55 Papers
214 Citations
Cuong Pham is an academic researcher from Posts and Telecommunications Institute of Technology. The author has contributed to research in topics: Computer science & Activity recognition. The author has an hindex of 14, co-authored 51 publications. Previous affiliations of Cuong Pham include Newcastle University & Japan Advanced Institute of Science and Technology.
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Papers
Rapid specification and automated generation of prompting systems to assist people with dementia
TL;DR: This paper describes a knowledge-driven method for automatically generating POMDP activity recognition and context-sensitive prompting systems for complex tasks and makes the case that the method could feasibly be used in a clinical or industrial setting.
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Masked face recognition with convolutional neural networks and local binary patterns
TL;DR: Tuming et al. as mentioned in this paper proposed a method that takes advantage of the combination of deep learning and Local Binary Pattern (LBP) features to recognize the masked face by utilizing RetinaFace, a joint extra-supervised and self-vised multi-task learning face detector.
CyRIS: a cyber range instantiation system for facilitating security training
Cuong Pham,Dat Tang,Ken-ichi Chinen,Razvan Beuran +3 more
- 08 Dec 2016
TL;DR: This paper proposes CyRIS (Cyber Range Instantiation System), a mechanism to automatically prepare and manage cyber ranges for cybersecurity education and training based on specifications defined by the instructors, and presents an evaluation of it.
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Integrated framework for hands-on cybersecurity training: CyTrONE
TL;DR: An integrated cybersecurity training framework is designed and implemented to address shortcomings by automating the training content generation and environment setup tasks and the results show that CyTrONE is well-suited for actual training activities in terms of features, usability and execution performance.
82
SensCapsNet: Deep Neural Network for Non-Obtrusive Sensing Based Human Activity Recognition
TL;DR: A method for recognizing human activity from wearable sensors based on a capsule network named SensCapsNet is proposed and a life logging application is developed which achieves a real-time computation and the accuracy rate greater than 80% for 5 common upper body activities.