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arXiv 2025年

作者： Wang, Jinping Song, Weiwei Chen, Hao Ren, Jinchang Zhao, Huimin School of Computer Sciences Guangdong Polytechnic Normal University Guangzhou510665 China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangdong Polytechnic Normal University Guangzhou510665 China Peng Cheng Laboratory Shenzhen518000 China Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom School of Computer Sciences Guangdong Polytechnic Normal University Guangzhou510640 China National Subsea Centre School of Computing Engineering and Technology Robert Gordon University AberdeenAB10 7AQ United Kingdom

World models significantly enhance hierarchical understanding, improving data integration and learning efficiency. To explore the potential of the world model in the remote sensing (RS) field, this paper proposes a label-efficient remote sensing world model for multimodal data fusion (FusDreamer). The FusDreamer uses the world model as a unified representation container to abstract common and high-level knowledge, promoting interactions across different types of data, i.e., hyperspectral (HSI), light detection and ranging (LiDAR), and text data. Initially, a new latent diffusion fusion and multimodal generation paradigm (LaMG) is utilized for its exceptional information integration and detail retention capabilities. Subsequently, an open-world knowledge-guided consistency projection (OK-CP) module incorporates prompt representations for visually described objects and aligns language-visual features through contrastive learning. In this way, the domain gap can be bridged by fine-tuning the pre-trained world models with limited samples. Finally, an end-to-end multitask combinatorial optimization (MuCO) strategy can capture slight feature bias and constrain the diffusion process in a collaboratively learnable direction. Experiments conducted on four typical datasets indicate the effectiveness and advantages of the proposed FusDreamer. The corresponding code will be released at https://***/ Cimy-wang/FusDreamer. © 2025, CC BY.

关键词： Contrastive Learning

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Siamese Generative Adversarial Predicting Network for Extremely Sparse data in Recommendation System

Siamese Generative Adversarial Predicting Network for Extrem...

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IEEE International Conference on Big data and Cloud computing (BdCloud)

作者： Qingxian Wang Renjian Zhang Kangkang Ma Bo Chen Jiufang Chen Xiaoyu Shi School of Computer Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China Intelligent Terminal Key Laboratory of SiChuan Province Yibin China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

In the recommendation system, user-item preferences are described by a High-Dimensional and Sparse (HiDS) matrix. Collaborative Filtering (CF)-based models have been widely adopted to solve unknown entries estimation. However, a CF-based model does not take the data distribution characteristic of an HiDS matrix into account, thereby its representation ability limited. To address this issue, this paper proposes to build a Siamese Generative Adversarial Predicting Network (SGAPN) to estimate the unknown entries in an HiDS matrix. Firstly, we build a model to learn the data distribution characteristic of an HiDS matrix. Secondly, based on the learned data distribution and observed entries, we build a model to estimate the unknown entries. Compared with the CF-based model, our model takes the data distribution of an HiDS matrix into account to address the unknown entries estimation. Experimental results over four HiDS matrices generated by industrial applications demonstrate that compared with several state-of-the-art models, the proposed model achieves competitive prediction accuracy.

关键词： Training Collaborative filtering Estimation Predictive models data models Sparse matrices

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Trust and Risk Based on Access Control Model in Social Internet of Things

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International Journal of Network Security 2023年第5期25卷 736-744页

作者： Zhang, Hongbin Liu, Jian Zhao, Dongmei Liu, Bin Wang, Yanmei Fan, Fan School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Scienc and Technology Shijiazhuang050000 China Hebei Geological Worker’s University Shijiazhuang050000 China

Integrating social networks and the Internet of Things has formed an emerging field of Social Internet of Things (IoT). Aiming to address the shortcomings of traditional TBAC (Trust based on Access Control) and ABAC (Attribute based on Access Control) in dynamic authorization and security, this paper proposes a hybrid access control model based on trust and risk for SIoT. The model supports social relationship-based trust assessment and risk prediction between SIoT nodes and integrates trust and risk values into SIoT security values through reasonable weight allocation. Furthermore, by simulating the safety requirements of different scenarios, it was verified that the model was efficient and more secure than the current models. © (2023). All Rights Reserved.

