Publications

2024

1. IGI

   Charting the Ethical Course: Navigating AI Advancements in Communication Education

   Huong Tra Le Nguyen*, Trung Tran*

   In Sanae Elmoudden, Jason S. Wrench (editors) The Role of Generative AI in the Communication Classroom

   Book
2. IGI

   Generative AI in terms of Its ethical problems for both teachers and learners: Striking a balance

   Huong Tra Le Nguyen*, Trung Tran*

   In Shalin Hai-Jew (editors) Generative AI in Teaching and Learning

   Book

2023

1. IGI

   Chatbots as motivational agents: Chatbots – The Value of a Digital Tool in Pedagogy

   Huong Tra Le Nguyen*, Trung Tran*

   In Mohammad Amin et al (editors) Trends, Applications, and Challenges of Chatbot Technology

   Book

2021

1. ICICT

   MAGNeto: An Efficient Deep Learning Method for the Extractive Tags Summarization Problem

   Hieu Trong Phung (1 and 2), Anh Tuan Vu (1), Tung Dinh Nguyen (1), Lam Thanh Do (1 and 2), Giang Nam Ngo (1), Trung Thanh Tran (1), Ngoc C. Lê (1 and 2) ((1) PIXTA Vietnam, Hanoi, Vietnam. (2) Hanoi University of Science and Technology, Ha Noi, Viet Nam.)

   The Seventh International Congress on Information and Communication Technology (ICICT), 2021

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   In this work, we study a new image annotation task named Extractive Tags Summarization (ETS). The goal is to extract important tags from the context lying in an image and its corresponding tags. We adjust some state-of-the-art deep learning models to utilize both visual and textual information. Our proposed solution consists of different widely used blocks like convolutional and self-attention layers, together with a novel idea of combining auxiliary loss functions and the gating mechanism to glue and elevate these fundamental components and form a unified architecture. Besides, we introduce a loss function that aims to reduce the imbalance of the training data and a simple but effective data augmentation technique dedicated to alleviates the effect of outliers on the final results. Last but not least, we explore an unsupervised pre-training strategy to further boost the performance of the model by making use of the abundant amount of available unlabeled data. Our model shows the good results as 90% F1 score on the public NUS-WIDE benchmark, and 50% F1 score on a noisy largescale real-world private dataset. Source code for reproducing the experiments is publicly available at: https://github.com/pixta-dev/labteam.

2018

1. Symposium

   An enhanced X-Vector Model for Noise-Robust, Text-Independent Speaker Identification”. Panasonic Technology Symposium 2018

   Truong, T.A, Tran, T.T, Vu, D.L

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   Our goal is todevelop an in-house, low-cost, scalable solution for closed-set text-independent speaker identification. Driven by Panasonic Corporation’s product development pathway, this research will be applicable a wide range of consumer and business-oriented products. Potential applications include recommendation systems for smart TVs, biometric authentication, and automatic transcription for conference meetings, among others. We propose three directions to enhance identification accuracy and noise-robustness: 1) retraining model on large open-source, noisy datasets; 2) omitting excessive data augmentation; 3) anew algorithm to detect non-voice samples. Our enhanced model achieves an increase in identification accuracy by 9% compared to existing state-of-the-art open-source solutions, and achieves non-voice detection accuracy of 97%. We are incorporating the speaker identification solution into a product prototype targeted at the Vietnamese B2B and B2C market.

2014

1. ComNavi-14

   Monitoring scintillations effects over Vietnam by means of a GNSS software receiver

   Workshop Communications and Navigations for the Development of Vietnam’s Marine Economy 2014

   Tran, T.T, Romero, R, Dovis, F

   Learning representations of data is an important problem in statistics and machine learning. While the origin of learning representations can be traced back to factor analysis and multidimensional scaling in statistics, it has become a central theme in deep learning with important applications in computer vision and computational neuroscience. In this article, we review recent advances in learning representations from a statistical perspective. In particular, we review the following two themes: (a) unsupervised learning of vector representations and (b) learning of both vector and matrix representations.

2013

1. IEEE

   Recent results in receiving and decoding signals from the Beidou system

   Duc Minh Truong*; Trung Thanh Tran*; Thuan Dinh Nguyen; Tung Hai Ta

   2013 International Conference on Localization and GNSS (ICL-GNSS)

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   Since December 27, 2012, the Beidou Navigation Satellite System officially started to operate. This event is a great opportunity for researchers in South East of Asia to receive and analyze the Beidou signals. After the official statement, the researchers at NAVIS centre monitored the broadcasted signal by using NAVISOFT- our Software Radio Receiver. This paper shows the analysis on the navigation message that was broadcasted by the Beidou satellites on the B1I bandwidth. In general, we were able to observe a valid ephemeris data on visible satellites. The successful PVT computation by using combinations of GEO and MEO/IGSO in static condition through code-phase measurements is indicated in this paper.