2,199 research outputs found
Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics
Mouse dynamics is a potential means of authenticating users. Typically, the
authentication process is based on classical machine learning techniques, but
recently, deep learning techniques have been introduced for this purpose.
Although prior research has demonstrated how machine learning and deep learning
algorithms can be bypassed by carefully crafted adversarial samples, there has
been very little research performed on the topic of behavioural biometrics in
the adversarial domain. In an attempt to address this gap, we built a set of
attacks, which are applications of several generative approaches, to construct
adversarial mouse trajectories that bypass authentication models. These
generated mouse sequences will serve as the adversarial samples in the context
of our experiments. We also present an analysis of the attack approaches we
explored, explaining their limitations. In contrast to previous work, we
consider the attacks in a more realistic and challenging setting in which an
attacker has access to recorded user data but does not have access to the
authentication model or its outputs. We explore three different attack
strategies: 1) statistics-based, 2) imitation-based, and 3) surrogate-based; we
show that they are able to evade the functionality of the authentication
models, thereby impacting their robustness adversely. We show that
imitation-based attacks often perform better than surrogate-based attacks,
unless, however, the attacker can guess the architecture of the authentication
model. In such cases, we propose a potential detection mechanism against
surrogate-based attacks.Comment: Accepted in 2019 International Joint Conference on Neural Networks
(IJCNN). Update of DO
The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences
Current smartphone operating systems regulate application permissions by
prompting users on an ask-on-first-use basis. Prior research has shown that
this method is ineffective because it fails to account for context: the
circumstances under which an application first requests access to data may be
vastly different than the circumstances under which it subsequently requests
access. We performed a longitudinal 131-person field study to analyze the
contextuality behind user privacy decisions to regulate access to sensitive
resources. We built a classifier to make privacy decisions on the user's behalf
by detecting when context has changed and, when necessary, inferring privacy
preferences based on the user's past decisions and behavior. Our goal is to
automatically grant appropriate resource requests without further user
intervention, deny inappropriate requests, and only prompt the user when the
system is uncertain of the user's preferences. We show that our approach can
accurately predict users' privacy decisions 96.8% of the time, which is a
four-fold reduction in error rate compared to current systems.Comment: 17 pages, 4 figure
Biometric Backdoors: A Poisoning Attack Against Unsupervised Template Updating
In this work, we investigate the concept of biometric backdoors: a template
poisoning attack on biometric systems that allows adversaries to stealthily and
effortlessly impersonate users in the long-term by exploiting the template
update procedure. We show that such attacks can be carried out even by
attackers with physical limitations (no digital access to the sensor) and zero
knowledge of training data (they know neither decision boundaries nor user
template). Based on the adversaries' own templates, they craft several
intermediate samples that incrementally bridge the distance between their own
template and the legitimate user's. As these adversarial samples are added to
the template, the attacker is eventually accepted alongside the legitimate
user. To avoid detection, we design the attack to minimize the number of
rejected samples.