关键词： Authorization

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A Large-scale Multiple-objective Method for Black-box Attack against Object Detection

arXiv

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arXiv 2022年

作者： Liang, Siyuan Li, Longkang Fan, Yanbo Jia, Xiaojun Li, Jingzhi Wu, Baoyuan Cao, Xiaochun State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Data Science Secure Computing Lab of Big Data The Chinese University of Hong Kong Shenzhen China Tencent AI Lab Shenzhen China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen China

Recent studies have shown that detectors based on deep models are vulnerable to adversarial examples, even in the black-box scenario where the attacker cannot access the model information. Most existing attack methods aim to minimize the true positive rate, which often shows poor attack performance, as another sub-optimal bounding box may be detected around the attacked bounding box to be the new true positive one. To settle this challenge, we propose to minimize the true positive rate and maximize the false positive rate, which can encourage more false positive objects to block the generation of new true positive bounding boxes. It is modeled as a multi-objective optimization (MOP) problem, of which the generic algorithm can search the Pareto-optimal. However, our task has more than two million decision variables, leading to low searching efficiency. Thus, we extend the standard Genetic Algorithm with Random Subset selection and Divide-and-Conquer, called GARSDC, which significantly improves the efficiency. Moreover, to alleviate the sensitivity to population quality in generic algorithms, we generate a gradient-prior initial population, utilizing the transferability between different detectors with similar backbones. Compared with the state-of-art attack methods, GARSDC decreases by an average 12.0 in the mAP and queries by about 1000 times in extensive experiments. Our codes can be found at https://***/LiangSiyuan21/ GARSDC. © 2022, CC BY.

关键词： Object detection

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Decentralized Non-Convex Learning with Linearly Coupled Constraints: Algorithm Designs and Application to Vertical Learning Problem

arXiv

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arXiv 2021年

作者： Zhang, Jiawei Ge, Songyang Chang, Tsung-Hui Luo, Zhi-Quan Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China The Guangdong Provincial Key Laboratory of Big Data Computing The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China

Motivated by the need for decentralized learning, this paper aims at designing a distributed algorithm for solving nonconvex problems with general linear constraints over a multi-agent network. In the considered problem, each agent owns some local information and a local variable for jointly minimizing a cost function, but local variables are coupled by linear constraints. Most of the existing methods for such problems are only applicable for convex problems or problems with specific linear constraints. There still lacks a distributed algorithm for solving such problems with general linear constraints under the nonconvex setting. To tackle this problem, we propose a new algorithm, called proximal dual consensus (PDC) algorithm, which combines a proximal technique and a dual consensus method. We show that under certain conditions the proposed PDC algorithm can generate an ∊-Karush-Kuhn-Tucker solution in O(1/∊) iterations, achieving the lower bound for distributed non-convex problems up to a constant. Numerical results are presented to demonstrate the good performance of the proposed algorithms for solving two vertical learning problems in machine learning over a multi-agent network. © 2021, CC BY.

关键词： Cost functions

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Abnormality Detection in Chest X-Ray Images Using Uncertainty Prediction Autoencoders 23rd

Abnormality Detection in Chest X-Ray Images Using Uncertaint...

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23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Mao, Yifan Xue, Fei-Fei Wang, Ruixuan Zhang, Jianguo Zheng, Wei-Shi Liu, Hongmei School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Pazhou Lab Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783030597245

Chest radiography is widely used in annual medical screening to check whether lungs are healthy or not. Therefore it would be desirable to develop an intelligent system to help clinicians automatically detect potential abnormalities in chest X-ray images. Here with only healthy X-ray images, we propose a new abnormality detection approach based on an autoencoder which outputs not only the reconstructed normal version of the input image but also a pixel-wise uncertainty prediction. Higher uncertainty often appears at normal region boundaries with relatively larger reconstruction errors, but not at potential abnormal regions in the lung area. Therefore the normalized reconstruction error by the uncertainty provides a natural measurement for abnormality detection in images. Experiments on two chest X-ray datasets show the state-of-the-art performance by the proposed approach. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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A Recommender System Based on Model Regularization Wasserstein Generative Adversarial Network*

A Recommender System Based on Model Regularization Wasserste...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qingxian Wang Qing Huang Kangkang Ma Xuerui Zhang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9781665442084

A recommender system (RS) commonly adopts a High-dimensional and sparse (HiDS) matrix to describe user-item preferences. Collaborative Filtering (CF)-based models have been widely adopted to address such an HiDS matrix. However, a CF-based model is unable to learn the property distribution characteristic of user’s preference from an HiDS matrix, thereby its representation ability is limited. To address this issue, this paper proposes a Model Regularization Wasserstein GAN(MRWGAN) to extract the distribution of user’s preferences. Its main ideas are two-fold: a) adopting an auto-encoder to implement the generator model of GAN; b) proposing a model-regularized Wasserstein distance as an objective function to training a GAN model. Empirical studies on four HiDS matrices from industrial applications demonstrate that compared with state-of-the-art models, the proposed model achieves higher prediction accuracy for missing data of an HiDS matrix.

