an University of Posts and Telecommunications

The advancement of terahertz (THz) communication technology drives the evolution of wireless communication systems,offering novel pathways and technical means for the development of future 6G communication *** wireless communication systems are often constrained by bandwidth limitations of electronic devices in high frequency ***,THz communication technology leverages the characteristics of electromagnetic waves to transcend these limitations,enabling communication athigher frequencies and wider bandwidths.

关键词： wireless terahertz over demonstration experimental signals transmission

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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MAUN:Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1139-1150页

作者： Qian Hu Jiayi Ma Yuan Gao Junjun Jiang Yixuan Yuan Electronic Information School Wuhan UniversityWuhan 430072China School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Electronic Engineering Chinese University of Hong KongHong Kong 999077China

Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging *** approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging *** some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative *** this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI ***,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information ***,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration *** experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and *** code is publicly available at https://***/HuQ1an/MAUN.

关键词： Deep folding iteration

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Revealing the Trends in the Academic Landscape of the Health Care System Using Contextual Topic Modeling

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Data Intelligence 2023年第4期5卷 923-946页

作者： Muhammad Inaam ul haq Qianmu Li School of computer science and Engineering Nanjing University of Science and Technology

The health care system encompasses the participation of individuals,groups,agencies,and resources that offer services to address the requirements of the person,community,and population in terms of *** to the rising debates on the healthcare systems in relation to diseases,treatments,interventions,medication,and clinical practice guidelines,the world is currently discussing the healthcare industry,technology perspectives,and healthcare *** gain a comprehensive understanding of the healthcare systems research paradigm,we offered a novel contextual topic modeling approach that links up the CombinedTM model with our healthcare Bert to discover the contextual topics in the domain of *** research work discovered 60 contextual topics among them fteen topics are the hottest which include smart medical monitoring systems,causes,and effects of stress and anxiety,and healthcare cost estimation and twelve topics are the ***,thirty-three topics are showing in-significant *** further investigated various clusters and correlations among the topics exploring inter-topic distance maps which add depth to the understanding of the research structure of this scientific *** current study enhances the prior topic modeling methodologies that examine the healthcare literature from a particular disciplinary *** further extends the existing topic modeling approaches that do not incorporate contextual information in the topic discovery process adding contextual information by creating sentence embedding vectors through transformers-based *** also utilized corpus tuning,the mean pooling technique,and the hugging face *** method gives a higher coherence score as compared to the state-of-the-art models(LSA,LDA,and Ber Topic).

关键词： Contextual topic modeling health care Bert content analysis health care system

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