publications

2024

1. ASSETS

   Toward Closed-Domain Conversational Item Listing Assistant for Improvement of Experiences of Older Adults in Customer-to-Customer (C2C) Marketplaces

   Subin Park, Hyunggu Jung, Bektur Ryskeldiev, and 5 more authors

   In Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility, 2024

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   Due to the increased availability of internet-based services, the number of e-commerce users among older adults is steadily growing. However, little is known about how older adults would adapt to modern e-commerce services, especially in the case of customer-to-customer (C2C) marketplaces as buyers and sellers. To address that, we propose a closed-domain conversational chatbot assistant that guides older adults through the product listing process. We evaluated the usability of our tool with 10 participants, consisting of a pre-test survey and a post-task interview. We found that while older adults proficiently operated with familiar functions such as text entry and photo taking, they faced challenges in understanding the purpose of each task and how tasks were related to each other. We suggest design implications for creating conversational chatbot assistants that help older adults sell items in C2C marketplaces.

2. IJCAI

   SaGol: Using MiniGPT-4 to Generate Alt Text for Improving Image Accessibility

   Yunseo Moon, Hyunmin Lee, SeungYoung Oh, and 1 more author

   In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, Aug 2024

   Demo Track

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   SaGol is an AI-powered application to improve image accessibility for people with visual impairments (PVI) users. Alternative (alt) text, a general method of web accessibility for PVI users, is text or phrases that describe images on a website in an understandable way. SaGol generates alt text with the images on the user’s smartphone using a vision large language model called MiniGPT-4. SaGol searches for similar images based on the generated alt text. We evaluated the length of alt text and the search accuracy. This paper shows a potential opportunity to improve image accessibility for PVI users.

3. JMIR

   A Deep Learning–Based Rotten Food Recognition App for Older Adults: Development and Usability Study

   Minki Chun, Ha-Jin Yu, and Hyunggu Jung

   JMIR Form Res, Jul 2024

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   Background: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items. Objective: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app. Methods: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants’ perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses. Results: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants’ perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory. Conclusions: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains.

4. DIB

   Creating a bias-free dataset with food delivery app reviews under data poisoning attack

   Hyunmin Lee, SeungYoung Oh, JinHyun Han, and 1 more author

   Data in Brief, Jun 2024

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   In online food delivery apps, customers write reviews to reflect their experiences. However, certain restaurants use a “review event” strategy to solicit favorable reviews from customers and boost their revenue. Review event is a marketing strategy where a restaurant owner gives free services to customers in return for a promise to write a review. Nevertheless, current datasets of app reviews for food delivery services neglect this situation. Furthermore, there appears to be an absence of datasets with reviews written in Korean. To solve this gap, this paper presents a dataset that contains reviews obtained from restaurants on a Korean app which use a review event strategy. A total of 128,668 reviews were gathered from 136 restaurants through crawling reviews using the Selenium library in Python. The dataset consists of detailed information of each review which contains information about ordered dishes, each review’s written time, whether the food image is included in the review or not, and various star ratings such as total, taste, quantity, and delivery ratings. This dataset supports an innovative process of preparing AI training data for achieving fairness AI by proposing a bias-free dataset of food delivery app reviews with data poisoning attacks as an example Additionally, the dataset is beneficial for researchers who are examining review events or analyzing the sentiment of food delivery app reviews.

5. HCIK

   Designing an LLM-based App to Improve Smartphone Usage Skills among Older Adults

   Minuk Kim, and Hyunggu Jung

   Proceedings of the HCI Korea, Jan 2024

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6. HCIK

   Exploring the Community of Sustainable AI Model Developers
on Hugging Face

   Taewon Yoo, and Hyunggu Jung

   Proceedings of the HCI Korea, Jan 2024

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7. HCIK

   Campus Connect: Designing an App to Improve Communications Between Students Through a Design Thinking Process

   Jungwoo Her, Hyun-seo Shin, Chan-yeong Lim, and 2 more authors

   Proceedings of the HCI Korea, Jan 2024

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8. HCIK

   Designing an Application to Support Software Entrepreneurs for Optimizing Technology Stack Selection

