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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68658 条记录，以下是181-190 订阅

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CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare 16th

CollectiveSFT: Scaling Large Language Models for Chinese Me...

引用

16th international conference on Social Robotics, ICSR + InnoBiz 2024

作者： Zhu, Jingwei Tan, Minghuan Yang, Min Li, Ruixue Alinejad-Rokny, Hamid University of Science and Technology of China Hefei China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Xiangshui County Party School Yancheng China School of Computer Science and Engineering The University of New South Wales Sydney Australia

ISBN: (纸本)9789819611508

The rapid progress in Large Language Models (LLMs) has prompted the creation of numerous benchmarks to evaluate their capabilities. This study focuses on the Comprehensive Medical Benchmark in Chinese (CMB) [25], showcasing how dataset diversity and distribution in supervised fine-tuning (SFT) may enhance LLM performance. Remarkably, We successfully trained a smaller base model to achieve scores comparable to larger models, indicating that a diverse and well-distributed dataset can optimize performance regardless of model size. This study suggests that even smaller models may reach high performance levels with carefully curated and varied datasets. By integrating a wide range of instructional content, our approach addresses potential issues such as data quality inconsistencies. Our results imply that a broader spectrum of training data may enhance a model’s ability to generalize and perform effectively across different medical scenarios, highlighting the importance of dataset quality and diversity in fine-tuning processes (https://***/CAS-SIAT-XinHai/CollectiveSFT). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Benchmarking Performance Dataset Diversity Fine-Tuning Methodologies Large Language Models Medical Applications

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High-Fidelity Stereoscopic Image Rain Removal with Texture Integrity and Disparity Consistency

High-Fidelity Stereoscopic Image Rain Removal with Texture I...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wei, Yanyan Zhang, Zhao Zhao, Zhong-Qiu Zhao, Yang Hong, Richang Yang, Yi Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei China The College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350368741

This paper tackles the challenge of stereoscopic image rain removal by focusing on enhancing texture integrity and disparity consistency. Existing stereoscopic rain removal techniques often fall short due to 1) disruptions in texture coherence caused by complex rain streaks, and 2) inaccuracies in disparity estimation from inadequate feature fusion. To overcome these limitations, we introduce the StereoIRR method, which incorporates: 1) a Long-range and Cross-view Interaction (LCI) framework that preserves texture integrity by mitigating rain's adverse effects on stereoscopic features, and 2) a Dual-view Mutual Attention mechanism that ensures disparity consistency by generating precise mutual attention maps for cross-view feature fusion. Our approach not only maintains the integrity of stereoscopic textures but also significantly reduces errors in disparity estimation. Extensive experiments demonstrate that StereoIRR consistently outperforms state-of-the-art monocular and stereoscopic methods on multiple benchmark datasets. © 2025 IEEE.

关键词： Dual-view mutual attention Image deraining StereoIRR Stereoscopic and multiview processing

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Preconditioning Strategies for RBF Interpolation 4th

Preconditioning Strategies for RBF Interpolation

引用

4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Egidi, Nadaniela Giacomini, Josephin Maponi, Pierluigi School of Science and Technology - Mathematics Division University of Camerino Via Madonna delle Carceri 9 MC Camerino62032 Italy

A preconditioning strategy for a data interpolation system through Radial Basis Functions (RBFs) is devised by exploiting the Jacobi method for matrix eigenvalues. The effectiveness of this preconditioner is tested wi... 详细信息

ISBN: (纸本)9783031812439

关键词： Interpolation

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Dynamic-static Feature Fusion with Multi-scale Attention for Continuous Blood Glucose Prediction

Dynamic-static Feature Fusion with Multi-scale Attention for...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gao, Jing Guo, Chenhua Liu, Yingshu Li, Peng Zhang, Jianing Liu, Meng School of Software Dalian University of Technology Dalian116620 China Department of Endocrinology Central Hospital of Dalian University of Technology Dalian116033 China School of Computer Science and Technology Dalian University of Technology Dalian116620 China

