2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)最新文献_第5页

Distantly Supervised Named Entity Recognition with Spy-PU Algorithm 基于Spy-PU算法的远程监督命名实体识别

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520707

Honghao Zheng, Hongtao Yu, Yinuo Hao, Yiteng Wu, Shaomei Li

引用次数: 1

Probability Preserving Discriminative Nonnegative Matrix Factorization 保概率判别非负矩阵分解

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520691

Liuyin Lin, Xin Shu, Jing Song, C. Yu

引用次数: 0

Error Correction Based on Transformer LM in Uyghur Speech Recognition 基于变换LM的维吾尔语语音识别纠错方法

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520740

Yan Zhang, Mijit Ablimit, A. Hamdulla

引用次数: 1

Moroccan Dialect “Darija” Automatic Speech Recognition: A Survey 摩洛哥方言“Darija”自动语音识别研究

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520690

Maria Labied, A. Belangour

{"title":"Moroccan Dialect “Darija” Automatic Speech Recognition: A Survey","authors":"Maria Labied, A. Belangour","doi":"10.1109/PRML52754.2021.9520690","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520690","url":null,"abstract":"Nowadays, human-machine interaction is growing swiftly, and Automatic Speech Recognition is gaining immense interest to make the daily routines much easier. This could be illustrated by the various applications of Speech Recognition in our daily lives, such as voice dictation, interactive voice response systems, device control, telephone applications, and others. Besides Automatic Speech Recognition, Natural language processing has gained significant improvements in terms of technologies and used approaches. Till today great results have been achieved in those Fields, especially for international languages such as English, Spanish, French, and Arabic. Whereas few results have been reached for dialects of languages such as the case of Moroccan dialect “Darija”. The growing use of Moroccan Darija on social media, videos, chatting and others, opens new research directions for Moroccan Darija speech recognition. The leading goal of this paper is to give a literature review on Moroccan Darija Automatic Speech Recognition. Through presenting the dialect specific constraints, the different works conducted in the field of Moroccan Darija speech recognition, and the progress made in recent years.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127482433","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

Concept Word Extraction for Bilingual Ontology Construction in Unstructured Text Environment* 非结构化文本环境下双语本体构建的概念词提取*

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520708

Turdi Tohti, Le Chang, A. Hamdulla, Hankiz Yilahun

引用次数: 1

Lifestyle Analysis via a Corpus of Disease-Fighting Weblogs 通过抗病博客语料库分析生活方式

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520697

Kazuyuki Matsumoto, Mopuaa Ryu, Minoru Yoshida, K. Kita

引用次数: 1

Research on Tibetan-Chinese Machine Translation Based on Multi-Strategy Processing 基于多策略处理的藏汉机器翻译研究

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520733

Saihu Liu, Jie Zhu, Zhensong Li, Zhixiang Luo

引用次数: 0

Rumor Detection Based on Improved Transformer 基于改进型变压器的谣言检测

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520704

Honghao Zheng, Hongtao Yu, Yinuo Hao, Yiteng Wu, Shaomei Li

引用次数: 0

Fingerprint Presentation Attack Detection by Learning in Frequency Domain 基于频域学习的指纹表示攻击检测

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520694

Wentian Zhang, Haozhe Liu, Feng Liu

{"title":"Fingerprint Presentation Attack Detection by Learning in Frequency Domain","authors":"Wentian Zhang, Haozhe Liu, Feng Liu","doi":"10.1109/PRML52754.2021.9520694","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520694","url":null,"abstract":"The anti-spoofing ability is of great importance to the development of automated fingerprint recognition systems. This paper proposes a novel Optical Coherence Technology (OCT)-based fingerprint Presentation Attack Detection (PAD) method from frequency domain. Unlike previous approaches, we design an Frequency Feature Disentangling (FFD) model to decompose OCT-based fingerprint B-scans into four different frequency subbands like Discrete Wavelet Transform (DWT). Through such disentangling, information superimposed in original image in spatial domain (e.g. discriminative PAD feature, invalid and redundant feature) can be separated respectively. We then let it learn different frequency codes to form their corresponding latent codes. Spoofness score which is used to distinguish PAs from bonafides is finally designed based on the latent codes. The experimental results, evaluated on the dataset with 93,200 bonafide B-scans from 137 fingers and 48,400 B-scans from 121 PAs, show that our method can remove some useless interference information superimposed in spatial domain by disentangling into frequency domain for effective PAD. In the instance-wise case, the proposed method achieves a minimum error (Err.) of 0.67%, which outperforms other compared methods and improves 81.89% than the best one.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129452648","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: 8

Deeply Fine-Tune a Convolutional Neural Network in Remote Sensing Image Classification: Easter Africa Countries (EAC) 深度微调卷积神经网络在遥感图像分类中的应用:东非国家(EAC)

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520703

M. J. Bosco, Wang Guoyin

{"title":"Deeply Fine-Tune a Convolutional Neural Network in Remote Sensing Image Classification: Easter Africa Countries (EAC)","authors":"M. J. Bosco, Wang Guoyin","doi":"10.1109/PRML52754.2021.9520703","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520703","url":null,"abstract":"Remote sensing is resource data accessible and easy to get in different areas without time-consuming. The traditional image recognition task was unlimited to better classification. A convolutional neural network (CNN) was introduced to improve remote sensing image classification accuracy by eliminating the intra-class and class similarity. Training CNN from scratch requires a large annotated dataset that is occasional in the remote sensing area. Transfer learning of CNN weights from another large non-remote sensing dataset can occasionally help overcome typical RS image applications. Transfer learning consists of fine-tuning CNN layers to better the new dataset. In this paper, all of the experiments were done on nine categories for dataset collected in east Africa community countries (EAC) using three state-of-the-art architectures based on the effect of fine-tuning and pre-trained weights of CNN. Results indicate that fine-tuning the entire network is not always a significant way; we compared it with a process of using VGG16-DensNet pre-trained weights and RF as machine learning classified results can be improved up to 97.60. Alternatively, fine-tuning the top blocks can save computational power and produce a more robust classifier.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127867346","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