Search

Your search keyword '"Bai, Guangdong"' showing total 6 results
Start Over Author "Bai, Guangdong" Topic machine learning
6 results on '"Bai, Guangdong"'

1. PrivColl: Practical Privacy-Preserving Collaborative Machine Learning

Author

Zhang, Yanjun, Bai, Guangdong, Li, Xue, Curtis, Caitlin, Chen, Chen, Ko, Ryan K. L., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Chen, Liqun, editor, Li, Ninghui, editor, Liang, Kaitai, editor, and Schneider, Steve, editor

Published
2020
Full Text
View/download PDF

2. Automated Parameter Determination for Enhancing the Product Configuration System of Renault: An Experience Report

Author

Xu, Hao, Baarir, Souheib, Ziadi, Tewfik, Essodaigui, Siham, Bossu, Yves, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bai, Guangdong, editor, Ishikawa, Fuyuki, editor, Ait-Ameur, Yamine, editor, and Papadopoulos, George A., editor

Published
2025
Full Text
View/download PDF

3. Supporting Cyber-Attacks and System Anomaly Detection Research with an Industry 4.0 Dataset

Author

Shi, Lei, Krishnan, Shanti, Wen, Sheng, Xiang, Yang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yuan, Xingliang, editor, Bai, Guangdong, editor, Alcaraz, Cristina, editor, and Majumdar, Suryadipta, editor

Published
2022
Full Text
View/download PDF

4. Leveraging Frame Aggregation in Wi-Fi IoT Networks for Low-Rate DDoS Attack Detection

Author

Tushir, Bhagyashri, Liu, Yuhong, Dezfouli, Behnam, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yuan, Xingliang, editor, Bai, Guangdong, editor, Alcaraz, Cristina, editor, and Majumdar, Suryadipta, editor

Published
2022
Full Text
View/download PDF

5. A Learning Methodology for Line-Rate Ransomware Mitigation with P4 Switches

Author

Friday, Kurt, Bou-Harb, Elias, Crichigno, Jorge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yuan, Xingliang, editor, Bai, Guangdong, editor, Alcaraz, Cristina, editor, and Majumdar, Suryadipta, editor

Published
2022
Full Text
View/download PDF

6. Dilated residual networks with multi-level attention for speaker verification.

Author

Wu, Yanfeng, Guo, Chenkai, Gao, Hongcan, Xu, Jing, and Bai, Guangdong

Subjects
*DELAY lines, *ERROR rates, *MACHINE learning, *FEATURE extraction
Abstract

With the development of deep learning techniques, speaker verification (SV) systems based on deep neural network (DNN) achieve competitive performance compared with traditional i-vector-based works. Previous DNN-based SV methods usually employ time-delay neural network, limiting the extension of the network for an effective representation. Besides, existing attention mechanisms used in DNN-based SV systems are only applied to a single level of network architectures, leading to insufficiently extraction of important features. To address above issues, we propose an effective deep speaker embedding architecture for SV, which combines a residual connection of one-dimensional dilated convolutional layers, called dilated residual networks (DRNs), with a multi-level attention model. The DRNs can not only capture long time-frequency context information of features, but also exploit information from multiple layers of DNN. In addition, the multi-level attention model, which consists of two-dimensional convolutional block attention modules employed at the frame level and the vector-based attention utilized at the pooling layer, can emphasize important features at multiple levels of DNN. Experiments conducted on NIST SRE 2016 dataset show that the proposed architecture achieves a superior equal error rate (EER) of 7.094% and a better detection cost function (DCF16) of 0.552 compared with state-of-the-art methods. Furthermore, the ablation experiments demonstrate the effectiveness of dilated convolutions and the multi-level attention on SV tasks. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results