Evaluation of the 2020 Investment Law in Vietnam

Objective: The study investigates the 2020 Investment Law in Vietnam. The study employs a set of aggregated data from previous studies, the results of a survey of 135 lecturers and lawyers. At the same time, the study also receives comments from experts experienced in the research field.   Method: We used qualitative research methods and quantitative research methods to evaluate and measure new contents in the 2020 Investment Law in Vietnam, which focuses on such contents as: Fundamental reforms in investment procedures, emphasizing the transparency of conditional business investment portfolios, the list of prohibited investment sectors, shortening the time view investment projects, apply information technology in investment procedures with foreign investment projects, etc.   Results: The results show that new contents in the 2020 Investment Law in Vietnam is highly appreciated. 

LIMITATIONS OF SECONDARY SCHOOL STUDENTS IN SOLVING A TYPE OF TASK RELATING TO THE EQUATION OF A CIRCLE: AN INVESTIGATION IN VIETNAM

In Vietnam, secondary school students learn the equation of a circle in Grade 10. Based on how to present this equation in the textbook “Geometry 10” and types of task for students, we believe that some limitations happen to students when they solve problems related to the equation of a circle. This paper reports the investigation of 845 students from the Mekong Delta-Vietnam. The results show that our prediction is correct.  Article visualizations

Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising

Multiresolution deep learning approaches, such as the U-Net architecture, have achieved high performance in classifying and segmenting images. However, these approaches do not provide a latent image representation and cannot be used to decompose, denoise, and reconstruct image data. The U-Net and other convolutional neural network (CNNs) architectures commonly use pooling to enlarge the receptive field, which usually results in irreversible information loss. This study proposes to include a Riesz-Quincunx (RQ) wavelet transform, which combines 1) higher-order Riesz wavelet transform and 2) orthogonal Quincunx wavelets (which have both been used to reduce blur in medical images) inside the U-net architecture, to reduce noise in satellite images and their time-series. In the transformed feature space, we propose a variational approach to understand how random perturbations of the features affect the image to further reduce noise. Combining both approaches, we introduce a hybrid RQUNet-VAE scheme for image and time series decomposition used to reduce noise in satellite imagery. We present qualitative and quantitative experimental results that demonstrate that our proposed RQUNet-VAE was more effective at reducing noise in satellite imagery compared to other state-of-the-art methods. We also apply our scheme to several applications for multi-band satellite images, including: image denoising, image and time-series decomposition by diffusion and image segmentation.Comment: Submitted to IEEE Transactions on Geoscience and Remote Sensing (TGRS

An Efficient Method for Generating Synthetic Data for Low-Resource Machine Translation – An empirical study of Chinese, Japanese to Vietnamese Neural Machine Translation

Data sparsity is one of the challenges for low-resource language pairs in Neural Machine Translation (NMT). Previous works have presented different approaches for data augmentation, but they mostly require additional resources and obtain low-quality dummy data in the low-resource issue. This paper proposes a simple and effective novel for generating synthetic bilingual data without using external resources as in previous approaches. Moreover, some works recently have shown that multilingual translation or transfer learning can boost the translation quality in low-resource situations. However, for logographic languages such as Chinese or Japanese, this approach is still limited due to the differences in translation units in the vocabularies. Although Japanese texts contain Kanji characters that are derived from Chinese characters, and they are quite homologous in sharp and meaning, the word orders in the sentences of these languages have a big divergence. Our study will investigate these impacts in machine translation. In addition, a combined pre-trained model is also leveraged to demonstrate the efficacy of translation tasks in the more high-resource scenario. Our experiments present performance improvements up to +6.2 and +7.8 BLEU scores over bilingual baseline systems on two low-resource translation tasks from Chinese to Vietnamese and Japanese to Vietnamese