   Hyunwoo Kang, Mose Park, and Hyunggu Jung

   Proceedings of the HCI Korea, Jan 2024

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2023

1. KSC

   GPT & Stable Diffusion-based Post-Reading Activity services

   SeYun Bae, Kyeongyoon Lee, JinSu Lee, and 2 more authors

   Proceedings of Korea Software Congress, Dec 2023

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2. CSCW

   Toward Value Scenario Generation Through Large Language Models

   Hyunggu Jung, Woosuk Seo, Seokwoo Song, and 1 more author

   In Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing, Oct 2023

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   We propose a method of generating value scenarios for design research by leveraging ChatGPT, an AI-powered chatbot based on large language models. Identifying the needs of a vulnerable population, such as North Korean defectors, is challenging for researchers. To address this, we introduce ChatGPT-generated value scenarios, an extension of scenario-based design that supports critical, systemic, long-term thinking in current design practice, technology development, and deployment. Using our proposed method, we created a prompt to generate value scenarios on ChatGPT. Based on our analysis of the generated scenarios, we identified that ChatGPT could generate plausible information about Value Implications. However, it lacks details on Pervasiveness and Systemic Effects. After discussing the limitations and opportunities of ChatGPT in generating value scenarios, we conclude with suggestions for how ChatGPT might be better used to generate value scenarios.

3. CSCW

   Visualizing the Carbon Intensity of Machine Learning Inference for Image Analysis on TensorFlow Hub

   Taewon Yoo, Hyunmin Lee, SeungYoung Oh, and 2 more authors

   In Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing, Oct 2023

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   The increasing performance of machine learning (ML) models necessitates greater computing resources, contributing to rising carbon intensity in ML computing and raising concerns about computational equity. Previous studies focused on developing tools that enable model developers to view the carbon intensity of the ML models in the training process. Still, little is known about how to support ML developers in online communities to explore the carbon intensity of ML models during inference. We developed MIEV, a model inference emission visualizer, that supports ML developers on TensorFlow Hub to explore the carbon intensity of image domain models during the model Inference phase. We also provide insights into designing technologies that promote collaborative work among ML developers to drive sustainable AI development processes. To the best of our knowledge, this is the first attempt to interactively visualize the carbon intensity of ML models in online communities during the Inference phase.

4. IEEE ACCESS

   SUV: Students’ Understanding Visualizer to Support Instructors in Synchronous Online Lectures

   Sungmin Na, Subin Park, Joonyoung Jun, and 6 more authors

   IEEE Access, Aug 2023

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   Due to the COVID-19 pandemic, university instructors shifted their classes online to ensure the continuation of numerous students’ learning. Although researchers created and evaluated tools to support students and instructors in classes in various settings, the tools to help university instructors in synchronous online education are still under-examined. To fill this gap, we introduce Students’ Understanding Visualizer (SUV), a system that visualizes students’ understanding in real-time during synchronous online lectures. To explore instructors’ experience and SUV ’s usability, we conducted a lab-based usability session with eight university instructors in South Korea by asking them to perform a series of tasks with SUV and share their experience. The interview results of this study revealed the benefits SUV would give in actual synchronous online lectures by delivering students’ understanding of lectures to instructors. Moreover, we found instructors’ challenges in using our system and potential features to be added to SUV to enhance the users’ experience. Based on the findings, we propose design opportunities for creating tools that allow instructors to give synchronous online lectures more effectively and in interactive ways.

5. ICML

   Toward Model Selection Through Measuring Dataset Similarity on TensorFlow Hub

   SeungYoung Oh, Hyunmin Lee, JinHyun Han, and 1 more author

   In ICML 2023 Workshop Artificial Intelligence and Human-Computer Interaction, Jul 2023
6. ICML

   Creating a Bias-Free Dataset of Food Delivery App Reviews with Data Poisoning Attacks

   Hyunmin Lee, SeungYoung Oh, JinHyun Han, and 1 more author

   In ICML 2023 Workshop Artificial Intelligence and Human-Computer Interaction, Jul 2023
7. 한국디자인학회

   Exploring User Experience According to the Relationship Between Gift Exchange Method and Intimacy: Focusing on KakaoTalk Birthday Gift

   Seohyeon Song, Hyunggu Jung, and Hyosun Kwon

   KSDS Conference Proceeding, May 2023

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8. 한국디자인학회

   Exploring User Preference on Cancellation/Refund Policy within Kakao Talk Gift Service