We design our method to cope with the weak assumptions for the attacker and
we evaluate the effectiveness of this approach on state-of-the-art face
recognition pipelines based on deep neural networks. We find that in scenarios
where the deep network is known, adversaries can successfully carry out the
attack over 70% of cases with less than ten injection attempts. Even in
black-box scenarios, we find that exploiting the transferability of adversarial
samples from surrogate models can lead to successful attacks in around 15% of
cases. Finally, we design a poisoning detection technique that leverages the
consistent directionality of template updates in feature space to discriminate
between legitimate and malicious updates. We evaluate such a countermeasure
with a set of intra-user variability factors which may present the same
directionality characteristics, obtaining equal error rates for the detection
between 7-14% and leading to over 99% of attacks being detected after only two
sample injections.Comment: 12 page
๋ฅ๋ฌ๋ ๊ธฐ๋ฐ ๊ณ ์ฅ ์ง๋จ์ ์ํ ์ ๋ณด ํ์ฉ ๊ทน๋ํ ๊ธฐ๋ฒ ๊ฐ๋ฐ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2021.8. ์ค๋ณ๋.๊ธฐ๊ณ ์์คํ
์ ์๊ธฐ์น ์์ ๊ณ ์ฅ์ ๋ง์ ์ฐ์
๋ถ์ผ์์ ๋ง๋ํ ์ฌํ์ , ๊ฒฝ์ ์ ์์ค์ ์ผ๊ธฐํ ์ ์๋ค. ๊ฐ์์ค๋ฐ ๊ณ ์ฅ์ ๊ฐ์งํ๊ณ ์๋ฐฉํ์ฌ ๊ธฐ๊ณ ์์คํ
์ ์ ๋ขฐ์ฑ์ ๋์ด๊ธฐ ์ํด ๋ฐ์ดํฐ ๊ธฐ๋ฐ ๊ณ ์ฅ ์ง๋จ ๊ธฐ์ ์ ๊ฐ๋ฐํ๊ธฐ ์ํ ์ฐ๊ตฌ๊ฐ ํ๋ฐํ๊ฒ ์ด๋ฃจ์ด์ง๊ณ ์๋ค. ๊ณ ์ฅ ์ง๋จ ๊ธฐ์ ์ ๋ชฉํ๋ ๋์ ๊ธฐ๊ณ ์์คํ
์ ๊ณ ์ฅ ๋ฐ์์ ๊ฐ๋ฅํ ๋นจ๋ฆฌ ๊ฐ์งํ๊ณ ์ง๋จํ๋ ๊ฒ์ด๋ค. ์ต๊ทผ ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง ๊ธฐ๋ฒ์ ํฌํจํ ๋ฅ๋ฌ๋ ๊ธฐ๋ฐ ๊ณ ์ฅ ์ง๋จ ๊ธฐ์ ์ ์์จ์ ์ธ ํน์ฑ์ธ์(feature) ํ์ต์ด ๊ฐ๋ฅํ๊ณ ๋์ ์ง๋จ ์ฑ๋ฅ์ ์ป์ ์ ์๋ค๋ ์ฅ์ ์ด ์์ด ํ๋ฐํ ์ฐ๊ตฌ๋๊ณ ์๋ค.
๊ทธ๋ฌ๋ ๋ฅ๋ฌ๋ ๊ธฐ๋ฐ์ ๊ณ ์ฅ ์ง๋จ ๊ธฐ์ ์ ๊ฐ๋ฐํจ์ ์์ด ํด๊ฒฐํด์ผ ํ ๋ช ๊ฐ์ง ๋ฌธ์ ์ ๋ค์ด ์กด์ฌํ๋ค. ๋จผ์ , ์ ๊ฒฝ๋ง ๊ตฌ์กฐ๋ฅผ ๊น๊ฒ ์์์ผ๋ก์จ ํ๋ถํ ๊ณ์ธต์ ํน์ฑ์ธ์๋ค์ ๋ฐฐ์ธ ์ ์๊ณ , ์ด๋ฅผ ํตํด ํฅ์๋ ์ฑ๋ฅ์ ์ป์ ์ ์๋ค. ๊ทธ๋ฌ๋ ๊ธฐ์ธ๊ธฐ(gradient) ์ ๋ณด ํ๋ฆ์ ๋นํจ์จ์ฑ๊ณผ ๊ณผ์ ํฉ ๋ฌธ์ ๋ก ์ธํด ๋ชจ๋ธ์ด ๊น์ด์ง์๋ก ํ์ต์ด ์ด๋ ต๊ฒ ๋๋ค๋ ๋ฌธ์ ๊ฐ ์๋ค. ๋ค์์ผ๋ก, ๋์ ์ฑ๋ฅ์ ๊ณ ์ฅ ์ง๋จ ๋ชจ๋ธ์ ํ์ตํ๊ธฐ ์ํด์๋ ์ถฉ๋ถํ ์์ ๋ ์ด๋ธ ๋ฐ์ดํฐ(labeled data)๊ฐ ํ๋ณด๋ผ์ผ ํ๋ค. ๊ทธ๋ฌ๋ ์ค์ ํ์ฅ์์ ์ด์ฉ๋๊ณ ์๋ ๊ธฐ๊ณ ์์คํ
์ ๊ฒฝ์ฐ, ์ถฉ๋ถํ ์์ ๋ฐ์ดํฐ์ ๋ ์ด๋ธ ์ ๋ณด๋ฅผ ์ป๋ ๊ฒ์ด ์ด๋ ค์ด ๊ฒฝ์ฐ๊ฐ ๋ง๋ค. ๋ฐ๋ผ์ ์ด๋ฌํ ๋ฌธ์ ๋ค์ ํด๊ฒฐํ๊ณ ์ง๋จ ์ฑ๋ฅ์ ํฅ์์ํค๊ธฐ ์ํ ์๋ก์ด ๋ฅ๋ฌ๋ ๊ธฐ๋ฐ ๊ณ ์ฅ ์ง๋จ ๊ธฐ์ ์ ๊ฐ๋ฐ์ด ํ์ํ๋ค.
