The debates and the long-awaited reform

Trung Tran, Phuong-Thao T. Trinh, Thu-Trang Vuong, Hiep-Hung Pham (2019). Chapter 1. The debates and the long-awaited reform. In Quan-Hoang Vuong, Trung Tran (Eds.), The Vietnamese Social Sciences at a Fork in the Road (pp. 17–32). Warsaw, Poland: De Gruyter / Sciendo. DOI:10.2478/9783110686081-006 Online ISBN: 9783110686081 © 2019 De Gruyter / Sciend

State-constraint static Hamilton-Jacobi equations in nested domains

We study state-constraint static Hamilton-Jacobi equations in a sequence of domains $\{\Omega_k\}_{k \in \mathbb{N}}$ in $\mathbb{R}^n$ such that $\Omega_k \subset \Omega_{k+1}$ for all $k\in \mathbb{N}$. We obtain rates of convergence of $u_k$, the solution to the state-constraint problem in $\Omega_k$, to $u$, the solution to the corresponding problem in $\Omega = \bigcup_{k \in \mathbb{N}} \Omega_k$. In many cases, the rates obtained are proven to be optimal. Various new examples and discussions are provided at the end of the paper.Comment: 23 pages, 1 figur

Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors

Current probabilistic models for activity recognition do not incorporate much sensory input data due to the problem of state space explosion. In this paper, we propose a model for activity recognition, called the Factored State-Abtract Hidden Markov Model (FS-AHMM) to allow us to integrate many sensors for improving recognition performance. The proposed FS-AHMM is an extension of the Abstract Hidden Markov Model which applies the concept of factored state representations to compactly represent the state transitions. The parameters of the FS-AHMM are estimated using the EM algorithm from the data acquired through multiple multi-modal sensors and cameras. The model is evaluated and compared with other existing models on real-world data. The results show that the proposed model outperforms other models and that the integrated sensor information helps in recognizing activity more accurately

A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment

To tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. These include context specific independence (CSI), mixtures of smaller multinomials and softmax function representations to compactly represent the state transitions of a large number of sensors. The model is evaluated on real-world data acquired through ubiquitous sensors in recognizing daily morning activities. The results show that the combination of CSI and mixtures of smaller multinomials achieves comparable performance with much fewer parameters.<br /

Data-driven structural health monitoring using feature fusion and hybrid deep learning

Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM

Strategic management accounting implementation: how the role of big data

The objective of this study is to investigate the factors that impactthe implementation of strategic management accounting (SMA)practices in Vietnamese enterprises. While SMA is considered a modernmanagement tool that provides relevant information for decisionmaking,as managers increasingly view SMA as a vital modern managementtool that provides valuable information for long-term strategiesand daily activities. Although many motivating factors can influencethe use of SMA, recent digitalization trends, particularly the applicationof big data, have the potential to enhance SMA implementation.However, factors such as managers’ perceptions of SMA, knowledge ofbig data, accountants, corporate culture, enterprise size, networking,information system (IS) quality, and market competition may also significantlyimpact the adoption of SMA techniques. This exploratorystudy employs a quantitative survey approach, and data is collectedusing purposive sampling techniques from 180 Vietnamese enterprises.The findings indicate that networking, enterprise size, corporate culture,big data, and IS quality have a positive relationship with the implementation of SMA practices. Moreover, the role of big data as anew factor impacting SMA practices is identified. The study’s contributionlies in providing insights into the influencing factors on SMApractices in Vietnamese enterprises, benefiting both practical managementand theoretical perspectives