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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Otoum, Yazan Singh, Paritosh Nayak, Amiya Algoma University School of Computer Science and Technology Canada University of Ottawa School of Electrical Engineering and Computer Science Canada

ISBN: (纸本)9798350351255

The Internet of Medical Things (IoMT) plays a pivotal role in healthcare, connecting a myriad of medical devices and applications for efficient patient care. However, the rising prevalence of cyberattacks targeting healthcare institutions for patient data underscores the critical need for robust security measures. This paper introduces a novel Homogenous Collaborative Machine Learning (HCML)-based model to enhance the security of healthcare-connected devices. Utilizing a Deep Neural Network (DNN) algorithm, our model constructs a tailored security framework by integrating multiple device 'edges', enhancing the system's ability to thwart cyber threats. We investigate the impact of incorporating additional edges on the model's performance, employing various metrics and assessing execution times with the IoT healthcare security dataset. Our findings reveal that, compared to conventional centralized learning methods, our proposed HCML model achieves superior generalization, incremental learning, and performance enhancement while maintaining stringent data privacy. This research contributes significantly to the IoMT field by providing a scalable and robust security solution adaptable to the evolving landscape of cyber threats. © 2024 IEEE.

关键词： Cyber attacks

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SCMotion: Predicting Human Motion Through Spatial Dependence and Center Anchoring 7

SCMotion: Predicting Human Motion Through Spatial Dependence...

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7th International Conference on computer Information science and Application technology, CISAT 2024

作者： Chen, Siqi Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350375107

Human motion prediction aims to predict future human motion based on past observations, playing important roles in several fields. However, previous works have often focused on the temporal sequential nature of human motion while neglecting the coordinated linkage of multiple components of the human body in space. Therefore, we introduce SCMotion, focused on predicting human motion using spatial dependencies. We divide the human body joints into four joint groups and predict each module individually. Additionally, SCMotion uses the more stable center of gravity of the human body as an anchor point for prediction, greatly enhancing accuracy. Our experiments on the H3.6M dataset demonstrate the excellent prediction capabilities of SCMotion. © 2024 IEEE.

关键词： Graph Convolutional Networks Human Motion Prediction Humanrobot Interaction

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Method for Relation Extraction Based on Entity Boundary Features 24

Method for Relation Extraction Based on Entity Boundary Feat...

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2024 7th International Conference on computer Information science and Artificial Intelligence, CISAI 2024

作者： Wang, Zining Ge, Jike Yang, Xiaolu Tan, Jie Xiang, Yu School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China

ISBN: (纸本)9798400707254

As a key task in natural language processing, the current knowledge extraction methods mostly involve joint extraction, simultaneously extracting named entities and relationships. When conducting relationship extraction, entities containing multiple words are usually represented using pooling or weighted sum methods, leading to the neglect of entity boundary information. To address the issue of reduced accuracy in relationship classification due to the introduction of intermediate noise information based on entity segment features, a relationship extraction method focusing on entity boundary features is proposed. Firstly, the semantic representation of the text is obtained using the pre-trained language model DeBERTa. Then, features of the head and tail positions of entities in the text are extracted and concatenated into a composite embedding vector containing information from these two key positions, used to enhance entity representation. By concatenating the composite embedding vectors of the head and tail entities, the final embedding vector of the entity pair is obtained, representing the information of each entity and enhancing the model’s understanding of entity boundaries and semantic information. Finally, the existence of relationships between entity pairs is determined by the relationship discriminator and relationship classifier, simultaneously classifying the relationship types of the entity pairs for relationship classification. Experimental results show that the proposed method achieves F1 scores of 89.3%, 94.4%, 94.2%, and 94.9% on the NYT, NYT*, WebNLG, and WebNLG* datasets, respectively, significantly outperforming the baseline models. Additionally, ablation experiments demonstrate the importance of the relationship discriminator, further confirming the superiority of the relationship extraction method focusing on entity boundary features. © 2024 Copyright held by the owner/author(s).

关键词： Boundary features

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Introduction

Signals and Communication Technology

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Signals and Communication technology 2024年 Part F1492卷 3-17页

作者： Tian, Yu-Chu Gao, Jing School of Computer Science Queensland University of Technology BrisbaneQLD Australia College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China

The main theme of this book is network analysis and architecture design for network planning. While there are various types of networks, this book specifically focuses on computer networks for data communication. Therefore, it uses the term networks to specifically refer to computer networks unless specified explicitly otherwise. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Automatic text inpainting and quality elevation in video sequences

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Multimedia Tools and Applications 2024年 1-34页

作者： Palivela, Lakshmi Harika Dharmalingam, Vivekanandan Gayathri, D. Bala School of Computer Science and Engineering Vellore Institute of Technology Chennai Vandalur-Kelambakkam Road Chennai600127 India Department of Information Technology Madras Institute of Technology Anna University Chennai India Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute Of Science and Technology Chennai India

Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text containing text strokes with their hazy backgrounds. Text in the real world uses a variety of font kinds, some of which are difficult to localize due to their chaotic shapes, varied shading degrees, and orientation *** text erasing may include the subtasks of text detection as well as text inpainting. Both subtasks require a large amount of data to be successful;but, existing approaches were limited by insufficient real-world data for scene-text elimination. Eventhough the existing works produced considerable performance improvement in scene text removal, they often leave many text remains like text strokes, thus producinglow-quality visual outcomes. Therefore, this paper proposes an automatic text inpainting and video quality elevation model by using the Improved Convolutional Network-based ***, the video samples are collected from the diverse datasets and then converted into frames. Next, the frames are deblurred using an enhanced Convolutional Neural Network (CNN) model that has three convolutional layers for accurately localizing the texts in frames. Subsequently, the texts are detected by utilizing the CLARA-based VGG-16 network. Afterward, the text strokes are removed using a convolutional Encoder and decoder network to eliminate the presence of text on complex backgrounds and textures. Here, the coordinates of text in the deblurred frames are used to crop out the text stroke regions. So, the texts are in-painted, and then, the text in-painted regions are pasted back to their original positions in the frames. Furthermore, the video quality is elevated with the help of the DenseNet-centric Enhancement network. The experimental outcomes demonstrate that the proposed model effectively removed scene texts and enhanced the video qu

关键词： Convolutional neural networks

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An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

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Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of Software Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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Blockchain-Enabled Federated Learning with Differential Privacy for Internet of Vehicles

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computers, Materials & Continua 2024年第10期81卷 1581-1593页

作者： Chi Cui Haiping Du Zhijuan Jia Yuchu He Lipeng Wang School of Information Science and Technology Zhengzhou Normal UniversityZhengzhou450044China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2500Australia

The rapid evolution of artificial intelligence(AI)technologies has significantly propelled the advancement of the Internet of Vehicles(IoV).With AI support,represented by machine learning technology,vehicles gain the capability to make intelligent *** a distributed learning paradigm,federated learning(FL)has emerged as a preferred solution in *** to traditional centralized machine learning,FL reduces communication overhead and improves privacy *** these benefits,FL still faces some security and privacy concerns,such as poisoning attacks and inference attacks,prompting exploration into blockchain integration to enhance its security *** paper introduces a novel blockchain-enabled federated learning(BCFL)scheme with differential privacy(DP)tailored for *** order to meet the performance demanding IoV environment,the proposed methodology integrates a consortium blockchain with Practical Byzantine Fault Tolerance(PBFT)consensus,which offers superior efficiency over the conventional public *** addition,the proposed approach utilizes the Differentially Private Stochastic Gradient Descent(DP-SGD)algorithm in the local training process of FL for enhanced privacy *** results indicate that the integration of blockchain elevates the security level of FL in that the proposed approach effectively safeguards FL against poisoning *** the other hand,the additional overhead associated with blockchain integration is also limited to a moderate level to meet the efficiency criteria of ***,by incorporating DP,the proposed approach is shown to have the(ε-δ)privacy guarantee while maintaining an acceptable level of model *** enhancement effectively mitigates the threat of inference attacks on private information.

关键词： Blockchain federated learning differential privacy Internet of Vehicles

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in engineering & sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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QPFFL: Advancing Federated Learning with Quantum-Resistance, Privacy, and Fairness

QPFFL: Advancing Federated Learning with Quantum-Resistance,...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Ran Li, Hongwei Qian, Xinyuan Liu, Xiaoyuan Jiang, Wenbo University of Electronic Science and Technology of China School of Computer Science and Engineering China

ISBN: (纸本)9798350351255

Federated Learning (FL) has gained prominence for collaborative training across multiple devices without data sharing. However, traditional FL overlooks two crucial aspects: collaborative fairness and privacy protection. Typically, all participants receive the same models, regardless of their contribution, and plaintext transmission of model gradients risks privacy. Existing fairness-enhancing approaches often increase privacy risks, while security-focused methods suffer from efficiency limitations, failing to provide a comprehensive solution against multiple threats simultaneously. To address these challenges, we propose QPFFL, a novel fair and secure FL framework. Firstly, we propose Privacy-Preserving Reputation Mechanism (PPRM) that assigns global models to users based on their performance during training, promoting fairness of FL. We employ Functional Encryption (FE) to enable efficient and quantum-resistant aggregation, securing user model parameters. Furthermore, a reputation threshold helps identify malicious behaviors. Theoretical analysis and experiments demonstrate QPFFL's effectiveness in thwarting various attacks without compromising privacy and efficiency, thereby providing a comprehensive solution for secure and fair FL. © 2024 IEEE.

关键词： Federated learning

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Provable Data Possession with Outsourced Tag Generation for AI-Driven E-Commerce

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computers, Materials & Continua 2025年第5期83卷 2719-2734页

作者： Yi Li Wenying Zheng Yu-Sheng Su Meiqin Tang School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China Department of Computer Science and Information Engineering National Chung Cheng UniversityChiayi621301TaiwanChina Advanced Institute of Manufacturing with High-tech Innovations National Chung Cheng UniversityChiayi621301TaiwanChina Department of Computer Science and Engineering Taiwan Ocean UniversityKeelung202301TaiwanChina School of Integrated Circuits Wuxi Vocational College of Science and TechnologyWuxi214028China

AI applications have become ubiquitous,bringing significant convenience to various *** e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy ***,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI ***,it is essential to verify the integrity of e-commerce *** existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in *** address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded ***,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its ***,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.

关键词： Provable data possession data auditing cloud computing e-commerce bloom filter

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