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A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

CAAI Transactions on Intelligence technology 2024年第1期9卷 153-166页

作者： Xiaoling Luo Wei Wang Yong Xu Zhihui Lai Xiaopeng Jin Bob Zhang David Zhang Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina The Department of Computer and Information Science University of MacaoMacaoMacaoChina The Chinese University of Hong Kong(Shenzhen) ShenzhenChina

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of *** present,deep convolutional neural networks have achieved promising performance in automatic DR detection *** convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for ***,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(*** and exudates)scattered throughout the whole *** proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR ***-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as *** Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained *** experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

关键词： image classification medical image processing pattern recognition

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Joint Routing and Scheduling for In-Vehicle Networks: A Deterministic Perspective

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Wenxun Wang, Wenyong Yu, Haisheng Wu, Bochun Zou, Sai Ni, Wei Zhang, Ji School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China International Institute of Next Generation Internet Macau University of Science and Technology Macau China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China College of Big Data and Information Engineering Guizhou University Guiyang China Data61 Business Unit CSIRO Canberra ACT Australia PA Company SC Branch China Telecom Co. LTD Chendu China

Deterministic transmission refers to the guaranteed delivery of data to its destination within a specific time frame along an optimal route, making it an indispensable prerequisite for the operation of autonomous vehicles. The in-vehicle networks (IVNs) face significant challenges in the deterministic transmission of large volumes of perception and control data from and to various vehicle components. In this paper, the cooperative scheduling and routing problem among multi-area control units (ACUs) in IVN is discussed from the view of deterministic transmission, and a ResVmix method is proposed to find the optimal two-layer assignment with ACU cooperation. The multi-cycle queuing and forwarding (multi-CQF) mechanism is employed to schedule the perceived data according to different quality of service (QoS) requirements. The cooperation among multiple ACUs enables deterministic routing of data. The two-layer optimization problems on multi-CQF and ACU are formulated as a+ decentralized partially observable Markov decision processes (Dec-POMDP) and solved using the enhanced multi-agent deep reinforcement learning (MADRL) method. Simulation results demonstrate that ResVmix significantly enhances the deterministic transmission performance of IVN. Compared to traditional shortest path algorithms and state-of-the-art MADRL methods, ResVmix achieves a 22.4% increase in arrival rate and a 20.1% increase in confirmed arrivals, respectively. IEEE

关键词： Reinforcement learning

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Expressive Access Control Encryption With Bilateral Policy Privacy for Consumer Electronics in the Metaverse

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IEEE Transactions on Consumer Electronics 2024年

作者： Peng, Zongfeng Peng, Changgen Xu, Dequan Ding, Hongfa Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Guizhou University of Finance and Economics College of Information Guiyang550025 China

The metaverse offers an unprecedented immersive experience and a fresh connection paradigm for consumers, which brings security threats related to unauthorized data access, potential misuse, and increasing attacks in the bilateral information flow. Therefore, it is necessary to provide an efficient and secure access control scheme. However, current efforts struggle to address these more complicated security issues. To do this, in this paper, we present an expressive access control encryption (EACE) scheme for bilateral control of information flow in metaverse-enabled consumer electronics. Our EACE supports fine-grained and bilateral control of senders and receivers by carefully taking advantage of access control encryption, dual-policy attribute-based encryption, and matchmaking encryption. Furthermore, we propose a novel sanitization algorithm with sender anonymity and bilateral policy privacy against the malicious sanitizer. We formalize the corresponding security definitions and give rigorous proofs. Performance analysis demonstrates that our EACE can simultaneously achieve bilateral control with policy privacy, sender anonymity against sanitizer, and lightweight decryption. The experimental results show that our proposal costs less computational and storage overhead in the symmetric bilinear group. The impressive performance indicates that our EACE is an excellent choice to balance security and efficiency when consumer electronics meet the metaverse. © 1975-2011 IEEE.

