Fusion of multiple self-diagnostic questionnaires into optimal diagnostic cut-offs and factor analysis for depression characterization of the Korean university student group.

Background: We were interested in developing a methodology for diagnosing the depression status of a focused population group, such as the Korean university student group, with higher accuracy. To this end, we proposed a method of fusing the data collected from multiple depression self-questionnaires aided by a psychiatrist's diagnosis. In particular, we found that the standard diagnostic cut-offs and factor analysis prepared for a general population by depression self-questionnaires are inadequate for a focused population with its unique cultural background.

Automated vehicle damage classification using the three-quarter view car damage dataset and deep learning approaches.

Automated procedures for classifying vehicle damage are critical in industries requiring extensive vehicle management. Despite substantial research demands, challenges in the field of vehicle damage classification persist due to the scarcity of public datasets and the complexity of constructing datasets. In response to these challenges, we introduce a Three-Quarter View Car Damage Dataset (TQVCD dataset), emphasizing simplicity in labeling, data accessibility, and rich information inherent in three-quarter views.

The Effect of Training on Localizing HoloLens-Generated 3D Sound Sources.

Sound localization is a crucial aspect of human auditory perception. VR (virtual reality) technologies provide immersive audio platforms that allow human listeners to experience natural sounds based on their ability to localize sound. However, the simulations of sound generated by these platforms, which are based on the general head-related transfer function (HRTF), often lack accuracy in terms of individual sound perception and localization due to significant individual differences in this function.

Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts.

Background: Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits.

Data-driven analysis on the performance evaluation of national R&D projects in Korea.

Research and development (R&D) is a crucial competency in both developing and developed countries. As a result, evaluating the performance of R&D programs has become a significant research topic for academic and governmental researchers. This study aims to investigate the impact of various factors, such as the characteristics of national R&D projects, research stages, technology types, and management institutions, on their performance.

Computational analysis of a collaboration network on human-computer interaction in Korea.

Since information and communication technology (ICT) has become one of the leading and essential fields for allowing developing countries to have the major growth engines, the majority of the countries have promoted collaboration in every ICT-related topics. In this study, we performed the trend and collaboration network analysis (CNA) in Korea for 2010-2019 among researchers who are related to human-computer interaction, one of the hottest research areas in ICT. Publication data were collected from SciVal, and the collaboration network was determined using degree, closeness, betweenness centralities, and PageRank.

Motivations for user satisfaction of mobile fitness applications: An analysis of user experience based on online review comments.

Considering that mobile fitness applications are one of the necessities in our lives, the user perspective toward the application is a prominent research topic in both academia and industry with the goal of improving such services. Thus, this study applies two different natural language processing approaches, bag-of-words, and sentiment analysis, to online review comments of the applications to examine the effects of user experience elements. The review dataset collected from 16,461 users, after pre-processing, revealed the notable roles of perceived affection and hedonic values in determining user satisfaction with the application, whereas the effect of user burden on satisfaction was marginal.

CRNet: a multimodal deep convolutional neural network for customer revisit prediction.

Unlabelled: Since mobile food delivery services have become one of the essential issues for the restaurant industry, predicting customer revisits is highlighted as one of the significant academic and research topics. Considering that the use of multimodal datasets has gained notable attention from several scholars to address multiple industrial issues in our society, we introduce CRNet, a multimodal deep convolutional neural network for predicting customer revisits. We evaluated our approach using two datasets [a customer repurchase dataset (CRD) and mobile food delivery revisit dataset (MFDRD)] and two state-of-the-art multimodal deep learning models.

FibVID: Comprehensive fake news diffusion dataset during the COVID-19 period.

As the SARS-CoV-2 (COVID-19) pandemic has run rampant worldwide, the dissemination of misinformation has sown confusion on a global scale. Thus, understanding the propagation of fake news and implementing countermeasures has become exceedingly important to the well-being of society. To assist this cause, we produce a valuable dataset called FibVID (Fake news information-broadcasting dataset of COVID-19), which addresses COVID-19 and non-COVID news from three key angles.

A lexicon-based approach to examine depression detection in social media: the case of Twitter and university community.

Globally, the number of people who suffer from depression is consistently increasing. Because both detecting and addressing the early stage of depression is one of the strongest factors for effective treatment, a number of scholars have attempted to examine how to detect and address early-stage depression. Recent studies have been focusing on the use of social media for depression detection where users express their thoughts and emotions freely.

Computational analysis of user experience and customer satisfaction with mobile food delivery services: Evidence from big data approaches.

Because of the COVID-19 global pandemic, mobile food delivery services have gained new prominence in our society. With this trend, the understanding of user experience in improving mobile food delivery services has gained increasing importance. To this end, we explore how user experience factors extracted by two natural language processing methods from comments of user reviews of mobile food delivery services significantly improve user satisfaction with the services.

A smart home dental care system: integration of deep learning, image sensors, and mobile controller.

Unlabelled: In this study, a home dental care system consisting of an oral image acquisition device and deep learning models for maxillary and mandibular teeth images is proposed. The presented method not only classifies tooth diseases, but also determines whether a professional dental treatment (NPDT) is required. Additionally, a specially designed oral image acquisition device was developed to perform image acquisition of maxillary and mandibular teeth.

Machine Learning for Mental Health in Social Media: Bibliometric Study.

Background: Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention.

Objective: We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media.

Social Acceptance of Renewable Energy Technologies in the Post-fukushima Era.

In 2011, the Fukushima nuclear accident occurred, and this had a strong effect on public perceptions of energy facilities and services that relate not only to nuclear energy, but also renewable energy resources. Moreover, the accident has also considerably affected national energy plans in both developing and developed countries. In South Korea, several studies have been conducted since the accident to investigate public perspectives toward particular energy technologies; however, few studies have investigated public perceptions of renewable-energy technologies and tracked the transitions.

Effects of Behavioral Risk Factors and Social-Environmental Factors on Non-Communicable Diseases in South Korea: A National Survey Approach.

Non-communicable diseases (NCDs) are one of the major health threats in the world. Thus, identifying the factors that influence NCDs is crucial to monitor and manage diseases. This study investigates the effects of social-environmental and behavioral risk factors on NCDs as well as the effects of social-environmental factors on behavioral risk factors using an integrated research model.

A deep learning model for detecting mental illness from user content on social media.

Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identify a user's mental state based on his/her posting information. To this end, we collected posts from mental health communities in Reddit.

Determinants for the success of regional ICT ventures: a close examination of South Korea.

Background: This study identifies the key motivational factors in enhancing economic performance and increasing new job opportunities for information and communication technology ventures (ICTVs) in South Korea and examines their potential causal relationships through structural equation modeling analysis on data collected from over 200 ICTVs located in Daedeok Innopolis.

Results: The results indicate that the economic performance of ICTVs is determined mainly by government support, innovation effort, and private equity and support. Government support and innovation effort are also positively associated with new job opportunities.

Can Coolness Predict Technology Adoption? Effects of Perceived Coolness on User Acceptance of Smartphones with Curved Screens.

This study proposes an acceptance model for curved-screen smartphones, and explores how the sense of coolness induced by attractiveness, originality, subcultural appeal, and the utility of the curved screen promotes smartphone adoption. The results of structural equation modeling analyses (N = 246) show that these components of coolness (except utility) increase the acceptance of the technology by enhancing the smartphones' affectively driven qualities rather than their utilitarian ones. The proposed coolness model is then compared with the original technology acceptance model to validate that the coolness factors are indeed equally effective determinants of usage intention, as are the extensively studied usability factors such as perceived ease of use and usefulness.