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International Conference on Computer Supported Cooperative Work in Design

作者： Xuanyan Liu Jinling He Hu Song Xinyun Cheng Luyun Hu Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Cloud-edge networks (CEN) has revolutionized the traditional network architecture. However, there are many security issues in CEN needing to be solved. In this case, CEN administrator should be able to detect network anomalies and find out the sources of anomalies rapidly, and respond in time to block anomalies. In order to protect network systems with CEN, we propose a mechanism consisting of two modules, including anomaly detection and anomaly traceback. The self-similarity anomaly detection is introduced for the first time to detect anomalies. In addition, we also propose an anomaly traceback method which utilizes the characteristics of CEN. An CEN testbed is established to evaluate the mechanism we proposed. Results of experiments show that the proposed mechanism can discover anomalies quickly and accurately, and trace anomaly sources and find anomaly paths accurately.

关键词： Federated learning Refining Process control Network security Network architecture Control systems Indexes

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security Compressed Sensing Image Encryption Algorithm Based on Elliptic Curve 9th

Security Compressed Sensing Image Encryption Algorithm Based...

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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Jin, Anan Li, Xiang Xiong, Qingzhi School of Information Engineering East China University of Technology Jiangxi Nanchang330013 China Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Jiangxi Nanchang330013 China Jiangxi Key Laboratory of Cybersecurity Intelligent Perception East China University of Technology Jiangxi Nanchang330013 China

ISBN: (纸本)9789819959679

Aiming to address the problems of high costs associated with the storage and transmission of environmental monitoring images, as well as potential security risks, this paper proposes a security compressed sensing image encryption algorithm based on elliptic curve cryptography. The algorithm introduces elliptic curve encryption technology within a compressed sensing framework, using elliptic curve cryptography to encrypt the matrix during the compression perception acquisition process. This enables secure acquisition of environmental monitoring encrypted images. Therefore, this paper presents a security compressed sensing framework to address security gaps in the compression perception reconstruction process and improve the security of environmental monitoring images. Experimental results show that the proposed algorithm effectively encrypts and reconstructs environmental monitoring images, with high security and resistance to cracking. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Compressed sensing

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Lgccn: A Label-Guided Contrastive Capsule Network for Few-Shot Text Classification

SSRN

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SSRN 2023年

作者： Xu, Yang Xu, Xiaolong Xiao, Fu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

Few-shot text classification aims to learn transferable knowledge from a limited dataset to perform classification tasks in unseen domains. Recently, metric-based meta-learning methods have demonstrated their advantages in few-shot text classification. However, the influence of randomly sampling support set on prototype quality has often been overlooked by these methods. In this paper, we propose a Label-Guided Contrastive Capsule Network (LGCCN) to address this issue. LGCCN leverages label information to guide supervised contrastive learning, using labels as anchors to enhance the clustering process, resulting in higher-quality sentence vectors. Furthermore, LGCCN employs label-guided dynamic routing process to iteratively generate a prototype, significantly mitigating the impact of random support set sampling. We conducted extensive experiments on four datasets, comparing LGCCN with recent methods. The results reveal that LGCCN, compared to the recent best work, has achieved an improvement of 10.1% and 2.1% in classification accuracy for 1-shot and 5-shot tasks, respectively. Additionally, ablation studies validate the effectiveness of each module in our approach. © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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End-to-End Remote Sensing Image Scene Classification with Vision Transformers 23

End-to-End Remote Sensing Image Scene Classification with Vi...

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6th International Conference on Big data Technologies, ICBDT 2023

作者： Yuan, Hao Liu, Kun Shi, Jiechuan Wang, Can Wang, Weiwei School of Information Science and Engineering University of Jinan Jinan250022 China China High-resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China QiLu Research Institute Jinan250200 China

ISBN: (纸本)9798400707667

In recent years, the development of deep learning technology has led to widespread attention on Vision Transformer (ViT) as an emerging image classification method. Remote sensing image classification is an important task in the field of remote sensing, with extensive application prospects. This paper aims to explore the remote sensing image classification method based on Vision Transformer, addressing the limitations of traditional convolutional neural networks in terms of global perception capability, context information retrieval, and positional encoding. The classification performance of the Vision Transformer model is evaluated and compared on remote sensing datasets. Vision Transformer is a deep neural network model based on self-attention mechanism that can capture the global context information in images and has achieved remarkable performance in various computer vision tasks. Furthermore, experimental results demonstrate that the remote sensing image classification method based on Vision Transformer exhibits outstanding accuracy and generalization ability. Compared to traditional convolutional neural networks, it can better capture the global features in remote sensing images and has better scalability when dealing with large-scale remote sensing image data. Experimental results on different remote sensing image datasets show that the model performs well compared to state-of-the-art methods. Specifically, Vision Transformer achieves average classification accuracies of 95.41%, 98.26%, 93.74% and 95.25% on the AID, UC-Merced, NWPU-RESISC45 and Optimal31 datasets, respectively. © 2023 ACM.

关键词： Neural network models

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Evaluation of Continual Learning Methods for Object Hardness Recognition

Evaluation of Continual Learning Methods for Object Hardness...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Liu, Yiwen Fang, Senlin Liu, Chengliang Wang, Jingnan Mai, Ke Zhang, Yupo Yi, Zhengkun Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Siat Branch Shenzhen518055 China City University of Macau Faculty of Data Science 999078 China

ISBN: (纸本)9798350327182

How to solve the problem of continual learning of robotic tactile perception in an open, dynamic environment is a pressing task. Although continual learning has been extensively studied in computer vision, the evaluation of those algorithms does not provide insights on whether their solutions may help continual learning in the context of tactile robotics. In this paper, we propose a continual learning scenario in the robotic tactile domain, and design experiments with tactile hardness dataset that allow us to compare the performance of many continual learning algorithms in the tactile domain. We examine the performance of regularization-based and replay methods in the tactile domain and discuss the effect of replay number on model performance. We show that regularization-based methods do not perform well on the tactile dataset. On the contrary, replay methods can maintain better anti-forgetting performance, but the number of model parameters will be larger, and the training time will be longer. We also found that the replay ratio should be higher than 0.2 to maintain the performance of the replay method on the tactile hardness dataset. © 2023 IEEE.

