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A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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Joint-Wise Distributed Perception Graph Convolutional Network for Skeleton-Based Action Recognition

Joint-Wise Distributed Perception Graph Convolutional Networ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Geng, Qiang Chen, Zhaoyu Li, Xin Li, Yangyang Li, Xing College of Computer Science and Software Engineering Hohai University China College of Information Science and Technology & Artificial Intelligence Nanjing Forestry University China

ISBN: (纸本)9798350368741

Recent studies have achieved remarkable results for action recognition with human skeletal data by utilizing graph convolutional models. Traditional approaches typically aggregate local spatio-temporal information bottom-up to form a single spatio-temporal global understanding. However, this method may fail to achieve multi-location perception for fine-grained action capture and may struggle to model subactions due to their varying durations. To solve these challenges, we design a Distributed Spatio-temporal Perception (DSP) module that innovatively treats each joint as an independent perception unit, performing joint-wise distributed multi-location perception in both spatial and temporal dimensions. In addition, we introduce an Anchor Pose-driven Subaction Encoding (APSE) module, which enhances informative clues for subaction reasoning through identifying the correlations between anchor pose and subactions formed by integrating distributed spatio-temporal perception features. Based on the above work, we propose a Joint-wise Distributed Perception Graph Convolutional Network (JDP-GCN). The experiments on three widely used datasets: NTU RGB+D 60, NTU RGB+D 120, and NW-UCLA, demonstrate that our method achieves state-of-the-art performance. © 2025 IEEE.

关键词： Distributed Perception Graph Convolutional Network Human Action Recognition Skeleton Modality

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models 16

VisioSLR: A Vision Data-Driven Framework for Sign Language V...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Ismail, Leila Shahin, Nada Tesfaye, Henok Hennebelle, Alain Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Transfer learning

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Lunar crater identification based on SA-BRU-Net 8

Lunar crater identification based on SA-BRU-Net

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8th International Conference on Electrical, Mechanical and computer engineering, ICEMCE 2024

作者： Lu, Youbei Wei, Ersa Chen, Jiaqi Liu, Xiangmei Hohai University College of Information Science and Engineering Changzhou China Hohai University College of Computer Science and Software Engineering Changzhou China Hohai University College of Artificial Intelligence and Automation Changzhou China

ISBN: (纸本)9798331506230

Identification of crater impacts is crucial during lunar exploration. Previous methods primarily rely on visual interpretation, which is time-consuming and lacks objective standards. Moreover, these methods often fail to accurately identify small craters. To address these issues, we propose a novel convolutional neural network, SA-BRU-Net (Spatial Attentive Bottleneck Residual U-Net). Building upon U-Net, SA-BRU-Net introduces spatial attention mechanisms and employs bottleneck residual blocks instead of traditional residual blocks. These improvements reduce network parameters while enhancing the identification accuracy of lunar craters. Experimental results demonstrate that our model achieves a recall rate of 81.38% and a precision of 79.03% on DEM images, outperforming other network models. © 2024 IEEE.

关键词： Convolutional neural networks

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Velocity Field-Based Surveillance Video Frame Deletion Detection Using Siamese Network 27th

Velocity Field-Based Surveillance Video Frame Deletion Detec...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Su, Yang Tan, ShunQuan Huang, Jiwu College of Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783031783111

Surveillance videos play a crucial role in providing evidences. However, the deletion of even a few frames can significantly impact the interpretation of events, while the deletion can be performed easily using video editing softwares without leaving visual traces. This paper introduces a novel method to detect video frame deletion based on velocity field characteristics. The main idea is to convert long videos into a feature sequence, which appears as a sequence containing outlier values for videos with frame deletions, and as a sequence without outlier values for original videos. The proposed approach employs a siamese network to distinguish frame deletions between adjacent frames. A video can be transformed into a feature sequence through the siamese network, and the resulting sequence is fed into a binary classifier for final classification. Experimental results demonstrate the effectiveness of our proposed approach, even for longer videos (e.g., 2000 frames) with minimal frame deletions (e.g., 1 frame). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital forensics

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Facial Emotion Recognition using Deep Learning (FERDL) 25

Facial Emotion Recognition using Deep Learning (FERDL)

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25th International Multi Topic Conference, INMIC 2023

作者： Ramzan, Hafiz Arslan Sohaib, Ahmed Ramzan, Sadia Kalsum, Tehmina Aqeel, Muhammad Majeed, Mohsin Khwaja Fareed University of Engineering and Information Technology Institute of Computer and Software Engineering Rahim Yar Khan Pakistan National University of Modern Languages Department of Computer Science Multan Pakistan University of Engineering & Technology Department of Software Engineering Taxila Pakistan

ISBN: (纸本)9798350317701

Facial Emotion Recognition is one of the in-demand and rapidly growing research topics in the domain of computer Vision (CV) and artificial intelligence (AI). The ability to identify or detect human emotions from real-time facial expressions (FEs) has vast conceivable applications in different domains, such as sentiment analysis, human-computer interaction, human resource management, security, and human psychology. In this paper, a Convolutional Neural Network (CNN) based deep learning model is trained with haar-cascade classifier to recognize the real-time FEs. The suggested model is specially trained to categorize the FEs into one of the seven emotion categories, namely six basic emotions (sad, happy, angry, surprised, disgusted, fear) and a neutral emotion. It includes several convolutional layers, as well as fully connected neurons followed by max-pooling layers, and soft-max activation function with the corresponding seven classes. ReLU activation functions along with various kernels to enhance filtering depth, and extraction of facial features. FER-2013 dataset is used for experimentation purpose. To improve the classification performance and model accuracy, a data augmentation technique is used for rescaling and horizontal flipping. The proposed model outperforms the previous related works by achieving a validation accuracy of 71.96% and training accuracy above 90%, with fewer epochs. © 2023 IEEE.

