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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Sakthivel, M. Sivanantham, S. Bharathiraja, N. Bala Krishna, N. Kamalraj, R. Kumar, V. Saravana Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Salem Sankari India Saveetha Institute of Medical and Technical Sciences Department of Cse Saveetha School of Engineering Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Biomedical Engineering Chennai India Mohan Babu University School of Computing Department of Ai&ml Andhra Pradesh Tirupati India University School of Cs and It Jayanagar Bangalore India Sreenidhi Institute of Science and Technology Department of It Hyderabad India

ISBN: (纸本)9798350359688

Website security is a big threat for both large companies and individual developers, the more advanced the technology employed, the more challenging it is to build secure website development procedures. Hackers can easily access vulnerabilities that were not patched during development and that were thereafter distributed. This could result in significant financial losses for the business or the person. Users who visit vulnerable websites may be subject to XSS attacks that could compromise their systems, or an unsecure database configuration may result in a potential data leak, in which case the password of every user who has registered on the website is compromised. Users who use the same password on numerous websites run the danger of identity theft. This study offers the most common vulnerabilities that should be fixed in any application, giving developers the crucial place to start. The overwhelming number of vulnerabilities in each application that is being developed served as the impetus for this project. Every developer, regardless of expertise level, needs a place to start in order to fix any vulnerabilities that may have appeared in their program. The goal of this project is to create a safe online application in accordance with the recommendations of the Open Web Application Security Project (OWSAP). This demonstrates how web application vulnerabilities can be mitigated through coding changes, configuration adjustments, and patch applications. The top ten vulnerabilities according to OWASP are SQL injections, faulty authentication, sensitive data exposure, faulty access control, and XML external entities. The web application's security has been examined and shown to have defenses in place for those vulnerabilities. © 2024 IEEE.

关键词： Information leakage

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Triplet Attention-Enhanced UNet Architectures for Advanced Skin Lesion Segmentation 8

Triplet Attention-Enhanced UNet Architectures for Advanced S...

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8th International Artificial Intelligence and data Processing Symposium, IDAP 2024

作者： Al-Huda, Zaid Al-Antari, Mugahed A. Algburi, Riyadh Nazar Ali Al-Maqtari, Omar Rajeh, Taha M Khan, Ghufran Ahmad Chengdu University Stirling College Chengdu610106 China Sejong University College of Ai Convergence Department of Artificial Intelligence and Data Science Seoul Korea Republic of Al-Farahidi University Aeronautical Technical engineering College of Technical Engineering Baghdad Iraq Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu China Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798331531492

ccurate segmentation of skin lesions is critical the early detection and successful treatment of skin cancer, particularly melanoma, which has a significant impact on patient survival rates. This paper investigates a novel approach to skin lesion segmentation by integrating the Triplet Attention mechanism into UNet-based architectures, aiming to enhance the precision and reliability of automated segmentation in der-moscopic images. The proposed model leverages the strengths of both UNet's encoder-decoder structure and the attention mechanism's ability to focus on critical features while suppressing irrelevant background noise. Additionally, an Adaptive Weighted Loss function is introduced to tackle the class imbalance often present in skin lesion datasets, thereby enhancing segmentation performance. Extensive experiments were conducted using publicly available datasets, demonstrating significant improvements in segmentation accuracy and robustness, particularly in challenging cases with unclear lesion boundaries and varying illumination conditions. Our results indicate that the Triplet Attention-Enhanced UNet model, combined with the Adaptive Weighted Loss function, outperforms existing methods, providing a more reliable tool for dermatologists in the early diagnosis and treatment planning of skin cancer. © 2024 IEEE.

