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arXiv 2025年

作者： Lu, Shao-Chien Yeh, Chen-Chen Cho, Hui-Lin Hsu, Chun-Chieh Hsu, Tsai-Ling Wu, Cheng-Han Shih, Timothy K. Lin, Yu-Cheng Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The field of music generation using Large Language Models (LLMs) is evolving rapidly, yet existing music notation systems, such as MIDI, ABC Notation, and MusicXML, remain too complex for effective fine-tuning of LLMs. These formats are difficult for both machines and humans to interpret due to their variability and intricate structure. To address these challenges, we introduce YNote, a simplified music notation system that uses only four characters to represent a note and its pitch. YNote’s fixed format ensures consistency, making it easy to read and more suitable for fine-tuning LLMs. In our experiments, we fine-tuned GPT-2 (124M) on a YNote-encoded dataset and achieved BLEU and ROUGE scores of 0.883 and 0.766, respectively. With just two notes as prompts, the model was able to generate coherent and stylistically relevant music. We believe YNote offers a practical alternative to existing music notations for machine learning applications and has the potential to significantly enhance the quality of music generation using LLMs. Copyright © 2025, The Authors. All rights reserved.

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A Key Performance Indicator to Analyze Swarm Learning Performances with EHR

A Key Performance Indicator to Analyze Swarm Learning Perfor...

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IEEE International Conference on Healthcare informatics (ICHI)

作者： Matteo Mantovani Riccardo Scheda Carlo Combi Stefano Diciotti Department of Computer Science University of Verona Italy Department of Electrical Electronic and Information Engineering University of Bologna Italy

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Swarm Learning (SL) has been recently proposed for distributed learning, where a group of individual centers perform a synchronized training. Unlike traditional machine learning models that rely on a central server, swarm learning distributes the learning process across multiple nodes. Each node independently processes data and contributes to the overall learning task. This collaboration allows the swarm to benefit from individual nodes' different data. Unlike federated learning, here model parameters are not handled by a central server but are randomly handled across each individual node. The intrinsic attention of swarm learning to data privacy makes it suitable for distributed health care analysis, where a clinical center wants to benefit from all the other ones in the swarm network. However, the benefit for a single center or for the whole network could vary depending on data distribution. In this paper, we want to analyze the performance of the swarm learning in a network with multiple nodes, where different data distribution scenarios are taken into account. This analysis will show the gain of the whole swarm network and a specific (reference) node, focusing on scenarios where this node has a different amount of data with respect to the other nodes. To perform a more analytical analysis, we introduce a new Key Performance Indicator (KPI) to measure such gain. We then applied this method using I CU data extracted from the MIMIC EHR database and discussed the results obtained by analyzing 5 nodes with different data distribution scenarios.

关键词： Training Key performance indicator MIMICs Distributed databases Medical services Data models Servers

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FlexSATE: Flexible and Distributed Traffic engineering with Supervised Learning in Ultra-Dense Low-Earth-Orbit Satellite Networks

FlexSATE: Flexible and Distributed Traffic Engineering with ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xiaoyu Liu Zitian Zhang Xiaohan Qin Haibo Zhou Lian Zhao School of Electronic Science and Engineering Nanjing University Nanjing China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou Department of Electrical Computer Biomedical Engineering Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The ultra-dense low earth orbit (UD-LEO) satellite network is being vigorously developed due to its great potential in providing global coverage and services. For the sake of improved network performance in resource-constrained satellite networks, multipath schemes are being explored. However, state-of-the-art multipath routing algorithms face the challenge when dealing with highly dynamic satellite network features (i.e., frequent traffic variation, link failures) and fail to exploit the simple grid topology to design fast yet efficient traffic engineering (TE) approaches. In this paper, we propose a novel distributed TE scheme called Flexible Satellite Traffic engineering (FlexSATE), which leverages global path computation coupled with distributed local routing decisions to improve the overall load balancing performance for ultra-dense LEO satellite networks. By constructing a minimum-hop binary tree (MHBT), we propose an MHBT-based k-segment Routing algorithm, which is capable of promptly discovering routing paths with low latency, high diversity, and good load balancing. To further enhance network transmission performance, we employ supervised learning into dynamic rate adaption, where FlexSATE employs centralized offline learning to derive insights from the globally optimal routing strategy and utilizes distributed deployment to predict the optimal distribution of traffic in real time. Our simulation results on a real-world typical Walker-delta type LEO constellation with 720 satellites show that FlexSATE outperforms some existing approaches with superior robustness and flexibility.

