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Pairing-Free Blockchain-Assisted Certificateless Aggregation Signcryption Scheme for VANETs

IEEE Internet of Things Journal 2025年第11期12卷 15545-15557页

作者： Wang, Yahui Peng, Cong Jia, Xiaoying Wen, Jiaming Zhang, Yuanyuan Xinyang Normal University School of Computer and Information Technology School of Software Xinyang464000 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China South-Central Minzu University School of Mathematics and Statistics Wuhan430074 China Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China University of Wollongong Institute of Cybersecurity and Cryptology School of Computing and Information Technology WollongongNSW2522 Australia Hubei University of Technology School of computer science Wuhan430072 China

Smart vehicle applications play a crucial role in intelligent transportation systems, enabling sensor-equipped vehicles to establish dynamic networks for efficient collection, sharing, and aggregation. This significantly enhances road security and efficiency by transmitting crucial information to traffic authorities. However, partial research on certificateless aggregation signcryption (CLAS) scheme reveals an intriguing phenomenon where each proposed scheme consistently exhibits numerous security vulnerabilities, particularly susceptible to public key replacement attacks. To address these challenges, this paper proposes a blockchain-assisted certificateless aggregation sign-cryption scheme (BACLAS), leveraging blockchain technology to securely store users' public key on a distributed ledger and prevent public key replacement attacks effectively. Furthermore, it is a provably secure communication scheme for real-world Vehicle-to-Infrastructure (V2I) communication while addressing practical security concerns. The BACLAS scheme ensures security in terms of existential unforgeability against adaptive chosen message attacks (EUF-CMA) and indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) based on the hardness assumption of the elliptic curve discrete logarithm problem and computational Diffie-Hellman problem in the random oracle model. The proposed scheme effectively reduces computational costs and time consumption, resulting in a significant reduction of the computation burden ranging from 50.02% to 88.28% compared to other competitive schemes. Moreover, it successfully addresses the key-escrow problem, thereby achieving enhanced security properties. © 2014 IEEE.

关键词： Vehicular ad hoc networks

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Implementing Asynchronous Jacobi Iteration on GPUs 13

Implementing Asynchronous Jacobi Iteration on GPUs

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13th IEEE/ACM Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems, ScalAH 2022

作者： Tsai, Yu-Hsiang Mike Nayak, Pratik Chow, Edmond Anzt, Hartwig Karlsruhe Institute of Technology Steinbuch Centre for Computing Germany Georgia Institute of Technology School of Computational Science and Engineering United States University of Tennessee Innovative Computing Laboratory United States

ISBN: (纸本)9781665475709

Computation on architectures that feature fine-grained parallelism requires algorithms that overcome load imbalance, inefficient memory access, serialization, and excessive synchronization. In this paper, we explore an algorithm for iteratively solving systems of linear equations that allows for asynchronous updates by different execution units and completely removes the need for synchronization. Methods of this type have been identified as potentially competitive for computations on Exascale machines, but practical implementations for GPU platforms have scarcely been studied. We present an asynchronous Jacobi iteration optimized for high-end GPUs, demonstrate the superiority of the algorithm over a highly tuned synchronous Jacobi iteration, and deploy the algorithm as production-ready implementation in the Ginkgo open source library. The ideas presented here on the algorithm design, implementation and performance can help guide the design of other asynchronous iterative methods on GPUs. © 2022 IEEE.

关键词： Iterative methods

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A Symplectic Discretization Based Proximal Point Algorithm for Convex Minimization

arXiv

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arXiv 2024年

作者： Yuan, Ya-Xiang Zhang, Yi Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

The proximal point algorithm plays a central role in non-smooth convex programming. The Augmented Lagrangian Method, one of the most famous optimization algorithms, has been found to be closely related to the proximal point algorithm. Due to its importance, accelerated variants of the proximal point algorithm have received considerable attention. In this paper, we first study an Ordinary Differential Equation (ODE) system, which provides valuable insights into proving the convergence rate of the desired algorithm. Using the Lyapunov function technique, we establish the convergence rate of the ODE system. Next, we apply the Symplectic Euler Method to discretize the ODE system to derive a new proximal point algorithm, called the Symplectic Proximal Point Algorithm (SPPA). By utilizing the proof techniques developed for the ODE system, we demonstrate the convergence rate of the SPPA. Additionally, it is shown that existing accelerated proximal point algorithm can be considered a special case of the SPPA in a specific manner. Furthermore, under several additional assumptions, we prove that the SPPA exhibits a finer convergence *** Codes 49J52, 65B99, 65K10, 65K15, 68Q25, 90C25, 90C26 © 2024, CC BY.

