• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.29 no.2
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• pp.47-53
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• 2018

Objectives: This study aimed to develop the attention-deficit hyperactivity disorder (ADHD)-After School Checklist (ASK) to evaluate the severity of ADHD symptoms and self-management ability in children and adolescents in South Korea. Additionally, we evaluated the reliability and validity of the scale. Methods: We developed the ASK to evaluate the effect of ADHD psychopathologies on self-management and interpersonal impulsivity. We investigated the reliability and validity of the scale with 1349 parents (male 56.9%; 1202 parents of non-ADHD children, 147 parents of children with ADHD) in Seoul and Gyeonggi Province, Korea. Results: According to the construct validity test using principal constant analysis with the varimax rotation method, two factors explained 60.7% of the cumulative variance in ASK scores. Cronbach's alpha for the whole scale was 0.71. There was no statistical difference between mean ASK scores at test and retest. Mean total ASK scores of the ADHD group were significantly higher than those of the non-ADHD group (p<0.001). Conclusion: The ASK can be used as a reliable and valid tool to evaluate not only self-management capability of children and adolescents with ADHD in their academic and everyday life, but also their impulsiveness in interpersonal relationships.

• Knowledge Management Research
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• v.21 no.4
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• pp.157-173
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• 2020

In accommodation sharing economy, customers take a risk of uncertainty about product quality, which is an important factor affecting users' satisfaction. This risk can be lowered by the information disclosed by the facility provider. Self-presentation of the hosts can make a positive effect on listing performance by eliminating psychological distance through emotional interaction with users. This paper analyzed the self-presentation text provided by Airbnb hosts and found key aspects in the text. In order to extract the aspects from the text, host descriptions were separated into sentences and applied the Attention-Based Aspect Extraction method, an unsupervised neural attention model. Then, we investigated the relationship between aspects in the host description and the listing performance via linear regression models. In order to compare their impact between the three facility types(Entire home/apt, Private rooms, and Shared rooms), the interaction effects between the facility types and the aspect summaries were included in the model. We found that specific aspects had positive effects on the performance for each facility type, and provided implication on the marketing strategy to maximize the performance of the shared economy.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.147-153
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• 2018

This study is about the interactive self-portrait which provides the experience of self-consciousness reflection of the viewer to modern people who are easily alienated in rapid social change. We proposed interactive self-portrait is implemented by an interactive mirror that reproduces the appearance of the viewer acquired using a webcam. The interactive mirror, which can directly project its own image, is drawn by searching news articles in real time and using the extracted characters as pixel information in real time. The viewer has the opportunity to experience a new style of active self-expression while watching his/herself composed of news characters that are issues of modern society. The virtual self-portrait designed with news characters can attract viewers' attention by visually expressing the interests of modern people and can act as an incentive to generate positive interaction.

• Educational Technology International
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• v.16 no.1
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• pp.58-83
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• 2015

Digital textbooks draw attention as a new format of educational material, using the advantages of information technology; this innovative learning tool requires consideration as a part of successful and effective learning. The main purpose of the article is to investigate the mediating role of self-regulation between digital literacy and learning outcomes (academic performance and learning motivation) when using digital textbooks as a learning tool in Middle School English. Both descriptive and regression analysis were used as data analyses methods. The main findings of this study were as follows: first, digital literacy and self-regulation significantly predicted academic performance and learning motivation; second, self-regulation fully mediated between digital literacy and academic performance; third, self-regulation partially mediated between digital literacy and learning motivation. The research results proved the effects of digital literacy and self-regulation on the learning outcomes and mediating role of self-regulation between digital literacy and learning outcomes. These results help to design and implement effective lessons when using a digital textbook in Middle school English.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.497-504
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• 2020

Constructing speaker embeddings in speaker verification is an important issue. In general, a self-attention mechanism has been applied for speaker embedding encoding. Previous studies focused on training the self-attention in a high-level layer, such as the last pooling layer. In this case, the effect of low-level layers is not well represented in the speaker embedding encoding. In this study, we propose Masked Cross Self-Attentive Encoding (MCSAE) using ResNet. It focuses on training the features of both high-level and low-level layers. Based on multi-layer aggregation, the output features of each residual layer are used for the MCSAE. In the MCSAE, the interdependence of each input features is trained by cross self-attention module. A random masking regularization module is also applied to prevent overfitting problem. The MCSAE enhances the weight of frames representing the speaker information. Then, the output features are concatenated and encoded in the speaker embedding. Therefore, a more informative speaker embedding is encoded by using the MCSAE. The experimental results showed an equal error rate of 2.63 % using the VoxCeleb1 evaluation dataset. It improved performance compared with the previous self-attentive encoding and state-of-the-art methods.