关键词： Dermatology

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Multi-agent Traffic Prediction via Denoised Endpoint Distribution

arXiv

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arXiv 2024年

作者： Liu, Yao Wang, Ruoyu Cao, Yuanjiang Sheng, Quan Z. Yao, Lina School of Computing Macquarie University Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia Data 61 CSIRO & School of Computer Science and Engineering University of New South Wales Sydney Australia

The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering historical features and interactions with surrounding entities, a complexity not as pronounced in lower-speed environments. Prior methods have assessed the spatio-temporal dynamics of agents but often neglected intrinsic intent and uncertainty, thereby limiting their effectiveness. We present the Denoised Endpoint Distribution model for trajectory prediction, which distinctively models agents’ spatio-temporal features alongside their intrinsic intentions and uncertainties. By employing Diffusion and Transformer models to focus on agent endpoints rather than entire trajectories, our approach significantly reduces model complexity and enhances performance through endpoint information. Our experiments on open datasets, coupled with comparison and ablation studies, demonstrate our model’s efficacy and the importance of its components. This approach advances trajectory prediction in high-speed scenarios and lays groundwork for future developments. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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Computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Analyze Decentralized Personal Health data using Solid, Digital Consent, and Federated Learning 14

Analyze Decentralized Personal Health Data using Solid, Digi...

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14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2023

作者： Sun, Chang van Soest, Johan Dumontier, Michel Institute of Data Science Faculty of Science and Engineering Maastricht University Maastricht Netherlands Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Maastricht Netherlands Brightlands Institute of Smart Society Faculty of Science and Engineering Maastricht University Maastricht Netherlands GROW School for Oncology and Reproduction Maastricht University Medical Centre Maastricht Netherlands

TIDAL is a Solid (SOcial Linked data)-based, citizen-centric data platform that facilitates interactions by citizens and researchers for health research. In this demonstration, we will show how TIDAL 1) can store personal data in Solid pods as RDF with well-known health-related vocabularies (e.g., SNOMED CT), 2) controls access to query fine-grained subsets of personal data, 3) enables researchers to post a human- and machine-readable digital consent using data Privacy Vocabulary, and 4) uses federated learning to analyze personal health data from multiple individuals using a Personal Health Train framework. TIDAL offers a new data paradigm of sharing and using personal data for research and ultimately increase the availability of personal data for societal relevant uses. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： data privacy

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A Framework for Identifying the Learners’ Engagement in E-learning Environments Using a Hybrid CNN Architecture

A Framework for Identifying the Learners’ Engagement in E-l...

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International Conference on computing, Electronics & Communications engineering (iCCECE)

作者： Tasfia Nuzhat Anny Jafrin Iqbal Chowdhury Tanveer Ahsan Istiaque Zahur Muhammed J. A. Patwary Mahdi H. Miraz Computer Science and Engineering International Islamic University Chittagong Mechanical Engineering Sonargaon University School of Computing and Data Science Xiamen University Malaysia

In the realm of online education, precise identification of the participants and their engagement is critical for optimal learning results. With the aim of precisely monitoring attendance and engagement in an online learning environment, this study suggests an end-to-end framework based on a hybrid convolutional neural network (CNN) architecture. The complete evaluation employs two separate CNN models: CNN-I for identifying learners’ faces in individual images and CNN-II for measuring engagement through facial expressions, eye gaze and head movement. Two custom datasets have been developed and annotated to allow for an in-depth assessment of the proposed approach. dataset-I contains 4000 individual face images, whereas dataset-II has 6720 images depicting various interactions of the learners, such as engaged, bored, frustrated, confused, drowsy and looking away. However, on dataset-I and the VGG-face dataset, the CNN-I model surpasses a deep CNN (DCNN) with superior accuracy rates of 95.25% and 88.60%, respectively. On dataset-II and the FER2013 dataset, the CNN-II model obtains similarly outstanding accuracy rates of 93.45% and 83.74%, respectively. The specialised CNN-I and CNN-II models outperform the hybrid CNN model in their particular tasks, albeit having an accuracy rate of 88%. The research highlights the enormous potential of straightforward architecture integrated with specific datasets, emphasising their computational effectiveness and efficiency in the domains of engagement and facial recognition for online learning.

