2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)最新文献_第3页

Sensitive Data Classification of Imbalanced Short Text Based on Probability Distribution BERT in Electric power industry 基于概率分布BERT的电力工业不平衡短文本敏感数据分类

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00034

Wensi Zhang, Xiao Liang, Yifang Zhang, Hanchen Su

{"title":"Sensitive Data Classification of Imbalanced Short Text Based on Probability Distribution BERT in Electric power industry","authors":"Wensi Zhang, Xiao Liang, Yifang Zhang, Hanchen Su","doi":"10.1109/PRMVIA58252.2023.00034","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00034","url":null,"abstract":"The exploitation of big data in industrial fields faces several challenges, such as data privacy and security, data integration and interoperability, and data analysis and visualization. Data privacy and security is a major concern, as the data collected from industrial fields often contain sensitive information. Due to the particularity of the industrial field, there are challenges in the utilization of big data. 1. The distribution of different categories data is extremely uneven; 2. There are a large number of industry terms in the short texts that constitute the metadata, which makes semantic representation difficult. These two challenges have a large impact on the application performance of existing models. In order to resolve the problems above, this paper proposes a pre-training model based on probability distribution, which for the classification of sensitive data in the power industry. The model consists of three modules: 1. The data enhancement module adopts the technology of synonym expansion and noise introduction, so that the model can extract the classification features of sensitive data with a small proportion; 2. The pre-training module adopts the BERT model, which can obtain the semantics of industry terms in short texts; 3. The probability prediction module is used to regularize the distribution of test data to meet the training data. Compared with the traditional classification model and the classification model based on deep learning, the F1-score can be improved by 36.68% and 6.39%.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129441801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Modal Cross-Attention Learning on Detecting Anomalies of Electrical Equipment 电气设备异常检测的多模态交叉注意学习

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00030

Zongluo Zhao, Zhixin Zhao, Qiangqiang Li, Jiaxi Zhuang, Xiaoming Ju

引用次数: 0

Iterative Uni-modal and Cross-modal Clustered Contrastive Learning for Image-text Retrieval 图像-文本检索的迭代单模态和跨模态聚类对比学习

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00009

Yi Zhu, Xiu Li

引用次数: 0

Application Research of Landscape Sculpture Design Aided by Computer Virtual Technology 计算机虚拟技术在景观雕塑设计中的应用研究

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00021

Yuran Sun

{"title":"Application Research of Landscape Sculpture Design Aided by Computer Virtual Technology","authors":"Yuran Sun","doi":"10.1109/prmvia58252.2023.00021","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00021","url":null,"abstract":"In recent years, due to the rapid development of the city, landscape sculpture has developed rapidly in the city. Due to its important role in regional beautification and humanistic education, the development of landscape sculpture has also become rich and colorful. With the application and promotion of computer virtual technology, facing the diversity and interaction of landscape culture, the wide application of computer virtual technology in landscape sculpture creation is an urgent need for the development of landscape sculpture. It has significant advantages in the production speed, cost reduction and customization of landscape sculpture, and optimizes the production process of landscape sculpture. This article discusses and analyzes the application method of computer virtual technology in landscape sculpture creation, and discusses how computer virtual technology intervenes in the creation of landscape sculpture, especially the material value and positive significance of computer virtual technology in sculpture landscape sculpture. According to the experimental research, the image frame size of the 3D landscape sculpture in the virtual space of this paper can store more than 48 frames, the real-time performance of the system is well guaranteed, and the speed and power output reach the best without affecting the response of other functions of the system Level.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121086070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on multi-AGV scheduling for intelligent storage based on improved genetic algorithm 基于改进遗传算法的智能存储多agv调度研究

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00041

Haowen Sun, Liming Zhao

引用次数: 0

Hybrid Attention Deep Adaptive Residual Graph Convolution Network for Few-shot Classification 基于混合注意深度自适应残差图卷积网络的少镜头分类

