The impact of recentralisation on FDI : evidence from a quasi-natural experiment

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant No. 502.02-2020.09.Although decentralised governance has been one of the most salient political regimes worldwide over the past few decades, many countries have started to realise various shortcomings associated with their decentralisation process. As a consequence, a number of central governments have attempted to pursue recentralisation reforms in order to reclaim authority from the localities. This government reform can lead to significant changes in institutional arrangements, and subsequently, may influence various aspects of socio-economic activities. However, the real impact of recentralisation reform still remains ambiguous. In this paper, we examine how recentralisation may affect foreign direct investment (FDI) inflows. We exploit the pilot recentralisation reform that temporarily abolished the intermediate legislative branches in some provinces in Vietnam as a quasi-natural experiment. The result shows that recentralisation leads to a significant reduction in FDI inflows. Our results are robust to a number of sensitivity analyses and falsification tests. Overall, our findings contribute to the literature on the determinants of FDI and provide new evidence on the real effect of recentralisation reform.Publisher PDFPeer reviewe

The dynamic relationship between greenfield investments, cross-border M&As, domestic investment and economic growth in Vietnam

Funding: NAFOSTED (grant number 502.02-2020.09).This paper investigates the dynamic linkages between different types of foreign direct investment (FDI), domestic investment and economic growth in Vietnam. We decompose the aggregated FDI level into its two major components: greenfield investments, and cross-border mergers and acquisitions (M&As). The empirical results reveal that greenfield investments and cross-border M&As exhibit different impacts on economic growth. While greenfield investments appear to complement domestic investment, which subsequently promotes long-run economic growth, cross-border M&As exert a significant crowd-out effect and subsequently impede growth in both the short- and the long-run. These results provide important implications for policies to attract FDI in order to stimulate sustainable growth.Publisher PDFPeer reviewe

Some factors affecting the effectiveness of social work activities in supporting drug addicts concentrated in No. II drug addiction treatment facility in Hoa Binh province, Vietnam

The article deals with the current situation of factors affecting the effectiveness of social work activities in supporting drug addicts at the No. II Hoa Binh drug rehabilitation facility. To achieve this goal, we conducted a random survey of 110 students undergoing detoxification at the center. Factors such as the characteristics of drug addicts; responsiveness of drug addiction treatment establishments; performance quality of social workers; Care and support of drug addicts' families. From there, propose measures to improve the effectiveness of social work activities in supporting drug addicts concentrated at detoxification establishments

Does BLEU Score Work for Code Migration?

Statistical machine translation (SMT) is a fast-growing sub-field of computational linguistics. Until now, the most popular automatic metric to measure the quality of SMT is BiLingual Evaluation Understudy (BLEU) score. Lately, SMT along with the BLEU metric has been applied to a Software Engineering task named code migration. (In)Validating the use of BLEU score could advance the research and development of SMT-based code migration tools. Unfortunately, there is no study to approve or disapprove the use of BLEU score for source code. In this paper, we conducted an empirical study on BLEU score to (in)validate its suitability for the code migration task due to its inability to reflect the semantics of source code. In our work, we use human judgment as the ground truth to measure the semantic correctness of the migrated code. Our empirical study demonstrates that BLEU does not reflect translation quality due to its weak correlation with the semantic correctness of translated code. We provided counter-examples to show that BLEU is ineffective in comparing the translation quality between SMT-based models. Due to BLEU's ineffectiveness for code migration task, we propose an alternative metric RUBY, which considers lexical, syntactical, and semantic representations of source code. We verified that RUBY achieves a higher correlation coefficient with the semantic correctness of migrated code, 0.775 in comparison with 0.583 of BLEU score. We also confirmed the effectiveness of RUBY in reflecting the changes in translation quality of SMT-based translation models. With its advantages, RUBY can be used to evaluate SMT-based code migration models.Comment: 12 pages, 5 figures, ICPC '19 Proceedings of the 27th International Conference on Program Comprehensio

Fabrication of TiO2 nanofibre photoelectrode for photoelectrochemical cells

The TiO2 nanofibres (NFs), prepared with the electrospinning method, acted as the photoanode in a photoelectrochemical cell (PEC) for hydrogen generation. The fabrication parameters of Ti/PVP (polyvinylpyrrolidone) fibres were determined with the field-emission scanning electron microscopy (FE-SEM) method. The structure and morphology of the TiO2 fibres were characterized by using X-ray diffraction (XRD), FE-SEM, transmission electron microscopy (TEM), and high-resolution transmission electron microscopy (HR-TEM). The average diameter of the TiO2 fibre is 132 ± 16 nm. A three-electrode potentiostat was used to study the photoelectrochemical properties of the photoanode. The density photocurrent reached the saturation value of 80 mA·cm–2 at 0.2 V under the irradiation of a Xenon lamp

Effective Feature Extraction Method for SVM-Based Profiled Attacks

Nowadays, one of the most powerful side channel attacks (SCA) is profiled attack. Machine learning algorithms, for example support vector machine, are currently used for improving the effectiveness of the attack. One issue when using SVM-based profiled attack is extracting points of interest, or features from power traces. So far, studies in SCA domain have selected the points of interest (POIs) from the raw power trace for the classifiers. Our work proposes a novel method for finding POIs that based on the combining variational mode decomposition (VMD) and Gram-Schmidt orthogonalization (GSO). That is, VMD is used to decompose the power traces into sub-signals (modes) of different frequencies and POIs selection process based on GSO is conducted on these sub-signals. As a result, the selected POIs are used for SVM classifier to conduct profiled attack. This attack method outperforms other profiled attacks in the same attack scenario. Experiments were performed on a trace data set collected from the Atmega8515 smart card run on the side channel evaluation board Sakura-G/W and the data set of DPA contest v4 to verify the effectiveness of our method in reducing number of power traces for the attacks, especially with noisy power traces

Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

Optimizing Boiler Efficiency by Data Mining Teciques: A Case Study

In a fertilizer plant, the steam boiler is the most important component. In order to keep the plant operating in the effective mode, the boiler efficiency must be observed continuously by several operators. When the trend of the boiler efficiency is going down, they may adjust the controlling parameters of the boiler to increase its efficiency. Since manual operation usually leads to unex-pectedly mistakes and hurts the efficiency of the system, we build an information system that plays the role of the operators in observing the boiler and adjusting the controlling parameters to stabilize the boiler efficiency. In this paper, we first introduce the architecture of the information system. We then present how to apply K-means and Fuzzy C-means algorithms to derive a knowledge base from the historical operational data of the boiler. Next, recurrent fuzzy neural network is employed to build a boiler simulator for evaluating which tuple of input values is the best optimal and then automatically adjusting controlling inputs of the boiler by the optimal val-ues. In order to prove the effectiveness of our system, we deployed it at Phu My Fertilizer Plant equipped with MARCHI boiler having capacity of 76-84 ton/h. We found that our system have improved the boiler efficiency about 0.28-1.12% in average and brought benefit about 57.000 USD/year to the Phu My Fertilizer Plant