๋ณธ ๋ฐ์ฌํ์๋
ผ๋ฌธ์์๋ ๋ฅ๋ฌ๋ ๊ธฐ๋ฐ ๊ณ ์ฅ ์ง๋จ ๊ธฐ์ ์ ์ฑ๋ฅ์ ํฅ์์ํค๊ธฐ ์ํ ์ธ๊ฐ์ง ์ ๋ณด ํ์ฉ ๊ทน๋ํ ๊ธฐ๋ฒ์ ๋ํ ์ฐ๊ตฌ๋ก 1) ๋ฅ๋ฌ๋ ์ํคํ
์ฒ ๋ด ๊ธฐ์ธ๊ธฐ ์ ๋ณด ํ๋ฆ์ ํฅ์์ํค๊ธฐ ์ํ ์๋ก์ด ๋ฅ๋ฌ๋ ๊ตฌ์กฐ ์ฐ๊ตฌ, 2) ํ๋ผ๋ฏธํฐ ์ ์ด ๋ฐ ์ผ์คํญ ์์ค์ ๊ธฐ๋ฐ์ผ๋ก ๋ถ์ถฉ๋ถํ ๋ฐ์ดํฐ ๋ฐ ๋
ธ์ด์ฆ ์กฐ๊ฑด ํ ๊ฐ๊ฑดํ๊ณ ์ฐจ๋ณ์ ์ธ ํน์ฑ์ธ์ ํ์ต์ ๋ํ ์ฐ๊ตฌ, 3) ๋ค๋ฅธ ๋๋ฉ์ธ์ผ๋ก๋ถํฐ ๋ ์ด๋ธ ์ ๋ณด๋ฅผ ์ ์ด์์ผ ์ฌ์ฉํ๋ ๋๋ฉ์ธ ์ ์ ๊ธฐ๋ฐ ๊ณ ์ฅ ์ง๋จ ๊ธฐ๋ฒ ์ฐ๊ตฌ๋ฅผ ์ ์ํ๋ค.
์ฒซ ๋ฒ์งธ ์ฐ๊ตฌ์์๋ ๋ฅ๋ฌ๋ ๋ชจ๋ธ ๋ด ๊ธฐ์ธ๊ธฐ ์ ๋ณด ํ๋ฆ์ ๊ฐ์ ํ๊ธฐ ์ํ ํฅ์๋ ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง ๊ธฐ๋ฐ ๊ตฌ์กฐ๋ฅผ ์ ์ํ๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ๋ค์ํ ๊ณ์ธต์ ์์ํ(feature map)์ ์ง์ ์ฐ๊ฒฐํจ์ผ๋ก์จ ํฅ์๋ ์ ๋ณด ํ๋ฆ์ ์ป์ ์ ์์ผ๋ฉฐ, ๊ทธ ๊ฒฐ๊ณผ ์ง๋จ ๋ชจ๋ธ์ ํจ์จ์ ์ผ๋ก ํ์ตํ๋ ๊ฒ์ด ๊ฐ๋ฅํ๋ค. ๋ํ ์ฐจ์ ์ถ์ ๋ชจ๋์ ํตํด ํ์ต ํ๋ผ๋ฏธํฐ ์๋ฅผ ํฌ๊ฒ ์ค์์ผ๋ก์จ ํ์ต ํจ์จ์ฑ์ ๋์ผ ์ ์๋ค.
