https://scholar.dgist.ac.kr/handle/20.500.11750/13861 2026-04-06T16:45:10Z https://scholar.dgist.ac.kr/handle/20.500.11750/57866 Title: Exploring Intervention Techniques to Alleviate Negative Emotions during Video Content Moderation Tasks as a Worker-centered Task Design Author(s): Lee, Dokyun; Seo, Sangeun; Park, Chanwoo; Kim, Sunjun; Chang, Buru; Song, Jean Y. Abstract: Videos are dynamic and multi-modal compared to other types of content, making automatic fltering difcult, which is why content moderators play a crucial role. However, video content moderators are exposed to more profound emotional labor because videos contain rich visual information, sometimes including even harmful content, such as violent or terrifying scenes. In this work, we explore the efect of six intervention techniques on alleviating negative emotions during video content moderation tasks. We conducted one online crowdsourcing experiment and two controlled user studies to fnd out that (i) interleaving with positive videos or (ii) cartoonization could signifcantly reduce negative emotions in the moderators. Participants reported that the advantages of these approaches are in helping reduce negative emotions at the time of moderation while existing approaches focus on post-task activities (e.g., relaxation, talking with others, or getting a hobby). We discuss the applicability of our fndings to broader tasks, including improvement in intervention techniques. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. 2024-06-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57551 Title: Serenus: Alleviating Low-Batery Anxiety Through Real-time, Accurate, and User-Friendly Energy Consumption Prediction of Mobile Applications Author(s): Lee, Sera; Jeong, Dae R.; Choi, Junyoung; Kwak, Jaeheon; Son, Seoyun; Song, Jean Young; Shin, Insik Abstract: Low-battery anxiety has emerged as a result of growing dependence on mobile devices, where the anxiety arises when the battery level runs low. While battery life can be extended through power-efficient hardware and software optimization techniques, low-battery anxiety will still remain a phenomenon as long as mobile devices rely on batteries. In this paper, we investigate how an accurate real-time energy consumption prediction at the application-level can improve the user experience in low-battery situations. We present Serenus, a mobile system framework specifically tailored to predict the energy consumption of each mobile application and present the prediction in a user-friendly manner. We conducted user studies using Serenus to verify that highly accurate energy consumption predictions can effectively alleviate low-battery anxiety by assisting users in planning their application usage based on the remaining battery life. We summarize requirements to mitigate users' anxiety, guiding the design of future mobile system frameworks. © 2024 Owner/Author. 2024-10-14T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57542 Title: FLUID-IoT : Flexible and Granular Access Control in Shared IoT Environments via-UI-Level Control Distribution Author(s): Lee, Sunjae; Jeong, Minwoo; Song, Daye; Choi, Junyoung; Son, Seoyun; Song, Jean Y.; Shin, Insik Abstract: The rapid growth of the Internet of Things (IoT) in shared spaces has led to an increasing demand for sharing IoT devices among multiple users. Yet, existing IoT platforms often fall short by offering an all-or-nothing approach to access control, not only posing security risks but also inhibiting the growth of the shared IoT ecosystem. This paper introduces FLUID-IoT, a framework that enables flexible and granular multi-user access control, even down to the User Interface (UI) component level. Leveraging a multi-user UI distribution technique, FLUID-IoT transforms existing IoT apps into centralized hubs that selectively distribute UI components to users based on their permission levels. Our performance evaluation, encompassing coverage, latency, and memory consumption, affirm that FLUID-IoT can be seamlessly integrated with existing IoT platforms and offers adequate performance for daily IoT scenarios. An in-lab user study further supports that the framework is intuitive and user-friendly, requiring minimal training for efficient utilization. 2024-05-14T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57543 Title: Find the Bot!: Gamifying Facial Emotion Recognition for Both Human Training and Machine Learning Data Collection Author(s): Yang, Yeonsun; Shin, Ahyeon; Kim, Nayoung; Woo, Huidam; Chung, John Joon Young; Song, Jean Young Abstract: Facial emotion recognition (FER) constitutes an essential social skill for both humans and machines to interact with others. To this end, computer interfaces serve as valuable tools for training individuals to improve FER abilities, while also serving as tools for gathering labels to train FER machine learning datasets. However, existing tools have limitations on the scope and methods of training non-clinical populations and also on collecting labels for machines. In this study, we introduce Find the Bot!, an integrated game that efectively engages the general population to support not only human FER learning on spontaneous expressions but also the collection of reliable judgment-based labels. We incorporated design guidelines from gamification, education, and crowdsourcing literature to engage and motivate players. Our evaluation (N=59) shows that the game encourages players to learn emotional social norms on perceived facial expressions with a high agreement rate, facilitating efective FER learning and reliable label collection all while enjoying gameplay. © 2024 Copyright held by the owner/author(s) 2024-05-13T15:00:00Z