Zobrazeno 1 - 10
of 238
pro vyhledávání: '"Hoang Trong, P."'
Autor:
Nguyen, Manh Duong, Nguyen, Trung Thanh, Pham, Huy Hieu, Hoang, Trong Nghia, Nguyen, Phi Le, Huynh, Thanh Trung
Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while storing their data locally. However, a significant ch
Externí odkaz:
http://arxiv.org/abs/2410.03070
Autor:
Nguyen, Minh Hieu, Nguyen, Huu Tien, Nguyen, Trung Thanh, Nguyen, Manh Duong, Hoang, Trong Nghia, Nguyen, Truong Thao, Nguyen, Phi Le
Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models in a decentralized manner, preserving data privacy by keeping local data on clients. However, evaluating the robustness of these models against data pertu
Externí odkaz:
http://arxiv.org/abs/2410.03067
Autor:
Hoang-Trong, Duong D., Tran, Khang, Trieu, Doan-An, Truong, Quan-Hao, Le, Van-Hoang, Phan, Ngoc-Loan
Creating soft-Coulomb-type (SC) molecular potential within single-active-electron approximation (SAE) is essential since it allows solving time-dependent Schr\"odinger equations with fewer computational resources compared to other multielectron metho
Externí odkaz:
http://arxiv.org/abs/2408.12627
Autor:
Son, Do Hai, Manh, Bui Duc, Khoa, Tran Viet, Trung, Nguyen Linh, Hoang, Dinh Thai, Minh, Hoang Trong, Alem, Yibeltal, Minh, Le Quang
Blockchain-based supply chain (BSC) systems have tremendously been developed recently and can play an important role in our society in the future. In this study, we develop an anomaly detection model for BSC systems. Our proposed model can detect cyb
Externí odkaz:
http://arxiv.org/abs/2407.15603
Offline optimization is an emerging problem in many experimental engineering domains including protein, drug or aircraft design, where online experimentation to collect evaluation data is too expensive or dangerous. To avoid that, one has to optimize
Externí odkaz:
http://arxiv.org/abs/2405.05349
Autor:
Sim, Rachael Hwee Ling, Zhang, Yehong, Hoang, Trong Nghia, Xu, Xinyi, Low, Bryan Kian Hsiang, Jaillet, Patrick
Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and reward each party based on shared data or model parameters but neglect th
Externí odkaz:
http://arxiv.org/abs/2404.01676
Autor:
Hoang, Trong-Vu, Nguyen, Quang-Binh, Ly, Duy-Nam, Le, Khanh-Duy, Nguyen, Tam V., Tran, Minh-Triet, Le, Trung-Nghia
Drawing is an art that enables people to express their imagination and emotions. However, individuals usually face challenges in drawing, especially when translating conceptual ideas into visually coherent representations and bridging the gap between
Externí odkaz:
http://arxiv.org/abs/2403.08876
Randomized smoothing has recently attracted attentions in the field of adversarial robustness to provide provable robustness guarantees on smoothed neural network classifiers. However, existing works show that vanilla randomized smoothing usually doe
Externí odkaz:
http://arxiv.org/abs/2310.07780
Autor:
Khoa, Tran Viet, Son, Do Hai, Nguyen, Chi-Hieu, Hoang, Dinh Thai, Nguyen, Diep N., Quynh, Tran Thi Thuy, Hoang, Trong-Minh, Ha, Nguyen Viet, Dutkiewicz, Eryk, Alsheikh, Abu, Trung, Nguyen Linh
With the escalating prevalence of malicious activities exploiting vulnerabilities in blockchain systems, there is an urgent requirement for robust attack detection mechanisms. To address this challenge, this paper presents a novel collaborative learn
Externí odkaz:
http://arxiv.org/abs/2308.15804
Data summarization is the process of generating interpretable and representative subsets from a dataset. Existing time series summarization approaches often search for recurring subsequences using a set of manually devised similarity functions to sum
Externí odkaz:
http://arxiv.org/abs/2308.13722