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Blockchain-based conditional privacy-preserving authentication scheme for vehicular ad hoc networks

Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 6273-6286页

作者： Gong, Cheng Xiong, Ling He, Xingyu Niu, Xianhua College of Computer and Software Engineering Xihua University Sichuan Chengdu610099 China National Key Laboratory of Science and Technology Communications University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Security and privacy are two challenging issues in Vehicular Ad-hoc Networks (VANETs), which have attracted widespread concern from industry and academia. However, the existing security schemes for VANETs environment have various problems, such as the unlinkability and the cross region communication,etc. To overcome these weaknesses, this paper designs a blockchain-based conditional privacy-preserving authentication scheme (BCPAS) for VANETs. The proposed scheme has the following two advantages: (1) Pseudonym ID is employed in authentication phase to ensure the real identity of vehicle is unlinkable. (2) Blockchain technology is adopted to make cross-region authentication more efficient. Compared with the most recent scheme, the computational efficiency of our scheme has improved 50% with 3 service management nodes, and 87% with 15 service management nodes in the Authentication and Member Secrets Generate Phase. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Authentication

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A Connectivity-Enhanced Multi-Task Learning based on Anatomical Priors for 3D Class-Balanced Pulmonary Airway Segmentation

A Connectivity-Enhanced Multi-Task Learning based on Anatomi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jia, Yan Dou, Yong Qiao, Peng Cheng, Yuqing Xu, Kele He, Zhouyu National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology Changsha China National University of Defense Technology College of Systems Engineering Changsha China

ISBN: (纸本)9798350386226

Accurate and efficient airway segmentation is essential for evaluating pulmonary diseases, aiding diagnosis, reducing the preoperative burden of airway identification, and minimizing patient discomfort during prolonged surgeries. However, current pulmonary airway reconstruction techniques are hindered by two major challenges: difficulty in accurately reconstructing fine airway branches due to the tendency to overlook small targets, and insufficient structural connectivity leading to frequent branch discontinuities within the airway tree. These limitations directly affect the clinical applicability of reconstructed airways. To overcome these challenges, a novel 3D pulmonary airway segmentation multi-task framework is proposed, designed to enhance the performance of existing backbone models. This approach integrates Anatomical Prior-Based Multi-Task Learning (AP-MTL) through the use of Gaussian-constructed connectivity-enhanced isosurfaces, significantly improving the network's ability to maintain airway continuity. Additionally, a Class-Balanced CT Density Distribution Reconstruction mechanism (DDR-CB) is introduced, further refining the model's capability to detect and segment fine airway branches. As a result of these enhancements, the model demonstrates a 11.5% average improvement in segmentation accuracy and connectivity compared to the baseline. The source code is publicly accessible at https://***/inexhaustible419/APMTLAirwaySegment. © 2024 IEEE.

关键词： Pulmonary diseases

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Joint Model Assignment and Resource Allocation for Cost-Effective Mobile Generative Services

Joint Model Assignment and Resource Allocation for Cost-Effe...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Gao, Shuangwei Yang, Peng Kong, Yuxin Lyu, Feng Zhang, Ning Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Central South University School of Computer Science and Engineering Changsha China University of Windsor Department of Electrical and Computer Engineering WindsorON Canada

ISBN: (纸本)9798350351255

Artificial Intelligence Generated Content (AIGC) services can efficiently satisfy user-specified content creation demands, but the high computational requirements pose various challenges to supporting mobile users at scale. In this paper, we present our design of an edge-enabled AIGC service provisioning system to properly assign computing tasks of generative models to edge servers, thereby improving overall user experience and reducing content generation latency. Specifically, once the edge server receives user requested task prompts, it dynamically assigns appropriate models and allocates computing resources based on features of each category of prompts. The generated contents are then delivered to users. The key to this system is a proposed probabilistic model assignment approach, which estimates the quality score of generated contents for each prompt based on category labels. Next, we introduce a heuristic algorithm that enables adaptive configuration of both generation steps and resource allocation, according to the various task requests received by each generative model on the edge. Simulation results demonstrate that the designed system can effectively enhance the quality of generated content by up to 4.7% while reducing response delay by up to 39.1% compared to benchmarks. © 2024 IEEE.

