Tuan-Hao Hoang

Le Quy Don Technical University

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i-10 index

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Co-Authors

Bob McKay

Australian National University

Daryl Essam

University of Canberra

Nguyễn Xuân Hoài

Ho Chi Minh City University of Technology

Xuan Hoai Nguyen

Carnegie Mellon University

Thanh-Toan Do

Monash University

Ngai‐Man Cheung

Singapore University of Technology and Design

Truong Son Pham

Le Quy Don Technical University

Tam Nguyen

University of Dayton

Vu Van Canh

Le Quy Don Technical University

Xuân Phương Nguyễn

Ho Chi Minh City University of Transport

Cooperative Institutions

Le Quy Don Technical University

Seoul National University

Vietnam Military Medical University

University of Adelaide

Singapore University of Technology and Design

UNSW Sydney

Ho Chi Minh City University of Technology

Vietnam National University Ho Chi Minh City

Queensland University of Technology

Australian National University

Author Statistics

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Vietnamese Document Classification Using Hierarchical Attention Networks

Advances in intelligent systems and computing (2019)

Khanh Nguyen Anh Viet Tuan-Hao Hoang

Vietnamese

Document classification

10.1007/978-981-13-9920-6_13

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Citations (1)

Privacy Preserving Classification in Two-Dimension Distributed Data

Luong The Dung Hồ Tú Bảo Nguyễn Thế Bình Tuan-Hao Hoang

Within the context of privacy preserving data mining, several solutions for privacy-preserving classification rules learning such as association rules mining have been proposed. Each solution was provided for horizontally or vertically distributed scenario. The aim of this work is to study privacy-preserving classification rules learning in two-dimension distributed data, which is a generalisation of both horizontally and vertically distributed data. In this paper, we develop a cryptographic solution for classification rules learning methods. The crucial step in the proposed solution is the privacy-preserving computation of frequencies of a tuple of values, which can ensure each participant's privacy without loss of accuracy. We illustrate the applicability of the method by using it to build the privacy preserving protocol for association rules mining and ID3 decision tree learning.

ID3

10.1109/kse.2010.38

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Citations (2)

Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing

arXiv (Cornell University) (2021)

Tuan-Hao Hoang Thanh-Toan Do Tam Nguyen Ngai‐Man Cheung

In this paper, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. We proposed a novel method, dubbed Cross-Modal Info-Max Hashing (CMIMH). First, to learn informative representations that can preserve both intra- and inter-modal similarities, we leverage the recent advances in estimating variational lower-bound of MI to maximize the MI between the binary representations and input features and between binary representations of different modalities. By jointly maximizing these MIs under the assumption that the binary representations are modelled by multivariate Bernoulli distributions, we can learn binary representations, which can preserve both intra- and inter-modal similarities, effectively in a mini-batch manner with gradient descent. Furthermore, we find out that trying to minimize the modality gap by learning similar binary representations for the same instance from different modalities could result in less informative representations. Hence, balancing between reducing the modality gap and losing modality-private information is important for the cross-modal retrieval tasks. Quantitative evaluations on standard benchmark datasets demonstrate that the proposed method consistently outperforms other state-of-the-art cross-modal retrieval methods.

Leverage (statistics)

Maximization

Modalities

Binary code

Benchmark (surveying)

Modality (human–computer interaction)

Source

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Citations (0)

Using compression to understand the distribution of building blocks in genetic programming populations

Bob McKay Jungseok Shin Tuan-Hao Hoang Xuan Hoai Nguyen Naoki Mori

Compression algorithms generate a predictive model of data, using the model to reduce the number of bits required to transmit the data (in effect, transmitting only the differences from the model). As a consequence, the degree of compression achieved provides an estimate of the level of regularity in the data. Previous work has investigated the use of these estimates to understand the replication of building blocks within genetic programming (GP) individuals, and hence to understand how different GP algorithms promote the evolution of repeated common structure within individuals. Here, we extend this work to the population level, and use it to understand the extent of similarity between sub-structures within individuals in GP populations.

Replication

Similarity (geometry)

10.1109/cec.2007.4424785

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Citations (12)

Solving Symbolic Regression Problems Using Incremental Evaluation In Genetic Programming

IEEE International Conference on Evolutionary Computation (2006)

Tuan-Hao Hoang Bob McKay Daryl Essam Nguyễn Xuân Hoài

In this paper, we show some experimental results using Incremental Evaluation with Tree Adjoining Grammar Guided Genetic Programming (DEVTAG) on two symbolic regression problems, a benchmark polynomial fitting problem in genetic programming, and a Fourier series problem (sawtooth problem). In our pilot study, we compare results with standard Genetic Programming (GP) and the original Tree Adjoining Grammar Guided Genetic Programming (TAG3P). Our results on the two problems are good, outperforming both standard GP and the original TAG3P.

