检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

来源：评论

学校读者我要写书评

暂无评论

A survey on graph neural network-based next POI recommendation for smart cities

引用

Journal of Reliable Intelligent Environments 2024年第3期10卷 299-318页

作者： Yu, Jian Guo, Lucas Zhang, Jiayu Wang, Guiling Department of Computer Science and Soft Engineering Auckland University of Technology Auckland New Zealand School of Information Science and Technology Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China

Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there’s an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially pivotal in smart cities, these systems aim to enhance user experiences by offering location recommendations tailored to past check-ins and visited POIs. Distinguishing itself from traditional POI recommendations, the next POI approach emphasizes predicting the immediate subsequent location, factoring in both geographical attributes and temporal patterns. This approach, while promising, faces with challenges like capturing evolving user preferences and navigating data biases. The introduction of Graph Neural Networks (GNNs) brings forth a transformative solution, particularly in their ability to capture high-order dependencies between POIs, understanding deeper relationships and patterns beyond immediate connections. This survey presents a comprehensive exploration of GNN-based next POI recommendation approaches, delving into their unique characteristics, inherent challenges, and potential avenues for future research. © The Author(s) 2024.

关键词： Location based services

来源：评论

学校读者我要写书评

暂无评论

An IoMT image crypto-system based on spatial watermarking and asymmetric encryption

引用

Multimedia Tools and Applications 2024年第39期83卷 86681-86706页

作者： Kahla, Mohammed El Habib Beggas, Mounir Laouid, Abdelkader AlShaikh, Muath Hammoudeh, Mohammad LIAP Laboratory University of El Oued PO Box 789 El Oued39000 Algeria Computer science department college of Computing and Informatics Saudi Electronic University Abi Bakr As Siddiq Rd Ar Rabi Riyadh11673 Saudi Arabia Information & amp Computer Science Department King Fahd University of Petroleum & ampMinerals Academic Belt Road Dhahran31261 Saudi Arabia

In the growing field of the Internet of Medical Things (IoMT), securing the transmission of medical images over public networks is a critical challenge. Medical images, being highly sensitive and often containing personally identifiable information, require robust protection against unauthorized access and tampering. This paper addresses this challenge by introducing a novel cryptosystem specifically tailored to the resource limitations inherent in IoMT environments. To meet the demand for protecting sensitive information in medical images, the proposed system integrates two layers of security: spatial watermarking and asymmetric encryption. At the core of our approach lies a newly developed, cost-effective spatial watermarking algorithm that seamlessly embeds watermarks within host images to facilitate tamper detection. Complementing this, we employ a resource-efficient Twin Message Fusion (TMF) encryption scheme to ensure confidentiality and integrity during transmission. The proposed cryptosystem is evaluated for both watermarking effectiveness and encryption robustness. Additionally, we assess the system’s suitability for IoMT environments in terms of cost-effectiveness. Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics have been used to assess watermarking resilience against various attacks, while encryption efficacy is analyzed through histogram and entropy evaluations. Furthermore, we compare the complexity of our cryptosystem with five recently proposed techniques in both traditional and IoMT environments, demonstrating superior execution time, reduced image size, and enhanced encryption efficiency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Accelerating local SGD for non-IID data using variance reduction

引用

Frontiers of computer science 2023年第2期17卷 73-89页

作者： Xianfeng LIANG Shuheng SHEN Enhong CHEN Jinchang LIU Qi LIU Yifei CHENG Zhen PAN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China Ant Financial Services Group Hangzhou 310000China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China

Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and ***,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence *** this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous *** extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence ***,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite *** from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical *** conduct experiments on three machine learning *** experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.

