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Automatic Repeat Request Design in AoI-Aware Broadcast With Heterogeneous Direct and Relay-Assisted Users

IEEE Internet of Things Journal 2025年

作者： Wang, Xueer Pan, Haoyuan Chan, Tse-Tin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

This paper investigates the design of automatic repeat request (ARQ) protocols in age of information (AoI)-aware broadcast systems with heterogeneous users, including both direct and relay-assisted users. In this setup, a direct user receives status updates directly via a single-hop link, while a relay-assisted user can receive status updates through either a direct single-hop link or a two-hop relay-assisted link. While ARQ is commonly used to ensure reliable transmission in error-prone wireless networks, previous studies suggest that ARQ does not improve the average AoI in single-hop networks. However, its impact on AoI in systems with relays, particularly those involving heterogeneous users, remains unclear. We address this gap by analyzing the average AoI under different ARQ strategies, introducing a transmission limit k ≥ 0 at the relay. Here, k = 0, k = 1, and k > 1 correspond to non-relay, non-ARQ-at-relay, and truncated-ARQ-at-relay strategies, respectively. Utilizing a unified Markov chain framework that models the transmission processes for each user type, we derive the theoretical average AoI. Our results show that the direct user benefits most from non-relay and non-ARQ strategies, similar to single-hop systems. In contrast, the relay-assisted user achieves optimal performance with truncated-ARQ-at-relay, leveraging both direct and relay-assisted links. For the overall system, applying a non-ARQ-at-relay approach strikes a balance in AoI between the direct and relay-assisted users, leading to a more stable and lower system-wide average AoI. © 2014 IEEE.

关键词： Mobile telecommunication systems

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A novel clinical artificial intelligence model for disease detection via retinal imaging

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The Innovation 2024年第2期5卷 11-12页

作者： Yidian Fu Liang Ma Sheng Wan Shengfang Ge Zhi Yang Department of Ophthalmology Ninth People’s HospitalShanghai JiaoTong University School of MedicineShanghai 200011China Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology Shanghai 200011China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of EducationNanjing 210000China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing 210000China

Artificial intelligence(AI)is an emerging field in which computerized systems are used to carry out complex tasks in place of *** AI algorithms have been developed for disease diagnosis and prediction and treatment recommendation across various clinical data types,e.g.,chest X-rays,electrocardiograms,and other radiological images.1 In ophthalmology,particularly,great progress has been made in AI systems over the past *** fundus photography(CFP)and optical coherence tomography(OCT),which are readily available in routine clinical practice,are both mainstream and useful retinal imaging modalities in *** September 2023,the 2023 Lasker-Debakey Clinical Medical Research Award was awarded to three scientists for their work on OCT for accurate retinal disease detection.

关键词： clinical routine diagnosis

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RaCMC: Residual-Aware Compensation Network with Multi-Granularity Constraints for Fake News Detection

arXiv

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arXiv 2024年

作者： Yu, Xinquan Sheng, Ziqi Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering Ministry of Education Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist in cross-modal feature fusion and refinement for classification. To address this, we present a residual-aware compensation network with multi-granularity constraints (RaCMC) for fake news detection, that aims to sufficiently interact and fuse cross-modal features while amplifying the differences between real and fake news. First, a multiscale residual-aware compensation module is designed to interact and fuse features at different scales, and ensure both the consistency and exclusivity of feature interaction, thus acquiring high-quality features. Second, a multi-granularity constraints module is implemented to limit the distribution of both the news overall and the image-text pairs within the news, thus amplifying the differences between real and fake news at the news and feature levels. Finally, a dominant feature fusion reasoning module is developed to comprehensively evaluate news authenticity from the perspectives of both consistency and inconsistency. Experiments on three public datasets, including Weibo17, Politifact and GossipCop, reveal the superiority of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Fake detection

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Clustering Interval and Triangular Granular Data: Modeling, Execution, and Assessment

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10000-10014页

作者： Yiming Tang Wenbin Wu Witold Pedrycz Jianwei Gao Xianghui Hu Zhaohong Deng Rui Chen Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada School of Computer and Information Hefei University of Technology Hefei China Faculty of Automatic Control Electronics and Computer Science Silesian University of Tecghnology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul TÃrkiye School of Computer Science and Engineering Southeast University Nanjing China School of Artificial Intelligence and Computer Jiangnan University Wuxi Jiangsu China