关键词： Lyapunov functions

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SYMPLECTIC EXTRA-GRADIENT TYPE METHOD FOR SOLVING GENERAL NON-MONOTONE INCLUSION PROBLEM

arXiv

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arXiv 2024年

In recent years, accelerated extra-gradient methods have attracted much attention by researchers, for solving monotone inclusion problems. A limitation of most current accelerated extra-gradient methods lies in their direct utilization of the initial point, which can potentially decelerate numerical convergence rate. In this work, we present a new accelerated extra-gradient method, by utilizing the symplectic acceleration technique. We establish the inverse of quadratic convergence rate by employing the Lyapunov function technique. Also, we demonstrate a faster inverse of quadratic convergence rate alongside its weak convergence property under stronger assumptions. To improve practical efficiency, we introduce a line search technique for our symplectic extra-gradient method. Theoretically, we prove the convergence of the symplectic extra-gradient method with line search. Numerical tests show that this adaptation exhibits faster convergence rates in practice compared to several existing extra-gradient type *** Codes 47J20, 47H05, 65K10, 65K15, 65Y20, 90C30, 90C52 © 2024, CC BY.

关键词： Lyapunov functions

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

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2nd International Conference computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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Jobstats: A Slurm-Compatible Job Monitoring Platform for CPU and GPU Clusters 23

Jobstats: A Slurm-Compatible Job Monitoring Platform for CPU...

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2023 Practice and Experience in Advanced Research computing, PEARC 2023

作者： Plazonic, Josko Halverson, Jonathan Comi, Troy OIT Research Computing Princeton University PrincetonNJ United States Princeton Institute for Computational Science and Engineering Princeton University PrincetonNJ United States OIT Research Computing Department of Chemical and Biological Engineering Princeton University PrincetonNJ United States

ISBN: (纸本)9781450399852

Job monitoring on high-performance computing clusters is important for evaluating hardware performance, troubleshooting failed jobs, identifying inefficient jobs and more. The combination of the Prometheus monitoring framework and the Grafana visualization toolkit has proven successful in recent years. This work shows how four Prometheus exporters can be configured for a Slurm cluster to provide detailed job-level information on CPU/GPU efficiencies and CPU/GPU memory usage as well as node-level Network File System (NFS) statistics and cluster-level General Parallel File System (GPFS) activity. A novel approach was devised to efficiently store a summary of this data in the Slurm database for each completed job. The open-source job monitoring platform introduced here can be used for batch, interactive and Open OnDemand jobs. Several tools are presented that use the Prometheus and Slurm databases to create dashboards, utilization reports and alerts. © 2023 Owner/Author.

关键词： Graphics processing unit

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Brain Tumor Detection and Classification using Vision Transformer (ViT) 3

Brain Tumor Detection and Classification using Vision Transf...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Sundar, G. Naveen Narmadha, D. Jerry, Nivin Abraham Thangavel, Senthil Kumar Shanmugam, Selvanayaki Kolandapalayam Ajibesin, Adeyemi Abel Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Mathematics and Computer Science Ashland University United States Department of Information Technology Cape Peninsula University of Technology South Africa

ISBN: (纸本)9798331532420

TThis study uses the Vision Transformer (ViT) architecture to present a sophisticated approach for brain tumor identification and classification. ViT models are assessed based on their capacity to extract global contextual information from MRI images. With an average accuracy of 97%, the refined ViT models surpass traditional CNN-based models on a dataset of brain tumor MRI images classified into glioma, meningioma, pituitary, and no-tumor classes. This study shows how ViT models can increase diagnostic precision, which makes them a good substitute for actual medical imaging applications. © 2024 IEEE.

关键词： Brain mapping

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Evidence for validity and reliability of a research-based assessment instrument on measurement uncertainty

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Physical Review Physics Education Research 2024年第2期20卷 020125页

作者： Gayle Geschwind Michael Vignal Marcos D. Caballero H. J. Lewandowski National Institute of Standards and Technology and the University of Colorado Boulder Colorado 80309 USA Department of Physics Department of Physics and Astronomy and CREATE for STEM Institute Department of Computational Mathematics Science and Engineering Department of Physics and Center for Computing in Science Education

The Survey of Physics Reasoning on Uncertainty Concepts in Experiments (SPRUCE) was designed to measure students’ proficiency with measurement uncertainty concepts and practices across ten different assessment objectives to help facilitate the improvement of laboratory instruction focused on this important topic. To ensure the reliability and validity of this assessment, we conducted a comprehensive statistical analysis using classical test theory. This analysis includes an evaluation of the test as a whole, as well as an in-depth examination of individual items and assessment objectives. We make use of a previously reported on scoring scheme involving pairing items with assessment objectives, creating a new unit for statistical analysis referred to as a “couplet.” The findings from our analysis provide evidence for the reliability and validity of SPRUCE as an assessment tool for undergraduate physics labs. This increases both instructors’ and researchers’ confidence in using SPRUCE for measuring students’ proficiency with measurement uncertainty concepts and practices to ultimately improve laboratory instruction. Additionally, our results using couplets and assessment objectives demonstrate how these can be used with traditional classic test theory analysis.