Symbolic Regression

Benchmark (surveying)

10.1109/cec.2006.1688570

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Citations (10)

Modeling The Causes Of Terrorism From Media News: An Innovative Framework Connecting Impactful Events With Terror Incidents

Truong Son Pham Tuan-Hao Hoang

Terrorism has become an increasingly relevant issue accounting for significant social, economic and political impact. Due to powerful media coverage on the subject, a lot of information is now publicly available, although normally found in an unstructured form. This research aims to better understand the connection between a collection of impactful events, such as external or internal conflicts and military operations, with terror events and its motivations. To this end, a framework was devised, starting with an online news scraper, coupled with machine learning and natural language processing techniques, capable of clustering keywords into the main topics found in the news. The results of these algorithms, in the form of structured data, were later fed to a modeling technique capable of finding, to a certain degree, the connections between topics and terror events. The approach presented in this work adopts a perspective that, to the best of our knowledge, has not been previously seen in specialized literature. Furthermore, this methodology constitutes the groundwork for open source intelligence, capable of being applied to various similar domains like the prediction of political risk index or economic risk index.

10.1109/kse.2018.8573353

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Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation

arXiv (Cornell University) (2018)

Thanh-Toan Do Tuan-Hao Hoang Dang-Khoa Le Tan Trung Pham Huu Le

Learning compact binary codes for image retrieval task using deep neural networks has attracted increasing attention recently. However, training deep hashing networks for the task is challenging due to the binary constraints on the hash codes, the similarity preserving property, and the requirement for a vast amount of labelled images. To the best of our knowledge, none of the existing methods has tackled all of these challenges completely in a unified framework. In this work, we propose a novel end-to-end deep learning approach for the task, in which the network is trained to produce binary codes directly from image pixels without the need of manual annotation. In particular, to deal with the non-smoothness of binary constraints, we propose a novel pairwise constrained loss function, which simultaneously encodes the distances between pairs of hash codes, and the binary quantization error. In order to train the network with the proposed loss function, we propose an efficient parameter learning algorithm. In addition, to provide similar / dissimilar training images to train the network, we exploit 3D models reconstructed from unlabelled images for automatic generation of enormous training image pairs. The extensive experiments on image retrieval benchmark datasets demonstrate the improvements of the proposed method over the state-of-the-art compact representation methods on the image retrieval problem.

Binary code

10.48550/arxiv.1802.07437

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Citations (0)

Developmental evaluation in Genetic Programming: The TAG-based frame work

International Journal of Knowledge-based and Intelligent Engineering Systems (2008)

Tuan-Hao Hoang Daryl Essam McKay RI Nguyễn Xuân Hoài

We build on our previous feasibility studies [18,20], which demonstrated the impact of evaluation during development in the DEVTAG system, and here present a full-fledged developmental system – Developmental Tree Adjoining Grammar Guided Genetic Prog

Frame work

10.3233/kes-2008-12106

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Citations (17)

Detect Wi-Fi Network Attacks Using Parallel Genetic Programming

Van Canh Vu Tuan-Hao Hoang

Wi-Fi network have been widely used nowadays. However, Intrusion Detection System (IDS) researches on Wi-Fi network were few and difficult since there was no common dataset between researchers on this area. Recently, Kolias et al. [2] published a comprehensive Wi-Fi network dataset extracting from real Wi-Fi traces, which is called the AWID dataset. Gene programming has proven effective in detecting network attacks, but the processing time is quite slow. Today, the development of GPU technology for high-speed parallel processing, the study of parallel programming solutions is essential. In this paper, we examined the Parallel Genetic Programming (Karoo GP) [13] in wireless attack detection to improve detection rates and processing time. The experiments showed that the processing time of Karoo GP was significantly improved compared to standard GP.

Parallel processing

10.1109/kse.2018.8573378

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Citations (3)

Analysing the Regularity of Genomes Using Compression and Expression Simplification

Springer eBooks (2007)

Jungseok Shin Moonyoung Kang Bob McKay Xuân Phương Nguyễn Tuan-Hao Hoang

Expression (computer science)

10.1007/978-3-540-71605-1_23

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Citations (10)