In current granular clustering algorithms, numeric representatives were selected by users or an ordinary strategy, which seemed simple; meanwhile, weight settings for granular data could not adequately express their structural characteristics. Aiming at these problems, in this study, a new scheme called a granular weighted kernel fuzzy clustering (GWKFC) algorithm is put forward. We propose the representative selection and granularity generation (RSGG) algorithm enlightened by the density peak clustering (DPC) algorithm. We build interval and triangular granular data on the strength of numeric representatives obtained by RSGG under the principle of justifiable granularity (PJG), in which we establish some combinations of functions and boundary constraints and prove their properties. Furthermore, we present a novel distance formula via the kernel function for granular data and design new weights to affect the coverage and specificity of granular data. In addition, based upon these factors, we come up with the GWKFC algorithm of granular clustering, and its performance with different granularity is assessed. To sum up, a macro framework involving granular modeling, granular clustering, and assessment has been set up. Lastly, the GWKFC algorithm and ten other granular clustering algorithms are compared by experiments on some artificial and UCI datasets together with datasets with large data or those of high dimensionality. It is found that the GWKFC algorithm can provide better granular clustering results by contrast with other algorithms. The originality is embodied as follows. First, we improve the previous density radius and present the RSGG algorithm to acquire numeric representatives. Second, we propose a new strategy to determine granular data boundaries and further obtain novel weights enlightened by the idea of volume. Lastly, we employ the kernel function to calculate the distance between granular data, which has a stronger spatial division ability than the pr

关键词： Clustering algorithms Data models Computational modeling Numerical models Kernel Phase change materials Granular computing Uncertainty Fuzzy sets Noise

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SecBCS: a secure and privacy-preserving blockchain-based crowdsourcing system

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science China(information sciences) 2020年第3期63卷 20-33页

作者： Chao LIN Debiao HE Sherali ZEADALLY Neeraj KUMAR Kim-Kwang Raymond CHOO Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and Engineering Wuhan University State Key Laboratory of Cryptology College of Communication and Information University of Kentucky Department of Computer Science and Engineering Thapar University Department of Information Systems and Cyber Security and Department of Electrical and Computer Engineering University of Texas at San Antonio

A robust and scalable crowd management infrastructure is crucial in addressing operational challenges when deploying high-density sensors and actuators in a smart city. While crowdsourcing is widely used in crowd management, conventional solutions, such as Upwork and Amazon Mechanical Turk, generally depend on a trusted third-party platform. There exist several potential security concerns(e.g., sensitive leakage, single point of failure and unfair judgment) in such a centralized paradigm. Hence, a recent trend in crowdsourcing is to leverage blockchain(a decentralized ledger technology) to address some of the existing limitations. A small number of blockchain-based crowdsourcing systems(BCSs) with incentive mechanisms have been proposed in the literature, but they are generally not designed with security in mind. Thus, we study the security and privacy requirements of a secure BCS and propose a concrete solution(i.e., SecBCS)with a prototype implementation based on JUICE.

关键词： crowdsourcing blockchain secure privacy-preserving smart contract

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AR-CNN: an attention ranking network for learning urban perception

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science China(information sciences) 2022年第1期65卷 164-174页

作者： Zhetao LI Ziwen CHEN Wei-Shi ZHENG Sangyoon OH Kien NGUYEN Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Data and Computer Science Sun Yat-sen University Department of Computer and Information Engineering Ajou University Graduate School of Engineering Chiba University

An increasing number of deep learning methods is being applied to quantify the perception of urban environments, study the relationship between urban appearance and resident safety, and improve urban appearance. Most advanced methods extract image feature representations from street-level images through conventional visual computation algorithms or deep convolutional neural networks and then directly predict the results using features. Unfortunately, these methods take color and texture information together during processing. Color and texture are prime image features, and they affect human perception and judgment differently. We argue that color and texture should be operated differently; therefore, we formulate an end-to-end learning methodology to process input images according to color and texture information before inputting it into the neural network. The processed images and the original image constitute three input streams for the triad attention ranking convolutional neural network(AR-CNN) model proposed in this *** accordance with the aspects of color and texture, an improved attention mechanism in the convolution layer is proposed. Our objective is to obtain the scores of humans on urban appearance in accordance with the prediction results computed from pairwise comparisons generated by the AR-CNN model.