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Revolutionizing Network Performance: The Active and Passive Service Path Performance Monitoring Analysis Method 10

Revolutionizing Network Performance: The Active and Passive ...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Wen, Jigang Chen, Yuxiang Jin, Kai Liu, Chuda Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan University Computer Science and Electronic Engineering Changsha China Changsha Aeronautical Vocation Technical College Changsha China

ISBN: (纸本)9798350312461

The Service Path Performance Monitoring Scheme is an advanced network performance management method that comprehensively monitors every link of the business system. Its primary goal is to quickly detect and resolve issues that affect key businesses, optimizing operational efficiency and fault-handling capabilities within the network. With customer-centric support in mind, the scheme features real-time performance monitoring, fast fault location, and analytical functions to aid operation and maintenance personnel. Furthermore, the scheme includes a centralized system that collects real-time analysis data from every network node, presenting a clear visual representation of the business system's working conditions. This graphical approach allows for faster interpretation and decision-making, maximizing operation and maintenance efficiency. Ultimately, the Service Path Performance Monitoring Scheme ensures the business network's performance and stability, enabling rapid issue detection and resolution to enhance customer satisfaction. © 2023 IEEE.

关键词： Network performance

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Investigation of DNA discontinuity for detecting tuberculosis

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology Information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

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Publication performance and trends in virtual reality research in education fields: a bibliometric analysis

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Multimedia Tools and Applications 2025年第10期84卷 6999-7026页

作者： Elaish, Monther M. Yadegaridehkordi, Elaheh Ho, Yuh-Shan Computer Science Department Information Technology Faculty University of Benghazi Benghazi Libya College of Information and Communications Technology School of Engineering and Technology CQUniversity Sydney Australia Trend Research Centre Asia University No. 500 Lioufeng Road Taichung41354 Taiwan

Virtual Reality (VR) technology has the potential to enhance education by providing immersive and engaging learning experiences that can improve teaching and learning outcomes. While there is a growing interest in utilizing VR in education settings, further research is needed to understand its pedagogical effectiveness and address associated considerations and challenges. This bibliometric study comprehensively analyzes 1,157 relevant articles from the Social science Citation Index (SSCI) and the science Citation Index Expanded (SCI-EPANDED) to gain insights into the current state of VR integration in education. The analysis revealed variation in VR adoption and research output across countries and institutions, underscoring the importance of collaboration and knowledge-sharing in the field. Key research areas and trends, such as the use of VR for skill development and training, were identified. Additionally, the study highlighted the need for more research on the pedagogical effectiveness of VR. The findings carry practical implications for guiding future research shaping policy decisions and advocating for a concerted effort to harness VR’s capabilities in education. This study serves as a practical roadmap, promoting the implementation and improvement of VR technology and fostering equitable and inclusive educational practices. © The Author(s) 2024.

关键词： Virtual reality

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A multi-strategy integrated improved YOLOv5 model and its application in target detection 4

A multi-strategy integrated improved YOLOv5 model and its ap...

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4th IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2024

作者： Li, Jia Liu, Ziyan Song, Yuxin Harbin Engineering University College of Computer Science and Technology Harbin China Hunan University of Technology and Business College of Intelligent Engineering and Intelligent Manufacturing Changsha China Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China

ISBN: (纸本)9798350361643

Deep learning techniques have the potential to significantly improve target detection speed and detection accuracy in autonomous driving. Most existing target detection algorithms have poor real-time performance and poor accuracy. Moreover, the algorithm is difficult to be deployed. Based on analysis of the existing problems of target detection in autonomous driving, this paper puts forward an improved YOLOv5 algorithm. First, the extract feature network of YOLOv5 is replaced with the FasterNet-T0 model to reduce model parameters. Next, in the fusion part (Neck) of the network, the attention mechanism CBAM is introduced to improve the accuracy of the target detection. Then, the Slim-Neck framework is presented to improve the computational efficiency of the model. Finally, considering that CIoU may be replaced by Inner-IoU to improve the accuracy and generalization of the model, in order to verify the effect of the improved model, this paper uses the improved YOLOv5 algorithm model to extract the specific categories from VOC2007 dataset and DOTA dataset. After experimentation. The results show that the model we made, the improved YOLOv5 algorithm model is capable of getting a 0.848 and 0.748mAP @ 0.5 of the VOC2007 dataset and DOTA dataset for the specific category extraction, which verifies its effectiveness. Compared with other algorithms, the improved YOLOv5s algorithm is more competitive. On the VOC2007 data set, mAP live/voc, 0.5 increased by 5.1% for a specific category acquisition, and mAP live/voc, 0.5:0.95 increased by 6.4% relative to the original YOLOv5s algorithm, and the number of parameters reduced by 39.2% from 7035811 to 4276490. On the DOTA dataset, mAP@0.5 increased by 0.9% for a specific category acquisition, and mAP@ 0.5:0.95 increased by 0.4%, which demonstrates that this method improves the accuracy and efficiency of vehicle detection to some extent. In this study, DOTA dataset was used as an experimental dataset to verify the performance of YOL

关键词： Autonomous vehicles

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VICTOR: Video Content-aware Partially Reliable Transmission over Multipath QUIC

