An Analysis of Value Chain in the Vietnam Construction Industry

A value chain in construction industry is implemented to ensure that a construction project is successful from the planning stages, all the way to the completed and approved building. Therefore, it is important to utilize a construction value chain because it ensures that the project delivers the maximum value for the least amount of capital. However, value chain analysis in the construction industry is little understood. The present study aims to analyze the status quo for the construction value chain in Vietnam. The findings indicated that there are three primary parts of the Vietnam construction value chain. The analysis results indicated that there are three primary parts of the Vietnam construction value chain which includes: (1) input factors (i.e., labors, materials, and machines); (2) construction processes (i.e., design, bidding, foundation construction, structural construction, and finishing works); (3) construction markets (i.e., civil construction market, industrial construction, infrastructure construction). The outcomes illustrated a comprehensive picture in the Vietnam construction value chain and build a strong platform to implement better appropriate tasks towards improving sustainable competitive advantages in the construction industry

KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All

Drawing inspiration from prompt tuning techniques applied to Large Language Models, recent methods based on pre-trained ViT networks have achieved remarkable results in the field of Continual Learning. Specifically, these approaches propose to maintain a set of prompts and allocate a subset of them to learn each task using a key-query matching strategy. However, they may encounter limitations when lacking control over the correlations between old task queries and keys of future tasks, the shift of features in the latent space, and the relative separation of latent vectors learned in independent tasks. In this work, we introduce a novel key-query learning strategy based on orthogonal projection, inspired by model-agnostic meta-learning, to enhance prompt matching efficiency and address the challenge of shifting features. Furthermore, we introduce a One-Versus-All (OVA) prototype-based component that enhances the classification head distinction. Experimental results on benchmark datasets demonstrate that our method empowers the model to achieve results surpassing those of current state-of-the-art approaches by a large margin of up to 20%

Determinants Influencing Entrepreneurial Intention in Hanoi, Vietnam

This research employed survey data from 204 students between two groups of  economics and technical majors in Hanoi city for assessing the impact levels of determinants on entrepreneurial intention. The results show that a number of determinants including Need for achievement, Self-efficacy, and Instrumental readiness have positive impacts on student’s entrepreneurial intention. Besides, this study is also to create a basis for  comparative students among different economics and technical majors, work exoperience, and gender. These findings are the basis to recommend policies and solutions to promote entrepreneurship movement in Vietnam. Keywords: Entrepreneurial intention, need for achievement, self-efficacy, instrumental readiness. DOI: 10.7176/EJBM/12-15-10 Publication date:May 31st 2020

Flat Seeking Bayesian Neural Networks

Bayesian Neural Networks (BNNs) provide a probabilistic interpretation for deep learning models by imposing a prior distribution over model parameters and inferring a posterior distribution based on observed data. The model sampled from the posterior distribution can be used for providing ensemble predictions and quantifying prediction uncertainty. It is well-known that deep learning models with lower sharpness have better generalization ability. However, existing posterior inferences are not aware of sharpness/flatness in terms of formulation, possibly leading to high sharpness for the models sampled from them. In this paper, we develop theories, the Bayesian setting, and the variational inference approach for the sharpness-aware posterior. Specifically, the models sampled from our sharpness-aware posterior, and the optimal approximate posterior estimating this sharpness-aware posterior, have better flatness, hence possibly possessing higher generalization ability. We conduct experiments by leveraging the sharpness-aware posterior with state-of-the-art Bayesian Neural Networks, showing that the flat-seeking counterparts outperform their baselines in all metrics of interest.Comment: Accepted at NeurIPS 202

A Vietnamese Handwritten Text Recognition Pipeline for Tetanus Medical Records

Machine learning techniques are successful for optical character recognition tasks, especially in recognizing handwriting. However, recognizing Vietnamese handwriting is challenging with the presence of extra six distinctive tonal symbols and vowels. Such a challenge is amplified given the handwriting of health workers in an emergency care setting, where staff is under constant pressure to record the well-being of patients. In this study, we aim to digitize the handwriting of Vietnamese health workers. We develop a complete handwritten text recognition pipeline that receives scanned documents, detects, and enhances the handwriting text areas of interest, transcribes the images into computer text, and finally auto-corrects invalid words and terms to achieve high accuracy. From experiments with medical documents written by 30 doctors and nurses from the Tetanus Emergency Care unit at the Hospital for Tropical Diseases, we obtain promising results of 2% and 12% for Character Error Rate and Word Error Rate, respectively

A Novel Self-organizing Fuzzy Cerebellar Model Articulation Controller Based Overlapping Gaussian Membership Function for Controlling Robotic System

This paper introduces an effective intelligent controller for robotic systems with uncertainties. The proposed method is a novel self-organizing fuzzy cerebellar model articulation controller (NSOFC) which is a combination of a cerebellar model articulation controller (CMAC) and sliding mode control (SMC). We also present a new Gaussian membership function (GMF) that is designed by the combination of the prior and current GMF for each layer of CMAC. In addition, the relevant data of the prior GMF is used to check tracking errors more accurately. The inputs of the proposed controller can be mixed simultaneously between the prior and current states according to the corresponding errors. Moreover, the controller uses a self-organizing approach which can increase or decrease the number of layers, therefore the structures of NSOFC can be adjusted automatically. The proposed method consists of a NSOFC controller and a compensation controller. The NSOFC controller is used to estimate the ideal controller, and the compensation controller is used to eliminate the approximated error. The online parameters tuning law of NSOFC is designed based on Lyapunov’s theory to ensure stability of the system. Finally, the experimental results of a 2 DOF robot arm are used to demonstrate the efficiency of the proposed controller

Study, Design and Construction of an Early Warning Environmental Radiation Monitoring Station

We report on the design and construction of an Early Warning Environmental Radiation Monitoring Station (EWERMS), used ONLINE in the environmental radiation monitoring and early warning network. It has a  high sensitivity and can send a prompt alarm signal via Internet to the emergency management office. It includes four gamma probes: one NaI(Tl) and three Geiger Mueller (GM) detectors. The NaI(TL) detector is used to monitor spectrum environmental radiation and measure the isotopic composition, the GM detectors are used to detect and measure high gamma ray rates. The instrument has been designed to be used outdoor and tolerate large and rapid temperature variations. The photomultiplier tubes (PMT), amplifiers and Analog To Digital (ADC) gains are stabilized using pulsed LEDs as precision reference light sources