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AuthUp: A Privacy-Preserving V2I Authentication Scheme for Vehicular Ad Hoc Networks

IEEE Transactions on Vehicular technology 2025年

作者： Li, Xincheng Yin, Xinchun Ning, Jianting Zhang, Tianwei Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology the Zhejiang Key Laboratory of Digital Fashion and Data Governance Hangzhou310018 China Jiangxi Vocational College of Science and Technology Nanchang330200 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science China College of Computer and Cyber Security Fuzhou350117 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Vehicular ad hoc networks (VANETs) play a crucial role in enhancing modern transportation systems, leading to the development of various authentication schemes to meet the stringent security and privacy requirements of vehicular communications. Many existing schemes rely on tamper- proof devices (TPDs) that store extensive secret parameters for identity authentication, making them susceptible to Sybil and side-channel attacks. This paper introduces a sophisticated privacy-preserving authentication protocol, referred to as Authentication and Update (AuthUp), specifically designed to solve the problem. A trusted authority (TA) issues one-time access tokens to vehicles during the registration process. After each vehicle-to-infrastructure (V2I) authentication process, cryptographic key material is updated without compromising privacy. Consequently, Sybil attacks are promptly identified and thwarted, as manipulated tokens fail authentication. Additionally, adversaries are prevented from accessing sensitive data stored in TPDs via side-channel attacks because of the dynamic updating of secret keys. A formal security proof validates the robustness of AuthUp, and extensive evaluations demonstrate its superior efficiency compared to alternative schemes, highlighting its suitability for VANET deployment. © 1967-2012 IEEE.

关键词： Side channel attack

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Beyond Precision and Recall: Measuring Search Engine Consistency Using Rank Stability

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IEEE Access 2025年 13卷 92242-92259页

作者： Lakshmi, S. Thinakaran, Rajermani Batri, Krishnan Sowrirajan, R. Murugan, Sivaram Jain University Department of Electronics and Communication Engineering Karnataka Bengaluru India INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Jain University School of Computer Science and Engineering Karnataka Bengaluru India Dr. N.G.P. Arts and Science College Department of Mathematics Tamil Nadu Coimbatore India Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Department of Computer Engineering Sivas58000 Turkey

Traditional information retrieval metrics such as precision, recall, and F-measure assess document relevance but fail to capture the stability of search rankings. Search engines frequently update their algorithms, leading to variations in retrieved documents and their rankings across identical queries. This study introduces a novel rank correlation measure that quantifies search result consistency by analyzing rank fluctuations in repeated queries, serving as a proxy for temporally separated searches. The proposed approach distinguishes between overlapping and non-overlapping documents, providing a detailed measure of ranking stability. Empirical evaluations comparing Google and Bing reveal that Google exhibits greater rank fluctuations for non-overlapping documents, whereas both search engines maintain relatively stable rankings for overlapping documents. These findings highlight the importance of rank-based evaluation frameworks in search engine analysis, extending beyond conventional retrieval metrics. By incorporating stability assessments, this research enhances the methodology for evaluating search consistency in dynamic web environments, contributing to process innovation in information retrieval (SDG 9: Industry, Innovation, and Infrastructure) and promoting fair, reliable access to information (SDG 16: Peace, Justice, and Strong Institutions). © 2013 IEEE.

关键词： Online searching

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A Multi-tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing

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IEEE Transactions on Consumer Electronics 2025年

作者： Lin, Haiyang Xiao, Bo Zhou, Xiaokang Zhang, Yonghong Liu, Xiaodong the School of Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China the School of Chemistry and Materials Science Nanjing University of Information Science and Technology Nanjing210044 China the Faculty of Business Data Science Kansai University Osaka565-0823 Japan the School of Automation Nanjing University of Information Science and Technology Nanjing210044 China the School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom

