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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 Yang, Xu Qi, Yong Xi'an Jiaotong University School of Computer Science and Technology China Xi'an University of Technology School of Computer Science and Engineering China Alibaba Group China Northwestern Polytechnical University School of Electronics and Information 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 Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

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Chinese Journal of electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of Software Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems

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computers, Materials & Continua 2024年第9期80卷 3573-3589页

作者： Qianyao Zhu Kaizhou Gao Wuze Huang Zhenfang Ma Adam Slowik Institute of Systems Engineering Macao University of Science and TechnologyMacao99078China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin75-453Poland

The flow shop scheduling problem is important for the manufacturing *** flow shop scheduling can bring great benefits to the ***,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted *** work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned ***,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are *** to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local *** of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during ***,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed *** experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random *** verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving *** Friedman test is executed on the results by five *** is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.

关键词： Distributed scheduling hybrid flow shop meta-heuristics local search Q-learning

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Joint Rain Streaks & Haze Removal Network for Object Detection

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computers, Materials & Continua 2024年第6期79卷 4683-4702页

作者： Ragini Thatikonda Prakash Kodali Ramalingaswamy Cheruku Eswaramoorthy K.V Department of Electronics and Communication Engineering National Institute of TechnologyWarangal506004India Department of Computer Science and Engineering National Institute of TechnologyWarangal506004India Department of Electronics and Communication Engineering Indian Institute of Information TechnologyDesign and ManufacturingKurnool518008India

In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant *** emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior ***,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational *** response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature *** methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map *** the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets.

关键词： Image deraining Selective Dense Residual Module(SDRM) Selective Kernel Fusion Module(SKFM) Selective KernelDense Residual M-Shaped Network(SKDRMNet)

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Medical Reports Simplification Using Large Language Models 23th

Medical Reports Simplification Using Large Language Models

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Almezhghwi, Khaled Hassan, Morad Ali Ghadedo, Adel Belhaj, Fairouz Shwehdi, Rabei Electrical and Electronics Engineering College of Electronics Technology Tripoli Tripoli Libya Computer Science Bani Waleed University Bani Waleed Libya Computer Science College of Electronics Technology Tripoli Tripoli Libya Computer Information Systems Higher Institute of Science and Technology Tripoli Libya Electrical and Electronics Engineering College of Electronics Technology Tripoli Tripoli Libya

ISBN: (纸本)9783031648120

Making medical reports easily understandable for a wider audience is a significant endeavor, and the recent advancements in deep learning and large language models offer a promising solution. In our research, we have introduced a fine-tuned Text-to-Text Transformer (T5) model to efficiently summarize these medical reports. Our model is trained and assessed on the publicly accessible Indiana Dataset. To gauge its effectiveness, we employ the ROUGE metrics. The outcomes we've achieved hold promise for the potential of this approach in enhancing medical report summarization. By harnessing the power of cutting-edge technology, we aim to make medical information more accessible and comprehensible to everyone. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Patient activity recognition for robust performance under varied illumination and cluttered background in indoor environment

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International Journal of computers and Applications 2024年第12期46卷 1219-1245页

作者： Sain, Manoj Kumar Laskar, Rabul Singha, Joyeeta Saini, Sandeep Department of Computer Science Banasthali Vidyapaith Niwai India Department of Electronics and Communication National Institute of Technology Silchar India

Human Activity Recognition (HAR) holds significant importance in health and human-machine interaction. However, recognizing actions from 2D information faces challenges like occlusion, illumination variation, cluttered backgrounds, and view invariance. These hurdles are particularly pronounced in indoor patient monitoring settings due to fluctuating lighting conditions and cluttered backgrounds, which compromise the accuracy of activity recognition systems. A new architecture named illuminationRevive has been proposed to tackle this issue. Which utilizes an encoder-decoder convolutional neural network and image post-processing blocks to enhance the image's visual appearance. A new dataset comprising seven indoor physical activities has been proposed, created with contributions from thirty individuals aged 20 to 45. A hybrid fusion architecture is proposed to classify activities, integrating motion sequence information and body joint features. The proposed classification model incorporates generated Skeleton Motion History Images (SMHIs), collected human joint motion features from video frames, and novel kinematic and geometric features within window frames as inputs. The model can extract spatial and temporal feature vectors by integrating ResNet50-ViT (Residual Network-50 layers, Vision Transformer) and CNN-BiLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) layers. The suggested classification model was evaluated alongside state-of-the-art models using the LNMIIT-SMD (The LNM Institute of Information technology-Skelton Motion Dataset) and established NTU-RGBD (Nanyang Technological University's Red Blue Green and Depth information) dataset. The evaluation aimed to assess the effectiveness of the proposed classification model architecture. Results demonstrate the superiority of the proposed model, achieving impressive accuracies of 98.21% on real-time data, 98.45% on the proposed dataset, and 97.12% on the NTU-RGBD dataset. © 2024 Informa UK

关键词： Convolutional neural networks

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Imbalanced Classification of Cultural Heritage Images Using Deep Neural Networks 1

