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

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Fingerprint Presentation Attack Detection by Learning in Frequency Domain 基于频域学习的指纹表示攻击检测
Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520694
Wentian Zhang, Haozhe Liu, Feng Liu
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.
抗欺骗能力对自动指纹识别系统的发展至关重要。提出了一种基于光学相干技术(OCT)的指纹呈现攻击检测方法。与以前的方法不同,我们设计了一个频率特征解纠缠(FFD)模型,将基于oct的指纹b扫描分解为四个不同的频率子带,如离散小波变换(DWT)。通过这种去纠缠,可以分别分离出在空间域中叠加在原始图像上的信息(如判别性PAD特征、无效和冗余特征)。然后让它学习不同的频率码,形成相应的潜码。最后基于潜码设计了用于区分真伪的欺骗评分。实验结果表明,该方法可以通过解纠缠到频域,去除空间域叠加的无用干扰信息,从而实现有效的PAD。在实例方面,该方法的最小误差(Err.)为0.67%,优于其他比较方法,比最佳方法提高了81.89%。
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引用次数: 8
Multi-task CNN for Abusive Language Detection 多任务CNN用于辱骂性语言检测
Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520387
Qingqing Zhao, Yue Xiao, Yunfei Long
Abusive language detection serves to ensure a compelling user experience via high-quality content. Different sub-categories of abusive language are closely related, with most aggressive comments containing personal attacks and toxic content and vice versa. We set a multi-task learning framework to detect different types of abusive content in a mental health forum to address this feature. Each classification task is treated as a subclass in a multi-class classification problem, with shared knowledge used for three related tasks: attack, aggression, and toxicity. Experimental results on three sub-types of Wikipedia abusive language datasets show that our framework can improve the net F1-score by 7.1%, 5.6%, and 2.7% in the attack, aggressive, and toxicity detection. Our experiments identified multi tasking framework act as an effective method in abusive language detection.
滥用语言检测有助于通过高质量的内容确保引人注目的用户体验。侮辱性语言的不同子类别是密切相关的,大多数攻击性评论包含人身攻击和有毒内容,反之亦然。为了解决这一问题,我们设置了一个多任务学习框架来检测心理健康论坛中不同类型的滥用内容。每个分类任务被视为多类分类问题中的一个子类,共享知识用于三个相关任务:攻击、攻击和毒性。在维基百科滥用语言数据集的三个子类型上的实验结果表明,我们的框架在攻击、攻击性和毒性检测方面可以将净f1分数提高7.1%、5.6%和2.7%。实验结果表明,多任务框架是一种有效的语言滥用检测方法。
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引用次数: 0
Multiclass Language Identification Using CNN-Bigru-Attention Model on Spectrogram of Audio Signals 基于CNN-Bigru-Attention模型的音频信号谱图多类语言识别
Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520702
Ma Xueli, Mijit Ablimit, A. Hamdulla
Aiming at the problems of low recognition rate and uneven distribution of language information in language identification tasks, a language recognition method based on the CNN-Bigru-Attention model is proposed. This method first extracts the spectrogram of audio signals and converts it into a gray-scale spectrogram as input, then uses CNN (convolutional neural network) to capture the local features, and extracts the temporal features through the Bigru (Bidirectional gated recurrent unit), and then local features and temporal features are passed to the attention mechanism layer to focus on the information related to the language features and suppress useless information. Finally the classes of language is output through the fully connected layer. Experiments on the Common voice dataset show that the method has achieved good results and improves the performance of language identification.
针对语言识别任务中存在的语言信息识别率低、分布不均匀等问题,提出了一种基于CNN-Bigru-Attention模型的语言识别方法。该方法首先提取音频信号的频谱图,并将其转换为灰度谱图作为输入,然后利用CNN(卷积神经网络)捕获局部特征,通过双向门控循环单元Bigru (Bidirectional gated recurrent unit)提取时间特征,然后将局部特征和时间特征传递给注意机制层,对与语言特征相关的信息进行集中处理,抑制无用信息。最后通过全连通层输出语言类。在通用语音数据集上的实验表明,该方法取得了良好的效果,提高了语言识别的性能。
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引用次数: 1
COVID-19 Fatality Rate Classification Using Synthetic Minority Oversampling Technique (SMOTE) for Imbalanced Class 基于合成少数过采样技术(SMOTE)的非平衡类COVID-19病死率分类
T. Oladunni, Justin Stephan, Lala Aicha Coulibaly
SARS-Cov-2 is not to be introduced anymore. The global pandemic that originated more than a year ago in Wuhan, China has claimed thousands of lives. Since the arrival of this plague, face mask has become part of our dressing code. The focus of this study is to design, develop and evaluate a COVID-19 fatality rate classifier at the county level. The proposed model predicts fatality rate as low, moderate, or high. This will help government and decision makers to improve mitigation strategy and provide measures to reduce the spread of the disease. Tourists and travelers will also find the work useful in planning of trips. Dataset for the experiment contained imbalanced fatality levels. Therefore, class imbalance was offset using SMOTE. Evaluation of the proposed model was based on precision, F1 score, accuracy, and ROC curve. Five learning algorithms were trained and evaluated. Experimental results showed the Bagging model has the best performance.
SARS-Cov-2不会再被引入。一年多前起源于中国武汉的全球大流行夺走了数千人的生命。自这场瘟疫到来以来,口罩已成为我们着装规范的一部分。本研究的重点是设计、开发和评估县一级的COVID-19病死率分类器。提出的模型将死亡率预测为低、中、高。这将有助于政府和决策者改进缓解战略,并提供减少疾病传播的措施。游客和旅行者也会发现这项工作对计划旅行很有用。实验数据集包含不平衡的死亡率水平。因此,使用SMOTE可以抵消类不平衡。对模型的评价基于精度、F1评分、准确度和ROC曲线。对五种学习算法进行了训练和评估。实验结果表明,Bagging模型的性能最好。
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引用次数: 0
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2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)
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