• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.1-8
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• 2019

Dialog system is becoming a new dominant interaction way between human and computer. It allows people to be provided with various services through natural language. The dialog system has a common structure of a pipeline consisting of several modules (e.g., speech recognition, natural language understanding, and dialog management). In this paper, we tackle a task of domain classification for the natural language understanding module by employing machine learning models such as convolutional neural network and random forest. For our dataset of seven service domains, we showed that the random forest model achieved the best performance (F1 score 0.97). As a future work, we will keep finding a better approach for domain classification by investigating other machine learning models.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.11-14
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• 2020

The importance of data in the development of deep learning is very high. Data with high morphological features are usually utilized in the domains where careful lens calibrations are needed by a human to capture those data. Synthesis of high morphological data for that domain can be a great asset to improve the classification accuracy of systems in the field. Unsupervised learning can be employed for this task. Generating photo-realistic objects of interest has been massively studied after Generative Adversarial Network (GAN) was introduced. In this paper, we propose Morpho-GAN, a method that unifies several GAN techniques to generate quality data of high morphology. Our method introduces a new suitable training objective in the discriminator of GAN to synthesize images that follow the distribution of the original dataset. The results demonstrate that the proposed method can generate plausible data as good as other modern baseline models while taking a less complex during training.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.73
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• pp.111-128
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• 2017

The main objective of this study is to examine the required aptitudes for undergraduate students who want to join export-oriented Small & Medium Enterprises(SMEs). 178 Dataset from a survey of exporting firms in Gwangju, Korea, were used to analyze the study. The results of the study are as follows ; First, the most required aptitude is 'the capability related to build up human relationship'. So students should learn negotiation skills in the college. In addition to this, student also try to join informal club and cultivate teamwork capabilities. Second, finding out a job in export-oriented SMEs is needed to equip with problem-solving capabilities. To do it, students should learn various subjects related to trade theory. Additionally, having some certificates like 'international trade master' can be better. Third, communication capabilities including foreign language and international business skills will be also required for students who are preparing for joining export-oriented SMEs. However, capabilities related to information technology and basic statistic skills does not have statistically significant correlation to recruitment intention. As a result, students who have such above-mentioned four aptitudes may have better position to find out jobs in export-oriented SMEs.

• Proceedings of the KSRS Conference
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• v.2
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• pp.868-871
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• 2006

Mortality of domestic people from cardiovascular disease ranked second, which followed that of from cancer last year. Therefore, it is very important and urgent to enhance the reliability of medical examination and treatment for cardiovascular disease. Heart Rate Variability (HRV) is the most commonly used noninvasive methods to evaluate autonomic regulation of heart rate and conditions of a human heart. In this paper, our aim is to extract a quantitative measure for HRV to enhance the reliability of medical examination for cardiovascular disease, and then develop a prediction method for extracting multi-parametric features by analyzing HRV from ECG. In this study, we propose a hybrid Bayesian classifier called FP-based Bayesian. The proposed classifier use frequent patterns for building Bayesian model. Since the volume of patterns produced can be large, we offer a rule cohesion measure that allows a strong push of pruning patterns in the pattern-generating process. We conduct an experiment for the FP-based Bayesian classifier, which utilizes multiple rules and pruning, and biased confidence (or cohesion measure) and dataset consisting of 670 participants distributed into two groups, namely normal and patients with coronary artery disease.

• Journal of Microbiology and Biotechnology
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• v.29 no.8
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• pp.1221-1229
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• 2019

Mycobacterium tuberculosis, a causative pathogen of tuberculosis (TB), still threatens human health worldwide. To find a novel drug to eradicate this pathogen, we tested taurine-5-bromosalicylaldehyde Schiff base (TBSSB) as an innovative anti-mycobacterial drug using Mycobacterium smegmatis as a surrogate model for M. tuberculosis. We investigated the antimicrobial activity of TBSSB against M. smegmatis by plotting growth curves, examined the effect of TBSSB on biofilm formation, observed morphological changes by scanning electron microscopy and transmission electron microscopy, and detected differentially expressed proteins using two-dimensional gel electrophoresis coupled with mass spectrometry. TBSSB inhibited mycobacterial growth and biofilm formation, altered cell ultrastructure and intracellular content, and inhibited cell division. Furthermore, M. smegmatis adapted itself to TBSSB inhibition by regulating the metabolic pathways and enzymatic activities of the identified proteins. NDMA-dependent methanol dehydrogenase, NAD(P)H nitroreductase, and amidohydrolase AmiB1 appear to be pivotal factors to regulate the M. smegmatis survival under TBSSB. Our dataset reinforced the idea that Schiff base-taurine compounds have the potential to be developed as novel anti-mycobacterial drugs.

