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arXiv 2023年

作者： Chiu, Ching-Yu Müller, Meinard Davies, Matthew E.P. Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The International Audio Laboratories Erlangen Germany The Department of Informatics Engineering Centre for Informatics and Systems of the University of Coimbra University of Coimbra Portugal The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called "predominant local pulses" (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2023, CC BY.

关键词： Dynamic programming

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Analysis and Design of University Teaching Equipment Management Information System

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Journal of Cyber Security 2021年第3期3卷 177-185页

作者： Yu Xue Xiang Jiao Chishe Wang Huanlun Chen Moayad Aloqaily School of Computer and Software Nanjing University of Information Science and TechnologyNanjingChina Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjingChina Faculty of Engineering Al Ain UniversityAl AinUnited Arab Emirates NUIST Students’Platform for Innovation and Entrepreneurship Training Program NanjingChina

Teaching equipment management is an important factor for colleges and universities to improve their teaching level,and its management level directly affects the service life and efficiency of teaching *** in recent years,our university recruitment of students scale is increasing year by year,the size of the corresponding teaching equipment is also growing,therefore to develop a teaching equipment management information system is necessary,not only can help universities to effective use of the existing teaching resources,also can update scrap equipment,related equipment maintenance,and build a good learning environment to students and to the improvement of the teaching quality of colleges and universities play a reliable safeguard *** paper first introduces some common development tools,and then analyzes the user functional requirements and data requirements of the system,and analyzes the feasibility of the system development from many aspects,finally based on B/S mode,using Java language,JSP technology and MySQL database design and implementation of a teaching equipment management information *** main functional modules of the system include equipment basic information management,equipment loan and return information management,equipment maintenance information management,equipment scrap information management,the interface of each functional module is shown in the paper.

关键词： Information systems teaching equipment management system analysis

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Design and Implementation of Enterprise Recruitment Mini program

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Journal of Cyber Security 2021年第3期3卷 125-132页

作者： Yu Xue Nan Wei Junyang Han Chishe Wang Moayad Aloqaily School of Computer and Software Nanjing University of Information Science and TechnologyNanjingChina Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjingChina Faculty of Engineering Al Ain UniversityAl AinUnited Arab Emirates NUIST Students’Platform for Innovation and Entrepreneurship Training Program NanjingChina

Since the 21st century,the Internet has been updated and developed at an alarming *** the same time,WeChat applets are constantly improving and introducing new *** an enterprise recruitment system based on WeChat applets for the majority of job seekers and recruiter users,provide job seekers with easy-to-reach employment opportunities,and provide a convenient and clear screening environment for job *** front-end part of the applet is developed using WeChat developer tools,and the back-end system is developed using *** Spring Boot+Spring MVC framework,implemented in Java *** is managed using MySql *** function of this company’s recruitment applet is similar to the ordinary traditional native recruitment *** achieves basic functions such as job search,job search,collection of jobs,delivery of resumes,viewing of the job search process,recruitment of job information,screening of job resumes,notification of interviews,etc.

关键词： Mini-program recruitment WeChat developer tools MyEclipse

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Model Target of Food Production and Farmers’ Incomes in Banten Province, Indonesia

SSRN

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SSRN 2023年

作者： Siagian, Viktor Siregar, Hermanto Fariyanti, Anna Nainggolan, Kaman Haloho, Lermansius Hutapea, Ronald Suratman Salim, Zamroni Wamaer, Demas da Silva, Helena Ekaswati, Rini Rusdiana, Supardi Marwati, Tri Toha, Mohammad Sondakh, Joula Marpaung, Imelda Research Center for Macro Economic and Finance Research Organization for Governance Economy and Community Welfare National Research and Innovation Agency Jakarta12710 Indonesia IPB University Faculty of Economics and Management Bogor16680 Indonesia College of Management and Computer Science Nusa Mandiri Jakarta12540 Indonesia Research Center for Cooperative Corporation and People's Economy National Research and Innovation Agency Jakarta12710 Indonesia Universitas Islam Malang Indonesia Research Center for Behavioral and Circular Economics National Research and Innovation Agency Jakarta12710 Indonesia Research Center for Animal Research Organization for Agriculture and Food National Research and Innovation Agency Cibinong-Bogor 16128 Indonesia Research Center for Food and Technology Processing Research Organization for Agriculture and Food National Research and Innovation Agency Serpong15314 Indonesia Ph.D. Study Program Natural Resources and Environmental Management Graduate School of IPB University Campus IPB Baranangsiang Bogor16144 Indonesia National Research and Innovation Agency Indonesia

The area of the rice harvest in Banten Province in 2021 was 319,558 ha with a production of 1,63 million tons of unhusked rice or with a productivity of 5.1 tons/ha (CBS,2022). The objectives of this study were: 1) to optimize the target of rice and corn production in Banten Province, and 2) to optimize the target farmers' income in Banten Province. The study method used was a survey, with purposive sampling at the farmer level. Data were analyzed qualitatively (uses descriptive tabulation) and quantitatively (uses goal programming). The results of this study were:1) The target of rice production in the Banten Province of 2.1 million tons of unhusked rice can be achieved with the optimal area of irrigated paddy of 299,755.7 ha and the optimal area of field/dry land of 87,750.9 ha. The target for maize production of 1.75 million tonnes in Banten Province can be achieved with an optimal planting area of 342.211.7 ha of dry land. 2) The optimal area of food crops to achieve the farmer's income target in Banten Province in 2018 is IDR. 8.79 trillion comes from the planting area of lowland rice covering an area of 391,222.1 ha, the corn planting area of 169,059.9 ha, soybean planting area of 23,074.82 ha and cassava planting area of 2,832.77 ha. © 2023, The Authors. All rights reserved.