   Gaeun Won, Hyunggu Jung, and Hyosun Kwon

   KSDS Conference Proceeding, May 2023

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9. HELIYON

   Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review

   Jinsun Jung, Hyungbok Lee, Hyunggu Jung, and 1 more author

   HELIYON, May 2023

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   Background Significant advancements in the field of information technology have influenced the creation of trustworthy explainable artificial intelligence (XAI) in the field of healthcare. Despite improved performance of XAI, XAI techniques have not yet been integrated into real-time patient care. Objective The aim of this systematic review is to understand the trends and gaps in research on XAI through an assessment of the essential properties of XAI and an evaluation of explanation effectiveness in the healthcare field. Methods A search of PubMed and Embase databases for relevant peer-reviewed articles on development of an XAI model using clinical data and evaluating explanation effectiveness published between January 1, 2011, and April 30, 2022, was conducted. All retrieved papers were screened independently by the two authors. Relevant papers were also reviewed for identification of the essential properties of XAI (e.g., stakeholders and objectives of XAI, quality of personalized explanations) and the measures of explanation effectiveness (e.g., mental model, user satisfaction, trust assessment, task performance, and correctability). Results Six out of 882 articles met the criteria for eligibility. AI users were the most frequently described stakeholders. XAI serves various purposes, including evaluation, justification, improvement, and learning from AI. Evaluation of the quality of personalized explanations is based on fidelity, explanatory power, interpretability, and plausibility. User satisfaction is the most frequently used measure of explanation effectiveness, followed by trust assessment, correctability, and task performance. The methods of assessing these measures also vary. Conclusion XAI research should address the lack of a comprehensive and agreed-upon framework for explaining XAI and standardized approaches for evaluating the effectiveness of the explanation that XAI provides to diverse AI stakeholders.

10. IUI

    WATAA: Web Alternative Text Authoring Assistant for Improving Web Content Accessibility

    Hyeonhak Jeong, Minki Chun, Hyunmin Lee, and 2 more authors

    In Companion Proceedings of the 28th International Conference on Intelligent User Interfaces, Mar 2023

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    Alternative (alt) text is essential for people with visual impairments to acquire information about image content through a screen reader. However, collecting images and creating alt text requires time and effort. To deal with this problem, automatically collecting images that have no alt text is essential to ensure that all image content in a web page has their alt text. Additionally, automatic alt text has limitations in accuracy and quality compared to human-created alt text despite the improvements in image recognition and natural language process technology. We present WATAA, a web alt text authoring assistant that collects images containing no alt text and suggests automatic alt text to help human alt text authors improve a web page’s accessibility.

11. IUI

    PORDE: Explaining Data Poisoning Attacks Through Visual Analytics with Food Delivery App Reviews

    Hyunmin Lee, Minki Chun, and Hyunggu Jung

    In Companion Proceedings of the 28th International Conference on Intelligent User Interfaces, Mar 2023

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    Artificial intelligence (AI) gives many benefits to our lives. However, biased AI models created by receiving data poisoning attacks may induce social problems. Therefore, developers must consider carefully whether the training data received a poison attack when developing an AI model. Data visualization is one of the methods to facilitate the analysis of the data required for checking if the training data received a poisoning attack. However, prior studies did not visualize real-world AI training data. Restaurant reviews in delivery apps are one of the cases of a poisoned dataset. Restaurants hold review events on delivery apps to encourage customers to write a positive review in return for certain rewards, thereby creating reviews with bias. In this study, we propose POisoned Real-world Data Explainer (PORDE) that explains data poisoning attacks through visual analytics with food delivery app reviews. The findings of this study suggest implications for securing safe training data and developing less biased AI models.

12. IUI

    Toward Keyword Generation Through Large Language Models

    Wanhae Lee, Minki Chun, Hyeonhak Jeong, and 1 more author

    In Companion Proceedings of the 28th International Conference on Intelligent User Interfaces, Mar 2023

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    It is essential to understand research trends for researchers, decision-makers, and investors. One way to analyze research trends is to collect and analyze author-defined keywords in scientific papers. Unfortunately, while author-defined keywords are beneficial to researchers aiming to figure out the trends of their research fields, 45% of scientific papers in Microsoft Academic Graph did not contain their author-defined keywords. Additionally, six of the top seven AI conferences neither collect nor disclose keywords. This paper proposes a method for generating the keywords using Galactica, a pre-trained large language model published by Meta. We evaluate this method’s performance by comparing the keywords provided by authors in the CoRL’22 and report characteristics of the generated keywords. Our study shows the F1 score of our proposed method was ten times better than that of previous studies, and 42.7% of the generated keywords are relevant to author-defined keywords.