๋ ๋ฒ์งธ ์ฐ๊ตฌ์์๋ ํ๋ผ๋ฏธํฐ ์ ์ด ๋ฐ ๋ฉํธ๋ฆญ ํ์ต ๊ธฐ๋ฐ ๊ณ ์ฅ ์ง๋จ ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ๋ณธ ์ฐ๊ตฌ๋ ๋ฐ์ดํฐ๊ฐ ๋ถ์ถฉ๋ถํ๊ณ ๋
ธ์ด์ฆ๊ฐ ๋ง์ ์กฐ๊ฑด ํ์์๋ ๋์ ๊ณ ์ฅ ์ง๋จ ์ฑ๋ฅ์ ์ป๊ธฐ ์ํด ๊ฐ๊ฑดํ๊ณ ์ฐจ๋ณ์ ์ธ ํน์ฑ์ธ์ ํ์ต์ ๊ฐ๋ฅํ๊ฒ ํ๋ค. ๋จผ์ , ํ๋ถํ ์์ค ๋๋ฉ์ธ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ฉํด ํ๋ จ๋ ์ฌ์ ํ์ต๋ชจ๋ธ์ ํ๊ฒ ๋๋ฉ์ธ์ผ๋ก ์ ์ดํด ์ฌ์ฉํจ์ผ๋ก์จ ๊ฐ๊ฑดํ ์ง๋จ ๋ฐฉ๋ฒ์ ๊ฐ๋ฐํ ์ ์๋ค. ๋ํ, semi-hard ์ผ์คํญ ์์ค ํจ์๋ฅผ ์ฌ์ฉํจ์ผ๋ก์จ ๊ฐ ์ํ ๋ ์ด๋ธ์ ๋ฐ๋ผ ๋ฐ์ดํฐ๊ฐ ๋ ์ ๋ถ๋ฆฌ๋๋๋ก ํด์ฃผ๋ ํน์ฑ์ธ์๋ฅผ ํ์ตํ ์ ์๋ค.
์ธ ๋ฒ์งธ ์ฐ๊ตฌ์์๋ ๋ ์ด๋ธ์ด ์ง์ ๋์ง ์์(unlabeled) ๋์ ๋๋ฉ์ธ์์์ ๊ณ ์ฅ ์ง๋จ ์ฑ๋ฅ์ ๋์ด๊ธฐ ์ํ ๋ ์ด๋ธ ์ ๋ณด ์ ์ด ์ ๋ต์ ์ ์ํ๋ค. ์ฐ๋ฆฌ๊ฐ ๋ชฉํ๋ก ํ๋ ๋์ ๋๋ฉ์ธ์์์ ๊ณ ์ฅ ์ง๋จ ๋ฐฉ๋ฒ์ ๊ฐ๋ฐํ๊ธฐ ์ํด ๋ค๋ฅธ ์์ค ๋๋ฉ์ธ์์ ์ป์ ๋ ์ด๋ธ ์ ๋ณด๊ฐ ์ ์ด๋์ด ํ์ฉ๋๋ค. ๋์์ ์๋กญ๊ฒ ๊ณ ์ํ ์๋ฏธ๋ก ์ ํด๋ฌ์คํฐ๋ง ์์ค(semantic clustering loss)์ ์ฌ๋ฌ ํน์ฑ์ธ์ ์์ค์ ์ ์ฉํจ์ผ๋ก์จ ์ฐจ๋ณ์ ์ธ ๋๋ฉ์ธ ๋ถ๋ณ ๊ธฐ๋ฅ์ ํ์ตํ๋ค. ๊ฒฐ๊ณผ์ ์ผ๋ก ๋๋ฉ์ธ ๋ถ๋ณ ํน์ฑ์ ๊ฐ์ง๋ฉฐ ์๋ฏธ๋ก ์ ์ผ๋ก ์ ๋ถ๋ฅ๋๋ ํน์ฑ์ธ์๋ฅผ ํจ๊ณผ์ ์ผ๋ก ํ์ตํ ์ ์์์ ์ฆ๋ช
ํ์๋ค.Unexpected failures of mechanical systems can lead to substantial social and financial losses in many industries. In order to detect and prevent sudden failures and to enhance the reliability of mechanical systems, significant research efforts have been made to develop data-driven fault diagnosis techniques. The purpose of fault diagnosis techniques is to detect and identify the occurrence of abnormal behaviors in the target mechanical systems as early as possible. Recently, deep learning (DL) based fault diagnosis approaches, including the convolutional neural network (CNN) method, have shown remarkable fault diagnosis performance, thanks to their autonomous feature learning ability.
