检索结果-内蒙古大学图书馆

IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoli Tang Han Yu Xiaoxiao Li College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Auction-based Federated Learning (AFL) has attracted extensive research interest due to its ability to motivate data owners (DOs) to join FL through economic means. While many existing AFL methods focus on providing decision support to model tusers (MUs) and the AFL auctioneer, decision support for data owners remains open. To bridge this gap, we propose a first-of-its-kind agent-oriented joint Pricing, Acceptance and Sub-delegation decision support approach for data owners in AFL (PAS-AFL). By considering a DO’s current reputation, pending FL tasks, willingness to train FL models, and its trust relationships with other DOs, it provides a systematic approach for a DO to make joint decisions on AFL bid acceptance, task sub-delegation and pricing based on Lyapunov optimization to maximize its utility. It is the first to enable each DO to take on multiple FL tasks simultaneously to earn higher income for DOs and enhance the throughput of FL tasks in the AFL ecosystem. Extensive experiments based on six benchmarking datasets demonstrate significant advantages of PAS-AFL compared to six alternative strategies, beating the best baseline by 28.77% and 2.64% on average in terms of utility and test accuracy of the resulting FL models, respectively.

关键词： Training Economics Systematics Federated learning Ecosystems Buildings Pricing

来源：评论

学校读者我要写书评

暂无评论

AGENT-ORIENTED JOINT DECISION SUPPORT FOR data OWNERS IN AUCTION-BASED FEDERATED LEARNING

arXiv

引用

arXiv 2024年

作者： Tang, Xiaoli Yu, Han Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada

关键词： Costs

来源：评论

学校读者我要写书评

暂无评论

RIS-Assisted Localization: A Novel Conditional Sample Mean Approach without CSI

arXiv

引用

arXiv 2025年

作者： Yao, Jiawei Mao, Yijie Chen, Mingzhe School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL United States

Reconfigurable intelligent surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. In this work, we propose a novel multiple RISs aided localization approach to address these challenges. The proposed method first estimates the angle-of-directions (AoDs) between the RISs and the user using the conditional sample mean approach, and then uses the estimated multiple AoD pairs to determine the user’s position. This approach only requires measuring the received signal strength at the BS for a set of randomly generated phase shifts across all RISs, thereby eliminating the need for real-time RIS phase shift design or user-to-BS pilot transmissions. Numerical results show that the proposed localization approach improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods. Copyright © 2025, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Monitoring Oceanographic and Cryosphere Changes: A Remote Sensing Approach to Climate-Induced Marine and Polar Dynamics

引用

Remote Sensing in Earth Systems sciences 2025年第2期8卷 496-509页

作者： Shaik, Reddi Khasim Jeslet, D. Santhi Aroulanandam, Vijay Vasanth Siva, B.A. Painam, Ranjith Kumar Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India Department of Computer Science M.G.R. College Hosur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattakalathur India Department of ECE Chennai Institute of Tech Chennai India Department of Electronics and Communication Engineering Kallam Haranadhareddy Institute of Technology (Autonomous) Guntur Chowdavaram India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai-62 Avadi India

Climate change has accelerated the dynamics of cryospheric and oceanographic systems, leading to rising sea levels, increased ice melt, and significant shifts in polar regions. Monitoring these changes is crucial, yet traditional observational methods are often impractical in remote, harsh environments. This study presents a novel approach to monitoring oceanographic and cryospheric changes induced by climate dynamics, using a cascaded long short-term memory (LSTM) and recurrent neural network (RNN) model with ICESat and supplementary remote sensing data. The model leverages ICESat’s high-resolution laser altimetry for precise measurements of ice thickness and ice sheet elevation, combined with data from MODIS, Sentinel-1, and CryoSat-2 for comprehensive insights. Our results indicate that the cascaded LSTM-RNN model achieves high predictive accuracy, with root mean square error (RMSE) values as low as 0.08 m for ice thickness and a mean absolute error (MAE) of 0.05 m. Additionally, it attains a correlation coefficient of 0.96, significantly outperforming other models like standalone LSTM (RMSE 0.12 m, MAE 0.09 m) and CNN-LSTM hybrid (RMSE 0.10 m, MAE 0.08 m). This model captures both seasonal and long-term trends in ice dynamics, highlighting its utility for robust climate monitoring. The successful integration of ICESat and other remote sensing data demonstrates the potential for advanced machine learning techniques to enhance predictions of polar and oceanographic changes, supporting climate adaptation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change Cryosphere Ice thickness ICESat LSTM-RNN Machine learning Oceanographic changes Predictive modeling Remote sensing Sea ice extent

来源：评论

学校读者我要写书评

暂无评论

When Zero-Trust Meets Federated Learning

When Zero-Trust Meets Federated Learning

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Xinran Wang, Dan Zhu, Yifei Chen, Weilong Chang, Zheng Han, Zhu The Hong Kong Polytechnic University Department of Computing Hong Kong Shanghai Jiao Tong University UM-SJTU Joint Institute Shanghai China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Jyväskylä Faculty of Information Technology Jyväskylä Finland University of Houston Department of Electrical and Computer Engineering HoustonTX United States Kyung Hee University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350351255