13. AMIA

    Toward Interactive Machine Learning Through Visualizing Predictions of Data Instances

    Hyeonhak Jeong, Minki Chun, and Hyunggu Jung

    In AMIA 2023 Informatics Summit, Mar 2023

    Poster
14. HCIK

    Link-it!: Designing an App Managing Website Links Through a Design Thinking Process

    Seongjae Lee, Haetae Lee, and Hyunggu Jung

    Proceedings of the HCI Korea, Feb 2023

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2022

1. KSC

   Development and Heuristic Evaluation of Rotten Food Recognition App User Interface for Older Adults

   Minki Chun, and Hyunggu Jung

   Proceedings of Korea Software Congress, Dec 2022

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2. KSC

   Comparison of the Probability Distribution of Two Labels Through Visual Analytics of a Multiclass Classification Model

   Hyeonhak Jeong, Minki Chun, and Hyunggu Jung

   Proceedings of Korea Software Congress, Dec 2022

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3. KSC

   System for Analyzing Bias of Data to Develop Artificial Intelligence Models: a Case Study of Food Delivery App Reviews

   Hyunmin Lee, and Hyunggu Jung

   Proceedings of Korea Software Congress, Dec 2022

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4. IEEE Access

   Development of Korean Food Image Classification Model Using Public Food Image Dataset and Deep Learning Methods

   Minki Chun, Hyeonhak Jeong, Hyunmin Lee, and 2 more authors

   IEEE Access, Dec 2022

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   Food image classification is useful in diet management apps for personal health management. Various methods for classifying food images in a particular country have been proposed in multiple studies. However, knowledge of Korean food image classification is limited. The objective of this study was to train a classification model for Korean food images. To train the classification model, we collected Korean food images from the AI-Hub platform, a public food image dataset. The images were pre-processed and augmented for model training. The proposed model was evaluated in an experiment using a Korean food image dataset and effectively classified Korean food images based on transfer learning. The findings of the study revealed that the model’s performance in classifying food images depended on the type of food. The findings have implications for the classification model training process using CNN and the Korean public food image dataset. Future work is required to improve the performance of a classification model, especially as it pertains to its poor performance for some food image types.

5. CSCW

   Exploring the Community of Model Publishers on TensorFlow Hub

   Taewon Yoo, Minki Chun, Yunjung Bae, and 2 more authors

   In Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing, Nov 2022

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   We explore the community of AI model publishers on TensorFlow Hub (TF Hub). While researchers identified the challenges AI model publishers and AI model users faced, little is known about how they interact with each other in an online community. The analysis of the metadata recorded on TF Hub revealed the models that the AI model publishers uploaded. Also, we found out how the models published by the AI model publishers were shared with other people on TF Hub. To our knowledge, this is the first attempt to explore the online community of AI model publishers sharing their models with each other.

6. KIISE

   Toward Fairness in Artificial Intelligence: Uncovering Food Delivery App Reviews with Poisoning Attacks

   Hyunmin Lee, Minki Chun, Hyeonhak Jeong, and 1 more author

   Communications of the Korean Institute of Information Scientists and Engineers, Nov 2022

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7. UAIS

   Exploring the Community of Older Adult Viewers on YouTube

   Jihyun Lee, Suin Gwak, Joonho Gwon, and 6 more authors

   Universal Access in the Information Society, Sep 2022

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   We report on a study of how older adult viewers use YouTube. We conducted semi-structured interviews with 15 older adults residing in the Republic of Korea who had recently watched videos on YouTube to understand their experiences. We identified themes regarding their perceptions and behaviors when interacting with videos and other viewers on YouTube through an open coding method. The analysis of the identified themes reveals that older adults use YouTube as a tool for collecting information that addresses their challenges when gathering information from media. Although older adult viewers worried about fake news and possible monetary charges, they were eager to watch videos on YouTube because they believed this information to be unbiased compared with media sources. To the best of our knowledge, this is the first interview study to investigate the community of older adult viewers on a video-based social media platform.

8. KCC

   Auto Labeling Restaurant Notices of Delivery Applications using Text Classification Model

   Hyunmin Lee, and Hyunggu Jung

   Proceedings of Korea Computer Congress, Jun 2022

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   Due to the outbreak of the COVID-19 pandemic and social distancing, the food delivery application market has shown rapid growth. This made customer reviews on restaurants of delivery applications influencing. However, fake reviews are being written to mislead customers with false information. Typically, review events held by restaurants encourage customers to leave positive reviews on the delivery applications, making it difficult for users to write objective and honest reviews. We try to solve this problem by creating a tool that labels restaurant notices promoting such review events. We first collected and preprocessed restaurant notice data and used the data to train and evaluate a classifier model.