Still, there are several issues that remain to be solved in the development of robust and industry-applicable deep learning-based fault diagnosis techniques. First, by stacking the neural network architectures deeper, enriched hierarchical features can be learned, and therefore, improved performance can be achieved. However, due to inefficiency in the gradient information flow and overfitting problems, deeper models cannot be trained comprehensively. Next, to develop a fault diagnosis model with high performance, it is necessary to obtain sufficient labeled data. However, for mechanical systems that operate in real-world environments, it is not easy to obtain sufficient data and label information. Consequently, novel methods that address these issues should be developed to improve the performance of deep learning based fault diagnosis techniques.
This dissertation research investigated three research thrusts aimed toward maximizing the use of information to improve the performance of deep learning based fault diagnosis techniques, specifically: 1) study of the deep learning structure to enhance the gradient information flow within the architecture, 2) study of a robust and discriminative feature learning method under insufficient and noisy data conditions based on parameter transfer and triplet loss, and 3) investigation of a domain adaptation based fault diagnosis method that propagates the label information across different domains.
The first research thrust suggests an advanced CNN-based architecture to improve the gradient information flow within the deep learning model. By directly connecting the feature maps of different layers, the diagnosis model can be trained efficiently thanks to enhanced information flow. In addition, the dimension reduction module also can increase the training efficiency by significantly reducing the number of trainable parameters.
The second research thrust suggests a parameter transfer and metric learning based fault diagnosis method. The proposed approach facilitates robust and discriminative feature learning to enhance fault diagnosis performance under insufficient and noisy data conditions. The pre-trained model trained using abundant source domain data is transferred and used to develop a robust fault diagnosis method. Moreover, a semi-hard triplet loss function is adopted to learn the features with high separability, according to the class labels.
