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arXiv 2023年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung The Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China The Department of Systems and Enterprises Stevens Institute of Technology United States School of Science and Technology Hong Kong Metropolitan University Hong Kong College of Engineering and Computer Science VinUni-Illinois Smart Health Center VinUniversity Viet Nam Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Game Design Faculty of Arts Uppsala University Sweden The School of Software Tiangong University Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer’s disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. Copyright © 2023, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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UAV-enabled social internet of vehicles: Roles, security issues and use cases 6th

UAV-enabled social internet of vehicles: Roles, security iss...

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6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020

作者： Tang, Chaogang Wei, Xianglin Liu, Chong Jiang, Haifeng Wu, Huaming Li, Qing School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China National University of Defense Technology Changsha410073 China The School of Engineering and Applied Science The George Washington University WashingtonDC20052 United States Center for Applied Mathematics Tianjin University Tianjin300072 China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9789811590306

Social internet of vehicle (SIoV), also termed vehicular social network (VSN), endeavors to integrate social networking related concepts into IoV, with an aim to make vehicles capable of social communication and low-cost infotainment service provisioning. In spite of potential prospects, some issues pertaining to SIoV remain to be addressed such as security and privacy. Specifically, we in this paper propose an Unmanned Aerial Vehicles (UAVs) enabled security framework to protect the security and privacy of SIoV. On one hand, we split the evolvement of SIoV into two phases and elaborate the roles and functionalities of vehicles in each stage;on the other hand, owing to high flexibility, fast deployment, and low-cost maintainability, we incorporate UAVs into SIoV with the purpose of accomplishing multiple functions including communication range extension, data processing improvement and security protection. Use cases are also given in hope to provide some insights within UAV enabled SIoV. © Springer Nature Singapore Pte Ltd 2020.

关键词： Unmanned aerial vehicles (UAV)

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IoT-ID: Robust IoT Device Identification Based on Feature Drift Adaptation

IoT-ID: Robust IoT Device Identification Based on Feature Dr...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Qi Chen Yubo Song Brendan Jennings Fan Zhang Bin Xiao Shang Gao Key Laboratory of Computer Network Technology of Jiangsu Province School of Cyber Science and Engineering Southeast University Purple Mountain Laboratories Telecommunications Software & Systems Group Waterford Institute of Technology The Hong Kong Polytechnic University

ISBN: (纸本)9781728181059

Internet of Things (IoT) devices deployed in publicly accessible locations increasingly encounter security threats from device replacement and impersonation attacks. Unfortunately, the limited memory and poor computing capability on such devices make solutions involving complex algorithms or enhanced authentication protocols untenable. To address this issue, device identification technologies based on traffic characteristics finger-printing have been proposed to prevent illegal device intrusion and impersonation. However, because of time-dependent distribution of traffic characteristics, these approaches often become less accurate over time. Meanwhile insufficient attention has been paid to the impact of possible changes on the accuracy of device identification. Therefore, we propose a novel feature selection method based on degree of feature drift and genetic algorithm to keep high accuracy and stability of device identification. The degree of feature drift— relevance of features through time and gain ratio are combined as a composite metric to filter out stable features. Furthermore, in order to perform equally well in device identification, we use the genetic algorithm to select the most discriminate feature subset. Experiments show that the accuracy of device recognition compared with other methods is increased from 86.4% to 94.5%, and the robustness of recognition is also improved.

关键词： Performance evaluation Protocols Fingerprint recognition Feature extraction Robustness Classification algorithms Object recognition

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Intrusion Detection Mechanism Based On Modular Neural Network

Intrusion Detection Mechanism Based On Modular Neural Networ...

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International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Hongyu Luo Kai Shi Fuqiang Qiao Yinghao Li School of Computer Science and Engineering Tianjin University of Technology Tianjin China College of Software and Communications Tianjin Sino-German University of Applied Sciences Tianjin China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

Nowadays, the complexity and diversity of network security issues have caused intrusion detection to become an important research topic. Aiming at the problems of traditional intrusion detection mechanisms, the modular neural network intrusion detection mechanism based on K-means similarity judgment is proposed. The method uses the Meanshift-Kmeans clustering algorithm to calculate the clustering center and data classification, then uses the similarity to determine the dynamic allocation of sub-modules, and finally uses the classifiers trained according to different types of intrusion to achieve intrusion detection. Through experiments on two different intrusion data sets, the experimental results prove the good detection efficiency and model stability of the model.

关键词： Adaptation models Heuristic algorithms Neural networks Intrusion detection Data models Stability analysis Security

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Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation

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Knowledge-Based Systems 2025年 324卷

作者： Qiangguo Jin Hui Cui Junbo Wang Changming Sun Yimiao He Ping Xuan Linlin Wang Cong Cong Leyi Wei Ran Su School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China Department of Computer Science and Information Technology La Trobe University Melbourne Australia CSIRO Data61 Sydney Australia Department of Computer Science School of Engineering Shantou University Guangdong China Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Shandong China Australian Institute of Health Innovation (AIHI) Macquarie University Australia School of Software Shandong University Shandong China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University Macao Special Administrative Region of China School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

Semi-supervised learning (SSL) has attracted considerable attention in medical image processing. The latest SSL methods use a combination of consistency regularization and pseudo-labeling to achieve remarkable success. However, most existing SSL studies focus on segmenting large organs, neglecting the challenging scenarios where there are numerous tumors or tumors of small volume. Furthermore, the extensive capabilities of data augmentation strategies, particularly in the context of both labeled and unlabeled data, have yet to be thoroughly investigated. To tackle these challenges, we introduce a straightforward yet effective approach, termed iterative pseudo-labeling based adaptive copy-paste supervision (IPA-CP), for tumor segmentation in CT scans. IPA-CP incorporates a two-way uncertainty based adaptive augmentation mechanism, aiming to inject tumor uncertainties present in the mean teacher architecture into adaptive augmentation. Additionally, IPA-CP employs an iterative pseudo-label transition strategy to generate more robust and informative pseudo labels for the unlabeled samples. Extensive experiments on both in-house and public datasets show that our framework outperforms state-of-the-art SSL methods in medical image segmentation. Ablation study results demonstrate the effectiveness of our technical contributions.