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Anti-Compression Contrastive Facial Forgery Detection

arXiv

引用

arXiv 2023年

作者： Huang, Jiajun Zhu, Xinqi Du, Chengbin Ma, Siqi Nepal, Surya Xu, Chang School of Computing Science University of Sydney Australia School of Engineering and Information Technology UNSW Canberra Australia CSIRO Data61 Australia

Forgery facial images and videos have increased the concern of digital security. It leads to the significant development of detecting forgery data recently. However, the data, especially the videos published on the Internet, are usually compressed with lossy compression algorithms such as H.264. The compressed data could significantly degrade the performance of recent detection algorithms. The existing anti-compression algorithms focus on enhancing the performance in detecting heavily compressed data but less consider the compression adaption to the data from various compression levels. We believe creating a forgery detection model that can handle the data compressed with unknown levels is important. To enhance the performance for such models, we consider the weak compressed and strong compressed data as two views of the original data and they should have similar representation and relationships with other samples. We propose a novel anti-compression forgery detection framework by maintaining closer relations within data under different compression levels. Specifically, the algorithm measures the pair-wise similarity within data as the relations, and forcing the relations of weak and strong compressed data close to each other, thus improving the discriminate power for detecting strong compressed data. To achieve a better strong compressed data relation guided by the less compressed one, we apply video level contrastive learning for weak compressed data, which forces the model to produce similar representations within the same video and far from the negative samples. The experiment results show that the proposed algorithm could boost performance for strong compressed data while improving the accuracy rate when detecting the clean data. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Towards trustworthy blockchain systems in the era of “Internet of value”: development, challenges, and future trends

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Science China(Information Sciences) 2022年第5期65卷 250-260页

作者： Hai JIN Jiang XIAO National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Since the advent of cryptocurrencies such as Bitcoin, blockchain, as their underlying technologies,has drawn a massive amount of attention from both academia and the industry. This ever-evolving technology inherits the “genes” of distributed systems, offering significant advantages of immutability, transparency,auditability, and tamper-resistance. These benefits help blockchain re-establish public confidence, and hold the significant promise of reliable information sharing and value transfer. Therefore, blockchain has become the foundation of crucial strategic deployments in countries across the world, and the fundamental basis for building the next generation Web 3.0 — “Internet of value”. In this article, we will start with unraveling the essential ingredients of blockchain technology, and showing the characteristics of each of these ingredients in the context of distributed systems. We will then present the core technical challenges that need to be addressed prior to unleashing its full potential, including its performance, scalability, and cross-chain interoperability. Finally, we will introduce the recent developments of blockchain systems, and discuss the future trends of the blockchain ecosystem.

关键词： blockchain distributed systems internet of value challenges development

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Graph Prompt Clustering

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5794-5805页

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530219 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Beijing100816 China South China Technology Commercialization Center Guangzhou510006 China Guangdong University of Technology School of Information Engineering Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Guangzhou510033 China Ministry of Education Beijing100044 China

Due to the wide existence of unlabeled graph-structured data (e.g., molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations, and seldom refer to the obvious differences in data distributions of distinct graph-level datasets. How to characteristically consider multiple graph-level datasets in a general well-designed model without prior knowledge is still challenging. In view of this, we propose a novel Graph Prompt Clustering (GPC) method. Within this model, there are two main modules, i.e., graph model pretraining as well as prompt and finetuning. In the graph model pretraining module, the graph model is pretrained by a selected source graph-level dataset with mutual information maximization and self-supervised clustering regularization. In the prompt and finetuning module, the network parameters of the pretrained graph model are frozen, and a groups of learnable prompt vectors assigned to each graph-level representation are trained for adapting different target graph-level datasets with various data distributions. Experimental results across six benchmark datasets demonstrate the impressive generalization capability and effectiveness of GPC compared with the state-of-the-art methods. © 1979-2012 IEEE.

关键词： Graph embeddings

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Effective degree of freedom for near-field plane-based XL-MIMO with tri-polarization

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Frontiers of Information Technology & Electronic engineering 2024年第12期25卷 1723-1731页

作者： Zhe WANG Jiayi ZHANG Wenhui YI Huahua XIAO Dusit NIYATO Bo AI School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China College of Computing and Data Science Nanyang Technological UniversitySingapore 639798Singapore State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE CorporationShenzhen 518057China

In this paper we study the effective degree of freedom(EDoF)for extremely large-scale multipleinput multiple-output(XL-MIMO)*** consider two XL-MIMO hardware designs,uniform planar array(UPA)based and continuous aperture(CAP)based XL-MIMO,as well as two representative near-field channel models:scalar Green function based and dyadic Green function with triple polarization based models.

关键词： field planar function

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