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14th International Conference on E-business, management and Economics, ICEME 2023

作者： Zeng, Yong Chen, Yan Fu, Yue Zhao, Mingming Institute of Strategic Development China Telecom Research Institute China School of Economics and Management Beijing University of Information Technology China

ISBN: (纸本)9798400708022

Most central banks around the world explore issuing retail central bank of digital currencies (CBDCs), China is one of the few countries that have made substantial progress. Compared to highly popular mobile payment, e-CNY is not widely used in pilot cities. An empirical study was carried out from the perspective of users, incorporating individual differences and e-CNY system characteristics into an expanded technology acceptance model. The results show that innovation has a significant effect on perceived ease of use, security and reachability have a significant effect on perceived usefulness, Meanwhile, there are no significant differences in model fit between early adopters and late adopters, and users perceive e-CNY payment attributes rather than cash value. Our study helps financial institutions to promote that CBDC should select the innovation-aware population and develop the financial value of e-CNY. Moreover, our study points to future research directions. © 2023 ACM.

关键词： Electronic money

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Mass balance of the Antarctic Ice Sheet from 2013 to 2018 estimated using the input-output method with updated remote sensing products

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Science China Earth Sciences 2023年第7期66卷 1478-1492页

作者： Yuan CHENG Gang HAI Xiangbin CUI Da LV Gang QIAO Rongxing LI Institute for the Conservation of Cultural Heritage School of Cultural Heritage and Information ManagementShanghai UniversityShanghai 200444China Center for Spatial Information Science and Sustainable Development Tongji UniversityShanghai 200092China College of Surveying and Geo-Informatics Tongji UniversityShanghai 200092China Polar Research Institute of China Shanghai 200136China

The Antarctic Ice Sheet(AIS)has been losing ice mass and contributing to the rise in the global sea-level(GSL)for the last 4 decades,as quantified by using satellite *** developed a framework for implementing the state-of-the-art input-output(IO)method that has the advantage of explicit estimation of the mass balance of individual glaciers,basins and the *** estimated the mass balance of the AIS from 2013 to 2018 using improved observations and updated datasets recently made available,including annual ice flow velocity maps from the Inter-mission Time Series of Land Ice Velocity and Elevation(ITS_LIVE)dataset,the Bed Machine and the Princess Elizabeth Land(PEL)Earth System Science Data(ESSD)datasets,and the surface mass balance from the RACMO 2.3 *** example,using the improved ice thickness data,the proposed method for ice discharge estimation enables a 10%reduction of uncertainty in ice *** the period of 2013–2018,an ice discharge acceleration of 6.9±6.5 Gt yr^(–2)in West Antarctica(WA)was detected,which contributed significantly to the estimated mass loss of~1069 Gt(–178.2±108.9 Gt yr^(–1))in the *** the other hand,Queen Maud Land,East Antarctica(EA),showed clearly a mass gain rate of 56.0±10.0 Gt yr^(–1)due to the regional increase in surface mass *** results extended the estimation period by 3 years in comparison to the published study using the same annual velocity maps from the ITS_LIVE ***,our results,along with those from other studies using the IO method,reassures the acceleration of recent mass loss in WA and Wilkes Land in EA,which are caused by glacier thinning and ice shelf basal *** with the long-term mass balance record since 1979,our results suggest that the mass loss in AIS accelerated in the last *** developed framework can be modified for mass balance estimation of the AIS or for other ice sheets by using velocity maps from other satellite data or from differen

关键词： Antarctic Ice Sheet Mass balance Input-output method BedMachine ITS_LIVE

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Automating Maritime Risk Data Collection and Identification Leveraging Large Language Models 24

Automating Maritime Risk Data Collection and Identification ...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Huang, Donghao Fu, Xiuju Yin, Xiaofeng Pen, Haibo Wang, Zhaoxia Singapore Management University School of Computing and Information Systems Singapore Mastercard Research and Development Singapore Agency for Science Technology and Research Institute of High Performance Computing Singapore Tianjin University School of Electrical and Information Engineering China

