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Professor Sung-Ju Lee’s Team Wins the Best Paper and the Methods Recognition Awards at the ACM CSCW
A research team led by Professor Sung-Ju Lee at the School of Electrical Engineering won the Best Paper Award and the Methods Recognition Award from ACM CSCW (International Conference on Computer-Supported Cooperative Work and Social Computing) 2021 for their paper “Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis”. Founded in 1986, CSCW has been a premier conference on HCI (Human Computer Interaction) and Social Computing. This year, 340 full papers were presented and the best paper awards are given to the top 1% papers of the submitted. Methods Recognition, which is a new award, is given “for strong examples of work that includes well developed, explained, or implemented methods, and methodological innovation.” Hyunsung Cho (KAIST alumus and currently a PhD candidate at Carnegie Mellon University), Daeun Choi (KAIST undergraduate researcher), Donghwi Kim (KAIST PhD Candidate), Wan Ju Kang (KAIST PhD Candidate), and Professor Eun Kyoung Choe (University of Maryland and KAIST alumna) collaborated on this research. The authors developed a tool that tracks and analyzes which features of a mobile app (e.g., Instagram’s following post, following story, recommended post, post upload, direct messaging, etc.) are in use based on a smartphone’s User Interface (UI) layout. Utilizing this novel method, the authors revealed which feature usage patterns result in regretful smartphone use. Professor Lee said, “Although many people enjoy the benefits of smartphones, issues have emerged from the overuse of smartphones. With this feature level analysis, users can reflect on their smartphone usage based on finer grained analysis and this could contribute to digital wellbeing.”
Image Analysis to Automatically Quantify Gender Bias in Movies
Many commercial films worldwide continue to express womanhood in a stereotypical manner, a recent study using image analysis showed. A KAIST research team developed a novel image analysis method for automatically quantifying the degree of gender bias in films. The ‘Bechdel Test’ has been the most representative and general method of evaluating gender bias in films. This test indicates the degree of gender bias in a film by measuring how active the presence of women is in a film. A film passes the Bechdel Test if the film (1) has at least two female characters, (2) who talk to each other, and (3) their conversation is not related to the male characters. However, the Bechdel Test has fundamental limitations regarding the accuracy and practicality of the evaluation. Firstly, the Bechdel Test requires considerable human resources, as it is performed subjectively by a person. More importantly, the Bechdel Test analyzes only a single aspect of the film, the dialogues between characters in the script, and provides only a dichotomous result of passing the test, neglecting the fact that a film is a visual art form reflecting multi-layered and complicated gender bias phenomena. It is also difficult to fully represent today’s various discourse on gender bias, which is much more diverse than in 1985 when the Bechdel Test was first presented. Inspired by these limitations, a KAIST research team led by Professor Byungjoo Lee from the Graduate School of Culture Technology proposed an advanced system that uses computer vision technology to automatically analyzes the visual information of each frame of the film. This allows the system to more accurately and practically evaluate the degree to which female and male characters are discriminatingly depicted in a film in quantitative terms, and further enables the revealing of gender bias that conventional analysis methods could not yet detect. Professor Lee and his researchers Ji Yoon Jang and Sangyoon Lee analyzed 40 films from Hollywood and South Korea released between 2017 and 2018. They downsampled the films from 24 to 3 frames per second, and used Microsoft’s Face API facial recognition technology and object detection technology YOLO9000 to verify the details of the characters and their surrounding objects in the scenes. Using the new system, the team computed eight quantitative indices that describe the representation of a particular gender in the films. They are: emotional diversity, spatial staticity, spatial occupancy, temporal occupancy, mean age, intellectual image, emphasis on appearance, and type and frequency of surrounding objects. Figure 1. System Diagram Figure 2. 