关键词： Satellites Heuristic algorithms Simulation Supervised learning Low earth orbit satellites Routing Load management Prediction algorithms Robustness Real-time systems

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IPFS: An Off-Chain Storage Solution for Blockchain 5th

IPFS: An Off-Chain Storage Solution for Blockchain

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Manpreet Gupta, Shikha Kumar, Deepak Raboaca, Maria Simona Goyal, S.B. Verma, Chaman Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Apex Institute of Technology Chandigarh University Mohali India National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Râmnicu Vâlcea Romania Faculty of Information Technology City University London Malaysia Department of Media and Educational Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9789811998751

Due to intrinsic properties such as transparency, immutability, decentralization, and cryptographic security, blockchain has emerged as a revolutionary technology. Despite its widespread application, blockchain is not used to store huge data due to the restricted storage capacity offered by each block. Furthermore, because the data contained in a block must be duplicated on many other nodes on the network, storage space is wasted. Off-chain storage is therefore required to store significant volumes of data while preserving the efficiency and performance of the network. Additionally, off-chain solutions such as Inter-Planetary File System (IPFS) would reduce the cost requirements of blockchain nodes as data stored and processed by these nodes would be reduced. Hence, strong decentralized storage is provided by IPFS that is a P2P-based content-addressed file sharing system that uses cryptographic hashes to store data. As a result, by combining blockchain with IPFS, an efficient file sharing system would be created. This paper presents a comprehensive IPFS architecture, a simplified version of this integrated approach, and potential applications that might assist to maximize the worth of both systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification

RerrFact: Reduced Evidence Retrieval Representations for Sci...

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2022 Workshop on Scientific Document Understanding, SDU 2022

作者： Rana, Ashish Khanna, Deepanshu Ghosal, Tirthankar Singh, Muskaan Singh, Harpreet Rana, Prashant Singh School of Business Informatics and Mathematics University of Mannheim Germany Department of Electronics & Communication Engineering Thapar Institute of Engineering & Technology India Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics Charles University Czech Republic Department of Computer Science & Engineering Thapar Institute of Engineering & Technology India

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate supporting and refuting evidence from credible scientific sources. The SciFact dataset and corresponding task provide a benchmarking leaderboard to the community to develop automatic scientific claim verification systems via extracting and assimilating relevant evidence rationales from source abstracts. In this work, we propose a modular approach that sequentially carries out binary classification for every prediction subtask as in the SciFact leaderboard. Our simple classifier-based approach uses reduced abstract representations to retrieve relevant abstracts. These are further used to train the relevant rationale-selection model. Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim. Experimentally, our system RerrFact with no fine-tuning, simple design, and a fraction of model parameters fairs competitively on the leaderboard against large-scale, modular, and joint modeling approaches. © 2022 Copyright for this paper by its authors.

关键词： Abstracting

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A New Classification Method for Drone-Based Crops in Smart Farming

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第9期16卷 164-174页

作者： Al-Rami, Bandar Alheeti, Khattab M. Ali Aldosari, Waleed M. Alshahrani, Saeed Matar Al-Abrez, Shahad Mahdi Dean of Faculty of Computer Studies Arab open University Riyadh Saudi Arabia College of Computer Sciences and Information Technology Computer Networking Systems Department University of Anbar Ramadi Iraq Department of Computer Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia Faculty of Computer Science and Information Technology Saudi Electronic University Riyadh Saudi Arabia

During the past decades, smart farming became one of the most important revolutions in the agriculture industry. Smart farming makes use of different communication technologies and modern information sciences for in-creasing the quality and quantity of the product. On the other hand, drones showed a major potential for enhancing imagery systems and re-mote sensing usage for many different applications such as crop classification, crop health monitoring and weed management. In this paper, an intelligent method for classifying crops is proposed to use a transfer learning approach based on a number of drone images. Moreover, the Convolution-al Neural Network (CNN) method is used as a classifier to improve efficiency for obtaining more accurate results in the training and testing phases. Various metrics are measured to evaluate the efficiency of the proposed model such as accuracy rate of detection, error rate and confusing matrix. It is found to be proven from the experimental results that the proposed method presents more efficient results with an accuracy detection rate of 92.93% © 2022. International Journal of Interactive Mobile *** Rights Reserved.

关键词： Drones

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Efficient and accurate numerical-projection of electromagnetic multipoles for scattering objects

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Frontiers of Optoelectronics 2023年第4期16卷 215-226页

作者： Wenfei Guo Zizhe Cai Zhongfei Xiong Weijin Chen Yuntian Chen School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117576Singapore Wuhan National Laboratory of Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Optics Valley Laboratory Wuhan 430074China

In this paper,we develop an efcient and accurate procedure of electromagnetic multipole decomposition by using the Lebedev and Gaussian quadrature methods to perform the numerical ***,we briefy review the principles of multipole decomposition,highlighting two numerical projection methods including surface and volume ***,we discuss the Lebedev and Gaussian quadrature methods,provide a detailed recipe to select the quadrature points and the corresponding weighting factor,and illustrate the integration accuracy and numerical efciency(that is,with very few sampling points)using a unit sphere surface and regular *** the demonstrations of an isotropic dielectric nanosphere,a symmetric scatterer,and an anisotropic nanosphere,we perform multipole decomposition and validate our numerical projection *** obtained results from our procedure are all consistent with those from Mie theory,symmetry constraints,and fnite element simulations.