关键词： distributed optimization variance reduction local SGD federated learning non-IID data

来源：评论

学校读者我要写书评

暂无评论

Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition 24

Decoupling General and Personalized Knowledge in Federated L...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Xinghao Liu, Xuefeng Niu, Jianwei Wang, Haolin Tang, Shaojie Zhu, Guogang Su, Hao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Department of Management Science and Systems University at Buffalo BuffaloNY United States School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798400706868

To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL) is to decouple general knowledge (shared among clients) and client-specific knowledge, as the latter can have a negative impact on collaboration if not removed. Existing PFL methods primarily adopt a parameter partitioning approach, where the parameters of a model are designated as one of two types: parameters shared with other clients to extract general knowledge and parameters retained locally to learn client-specific knowledge. However, as these two types of parameters are put together like a jigsaw puzzle into a single model during the training process, each parameter may simultaneously absorb both general and client-specific knowledge, thus struggling to separate the two types of knowledge effectively. In this paper, we introduce FedDecomp, a simple but effective PFL paradigm that employs parameter additive decomposition to address this issue. Instead of assigning each parameter of a model as either a shared or personalized one, FedDecomp decomposes each parameter into the sum of two parameters: a shared one and a personalized one, thus achieving a more thorough decoupling of shared and personalized knowledge compared to the parameter partitioning method. In addition, as we find that retaining local knowledge of specific clients requires much lower model capacity compared with general knowledge across all clients, we let the matrix containing personalized parameters be low rank during the training process. Moreover, a new alternating training strategy is proposed to further improve the performance. Experimental results across multiple datasets and varying degrees of data heterogeneity demonstrate that FedDecomp outperforms state-of-the-art methods up to 4.9%. The code is available at https://***/XinghaoWu/FedDecomp © 2024 ACM.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Deep learning:Applications,architectures,models,tools,and frameworks:A comprehensive survey

引用

CAAI Transactions on Intelligence Technology 2023年第3期8卷 581-606页

作者： Mehdi Gheisari Fereshteh Ebrahimzadeh Mohamadtaghi Rahimi Mahdieh Moazzamigodarzi Yang Liu Pijush Kanti Dutta Pramanik Mohammad Ali Heravi Abolfazl Mehbodniya Mustafa Ghaderzadeh Mohammad Reza Feylizadeh Saeed Kosari School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)ShenzhenChina Department of Cognitive Computing Institute of Computer Science and EngineeringSaveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science Islamic Azad UniversityTehranIran Computer Engineering Department Bu‐Ali Sina UniversityHamedanIran Department of Mathematics and Statistics Iran University of Science and TechnologyTehranIran Department of Mathematics and Statistics University of Northern British ColumbiaBCCanada Peng Cheng Laboratory ShenzhenChina Deprtment of Technology Jodhpur Institute of Engineering&TechnologyJodhpurIndia Department of Computer Engineering Sadjad University of TechnologyMashhadIran Department of Electronics and Communications Engineering Kuwait College of Science and TechnologyDoha DistrictKuwait Department of Artificial Intelligence Smart University of Medical SciencesTehranIran Department of Industrial Engineering Shiraz BranchIslamic Azad UniversityShirazIran Institute of Computing Science and Technology Guangzhou UniversityGuangzhouChina

Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new *** using DL,the extraction of advanced data representations and knowledge can be made *** effective DL techniques help to find more hidden *** learning has a promising future due to its great performance and *** need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively.A survey on DL ways,advantages,drawbacks,architectures,and methods to have a straightforward and clear understanding of it from different views is explained in the ***,the existing related methods are compared with each other,and the application of DL is described in some applications,such as medical image analysis,handwriting recognition,and so on.

关键词： data mining data privacy deep learning

来源：评论

学校读者我要写书评

暂无评论

RAJA Performance Suite: Performance Portability Analysis with Caliper and Thicket

RAJA Performance Suite: Performance Portability Analysis wit...