9. KCC

   Development of Visual Analytics Tool for Explaining Data Characteristics and Model Performance Indicators

   Minki Chun, and Hyunggu Jung

   Proceedings of Korea Computer Congress, Jun 2022

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   A deep learning method has been widely used in various fields for many years. However, due to the characteristic of the deep learning method, it is difficult for decision-makers to trust the results inferred from the deep learning model. To support model developers in improving their models, we developed a visual analytics tool that allows users to review the recall, precision, and images of each class by displaying the images on a coordinate plane. The tool allows model developers to understand the training results by supporting them in interpreting the characteristics of the dataset used to train the model.

10. ECSCW

    Toward an AI-assisted Assessment Tool to Support Online Art Therapy Practices: A Pilot Study

    Woosuk Seo, Joonyoung Jun, Minki Chun, and 5 more authors

    In Proceedings of 20th European Conference on Computer-Supported Cooperative Work, Jun 2022

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    Artificial intelligence (AI) has been widely used to assist art therapists with artwork assessments by providing objective information. While prior studies showed that AI-assisted tools are feasible to improve drawing analysis in in-person art therapy practices, the use of those tools in online art therapy is still under-examined. To fill the gap, we created a prototype of an AI-assisted tool for online therapy in a House-Tree-Person (HTP) test scenario and ran lab-based usability sessions with 10 art therapists in which they used our proposed prototype to complete predefined tasks. We then conducted semi-structured interviews with the participants to understand their acceptance and concerns about the prototype. The findings revealed the unique needs of art therapists and opportunities of using AI-assisted tools to improve online art therapy practices. Based on these findings, we suggest implications for creating AI-assisted tools that meet specific needs of art therapists in online therapy sessions, and further discuss future directions of research about AI-assisted tools for art therapists in online settings.

11. UAIS

    Understanding Experiences of Older Adults in Virtual Reality Environments with a Subway Fire Disaster Scenario

    Subin Park, Hansu Lee, Minji Kwon, and 2 more authors

    Universal Access in the Information Society, Mar 2022

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    Training to cope with fire disaster situations utilizing virtual reality (VR) environments as educational materials is becoming universalized. It is widely accepted that VR enables young adult trainees to undergo tactical training in potential disaster situations. However, little is known whether older adults are satisfied with using such a technique for disaster response training. To address this insufficiency, we created a VR environment in a subway fire disaster scenario and conducted an experiment, wherein 13 young and 9 older adults were asked to explore the VR environment. We then conducted surveys and semi-structured interviews involving the participants to understand their experiences. The analysis of results revealed that compared to young adults, older adults tended to move greater distances, take longer times, felt less afraid and scared, and experienced less difficulty in manipulating the controller. Subsequently, we suggest design opportunities for creating VR environments for more effective training of older adults.

12. AAAI

    Creating an Explainable Artificial Intelligence Framework to Increase Nurses’ Confidence in an Interhospital Transfer Scenario

    Hyunggu Jung, Hyungbok Lee, Jinsun Jung, and 1 more author

    In Association for the Advancement of Artificial Intelligence 2022 Workshop: Trustworthy AI for Healthcare, Mar 2022

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13. HCIK

    Designing a Trip-plan Sharing App Through a Design Thinking Process

    Hyeonhak Jeong, Younguk Kim, and Hyunggu Jung

    Proceedings of the HCI Korea, Jan 2022

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14. HCIK

    Designing an App to Improve Experience of Workers Waiting for an Elevator During the COVID-19 Pandemic

    Minsul Kang, JinHyun Han, and Hyunggu Jung

    Proceedings of the HCI Korea, Jan 2022

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2021

1. Nursing Informatics Research Trends: Findings from an International Survey

   Laura-Maria Peltonen, Raji Nibber, Lorraine Block, and 8 more authors

   Studies in Health Technology and Informatics, Dec 2021

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2. KOSMI

   An Explainable AI Tool to Support Decision-making in an Emergency Patients Transfer Request Scenario

   Hyungbok Lee, Hyunggu Jung, Jinsun Jung, and 1 more author

   Korean Society of Medical Informatics, Nov 2021
3. CSCW

   Exploring the Experiences of Student Volunteer and Student Volunteer Chair Communities at Academic Conferences

   Subin Park, Heejae Jung, Jae Won Choi, and 4 more authors

   Proceedings of the ACM on Human-Computer Interaction, Oct 2021

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4. CSCW

   Exploring the Experiences of Streamers with Visual Impairments

   Joonyoung Jun, Woosuk Seo, Jihyeon Park, and 2 more authors

   Proceedings of the ACM on Human-Computer Interaction, Oct 2021

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5. IJLTER

   Exploring University Instructors’ Challenges in Online Teaching and Design Opportunities during the COVID-19 Pandemic: A Systematic Review