关键词： ranking network urban perception attribute learning attention network colour and texture

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Implementation of a Tunisian Coin Recognition Embedded System with Deep Learning

Implementation of a Tunisian Coin Recognition Embedded Syste...

引用

Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Amina Kessentini Rania Ghanem Tasnim Ben Mouhamed Nouri Masmoudi National Engineering School of Sfax University of Sfax Laboratory of Electronics and Information Technologies Safx Tunisia Department of industrial computing Higher Institute of Computer Science and Multimedia Gabes Gabes Tunisia

ISBN: (数字)9798350389838

ISBN: (纸本)9798350389845

Managing a large volume of coins poses a complex challenge in various fields, particularly within Tunisian administrations, specifically in terms of counting and classification. In this case, several coin recognition systems are distinguished, such as systems based on mechanical, electromagnetic, and image processing methods. However, these systems may suffer from accuracy and reliability shortcomings and are sensitive to deception. To avoid these problems, we have developed an intelligent system based on convolutional models (CNN) with the aim of automating coin detection, classification, and counting. Additionally, we have expanded our database by integrating a generative neural network model (GAN). The main goal of our paper is to develop a robust and precise solution capable of efficiently processing a large volume of coins quickly and presenting the results in a mobile application connected to the system. Our work combines the latest technologies to assist Tunisian administrations in better managing their finances. The accuracy result has achieved up to 97.8 % with real time detection.

关键词： Deep learning Measurement Accuracy Webcams Neural networks Mobile communication Software Real-time systems Software reliability Mobile applications

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Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

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computers, Materials & Continua 2022年第4期71卷 159-170页

作者： Dezheng Zhang Qi Jia Shibing Yang Xinliang Han Cong Xu Xin Liu Yonghong Xie School of Computer&Communication Engineering University of Science&Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Inspur Electronic Information Industry Co. Ltd.&State Key Laboratory of High-End Server&Storage TechnologyJinan250101China Surgical Simulation Research Lab Department of SurgeryUniversity of AlbertaEdmontonT6G 2E1AlbertaCanada

Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in *** analyze the reasoning path patterns from symptom to syndromes on the knowledge *** are two kinds of path patterns in the knowledge graph:one-hop and *** one-hop path pattern maps the symptom to syndromes *** two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic *** the different support strengths for the knowledge paths in reasoning,we design a dynamic weight *** utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score *** proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path *** evaluate the method with clinical records and clinical practice in *** preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in ***,the method is robust and explainable under the guide of the knowledge *** could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.

关键词： Traditional Chinese medicine automated diagnosis knowledge graph Naïve Bayes syndrome differentiation

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WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equivalent Permittivity

WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equi...

引用

2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Hu, Pengming Yang, Weidong Wang, Xuyu Mao, Shiwen Shen, Erbo Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Knight Foundation School of Computing and Information Sciences Florida International University FL33199 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States

ISBN: (纸本)9781665494274

Moisture content is one of the important indexes of food storage security. The existing detection methods are time-consuming and high cost such that it is difficult to realize online moisture detection. In this paper, according to the dielectric properties of wheat, we propose a wheat moisture content detection system with commercial Wi-Fi devices, termed WiWm-EP. First, we introduce the relationship between the moisture content of wheat and its dielectric constant. Then, we establish an equivalent permittivity (EP) model to characterize wheat moisture content, where the EP can be calculated from channel state information (CSI) of the dual antenna model. Besides, we build the fitting function between the EP and the moisture content as the wheat moisture detection model. Finally, we evaluate the performance of the system through different experiments. The average relative error of the detection results of five wheat samples with different moisture contents is less than 3%. © 2023 IEEE.

关键词： Permittivity

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Questionable Fairness in Federated Learning

Questionable Fairness in Federated Learning

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Conference on Data science and Machine Learning Applications (CDMA)

作者： Raed Alharbi Truc Nguyen Nooshin Yousefzadeh Ahmed Aljohani College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Computatioual Science Center National Renewable Energy Laboratory Golden CO USA Computer and Information Science and Engineering Department University of Florida Gainesville FL USA Computer Science and Engineering University of North Texas Denton USA

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. This paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. The experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning.

关键词： Training Deep learning Data privacy Federated learning Prevention and mitigation Distributed databases Data science Data models Servers Multiaccess communication

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