关键词： Agriculture

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Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study

arXiv

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arXiv 2023年

作者： Zhang, Ruichen Xiong, Ke Du, Hongyang Niyato, Dusit Kang, Jiawen Shen, Xuemin Poor, H. Vincent The Engineering Research Center of Network Management Technology High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Improved diabetes disease prediction IWFO model using machine learning algorithms

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AIP Conference Proceedings 2024年第1期3161卷

作者： R. Gunavathi V. Sivakumar B. Senthil Kumar S. Vijayalakshmi Associate professor School of Science Department of Data Science Christ University Pune India Associate Professor & Program Leader School of Computing Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Assistant Professor Department of Computer Science Sree Narayana Guru College Coimbatore India

Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level.

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Generating theorems for schwarzschild static Einstein-Gauss-Bonnet metrics

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AIP Conference Proceedings 2024年第1期3016卷

作者： Kunlapat Sansuk Petarpa Boonserm Tritos Ngampitipan Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Thailand Center of Excellence in Physics Ministry of Higher Education Science Research and Innovation 328 Si Ayutthaya Road Bangkok 10400 Thailand Physics Program Faculty of Science Chandrakasem Rajabhat University Bangkok 10900 Thailand

The modified Einstein’s field equations, that is the Einstein-Gauss-Bonnet, explain a higher dimensional curvature gravity which is expressed in terms of the Lanczos tensor and love lock. In this paper, we will find the constraint to solve the Einstein-Gauss-Bonnet, and use the constraint to obtain the differential equation and generate the solution generating theorems that apply to perfect fluid spheres. Finally, we analyze the result of the theorems and determine how to classify the metrics using these theorems.

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A Data Concept Map for the Data Driven Enterprise Using Smart Technologies 20

A Data Concept Map for the Data Driven Enterprise Using Smar...

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3rd International Conference on Big Data Technologies, ICBDT 2020

作者： Yeh, Tzu-Chen Chen, Hsiu-Chun Jwo, Jung-Sing Lin, Ching-Sheng Lee, Cheng-Hsiung Digiwin Software Co. Ltd 22F No.1 Lane1377 Jiangchang Rd Jingan District Shanghai200072 China Master Program of Digital Innovation Department of Computer Science Tunghai University Taichung City40704 Taiwan

ISBN: (纸本)9781450387859

Data Driven innovation (DDI) is one of the main pillars leading to improved productivity and economic growth for companies. The adoption and use of DDI may determine their competitive advantages and innovation capabilities to exploit data. However, it is known that the key to DDI lies in the data itself. Comparing the use of data and the creation of innovation, the way how data flows, is collected and integrated is no less pressing. In this paper, we present the Data Concept Map to take into account the human factor for digitalization challenges and benefit organizations, especially for small- A nd medium sized enterprises, in terms of saving costs, improving flexibility and marching towards Data Driven Enterprise. We introduce the concept of utilizing smart technologies as a channel to work with humans for expediting data collection, system integration and information flow. An experimental case study is conducted for a manufacturing application to demonstrate the effectiveness of the proposed approach. © 2020 ACM.

关键词： Human engineering

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Packet warriors: An academic mobile action game for promoting OSI model concepts to learners

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2019年第6期13卷 41-51页

作者： Uiphanit, Thanakorn Bhattarakosol, Pattarasinee Suanpong, Kwanrat Iamsupasit, Sompoch Technopreneurship and Innovation Management Program Chulalongkorn University Bangkok10330 Thailand Technopreneurship and Innovation Management Program Suan Sunandha Rajabhat University Bangkok Thailand Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Commerce Chulalongkorn Business School Chulalongkorn University Bangkok10330 Thailand Faculty of Psychology Chulalongkorn University Bangkok10330 Thailand

This research aims to develop a game to teach the theoretical concepts of the OSI reference model to students who must learn data communication and networking as a compulsory or an elective subject . The survey was administered to most students at Suan Sunandha Rajabhat University by a questionnaire to assess a suitable game for students of this generation. The result showed that action games are the most popular game for them. Then, a script of the OSI reference model was drawn based on the theoretical contents and the software design and implementation were performed. After finishing this development process, 19 third-year undergraduate students majoring in information sciences at Suan Sunandha Rajabhat University tested this system. The evaluation process was performed using pretest and posttest data analyzed by a paired samples t-test at a 95% confidence level. The result of this study shows that the action game 'Packet Warrior' is an efficient tool to help students improve their learning capabilities via fun and good relationships with friends. © 2019, International Association of Online Engineering.

关键词： Students

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