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pro vyhledávání: '"Danh An"'
Federated Class-Incremental Learning (FCIL) increasingly becomes important in the decentralized setting, where it enables multiple participants to collaboratively train a global model to perform well on a sequence of tasks without sharing their priva
Externí odkaz:
http://arxiv.org/abs/2407.11078
Decentralized applications (DApps) have gained prominence with the advent of blockchain technology, particularly Ethereum, providing trust, transparency, and traceability. However, challenges such as rising transaction costs and block confirmation de
Externí odkaz:
http://arxiv.org/abs/2406.12244
Federated Learning (FL) is a promising paradigm that offers significant advancements in privacy-preserving, decentralized machine learning by enabling collaborative training of models across distributed devices without centralizing data. However, the
Externí odkaz:
http://arxiv.org/abs/2405.20431
Publikováno v:
Tạp chí Khoa học Đại học Cần Thơ, Vol 57, Iss 3 (2021)
Trong Địa Vật lý thăm dò, lời giải bài toán ngược trường thế giữ vai trò rất quan trọng, góp phần minh giải định lượng các thông số đặc trưng của nguồn trường gây ra dị thường khảo sát,
Externí odkaz:
https://doaj.org/article/3c1754a238bd42a387dedde90b04cfda
The adoption of the Internet of Things (IoT) deployments has led to a sharp increase in network traffic as a vast number of IoT devices communicate with each other and IoT services through the IoT-edge-cloud continuum. This network traffic increase p
Externí odkaz:
http://arxiv.org/abs/2404.19492
Unsupervised Domain Adaptation (UDA) has shown significant advancements in object detection under well-lit conditions; however, its performance degrades notably in low-visibility scenarios, especially at night, posing challenges not only for its adap
Externí odkaz:
http://arxiv.org/abs/2404.01988
Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Generalization (FedDG) attempts to train a global model using collaborative clients in a
Externí odkaz:
http://arxiv.org/abs/2403.15605
We investigate many-body topological and transport properties of a one-dimensional Su-Schrieffer-Heeger (SSH) topological chain coupled to the quantum field of a cavity mode. The quantum conductance is determined via Green's function formalism in ter
Externí odkaz:
http://arxiv.org/abs/2402.19244
Autor:
Wong, Kok-Seng, Nguyen-Duc, Manh, Le-Huy, Khiem, Ho-Tuan, Long, Do-Danh, Cuong, Le-Phuoc, Danh
Nowadays, billions of phones, IoT and edge devices around the world generate data continuously, enabling many Machine Learning (ML)-based products and applications. However, due to increasing privacy concerns and regulations, these data tend to resid
Externí odkaz:
http://arxiv.org/abs/2305.19831
Autor:
Yuan, Jicheng, Le-Tuan, Anh, Nguyen-Duc, Manh, Tran, Trung-Kien, Hauswirth, Manfred, Le-Phuoc, Danh
The availability of vast amounts of visual data with heterogeneous features is a key factor for developing, testing, and benchmarking of new computer vision (CV) algorithms and architectures. Most visual datasets are created and curated for specific
Externí odkaz:
http://arxiv.org/abs/2309.13610