ISBN: (纸本)9798331530631

Maritime risk research is crucial yet challenging for improving safety, efficiency, and sustainability in maritime operations. This paper presents an innovative method for automating the collection and identification of risk data related to global maritime risks from news sources, addressing the limitations of traditional manual methods. To evaluate the proposed method, different learning-based models, including conventional machine learning approaches and advanced Large Language Models (LLMs) such as GPT-4 and LLaMA-3.1, are comprehensively studied for comparison. In addition, not only do we use popular evaluation metrics to assess the proposed method, but we also introduce a new evaluation metric, called the "Ratio of Valid Categories (RVC),"to evaluate model reliability. The merits of the proposed method are demonstrated across different evaluation metrics. The research results show that the proposed LLM-based methods, particularly the GPT-4-based method, consistently outperform traditional models, significantly improving both the efficiency and accuracy of maritime risk data collection and identification. Our findings contribute to the expanding literature on LLM applications in risk management, demonstrating their potential to transform data collection and identification practices. © 2024 IEEE.

关键词： Contrastive Learning

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An Automatic Threshold Selection Using ALO for Healthcare Duplicate Record Detection with Reciprocal Neuro-Fuzzy Inference System

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Computers, Materials & Continua 2023年第3期74卷 5821-5836页

作者： Ala Saleh Alluhaidan Pushparaj Anitha Subbappa Ved Prakash Mishra P.V.Chandrika Anurika Vaish Sarthak Sengupta Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhKSA.P.O.Box:8442811671 Department of Electronics and Communication Engineering National Institute of Technical Teacher Training and ResearchChandigarh160019India Department of Periodontology JSS Dental College&HospitalJSS Academy of Higher Education and ResearchMysuru570015India Department of Computer Science and Engineering Amity UniversityDubai345019United Arab Emirates Department of Management/Economics Prin LN Welingkar Institute of Management Development and ResearchMumbai400019India Department of Management Studies Indian Institute of Information TechnologyAllahabad211012India

ESystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt *** still have been pioneering collections of massive volumes of health *** detections involve discovering records referring to the same practical components,indicating tasks,which are generally dependent on several input parameters that experts *** linkage specifies the issue of finding identical records across various data *** similarity existing between two records is characterized based on domain-based similarity functions over different ***-duplication of one dataset or the linkage of multiple data sets has become a highly significant operation in the data processing stages of different data mining *** objective is to match all the records associated with the same *** measures have been in use for representing the quality and complexity about data linkage algorithms,and many other novel metrics have been *** outline of the problem existing in themeasurement of data linkage and de-duplication quality and complexity is *** article focuses on the reprocessing of health data that is horizontally divided among data custodians,with the purpose of custodians giving similar features to sets of *** first step in this technique is about an automatic selection of training examples with superior quality from the compared record pairs and the second step involves training the reciprocal neuro-fuzzy inference system(RANFIS)*** the Optimal Threshold classifier,it is presumed that there is information about the original match status for all compared record pairs(i.e.,Ant Lion Optimization),and therefore an optimal threshold can be computed based on the respective ***,Clinical Decision(CD),and Cork Open research Archive(CORA)data repository help analyze the proposed method with evaluated benchmarks with current techniques.

关键词： Duplicate detection healthcare record linkage dataset pre-processing reciprocal neuro-fuzzy inference system and ant lion optimization fuzzy system

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Converting Vocal Performances into Sheet Music Leveraging Large Language Models 24

Converting Vocal Performances into Sheet Music Leveraging La...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Jiang, Jinjing Teo, Nicole Pen, Haibo Ho, Seng-Beng Wang, Zhaoxia Mastercard Research and Development Singapore Singapore Singapore Management University School of Computing and Information Systems Singapore Singapore Tianjin University China School of Electrical and Information Engineering China AI Institute Global 139955 Singapore

ISBN: (纸本)9798331530631

Advanced natural language processing (NLP) models are increasingly applied in music composition and performance, particularly in generating vocal melodies and simulating singing voices. While NLP techniques have been successfully used to analyze vocal performance data, providing insights into performance quality and style, the automatic transcription of vocal performances into sheet music remains a complex challenge. Existing tools for manual transcription are often insufficient due to the intricate dynamics of vocal expression. This study addresses the challenge of automating the conversion of vocal performances into sheet music using a combination of novel techniques, including large language models (LLMs). We present a method that successfully translates vocal audio input into display-ready sheet music. Our findings highlight the strengths and limitations of various approaches, particularly in the transcription of a cappella performances into notes and lyrics. This research contributes to the expanding field of NLP-driven music analysis and showcases the potential of these models to revolutionize vocal transcription in the future. © 2024 IEEE.

