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The Factors Affecting Corporate Income Tax Non-Compliance: A Case Study in Vietnam
NGUYEN, Loan Thi,NGUYEN, Anh Hong Viet,LE, Hac Dinh,LE, Anh Hoang,TRUONG, Tu Tuan Vu Korea Distribution Science Association 2020 The Journal of Asian Finance, Economics and Busine Vol.7 No.8
In many countries, the Government enacts tax laws in order to manage tax collection and regulate the macro-economy. According to Noor, Jamaludin, Omar, and Aziz (2013), tax non-compliance is a growing concern because of its negative effects on the state budget. The main objectives of this article are to identify the factors affecting corporate income tax non-compliance of enterprises in Ho Chi Minh City in accordance with the current situation of Vietnamese tax administration. We use several research methods, including the exploitation of information and practical experiences from both taxpayers and tax authorities; with Probit regression model on a sample of 187 enterprises that have been inspected or examined by tax authorities in Vietnam during the period from 2013 to 2017.The article identified eight factors affecting corporate income tax (CIT) non-compliance: (1) working capital/total assets; (2) revenue/total assets; (3) total debt/total assets; (4) loss in the previous year; (5) receivables/revenue; (6) the size of enterprises; (7) tax administrative penalties/tax payable; and (8) business field. In particular, the tax non-compliance was studied as a violation of Vietnamese tax laws by enterprises declaring an insufficient amount of CIT payable to the State budget.
Gabor Filter-based Feature Extraction for Human Activity Recognition
Nguyen Anh Tu(윈안두),Young Koo Lee(이영구),Sungyoung Lee(이승룡) 한국정보과학회 2011 한국정보과학회 학술발표논문집 Vol.38 No.1C
Recognizing human activities from image sequences is an active area of research in computer vision. Most of the previous work on activity recognition focuses on recognition from a single view and ignores the issue of view invariance. In this paper, we present an independent Gabor features (IGFs) method comes from the derivation of independent Gabor features in the feature extraction stage. The Gabor transformed human image exhibit strong characteristics of spatial locality, scale and orientation selectivity.
ML-HDP: A Hierarchical Bayesian Nonparametric Model for Recognizing Human Actions in Video
Tu, Nguyen Anh,Huynh-The, Thien,Khan, Kifayat Ullah,Lee, Young-Koo Institute of Electrical and Electronics Engineers 2019 IEEE transactions on circuits and systems for vide Vol.29 No.3
<P>Action recognition from videos is an important area of computer vision research due to its various applications, ranging from visual surveillance to human–computer interaction. To address action recognition problems, this paper presents a framework that jointly models multiple complex actions and motion units at different hierarchical levels. We achieve this by proposing a generative topic model, namely, multi-label hierarchical Dirichlet process (ML-HDP). The ML-HDP model formulates the co-occurrence relationship of actions and motion units, and enables highly accurate recognition. In particular, our topic model possesses the three-level representation in action understanding, where low-level local features are connected to high-level actions via mid-level atomic actions. This allows the recognition model to work discriminatively. In our ML-HDP, atomic actions are treated as latent topics and automatically discovered from data. In addition, we incorporate the notion of class labels into our model in a semi-supervised fashion to effectively learn and infer multi-labeled videos. Using discovered topics and inferred labels, which are jointly assigned to local features, we present the straightforward methods to perform three recognition tasks including action classification, joint classification and segmentation of continuous actions, and spatiotemporal action localization. In experiments, we explore the use of three different features and demonstrate the effectiveness of our proposed approach for these tasks on four public datasets: KTH, MSR-II, Hollywood2, and UCF101.</P>
Innovation Capacity of Student: A Case Study in Vietnam
Anh Duc DO,Nguyen Nguyen Thao PHAM,Thi Minh Phuong NGUYEN,Van Son TU,Cam Nhung NGUYEN,Hai Duong NGUYEN 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.5
This study aimed to explore the factors affecting the innovation capacity of students at the National Economics University, Vietnam. Researchers used the innovation capacity model based on six factors, including personality traits, future orientation, creative skills, social interaction, content knowledge, and management skills. The empirical analysis used data from the survey of 303 students at National Economics University, Vietnam, with reliable tools (SPSS 26.0 software). The data were analyzed by testing the reliability of the scales, correlation analysis, and Pearson’ Linear Correlation Coefficient, exploratory factor analysis, as well as regression model based on the survey data. The research results identified the following factors affecting innovation capacity of students: management skills, social interaction, and personality traits have the strongest impact on innovation capacity of students; content knowledge has the following strongest effects on innovation capacity of students; and finally the creative skills that affects on innovation capacity of students. There is also a positive relationship between all the factors and innovation capacity of students. The result can serve as useful reference sources for scholars who are interested in the innovation field. It also helps university’s managers and policymakers build the appropriate environment to improve innovation capacity of students.