关键词： Differential privacy

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Fuzzy Password Authentication key Exchange Protocol in Universal Composable Framework for Blockchain Privacy Protection

SSRN

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SSRN 2024年

作者： Chen, Qihong Peng, Changgen Xu, Dequan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Big Data Academy Guizhou University Guiyang550025 China

In this paper, we construct a lattice-based fuzzy password authentication key exchange(fPAKE) protocol in the framework of Universal Composable(UC) security. Through the known password authentication key exchange (PAKE) scheme, the Randomised Fuzzy Equality(RFE) protocol and the Oblivious Transfer(OT) protocol are introduced to improve PAKE into a variant of PAKE-fPAKE. Firstly, the users go through two rounds of Oblivious Transfer protocol, and then the key exchange is achieved based on the information exchanged. fPAKE satisfies that even if there is noise in the passwords between users, key exchange is still *** is suitable for more application scenarios compared to PAKE, and the construction is UC-secure, which can be more securely and efficiently applied to blockchain scenarios. © 2024, The Authors. All rights reserved.

关键词： Blockchain

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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning 7

Tensor Graph Convolutional Network for Dynamic Graph Represe...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application For Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9798350363173

Dynamic graphs (DG) represent evolving interactions between entities in various real-world scenarios. Many existing DG representation learning models employ a combination of graph convolutional networks and sequence neural networks to capture spatial-temporal dependencies. However, this hybrid approach often fails to effectively capture the spatial-temporal continuity inherent in DGs. In this paper, we propose a novel approach called the Tensor Graph Convolutional Network (TGCN) to learn DG representations within a unified convolution framework based on tensor products. Our approach is founded on two key principles: (a) representing DG information in tensor form, and (b) leveraging tensor product operations to design a graph convolutional network that simultaneously models spatial and temporal features. Experimental results on three real-world DG datasets demonstrate that our proposed model achieves state-of-the-art performance. © 2024 IEEE.

关键词： Tensors

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Threshold Attention Network for Semantic Segmentation of Remote Sensing Images

arXiv

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arXiv 2025年

作者： Long, Wei Zhang, Yongjun Cui, Zhongwei Xu, Yujie Zhang, Xuexue State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China School of Mathematics and Big Data Guizhou Education University Guiyang550018 China

Semantic segmentation of remote sensing images is essential for various applications, including vegetation monitoring, disaster management, and urban planning. Previous studies have demonstrated that the self-attention mechanism (SA) is an effective approach for designing segmentation networks that can capture long-range pixel dependencies. SA enables the network to model the global dependencies between the input features, resulting in improved segmentation outcomes. However, the high density of attentional feature maps used in this mechanism causes exponential increases in computational complexity. Additionally, it introduces redundant information that negatively impacts the feature representation. Inspired by traditional threshold segmentation algorithms, we propose a novel threshold attention mechanism (TAM). This mechanism significantly reduces computational effort while also better modeling the correlation between different regions of the feature map. Based on TAM, we present a threshold attention network (TANet) for semantic segmentation. TANet consists of an attentional feature enhancement module (AFEM) for global feature enhancement of shallow features and a threshold attention pyramid pooling module (TAPP) for acquiring feature information at different scales for deep features. We have conducted extensive experiments on the ISPRS Vaihingen and Potsdam datasets. The results demonstrate the validity and superiority of our proposed TANet compared to the most state-of-the-art models. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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MoCha-Stereo: Motif Channel Attention Network for Stereo Matching

