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. In this study, we aimed to investigate the disparities between the perceived locations of sound sources by users and the locations generated by the platform. Our goal was to determine if it is possible to train users to adapt to the platform-generated sound sources. We utilized the Microsoft HoloLens 2 virtual platform and collected data from 12 subjects based on six separate training sessions arranged in 2 weeks. We employed three modes of training to assess their effects on sound localization, in particular for studying the impacts of multimodal error, visual, and sound guidance in combination with kinesthetic/postural guidance, on the effectiveness of the training. We analyzed the collected data in terms of the training effect between pre- and post-sessions as well as the retention effect between two separate sessions based on subject-wise paired statistics. Our findings indicate that, as far as the training effect between pre- and post-sessions is concerned, the effect is proven to be statistically significant, in particular in the case wherein kinesthetic/postural guidance is mixed with visual and sound guidance. Conversely, visual error guidance alone was found to be largely ineffective. On the other hand, as far as the retention effect between two separate sessions is concerned, we could not find any meaningful statistical implication on the effect for all three error guidance modes out of the 2-week session of training. These findings can contribute to the improvement of VR technologies by ensuring they are designed to optimize human sound localization abilities.

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. Specifically, we focus on identifying key factors that influence the efficiency of national R&D investments in South Korea. To achieve this, we compiled a dataset of 98,224 government-funded R&D projects conducted between 2016 and 2019. The dataset includes information on project characteristics (research stage, technology types, and management institutions) as well as outcomes (patent applications, patent registrations, publications, royalties, and sales). Through factorial Kruskal-Wallis tests, we found that the research stage and technology type significantly affected the project outcomes, while the research stage did not significantly influence royalty and sales amounts. Additionally, our analysis of South Korean research management institutions revealed variations in their overall performance, suggesting differences in management capabilities among institutions. Based on these findings, we provide insights into setting appropriate research goals for each project, considering their unique characteristics. Finally, we discuss the implications and limitations of this study.

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. OBJECTIVE: In this study, we analyzed the distinctive linguistic characteristics in users' posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process. METHODS: Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health-related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks. RESULTS: We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case. CONCLUSIONS: By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients' mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online.

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. Several implications, as well as limitations of the study, were examined incorporating the findings with the statistical results.

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

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. The results showed that CRNet obtained accuracies and Fi-Scores of 0.9575 (CRD) and 0.9436 (MFDRD) and 0.9730 (CRD) and 0.9509 (MFDRD), respectively, thus achieving higher performance levels than current state-of-the-art multimodal frameworks (accuracy: 0.7417-0.9012; F1-Score: 0.7461-0.9378). Future research should aim to address other resources that can enhance the proposed framework (e.g., metadata information). Supplementary Information: The online version contains supplementary material available at 10.1186/s40537-022-00674-4.

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

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. Two evaluation metrics, namely, tooth disease and NPDT classifications, were examined using 610 compounded and 5251 tooth images annotated by an experienced dentist with a Doctor of Dental Surgery and another dentist with a Doctor of Dental Medicine. In the tooth disease and NPDT classifications, the proposed system showed accuracies greater than 96% and 89%, respectively. Based on these results, we believe that the proposed system will allow users to effectively manage their dental health by detecting tooth diseases by providing information on the need for dental treatment. Supplementary Information: The online version contains supplementary material available at 10.1007/s12652-021-03366-8.

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. With this trend, we examine two-step approaches for early-stage depression detection. First, we propose a depression post-classification model using multiple languages Twitter datasets (Korean, English, and Japanese) to improve the applicability of the proposed model. Moreover, we built a depression lexicon for each language, which mental health experts verified. Then, we applied the proposed model to a more specific user group dataset, a community of university students (Everytime), to examine whether the model can be employed to address depression posts in more specific user groups. The classification results present that the proposed model and approach can effectively detect depression posts of a general user group (Twitter), as well as specific user group datasets. Moreover, the implemented models and datasets are publicly available.

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. The results of two multiple regression analyses show that sentiment dimension factors, as well as usability, usefulness, and affection, have notable effects on satisfaction with the applications. Based on several findings of this study, we examine the significant implications and present the limitations of the study.

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. Hence, key researchers were identified based on their centrality metrics. The dataset contained 7,155 publications, thus reflecting the contributions of a total of 243 authors. The results of our data analysis demonstrated that key researchers can be identified via CNA; this aspect was not evident from the results of the most productive researchers. Additionally, on the basis of the results, the implications and limitations of this study were analyzed.

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. METHODS: Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. RESULTS: We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. CONCLUSIONS: The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.

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. This study used a dataset from the 2017 Korea National Health and Nutrition Examination Survey. After filtering incomplete responses, 5462 valid responses remained. Items including one's social-environmental factors (household income, education level, and region), behavioral factors (alcohol use, tobacco use, and physical activity), and NCDs histories were used for analyses. To develop a comprehensive index of each factor that allows comparison between different concepts, the researchers assigned scores to indicators of the factors and calculated a ratio of the scores. A series of path analyses were conducted to determine the extent of relationships among NCDs and risk factors. The results showed that social-environmental factors have notable effects on stroke, myocardial infarction, angina, diabetes, and gastric, liver, colon, lung, and thyroid cancers. The results indicate that the effects of social-environmental and behavioral risk factors on NCDs vary across the different types of diseases. The effects of social-environmental factors and behavioral risk factors significantly affected NCDs. However, the effect of social-environmental factors on behavioral risk factors was not supported. Furthermore, social-environmental factors and behavioral risk factors affect NCDs in a similar way. However, the effects of behavioral risk factors were smaller than those of social-environmental factors. The current research suggests taking a comprehensive view of risk factors to further understand the antecedents of NCDs in South Korea.

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. First, we provide truth and falsehood (T/F) indicators of news items, as labeled and validated by several fact-checking platforms (e.g., Snopes and Politifact). Second, we collect spurious-claim-related tweets and retweets from Twitter, one of the world's largest social networks. Third, we provide basic user information, including the terms and characteristics of "heavy fake news" user to present a better understanding of T/F claims in consideration of COVID-19. FibVID provides several significant contributions. It helps to uncover propagation patterns of news items and themes related to identifying their authenticity. It further helps catalog and identify the traits of users who engage in fake news diffusion. We also provide suggestions for future applications of FibVID with a few exploratory analyses to examine the effectiveness of the approaches used.

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. By analyzing and learning posting information written by users, our proposed model could accurately identify whether a user's post belongs to a specific mental disorder, including depression, anxiety, bipolar, borderline personality disorder, schizophrenia, and autism. We believe our model can help identify potential sufferers with mental illness based on their posts. This study further discusses the implication of our proposed model, which can serve as a supplementary tool for monitoring mental health states of individuals who frequently use 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. Therefore, this study examines the trend of South Korean public's perceptions of renewable-energy technologies. Based on data collected in 2016, we validated the structural connections and determined that trust, benefits, risks, and attitude were key determinants of the public's desire to adopt these technologies; specifically, public attitude was found to be the greatest determinant of this desire. Based on the results, both implications and limitations are examined.

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. CONCLUSIONS: The theoretical, industrial implications of the key findings, and recommendations for the Korean government are discussed.

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.