31 citations · 31 across the 6 of their papers we have counts for
6 papers
Studying Various Activation Functions and Non-IID Data for Machine Learning Model Robustness
Long Dang, Thushari Hapuarachchi, Kaiqi Xiong +1
Adversarial training is an effective method to improve the machine learning (ML) model robustness. Most existing studies typically consider the Rectified linear unit (ReLU) activat…
Securing Traffic Sign Recognition Systems in Autonomous Vehicles
Thushari Hapuarachchi, Long Dang, Kaiqi Xiong
Deep Neural Networks (DNNs) are widely used for traffic sign recognition because they can automatically extract high-level features from images. These DNNs are trained on large-sca…
SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks
Long Dang, Thushari Hapuarachchi, Kaiqi Xiong +1
As the development of autonomous and connected vehicles advances, the complexity of modern vehicles increases, with numerous Electronic Control Units (ECUs) integrated into the sys…
Improving Machine Learning Robustness via Adversarial Training
Long Dang, Thushari Hapuarachchi, Kaiqi Xiong +1
As Machine Learning (ML) is increasingly used in solving various tasks in real-world applications, it is crucial to ensure that ML algorithms are robust to any potential worst-case…
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks
Jing Lin, Long Dang, Mohamed Rahouti +1
Many state-of-the-art ML models have outperformed humans in various tasks such as image classification. With such outstanding performance, ML models are widely used today. However,…
Compressive Features in Offline Reinforcement Learning for Recommender Systems
Hung Nguyen, Minh Nguyen, Long Pham +1
In this paper, we develop a recommender system for a game that suggests potential items to players based on their interactive behaviors to maximize revenue for the game provider. O…