引用

2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Pearce, Olga Burmark, Jason Hornung, Rich Bogale, Befikir Lumsden, Ian Mckinsey, Michael Yokelson, Dewi Boehme, David Brink, Stephanie Taufer, Michela Scogland, Tom Lawrence Livermore National Laboratory LivermoreCA United States University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States Texas A&M University Department of Computer Science and Engineering College StationTX United States

ISBN: (纸本)9798350355543

Maintaining performant code in a world of fast-evolving computer architectures and programming models poses a significant challenge to scientists. Typically, benchmark codes are used to model some aspects of a large application code's performance, and are easier to build and run. Such benchmarks can help assess the effects of code or algorithm changes, system updates, and new hardware. However, most performance benchmarks are not written using a wide range of GPU programming models. The RAJA Performance Suite provides a comprehensive set of computational kernels implemented in a variety of programming models. We integrated the performance measurement and analysis tools Caliper and Thicket into the RAJA Performance Suite to facilitate performance comparison across kernel implementations and architectures. This paper describes the RAJA Performance Suite, performance metrics that can be collected, and experimental analysis with case studies. The RAJA Performance Suite and integration of Caliper and Thicket enabled us to effectively measure the variety of implementations on the variety of hardware, and automatically characterize subsets of kernels which exhibit similar bottlenecks - and therefore perform similarly on new architectures which provide a different balance between resources such as FLOPS and memory bandwidth. We definitively demonstrate that the most memory bound kernels show the most performance gains on architectures with high-bandwidth memory (HBM), and that the kernels that have other bottleneck may, to a lesser extent, benefit from the higher-FLOPS GPUs. © 2024 IEEE.

关键词： benchmarking high performance computing performance analysis performance portability

来源：评论

学校读者我要写书评

暂无评论

Imaging and structure analysis of ferroelectric domains,domain walls,and vortices by scanning electron diffraction

引用

npj Computational Materials 2024年第1期10卷 2154-2161页

作者： Ursula Ludacka Jiali He Shuyu Qin Manuel Zahn Emil Frang Christiansen Kasper A.Hunnestad Xinqiao Zhang Zewu Yan Edith Bourret István Kézsmárki Antonius T.Jvan Helvoort Joshua Agar Dennis Meier Department of Materials Science and Engineering NTNU Norwegian University of Science and TechnologyTrondheimNorway Department of Computer Science and Engineering Lehigh UniversityBethlehemPAUSA Department of Mechanical Engineering and Mechanics Drexel UniversityPhiladelphiaPAUSA Experimental Physics V University of AugsburgAugsburgGermany Department of Physics NTNU Norwegian University of Science and TechnologyTrondheimNorway Department of Physics ETH ZurichZürichSwitzerland Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCAUSA

Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the *** electronic and quantum materials,this new capability gives access to,for example,emergent chiral structures and symmetry-breaking distortions that underpin functional *** nanoscale structural features with statistical significance,however,is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms,which often results in a low signal-to-noise ratio and the superposition of multiple signals that are challenging to deconvolute.

关键词： properties structure ferroelectric

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

引用

Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Novel 1-$\varphi$ High-Voltage Boosting Transformerless Inverter Topology with Optimal Power Components and Negligible Leakage Currents

引用

IEEE Transactions on Industry Applications 2023年第5期59卷 1-14页

作者： Chamarthi P.K. Muduli U.R. Moursi M.S.E. Al-Durra A. Khadkikar V. Hosani K.A. EL-Fouly T.H.M. Real-Time Control and Optimization Laboratory (RT-COOL) in Holcombe Department of Electrical and Computer Engineering Clemson University USA APEC Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

Inverter topologies for integrating a rooftop photovoltaic (PV) unit into a microgrid are becoming increasingly complex. This paper proposes a high-voltage boosting transformerless inverter (HVBTI) topology for enhancing such applications. The coupled inductor-based high voltage gain feature of the HVBTI configuration allows power to be delivered into the grid from a lower voltage PV source without using higher duties. In addition, HVBTI suppresses leakage current as the common connection shared between the

$-ve$

terminal of the PV source and the grid neutral point. In comparison to existing topologies, the HVBTI topology uses a compact pulse width modulation strategy to control only six controllable switching devices. Again, using lower-rated switching devices is more cost-effective while increasing reliability and efficiency. In a laboratory prototype of a 1 kVA grid integrated system, the proposed HVBTI configuration is validated, and the maximum efficiency of the HVBTI is estimated at approximately 95%. Author

关键词： Boosting Inverter Inductors Inverters Leakage currents Low Switch Count Microgrid Rooftop Photovoltaics Switches Topology Transformers Voltage Voltage Gain

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：