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Provably Secure Conditional-Privacy Access Control Protocol for Intelligent Customers-Centric Communication in VANET

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1747-1756页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Bashir, Ali Kashif Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BH United Kingdom Woxsen University Woxsen School of Business Hyderabad502345 India Lebanese American University Department of Computer Science and Mathematics Beirut1102 2801 Lebanon Illinois Institute of Technology Information Technology and Management ChicagoIL60555 United States

Globally, the development of Intelligent Cyber-Physical Transportation Systems (ICTS) aims to tackle several challenges, including reducing traffic accidents and fuel usage, alleviating congestion, shortening travel time, and enhancing overall transportation safety. These systems leverage advanced customer-centric communication and networked control methods, such as inter-vehicle, vehicle-to-roadside (V2R), and vehicle-to-vehicle (V2V) communication through the use of vehicular ad hoc networks (VANETs) to cover all aspects of transportation-based information. In existing systems, once devices are registered with a Trusted Authority (TA), subsequent authentication still relies on the TA's assistance. However, these devices typically remain stationary, and frequent interaction with the TA becomes impractical and costly in highly mobile VANET environments. To address this challenge, we propose a secure access control protocol with conditional privacy for VANETs. Unlike other protocols, our protocol does not require the TA's involvement during authentication between vehicles and RSUs. Additionally, our protocol leverages pseudonym mechanisms to provide conditional privacy, enabling legitimate vehicles to remain anonymous while malicious ones can be tracked. Our proposed scheme is supported by both formal and informal security analyses and has been shown to be secure against several known attacks in VANETs. Furthermore, compared to relevant studies, our protocol achieves 30.2505% of efficiency in terms of computation cost and 11.09276% of efficiency in terms of communication cost, respectively. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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A Joint Gradient and Loss Based Clustered Federated Learning Design

A Joint Gradient and Loss Based Clustered Federated Learning...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Lin, Licheng Yang, Zhaohui Wu, Yusen Liu, Yuchen Chen, Mingzhe Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States University of Miami Frost Institute for Data Science and Computing Coral GablesFL33146 United States

ISBN: (纸本)9798350351255

In this paper, a novel clustered FL framework that enables distributed edge devices with non-IID data to independently form several clusters in a distributed manner and implement FL training within each cluster is proposed. In particular, our designed clustered FL algorithm must overcome two challenges associated with FL training. First, the server has limited FL training information (i.e., the parameter server can only obtain the FL model information of each device) and limited computational power for finding the differences among a large amount of devices. Second, each device does not have the data information of other devices for device clustering and can only use global FL model parameters received from the server and its data information to determine its cluster identity, which will increase the difficulty of device clustering. To overcome these two challenges, we propose a joint gradient and loss based distributed clustering method in which each device determines its cluster identity considering the gradient similarity and training loss. The proposed clustering method not only considers how a local FL model of one device contributes to each cluster but also the direction of gradient descent thus improving clustering speed. By delegating clustering decisions to edge devices, each device can fully leverage its private data information to determine its own cluster identity, thereby reducing clustering overhead and improving overall clustering performance. Simulation results demonstrate that our proposed clustered FL algorithm can reduce clustering iterations by up to 99% compared to the existing baseline. © 2024 IEEE.

关键词： Federated learning

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HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning

HiSMatch: Historical Structure Matching based Temporal Knowl...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Li, Zixuan Hou, Zhongni Guan, Saiping Jin, Xiaolong Peng, Weihua Bai, Long Lyu, Yajuan Li, Wei Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Baidu Inc

A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (subject, relation, object, timestamp) to describe dynamic facts. TKG reasoning has facilitated many real-world applications via answering such queries as (query entity, query relation, ?, future timestamp) about future. This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps. In addition, recent KGs provide background knowledge of all the entities, which is also helpful for the matching. Thus, in this paper, we propose the Historical Structure Matching (HiSMatch) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Besides, it adopts another encoder to integrate the background knowledge into the model. TKG reasoning experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6% performance improvement in MRR, compared to the state-of-the-art baselines. © 2022 Association for Computational Linguistics.

关键词： Knowledge graph

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Towards harmless rawlsian fairness regardless of demographic prior 24

Towards harmless rawlsian fairness regardless of demographic...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xuanqian Wang Jing Li Ivor W. Tsang Yew-Soon Ong School of Computer Science and Engineering Beihang University China Institute of High-Performance Computing Agency for Science Technology and Research Singapore and Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High-Performance Computing Agency for Science Technology and Research Singapore and Centre for Frontier AI Research Agency for Science Technology and Research Singapore and College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798331314385

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the training set, namely harmless Rawlsian fairness. We ascertain that such a fairness requirement with no prior demographic information essential promotes training losses to exhibit a Dirac delta distribution. To this end, we propose a simple but effective method named VFair to minimize the variance of training losses inside the optimal set of empirical losses. This problem is then optimized by a tailored dynamic update approach that operates in both loss and gradient dimensions, directing the model towards relatively fairer solutions while preserving its intact utility. Our experimental findings indicate that regression tasks, which are relatively unexplored from literature, can achieve significant fairness improvement through VFair regardless of any prior, whereas classification tasks usually do not because of their quantized utility measurements. The implementation of our method is publicly available at https://***/wxqpxw/VFair.

