Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning最新文献_第3页

Global-Local Framework for Medical Image Segmentation with Intra-class Imbalance Problem 一类内不平衡问题下医学图像分割的全局-局部框架

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590071

Yifan Zhou, Bing Yang, Xiaolu Lin, Risa Higashita, Jiang Liu

{"title":"Global-Local Framework for Medical Image Segmentation with Intra-class Imbalance Problem","authors":"Yifan Zhou, Bing Yang, Xiaolu Lin, Risa Higashita, Jiang Liu","doi":"10.1145/3590003.3590071","DOIUrl":"https://doi.org/10.1145/3590003.3590071","url":null,"abstract":"Deep learning methods have been demonstrated effective in medical image segmentation tasks. The results are affected by data imbalance problems. The inter-class imbalance is often considered, while the intra-class imbalance is not. The intra-class imbalance usually occurs in medical images due to external influences such as noise interference and changes in camera angle, resulting in insufficient discriminative representations within classes. Deep learning methods are easy to segment regions without complex textures and varied appearances. They are susceptible to the intra-class imbalance problem in medical images. In this paper, we propose a two-stage global-local framework to solve the intra-class imbalance problem and increase segmentation accuracy. The framework consists of (1) an auxiliary task network(ATN), (2) a local patch network(LPN), and (3) a fusion module. The ATN has a shared encoder and two separate decoders that perform global segmentation and key points localization. The key points guide to generating the fuzzy patches for the LPN. The LPN focuses on challenging patches to get a more accurate result. The fusion module generates the final output according to the global and local segmentation results. Furthermore, we have performed experiments on a private iris dataset with 290 images and a public CAMUS dataset with 1800 images. Our method achieves an IoU of 0.9280 on the iris dataset and an IoU of 0.8511 on the CAMUS dataset. The results on both datasets show that our method achieves superior performance over U-Net, CE-Net, and U-Net++.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132347222","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-dimensional analysis of urban shrinkage problem in Liaoning Province based on multi-index system, grey correlation analysis and BP neural network with particle swarm optimization 基于多指标体系、灰色关联分析和粒子群优化BP神经网络的辽宁省城市收缩问题多维分析

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590016

Zhenyu Fang, Jun Yu Li, Junyu Xiong, Xin Wang

{"title":"Multi-dimensional analysis of urban shrinkage problem in Liaoning Province based on multi-index system, grey correlation analysis and BP neural network with particle swarm optimization","authors":"Zhenyu Fang, Jun Yu Li, Junyu Xiong, Xin Wang","doi":"10.1145/3590003.3590016","DOIUrl":"https://doi.org/10.1145/3590003.3590016","url":null,"abstract":"The rapid development of urbanization in modern China is accompanied by the increasingly serious problem of urban shrinkage. To provide an effective analytical model for the urban shrinkage problem, this paper takes Liaoning Province, which is one of the typical provinces with a serious urban shrinkage issue in China, as an example. Based on the data from 30 cities in Liaoning Province in recent years, this paper constructs a multi-index system for shrinking cities to evaluate and classify the shrinkage degree of 30 cities. The grey relation analysis model is also used to quantitatively analyze the influence of various factors on the shrinking city population, while the back-propagation neural network algorithm model optimized with particle swarm optimization is also applied to predict the development trend of shrinking cities. The results present the shrinking properties of 30 cities and correlations between different city indicators, as well as the predictive development trend of the shrinking city.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133833060","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

Twitter stance detection using deep learning model with FastText Embedding 使用快速文本嵌入的深度学习模型进行Twitter姿态检测

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590102

Yongqing Deng, Yongzhong Huang

{"title":"Twitter stance detection using deep learning model with FastText Embedding","authors":"Yongqing Deng, Yongzhong Huang","doi":"10.1145/3590003.3590102","DOIUrl":"https://doi.org/10.1145/3590003.3590102","url":null,"abstract":"The interactivity of social media platforms allows a large number of users to comment on different political or social issues to express their views, and identifying users' stances from online comment texts helps the government to monitor public opinion more effectively. The automatic recognition of stance information in comment text has become a new research hotspot in the field of natural language processing. Most of the existing text stance analysis corpus focuses on political topics in European and American countries, and high-quality stance analysis corpus research on political topics in Southeast Asian countries is relatively scarce. In order to stimulate this research direction, this paper provides a dataset about the 2022 Philippine presidential election, which annotates the stance information of the two popular presidential candidates and provides reliable data support for subsequent stance analysis model research. Next, we build a stance detection model of hybrid deep neural networks based on BiLSTM, CNN, and Attention, and we demonstrate its effectiveness on multiple datasets and obtain the best results on the SemEval-2016 dataset. In addition, we compare FastText and Word2Vec, two pre-trained word embeddings for word encoding, and discuss which word embedding is preferred in stance detection tasks. This result shows that the stance analysis model proposed in this paper can be effectively applied to Twitter text stance data.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116143744","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

Federated Learning-Based Intrusion Detection Method for Smart Grid 基于联邦学习的智能电网入侵检测方法