—In the domain of consumer electronics, vehicular edge computing (VEC) technology is emerging as a novel data processing paradigm within vehicular networks. By sending tasks related to vehicular applications to the edge, this model makes it easier for computing power to be spread out. This lets interactive services respond quickly. Nevertheless, the computational resources at edge servers are inherently limited and often tasked with handling multiple concurrent operations. The inefficacious allocation of these resources significantly impairs the efficiency of task offloading. Additionally, indiscriminate offloading could overwhelm the servers, detrimentally impacting the performance of subsequent tasks. To circumvent these challenges, this study introduces a multi-tier offloading model predicated on game theory principles. This framework aims to optimize resource utilization at the edge while accounting for server load to ensure the timely execution of latency-sensitive tasks. To evaluate this model, this paper created a simulation environment specifically for video game tasks in consumer electronics. The experimental results show that the multi-tier offloading model can effectively relieve the load pressure on the edge server. The task failure rate of the multi-tier offloading model remains at the lowest level compared with several state-of-the-art algorithms, significantly reducing the execution delay of tasks and being able to meet the requirements of consumer electronics applications. © 2025 IEEE.

关键词： Computation offloading

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Early detection of Alzheimer’s disease progression stages using hybrid of CNN and transformer encoder models

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Scientific Reports 2025年第1期15卷 1-22页

作者： Almalki, Hassan Khadidos, Alaa O. Alhebaishi, Nawaf Senan, Ebrahim Mohammed Department of Information Technology College of Technology for Communications and Information Technical and Vocational training Corporation Riyadh Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science College of Applied Sciences Hajjah University Hajjah Yemen Department of Artificial Intelligence Faculty of Computer Science and Information Technology Al-Razi University Sana’a Yemen

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects memory and cognitive functions. Manual diagnosis is prone to human error, often leading to misdiagnosis or delayed detection. MRI techniques help visualize the fine tissues of the brain cells, indicating the stage of disease progression. Artificial intelligence techniques analyze MRI with high accuracy and extract subtle features that are difficult to diagnose manually. In this study, a modern methodology was designed that combines the power of CNN models (ResNet101 and GoogLeNet) to extract local deep features and the power of Vision Transformer (ViT) models to extract global features and find relationships between image spots. First, the MRI images of the Open Access Imaging Studies Series (OASIS) dataset were improved by two filters: the adaptive median filter (AMF) and Laplacian filter. The ResNet101 and GoogLeNet models were modified to suit the feature extraction task and reduce computational cost. The ViT architecture was modified to reduce the computational cost while increasing the number of attention vertices to further discover global features and relationships between image patches. The enhanced images were fed into the proposed ViT-CNN methodology. The enhanced images were fed to the modified ResNet101 and GoogLeNet models to extract the deep feature maps with high accuracy. Deep feature maps were fed into the modified ViT model. The deep feature maps were partitioned into 32 feature maps using ResNet101 and 16 feature maps using GoogLeNet, both with a size of 64 features. The feature maps were encoded to recognize the spatial arrangement of the patch and preserve the relationship between patches, helping the self-attention layers distinguish between patches based on their positions. They were fed to the transformer encoder, which consisted of six blocks and multiple vertices to focus on different patterns or regions simultaneously. Finally, the MLP classification layers classify each

关键词： Alzheimer’s disease CNN-ViT GoogLeNet Hybrid Models ResNet101 Vision transformer

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 dataset

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Computers, Materials and Continua 2025年第3期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Data Science and Engineering Indian Institute of Science Education and Research Bhopal 462066 India Research Institute of Marine Systems Engineering Seoul National University Seoul 08826 Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad 201204 India Battery System Modelling Department CLARIOS VARTA Hannover GmbH Hanover 30419 Germany Science and Innovation Group Bureau of Meteorology Brisbane 4000 Australia

Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an R 2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural Networks (CNN) and Radial Basis Function Networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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Integrating Kelly Criterion with Technical Indicators for VN30 Stock Market 2nd

Integrating Kelly Criterion with Technical Indicators for ...

