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检索条件"任意字段=2021 IEEE International Conference on Data Science and Computer Application, ICDSCA 2021"

共 16680 条记录，以下是1-10 订阅

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Proceedings of 2021 ieee international conference on data science and computer application, icdsca 2021

Proceedings of 2021 IEEE International Conference on Data Sc...

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2021 ieee international conference on data science and computer application, icdsca 2021

ISBN: (纸本)9781665440530

The proceedings contain 182 papers. The topics discussed include: optimal dispatch of household microgrid with electric vehicle based on demand response;research on insulator defect detection and recognition in overhead transmission line based on convolutional neural network;a service driver based application execution and development method in multi-cloud context;a support vector machine model based on mixed time series prediction;discriminant sensitive harmonic correlation fault diagnosis for rolling bearing;fault tolerance technique based on state real-time synchronization;estimation model of environmental leakage of plastic waste along the Yangtze River based on remote sensing data and UAV data;and communication signal modulation recognition based on time-frequency domain analysis and Bayesian optimized decision tree algorithm.

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2024 ieee 4th international conference on data science and computer application, icdsca 2024

2024 IEEE 4th International Conference on Data Science and C...

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4th ieee international conference on data science and computer application, icdsca 2024

ISBN: (纸本)9798350368239

The proceedings contain 150 papers. The topics discussed include: algorithm for data format conversion and compatibility guarantee in technical platforms for cross platform application integration;research on the application of high-performance electronic devices in computer hardware acceleration;research on multimodal data fusion and analysis methods driven by knowledge graph;research on the new generation of network data security protection technology for zero-trust environment;the separated channel simultaneous wireless power and data transmission system based on double-layer orthogonal dd coils;data security protection technology for power terminal devices based on security chips;image steganography without embedding based on style transfer and zero-watermarking;and simulation platform architecture design for side channel attack.

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2023 ieee 3rd international conference on data science and computer application, icdsca 2023

2023 IEEE 3rd International Conference on Data Science and C...

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2023 ieee 3rd international conference on data science and computer application, icdsca 2023

ISBN: (纸本)9798350341546

The proceedings contain 306 papers. The topics discussed include: CloneBAS: a code clone detection method based on abstract syntax tree and Simhash;research on the a-star algorithm based on path finding;study on vibration isolation characteristics of UHVDC converter valve saturable reactor;a market transaction model based on blockchain technology for multimicrogrid networks;optimization research on radar signal processing technology based on MTI-MTD;stock prediction using evolutionary attention-based LSTM;the reasons and improvement measures for the long time of replacing cross arm with 10 kV live;research on accurate three-dimensional modeling technology and intelligent mining in coal mines;evaluation on power equipment operation status monitoring system based on data mining;exploring deep learning-based visual localization techniques;and intelligent detection and recognition of pulley paying-off defects based on improved YOLOv5.

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2022 ieee 2nd international conference on data science and computer application, icdsca 2022

2022 IEEE 2nd International Conference on Data Science and C...

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2nd ieee international conference on data science and computer application, icdsca 2022

ISBN: (纸本)9781665472005

The proceedings contain 284 papers. The topics discussed include: user angle estimation based on intelligent reflecting surface assisted communication;alfa matcher: supervised and unsupervised product entity matching model;research on multiple automated guided vehicles path planning to reduce default time;research on the strategy of automobile software test case generation based on state chart;finding robust low dimensional features for sleep detection using EEG data;predicting the price direction of bitcoin using twitter data and machine learning;measurement index system and empirical study of consumers' continuous trust on social e-commerce platforms-based on the ga-bp network;a topology identification method of low-voltage distribution network based on structured manifold and density peaks clustering;learner modeling framework based on learning analytics;and covid-19 chest radiography images analysis based on integration of image preprocess and ensemble learning model.

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An Interactive Evaluation Framework for Empathetic Response Generation

An Interactive Evaluation Framework for Empathetic Response ...