VICTOR: Video Content-aware Partially Reliable Transmission ...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Li, Yahui Han, Biao Han, Xueqiang Ji, Xiaolan Song, Congxi Chen, Guo National University of Defense Technology School of Computer Changsha China Hunan University College of Computer Science and Electronic Engineering Changsha China

ISBN: (纸本)9798350333336

Over the past few years, video streaming has been dominating the internet traffic. The Quick UDP Internet Connections (QUIC) protocol and its support for both reliable and un-reliable transmission may exhibit attractive advantages for video streaming. Multipath transport techniques like Multi-Path QUIC (MPQUIC) are introduced to aggregate the bandwidth of multiple links and to provide reliable transmission in poor network conditions. However, how to leverage unreliable transmission for video streaming over MPQUIC has not been explored yet. Motivated by extensive experimental observations, we propose VICTOR, video content-aware partially reliable transmission over multipath QUIC. Specifically, VICTOR exploits both reliable and unreliable transmission to improve the quality of experience (QoE) perceived by users. Besides, the scheduling mechanism of MPQUIC is examined and VICTOR split video frames over multiple paths with the perception of QoE. We evaluate VICTOR experimentally in both simulations and a lab testbed. The results reveal that VICTOR can achieve better performance compared with the latest MPTCP and MPQUIC protocols. © 2023 IEEE.

关键词： Video streaming

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Multi-Scale Fusion and Decomposition Network for Single Image Deraining

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IEEE Transactions on Image Processing 2024年 33卷 191-204页

作者： Wang, Qiong Jiang, Kui Wang, Zheng Ren, Wenqi Zhang, Jianhui Lin, Chia-Wen Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China Hangzhou Dianzi University College of Computer Science and Technology Hangzhou310018 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated remarkable success in low-level vision tasks, such as image super-resolution, deraining, and dehazing. The former excels in acquiring local connections with translation equivariance, while the latter is better at capturing long-range dependencies. However, both CNNs and Transformers suffer from individual limitations, such as limited receptive field and weak diversity representation of CNNs during low efficiency and weak local relation learning of SA. To this end, we propose a multi-scale fusion and decomposition network (MFDNet) for rain perturbation removal, which unifies the merits of these two architectures while maintaining both effectiveness and efficiency. To achieve the decomposition and association of rain and rain-free features, we introduce an asymmetrical scheme designed as a dual-path mutual representation network that enables iterative refinement. Additionally, we incorporate high-efficiency convolutions throughout the network and use resolution rescaling to balance computational complexity with performance. Comprehensive evaluations show that the proposed approach outperforms most of the latest SOTA deraining methods and is versatile and robust in various image restoration tasks, including underwater image enhancement, image dehazing, and low-light image enhancement. The source codes and pretrained models are available at https://***/qwangg/MFDNet. © 1992-2012 IEEE.

关键词： Image reconstruction

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Analysis and Research on BOX64's Core Technologies for Cross-Architecture Software Migration

Analysis and Research on BOX64's Core Technologies for Cross...

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2023 International Conference on Frontiers of Robotics and Software engineering, FRSE 2023

作者： Zhang, Jin Shan, Zehu Liu, Xiaodong Wang, Wenzhu Li, Zhuoheng Peng, Long Yu, Jie Hunan Normal University College of Computer and Communication Engineering Changsha University of Science and Technology College of Information Science and Engineering Changsha China Hunan Normal University College of Information Science and Engineering Changsha China National University of Defense Technology College of Computer Science Changsha China Haihe Lab of Itai Tianjin China Kylin Software Tianjin China

ISBN: (纸本)9798350301113

In the current context of increasingly diverse processor architectures and complex application requirements, how to seamlessly migrate software written for specific architectures (such as X86) to other architectures (such as ARM) has become a critical and urgent issue to resolve. This research primarily explores the key roles of WRAPPER and Dynarec technologies in this process. WRAPPER technology encapsulates the library function calls and system calls of X86_64 Linux programs, enabling them to run seamlessly and efficiently on the ARM64 Linux platform, thereby achieving cross-architecture software compatibility. Dynamic recompilation technology, on the other hand, can dynamically convert the binary code of the source architecture into the binary code of the target architecture during runtime, further enhancing program performance. Through in-depth theoretical analysis and empirical testing, the research team has found that there is still potential for optimization in these two technologies regarding library function calls and memory management. The team conducted a comprehensive theoretical analysis and evaluation of possible optimization strategies, expecting to further improve software performance on non-X86_64 architecture platforms through the implementation of these strategies. This has practical research value for the development and porting of software on non-X86_64 architecture platforms. © 2023 IEEE.

关键词： Binary codes

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