引用

2nd International Conference on Intelligent Systems and data science, ISDS 2024

作者： Dinh, Dao Lan Vy Tran, Ngoc Hang May, Vo Huyen Khanh Tran, Hung Minh, Tran Duc Lan, Dang Thu Vo, Van Nhan DATCOM Lab Faculty of Data Science and Artificial Intelligence College of Technology National Economics University Hanoi Viet Nam Faculty of Business and Economics Phenikaa University Yen Nghia Ha Dong Hanoi Viet Nam Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

ISBN: (纸本)9789819796151

Recently algorithmic trading has revolutionized financial markets. This study investigates the application of the Kelly criterion and technical indicators pairs (relative strength index RSI, moving average convergence divergence (MACD) on-balance volume (OBV), Bollinger bands (BB)) to optimize trading strategies within the Vietnamese VN30 index. Historical data from 2021–2024 was split into 2 parts, one is to find parameters for Kelly Criteria, another is used to analyze and backtest the proposed models. Results indicate that combining RSI and MACD is effective during downtrends, while RSI with OBV and BB performs better in uptrends. The Kelly Criterion consistently enhances risk management during periods, especially in downtrends. This research fills a gap in the literature and offers a foundation for future studies on algorithmic trading in emerging markets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk management

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Towards A Hybrid Quantum Differential Privacy

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IEEE Journal on Selected Areas in Communications 2025年

作者： Song, Baobao Pokhrel, Shiva Raj Vasilakos, Athanasios V. Zhu, Tianqing Li, Gang University of Technology Sydney Faculty of Engineering and IT Ultimo SydneyNSW2007 Australia Deakin University School of IT GeelongVIC3125 Australia College of Computer Science and Information Technology IAU Department of Networks and Communications Dammam P.O. Box 1982 Dammam31441 Saudi Arabia Grimstad Norway City University of Macau Faculty of Data Science Macau China

Quantum computing offers unparalleled processing power but raises significant data privacy challenges. Quantum Differential Privacy (QDP) leverages inherent quantum noise to safeguard privacy, surpassing traditional DP. This paper develops comprehensive noise profiles, identifies noise types beneficial for QDP, and highlights the need for practical implementations beyond theoretical models. Existing QDP mechanisms, limited to single noise sources, fail to reflect the multi-source noise reality of quantum systems. We propose a resilient hybrid QDP mechanism utilizing channel and measurement noise, optimizing privacy budgets to balance privacy and utility. Additionally, we introduce Lifted Quantum Differential Privacy, offering enhanced randomness for improved privacy audits and quantum algorithm evaluation. © 1983-2012 IEEE.

关键词： Qubits

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Citizenship Engagement with Government Electronic Services a Case Study of “Companies Control Department of Jordan”

Citizenship Engagement with Government Electronic Services a...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Ebtisal Dawood Amani Abu Zaid Ahmad Al-qerem Mohammad A Al Khaldy Hussain Turki Computer Science Department Faculty of Information Technology Zarqa University Jordan Zarqa Jordan Business Intelligence Data Analytics University of Petra Amman Jordan University Headquarter University Of Anbar Ramadi Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper aims to analyze the impact of digital transformation on civic engagement and governance, with a focus on the Companies Control Department (CCD) in Jordan. Traditional methods of civic engagement suffer from slow procedures, poor accessibility, and lack of democratic citizen engagement. The paper explores how technology, particularly mobile applications, can address these issues by enabling citizens to access government information and interact with official bodies when needed. The paper also discusses the benefits of digital initiatives at the CCD, which include providing information and statistical data on the number of registered companies and their capital through a monthly statistical bulletin and an annual report, in addition to ensuring the accuracy and validity of information related to registered companies and their partners for the benefit of official bodies and judicial authorities. The paper also highlights the challenges associated with implementing digital solutions, such as cybersecurity threats, legal complexities, and obstacles resulting from limited digital transformation. Based on the digital services provided by the CCD, the study demonstrates how the implementation of ICT strategies and digital innovations contribute to enhancing transparency, accountability, and efficiency of public services. It also provides recommendations aimed at enabling Jordanian civil society to benefit from technology to enhance its participation and contribute to developing the national digital agenda.