• 대한상한금궤의학회지
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• v.15 no.1
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• pp.85-115
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• 2023

Objectives : This study reports on the cases of four ADHD patients who treated with herbal medication based on Disease Pattern Identification Diagnostic System (DPIDS). Methods : Two children and two adults were diagnosed with ADHD according to DSM-V, and Galgeunhwangryeonhwanggeum-tang was administered. The parents of the two children completed the Korean ADHD Rating Scale (K-ARS) before and after treatment, while the other patients used the Korean ADHD Self Rating Scale (K-ASRS). The patients' somatic symptoms and other issues were evaluated through interviews. Results : In the first case, the K-ARS scale exhibited a change from 28 to 3. The patient demonstrated improvements in attention, concentration, and social behavior, along with the elimination of self-injurious behavior. In the second case, the K-ARS scale showed a change from 41 to 9. The patient in the third case demonstrated improved chronic fatigue, increased attention, work efficiency, and enhanced social skills, leading to maintaining employment for over six months for the first time in their life after the treatments. Additionally, the patient in the fourth case demonstrated improved work concentration and relief in nocturia. Conclusions : In this study, four patients exhibited improvements in symptoms of attention deficit, hyperactivity, and impulsivity before and after herbal treatment, leading to enhanced academic and social relationships. As the patients progressed in their health restoration, symptoms related to the genitourinary system also showed improvement.

• Journal of Engineering Education Research
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• v.27 no.2
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• pp.13-24
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• 2024

This research aims to examine the effects of engineering students' self-regulated learning strategies on writing self-efficacy, perceptions of writing feedback, and learning presence. To achieve this purpose, firstly, differences in self-regulated learning strategies, writing self-efficacy, perceptions of writing feedback, and learning presence were investigated among engineering and non-engineering students. Secondly, the effects of self-regulated learning strategies, as perceived by engineering students, on writing self-efficacy, perceptions of writing feedback, and learning presence were explored. A total of 196 engineering and non-engineering students from one university in Korea responded to a survey based on a four-variable scale. The findings were as follows: firstly, there were significant differences in self-regulated learning strategies, writing self-efficacy, perceptions of writing feedback, and learning presence by major. Secondly, positive correlations between self-regulated learning strategies, writing self-efficacy, perceptions of writing feedback, and learning presence were identified in terms of sub-factors of those variables. Thirdly, engineering students' self-regulated learning strategies predicted writing self-efficacy, perceptions of writing feedback, and learning presence. The practical implications of these findings are discussed herein, with particular attention to education for the promotion of self-regulated learning strategies and their application to writing courses, as well as diverse learning environments.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.30-42
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• 2020

Unlike in previous generations of mobile technology, machine learning (ML)-based self-healing research trend are currently attracting attention to provide high-quality, effective, and low-cost 5G services that need to operate in the HetNets scenario where various wireless transmission technologies are added. Self-healing plays a vital role in detecting and mitigating the faults, and confirming that there is still room for improvement. We analyzed the research trend in self-healing framework and ML-based fault detection, fault diagnosis, and fault compensation. We propose that to ensure that self-healing is a proactive instead of being reactive, we have to design an ML-based self-healing framework and select a suitable ML algorithm for fault detection, diagnosis, and outage compensation.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.21 no.1
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• pp.11-16
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• 2010

This review aimed to assist clinicians in the identification and assessment of adult attention-deficit hyperactivity disorder (ADHD) with an emphasis on diagnostic and rating instruments. Pubmed and RISS were utilized to identify relevant studies and critical reviews on the diagnosis and assessment of adult ADHD, published between 1988 and 2010. The Adult ADHD Self-Report Scale-v1.1, the ADHD Rating Scale-IV, the Conners Adult ADHD Rating Scale, and the Current Symptoms Scale have been utilized for self-reporting of current ADHD symptoms. The Brown ADD Rating Scale, the ADHD Rating Scale-IV, the Current Symptoms Scale, and the Conners Adult ADHD Rating Scale have also been evaluated by an observer. The Childhood Symptom Scale and the Wender-Utah Rating Scale have been used for retrospective assessment of childhood ADHD symptoms and the Adult ADHD Investigator Symptom Rating Scale, the Adult Interview, the Brown ADD Diagnostic Form, the Conners adult ADHD diagnostic interview for DSM-IV, and the Wender-Reimherr Interview have been available as comprehensive diagnostic interviews. There is a wide variety of instruments available with respect to adult ADHD. The choice of appropriate instruments is essential for achieving accurate diagnosis and assessment of this disorder.