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SSRN 2023年

作者： Han, Keji Ge, Yao Wang, Ruchuan Li, Yun Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

Deep neural networks (DNNs) are demonstrated to be vulnerable to the adversarial example crafted by the adversary to fool the target model. Adversarial training and adversarial example detection are two popular methods to mitigate the adversarial issue. The adversarial training makes the target model recognize the adversarial example correctly for the image classification task, while it does not generalize well. The adversarial detection generalizes well but cannot help the target model recognize the adversarial example. We first define the label recovery task to address the adversarial issue for DNNs. Then we propose a new generative classifier and apply it to the adversarial example label recovery task. The proposed method is named as Detection and Label Recovery (DLR) defense method, which consists of Detector and Recover. Detector feeds the legitimate and adversarial examples to the target model and Recover, respectively. The Recover explores the ground-truth label of the detected adversarial example. DLR combines the merits of adversarial training and adversarial example detection. Experimental results demonstrate that our method outperforms some state-of-the-art adversarial training methods. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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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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EFIE: Efficient and Fine-Grained Image Encryption Algorithm With Permutation-Substitution Synchronization

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IEEE Transactions on Consumer Electronics 2025年

作者： Xie, Dong Zhang, Guiting Yang, Menglin Chen, Fulong Wang, Taochun Hu, Peng Anhui Normal University Anhui Provincial Key Laboratory of Industrial Intelligence Data Security Anhui Engineering Research Centers of Medical Big Data Intelligent System School of Computer and Information Wuhu241002 China

Currently, both pixel-level and bit-level image encryption algorithms demonstrate excellent visual performance. However, most high-security algorithms usually involve complex encryption steps, which increase the time complexity. In addition, some algorithms are challenging to adjust flexibly according to specific scenarios, making it difficult to achieve a balance between security and efficiency. In this paper, we propose an efficient and fine-grained image encryption algorithm, named EFIE. The main step of EFIE is the proposed permutation-substitution synchronization procedure, which is much more efficient compared to existing image encryption solutions. To our knowledge, we first introduce the concept of fine-grained encryption, which allows the encryptor to flexibly set the encryption unit (e.g., 1-bit, 2-bit, or 1-pixel). The smaller the length of the encryption unit, the longer the encryption time and the higher the level of security. The experimental results and various security analyses demonstrate that EFIE provides excellent encryption performance. When the encryption unit is 1 bit, encrypting a 512 × 512 color image requires only about 2.34 seconds. © 1975-2011 IEEE.

关键词： Encryption algorithms

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Mixup data Augmentation and Channel-Spatial Attention Mechanisms for Facial Emotion Recognition

Mixup Data Augmentation and Channel-Spatial Attention Mechan...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Dewei Yu Chuijian Kong Xianpiao Tang Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Guiyang China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal Universitya Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the rapid advancement of computer vision, facial expression recognition has made significant progress in areas like human-computer interaction, mental health, driver assistance, and education. However, traditional facial expression recognition techniques exhibit poor robustness under complex backgrounds and varying lighting conditions, making them susceptible to environmental noise and challenging to accurately capture and classify complex emotional states. Additionally, these techniques often lack in data augmentation and model generalization capabilities, leading to overfitting on training samples and insufficient recognition of unseen samples. To address these limitations, this paper proposes a novel convolutional neural network model—HSCNet (A Hybrid data Augmentation and Channel-Spatial Attention Mechanisms Neural Network). The HSCNet model first employs the Mixup data augmentation technique to generate new training samples, then utilizes the Res2Net50 backbone network combined with the CBAM attention mechanism and average-max pooling techniques to extract significant features while suppressing redundant ones. This approach not only enhances feature extraction accuracy but also significantly improves the model’s generalization ability and robustness in facial expression recognition tasks. Finally, tested on the public dataset FERPlus, which includes eight types of emotions, the HSCNet model achieved an accuracy rate of 89.72%, demonstrating excellent performance and robustness.

关键词： Training Emotion recognition Attention mechanisms Accuracy Face recognition Lighting Feature extraction data augmentation data models Robustness

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Transparent Four-Port MIMO DRAs Decoupled by Conformal Strips

Transparent Four-Port MIMO DRAs Decoupled by Conformal Strip...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： He, Chao Yang, Nan Leung, Kwok Wa Liaoning Technical University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Kowloon Hong Kong CityU Shenzhen Research Institute Information and Communication Technology Centre Shenzhen China

ISBN: (纸本)9798350355581

A four-port multi-input multi-output (MIMO) dielectric resonator (DR) antenna (DRA) is presented in this paper. Combing four rectangular DRs together can form a single DR element. With shape modification and conformal strips, the four ports can be decoupled. To verify the design idea, a transparent prototype can be designed and fabricated for 2.4-GHz band applications. The measured results show an impedance bandwidth of 5.8% and an isolation level of 24 dB. © 2024 IEEE.

