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第42届中国控制会议

作者： Shuwen Feng Xin Hu Xinjiang Wei Huifeng Zhang Shaoyan Cui School of Mathematics and Statistics Science Ludong University Shandong Provincial Big Data Development and Innovation Laboratory of Public Resources

This paper presents an event-triggered anti-disturbance control scheme for the trajectory tracking of ships with timevarying ocean disturbances and actuator *** combining the disturbance observer with the auxiliary dynamic system,a robust disturbance observer-based control is designed with the backstepping technique and event-triggered *** disturbance observer provides the on-line time-varying disturbance *** auxiliary dynamic system helps to reduce the actuator saturation *** event-triggered mechanism avoids the wear and tear of actuators through implementing the control signals on triggering *** is theoretically proven that the event-triggered anti-disturbance controller achieves the trajectory tracking and all signals in the closed-loop tracking control systems are uniformly ultimately *** simulations on a 1:70 scaled model ship confirm the event-triggered anti-disturbance tracking control.

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Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps

Chat2Brain: A Method for Mapping Open-Ended Semantic Queries...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wei, Yaonai Zhong, Tianyang Zhang, Songyao Li, Xiao Zhang, Tuo Zhao, Lin Liu, Zhengliang Shang, Muheng Liu, Tianming Zhang, Han Ma, Chong Du, Lei Han, Junwei Northwestern Polytechnical University School of Automation Xi'an China Northwest University School of Information Science&Technology Xi'an China University of Georgia School of Computing GA United States Northwest University Network and Data Center Xi'an China

ISBN: (纸本)9798350337488

Over decades, neuroscience has accumulated a wealth of research results in the text modality that can be used to explore cognitive processes. Meta-analysis is a typical method that successfully establishes a link from text queries to brain activation maps using these research results, but it still relies on an ideal query environment. In practical applications, text queries used for meta-analyses may encounter issues such as semantic redundancy and ambiguity, resulting in an inaccurate mapping to brain images. On the other hand, large language models (LLMs) like ChatGPT have shown great potential in tasks such as context understanding and reasoning, displaying a high degree of consistency with human natural language. Hence, LLMs could improve the connection between text modality and neuroscience, resolving existing challenges of meta-analyses. In this study, we propose a method called Chat2Brain that combines LLMs to basic text-2-image model, known as Text2Brain, to map open-ended semantic queries to brain activation maps in data-scarce and complex query environments. By utilizing the understanding and reasoning capabilities of LLMs, the performance of the mapping model is optimized by transferring text queries to semantic queries. We demonstrate that Chat2Brain can synthesize anatomically plausible neural activation patterns for more complex tasks of text queries. © 2023 IEEE.

关键词： image generation large language model meta-analysis

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Applications of Explainable Artificial Intelligent Algorithms to Medicine: A Bibliographic Study 2nd

Applications of Explainable Artificial Intelligent Algorithm...

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2nd World Conference on Intelligent and 3D Technologies, WCI3DT 2023

作者： Wang, Mini Han Xing, Lumin Yu, Xiangrong Yan, Chenyu Zhou, Ruoyu Chong, Kelvin K. L. Wang, Fengling Zhou, Xiaoshu Chen, Guoqiang Wu, Qing Lin, Zhiyuan Zeng, Peijin Xiao, Qide The Faculty of Data Science City University of Macau Macau China The Faculty of Medicine The Chinese University of Hong Kong Hong Kong Guanghui Hou Zhuhai China The Department of AI and Big Data Application Zhuhai Institute of Advanced Zhuhai China The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital Jina China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou China Hezhou University Hezhou China Gree Polytechnic Zhuhai China Beijing Institute of Technology Zhuhai China Zhuhai Science and Technology Development Promotion Association Zhuhai China