40 Hollywood and Korean Films Analyzed in the Study According to the emotional diversity index, the depicted women were found to be more prone to expressing passive emotions, such as sadness, fear, and surprise. In contrast, male characters in the same films were more likely to demonstrate active emotions, such as anger and hatred. Figure 3. Difference in Emotional Diversity between Female and Male Characters The type and frequency of surrounding objects index revealed that female characters and automobiles were tracked together only 55.7 % as much as that of male characters, while they were more likely to appear with furniture and in a household, with 123.9% probability. In cases of temporal occupancy and mean age, female characters appeared less frequently in films than males at the rate of 56%, and were on average younger in 79.1% of the cases. These two indices were especially conspicuous in Korean films. Professor Lee said, “Our research confirmed that many commercial films depict women from a stereotypical perspective. I hope this result promotes public awareness of the importance of taking prudence when filmmakers create characters in films.” This study was supported by KAIST College of Liberal Arts and Convergence Science as part of the Venture Research Program for Master’s and PhD Students, and will be presented at the 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) on November 11 to be held in Austin, Texas. Publication: Ji Yoon Jang, Sangyoon Lee, and Byungjoo Lee. 2019. Quantification of Gender Representation Bias in Commercial Films based on Image Analysis. In Proceedings of the 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW). ACM, New York, NY, USA, Article 198, 29 pages. https://doi.org/10.1145/3359300 Link to download the full-text paper: https://files.cargocollective.com/611692/cscw198-jangA--1-.pdf Profile: Prof. Byungjoo Lee, MD, PhD firstname.lastname@example.org http://kiml.org/ Assistant Professor Graduate School of Culture Technology (CT) Korea Advanced Institute of Science and Technology (KAIST) https://www.kaist.ac.kr Daejeon 34141, Korea Profile: Ji Yoon Jang, M.S. email@example.com Interactive Media Lab Graduate School of Culture Technology (CT) Korea Advanced Institute of Science and Technology (KAIST) https://www.kaist.ac.kr Daejeon 34141, Korea Profile: Sangyoon Lee, M.S. Candidate firstname.lastname@example.org Interactive Media Lab Graduate School of Culture Technology (CT) Korea Advanced Institute of Science and Technology (KAIST) https://www.kaist.ac.kr Daejeon 34141, Korea (END)
Successful Development of Excavation System of Biomarkers containing Protein Decomposition Control Enzyme Information
A Korean team of researchers successfully developed a biomarker excavation system named E3Net that excavates biomarkers containing information of the enzymes that control the decomposition of proteins. The development of the system paved the possibility of development of new high quality biomarkers. *Biomarker: Molecular information of unique patterns derived from genes and proteins that allow the monitoring of physical changes from genetic or environmental causes. Professor Lee Kwan Soo’s team (Department of Biological Sciences) composed of Doctorate candidate Han Young Woong, Lee Ho Dong Ph.D. and Professor Park Jong Chul published a dissertation in the April edition of Molecular and Cellular Proteomics. (Dissertation Title: A system for exploring E3-mediated regulatory networks of cellular functions). Professor Lee’s team compiled all available information of the enzyme that controls protein decomposition (E3 enzyme) and successfully compiled the inter-substrate network by extracting information from 20,000 biology related data base dissertations. The result was the development of the E3Net system that analyzes the related cell function and disease. Cells have a system that produces, destroys, and recycles proteins in response to the ever changing environmental conditions. Error in these processes leads to disease. Therefore finding the relationship between E3 enzymes that control the decomposition of proteins and the substrates will allow disease curing and prevention to become much easier. E3 enzyme is responsible for 80% of the protein decomposition and is therefore predicted to be related to various diseases. However the information on E3 enzyme and inter-substrate behavior are spread out among numerous dissertations and data bases which prevented methodological analysis of the role of the related cells and characteristics of the disease itself. Professor Lee’s team was successful in creating the E3Net that compiled 2,201 pieces of E3 substrate information, 4,896 pieces of substrate information, and 1,671 pieces of inter-substrate relationship information. This compilation allows for the systematic analysis of cells and diseases. The newly created network is 10 times larger than the existing network and is the first case where it is possible to accurately find the cell function and related diseases. It is anticipated that the use of the E3Net will allow the excavation of new biomarkers for the development of personalized drug systems. The research team applied the E3Net to find tens of new candidate biomarkers related to the major modern diseases like diabetes and cancer.
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