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Sapdo Utomo, Adarsh Rouniyar, Hsiu-Chun Hsu and Pao-Ann Hsiung
Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML). However, FL models are susceptible to...
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Jianzhuo Yan, Lihong Chen, Yongchuan Yu, Hongxia Xu, Qingcai Gao, Kunpeng Cao and Jianhui Chen
With the rapid development of the internet and social media, extracting emergency events from online news reports has become an urgent need for public safety. However, current studies on the text mining of emergency information mainly focus on text class...
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Peng Wang, Jingju Liu, Dongdong Hou and Shicheng Zhou
The application of cybersecurity knowledge graphs is attracting increasing attention. However, many cybersecurity knowledge graphs are incomplete due to the sparsity of cybersecurity knowledge. Existing knowledge graph completion methods do not perform w...
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Smita Mahajan, Shruti Patil, Moinuddin Bhavnagri, Rashmi Singh, Kshitiz Kalra, Bhumika Saini, Ketan Kotecha and Jatinderkumar Saini
This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in environments created by a generative adversarial network (GAN). This is a first step towards the greater goal of developing fast-learning and robust RL age...
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Viacheslav Moskalenko, Vyacheslav Kharchenko, Alona Moskalenko and Sergey Petrov
Modern trainable image recognition models are vulnerable to different types of perturbations; hence, the development of resilient intelligent algorithms for safety-critical applications remains a relevant concern to reduce the impact of perturbation on m...
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Bin Yang, Muhammad Haseeb Arshad and Qing Zhao
Powered by advances in information and internet technologies, network-based applications have developed rapidly, and cybersecurity has grown more critical. Inspired by Reinforcement Learning (RL) success in many domains, this paper proposes an Intrusion ...
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Weimin Zhao, Sanaa Alwidian and Qusay H. Mahmoud
Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms. Adversarial training is one of the methods used to defend against the thr...
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Ulrich Aïvodji, François Bidet, Sébastien Gambs, Rosin Claude Ngueveu and Alain Tapp
The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial train...
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Junhyung Kwon and Sangkyun Lee
Despite the advance in deep learning technology, assuring the robustness of deep neural networks (DNNs) is challenging and necessary in safety-critical environments, including automobiles, IoT devices in smart factories, and medical devices, to name a fe...
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Hui Tao, Jun He, Quanjie Cao and Lei Zhang
Domain adaptation is critical to transfer the invaluable source domain knowledge to the target domain. In this paper, for a particular visual attention model, saying hard attention, we consider to adapt the learned hard attention to the unlabeled target ...
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Shayan Taheri, Milad Salem and Jiann-Shiun Yuan
In this work, we propose ShallowDeepNet, a novel system architecture that includes a shallow and a deep neural network. The shallow neural network has the duty of data preprocessing and generating adversarial samples. The deep neural network has the duty...
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Soo Hyun Bae, Inkyu Choi and Nam Soo Kim
Most of the recently proposed deep learning-based speech enhancement techniques have focused on designing the neural network architectures as a black box. However, it is often beneficial to understand what kinds of hidden representations the model has le...
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Xianfeng Gao, Yu-an Tan, Hongwei Jiang, Quanxin Zhang and Xiaohui Kuang
These years, Deep Neural Networks (DNNs) have shown unprecedented performance in many areas. However, some recent studies revealed their vulnerability to small perturbations added on source inputs. Furthermore, we call the ways to generate these perturba...
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Cheng-Bin Jin, Hakil Kim, Mingjie Liu, In Ho Han, Jae Il Lee, Jung Hwan Lee, Seongsu Joo, Eunsik Park, Young Saem Ahn and Xuenan Cui
Magnetic resonance imaging (MRI) plays a significant role in the diagnosis of lumbar disc disease. However, the use of MRI is limited because of its high cost and significant operating and processing time. More importantly, MRI is contraindicated for som...
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Albatul Albattah and Murad A. Rassam
Deep learning (DL) models are frequently employed to extract valuable features from heterogeneous and high-dimensional healthcare data, which are used to keep track of patient well-being via healthcare monitoring systems. Essentially, the training and te...
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James Msughter Adeke, Guangjie Liu, Junjie Zhao, Nannan Wu and Hafsat Muhammad Bashir
Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training i...
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Weijie Zhang, Lanping Zhang, Xixi Zhang, Yu Wang, Pengfei Liu and Guan Gui
Network traffic classification (NTC) has attracted great attention in many applications such as secure communications, intrusion detection systems. The existing NTC methods based on supervised learning rely on sufficient labeled datasets in the training ...
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Luigi Gianpio Di Maggio, Eugenio Brusa and Cristiana Delprete
The Intelligent Fault Diagnosis of rotating machinery calls for a substantial amount of training data, posing challenges in acquiring such data for damaged industrial machinery. This paper presents a novel approach for generating synthetic data using a G...
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Juan M. Perero-Codosero, Fernando M. Espinoza-Cuadros and Luis A. Hernández-Gómez
This paper describes a comparison between hybrid and end-to-end Automatic Speech Recognition (ASR) systems, which were evaluated on the IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge. Deep Neural Networks (DNNs) are becoming the most promisi...
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