ISBN: (纸本)9789819721436

In recent years, Explainable Artificial Intelligent (XAI) algorithms have been widely applied and deeply studied in the field of medicine. Based on the bibliometric method, this paper summarizes and analyses 315 non-repetitive related literature retrieved from the literature databases of Scopus and Web of science (WOS). Developing trends, key authors, contributed countries, journals, references, and keywords are explored. Based on the method of text mining, representative articles, and their core opinions are qualitatively analyzed. Methods are clustered, applications, strengths, and drawbacks are discussed, and issues and future directions are exhibited. Findings show that applications of the interpretable algorithm to the medical field have vital potential clinical value. Medical diagnosis based on computer vision with an explainable mechanism is aimed at breaking the trust between artificial intelligence and medicine disciplines. In addition, cooperative research and talent training in this interdisciplinary area are also worthy of attention in the future. Thus, a reference significance for XAI applied to the medical field is contributed by this study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Medicinal chemistry

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Domain Generalized Solar Cell Defect Segmentation Based on Shape-Aware and Multi-View Meta-Learning 42

Domain Generalized Solar Cell Defect Segmentation Based on S...

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42nd Chinese Control Conference, CCC 2023

作者： Liu, Kun Yang, Ying Mao, Jingkun Liu, Weipeng School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300130 China Tianjin University of Technology Department of Automation Tianjin300384 China Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin300401 China

ISBN: (纸本)9789887581543

Various defects are inevitably generated in the manufacturing process of solar cells. Deep learning-based methods for defect segmentation under closed situation have achieved remarkable progress. Due to the difference of imaging condition and camera parameter under different production line, there are large differences in brightness distribution of solar cell images. The model trained under closed situation cannot achieve good performance in opened situation such as multi-production lines. In this paper, a new shape-aware and multi-view meta-learning scheme is proposed to improve the model generalization performance for single-domain solar cell defect segmentation. The scheme roots in gradient-based meta-learning. Multi-brightness view information is yielded from the generated augmented images, and explicitly simulating domain shift with virtual meta-train and meta-test during training to mitigate overfitting in single-source domain train and unstable prediction. Importantly, considering tiny and faint solar cell defects are not easily identified under different image brightness conditions, shape edge constraint loss is proposed to encourage shape compactness and shape smoothness of segmentations. Experimental results show that the proposed method has high segmentation accuracy, which outperforms the state-of-the-art methods. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Solar cells

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Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks

arXiv

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arXiv 2024年

作者： Li, Ziqiang Zeng, Yueqi Xia, Pengfei Liu, Lei Fu, Zhangjie Li, Bin Nanjing University of Information Science and Technology China Big Data and Decision Lab University of Science and Technology of China China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System China

With the burgeoning advancements in the field of natural language processing (NLP), the demand for training data has increased significantly. To save costs, it has become common for users and businesses to outsource the labor-intensive task of data collection to third-party entities. Unfortunately, recent research has unveiled the inherent risk associated with this practice, particularly in exposing NLP systems to potential backdoor attacks. Specifically, these attacks enable malicious control over the behavior of a trained model by poisoning a small portion of the training data. Unlike backdoor attacks in computer vision, textual backdoor attacks impose stringent requirements for attack stealthiness. However, existing attack methods meet significant trade-off between effectiveness and stealthiness, largely due to the high information entropy inherent in textual data. In this paper, we introduce the Efficient and Stealthy Textual backdoor attack method, EST-Bad, leveraging Large Language Models (LLMs). Our EST-Bad encompasses three core strategies: optimizing the inherent flaw of models as the trigger, stealthily injecting triggers with LLMs, and meticulously selecting the most impactful samples for backdoor injection. Through the integration of these techniques, EST-Bad demonstrates an efficient achievement of competitive attack performance while maintaining superior stealthiness compared to prior methods across various text classifier datasets. © 2024, CC BY.

关键词： Deep neural networks

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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Privacy Protection for Image Sharing Using Reversible Adversarial Examples

Privacy Protection for Image Sharing Using Reversible Advers...