Imbalanced Classification of Cultural Heritage Images Using ...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Wisanmongkol, Juthatip Sanpechuda, Taweesak Lim, Sambat Kovavisaruch, La-Or National Electronics and Computer Technology Center Pathum Thani Thailand

ISBN: (纸本)9798350365016

We investigate the use of deep learning in cultural heritage image classification when the number of samples is not uniformly distributed among the classes. Two intuitive methods, data resampling and transfer learning, are considered to address the impact of the imbalanced dataset. Results show that classification performance can be improved by using the two methods. Compared with the baseline convolutional neural network, the overall F1 scores computed over all classes show slight improvements of 2.67% and 4.56% for the data oversampling and transfer learning methods. However, when the per-class F1 scores are considered, the upgrades can be as high as 64.77% and 67.35% for the data oversampling and transfer learning methods, respectively. © 2024 IEEE.

关键词： classification cultural heritage digitization image imbalanced museums

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Resource-Efficient Image Retrieval: A Study of Local Patterns Versus Deep Learning Models 4th

Resource-Efficient Image Retrieval: A Study of Local Patte...

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4th International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2023

作者： Arora, Nitin Sharma, Subhash C. Electronics and Computer Discipline Indian Institute of Technology Roorkee India

ISBN: (纸本)9789819752300

This research paper delves into the ongoing debate between traditional local patterns and advanced deep learning models in the context of image retrieval from less complex datasets. Through a comprehensive comparative analysis, the study examines the trade-offs and advantages of employing these approaches for retrieving images from a standard dataset. Emphasis is placed on understanding how these methods impact computational resource usage, including CPU/GPU utilization and memory consumption. Our findings reveal that deep learning models show promise in handling complex image datasets, while local patterns, known for their resource efficiency, may struggle with capturing fine-grained visual details. This research underscores the importance of considering specific requirements and resource efficiency when choosing between these methods. In an era marked by the exponential growth of image data, this study contributes to the broader discourse, encouraging continued exploration and innovation for more effective and efficient image retrieval solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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The Impact of Variable Focal Lengths in Head Mounted Display

The Impact of Variable Focal Lengths in Head Mounted Display

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2024 International Conference on Recent Advances in Electrical, electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Reddy, Ashik Venkat Bandaru, Pavan Kumar Yaswanth, Gamidi Naga Jiavana, Ferents Koni SRM Institute of Science and Technology Electronics and Computer Engineering India

ISBN: (纸本)9798350354539

Significant advances have been made in augmented reality (AR) technology, which provides immersive experiences by superimposing digital content over the real world. However, there are issues with how Head-Mounted Displays (HMDs) affect user experiences, especially when they have varying focal lengths, given their widespread use in augmented reality applications. Examining how varied focal changes in head-mounted displays affect users' experiences in augmented reality (AR) environments, this article seeks to answer important queries about visual comfort, depth perception, cognitive burden, and myopia progression rates. Fifteen individuals participated in an extensive user study utilizing HMDs with programmable focal length settings while performing augmented reality tasks. Subjective assessments of visual comfort, depth perception, and cognitive load following exposure to various focal length alterations were obtained from the data. Additionally, objective measurements of physiological reactions and task performance were gathered. Based on assessments of subjects' visual acuity taken before and after the trial, myopia progression rates were computed. The findings showed a strong relationship between user experiences and varied focal adjustments in HMDs. Data demonstrates that using varifocal lenses, especially at specific focal length settings, improves both visual comfort and depth perception accuracy. However, employing HMDs with varied focal lengths, statistically higher rates of myopia progression and higher cognitive load were noted, suggesting possible long-term impacts on visual health. © 2024 IEEE.

关键词： Augmented reality

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Dynamic Indian Sign Language Sentence Captioning System Using Machine Learning 5th

Dynamic Indian Sign Language Sentence Captioning System Usin...

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5th International Conference on Electrical and electronics Engineering, ICEEE 2024

作者： Kulkarni, Vedant Dattatray Bajaj, Anushree Shrigopal Marathe, Himanshu Deshmukh, Mandar Sreemathy, R. Jagdale, Jayashree Department of Information Technology Pune Institute of Computer Technology Pune India Department of Electronics and Telecommunication Engineering Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789819791118

Sign language serves as a vital mode of communication for the deaf and hard of hearing community, yet access to sign language content remains limited due to the lack of accurate and timely captioning. In this paper, a comprehensive comparison has been made between Long Short-Term Memory (LSTM)- and Support Vector Machine (SVM)-based neural networks for sign language recognition. Our approach uses LSTM networks to effectively recognize and translate sign language gestures into textual descriptions. We leverage a large dataset of sign language videos paired with English captions for training and evaluation. Through extensive experimentation, we demonstrate the effectiveness of our system in accurately captioning sign language videos, thereby enhancing accessibility for individuals with hearing impairments. Our self-curated dataset includes 28 unique sentences on which our LSTM-based model achieved an accuracy of 90% whereas it was 97% for SVM-based approach. Our research contributes to advancing the field of accessibility technology and holds promise for real-world applications in education, entertainment, and communication for the deaf and hard of hearing community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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