关键词： Robotics

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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An online sparse least square support vector regression and particle filter for remaining useful life prediction of capacitive-type pressure transducer 3

An online sparse least square support vector regression and ...

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2024 3rd International Conference on Aerospace and Control Engineering, ICoACE 2024

作者： Wang, Senlin Dai, Lingfeng Guo, Mingzhe Lu, Yikang Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fujian Fuzhou362200 China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou362200 China Fujian College University of Chinese Academy of Sciences Fujian Fuzhou350002 China School of Advanced Manufacturing Fuzhou University Fujian Quanzhou350108 China School of College of Computer and Cyberspace Security Fujian Normal University Fujian350007 China

This paper proposes a novel online sparse least square support vector regression without bias for forecasting capacitive type pressure transducer remaining useful life prediction (RUL). The proposed approach is based on mechanism and statistical knowledge. The impedance variables are applied to calibrate the correlation between pressure capacitance capacity degradation and the resistance value to improve the prediction precision. Furthermore, a particle filter algorithm is proposed to estimate the pressure capacitance impedance fade parameters, and then the probability density function of the predicted RUL value is obtained by Gaussian. Lastly, experimental results consider #5 and #6. The pressure capacitance data set verifies the hybrid RUL prognostics strategy with high accuracy and robust stability. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Support vector regression

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Learning Multimodal Attention Mixed with Frequency Domain Information as Detector for Fake News Detection

Learning Multimodal Attention Mixed with Frequency Domain In...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zihan Ma Huan Liu Zhi Zeng Hao Guo Xiang Zhao Minnan Luo School of Computer Science and Technology Xi’an Jiaotong University Xi'an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi'an China Laboratory for Big Data and Decision Nation University of Defence Technology Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Detecting fake news on social media has become a crucial task in combating online misinformation and countering malicious propaganda. Existing methods rely on semantic consistency across modalities to fuse features and determine news authenticity. However, cunning fake news publisher manipulate image to ensure a high level of semantic consistency between news post and image, making it more difficult to distinguish fake news. To this end, we propose MHFFD (Mixed High-Frequency Feature Detector), a novel fake news detection framework that utilizes token-level semantic consistency evaluation to identify key elements in news content and provide guidance for discovering image manipulation and learning better news representations. Extensive experiments demonstrate that MHFFD outperforms state-of-the-art methods on two widely used fake news detection datasets. Further research also validates the effectiveness of token-level semantic alignment and manipulation detection.

关键词： Social networking (online) Fuses Frequency-domain analysis Semantics Detectors Feature extraction Fake news

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A Fraud Detection Approach Based on Combined Feature Weighting 10th

A Fraud Detection Approach Based on Combined Feature Weighti...

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10th EAI International Conference on Cloud Computing, CloudComp 2020

作者： Liu, Xiaoqian Yu, Chenfei Xia, Bin Gu, Haiyan Wang, Zhenli Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Nanjing China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Nanjing China

ISBN: (纸本)9783030699918

data mining technology has yielded fruitful results in the area of crime discovery and intelligent decision making. Credit card is one of the most popular payment methods, providing great convenience and efficiency. However, due to the vulnerabilities of credit card transactions, criminals are able to commit fraud to infringe on the interests of the state and citizens. How to discover potential fraudsters while guaranteeing high efficiency becomes an extremely valuable problem to solve. In this work, we talk about the advantages and disadvantages of different models to detect credit card fraud. We first introduce the data preprocessing measures for handling imbalanced fraud detection dataset. Then we compare related models to implement fraudster recognition. We also propose a feature selection approach based on combined feature weights. Some future research interests are also envisioned. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Crime

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COMMITSHIELD: Tracking Vulnerability Introduction and Fix in Version Control systems

arXiv

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arXiv 2025年

作者： Wu, Zhaonan Zhao, Yanjie Wei, Chen Wan, Zirui Liu, Yue Wang, Haoyu Huazhong University of Science and Technology Wuhan China MYbank Ant Group Hangzhou China Monash University Melbourne Australia Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Version control systems are commonly used to manage open-source software, in which each commit may introduce new vulnerabilities or fix existing ones. Researchers have developed various tools for detecting vulnerabilities in code commits, but their performance is limited by factors such as neglecting descriptive data and challenges in accurately identifying vulnerability introductions. To overcome these limitations, we propose COMMITSHIELD, which combines the code analysis capabilities of static analysis tools with the natural language and code understanding capabilities of large language models (LLMs) to enhance the accuracy of vulnerability introduction and fix detection by generating precise descriptions and obtaining rich patch contexts. We evaluate COMMITSHIELD using the newly constructed vulnerability fix dataset, CommitVulFix, and a cleaned vulnerability introduction dataset. Experimental results indicate that COMMITSHIELD improves recall by 74%-77% over state-of-the-art methods in the vulnerability fix detection task, and its F1-score improves by 15%-27% in the vulnerability introduction detection task. Copyright © 2025, The Authors. All rights reserved.

关键词： Open source software

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