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IEEE International Conference on Communications (ICC)

作者： Ping Yin Wei Chen Jiaxi Zheng Yiting Zhang Lifa Wu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Online image sharing on social media platforms faces information leakage due to deep learning-aided privacy attacks. To avoid these attacks, this paper proposes a privacy protection mechanism for image sharing without changing the visual effect, which is based on reversible adversarial examples. Specifically, social media platform users can change the class activation feature to convert the original image into an adversarial image before sharing. When users want to restore the adversarial image to the original image, they can use an improved generative adversarial network model to restore it. The experimental results prove that the conversion model in this paper can effectively prevent privacy attacks from analyzing and stealing users' private information while having no visual impact. At the same time, the proposed restoration model can restore the adversarial examples with high accuracy.

关键词： Training Privacy Image recognition Social networking (online) Perturbation methods Visual effects Information leakage

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A Novel Design of Fatigue Driving Detector with mmWave Sensor AWR1642boost 23

A Novel Design of Fatigue Driving Detector with mmWave Senso...

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2023 International Conference on Intelligent Computing and Its Emerging Applications, ICEA 2023

作者： Tseng, Shih-Pang Hsiao, I-Hui Wang, Jhing-Fa School of Software and Big Data Changzhou College of Information Technology Changzhou China Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9798400709050

According to statistics from the Ministry of Transportation, distracted driving is one of the main causes of traffic accidents. In Taiwan, approximately 20% of total accidents each year are attributed to distracted or fatigued driving, ranking it as the second leading cause of accidents. Typically, drivers start to feel fatigued and experience decreased reaction and decision-making abilities after driving continuously for 2-3 hours. Post-meal and midnight driving also tend to induce drowsiness more than other times. Additionally, alcohol consumption, drug use, and the use of hypnotic medication can lead to loss of consciousness. Fatigued driving poses a serious public safety issue, endangering both drivers and other road users. Therefore, developing effective methods for detecting driver fatigue is crucial. Based on existing research, employing facial monitoring systems can reduce accident rates by 10% to 20%. However, these systems are limited by lighting conditions. Thus, this paper proposes the use of millimeter-wave technology to realize a fatigue detection system, effectively addressing the high reliance of facial monitoring systems on lighting conditions. In this study, data collection was performed using the AWR1642boost, a highly integrated millimeter-wave radar development board developed by Texas Instruments. Subsequently, a Multi-experts system was developed to integrate and analyze four features: nodding, yawning, breathing, and heart rate. The system employed a Support Vector Machine (SVM) model to improve the accuracy of fatigue detection in driving scenarios. Experimental results demonstrated that the system achieved an accuracy rate of 90%, effectively discerning whether the driver was experiencing fatigue. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Highway accidents

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Downstream-agnostic Adversarial Examples

Downstream-agnostic Adversarial Examples

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International Conference on Computer Vision (ICCV)

作者： Ziqi Zhou Shengshan Hu Ruizhi Zhao Qian Wang Leo Yu Zhang Junhui Hou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan University School of Information and Communication Technology Griffith University Department of Computer Science City University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial *** this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://***/CGCL-codes/AdvEncoder.

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An Image Super Resolution Algorithm Using Reparameterization 18th

An Image Super Resolution Algorithm Using Reparameterization

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18th Chinese Intelligent Systems Conference, CISC 2022

作者： Cai, Qiang Chang, Haodong Li, Haisheng Wan, Ruyi Chen, Xiaoming School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China National Engineering Laboratory for Agri-Product Quality Traceability Beijing100048 China

ISBN: (纸本)9789811962257

At present, the residual-based deep network model mainly adopts a multi-branch structure, but this structure increases the memory access cost, and the reasoning time is not as good as that of a single-branch network. In this work, we propose a re-parameterized construction structure based on EDSR, namely Re-parameterized Deep Super-Resolution (RepEDSR). The training and inference processes are decoupled to speed up inference without sacrificing performance. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm outperforms existing methods in terms of speed and accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical resolving power

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