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

作者： Lei, Xixi Li, Changqun He, Liang Lin, Xin School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9798350368741

Empathetic response generation is a significant domain in Natural Language Processing (NLP). Its development is a critical step toward achieving humanized AI systems. However, current evaluations of empathetic dialogue models are primarily single-turn and static, leading to bias between evaluation results and real-world multi-turn interaction performance. To overcome the longstanding challenge, we propose a novel Interactive Empathy Evaluation Framework (IEEF). It eliminates the bias by facilitating a human-free multi-turn interaction evaluation. Specifically, for human-free interaction, we design a user simulator using reinforcement learning, leveraging a reward model based on LLM scoring. For evalution, we introduce a series of empathy-related metrics based on LLM. The experiments show that IEEF's evaluation results are highly correlated with real-world multi-turn interaction performance, demonstrating its alignment with human preferences in empathy evaluation. © 2025 ieee.

关键词： Dialogue System Empathetic Dialogue Empathy Metrics LLM Scoring Natural Language Processing

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Multi-scale Spectral Mixture Neural Operator

Multi-scale Spectral Mixture Neural Operator

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

作者： Jing, Fengrui Ding, Hongzhen Song, Tao Qingdao Institute of Software College of Computer Science and Technology Qingdao China

ISBN: (纸本)9798350368741

Seeking effective numerical approximations for partial differential equations (PDEs) is a major challenge in modern science and technology. Recently, AI-inspired data-driven solvers, such as neural operators, have achieved great success in quickly PDE solving. However, in the design of neural operators, the processing of frequency domain information is crucial, while the importance of low-frequency information is excessively overlooked. In order to reduce low-frequency error in PDE solving, we propose the multi scale spectral mixture neural operator (MSSMNO) architecture and design a residual learning structure that transfers residuals in multi-scale cycle by combining the frequency domain learning patterns of the FNO method. Meanwhile, we designed interpolation operator and restriction operator to effectively transmit and reconstruct high-frequency information in MSSMNO. Experimentally, MSSMNO achieves state-of-the-art and yields a relative error reduction of 21.9% averaged on four classical benchmarks. © 2025 ieee.

关键词： neural operator PDE

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MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network for Mpox Detection

MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network ...

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

作者： Yue, Yubiao Xue, Jun Liang, Haihuang Li, Zhenzhang Wang, Yufeng School of Biomedical Engineering Guangzhou Medical University Guangzhou China School of Computer Science and Technology Anhui University Hefei China School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou China Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350368741

Due to the lack of effective mpox detection tools, the mpox virus continues to spread worldwide and has been once again declared a public health emergency of international concern by the World Health Organization. Lightweight deep learning model-based detection systems are crucial to alleviate mpox outbreaks since they are suitable for widespread deployment, especially in resource-limited scenarios. However, the key to its successful application depends on ensuring that the model can effectively model local features and long-range dependencies in mpox lesions while maintaining lightweight. Inspired by the success of Mamba in modeling long-range dependencies and its linear complexity, we proposed a lightweight hybrid architecture called MpoxMamba for efficient mpox detection. MpoxMamba utilizes depth-wise separable convolutions to extract local feature representations in mpox skin lesions and greatly enhances the model's ability to model the global contextual information by grouped Mamba modules. Notably, MpoxMamba's parameter size and FLOPs are 0.77M and 0.53G, respectively. Experimental results on two widely recognized benchmark datasets demonstrate that MpoxMamba outperforms state-of-the-art lightweight models and existing mpox detection methods. Importantly, we developed a web-based online application to provide free mpox detection. The source code of MpoxMamba and the developed online application are available at https://***/YubiaoYue/MpoxMamba. © 2025 ieee.

关键词： Deep Leraning Lightweight Model Mpox Detection Vision Mamba

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A Framework Based on data Augmentation for Knowledge Graph Entity Typing

A Framework Based on Data Augmentation for Knowledge Graph E...