   Sungmin Na, and Hyunggu Jung

   International Journal of Learning, Teaching and Educational Research, Oct 2021

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6. AsianCHI

   CO-ALERT: A Tool for Personalizing COVID-19 Emergency Alert Messages to Support Older Adults

   Minki Chun, Suin Gwak, and Hyunggu Jung

   In Asian CHI Symposium 2021, May 2021

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7. IUI

   MonoPass: A Password Manager without Master Password Authentication

   Hyeonhak Jeong, and Hyunggu Jung

   In 26th International Conference on Intelligent User Interfaces-Companion, Apr 2021

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8. IUI

   LectYS: A System for Summarizing Lecture Videos on YouTube

   Taewon Yoo, Hyewon Jeong, Donghwan Lee, and 1 more author

   In 26th International Conference on Intelligent User Interfaces-Companion, Apr 2021

   HTML PDF
9. JMIR

   A novel food record app for dietary assessments among older adults with type 2 diabetes: development and usability study

   Hyunggu Jung, George Demiris, Peter Tarczy-Hornoch, and 1 more author

   JMIR Formative Research, Feb 2021

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10. UAIS

    Challenges and opportunities to improve the accessibility of YouTube for people with visual impairments as content creators

    Woosuk Seo, and Hyunggu Jung

    Universal Access in the Information Society, Feb 2021

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11. HCIK

    A Study on Utilizing COVID-19 Emergency Notifications through a Design Thinking Process

    Suin Gwak, Minki Chun, and Hyunggu Jung

    Proceedings of the HCI Korea, Feb 2021

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12. HCIK

    Bounce: Designing an EKG Monitoring Application Using a Wearable Sensor through a Design Thinking Process

    Shinsil Kang, Young-Soo Song, Huimin Hu, and 1 more author

    Proceedings of the HCI Korea, Feb 2021

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2020

1. KMS

   DALT: Deep Learning Assisted Labeling Task

   Wanhae Lee, and Hyunggu Jung

   In Proceedings of the 2020 Summer Conference of the Korea Multimedia Society, Aug 2020

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2. AsianCHI

   Challenges and Opportunities for Accessible Seoul Metropolitan Buses: An Interview Study of People with Visual Impairments

   Subin Park, Gyeonghun Kim, and Hyunggu Jung

   In Proceedings of the 2020 Symposium on Emerging Research from Asia and on Asian Contexts and Cultures, Apr 2020

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3. AsianCHI

   Uncovering CHI Reviewers Needs and Barriers

   Wanhae Lee, Minji Kwon, Yewon Hyun, and 3 more authors

   In Proceedings of the 2020 Symposium on Emerging Research from Asia and on Asian Contexts and Cultures, Apr 2020

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4. AsianCHI

   Challenges and Design Opportunities for Easy, Economical, and Accessible Offline Shoppers with Visual Impairments

   Jihyun Lee, Jinsol Kim, and Hyunggu Jung

   In Proceedings of the 2020 Symposium on Emerging Research from Asia and on Asian Contexts and Cultures, Apr 2020

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5. AsianCHI

   BYE-TAL: Designing a Smartphone App for Sustainable Self-Healthcare through Design Thinking Process

   Minji Kwon, Jihyun Lee, Wanhae Lee, and 1 more author

   In Proceedings of the 2020 Symposium on Emerging Research from Asia and on Asian Contexts and Cultures, Apr 2020

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6. AsianCHI

   Analyzing Bias of Comments on Political News Articles to Facilitate Transparent Online Communities

   Joonho Gwon, Minji Kwon, and Hyunggu Jung

   In Proceedings of the 2020 Symposium on Emerging Research from Asia and on Asian Contexts and Cultures, Apr 2020

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7. UAIS

   Understanding the community of blind or visually impaired vloggers on YouTube

   Woosuk Seo, and Hyunggu Jung

   Universal Access in the Information Society, Jan 2020

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2019

1. VRST

   Exploring Experiences of Virtual Reality among Young and Older Adults in a Subway Fire Scenario: a Pilot Study

   Minyeong Seo, Hansu Lee, Seungmi Choi, and 4 more authors

   In 25th ACM Symposium on Virtual Reality Software and Technology, Nov 2019

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2. UKC

   Category Classification of Educational Videos on YouTube through Machine Learning Approaches using Video Titles

   Taewon Yoo, and Hyunggu Jung

   Aug 2019

   Poster
3. UKC

   A Prototype of Non-smoking Aid Application Based on Features and User Interfaces to Support People with Low Vision