Finally, the last research thrust proposes a label information propagation strategy to increase the fault diagnosis performance in the unlabeled target domain. The label information obtained from the source domain is transferred and utilized for developing fault diagnosis methods in the target domain. Simultaneously, the newly devised semantic clustering loss is applied at multiple feature levels to learn discriminative, domain-invariant features. As a result, features that are not only semantically well-clustered but also domain-invariant can be effectively learned.Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Research Scope and Overview 3
1.3 Dissertation Layout 6
Chapter 2 Technical Background and Literature Review 8
2.1 Fault Diagnosis Techniques for Mechanical Systems 8
2.1.1 Fault Diagnosis Techniques 10
2.1.2 Deep Learning Based Fault Diagnosis Techniques 15
2.2 Transfer Learning 22
2.3 Metric Learning 28
2.4 Summary and Discussion 30
Chapter 3 Direct Connection Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis 31
3.1 Directly Connected Convolutional Module 33
3.2 Dimension Reduction Module 34
3.3 Input Vibration Image Generation 36
3.4 DC-CNN-Based Fault Diagnosis Method 40
3.5 Experimental Studies and Results 45
3.5.1 Experiment and Data Description 45
3.5.2 Compared Methods 48
3.5.3 Diagnosis Performance Results 51
3.5.4 The Number of Trainable Parameters 56
3.5.5 Visualization of the Learned Features 58
3.5.6 Robustness of Diagnosis Performance 62
3.6 Summary and Discussion 67
Chapter 4 Robust and Discriminative Feature Learning for Fault Diagnosis Under Insufficient and Noisy Data Conditions 68
4.1 Parameter transfer learning 70
4.2 Robust Feature Learning Based on the Pre-trained model 72
4.3 Discriminative Feature Learning Based on the Triplet loss 77
4.4 Robust and Discriminative Feature Learning for Fault Diagnosis 80
4.5 Experimental Studies and Results 84
4.5.1 Experiment and Data Description 84
4.5.2 Compared Methods 85
4.5.3 Experimental Results Under Insufficient Data Conditions 86
4.5.4 Experimental Results Under Noisy Data Conditions 92
4.6 Summary and Discussion 95
Chapter 5 A Domain Adaptation with Semantic Clustering (DASC) Method for Fault Diagnosis 96
5.1 Unsupervised Domain Adaptation 101
5.2 CNN-based Diagnosis Model 104
5.3 Learning of Domain-invariant Features 105
5.4 Domain Adaptation with Semantic Clustering 107
5.5 Proposed DASC-based Fault Diagnosis Method 109
5.6 Experimental Studies and Results 114
5.6.1 Experiment and Data Description 114
5.6.2 Compared Methods 117
5.6.3 Scenario I: Different Operating Conditions 118
5.6.4 Scenario II: Different Rotating Machinery 125
5.6.5 Analysis and Discussion 131
5.7 Summary and Discussion 140
Chapter 6 Conclusion 141
6.1 Contributions and Significance 141
6.2 Suggestions for Future Research 143
References 146
๊ตญ๋ฌธ ์ด๋ก 154๋ฐ
Improving Classification in Single and Multi-View Images
Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that differentiate two given input images. The metric proposed achieves state-of-the-art Fฮฒ measure. In our second paper, we explore multi-view data classification. We investigate the application of Generative Adversarial Networks GANs on Multi-view data image classification and few-shot learning. Experimental results show that our method outperforms state-of-the-art research. In our third paper, we take on the challenge of improving ResNet backbone model. For this task, we focus on improving channel attention mechanisms. We utilize Discrete Wavelet Transform compression to address the channel representation problem. Experimental results on ImageNet shows that our method outperforms baseline SENet-34 and SOTA FcaNet-34 at no extra computational cost. In our fourth paper, we investigate further the potential of orthogonalization of filters for extraction of diverse information for channel attention. We prove that using only random constant orthogonal filters is sufficient enough to achieve good channel attention. We test our proposed method using ImageNet, Places365, and Birds datasets for image classification, MS-COCO for object detection, and instance segmentation tasks. Our method outperforms FcaNet, and WaveNet and achieves the state-of-the-art results
Improving Classification in Single and Multi-View Images
Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that differentiate two given input images. The metric proposed achieves state-of-the-art Fฮฒ measure. In our second paper, we explore multi-view data classification. We investigate the application of Generative Adversarial Networks GANs on Multi-view data image classification and few-shot learning. Experimental results show that our method outperforms state-of-the-art research. In our third paper, we take on the challenge of improving ResNet backbone model. For this task, we focus on improving channel attention mechanisms. We utilize Discrete Wavelet Transform compression to address the channel representation problem. Experimental results on ImageNet shows that our method outperforms baseline SENet-34 and SOTA FcaNet-34 at no extra computational cost. In our fourth paper, we investigate further the potential of orthogonalization of filters for extraction of diverse information for channel attention. We prove that using only random constant orthogonal filters is sufficient enough to achieve good channel attention. We test our proposed method using ImageNet, Places365, and Birds datasets for image classification, MS-COCO for object detection, and instance segmentation tasks. Our method outperforms FcaNet, and WaveNet and achieves the state-of-the-art results
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