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

作者： Li, Zepeng Zhai, Minyu Li, Xiao Huang, Rikui Liang, Chenhui Hu, Bin School of Information Science & Engineering Lanzhou University Lanzhou China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

The task of knowledge graph entity typing (KGET) aims to infer the missing types for entities in knowledge graphs, which is a significant subtask of knowledge graph completion (KGC). In despite of its progress, we observe that the sparsity of the dataset greatly affects the task itself as well as downstream tasks. In this paper, we propose a framework to alleviate this problem, which consists of data augmentation and type inference. We introduce a Statistics-based Entity Type data Augmentation (SET-DA) method in data augmentation phase, which calculates a type probability distribution for each entity by statistically determining the global relation-type statistics information, and then employ embedding-based KGET models for type inference. Experimental results on two widely used datasets indicate that the proposed framework can solve the data sparsity problem in the task of KGET and the performance of models trained with data augmentation by SET-DA significantly outperforms previous state-of-the-art methods. © 2025 ieee.

关键词： data Augmentation Knowledge Graph Completion Knowledge Graph Entity Typing

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Leveraging Web Technologies for Enhanced Customer Satisfaction data in Public Health: A Case Study of a Digital Solution

Leveraging Web Technologies for Enhanced Customer Satisfacti...

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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Gloria, Emanuel Diaz, Ken Celis, Viel Aurea Sampedro, Gabriel Avelino Universiteit Hasselt Faculty of Science Hasselt Belgium University of the Philippines Open University Faculty of Information and Communication Studies Laguna Philippines University of the Philippines Diliman Department of Computer Science Quezon City Philippines

ISBN: (纸本)9798331510756

The Department of Health has initiated efforts to enhance the collection and analysis of customer satisfaction data in public health services, as highlighted by Department Memorandum No. 2021-0100, which introduced the Updated Customer Satisfaction Survey Form. However, resource constraints have prevented the full realization of digitalization's potential to streamline this process. This study addresses this gap by leveraging web technologies to develop a tailored web-based application that meets the requirements outlined in the memorandum and aligns with industry standards. Utilizing a Waterfall methodology, the team integrates embedded analytics for the front end and employs MySQL via PhpMyAdmin for robust backend support in the development process. Through systematic prototyping, implementation, and testing, the study demonstrates the efficacy of this digital solution in improving the process of collecting data on customer satisfaction. The results highlight the transformative potential of technological innovation in modernizing public health service delivery, facilitating evidence-based decision-making, and ultimately improving healthcare quality for all stakeholders involved. © 2025 ieee.

关键词： Electronic health record

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Siamese Network for EEG Classification of Motor Execution and Imagery 5

Siamese Network for EEG Classification of Motor Execution an...

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5th ieee international conference on Power, Electronics and computer applications, ICPECA 2025

作者： Li, Zhi Chen, Cai Peng, Fulai Lv, Danyang Tian, Chongxuan Meng, Xiangwei Zhang, Ningling Wu, Fengxia Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan250000 China Biomedical Engineering Institute School of Control Science and Engineering Shandong University Jinan250061 China School of Basic Medical Sciences Shandong University Jinan250000 China

ISBN: (纸本)9798331533694

Motor imagery (MI), the mental simulation of movement without physical execution, is a pivotal aspect of brain-computer interface (BCI) research, facilitating direct communication between the brain and external devices. Non-invasive BCIs, particularly those utilizing electroencephalogram (EEG) signals, face inherent challenges, including high-dimensional data, temporal dependencies, and variability across subjects. This study investigates the potential of Siamese networks-A deep learning framework grounded in contrastive learning-for single-channel EEG classification in MI tasks. Through a series of comparative experiments employing identical parameter settings, including ten-fold cross-validation, we demonstrate that Siamese networks outperform convolutional neural networks (CNNs), yielding an average classification accuracy of approximately 10% higher. These findings underscore the superior capability of Siamese networks in capturing subtle temporal patterns and mitigating inter-subject variability, positioning them as a promising alternative to traditional CNN-based approaches in EEG-based MI classification. © 2025 ieee.

关键词： Electroencephalography

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