   Yiji Bae, and Hyunggu Jung

   In Proceedings of the 2019 US-Korea Conference on Science, Technology and Entrepreneurship, Aug 2019

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4. UKC

   A Study on the Utilization and Module Configuration Diagram of the User Path in Virtual Reality Using Unity

   Minyeong Seo, and Hyunggu Jung

   In Proceedings of the 2019 US-Korea Conference on Science, Technology and Entrepreneurship, Aug 2019

   Poster
5. MobiSys

   SEEjang: Smart, Easy, and Economical Offline Shopping Assist App Development through a Design Thinking Proces (poster)

   Jihyun Lee, Gyeongcheol Park, and Hyunggu Jung

   In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, Jun 2019

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6. KOSMI

   A Study on the Utilization and Module Configuration Diagram of the User Path in Virtual Reality Using Unity

   In Proceedings of the Korean Society of Medical Informatics, Jul 2019
7. AsianCHI

   Evaluation of mobile applications for disaster responses through personas and scenarios

   Heejae Jung, and Hyunggu Jung

   In Proceedings of Asian CHI Symposium 2019: Emerging HCI Research Collection, May 2019

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8. The current state of Nursing Informatics–An international cross-sectional survey

   Laura-Maria Peltonen, Lisiane Pruinelli, Charlene Ronquillo, and 8 more authors

   Finnish Journal of eHealth and eWelfare, May 2019

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9. KCC

   Design of Cohort Analysis System for Colon Cancer Patients based on the Combination of Environmental Factor

   Han-Sub Shin, Gang-Min Park, Hyunggu Jung, and 1 more author

   Proceedings of Korea Computer Congress, Jun 2019

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10. KCC

    Development and Usability Study of an Offline Shopping Assist App through Design Thinking Process.

    Jihyun Lee, Gyeongcheol Park, and Hyunggu Jung

    Proceedings of Korea Computer Congress, Jun 2019

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11. KCC

    Category Classification of Educational Videos on YouTube through Machine Learning Approaches

    Taewon Yoo, and Hyunggu Jung

    Proceedings of Korea Computer Congress, Jun 2019

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12. HCIK

    Analysis of Conflicts in Romantic Relationships on Mobile Messengers

    Young-Ji Koh, Hyunggu Jung, and Uran Oh

    The HCI Society of Korea, Jun 2019

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13. HCIK

    Design a Crime Rate-based Recommended Routes Guidance Application

    Hyeonjin Jeon, and Hyunggu Jung

    Proceedings of the HCI Korea, Feb 2019

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14. HCIK

    Needs Analysis Based on Vlogging Career

    Taewon Yoo, and Hyunggu Jung

    Proceedings of the HCI Korea, Feb 2019

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15. HCIK

    A Study on the Analysis of Personas and Scenarios for Earthquake Avoidance in School

    Yiji Bae, and Hyunggu Jung

    Proceedings of the HCI Korea, Feb 2019

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2018

1. APHA

   Opportunities to Reduce Lung Cancer Disparities Among African Americans through Informatics: A Systematic Review

   KiBeom Kwon, and Hyunggu Jung

   In APHA’s 2018 Annual Meeting & Expo (Nov. 10-Nov. 14), Nov 2018

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2. IEEE

   Understanding users’ privacy attitudes through subjective and objective assessments: An instagram case study

   Kyungsik Han, Hyunggu Jung, Jin Yea Jang, and 1 more author

   Computer, Jun 2018

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3. TVX

   Understanding blind or visually impaired people on youtube through qualitative analysis of videos

   Woosuk Seo, and Hyunggu Jung

   In Proceedings of the 2018 ACM International Conference on Interactive Experiences for TV and Online Video, Jun 2018

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2017

1. ASSETS

   Exploring the community of blind or visually impaired people on YouTube

   Woosuk Seo, and Hyunggu Jung

   In Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility, Oct 2017

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2. CSCW

   Personas and scenarios to design technologies for North Korean defectors with depression

   Hyunggu Jung, Woosuk Seo, and Michelle Cha

   In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, Feb 2017

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3. ICIHT

   Designing community of practice systems: A value sensitive approach

   Abdulwahhab O Alshammari, and Hyunggu Jung

   In 2017 International Conference on Informatics, Health & Technology (ICIHT), Feb 2017

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2016

1. AMIA

   Development of a novel markov chain model for the prediction of head and neck squamous cell carcinoma dissemination

