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arXiv 2022年

作者： Wang, Yuntao Su, Zhou Benslimane, Abderrahim Xu, Qichao Dai, Minghui Li, Ruidong School of Cyber Science and Engineering Xi’an Jiaotong University China Laboratory of Computer Sciences Avignon University France School of Mechatronic Engineering and Automation Shanghai University China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Department of Electrical and Computer Engineering Kanazawa University Japan

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs’ participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the presence of network dynamics and UAVs’ multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fairness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV’s collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Seamless Interaction Design with Coexistence and Cooperation Modes for Robust Human-Robot Collaboration

arXiv

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arXiv 2022年

作者： Huang, Zhe Mun, Ye-Ji Li, Xiang Xie, Yiqing Zhong, Ninghan Liang, Weihang Geng, Junyi Chen, Tan Driggs-Campbell, Katherine The Department of Electrical and Computer Engineering The University of Illinois Urbana-Champaign United States The Department of Computer Science The University of Illinois Urbana-Champaign United States The Robotics Institute Carnegie Mellon University United States The Coordinated Science Laboratory The University of Illinois Urbana-Champaign United States

A robot needs multiple interaction modes to robustly collaborate with a human in complicated industrial tasks. We develop a Coexistence-and-Cooperation (CoCo) human-robot collaboration system. Coexistence mode enables the robot to work with the human on different sub-tasks independently in a shared space. Cooperation mode enables the robot to follow human guidance and recover failures. A human intention tracking algorithm takes in both human and robot motion measurements as input and provides a switch on the interaction modes. We demonstrate the effectiveness of CoCo system in a use case analogous to a real world multi-step assembly task. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine design

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A Hybrid Swarm Intelligence Model for Efficient Query Optimization in Distributed Databases Using Genetic and Ant Colony Algorithms

A Hybrid Swarm Intelligence Model for Efficient Query Optimi...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Jayasri, Nemala Santosh Kumar, B. Singh, Navdeep Singh, Ajeet Al-Rubaye, Taqi Mohammed Khattab Rao, Mallikarjuna Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India New Horizon College of Engineering Department of Computer Science Engineering Bangalore India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technologies The Islamic University Department of Medical Laboratory Technology Najaf Iraq Griet Department of Cse Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

Optimisation of queries in distributed databases is more imperative to enhance the processing rate and use of resources with limited computational abilities. This work presents a novel GA-ACO hybrid swarm intelligence model to address the query optimization problem. The optimization effort of the proposed model starts with GA for an initial population of probable paths for query execution which uses the concept of evolution to select diverse solutions. After that, ACO strengthens this set by the application of pheromone-based reinforcement that allows adjusting of a query paths toward the best solution or its approximation with the following number values. In this way the enhancement of the convergence rates and the scalability of the proposed solution when distributed databases are used can be explained. The model was tested with artificial query logs with DEAP being used for the implementation of the genetic algorithm while ant colony operations were implemented using Python built ACO libraries. Performance analysis shows the success of the developed model in decreasing query response time as follows: This essentially complex hybrid mechanism shows potential for future use in other areas with large data sets and efficient query handling. © 2025 IEEE.

关键词： Ant colony optimization

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A general survey on medical image super-resolution via deep learning

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computers in Biology and Medicine 2025年 193卷 110345-110345页

作者： Yu, Miao Xu, Zhenghua Lukasiewicz, Thomas Public Health Sciences and Engineering College Tianjin University of Traditional Chinese Medicine Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin China Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health School of Health Sciences and Biomedical Engineering Hebei University of Technology China Institute of Logic and Computation TU Wien Vienna Austria Department of Computer Science University of Oxford Oxford United Kingdom

Medical image super-resolution (SR) is a classic regression task in low-level vision. Limited by hardware limitations, acquisition time, low radiation dose, and other factors, the spatial resolution of some medical images is not sufficient. To address this problem, many different SR methods have been proposed. Especially in recent years, medical image SR networks based on deep learning have been vigorously developed. This survey provides a modular and detailed introduction to the key components of medical image SR technology based on deep learning. In this paper, we first introduce the background concepts of deep learning and medical image SR task. Subsequently, we present a comprehensive analysis of the key components from the perspectives of effective architecture, upsampling module, learning strategy, and image quality assessment of medical image SR networks. Furthermore, we focus on the urgent problems that need to be addressed in the medical image SR task based on deep learning. And finally we summarize the trends and challenges of future development. © 2025 Elsevier Ltd

