Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names

Every human has their own name, a fundamental aspect of their identity and cultural heritage. The name often conveys a wealth of information, including details about an individual's background, ethnicity, and, especially, their gender. By detecting gender through the analysis of names, researchers can unlock valuable insights into linguistic patterns and cultural norms, which can be applied to practical applications. Hence, this work presents a novel dataset for Japanese name gender detection comprising 64,139 full names in romaji, hiragana, and kanji forms, along with their biological genders. Moreover, we propose Gendec, a framework for gender detection from Japanese names that leverages diverse approaches, including traditional machine learning techniques or cutting-edge transfer learning models, to predict the gender associated with Japanese names accurately. Through a thorough investigation, the proposed framework is expected to be effective and serve potential applications in various domains.Comment: This paper is accepted for presentation at ISDA'2

Effect of shear deformations due to bending and warping on the buckling resistances of thin-walled steel beams

The present paper successfully develops a closed form solution based on a shear deformation theory for elastic lateral-torsional buckling analyses of simply supported thin-walled steel beams. The theory captures the shear effects caused by transverse bending, lateral bending and warping deformations. The closed form solution is successfully validated against 3 dimensional finite element analyses conducted in commercial software. Through various comparisons between the buckling resistances based on a non-shear deformation theory and the buckling resistances based on the present shear deformation theory, the present study finds that (i) the effect of shear deformations on the buckling resistances decreases when the beam span increases, (ii) the effect of shear deformations on the buckling resistance is sensitive with the change of the flange width, and (iii) the effect of shear deformations in general is also influenced by the change of the section depth, and the flange and web thicknesses

SURVEYING THE VIETNAMESE YOUTH ON THE NEGATIVE IMPACT OF SOCIAL MEDIA

In the context of globalization and the rapid development of the Internet, social networks have become an indispensable part of the lives of citizens in the 21st century. In addition to helping people communicate and connect, wireless platforms bring benefits to work, study, and entertainment. However, faced with the staggering increase in the use of social networks, many argue that they can have negative impacts on users, particularly those who are studying or working. This study aims to provide readers with an overview of the negative impacts of social networks on Vietnamese youth. The research data was collected by gathering reputable sources and surveying young people born between 1995 and 2010, belonging to Generation Z, who are living, studying, and working in major cities in Vietnam and using social networks. Through statistical analysis and data processing, the results show that the use of communication platforms has a negative impact on the productivity and health of Vietnamese youth. To minimize the negative impacts on daily life, young people should consider the amount of time they spend using social networks and the content they publish. Additionally, protecting personal information and building positive communities is necessary to avoid unnecessary risks

EVALUATION OF SOLAR RADIATION ESTIMATED FROM HIMAWARI-8 SATELLITE OVER VIETNAM REGION

The development of Solar energy system is growing rapidly in Vietnam in recent years by encouragement of the Government in renewable energy. Requirement for accurate knowledge of the solar radiation reaching the surface is increasingly important in the successful deployment of Solar photovoltaic plants. However, measurements of different components of solar resources including direct normal irradiance (DNI) and global horizontal irradiance (GHI) are limited to few stations over whole country. Satellite imagery provides an ability to monitor the surface radiation over large areas at high spatial and temporal resolution as alternatives at low cost. Observations from the new Japanese geostationary satellite Himawari-8 produce imagery covering Asia-Pacific region, permitting estimation of GHI and DNI over Vietnam at 10-minute temporal resolution. However, accurate comparisons with ground observations are essential to assess their uncertainty. In this study, we evaluated the Himawari-8 radiation product AMATERASS provided by JST/CREST TEEDDA using observations recorded at 5 stations in different regions of Vietnam. The result shows good agreement between satellite estimation and observed data with high correlation of range 0.92-0.94, but better in clear-sky episodes.Because of AMATERASS outperform, we used it for validating ERA-Interim reanalysis in the spatial scale. The comparison was made dividedly for 7 climate zones and 4 seasons. The conclusion is that ERA-Interim is also well associated with satellite-based estimates in seasonal trend for all season, but in average the reanalysis has negative bias towards satellite estimates. This underestimation is more pronounced in the months of JJA and SON periods and in the north part of Vietnam because of unpredicted cloud in the ERA reanalysis

Research on Using Dolomite Aggregate as Cement Treated Base for Highway Pavement Construction in Ninh Binh, Vietnam

Dolomite is commonly used in the construction of highway pavement in the world. However, there are still no concrete specifications or regulations on the use of dolomite for highway construction. Dolomite is available in huge quantities in NinhBinh Province. This is a high potential material for grain bases of highway pavement structure. The alternative material could be a considerable contribution to diversify the supply of aggregate resources for highway pavement construction in the province, and thus contribute to the conservation of natural landscape heritages and limestone resources for related building materials manufacturing industries. In order to evaluate the use of dolomite in highway pavement construction, a research program is conducted to test the working capacity of the cement treated dolomite aggregate, which is intended to use as upper base material in pavement structure. The experimental results showed that the mechanical indicators of the mixture satisfy the requirements for the base layers of highway pavement structure