ISBN: (纸本)9798350368741

Accurate continuous blood glucose prediction is an effective and direct method for treating type 2 diabetes mellitus. However, current methods are commonly single-domain single-scale blood glucose prediction models. That is, they only learn time correlations within constant time steps of continuous blood glucose data, to mine fluctuation patterns, which limits the model effectiveness and robustness. To this end, a novel dynamic-static feature fusion with multi-scale attention method (DSfusion) is proposed for accurately predicting continuous blood glucose. Specifically, DSfusion designs the cross-domain complementary augmentation via Fourier cycle transformation for learning inherent frequency features. Then, DSfusion devises the multi-scale dependence aggregation based on the attention mechanism with various scale receptive fields to capture time correlations and feature correlations across different time steps. Meanwhile, DSfusion utilizes the static feature enhancement to integrate multi-domain information, i.e., static and dynamic features, into a unified prediction architecture, which greatly boosts the model performance. Finally, comprehensive experiments are performed on the real-world blood glucose dataset, and the results confirm the excellence of DSfusion. © 2025 IEEE.

关键词： continuous blood glucose prediction dynamic-static feature fusion multi-scale attention

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Small-Scale Short-Term Spatio-Temporal Rainfall Prediction Based on EBK, GCN and Bi-LSTM Model 24

Small-Scale Short-Term Spatio-Temporal Rainfall Prediction B...

引用

8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Cai, Jiajing Ji, Xiang Zhou, Jinghe Peng, Ziyang College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

The field of meteorology and hydrology is significantly impacted by the spatial-temporal prediction of rainfall. Rainfall exhibits temporal correlation and periodicity in addition to significant spatial correlation. Currently, the primary objective of the majority of spatiotemporal rainfall prediction models is to extract the local temporal characteristics of rainfall events. We suggest an enhanced model for spatio-temporal rainfall prediction that is based on GCN and Bi-LSTM in order to overcome this obstacle. The model takes advantage of the EBK method in spatial interpolation to effectively generate the spatial distribution of small-scale short-term rainfall. The residual connection is implemented to derive spatial features on the basis of the GCN network, and the sequential operation of dilated convolution is converted to a parallel operation. Subsequently, the inflated sequence is fused into Bi-LSTM to extract short-term local features and macroscopic long-term characteristics of rainfall. To confirm the efficacy of the proposed model, the validation experiment employs historical rainfall data from the National Climatic Data Center for testing. The experimental results demonstrate that ESFEM-DDTM outperforms traditional prediction models in the prediction of spatio-temporal rainfall. The model’s mean absolute error, mean absolute percentage error, and root mean square error are 2.6725, 123.153%, and 5.1447, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction 31

CACA: Context-Aware Cross-Attention Network for Extractive A...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Chen, Bingfeng Xu, Haoran Luo, Yongqi Xu, Boyan Cai, Ruichu Hao, Zhifeng School of Computer Science Guangdong University of Technology China China College of Science Shantou University China

ISBN: (纸本)9798891761964

Aspect Sentiment Quad Prediction(ASQP) enhances the scope of aspect-based sentiment analysis by introducing the necessity to predict both explicit and implicit aspect and opinion terms. Existing leading generative ASQP approaches do not modeling the contextual relationship of the review sentence to predict implicit terms. However, introducing the contextual information into the pre-trained language models framework is non-trivial due to the inflexibility of the generative encoder-decoder architecture. To well utilize the contextual information, we propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network. When implicit terms are present, the Context-Aware Cross-Attention Network enhances the alignment of aspects and opinions, through alternating updates of explicit and implicit representations. Additionally, contrastive learning is introduced in the implicit representation learning process. Experimental results on three benchmarks demonstrate the effectiveness of CACA. Our implementation will be open-sourced at https://***/DMIRLAB-Group/CACA. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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CSClust: A Luxury Goods Image Authentication Algorithm Based on Contrastive Clustering 24

CSClust: A Luxury Goods Image Authentication Algorithm Based...

引用

8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Chao, Yaxin Yang, Yu Zhou, Linna Zou, Jixin Lei, Min School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Institude of Forensic Science of China Beijing China

ISBN: (纸本)9798400710094

The aim of our paper is to introduce CSClust, a contrastive spectral clustering model for luxury goods image authentication. The approach involves fine-tuning a Vision Transformer (ViT) pretrained model to enhance representation learning. Utilizing a spectral clustering network, the model embeds latent representations into the feature space and performs subsequent clustering. The training process is conducted in an unsupervised manner on self-generated augmented data, incorporating both instance-level and cluster-level contrastive losses. Experiments have demonstrated that the proposed method exhibits superior accuracy and generalization capability in luxury goods image authentication, and has also achieved state-of-the-art results on the CIFAR-100, ImageNet-10, and ImageNet-dogs datasets. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Visualization Analysis of Online Education Based on Vue Framework 24

Visualization Analysis of Online Education Based on Vue Fram...

