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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Dong, Zhiqing Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo University of Science and Technology Liaoning School of Computer Science and Software Engineering Anshan China University of Science and Technology Liaoning School of Electronics and Information Engineering Anshan China

ISBN: (纸本)9798350325294

In this paper, a color image encryption scheme based on 4D hyperchaos system and RSA algorithm is proposed. Firstly, RSA algorithm is used to generate the initial value of 4D hyperchaotic system, and the key stream is iteratively generated. Then, the color image is divided into RGB three channels. In order to change the position of image pixels, Arnold scrambling is performed on the image. Finally, simultaneous scrambling and diffusion algorithms are used to encrypt the preprocessed images for a second time. Experimental results show that the encryption scheme proposed in this paper is effective. Compared with the existing algorithms, this encryption scheme improves the encryption efficiency, has strong anti-aggression and robustness. © 2023 IEEE.

关键词： Cryptography

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On the Adversarial Robustness of Hierarchical Classification

On the Adversarial Robustness of Hierarchical Classification

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Ran Zeng, Simeng Wu, Wenhui Jia, Yuheng Ng, Wing W. Y. Wang, Xizhao School of Mathematical Sciences Shenzhen University Shenzhen518060 China College of Electronic and Information Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering South China University of Technology Guangzhou510006 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781665410205

Deep neural networks (DNNs) have demonstrated remarkable success on various learning problems, but they face a formidable challenge in the form of adversarial attacks. Especially, when dealing with complex classification tasks for numerous classes with a hierarchical structure, the adversarial robustness of a DNN model may drop seriously. In this paper, we investigate the adversarial robustness of DNN models on such complex classification tasks. In response, we propose a two-stage hierarchical classification framework, which is composed of a coarse-grained classifier and a series of fine-grained classifiers. A data correction sampling module is designed between the two stages, in order to mitigate the influence of misclassification caused by the coarse-grained classifier;and a discriminative filter learning module is employed in the fine-grained classification, in order to gain better distinguish abilities among fine-grained categories. Experiments on the well-known dataset CIFAR-100 and a newly-constructed hierarchical dataset mini-ImageNet76 demonstrate that employing a hierarchical framework can effectively improve the model robustness on such complex classification tasks. © 2024 IEEE.

关键词： Generative adversarial networks

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DivGCL: A Graph Contrastive Learning Model for Diverse Recommendation 39

DivGCL: A Graph Contrastive Learning Model for Diverse Recom...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Gong, Wenwen Geng, Yangliao Zhang, Dan Zhu, Yifan Xu, Xiaolong Xiang, Haolong Beheshti, Amin Zhang, Xuyun Qi, Lianyong School of Information and Electrical Engineering China Agricultural University China School of Computer Science & Technology Beijing Jiaotong University China Department of Computer Science and Technology Tsinghua University China School of Computer Science Beijing University of Posts and Telecommunications China School of Software Nanjing University of Information Science and Technology China School of Computing Macquarie University Australia College of Computer Science and Technology China University of Petroleum [East China China State Key Lab. for Novel Software Technology Nanjing University China

ISBN: (纸本)157735897X

Graph Contrastive Learning (GCL), as a primary paradigm of graph self-supervised learning, spurs a fruitful line of research in tackling the data sparsity issue by maximizing the consistency of user/item embeddings between different augmented views with random perturbations. However, diversity, as a crucial metric for recommendation performance and user satisfaction, has received rather little attention. In fact, there exists a challenging dilemma in balancing accuracy and diversity. To address these issues, we propose a new Graph Contrastive Learning model for Diversifying recommendations (DivGCL). Inspired by the excellence of the determinant point process (DPP), DivGCL adopts a DPP likelihood-based loss function to achieve an ideal trade-off between diversity and accuracy, optimizing it jointly with the advanced Gaussian noise-augmented GCL objective. Extensive experiments on four popular datasets demonstrate that DivGCL surpasses existing approaches in balancing accuracy and diversity, with an improvement of 23.47% at T@20 (abbreviation for trade-off metric) on ML-1M. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Self-supervised learning

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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A Hypergraph Transformer With Adaptive EdgeConv for Skeleton-based Action Recognition 7

A Hypergraph Transformer With Adaptive EdgeConv for Skeleton...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhou, Fengyu Liu, Jun Zhang, Dazhi Huang, Zengxi School of Computer Science and Software Engineering Xihua University Chengdu China Sichuan Police College Criminal Science and Technolog Department Luzhou China

ISBN: (纸本)9798350350890

Skeleton-based action recognition is a crucial task in computer vision, aiming to perform pattern recognition on a skeleton sequence to identify the actions represented by the skeleton's semantics. However, current mainstream skeleton-based methods primarily rely on global edge information. Unimportant edges may influence this approach, limiting the model's performance in action recognition tasks. Additionally, because these methods treat all information equally, they may lead to resource wastage. This paper proposes a Hypergraph Transformer-based action recognition method using Adaptive EdgeConv Module(AEM) to address these issues effectively. In this method, we introduce Adaptive Edge Graph Convolution Networks(AEGCNs), which utilize the AEM to adaptive obtain edge sets and edge set weights, thereby adaptive identifying key edge sets. With this information, the model can dynamically adjust the importance of different edge sets based on various samples. This approach improves the accuracy of action recognition and reduces the loss of computational resources. Our experimental results on two challenging benchmark datasets (i.e., NTU RGB+D and NTU RGB+D 120) show that the proposed method is comparable to state-of-the-art methods. © 2024 IEEE.

