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A New Speech Encoder Based on Dynamic Framing Approach

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1259-1276页

作者： Renyuan Liu Jian Yang Xiaobing Zhou Xiaoguang Yue School of Information Science and Engineering Yunnan UniversityKunming650500China Rattanakosin International College of Creative Entrepreneurship Rajamangala University of Technology RattanakosinNakhon Pathom73170Thailand Department of Computer Science and Engineering School of SciencesEuropean University CyprusNicosia1516Cyprus ciicesi ESTGPolitécnico do PortoFelgueiras4610-156Portugal

Latent information is difficult to get from the text in speech *** show that features from speech can get more information to help text *** the field of speech encoding,a lot of work has been conducted on two *** first aspect is to encode speech frame by *** second aspect is to encode the whole speech to a *** the scale in these aspects is ***,encoding speech with an adjustable scale for more latent information is worthy of *** current alignment approaches only support frame-by-frame encoding and speech-to-vector *** remains a challenge to propose a new alignment approach to support adjustable scale speech *** paper presents the dynamic speech encoder with a new alignment approach in conjunction with frame-by-frame encoding and speech-to-vector *** speech feature fromourmodel achieves three ***,the speech feature can reconstruct the origin speech while the length of the speech feature is equal to the text ***,our model can get text embedding fromspeech,and the encoded speech feature is similar to the text embedding ***,it can transfer the style of synthesis speech and make it more similar to the given reference speech.

关键词： Speech synthesis dynamic framing convolution network speech encoding

Mu-Net:Multi-Path Upsampling Convolution Network for Medical Image Segmentation

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Computer Modeling in Engineering & Sciences 2022年第4期131卷 73-95页

作者： Jia Chen Zhiqiang He Dayong Zhu Bei Hui Rita Yi Man Li Xiao-Guang Yue School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Department of Economics and Finance/Sustainable Real Estate Research Center Hong Kong Shue Yan UniversityHongkongChina Rattanakosin International College of Creative Entrepreneurship Rajamangala University of Technology RattanakosinNakhon Pathom73170Thailand Department of Computer Science and Engineering School of SciencesEuropean University CyprusNicosia1516Cyprus ciicesi ESTGPolit′ecnico do Porto4610-156FelgueirasPortugal

Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment *** 2015,U-Net-based approaches have been widely used formedical image *** purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature ***,the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level *** high-level information can make the segmentationmore *** this paper,we propose MU-Net,a novel,multi-path upsampling convolution network to retain more high-level *** MU-Net mainly consists of three parts:contracting path,skip connection,and multi-expansive *** proposed MU-Net architecture is evaluated based on three different medical imaging *** experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different *** the same time,the computational efficiency is significantly improved by reducing the number of parameters by more than half.

关键词： Medical image segmentation MU-Net(multi-path upsampling convolution network) U-Net clinical diagnosis encoder-decoder networks

Remote Sensing Data Processing Process Scheduling Based on Reinforcement Learning in Cloud Environment

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Computer Modeling in Engineering & Sciences 2023年第6期135卷 1965-1979页

作者： Ying Du Shuo Zhang Pu Cheng Rita Yi Man Li Xiao-Guang Yue School of Computer and Information Engineering Henan UniversityKaifeng475004China Henan Province Engineering Research Center of Spatial Information Processing Henan UniversityKaifeng475004China School of Software Henan UniversityKaifeng475004China Department of Economics and Finance/Sustainable Real Estate Research Center Hong Kong Shue Yan UniversityHong Kong999077China Department of Computer Science and Engineering European University CyprusNicosia1516Cyprus ciicesi-ESTG Politécnico do PortoFelgueiras4610-156Portugal

Task scheduling plays a crucial role in cloud computing and is a key factor determining cloud computing *** solve the task scheduling problem for remote sensing data processing in cloud computing,this paper proposes a workflow task scheduling algorithm—Workflow Task Scheduling Algorithm based on Deep Reinforcement Learning(WDRL).The remote sensing data process modeling is transformed into a directed acyclic graph scheduling ***,the algorithm is designed by establishing a Markov decision model and adopting a fitness calculation ***,combine the advantages of reinforcement learning and deep neural networks to minimize make-time for remote sensing data processes from *** experiment is based on the development of CloudSim and Python and compares the change of completion time in the process of remote sensing *** results showthat compared with several traditionalmeta-heuristic scheduling algorithms,WDRL can effectively achieve the goal of optimizing task scheduling efficiency.

关键词： Cloud computing reinforcement learning remote sensing task scheduling

Volunteering and Depression among Older Adults: An Empirical Analysis Based on CLASS 2018

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International Journal of Mental Health Promotion 2023年第3期25卷 403-419页

作者： Zhendong Wu Chen Xu Liyan Zhang Yang Wang George W.Leeson Gong Chen Julien S.Baker Xiao-Guang Yue School of Law Humanities and SociologyWuhan University of TechnologyWuhan430070China Beijing Institute of Aerospace Information Defense Technology Academy of CASICBeijing100013China The People’s Government of Niulanshan Town Beijing101301China Institute of Population Research Peking UniversityBeijing100091China Oxford Institute of Population Ageing OxfordOX26PRUK Department of Sport Physical Education and HealthHong Kong Baptist UniversityHong Kong999077China Department of Computer Science and Engineering European University CyprusNicosia1516Cyprus ciicesi-ESTG Politécnico do PortoFelgueiras4610-156Portugal

Introduction:Older adults are prone to high levels of depression due to their deteriorating physical functions and shrinking social networks after *** as an important social activity is essential for alleviating depression by building social *** paper aims to examine the effect of volunteering on depression among older adults by using China Longitudinal Aging Social Survey(CLASS 2018)***:This study uses descriptive analysis and chi-square tests to show differences in demographic factors of older adults’volun-teerism participation,followed by bivariate correlation analysis to examine the correlation between the vital ***,stratified linear regression analysis is used to research the significant level and impact between volunteering and degree of expertise,frequency,and variety of ***:8,459 older adults are included in *** research reveals that older adults who are younger,live in urban areas,are married,or have a higher degree of education tend to have fewer depressive ***,participation in volun-teering(OR=0.90,95%CI:0.8,1.1,p<0.001),as well as that demands specialized skills(OR=0.51,95%CI:0.30,0.2,p<0.001),more frequency of participation(OR=1.85,95%CI:1.53,2.18,p<0.001),and a wider variety of activities(OR=0.21,95%CI:0.12,0.29,p<0.001),all have a positive inﬂuence on depression ***/Conclusion:Older adults who participate in voluntary services have lower depression symptoms and should be encouraged to use their professional skills and increase participation frequency and variety in this *** article suggests that governments should help older adults participate in voluntary services by time bank which will further strengthen social ties,rebuild social networks and alleviate depression symptoms of older adults.

关键词： Volunteering depression mental health older adults China time banking