关键词： Damage detection

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Coded Residual Transform for Generalizable Deep Metric Learning

arXiv

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arXiv 2022年

作者： Kan, Shichao Liang, Yixiong Li, Min Cen, Yigang Wang, Jianxin He, Zhihai School of Computer Science and Engineering Central South University Hunan Changsha410083 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Pengcheng Lab Shenzhen518066 China

A fundamental challenge in deep metric learning is the generalization capability of the feature embedding network model since the embedding network learned on training classes need to be evaluated on new test classes. To address this challenge, in this paper, we introduce a new method called coded residual transform (CRT) for deep metric learning to significantly improve its generalization capability. Specifically, we learn a set of diversified prototype features, project the feature map onto each prototype, and then encode its features using their projection residuals weighted by their correlation coefficients with each prototype. The proposed CRT method has the following two unique characteristics. First, it represents and encodes the feature map from a set of complimentary perspectives based on projections onto diversified prototypes. Second, unlike existing transformer-based feature representation approaches which encode the original values of features based on global correlation analysis, the proposed coded residual transform encodes the relative differences between the original features and their projected prototypes. Embedding space density and spectral decay analysis show that this multi-perspective projection onto diversified prototypes and coded residual representation are able to achieve significantly improved generalization capability in metric learning. Finally, to further enhance the generalization performance, we propose to enforce the consistency on their feature similarity matrices between coded residual transforms with different sizes of projection prototypes and embedding dimensions. Our extensive experimental results and ablation studies demonstrate that the proposed CRT method outperform the state-of-the-art deep metric learning methods by large margins and improving upon the current best method by up to 4.28% on the CUB dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Privacy-Preserving Federated Learning Framework with Resisting Source Inference Attacks in Smart Healthcare

SSRN

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SSRN 2024年

作者： Chenghe, Dong Xu, Guangquan Zhang, Jianhong Bai, Hongpeng Hao, Peng College of Intelligence and Computing Tianjin University Tianjin300354 China School of Big Data Qingdao Huanghai University Qingdao China Tianjin300350 China Department of Electrical and Computer Engineering North China University of Technology Beijing100144 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

Federated Learning (FL) is a distributed machine learning technology that is extensive applications in smart healthcare, since it allows multiple medical institutions to jointly train medical diagnostic models without sharing their local sensitive dataset with others. Despite its benefits, FL is susceptible to source inference attacks and model poisoning. To avoid these issues, we propose a federated learning framework for smart healthcare that preserves privacy and employs certificateless ring signatures to counteract potential attacks. During the collaborative training process of the FL model, certificateless ring signatures are used to obscure the origin of parameter updates, ensuring the anonymity of the parameter sources. This approach has the advantage of being computationally efficient due to no complicated pairing operations. Furthermore, compared with the previous certificateless ring signature schemes, our proposed scheme is more effective in reducing the success rate of source inference attacks, and is efficient in terms of computation and communication overhead. © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Infinite-Q guided modes radiate in the continuum

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Physical Review B 2023年第11期107卷 115415-115415页

作者： Kaili Sun Heng Wei Weijin Chen Yang Chen Yangjian Cai Cheng-Wei Qiu Zhanghua Han Shandong Provincial Key Laboratory of Optics and Photonic Devices Center of Light Manipulation and Applications School of Physics and Electronics Shandong Normal University Jinan 250358 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore 117583 Chinese Academy of Sciences Key Laboratory of Mechanical Behavior and Design of Materials Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei China

Guided modes in photonic structures, with broadband infinite-quality factors, are inaccessible from the far field due to the momentum mismatch. They become leaky modes in the continuum when the mismatch is compensated for by introducing periodic perturbations to form photonic structures. However, the quality factors (Q factors) of such leaky modes deteriorate significantly in most regions of the k-space except at a few discrete high-symmetry points. It is an intriguing question as to whether guided modes can hop above the light cone and yet maintain high Q. Here, we propose a double-band-folding strategy to achieve high-Q leaky modes in compound lattices, exemplified with a one-dimensional grating and a two-dimensional zigzag array of dielectric disks. The Q factor of those leaky modes can be made ultrahigh at arbitrarily any incident angles, showing that such modes do not originate from bound states in the continuum (BICs) above the light cone. Our findings provide unique insight for elucidating the relations between guided modes, BICs, quasi-BICs, and radiation. They further provide a generalized recipe for numerous optical applications such as all-dielectric sensing, lasing, and nonlinear generation with multiple inputs.