关键词： Musculoskeletal system

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Imitation Learning from Purified Demonstrations 41

Imitation Learning from Purified Demonstrations

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Predicting Plain Text Imageability for Faithful Prompt-Conditional Image Generation 21st

Predicting Plain Text Imageability for Faithful Prompt-Cond...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Chen, Yufeng Yue, Guanghui Liu, Weide Lv, Chenlei Wang, Ruomei Zhou, Fan Zhao, Baoquan School of Artificial Intelligence Sun Yat-sen University Zhuhai China School of Biomedical Engineering Shenzhen University Shenzhen China Harvard Medical School Harvard University Boston United States College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9789819601219

Text imageability is often used to quantize the ease with which a natural language description can invoke a mental image in a reader. With the proliferation of artificial intelligence powered text-to-image generation models, it will likely play an even more significant role in bridging the gap between language and visual representation. Unfortunately, automatically suggesting proper imageable textural prompts from a piece of plain text has scarcely been systematically investigated. In this paper, we narrow the gap by introducing a novel framework for text imageability assessment to automatically predict whether a piece of plain text and a prompt is highly imageable to be fed into a text-to-image model for faithful image generation. We have also developed a new visual-text dataset, named Ted1.6k, to facilitate model training and validation. Experiment results demonstrate the effectiveness of the proposed method in promoting prompt-guided image generation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image representation

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Prior Information Guided Coarse-to-fine Dual-branch Encoding Network for Fovea Localization and Optic Disc/Cup Segmentation

Prior Information Guided Coarse-to-fine Dual-branch Encoding...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Xie, Hai Zhao, Jia Zhao, Danrui Huang, Limin Lei, Baiying Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Southwest University School of Computer Information & Science Shenzhen China

ISBN: (纸本)9798350337488

Fundus images are commonly used to document the presence and severity of various retinal degenerative diseases, where the fovea, optic disc (OD), and optic cup (OC) serve as important anatomical landmarks. Locating and segmenting these landmarks are crucial for clinical diagnosis and treatment. Many existing methods treat the recognition of the fovea, OD, and OC as separate tasks without incorporating any clinical prior knowledge related to various anatomical structures. In this paper, we propose a prior information guided coarse-to-fine dual-branch encoding network, which enables fovea localization and OD/OC segmentation. In coarse stage, we employ a dual-branch network consisting of convolutional neural network (CNN) and Transformer to encode local and global features, and then utilize multi-scale feature fusion techniques to merge the extracted semantic features, aiming to enhance the localization accuracy. In addition, we effectively use the distance information from each pixel to the landmark of interest, and output the results of distance map and heat map regression as prior information to further guide the network to learn the positional relationship between fovea and OD. In fine stage, we refine the region of interest (ROI) of the OD, balance the distribution of the OD and OC using polar coordinate transformation (PCT), extract critical boundary features using the boundary attention module (BAM), and improve the generalization performance of our method through model ensemble strategy. Extensive experimental results demonstrate that our proposed method outperforms existing state-of-the-art (SOTA) methods on the publicly available GAMMA and REFUGE datasets. © 2023 IEEE.

关键词： coarse-to-fine dual-branch fovea localization optic disc and cup segmentation Prior information

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Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

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computers, Materials & Continua 2023年第7期76卷 849-859页

作者： Baowei Wang Neng Wang Yuxiao Zhang Zenghui Xu Junhao Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044JiangsuChina Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044JiangsuChina

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data *** speaking,EHRs are widely used in blockchain-based medical data *** are valuable private assets of patients,and the ownership belongs to *** recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit *** order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate *** paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted *** the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical *** addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical *** the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the *** analysis and security analysis illustrate the completeness and feasibility of the scheme.

关键词： Electronic health records cross-chain medical certificate blockchain data deletion and recovery

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Multi-Scale Promoted Self-Adjusting Correlation Learning for Facial Action Unit Detection

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IEEE Transactions on Affective Computing 2025年第2期16卷 697-711页

作者： Liu, Xin Yuan, Kaishen Niu, Xuesong Shi, Jingang Yu, Zitong Yue, Huanjing Yang, Jingyu Tianjin University School of Electrical and Information Engineering Tianjin300072 China Lappeenranta-Lahti University of Technology Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta53850 Finland Beijing Institute for General Artificial Intelligence Beijing100080 China Xi’an Jiaotong University School of Software Engineering Xi’an710049 China Great Bay University Dongguan523000 China

Facial Action Unit (AU) detection is a crucial task in affective computing and social robotics as it helps to identify emotions expressed through facial expressions. Anatomically, there are innumerable correlations between AUs, which contain rich information and are vital for AU detection. Previous methods used fixed AU correlations based on expert experience or statistical rules on specific benchmarks, but it is challenging to comprehensively reflect complex correlations between AUs via hand-crafted settings. There are alternative methods that employ a fully connected graph to learn these dependencies exhaustively. However, these approaches can result in a computational explosion and high dependency with a large dataset. To address these challenges, this paper proposes a novel self-adjusting AU-correlation learning (SACL) method with less computation for AU detection. This method adaptively learns and updates AU correlation graphs by efficiently leveraging the characteristics of different levels of AU motion and emotion representation information extracted in different stages of the network. Moreover, this paper explores the role of multi-scale learning in correlation information extraction, and design a simple yet effective multi-scale feature learning (MSFL) method to promote better performance in AU detection. By integrating AU correlation information with multi-scale features, the proposed method obtains a more robust feature representation for the final AU detection. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on widely used AU detection benchmark datasets, with only 28.7% and 12.0% of the parameters and FLOPs of the best method, respectively. © 2010-2012 IEEE.

关键词： Contrastive Learning

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