关键词： Microwave antennas

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Three-Mode, Three-Band QMSIW Filters Based on CSRRs

Three-Mode, Three-Band QMSIW Filters Based on CSRRs

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Congying Wang Mingming Gao Ya He Xinyu Wang Wenxian Yang Chang Ge College of Electronics and Information Engineering Liaoning Technicial University Huludao Liaoning Province China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

In this study, a three-mode, three-band filter is developed to fulfill the prerequisites of contemporary satellite communication systems. A technique that integrates a QMSIW cavity and a microstrip line structure is proposed. This structure improves the impedance matching of the filter and also satisfies the need for miniaturization. However, it suppresses the second mode, and to enhance the resonance of the second mode, two complementary square open resonance rings (CSRRs) are introduced on the QMSIW *** structure yields five transmission zeros for the three-band filter, offering advantages such as compact size, low loss, and effective out-of-band rejection.

关键词： Structural rings Matched filters Microstrip filters Spaceborne radar Resonator filters Millimeter wave radar Resonance

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Explore the collision safety of tram and passenger vehicle under disparate right of way 6

Explore the collision safety of tram and passenger vehicle u...

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6th International Conference on Traffic Engineering and Transportation System, ICTETS 2022

作者： Guo, Yueling Liu, Yugang Xiao, Hanlin Ye, Jinsong School of Transportation and Logistics Southwest Jiaotong University Sichuan Province Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Sichuan Province Chengdu610031 China National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Sichuan Province Chengdu610031 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport China Academy of Transportation Sciences Beijing100029 China

ISBN: (纸本)9781510663053

Unlike traditional railway vehicles, the particularity of tram right of way determines the multiple and diversity of its accident forms. Based on the analysis of accident data at home and abroad, this paper determines the three road right management modes with the highest accident rate. On this basis, three collision scenarios are designed between a three-module 100% low-floor tram and various types of passenger vehicles. In this paper, we innovatively use PC-Crash crash simulation combined with SPSS quantitative analysis to study the accident characteristics of tram and passenger vehicle collisions under disparate right of way. The results show that the occupant's living space is smaller in 45° side impact under the semi-independent right of way. Under the mixed right of way, when the tram is used as a rear-end vehicle, the collision interface force is more intense. At the same collision speed, the displacement of the vehicle body and the invasion of the living space will be larger under the mixed right of way. Finally, based on the simulation results and causal analysis method, an accurate and highly flexible accident cause analysis framework is formed. From the aspects of single influence and comprehensive influence, the factors affecting the safety of tram operation under diverse road right management modes are analyzed, and tailored suggestions are given to improve the safety of the track system. © 2023 SPIE.

关键词： Highway planning

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DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

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Research on named entity recognition method for high-speed railway technology transformation project text 4

Research on named entity recognition method for high-speed r...

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2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Lian, Xiaoqin Bao, Kexin Gao, Chao Gong, Yonggang Wu, Yanhua Cheng, Zhibo School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing100081 China

High-speed railway technology transformation projects are crucial to the layout of high-speed rail transportation production, high-speed rail safety, and high-speed rail operation and management. However, the data of high-speed railway technology transformation projects is mainly stored in the form of unstructured text, and the relevant personnel of high-speed railroad often need to manually go through the form to extract the key information of the project data and enter it into the system, with a low degree of automation and intelligence. Therefore, to improve the efficiency and quality of processing the data of high-speed railway technology transformation projects, and help the intelligent development of high-speed railways, this paper constructs the RoBERTa-BiLSTM-CRF fusion model to automate the identification of key information entities in the data of high-speed railway technology transformation project. The experimental data were selected as technology transformation project documents of a railway bureau from 2013 to 2022, and six defined entity types were identified. Experimental results show that the constructed model can effectively identify six types of defined entities, and the F1 value of each type of entity is above 90%. In addition, compared with other commonly used models, the F1 value has been significantly improved. The fusion model proposed in this paper can be used as a method to automatically extract key information for high-speed rail technology transformation projects. © Published under licence by IOP Publishing Ltd.

关键词： Railroads

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A Study on Application of Artificial Intelligence in College English Teaching Management and Evaluation 3

A Study on Application of Artificial Intelligence in College...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Feng, Tao Deng, Jianjie Liu, Yiwen Gao, Yanxia Peng, Ling Fu, Jinrong School of Foreign Languages Huaihua University Hunan Huaihua418000 China School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China School of Computing Science University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9798400716775

With the rapid development of artificial intelligence, AI technology has been widely applied in various industries, including education. In English teaching, AI technology provides teachers with objective and instructive feedback, offers students a more personalized learning experience, helping them improve their English language skills, etc., and brings new development opportunities and challenges to English teaching as well. However, there are still some problems that need to be solved in college English teaching in our university. For this reason, based on the application of AI technology in college English teaching, this paper mainly explores the impact and application countermeasures of AI on college English teaching from the following four aspects: individuals with differences, teaching management, classroom teaching and teaching evaluation. In addition, the profound integration of college English education and AI will foster the advancement and transformation of college English instruction, ultimately serving as a catalyst for its reform. © 2023 ACM.

关键词： Artificial intelligence

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