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8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Shi, Jinmei Wen, Xinyuan Dong, Chunyang Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

Digitalization of education is one of the major strategies for my country’s current education reform and development, and market-oriented online education companies are one of the frontier practitioners in promoting digitalization of education. The work is based on the Vscode programming tool, and uses the Echarts graphics library and the Vue framework to perform Web front-end data visualization interaction design in a knowledge graph visualization manner. Use the Scrapy crawler framework in Python to crawl data from relevant websites, summarize and analyze the industry status, forward-looking areas and research hotspots of online education worldwide, and show the current development history and industry prospects of the global online education industry. By adding the random forest algorithm, we can deeply analyze the profound impact of my country’s double reduction policy on the development of domestic online education and the establishment of a new generation of industry standards. The relevant literature data of the past ten years from CNKI were exported, and the screened literature data were imported into the bibliometric tool COOC and the visualization software CiteSpace for sorting and analysis, presenting the research status and hot trends in the field of online education industry. Help relevant practitioners, researchers, learners, etc. to enhance their business understanding in the field of online education, and provide reference for the digital transformation of education in educational institutions at all levels and types. © 2024 Copyright held by the owner/author(s).

关键词： Decision trees

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A Tennis Momentum Analysis Method Based on Gaussian Dynamics and Machine Learning 24

A Tennis Momentum Analysis Method Based on Gaussian Dynamics...

引用

3rd international conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Long, Zheng Li, Na Sun, Mengqing Luo, Jiawen Pan, Ting Yin, Yujie Yang, Xinhua School of Computer and Communication Lanzhou University of Technology Gansu Lanzhou China School of Economics and Management Lanzhou University of Technology Gansu Lanzhou China College of Energy and Power Engineering Beihang University Beijing Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing Beijing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China College of Electrical and Information Engineering Lanzhou University of Technology Gansu Lanzhou China

ISBN: (纸本)9798400718120

Momentum analysis in tennis matches has predominantly focused on qualitative methods, lacking systematic quantitative approaches. This study proposes a novel method that integrates Gaussian dynamics models with machine learning techniques to quantitatively analyze and predict momentum changes in tennis matches. By selecting and defining key indicators such as serve success rate and scoring rate, we developed an algorithm to identify and predict momentum shifts. The proposed method employs ensemble learning techniques, specifically Stacking, to combine multiple machine learning models, enhancing prediction accuracy and execution efficiency. Experimental validation using data from major tennis tournaments, including the 2023 Wimbledon Championships, demonstrates the effectiveness of the proposed approach. The results show that the Stacking model outperforms individual models in accuracy and robustness, providing scientific decision support for coaches and players. This method has low computational requirements, is simple to implement, and considers a wide range of variables, making it highly efficient. Future research will focus on further optimizing the model and applying it to other sports and match types. © 2024 Copyright held by the owner/author(s).

关键词： Gaussian dynamics model

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Distributed-computing based versatile healthcare services framework for diagnostic markers 2nd

Distributed-computing based versatile healthcare services fr...

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2nd international conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Chunduri, Venkata Posinasetty, Bhargavi Son, Mukesh Byeon, Haewon Department of Mathematics & Computer Science Indiana State University United States The University of Southern Mississippi HattiesburgMS United States Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Pune Tathawade India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

ISBN: (纸本)9781032738659

Recently, there has been a boom in imaginative medical services technologies that have enhanced the delivery of medical care data. Human-to-human and gadget-to-gadget networks are crucial in people’s lives and function 24 hours a day, seven days a week. MobileCloud-Computing becomes a crucial tool in this regard. Given the tremendous improvements in the Internet of Things, such a combination has become a major worry. This study developed a framework that included two essential application components: flexible usage and a server request. The server application records patient data while a Counterfeit Neural Network (CNN) module distinguishes between the two stroke subtypes. Similarly, as a framework for stroke patients, our model ensures accessibility, security, and adaptability by utilising the Stroke dataset for CNN computation and the Multilayer Perceptron Algorithm (MLP), which has been completed without precedent for working with large amounts of data in this extension. © 2025 The Author(s).

关键词： Hospital data processing

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