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00007

Guangyi Liu, Qifan Liu, Wenming Cao

{"title":"Hybrid Attention Deep Adaptive Residual Graph Convolution Network for Few-shot Classification","authors":"Guangyi Liu, Qifan Liu, Wenming Cao","doi":"10.1109/PRMVIA58252.2023.00007","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00007","url":null,"abstract":"Few-shot learning is a challenging task in the field of machine learning that aims to acknowledge novel class with a few amount of labeled samples. To address this problem, researchers have proposed several methods, with metric-based methods being one of the most effective approaches. These methods learn a transferable embedding space for classification by computing the similarity between samples. In this context, Graph Neural Networks (GNNs) have been employed to describe the association among support samples and query samples. However, existing GNN-based methods face limitations in their capability to achieve deeper layers, which restricts their ability to effectively transport information from the support images to the query images. To overcome the limitation, we propose a deep adaptive residual graph convolution network with deeper layers that better explores the relationship between support and query sets. Additionally, we design a hybrid attention module to learn the metric distributions, which helps to alleviate the over-fitting problem that can occur with few samples. The proposed method has been shown to be effective through comprehensive experimentation on five benchmark datasets.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"25 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117055596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment Analysis of the COVID-19 Epidemic Based on Deep Learning 基于深度学习的COVID-19疫情情绪分析

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00040

Yingying Mei, Yuanyuan Wang

引用次数: 0

Research on fear mental resilience training based on virtual reality and dynamic decision fusion 基于虚拟现实和动态决策融合的恐惧心理弹性训练研究

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00045

Yangzhao Yu, Bin He, Guangjie Yu, Faxin Zhong

{"title":"Research on fear mental resilience training based on virtual reality and dynamic decision fusion","authors":"Yangzhao Yu, Bin He, Guangjie Yu, Faxin Zhong","doi":"10.1109/PRMVIA58252.2023.00045","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00045","url":null,"abstract":"Effective resilience training can prevent early post-traumatic stress disorder, but limitations in emotion induction and recognition make it extremely challenging. Thus, this paper presents a fear mental resilience training that uses virtual reality exposure therapy and introduces two key techniques - construction of virtual scenarios and dynamic weighted decision fusion. Firstly, virtual reality (VR) is proposed to construct three disaster scenarios to induce different level of fear emotion and combining VR with stroop test to improve ecological validity. Then, three different weights are designed by analyzing the modal and cross-modal information to establish a fear emotion classification model based on dynamic weighted decision fusion. Finally, combining VR scenarios with exposure therapy to achieve progressive fear resilience training. And evaluate the training effect according to the individual’s emotional state and stroop performance level. The results demonstrate the designed VR scenarios can effectively induce fear, the proposed data fusion method realizes dynamic weighted fusion according to the weight design, effectively integrates multimodal data information, thereby improving the classification performance of the model. And the mental resilience training based on VR and dynamic weighted decision fusion methods is of great significance for enhancing the mental resilience of the subjects.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133415966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

University Innovation Lab full-space 3D visualization display system based on 3D real sense and panoramic technology 基于三维真实感和全景技术的大学创新实验室全空间三维可视化显示系统

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00051

Kaixin Su, Xianhui Yu, Xiaoying Cai, Yangfeng Lai

{"title":"University Innovation Lab full-space 3D visualization display system based on 3D real sense and panoramic technology","authors":"Kaixin Su, Xianhui Yu, Xiaoying Cai, Yangfeng Lai","doi":"10.1109/prmvia58252.2023.00051","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00051","url":null,"abstract":"Building a campus innovation laboratory with the characteristics of \"3D visualization\", \"informatization\" and \"digitalization\" is the basis for the construction of a smart campus. The majority of the current campus visualization systems were created utilizing conventional modeling tools along with GIS platforms for C/S architecture. It tends to be flat, has a poor level of visualization, and lacks consistent integration of model information. It has increasingly become necessary to find a solution to the problem of how to achieve real-time acquisition and full-space three-dimensional display of innovation laboratories in the Internet environment. This study enhances the interactivity of the innovation laboratory display using the Pano2VR fusion of panorama and video. It also builds an indoor and outdoor model of the innovation laboratory, employs cutting-edge layered display technology to create the floor’s layered display effect, integrates the indoor and outdoor expressions of the campus, and builds a Web-based three-dimensional visualization management system for innovative campuses to realize online learning. This method can more effectively address the issues with shared labs and limited offline space that currently plague institutions.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134111414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contrastive Learning with Part Assignment for Fine-grained Ship Image Recognition 基于部件分配的对比学习细粒度船舶图像识别

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00048

Zhilin Zhang, Ting Zhang, Zhaoying Liu, Yujian Li

引用次数: 0