关键词： Multipole decomposition Numerical quadrature Light scattering

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Low-temperature conformal vacuum deposition of OLED devices using close-space sublimation

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Journal of Semiconductors 2023年第9期44卷 62-67页

作者： Bryan Siu Ting Tam Shou-Cheng Dong Ching W.Tang State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Institute for Advanced Study The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina

Close-space sublimation(CSS)has been demonstrated as an alternative vacuum deposition technique for fabricating organic light-emitting diodes(OLEDs).CSS utilizes a planar donor plate pre-coated with organic thin films as an area source to rapidly transfer the donor film to a device substrate at temperatures below 200℃.CSS is also conformal and capable of depositing on odd-shaped substrates using flexible donor *** evaporation behaviors of organic donor films under CSS were fully characterized using model OLED materials and CSS-deposited films exhibited comparable device performances in an OLED stack to films deposited by conventional point *** low temperature and conformal nature of CSS,along with its high material utilization and short process time,make it a promising method for fabricating flexible OLED displays.

关键词： close-space sublimation OLED thin film low temperature vacuum deposition

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Moisture Content Prediction of Sugi Wood Drying Using Deep LSTM AE Minimizing Perturbed Error

Moisture Content Prediction of Sugi Wood Drying Using Deep L...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Ting Ng, Wing W. Y. Zhang, Mingyang Zhang, Xueli Zhang, Jianjun Deng, Mingcong School of Medicine Department of Radiology Guangzhou First People's Hospital South China University of Technology Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Tokyo University of Agriculture and Technology Department of Electrical and Electronic Engineering Tokyo Japan

ISBN: (纸本)9798350337020

Wood drying technology plays a key role in extending service lifetime of wood as the moisture content has a great influence on the wood quality. This paper presents a moisture content prediction model based on the deep long short-term memory (LSTM) autoencoders with stochastic sensitivity (DLASS) to extract a hidden representation of input data. The DLASS uses multiple LSTM encoders to learn more informative hidden representations from unseen samples, which are then decoded using multiple LSTM decoders. The DLASS is trained by minimizing the perturbed error from historical moisture content data. Furthermore, a nonlinear fully connected feedforward neural network as a regression layer is applied to predict moisture content using hidden representations learned by the DLASS. The DLASS is applied to real-world industrial data of Sugi wood processed by a drying kiln made by SECEA from August 4 to 19, 2008. Multiple test cases and comparisons with existing classical and state-of-the-art models show that the DLASS model yields more accurate moisture content prediction results and has high generalization capability. To be specific, the DLASS yields the lowest MAE (0.026), MAPE (0.280), and RMSE (0.058) for predicting moisture content during the wood drying process. © 2023 IEEE.

关键词： autoencoders LSTM Moisture content prediction stochastic sensitivity

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Optimization Models and Educational Teaching Research in Agricultural Logistics System 2

Optimization Models and Educational Teaching Research in Agr...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied informatics, ACCAI 2023

作者： Karthick, S. Kumar, B. Anjanee Sethi, Puneet Singh, Niraj Kr Srivastava, Supriya Prasanth, P Vishnu Srm Institute of Science and Technology Department of Computer Science and Engineering Ghaziabad India Dadi Institute of Engineering and Technology Department of Electronic and Communication Engineering India Teerthanker Mahaveer University Moradabad India Amity University Amity Institute of Applied Sciences India Shoolini University Faculty of Management Sciences Solan India Mohan Babu University Tirupati India

ISBN: (纸本)9798350315905

A spic and span exhaustive disciplinesare logistics science. It is essential to the headway of the board and creation principles as well as the development of the public economy. To foster a powerful agricultural economy, the undertaking of developing new agricultural corporate substances is vital. The old techniques for agricultural item assembling and circulation are as of now not ready to fulfill the requirements of production network advancement and market informatization. To resolve the issues traditional strategies welcome, investigating new agricultural item creation and conveyance techniques is fundamental. By coordinating agricultural item store network assets, eliminating wasteful connections in the course of agricultural items, reducing expenses, accomplishing discernibility of agricultural items, and guaranteeing the quality and security of agricultural items, we make an advancement system for agricultural item supply chains, drove by enormous information specialist coops. Given this, this article proposes a model for a coordinated administration stage-based agricultural item store network, and it conducts broad research on the logistics activity rationale of the chain to give a hypothetical establishment and information support for the plan and improvement of the administration stage. © 2023 IEEE.

关键词： Supply chains

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