Research on Using Dolomite Aggregate as Cement Treated Base for Highway Pavement Construction in Ninh Binh, Vietnam

Dolomite is commonly used in the construction of highway pavement in the world. However, there are still no concrete specifications or regulations on the use of dolomite for highway construction. Dolomite is available in huge quantities in NinhBinh Province. This is a high potential material for grain bases of highway pavement structure. The alternative material could be a considerable contribution to diversify the supply of aggregate resources for highway pavement construction in the province, and thus contribute to the conservation of natural landscape heritages and limestone resources for related building materials manufacturing industries. In order to evaluate the use of dolomite in highway pavement construction, a research program is conducted to test the working capacity of the cement treated dolomite aggregate, which is intended to use as upper base material in pavement structure. The experimental results showed that the mechanical indicators of the mixture satisfy the requirements for the base layers of highway pavement structure

Multifactorial Evolutionary Algorithm For Clustered Minimum Routing Cost Problem

Minimum Routing Cost Clustered Tree Problem (CluMRCT) is applied in various fields in both theory and application. Because the CluMRCT is NP-Hard, the approximate approaches are suitable to find the solution for this problem. Recently, Multifactorial Evolutionary Algorithm (MFEA) has emerged as one of the most efficient approximation algorithms to deal with many different kinds of problems. Therefore, this paper studies to apply MFEA for solving CluMRCT problems. In the proposed MFEA, we focus on crossover and mutation operators which create a valid solution of CluMRCT problem in two levels: first level constructs spanning trees for graphs in clusters while the second level builds a spanning tree for connecting among clusters. To reduce the consuming resources, we will also introduce a new method of calculating the cost of CluMRCT solution. The proposed algorithm is experimented on numerous types of datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, partially on large instance

Academic Staffs’ Participation in University Governance Towards Autonomy: Practice at Two University Models in Vietnam

Vietnamese universities are now in a state of “diverse governing bodies” and the Ministry of Education and Training is responsible for their expertise. This model can cause overlapping or loosened management by many agencies simultaneously managing. Vietnamese universities need to promote autonomy and accountability in management. This study was conducted in 2018-2019 with 322 lecturers and educational managers working at two Vietnamese public universities governed by different autonomy policies. This research analyses the academic staff’ s participation in university governance toward autonomy. The research results show that (i) there is no difference between the two universities in the level of participation in governance activities; (ii) The academic staff’ s participation levels are positively correlated from low to moderate levels according to the effectiveness of participating in activities; (iii) The higher the participation level, the higher the scientific research results are for domestic publication. However, it is not a significant case for international publication

Research of multi-response optimization of milling process of hardened S50C steel using minimum quantity lubrication of Vietnamese peanut oil

This study aims to build a regression model when surveying the milling process on S50C steel using Minimum Quantity Lubrication (MQL) of Vietnamese peanut oil-based on Response Surface Methodology. The paper analyses and evaluates the effect of cutting parameters, flow rates, and pressures in minimum quantity lubrication system on cutting force and surface roughness in the milling process of S50C carbon steel materials after heat treatment (reaching a hardness of 52 HRC). The Taguchi method, one of the most effective experimental planning methods nowadays, is used in this study. The statistical analysis software, namely Minitab 19, is utilized to build a regression model between parameters of the cutting process, flow rates and pressures of the minimum quantity lubrication system and the cutting force, surface roughness of the part when machining on a 5-axis CNC milling machine. Thereby analyzing and predicting the effect of cutting parameters and minimum quantity lubrication conditions on the surface roughness and cutting force during machining to determine the influence level them. In this work, the regression models of Ra and F were achieved by using the optimizer tool in Minitab 19. Moreover, the multi-response optimization problem was solved. The optimum cutting parameters and lubricating conditions are as follows: Cutting velocity Vc=190.909 m/min, feed rate fz=0.02 mm/tooth, axial depth of cut ap=0.1 and nozzle pressure P=5.596 MPa, flow rate Q=108.887 ml/h. The output parameters obtained from the above parameters are Ra=0.0586  and F=162.035 N, respectively. This result not only provides the foundation for future research but also contributes reference data for the machining proces

VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning.

In this study, we introduce an ensemble selection method for deep ensemble systems called VEGAS. The deep ensemble models include multiple layers of the ensemble of classifiers (EoC). At each layer, we train the EoC and generates training data for the next layer by concatenating the predictions for training observations and the original training data. The predictions of the classifiers in the last layer are combined by a combining method to obtain the final collaborated prediction. We further improve the prediction accuracy of a deep ensemble model by searching for its optimal configuration, i.e., the optimal set of classifiers in each layer. The optimal configuration is obtained using the Variable-Length Genetic Algorithm (VLGA) to maximize the prediction accuracy of the deep ensemble model on the validation set. We developed three operators of VLGA: roulette wheel selection for breeding, a chunk-based crossover based on the number of classifiers to generate new offsprings, and multiple random points-based mutation on each offspring. The experiments on 20 datasets show that VEGAS outperforms selected benchmark algorithms, including two well-known ensemble methods (Random Forest and XgBoost) and three deep learning methods (Multiple Layer Perceptron, gcForest, and MULES)