• Smart Media Journal
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• v.8 no.2
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• pp.46-57
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• 2019

In recent research, deep learning-based methods have achieved state-of-the-art performance in various computer vision tasks. However, these methods are commonly supervised, and require huge amounts of annotated data to train. Acquisition of data demands an additional costly effort, particularly for the tasks where it becomes challenging to obtain large amounts of data considering the time constraints and the requirement of professional human diligence. In this paper, we present a data level synthetic sampling solution to learn from small and imbalanced data sets using Generative Adversarial Networks (GANs). The reason for using GANs are the challenges posed in various fields to manage with the small datasets and fluctuating amounts of samples per class. As a result, we present an approach that can improve learning with respect to data distributions, reducing the partiality introduced by class imbalance and hence shifting the classification decision boundary towards more accurate results. Our novel method is demonstrated on a small dataset of 2789 tomato plant disease images, highly corrupted with class imbalance in 9 disease categories. Moreover, we evaluate our results in terms of different metrics and compare the quality of these results for distinct classes.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.59-71
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• 2022

Purpose - We investigate how firms transfer financial risks to employees in a form of flexible employment contracts and layoffs. Design/methodology/approach - Based on the literature on the prevalence of shareholder value ideology and the associated 'risk shift', we examined how stock price volatility is associated with a firm's use and hiring of nonstandard employees, and the number of employees lay-offed. We test our hypotheses using a longitudinal, multi-source, dataset of Korean firms from 2003 to 2011. Findings - We found support for the relationship between stock price volatility and flexible employment contracts and layoffs after controlling for actual risks such as increased debt or decreased sales. However, we found that the relationship is moderated by the power of professional CEOs relative to that of shareholders, in that powerful CEOs are more likely to transfer the external risks, i.e. stock price volatility, to employees. Research implications or Originality - This study contributes the emerging stream of literature that explore the effect of stock market pressures and governance structures on human resource management.

• Mycobiology
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• v.49 no.6
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• pp.559-581
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• 2021

The coastal marine ecosystems of Vietnam are one of the global biodiversity hotspots, but the biodiversity of marine fungi is not well known. To fill this major gap of knowledge, we assessed the genetic diversity (ITS sequence) of 75 fungal strains isolated from 11 surface coastal marine and deeper waters in Nha Trang Bay and Van Phong Bay using a culture-dependent approach and 5 OTUs (Operational Taxonomic Units) of fungi in three representative sampling sites using next-generation sequencing. The results from both approaches shared similar fungal taxonomy to the most abundant phylum (Ascomycota), genera (Candida and Aspergillus) and species (Candida blankii) but were different at less common taxa. Culturable fungal strains in this study belong to 3 phyla, 5 subdivisions, 7 classes, 12 orders, 17 families, 22 genera and at least 40 species, of which 29 species have been identified and several species are likely novel. Among identified species, 12 and 28 are new records in global and Vietnamese marine areas, respectively. The analysis of enzyme activity and the checklist of trophic mode and guild assignment provided valuable additional biological information and suggested the ecological function of planktonic fungi in the marine food web. This is the largest dataset of marine fungal biodiversity on morphology, phylogeny and enzyme activity in the tropical coastal ecosystems of Vietnam and Southeast Asia. Biogeographic aspects, ecological factors and human impact may structure mycoplankton communities in such aquatic habitats.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.207-209
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• 2022

In the IoT environment, it is possible to collect life pattern data by recognizing human physical activity from smart devices. In this paper, the proposed model consists of a prediction stage and a recommendation stage. The prediction stage predicts the scale of anxiety and depression by using logistic regression and k-nearest neighbor algorithm through machine learning on the dataset collected from life pattern data. In the recommendation step, if the symptoms of anxiety and depression are classified, the principal component analysis algorithm is applied to recommend food and light exercise that can improve them. It is expected that the proposed anxiety/depression prediction and food/exercise recommendations will have a ripple effect on improving the quality of life of individuals.