Nguyen Duc Anh,Pham Van Thanh,Doan Tu Lap,Nguyen Tuan Khai,Tran Van An,Tran Duc Tan,Nguyen Huu An,Dang Nhu Dinh 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.2
Forest fires inflict great losses of human lives and serious damages to ecological systems. Hence, numerous fire detection methods have been proposed, one of which is fire detection based on sensors. However, these methods reveal several limitations when applied in large spaces like forests such as high cost, high level of false alarm, limited battery capacity, and other problems. In this research, we propose a novel forest fire detection method based on image processing and correlation coefficient. Firstly, two fire detection conditions are applied in RGB color space to distinguish between fire pixels and the background. Secondly, the image is converted from RGB to YCbCr color space with two fire detection conditions being applied in this color space. Finally, the correlation coefficient is used to distinguish between fires and objects with fire-like colors. Our proposed algorithm is tested and evaluated on eleven fire and non-fire videos collected from the internet and achieves up to 95.87% and 97.89% of F-score and accuracy respectively in performance evaluation.
Anh Tuan Nguyen,Jae-Hung Han,Anh Tu Nguyen 한국항공우주학회 2017 International Journal of Aeronautical and Space Sc Vol.18 No.3
This paper studies the applicability of an efficient numerical model based on artificial neural networks (ANNs) to predict the dynamic responses of the wing structure of an airplane due to atmospheric turbulence in the time domain. The turbulence velocity is given in the form of a stationary Gaussian random process with the von Karman power spectral density. The wing structure is modeled by a classical beam considering bending and torsional deformations. An unsteady vortex-lattice method is applied to estimate the aerodynamic pressure distribution on the wing surface. Initially, the trim condition is obtained, then structural dynamic responses are computed. The numerical solution of the wing structure’s responses to a random turbulence profile is used as a training data for the ANN. The current ANN is a three-layer network with the output fed back to the input layer through delays. The results from this study have validated the proposed low-cost ANN model for the predictions of dynamic responses of wing structures due to atmospheric turbulence. The accuracy of the predicted results by the ANN was discussed. The paper indicated that predictions for the bending moments are more accurate than those for the torsional moments of the wing structure.
Nguyen, Anh Tuan,Han, Jae-Hung,Nguyen, Anh Tu The Korean Society for Aeronautical and Space Scie 2017 International Journal of Aeronautical and Space Sc Vol.18 No.3
This paper studies the applicability of an efficient numerical model based on artificial neural networks (ANNs) to predict the dynamic responses of the wing structure of an airplane due to atmospheric turbulence in the time domain. The turbulence velocity is given in the form of a stationary Gaussian random process with the von Karman power spectral density. The wing structure is modeled by a classical beam considering bending and torsional deformations. An unsteady vortex-lattice method is applied to estimate the aerodynamic pressure distribution on the wing surface. Initially, the trim condition is obtained, then structural dynamic responses are computed. The numerical solution of the wing structure's responses to a random turbulence profile is used as a training data for the ANN. The current ANN is a three-layer network with the output fed back to the input layer through delays. The results from this study have validated the proposed low-cost ANN model for the predictions of dynamic responses of wing structures due to atmospheric turbulence. The accuracy of the predicted results by the ANN was discussed. The paper indicated that predictions for the bending moments are more accurate than those for the torsional moments of the wing structure.
HER2 Status and Its Heterogeneity in Gastric Carcinoma of Vietnamese Patient
Dang Anh Thu Phan,Vu Thien Nguyen,Thi Ngoc Ha Hua,Quoc Dat Ngo,Thi Phuong Thao Doan,Sao Trung Nguyen,Anh Tu Thai,Van Thanh Nguyen 대한병리학회 2017 Journal of Pathology and Translational Medicine Vol.51 No.4
Background: Human epidermal growth factor receptor 2 (HER2) is related to the pathogenesis and poor outcome of numerous types of carcinomas, including gastric carcinoma. Gastric cancer patients with HER2 positivity have become potential candidates for targeted therapy with trastuzumab. Methods: We investigated 208 gastric cancer specimens using immunohistochemistry (IHC), fluorescence in situ hybridization and dual in situ hybridization (ISH). We also investigated the concordance between IHC and ISH. The correlation between HER2 status and various clinicopathological findings was also investigated. Results: In total, 15.9% (33/208) and 24.5% (51/208) of gastric cancers showed HER2 gene amplification and protein overexpression, respectively. A high level of concordance between ISH and IHC analyses (91.3%, κ = 0.76) was found. A significant correlation between HER2 status and intestinal-type (p < .05) and differentiated carcinomas (p < .05) was also noted. The HER2 heterogeneity was high in gastric cancers; we found 68.8% phenotypic heterogeneity and 57.6% genotypic heterogeneity. Heterogeneity in HER2 protein expression and gene amplification showed a close association with diffuse histologic type and IHC 2+. Conclusions: HER2 protein overexpression and gene amplification were detected in 24.5% and 15.9% of gastric cancer specimens, respectively. Intestinal-type showed a higher level of HER2 protein overexpression and gene amplification than diffuse type. HER2 status also showed a significant relationship with well- and moderately-differentiated carcinomas. The ratio of phenotypic and genotypic heterogeneity of HER2 was high in gastric carcinomas and was associated with HER2 IHC 2+ and diffuse histologic type.