   Hyunggu Jung, Anthony Law, Eli Grunblatt, and 4 more authors

   In AMIA Annual Symposium Proceedings, Nov 2016

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2. AMIA

   Nurse informaticians report low satisfaction and multi-level concerns with electronic health records: results from an international survey

   Maxim Topaz, Charlene Ronquillo, Laura-Maria Peltonen, and 8 more authors

   In AMIA Annual Symposium Proceedings, Nov 2016

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3. Coordinated Markov Modeling of Cancer Metastasis from Multiple Primary Site

   Hyunggu Jung, Anthony Law, Esther Wu, and 1 more author

   In Proceedings of the Conference of the Asia Pacific Association for Medical Informatics, Nov 2016

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4. Development of a Medication Information Generator System for Older Adults

   Hyunggu Jung, and Jiyoun Lee

   In 2016 IEEE International Conference on Healthcare Informatics (ICHI), Oct 2016

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5. Mutiv: Music-based Mobile Application to Support Joggers

   Sangwook Lee, and Hyunggu Jung

   In 2016 IEEE International Conference on Healthcare Informatics (ICHI), Oct 2016

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6. A Model of Regional Tumor Metastasis of Squamous Cell Carcinoma of the Tongue

   In Proceedings of the American Head and Neck Society (AHNS) 9th International Conference on Head and Neck Cancer, Jul 2016

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7. Current Trends in Nursing Informatics: Results of an International Survey.

   Laura-Maria Peltonen, Dari Alhuwail, Samira Ali, and 8 more authors

   Studies in Health Technology and Informatics, Jun 2016

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8. Nursing informatics research priorities for the future: recommendations from an international survey

   LM Peltonen, M Topaz, C Ronquillo, and 14 more authors

   Nursing Informatics 2016: EHealth for All: Every Level Collaboration–From Project to Realization, Jun 2016

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9. Advancing nursing informatics in the next decade: recommendations from an international survey

   M Topaz, C Ronquillo, LM Peltonen, and 14 more authors

   Nursing Informatics 2016: EHealth for All: Every Level Collaboration–From Project to Realization, Jun 2016

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10. CHI

    ’MASTerful’Matchmaking in Service Transactions: Inferred Abilities, Needs and Interests versus Activity Histories

    Hyunggu Jung, Victoria Bellotti, Afsaneh Doryab, and 7 more authors

    In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, May 2016

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2015

1. AMIA

   Biological Model Development as an Opportunity to Provide Content Auditing for the Foundational Model of Anatomy Ontology

   Lucy L Wang, Eli Grunblatt, Hyunggu Jung, and 2 more authors

   In AMIA Annual Symposium Proceedings, Nov 2015

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2. SIGDOC

   Designing tools to support advanced users in new forms of social media interaction

   Hyunggu Jung, Sungsoo Hong, Perry Meas, and 1 more author

   In Proceedings of the 33rd Annual International Conference on the Design of Communication, Jul 2015

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3. AMIA

   Examining researcher needs and barriers for using electronic health data for translational research

   Kari A Stephens, E Sally Lee, Hossein Estiri, and 1 more author

   AMIA Summits on Translational Science Proceedings, Mar 2015

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4. AMIA

   Visualizing anomalies in electronic health record data: the variability explorer tool

   Hossein Estiri, Ya-Fen Chan, Laura-Mae Baldwin, and 3 more authors

   AMIA Summits on Translational Science Proceedings, Mar 2015

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2014

1. VAHC

   Interactive Demographic Visualization of Multiple Facilities across Time

   Hyunggu Jung, Sally E Lee, Hossein Estiri, and 1 more author

   In Proceedings of the 2014 Workshop on Visual Analytics in Healthcare (VAHC), Nov 2014

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2013

1. UKC

   Task-Decomposing Anonymous Crowds

   Hyunggu Jung, and Joseph Lau

   In Proceedings of the 2013 US-Korea Conference on Science, Technology and Entrepreneurship (UKC), Aug 2013

2011

1. UMUAI

   A user modeling approach for reasoning about interaction sensitive to bother and its application to hospital decision scenarios

   Robin Cohen, Hyunggu Jung, Michael W Fleming, and 1 more author

   User Modeling and User-Adapted Interaction, Jan 2011

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2010

1. Canadian AI

   A model for reasoning about interaction with users in dynamic, time critical environments for the application of hospital decision making

   Hyunggu Jung, and Robin Cohen

   In Canadian Conference on Artificial Intelligence, May 2010

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2. Canadian AI

   Reasoning about interaction for hospital decision making

   Hyunggu Jung

   In Canadian Conference on Artificial Intelligence, May 2010

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