关键词： Natural language processing systems

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Impact of urban sprawl on land surface temperature in the Mashhad City,Iran:A deep learning and cloudbased remote sensing analysis

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Journal of Arid Land 2025年第3期17卷 285-303页

作者： Komeh ZINAT Hamzeh SAEID Memarian HADI Attarchi SARA LU Linlin Naboureh AMIN Alavipanah KAZEM SEYED Department of Remote Sensing and GIS(Geographic Information System) University of TehranTehran 14178-53933Iran Department of Watershed Management University of BirjandBirjand 97174-34765Iran Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China University of Chinese Academy of Sciences Beijing 100049China

The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development *** study aims to establish a model that maps the correlation between changes in land use and land surface temperature(LST)in the Mashhad City,northeastern *** the Google Earth Engine(GEE)platform,we calculated the LST and extracted land use maps from 1985 to *** convolutional neural network(CNN)approach was utilized to deeply explore the relationship between the LST and land *** obtained results were compared with the standard machine learning(ML)methods such as support vector machine(SVM),random forest(RF),and linear *** results revealed a 1.00°C–2.00°C increase in the LST across various land use *** variation in temperature increases across different land use types suggested that,in addition to global warming and climatic changes,temperature rise was strongly influenced by land use *** LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C,while forest lands experienced the smallest increase of 1.19°*** developed CNN demonstrated an overall prediction accuracy of 91.60%,significantly outperforming linear regression and standard ML methods,due to the ability to extract higher level ***,the deep neural network(DNN)modeling indicated that the urban lands,comprising 69.57%and 71.34%of the studied area,were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030,*** conclusion,the LST predictioin framework,combining the GEE platform and CNN method,provided an effective approach to inform urban planning and to mitigate the impacts of UHI.

关键词： convolutional neural network machine learning Google Earth Engine land use change random forest

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Status and trends in the stability of the three largest ice shelves in Antarctica

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Science China Earth Sciences 2024年第8期67卷 2615-2632页

作者： Rongxing LI Guojun LI Lu AN Menglian XIA Da LV Yuan CHENG Aiguo ZHAO Tian CHANG Xiangbin CUI Shengkai ZHANG Shijie LIU Yixiang TIAN Weian WANG Center for Spatial Information Science and Sustainable Development Applications Tongji UniversityShanghai 200092China College of Surveying and Geo-informatics Tongji UniversityShanghai 200092China Postdoctoral Research Station of Shanghai Surveying and Mapping Institute Shanghai 200063China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Shanghai 200063China Institute for Conservation of Cultural Heritage School of Cultural Heritage and Information ManagementShanghai UniversityShanghai 200444China Polar Research Institute of China Shanghai 200136China Chinese Antarctic Center of Surveying and Mapping Wuhan UniversityWuhan 430079China

The Ross,Filchner-Ronne,and Amery ice shelves are the three largest ice shelves in Antarctica,playing a crucial role in supporting the Antarctic ice ***,current studies on the stability of the three largest ice shelves primarily focus on singular or limited factors,lacking a comprehensive assessment of multiple *** systematically and in-depth study the stability and trend of the three largest ice shelves,we comprehensively collected and analyzed key parameters,including elevation changes,basal melting,surface meltwater,major rifts propagation rate,suture zones,ice front area change rate,grounding lines,ice velocity,and mass ***,we selected the collapsed Larsen B Ice Shelf(LBIS),the rapidly changing and structurally weakened Pine Island Ice Shelf(PIIS),and the accelerating Totten Ice Shelf(TIS)as reference ice *** comparing and analyzing the key parameters between these reference ice shelves and the three largest ice shelves,we find the status and trends in the stability of the *** findings reveal that most key parameters of the three largest ice shelves present relatively minor variations compared to those of the reference ice ***,50%of the parameters are smaller than those of the accelerating TIS,88%are smaller than those of the rapidly changing PIIS,and all parameters are smaller than those of the collapsed ***,after analyzing parameters that are not smaller than those of the TIS,it is observed that they remain in a stable ***,the three largest ice shelves are currently undergoing natural changes that do not threaten their stability in the short ***,the evolution of the ice shelves under global climate change remains uncertain,making long-term observation and monitoring essential to assess their impact on sea level rise.

关键词： Three largest ice shelves in Antarctica Key parameters Remote sensing monitoring Ice shelf stability Sea level change

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YOLO-CD: YOLOv5-Based Neural Network for Empty Position Recognition of Dispatch Center 4

YOLO-CD: YOLOv5-Based Neural Network for Empty Position Reco...