关键词： Training Analytical models Predictive models Generative adversarial networks Linear programming data models Sparse matrices

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Addressing underestimation and explanation of retinal fundus photo-based cardiovascular disease risk score: Algorithm development and validation

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Computers in Biology and Medicine 2025年 189卷 109941-109941页

作者： Lin, Zhihong Liu, Chi Shi, Danli Wang, Yueye Hu, Wenyi Henwood, Jacqueline Kiburg, Katerina van Wijngaarden, Peter Clark, Malcolm Shang, Xianwen Han, Xiaotong Zhang, Lei He, Mingguang Ge, Zongyuan The AIM for Health Lab Monash University Melbourne Australia Faculty of Engineering Monash University Melbourne Australia Faculty of Data Science City University of Macau China Centre for Eye Research Australia University of Melbourne Melbourne Australia Ophthalmology Department of Surgery University of Melbourne Melbourne Australia School of Optometry The Hong Kong Polytechnic University Hong Kong Department of General Practice University of Melbourne Melbourne Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangdong Provincial Clinical Research Center for Ocular Diseases Guangdong Guangzhou China Central Clinical School Faculty of Medicine Monash University Australia Clinical Medical Research Center Children's Hospital of Nanjing Medical University China The Hong Kong Polytechnic University Hong Kong Faculty of Information Technology Monash University Melbourne Australia

Objective: To resolve the underestimation problem and investigate the mechanism of the AI model which employed to predict cardiovascular disease (CVD) risk scores from retinal fundus photos. Methods: An ordinal regression Deep Learning (DL) model was proposed to predict 10-year CVD risk scores. The mechanism of the DL model in understanding CVD risk was explored using methods such as transfer learning and saliency maps. Results: Model development was performed using data from 34,652 participants with good-quality fundus photographs from the UK Biobank and a dataset for external validation collected in Australia comprised of 1376 fundus photos of 401 participants with a desktop retinal camera and a portable retinal camera. The mean [SD] risk-level accuracies across cross-validation folds was 0.772 [0.008], while AUROC for over moderate risk was 0.849 [0.005] and the AUROC for high risk was 0.874 [0.007] on the UK Biobank dataset. The risk-level accuracy for images acquired with the desktop camera data was 0.715, and the accuracy for portable camera data was 0.656 on the external dataset. Conclusions: The DL model described in this study has minimized the underestimation problem. Our analysis confirms that the DL model learned CVD risk score prediction primarily from age- and sex-related image representation. Model performance was only slightly degraded when features such as the retinal vessels and colours were removed from the images. Our analysis identified some image features associated with high CVD risk status, such as the peripheral small vessels and the macula areas. © 2025

关键词： Ophthalmology

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Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey

arXiv

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arXiv 2025年

作者： Tian, Shuang Zhang, Tao Liu, Jiqiang Wang, Jiacheng Wu, Xuangou Zhu, Xiaoqiang Zhang, Ruichen Zhang, Weiting Yuan, Zhenhui Mao, Shiwen Kim, Dong In School of Software Engineering Beijing Jiaotong University Beijing100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Technology An hui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui University of Technology Anhui243002 China College of Computing and Data Science Nanyang Technological University Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

—With the rapid development of technology and the acceleration of digitalisation, the frequency and complexity of cyber security threats are increasing. Traditional cybersecurity approaches, often based on static rules and predefined scenarios, are struggling to adapt to the rapidly evolving nature of modern cyberattacks. There is an urgent need for more adaptive and intelligent defence strategies. The emergence of Large Language Model (LLM) provides an innovative solution to cope with the increasingly severe cyber threats, and its potential in analysing complex attack patterns, predicting threats and assisting real-time response has attracted a lot of attention in the field of cybersecurity, and exploring how to effectively use LLM to defend against cyberattacks has become a hot topic in the current research field. This survey examines the applications of LLM from the perspective of the cyber attack lifecycle, focusing on the three phases of defense reconnaissance, foothold establishment, and lateral movement, and it analyzes the potential of LLMs in Cyber Threat Intelligence (CTI) tasks. Meanwhile, we investigate how LLM-based security solutions are deployed and applied in different network scenarios. It also summarizes the internal and external risk issues faced by LLM during its application. Finally, this survey also points out the facing risk issues and possible future research directions in this domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

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Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

Model-Based Reinforcement Learning for Quantized Federated L...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning Mathematical models

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