关键词： Light propagation, transmission & absorption Metasurfaces Nanophotonics Photonic crystals

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A2B: Anchor to Barycentric Coordinate for Robust Correspondence

arXiv

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arXiv 2023年

作者： Zhao, Weiyue Lu, Hao Cao, Zhiguo Li, Xin The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Lane Department of Computer Science and Electrical Engineering West Virginia University Commonwealth of Virginia Morgantown 15461 United States

There is a long-standing problem of repeated patterns in correspondence problems, where mismatches frequently occur because of inherent ambiguity. The unique position information associated with repeated patterns makes coordinate representations a useful supplement to appearance representations for improving feature correspondences. However, the issue of appropriate coordinate representation has remained unresolved. In this study, we demonstrate that geometric-invariant coordinate representations, such as barycentric coordinates, can significantly reduce mismatches between features. The first step is to establish a theoretical foundation for geometrically invariant coordinates. We present a seed matching and filtering network (SMFNet) that combines feature matching and consistency filtering with a coarse-to-fine matching strategy in order to acquire reliable sparse correspondences. We then introduce DEGREE, a novel anchor-to-barycentric (A2B) coordinate encoding approach, which generates multiple affine-invariant correspondence coordinates from paired images. DEGREE can be used as a plug-in with standard descriptors, feature matchers, and consistency filters to improve the matching quality. Extensive experiments in synthesized indoor and outdoor datasets demonstrate that DEGREE alleviates the problem of repeated patterns and helps achieve state-of-the-art performance. Furthermore, DEGREE also reports competitive performance in the third Image Matching Challenge at CVPR 2021. This approach offers a new perspective to alleviate the problem of repeated patterns and emphasizes the importance of choosing coordinate representations for feature correspondences. Copyright © 2023, The Authors. All rights reserved.

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An Explainable Convolutional Neural Networks for Automatic Segmentation of the Left Ventricle in Cardiac MRI 11

An Explainable Convolutional Neural Networks for Automatic S...

引用

11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Liu, Jun Deng, Feng Yuan, Geng Lin, Xue Song, Houbing Wang, Yanzhi Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science and Technology University Beijing China Department of Electrical and Computer Engineering College of Engineering Northeastern University BostonMA United States Embry-Riddle Aeronautical University Daytona BeachFL United States

ISBN: (纸本)9781643682402

Recently, the study on model interpretability has become a hot topic in deep learning research area. Especially in the field of medical imaging, the requirements for safety are extremely high;Moreover, it is very important for the model to be able to explain. However, the existing solutions for left ventricular segmentation by convolutional neural networks are black boxes;explainable CNNs remains a challenge;explainable deep learning models has always been a task often overlooked in the entire data science lifecycle by data scientists or deep learning engineers. Because of very limited medical imaging data, most solutions currently use transfer learning methods to transfer the model which used on large-scale benchmark data sets (such as ImageNet) to fine tune medical imaging models. Consequently, a large amount of useless parameters are generated, resulting in further barrier for the model to provide a convincing explanation. This paper presents a novel method to automatically segment the Left Ventricle in Cardiac MRI by explainable convolutional neural networks with optimized size and parameters by our enhanced Deep Learning GPU Training System. It is very suitable for deployment on mobile devices. We simplify deep learning tasks on DIGITS systems, monitoring performance, and displaying the heat map of each layer of the network with advanced visualizations in real time. Our experiment results demonstrated that the proposed method is feasible and efficient. © 2022 The authors and IOS Press.

关键词： Image segmentation

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COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

arXiv

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arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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