关键词： Charge coupled devices Technological innovation Machine learning algorithms Law Digital transformation Government Companies Machine learning Mobile applications Monitoring

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2D-DOA for a Monostatic ULA EMVS-MIMO Radar Based on RC-PARAFAC

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IEEE Sensors Journal 2025年

作者： Wang, Jianlong Shi, Junpeng Wen, Fangqing China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hvdroelectric Engineering College of Computer and Information Technology Yichang443002 China National University of Defense Technology College of Electronic Countermeasure Hefei230037 China City University of Macau College of Computer and Information Technology China Three Gorges University Faculty of Data Science 999078 China

The electromagnetic vector sensor (EMVS) embedded multiple-input multiple-output (MIMO) radar is an emerging technology capable of two-dimensional (2D) direction of arrival (DOA) estimation. In this paper, we propose a low-complexity algorithm for monostatic uniform linear array (ULA) EMVS-MIMO radar to rapidly estimate 2D-DOA. First, we decompose the received signal into a third-order tensor using parallel factor analysis (PARAFAC). Next, we eliminate data redundancy through a reduced complexity (RC) process. Then, we use the least squares method to extract ambiguous elevation angle information from the spatial response matrix and apply the vector cross-product (VCP) technique to the electromagnetic vector to achieve unambiguous 2D-DOA estimation. Finally, we combine these estimates to achieve high-resolution, unambiguous elevation angle estimation. The proposed algorithm is suitable for large-scale and sparse EMVS-MIMO radar systems. © 2001-2012 IEEE.

关键词： Direction of arrival

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“Weibing” in traditional Chinese medicine—biological basis and mathematical representation of disease-susceptible state

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Acta Pharmaceutica Sinica B 2025年第5期15卷 2363-2371页

作者： Sun, Wanyang Wang, Rong Ouyang, Shuhua Liang, Wanli Duan, Junwei Gong, Wenyong Hu, Lianting Chen, Xiujuan Li, Yifang Kurihara, Hiroshi Yao, Xinsheng Gao, Hao He, Rongrong Institute of Traditional Chinese Medicine and Natural Products College of Pharmacy/Guangdong Engineering Research Center of Traditional Chinese Medicine & Disease Susceptibility/Guangdong Engineering Research Center of Traditional Chinese Medicine & Health Products/International Cooperative Laboratory of TCM Modernization and Innovative Drug Development of Chinese Ministry of Education (MOE)/Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research/State Key Laboratory of Bioactive Molecules and Druggability Assessment Jinan University Guangzhou 510632 China State Key Laboratory of Quality Research in Chinese Medicine Macau University of Science and Technology Macau 999078 China College of Information Science and Technology Jinan University Guangzhou 510632 China Faculty of Data Science City University of Macau Macau 999078 China Medical Big Data Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou 510632 China The Data Center Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital) Tongji Medical College Huazhong University of Science and Technology Wuhan 430016 China

“Weibing” is a fundamental concept in traditional Chinese medicine (TCM), representing a transitional state characterized by diminished self-regulatory abilities without overt physiological or social dysfunction. This perspective delves into the biological foundations and quantifiable markers of Weibing, aiming to establish a research framework for early disease intervention. Here, we propose the “Health Quadrant Classification” system, which divides the state of human body into health, sub-health, disease-susceptible state, and disease. We suggest the disease-susceptible stage emerges as a pivotal point for TCM interventions. To understand the intrinsic dynamics of this state, we propose laboratory and clinical studies utilizing time-series experiments and stress-induced disease susceptibility models. At the molecular level, bio-omics technologies and bioinformatics approaches are highlighted for uncovering intricate changes during disease progression. Furthermore, we discuss the application of mathematical models and artificial intelligence in developing early warning systems to anticipate and avert the transition from health to disease. This approach resonates with TCM's preventive philosophy, emphasizing proactive health maintenance and disease prevention. Ultimately, our perspective underscores the significance of integrating modern scientific methodologies with TCM principles to propel Weibing research and early intervention strategies forward. © 2025 The Authors

关键词： Disease-susceptible state Mathematical modeling Preventive medicine Traditional Chinese medicine Weibing

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