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Empowering Breast Cancer Detection with AI: A Modified Support Vector Machine Approach for Improved Classification Accuracy 4

Empowering Breast Cancer Detection with AI: A Modified Suppo...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Shiny, K.V. Sujatha, T. N.r, Wilfred Blessing College of Computing and Information Sciences University of Technology and Applied Information Technology Department Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India College of Computing and Information Sciences Univesity of Technology and Applied Sciences Information Technology Department Ibri Oman

ISBN: (纸本)9798350385793

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Maxflow-Based Bounds for Low-Rate Information Propagation over Noisy Networks

arXiv

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arXiv 2023年

作者： Ling, Yan Hao Scarlett, Jonathan Department of Computer Science Department of Mathematics School of Computing National University of Singapore Singapore Institute of Data Science United States

We study error exponents for the problem of low-rate communication over a directed graph, where each edge in the graph represents a noisy communication channel, and there is a single source and destination. We derive maxflow-based achievability and converse bounds on the error exponent that match when there are two messages and all channels satisfy a symmetry condition called pairwise reversibility. More generally, we show that the upper and lower bounds match to within a factor of 4. We also show that with three messages there are cases where the maxflow-based error exponent is strictly suboptimal, thus showing that our tightness result cannot be extended beyond two messages without further assumptions. Copyright © 2023, The Authors. All rights reserved.

关键词： Directed graphs

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EFFICIENT NONLINEAR MULTISCALE SPECTRAL GFEM APPLIED TO COMPOSITE AEROSPACE STRUCTURES 23

EFFICIENT NONLINEAR MULTISCALE SPECTRAL GFEM APPLIED TO COMP...

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23rd International Conference on Composite Materials, ICCM 2023

作者： Bénézech, J. Seelinger, L. Reinarz, A. Dodwell, T.J. Butler, R. Scheichl, R. Material & Structure Research Centre University of Bath Bath United Kingdom Interdisciplinary Center for Scientific Computing Institute for Applied Mathematics Heidelberg University Heidelberg Germany Department of Computer Science Durham University Durham United Kingdom Institute of Data Science and AI University of Exeter Exeter United Kingdom DigiLab Exeter United Kingdom

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Artificial Intelligence based Facial Emotion Recognition with Deep Neural GAN Augmentation 8

Artificial Intelligence based Facial Emotion Recognition wit...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Menaka, D. Priya, D. Karthika Lenus, C. Reeda Ashwini, A. Shermila, P. Josephin Prathaban, Banu Priya Noorul Islam Centre for Higher Education Department of Electronics and Instrumentation Engineering Kumaracoil India Easwari Engineering College Department of Artificial Intelligence and Data Science Ramapuram Chennai India S.A.Engineering College Department of Physics Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India R.M.K.College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India Srm Institute of Science and Technology Kattankulathur School of Computing Department of Networking and Communication Chennai India

ISBN: (纸本)9798350367904

Facial recognition system in computer vision is widely used in human-computer interface and clinical diagnostics. Nonetheless, achieving high accuracy in this task still poses a challenge due to issues of inadequate amount and imbalance of data to train the models as well as issues of generalization and robustness. The findings of this research propose the use of Generative Adversarial Network (GAN) data augmentation for face emotion classification specifically for this approach. The proposed strategy is based on the generation of artificial 'emotional' images on the face using Deep Neural Self Attention GANs (DNSAGANs). This way, to capture all the variations of facial expressions the GAN takes a large number of images and thus makes the augmented data richer and more realistic. This larger data set is then used to train a deep learning architecture that performs the face emotion classification thereby enabling the network to learn enhanced discriminative features. To collect face photos the FER2013 dataset is used. The training process, entrained the generator and discriminator to minimize the mean squared error while the discriminator was simultaneously trained to robustly distinguish generated images from real ones. Altogether, the proposed Adam optimizer was more accurate than Adagrad and Adadelta for all epochs. The percentage of accuracy is about 88.64 % for 100 epochs using the proposed optimizer techniques. © 2024 IEEE.

关键词： Generative adversarial networks

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Timeliness-Aware Computation Offloading Strategies for IIoT Networks

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IEEE Transactions on Network science and Engineering 2025年

作者： Ernest, Tan Zheng Hui Madhukumar, A.S. Singapore Institute of Technology Infocomm Technology Cluster Singapore Nanyang Technological University College of Computing and Data Science Singapore

This paper investigates the peak age of information (PAoI) violation probability and mean PAoI of computation offloading strategies in multi-access edge computing-enabled (MEC-enabled) industrial Internet-of-Things (IIoT) networks. In particular, a comprehensive PAoI analysis framework for computation offloading strategies is proposed in this work. Through closed-form cumulative distribution function (CDF) expressions derived for received signal-to-interference-plus-noise ratios (SINRs) and PAoI arising from tandem M/M/1 queues, new closed-form expressions for PAoI violation probability and mean PAoI are obtained for the uplink timeliness-aware (UTA), joint uplink-and-computing timeliness-aware (JUCTA), and cloud-only (CL) computation offloading strategies. Extensive analysis demonstrate that the proposed UTA and JUCTA strategies outperform the CL strategy in MEC-enabled IIoT networks and are thus viable to support mission-critical IIoT applications. Crucially, it is also shown that the PAoI violation probability and mean PAoI of the considered computation offloading strategies hinges greatly on computation delay, communications radius, and task generation rates. © 2013 IEEE.

关键词： Queueing networks

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GoMatching: a simple baseline for video text spotting via long and short term matching 24

GoMatching: a simple baseline for video text spotting via lo...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haibin He Maoyuan Ye Jing Zhang Juhua Liu Bo Du Dacheng Tao School of Computer Science National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science at Nanyang Technological University

ISBN: (纸本)9798331314385

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets.

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