MoCha-Stereo: Motif Channel Attention Network for Stereo Mat...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Ziyang Chen Wei Long He Yao Yongjun Zhang Bingshu Wang Yongbin Qin Jia Wu The State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Artificial Intelligence Guizhou University College of Software Northwest Polytechnical University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Learning-based stereo matching techniques have made significant progress. However, existing methods inevitably lose geometrical structure information during the feature channel generation process, resulting in edge detail mis-matches. In this paper, the Motif Channel Attention Stereo Matching Network (MoCha-Stereo) is designed to address this problem. We provide the Motif Channel Correlation Volume (MCCV) to determine more accurate edge matching costs. MCCV is achieved by projecting motif channels, which capture common geometric structures in feature channels, onto feature maps and cost volumes. In addition, edge variations in the reconstruction error map also affect details matching, we propose the Reconstruction Error Motif Penalty (REMP) module to further refine the full-resolution disparity estimation. REMP integrates the frequency information of typical channel features from the re-construction error. MoCha-Stereo ranks 1st on the KITTI-2015 and KITTI-2012 Reflective leaderboards. Our structure also shows excellent performance in Multi- View Stereo.

关键词： computer vision Costs Correlation Accuracy Estimation Pattern matching

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Cryptanalysis and Improvement of 'Group public key Encryption Supporting Equality Test Without Bilinear Pairings'

SSRN

引用

SSRN 2023年

作者： Tian, Youliang Zhang, Qijia State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

public key encryption with equality test (PKEET) is a novel primitive which supports equality comparisons on two encrypted messages. Currently, most of the existing PKEET schemes are based on bilinear pairing and require heavy computational overheads. To address this issue, Shen et al. recently proposed an efficient group public key encryption supporting equality test without bilinear pairings scheme. Compared with other schemes, their scheme reduces the usage of expensive bilinear pairing operations and enjoys higher computation efficiency. They claimed that their scheme achieved one-wayness security in the random oracle model and resisted offline message recovery attack. In this letter, we analyze Shen et al.’s scheme through two concrete attacks and demonstrate that their scheme can not support the above two security requirements. An improved scheme is provided to overcome the security vulnerabilities in their scheme. © 2023, The Authors. All rights reserved.

关键词： public key cryptography

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Confidence-aware Contrastive Learning for Selective Classification 41

Confidence-aware Contrastive Learning for Selective Classifi...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Wu, Yu-Chang Lyu, Shen-Huan Shang, Haopu Wang, Xiangyu Qian, Chao National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China

Selective classification enables models to make predictions only when they are sufficiently confident, aiming to enhance safety and reliability, which is important in high-stakes scenarios. Previous methods mainly use deep neural networks and focus on modifying the architecture of classification layers to enable the model to estimate the confidence of its prediction. This work provides a generalization bound for selective classification, disclosing that optimizing feature layers helps improve the performance of selective classification. Inspired by this theory, we propose to explicitly improve the selective classification model at the feature level for the first time, leading to a novel Confidence-aware Contrastive Learning method for Selective Classification, CCL-SC, which similarizes the features of homogeneous instances and differentiates the features of heterogeneous instances, with the strength controlled by the model's confidence. The experimental results on typical datasets, i.e., CIFAR-10, CIFAR-100, CelebA, and ImageNet, show that CCL-SC achieves significantly lower selective risk than state-of-the-art methods, across almost all coverage degrees. Moreover, it can be combined with existing methods to bring further improvement. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest 27

The Role of Depth, Width, and Tree Size in Expressiveness of...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Lyu, Shen-Huan Wu, Jin-Hui Zheng, Qin-Cheng Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China

ISBN: (纸本)9781643685489

Random forests are classical ensemble algorithms that construct multiple randomized decision trees and aggregate their predictions using naive averaging. Zhou and Feng [51] further propose a deep forest algorithm with multi-layer forests, which outperforms random forests in various tasks. The performance of deep forests is related to three hyperparameters in practice: depth, width, and tree size, but little has been known about its theoretical explanation. This work provides the first upper and lower bounds on the approximation complexity of deep forests concerning the three hyperparameters. Our results confirm the distinctive role of depth, which can exponentially enhance the expressiveness of deep forests compared with width and tree size. Experiments validate these theoretical findings. The detailed proof and code are available in the full version [31]. © 2024 The Authors.

关键词： Random forests

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