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4th International Conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Liu, Bin Zheng, Zengrong Wang, Peng Shu, Lichun Wang, Jun Wu, Yunyang Liu, Shengqiang Zhang, Xiaobo Zhao, Hongfei Shenmu Energy Administration Shaanxi China Information Research Institute Ministry of Emergency Management Beijing China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Shenmu Information Industry Development Group Co. Ltd Shaanxi China

ISBN: (纸本)9798350380323

The mine dispatch room plays a critical role in ensuring workplace safety by enabling security personnel to monitor and respond swiftly to emergencies. Detecting whether the dispatch room is unoccupied is essential to maintain continuous monitoring and prevent potential safety risks. This study addresses the challenge of automated vacancy detection in dispatch rooms, which is complicated by unique environmental characteristics and the issue of imbalanced sample categories. To overcome these challenges, we utilized screenshots extracted from a mine's safety analysis and warning system as experimental data. An improved YOLOv5 model was proposed, integrating the Convolutional Block Attention Module (CBAM) to enhance its ability to focus on critical features of the human body while reducing the influence of background noise. Furthermore, a decoupling strategy was applied to mitigate recognition errors caused by low-sample categories, improving model robustness in detecting rare but critical scenarios. Experimental results validate the enhanced performance of the proposed model, showcasing its capability to accurately identify whether the dispatch room is vacant in real-world conditions. By enabling real-time monitoring and alerting, this research contributes to the automation of safety protocols in mining operations, reducing the reliance on manual supervision and improving overall safety management efficiency. This methodology offers a scalable solution for intelligent safety monitoring systems across similar industrial settings. © 2024 IEEE.

关键词： Deep neural networks

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Burden of Vaginitis Among Chinese Women Aged 18-74 Years——Five Provinces, China, 2023

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China CDC weekly 2024年第49期6卷 1299-1305页

作者： Zixuan Fan Chenyang Pei Jing Ma Chengdong Xu Zhenbo Wang Yuanli Liu Yuehua Hu School of Health Policy and Management Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijingChina Key Laboratory of Regional Sustainable Development Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina Institute for Hospital Management of Tsinghua University Shenzhen CityGuangdong ProvinceChina State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Office of Epidemiology Chinese Center for Disease Control and PreventionBeijingChina

What is already known about this topic?Vaginitis is a prevalent and treatable gynecological condition in women that is associated with adverse reproductive outcomes,human papillomavirus(HPV)infection,and elevated human immunodeficiency virus(HIV)transmission *** is added by this report?This study identifies elevated infection risks among women aged 35–54 years,those with three or more pregnancies,and individuals who smoke or consume alcohol,with notably higher prevalence rates in Hubei and Yunnan *** findings demonstrate that awareness of cervical cancer screening serves as a protective factor,while emphasizing screening’s crucial role in early vaginitis detection.

关键词： elevated protective smoke

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Specifying the Virtual Reality Approach in Mobile Gaming Using Unity Game Engine 2nd

Specifying the Virtual Reality Approach in Mobile Gaming Usi...

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2023

作者： Avinash, Sharma Deshmukh, Pratibha Jamsandekar, Pallavi Kumbhar, R.D. Kharade, Jyoti Rajendran, Rahul Haryana Mullana-Ambala India Bharati Vidyapeeth’s Institute of Management and Information Technology Maharashtra Navi Mumbai India Bharati Vidyapeeth’s Institute of Management and Rural Development Administration Maharashtra Sangli India Rayat Shikshan Sanstha’s Karmaveer Bhaurao Patil Institute of Management Studies and Research Maharashtra Satara India

ISBN: (纸本)9789819919451

Mobile based gaming application development is an innovative recent trend in the area of mobile computing. The paper explores Hypercasual Infinity Virtual Reality (VR) mobile gaming application designed and developed using Unity Game Engine (UGE) as a mobile application. Mobile gaming applications are becoming successful in the media and entertainment industries and are easily available in Google play store. The research paper highlights the various activities like analysis of various domain requirements, user requirements and system requirements, design and development activities. Hypercasual Infinity captures the features for players with coins information, selection of different players, use of coins, display player list etc. Hypercasual Infinity is an effective learning game-based tool useful for increasing computational logic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Virtual reality

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