关键词： Emotion Recognition

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Convergence Track Based Adaptive Differential Evolution Algorithm(CTbADE)

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computers, Materials & Continua 2022年第7期72卷 1229-1250页

作者： Qamar Abbas Khalid Mahmood Malik Abdul Khader Jilani Saudagar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdullah AlTameem Mohammed AlKhathami Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Engineering Oakland UniversityRochesterMIUSA Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research *** crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima *** the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying *** adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this *** proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE *** results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE *** algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.

关键词： Differential evolution function optimization convergence track parameter sequence adaptive control parameters

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BFT Consensus Algorithms 10

BFT Consensus Algorithms

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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： Zhang, Xiaojun Zhong, Weiyu Yang, Ce Chen, Lin Liao, Jing Xiong, Naixue QuanZhou University of Information Engineering Software College QuanZhou362000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

With the advent of Bitcoin, the blockchain as the underlying technology of Bitcoin has entered people's field of vision. In recent years, the blockchain and its related technologies have developed rapidly. The consensus algorithm is the key to the realization of blockchain technology. There will be Byzantine nodes in the process of reaching consensus among the nodes in the blockchain. Byzantine nodes are tolerated in the blockchain to achieve consensus. Consensus is an essential performance of consensus algorithms. This paper first introduces blockchain technology and the application of consensus algorithms in it;secondly, introduces the classic PBFT algorithm;then, introduces some existing consensus algorithms;finally summarizes the research status and challenges of consensus algorithms, and analyzes its Prospects. © 2023 IEEE.

关键词： Blockchain

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DDPG Based Dynamic Scheduling Optimization Mechanism in Real Switch 15

DDPG Based Dynamic Scheduling Optimization Mechanism in Real...

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15th International Conference on Communication software and Networks, ICCSN 2023

作者： Wang, Xue Wang, Xingwei Jia, Jie Lu, Xijia Huang, Min School of Computer Science and Engineering Northeastern University China Software College Northeastern University China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350329360

Nowadays, more and more traffic with different service requirements is transmitted simultaneously in the network, resulting in network congestion. In order to improve network Quality of Service (QoS), traffic will be classified and processed according to its requirements. The switch uses fair queuing and its extension schemes to schedule it to reduce the packet loss rate. However, statistical data such as packet arrival rate cannot be accurately obtained in the switches, and discrete reinforcement learning cannot model dynamically changing traffic, making existing dynamic bandwidth allocation solutions challenging to implement. Meanwhile, the same weight configuration will obviously cause the packet loss rate to fluctuate, so it is challenging to design an effective reward function. This paper proposes a dynamic scheduling optimization mechanism based on Deep Deterministic Policy Gradient (DDPG), called DDSO, in an actual switch to schedule flows with different service requirements. Firstly, the flow scheduling problem is modeled as a Markov Decision Process (MDP), and DDPG is used to find the optimal queue weight configuration. Queue weight adjustment is a continuous control problem, and DDPG can solve this problem very well. Secondly, we introduce two one-dimensional arrays to design a new reward function, to reduce the influence of packet loss rate fluctuation on reward determination. At last, this mechanism is evaluated through a hardware testbed in two scenarios. The results show that DDSO can effectively schedule the flows with different service requirements and alleviate network congestion. © 2023 IEEE.

关键词： Reinforcement learning

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Knowledge Graph and Knowledge Reasoning:A Systematic Review

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Journal of Electronic science and Technology 2022年第2期20卷 159-186页

作者： Ling Tian Xue Zhou Yan-Ping Wu Wang-Tao Zhou Jin-Hao Zhang Tian-Shu Zhang Kashi Institute of Electronics and Information Industry Kashi 844099 Shenzhen Institute of Information Technology Shenzhen 518172 the School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054 the School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054

The knowledge graph(KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of KG and KG reasoning is provided. It introduces an overview of KGs, including representation, storage, and essential technologies. Specifically, it summarizes several types of knowledge reasoning approaches, including logic rules-based, representation-based, and neural network-based methods. Moreover, this paper analyzes the representation methods of knowledge hypergraphs. To effectively model hyper-relational data and improve the performance of knowledge reasoning, a three-layer knowledge hypergraph model is proposed. Finally, it analyzes the advantages of three-layer knowledge hypergraphs through reasoning and update algorithms which could facilitate future research.

关键词： methods. representation reasoning

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