关键词： Assessment Research methodology

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Molecular exchange and passivation at interface afford high-performing perovskite solar cells with efficiency over 24%

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Journal of Energy Chemistry 2023年第7期82卷 219-227,I0005页

作者： Jianjun Sun Wangchao Chen Yingke Ren Yunjuan Niu Zhiqian Yang Li'e Mo Yang Huang Zhaoqian Li Hong Zhang Linhua Hu Key Laboratory of Photovoltaic and Energy Conservation Materials Institute of Solid State PhysicsHFIPSChinese Academy of SciencesHefei 230031AnhuiChina School of Chemistry and Chemical Engineering Anhui Province Key Laboratory of Advanced Catalytic Materials and Reaction EngineeringHefei University of TechnologyHefei 230009AnhuiChina College of Science Hebei University of Science and TechnologyShijiazhuang 050018HebeiChina Hebei Computational Optical Imaging and Photoelectric Detection Technology Innovation Center Hebei International Joint Research Center for Computational Optical Imaging and Intelligent SensingSchool of Mathematics and Physics Science and EngineeringHebei University of EngineeringHandan 056038HebeiChina University of Science and Technology of Chin1 Anhui 230026HefeiChina

The interface is crucial for perovskite solar cells(PSCs).However,voids at interfaces induced by the trapped hygroscopic dimethyl sulfoxide(DMSO)can reduce charge extraction and accelerate the film degradation,seriously damaging the efficiency and *** this work,4,4’-dinonyl-2,2’-dipyridine(DN-DP),a Lewis base with long alkyl chains is introduced to solve this *** calculated and experimental results confirm that the dipyridyl group on DN-DP can more strongly coordinate with Pb^(2+)than that of the S=O group on *** strong coordination effect plays a crucial role in removing the DMSO-based adduct and reducing the formation of *** to the electron-donating properties of pyridine,the existence of DN-DP in the perovskite film can passivate the defects and optimize the energy level alignment of the perovskite *** open-circuit voltage(VOC)of the DN-DP-based PSC is improved from 1.107 V(control device)to 1.153 V,giving rise to a power conversion efficiency(PCE)of24.02%.Furthermore,benefiting from the moisture resistance stemming from the hydrophobic nonyl group,the PCE retains 90.4%of the initial performance after 1000 h of storage in the ambient condition.

关键词： Perovskite solar cell Anti-solvent 4 4′-Dinonyl-2 2′-dipyridine Optimized interfaceStability

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A Deep Learning Framework with Attention Mechanism for the Classification of Mild Cognitive Impairment using Brain MRI

A Deep Learning Framework with Attention Mechanism for the C...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： T. Illakiya B. Vidhya S. Prince Chelladurai S. Hariprasad Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Mild Cognitive Impairment (MCI) refers to a neurological disorder marked by observable changes in cognitive decline. MCI signifies an intermediate phase linking normal aging and Alzheimer's Disease (AD). It is typically categorized into two types: Early MCI (EMCI) and Late MCI (LMCI), representing varying levels of cognitive impairment. An effective classification between EMCI and LMCI is vital, as it allows timely interventions that can potentially slow the progression of AD, improving patient outcomes and quality of life. Identifying the subtle features and patterns in EMCI and LMCI is essential for early diagnosis and effective intervention. This work proposes a novel Deep Learning (DL) network integrating EfficientNet-B0, a Global Context Network (GCN), and Neighborhood Attention blocks. The EfficientNet-B0 acts as the foundation for feature extraction in the proposed model. It uses its efficient architecture and pre-trained capabilities to capture meaningful patterns. To further enhance feature representation, a Global Context Network (GCN) is employed to capture long-range dependencies and global semantic information. Additionally, a Neighborhood Attention Module is used to refine local features by focusing on important regions in the MRI scans. The proposed framework balances global semantic understanding and localized feature extraction, resulting in improved classification performance. With systematic training and evaluation, the model achieves an accuracy of 95.3%, outperforming existing approaches and providing a better solution for neuroimaging-based MCI subtype classification.

关键词： Deep learning Training Accuracy Systematics Magnetic resonance imaging Visual analytics Semantics Feature extraction Brain modeling Convolutional neural networks

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