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590060

Dong Bin, Xin Li, Chunyan Yang, Songming Han, Ying Ling

{"title":"Federated Learning-Based Intrusion Detection Method for Smart Grid","authors":"Dong Bin, Xin Li, Chunyan Yang, Songming Han, Ying Ling","doi":"10.1145/3590003.3590060","DOIUrl":"https://doi.org/10.1145/3590003.3590060","url":null,"abstract":"Power systems have revealed serious security problems in the process of gradual opening, and intrusion detection as an important security defense measure can detect potential intrusions in a timely manner. In the big data environment of electric power, there are information silos between different electric power data owners, and in order to obtain intrusion detection models with better performance, traditional methods need to fuse data from all parties, which often brings difficulties in information security and data privacy protection. In this paper, we propose a distributed intrusion detection framework based on federated learning and apply it to network traffic data analysis. The framework aims to ensure the information security of each local power data while establishing a collection of decentralized data and completing the joint training of models from multiple data sources. The experimental results show that the scheme achieves 98.1% accuracy on the simulated data set, which is better than other commonly used intrusion detection algorithms. In addition, the method well ensures the security and privacy of data because the data are not interoperable among each participant under the federated learning mechanism.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115152566","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

Health monitoring system for elderly people based on Raspberry Pi 基于树莓派的老年人健康监测系统

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590057

Qingsong Peng

{"title":"Health monitoring system for elderly people based on Raspberry Pi","authors":"Qingsong Peng","doi":"10.1145/3590003.3590057","DOIUrl":"https://doi.org/10.1145/3590003.3590057","url":null,"abstract":"We present a comprehensive overview of the application of Raspberry Pi in the field of health monitoring for elderly people with disabilities. Firstly we discuss the advantages of using artificial intelligence technology for health monitoring of elderly people, and the significance of using information technology devices to achieve health monitoring for the elderly, while keeping the cost of the devices low. And then we examine the development of Raspberry Pi and its advantages for health monitoring of elderly people with disabilities, such as its low cost, portability, and ease of use. After that we outline the methods of collecting data for health monitoring of elderly people, such as using sensors to measure heart rate, oxygen levels, and blood pressure, and integrating these sensors into a single device. We also discuss the implementation of a Raspberry Pi-based health monitoring system for elderly people, and the ways in which health data can be utilized to optimize the performance of the system. The work provides useful insights for those who are interested in using Raspberry Pi for health monitoring applications for elderly people with disabilities.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123338827","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: 1

A Histo-Puzzle Network for Weakly Supervised Semantic Segmentation of Histological Tissue Type 用于组织类型弱监督语义分割的组织谜题网络

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590095

Tengyun Ma, Guotian He, Lin Chen, Yuanchang Lin

引用次数: 0

An Emotion Recognition Method Based On Feature Fusion and Self-Supervised Learning 基于特征融合和自监督学习的情绪识别方法

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590041

Xuan-Nam Cao, Ming Sun

{"title":"An Emotion Recognition Method Based On Feature Fusion and Self-Supervised Learning","authors":"Xuan-Nam Cao, Ming Sun","doi":"10.1145/3590003.3590041","DOIUrl":"https://doi.org/10.1145/3590003.3590041","url":null,"abstract":"Emotional diseases being represented in many kinds of human mental and cardiac problems, demanding requirements are imposed on accurate emotion recognition. Deep learning methods have gained widespread application in the field of emotion recognition, utilizing physiological signals. However, many existing methods rely solely on deep features, which can be difficult to interpret and may not provide a comprehensive understanding of physiological signals. To address this issue, we propose a novel emotion recognition method based on feature fusion and self-supervised learning. This approach combines shallow features and deep learning features, resulting in a more holistic and interpretable approach to analyzing physiological signals. In addition, we transferred the self-supervised learning method from processing images to signals, which learns sophisticated and informative features from unlabeled signal data. Our experimental results are conducted on WESAD, a publicly available dataset and the proposed model shows significant improvement in performance, which confirms the superiority of our proposed method compared to state-of-the-art methods.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126580949","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

Detecting Arbitrary-oriented Objects in Remote Sensing Imagery with Segmentation-Aware Mask 基于分割感知掩模的遥感图像任意方向目标检测

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590032

Jiali Wei, Bo Hua, Fei Gao, Huan Zhang, Jiangwei Fan, Shuran Zhang

引用次数: 0

RhySpeech: A Deployable Rhythmic Text-to-Speech Based on Feed-Forward Transformer for Reading Disabilities RhySpeech:一种可部署的基于前馈变压器的有节奏文本到语音的阅读障碍

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590062

Yi-Hsien Lin

引用次数: 0

Gaussian-guided character erasure for data augment of industrial characters 工业字符数据扩充的高斯制导字符擦除

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590077

Hongchao Gao, Chao Yao, Zhennan Wang

{"title":"Gaussian-guided character erasure for data augment of industrial characters","authors":"Hongchao Gao, Chao Yao, Zhennan Wang","doi":"10.1145/3590003.3590077","DOIUrl":"https://doi.org/10.1145/3590003.3590077","url":null,"abstract":"The application of scene text erasure technology in privacy protection, camera-based virtual reality translation and image editing has attracted more and more research interests. Recent efforts on scene text erasing have shown promising results. We utilize text removal methods as a component of industrial characters generation procedure to generate large-scale synthetic character images so as to mitigate the issue of insufficient samples in the recognition task of industrial characters. Existing character erasure models has achieved good performance in natural scenes. However, in industrial scenes, these erasure networks are easily affected by salient no-character regions leading to the attention shift. To overcome this limitation, we proposed a character erasure network based on attention mechanism which embed an additional region awareness layer to guide attention to the correct character regions. Meanwhile, we devise a gaussian heat map supervision method for learning additional region awareness layer. The experiments show that the proposed method performs favourably on four industrial character datasets.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129126890","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