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Correction: HCLR-Net: Hybrid Contrastive Learning Regularization with Locally Randomized Perturbation for Underwater Image Enhancement

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International Journal of computer Vision 2024年第11期132卷 5490-5490页

作者： Zhou, Jingchun Sun, Jiaming Li, Chongyi Jiang, Qiuping Zhou, Man Lam, Kin-Man Zhang, Weishi Fu, Xianping School of Information Science and Technology Dalian Maritime University Dalian Liaoning China College of Computer Science Nankai University Tianjin China School of Information Science and Engineering Ningbo University Ningbo China S-Lab Nanyang Technological University Singapore Singapore Department of Electrical and Electronic Engineering Hong Kong Polytechnic University Hong Kong China

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Deep Denoising Sparse Coding

Deep Denoising Sparse Coding

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yijie Wang Bo Yang School of Software Engineering Xi'an Jiaotong University Xi'an China School of Computer Science Xi'an Polytechnic University Xi'an China

ISBN: (数字)9781728192284

ISBN: (纸本)9781728185361

Single-cell Ribonucleic Acid sequencing (scRNA-seq) has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. Clustering transcriptomes profiled by scRNA-seq has been routinely conducted to reveal cell heterogeneity and diversity. In fact, scRNA-seq data contain an abundance of dropout events that lead to zero expression measurements. These dropout events may be the result of technical sampling effects or real biology arising from stochastic transcriptional activity. Therefore clustering analysis of scRNA-seq data remains a statistical and computational challenge. Here, we have developed Deep Denoising Sparse Coding (DDSC), a deep clustering method combine autoencoder and sparse coding approach. Based on six real datasets from five representative single-cell sequencing platforms, DDSC outperformed some state-of-the-art methods under various clustering performance metrics and exhibited improved scalability. Its accuracy and efficiency make DDSC a promising algorithm for clustering large-scale scRNA-seq data.

关键词： Sequential analysis Clustering methods Noise reduction Clustering algorithms Encoding Sparse matrices Unsupervised learning

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Multi-modal data information alignment based SOH estimation for lithium-ion batteries using a local–global parallel CNN-Transformer Network

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Journal of Energy Storage 2025年 129卷

作者： Xuanang Gui Junrong Du Qianlong Wang Huan Zhao Yuheng Cheng Junhua Zhao School of Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 China National Computer System Engineering Research Institute of China Beijing 100083 China Business School Nankai University Tianjin 300071 China Department of Building Environment and Energy Engineering The Hong Kong Polytechnic University Hong Kong China

Accurate State of Health (SOH) estimation of lithium-ion batteries is crucial for safe and reliable operation. However, real-world applications often lack comprehensive labeled data across diverse operating conditions, hindering data-driven methods’ performance in unseen scenarios. To address this, we propose a novel Multi-Modal Data Information Alignment Local–Global Parallel CNN-Transformer Network (MM-LG-CNNT) for cross-domain SOH estimation. MM-LG-CNNT leverages abundant labeled data from source domain to improve accuracy in target domain with limited unlabeled data. Unlike traditional methods relying solely on raw sequential data, our framework incorporates multi-modal data: raw degradation sequences and their corresponding image representations from recurrence plots to capture both the local and global degradation information. A multi-information alignment strategy, combining Maximum Mean Discrepancy (MMD), Correlation Alignment (CORAL), and adversarial learning, aligns both data structure and domain label information to improve the estimation accuracy. A parallel CNN-Transformer architecture efficiently extracts local degradation patterns and long-range dependencies. Extensive experiments demonstrate the effectiveness and superiority of MM-LG-CNNT for SOH estimation. The proposed framework offers a promising solution for practical, accurate, and robust SOH estimation under limited data availability.

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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Dual Representation Learning for One-Step Clustering of Multi-View Data

arXiv

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arXiv 2022年

作者： Zhang, Wei Deng, Zhaohong Choi, Kup-Sze Wang, Jun Wang, Shitong The School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Media Design and Software Technology Wuxi214122 China The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China ZJLab Shanghai200433 China The Centre for Smart Health The Hong Kong Polytechnic University Hong Kong The Key laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 China

Multi-view data are commonly encountered in data mining applications. Effective extraction of information from multi-view data requires specific design of clustering methods to cater for data with multiple views, which is non-trivial and challenging. In this paper, we propose a novel one-step multi-view clustering method by exploiting the dual representation of both the common and specific information of different views. The motivation originates from the rationale that multi-view data contain not only the consistent knowledge between views but also the unique knowledge of each view. Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole. With this framework, the representation learning and clustering partition mutually benefit each other, which effectively improves the clustering performance. Results from extensive experiments conducted on benchmark multi-view datasets clearly demonstrate the superiority of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Cluster analysis

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