Nowadays, Federated Learning (FL) has emerged as a promising and critical machine learning scheme to protect data privacy and reduce communication overhead. As the scale and connectivity expand in the FL system, enhancing the model's robustness against security threats from malicious clients grows ever more critical. An effective defensive solution involves selecting benign clients appropriately, thereby mitigating the vulnerability of the FL system to malicious attacks. However, clients exhibit varying behaviors over time, which complicates the task of accurately modeling their future trustworthiness. Moreover, blindly trusting clients with high trust values poses risks, given the potential for severe losses from betrayal. To tackle these problems, we propose a zero-trust policy in FL aimed at establishing continuous trust in each client while maintaining skepticism towards potential betrayal attacks. Specifically, we develop a Dirichlet-based trust evaluation technique to enable a comprehensive selection of trustworthy participants. This technique leverages the posterior distribution to estimate clients' trust values from their evolving behavior records over time. Then, we anticipate potential betrayal from a selected client and formulate a min-max optimization problem to minimize the worst-case betrayal loss, thereby boosting the system's betrayalaware robustness. Next, we convert this problem into a convex optimization problem and utilize the interior point method for resolution. We conduct extensive simulations to validate the efficacy of our proposed zero-trust policy in accurately assessing trust and enhancing the model's robustness to betrayal. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Physics-based fuid simulation in computer graphics: Survey, research trends, and challenges

引用

Computational Visual Media 2024年第5期10卷 803-858页

作者： Xiaokun Wang Yanrui Xu Sinuo Liu Bo Ren Jiri Kosinka Alexandru C.Telea Jiamin Wang Chongming Song Jian Chang Chenfeng Li Jian Jun Zhang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering and School of Intelligence Science and Technologyand Shunde Innovation School University of Science and Technology BeijingBeijing 100083China National Center for Computer Animation Faculty of Media and CommunicationBournemouth UniversityPoole BH125BBUK. Bernoulli Institute University of GroningenGroningen 9700 AKthe Netherlands School of Computer Science Peking UniversityBeijing 100871China. TMCC College of Computer ScienceNankai UniversityTianjin 300350China Department of Information and Computing Sciences Utrecht UniversityUtrecht 3584 CCthe Netherlands. Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwansea SA18ENUK. Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China.

Physics-based fluid simulation has played an increasingly important role in the computer graphics *** methods in this area have greatly improved the generation of complex visual effects and its computational *** techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine *** parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research *** this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last *** by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and *** a practical perspective,we give an overview of existing implementations available for the above methods.

关键词： computer graphics physical simulation fluid simulation fluid coupling

来源：评论

学校读者我要写书评

暂无评论

Ripple Sparse Self-Attention for Monaural Speech Enhancement 48

Ripple Sparse Self-Attention for Monaural Speech Enhancement

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Qiquan Zhu, Hongxu Song, Qi Qian, Xinyuan Ni, Zhaoheng Li, Haizhou University of New South Wales School of Electrical Engineering and Telecommunications Australia National University of Singapore Department of Electrical and Computer Engineering Singapore Alibaba Group China Meta United States The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally prohibited for long speech recordings. Moreover, it allows each time frame to attend to all time frames, neglecting the strong local correlations of speech signals. This study presents a simple yet effective sparse self-attention for speech enhancement, called ripple attention, which simultaneously performs fine- and coarse-grained modeling for local and global dependencies, respectively. Specifically, we employ local band attention to enable each frame to attend to its closest neighbor frames in a window at fine granularity, while employing dilated attention outside the window to model the global dependencies at a coarse granularity. We evaluate the efficacy of our ripple attention for speech enhancement on two commonly used training objectives. Extensive experimental results consistently confirm the superior performance of the ripple attention design over standard full self-attention, blockwise attention, and dual-path attention (Sep-Former) in terms of speech quality and intelligibility. © 2023 IEEE.

关键词： Speech enhancement

来源：评论

学校读者我要写书评

暂无评论

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

引用

computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

来源：评论

学校读者我要写书评

暂无评论

Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Cloud computing Based POC Diagnostic Device: Rapid Infectious Disease Testing by data Analytics 1

Cloud Computing Based POC Diagnostic Device: Rapid Infectiou...

引用

1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Basha, H. Anwer Ramalingam, L. Hasan, Md Ashfaqul Ananda, M.H. Velmurugan, S. Maniraj, S.P. Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Reva University School of Electrical and Electronics Engineering Karnataka Bangalore India Tjs Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350330915

To meet the growing need for affordable, efficient, and accurate diagnostic tools, the system has developed a Raspberry Pi-based Point-of-Care (POC) gadget for speedy and reliable detection of infectious illnesses. This groundbreaking gadget, built on the flexible and powerful Raspberry Pi computer, ushers in a new era of state-of-the-art POC diagnostics by combining molecular and image analysis methods. Complex testing for pathogens like COVID-19 and malaria may be carried out using this state-of-the-art system, which integrates a camera module and custom biosensors. The device's software parses sensor data, improves diagnostic precision using machine learning techniques, and presents a straightforward interface for clinicians and patients alike. This study takes a holistic approach by considering all relevant factors, including adherence to regulations, user education, upkeep, and data management. Our POC diagnostic gadget driven by a Raspberry Pi provides a low-cost, high-throughput method for diagnosing illness, which may have far-reaching implications for the future of healthcare delivery in both under-resourced and well-endowed regions. © 2023 IEEE.

关键词： Healthcare Infectious Disease Internet of Things, Cloud computing Point-of-Care Diagnostics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：