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Professor Shin-Hyun Kim Receives the Young Scientist Award
Professor Shin-Hyun Kim from the Department of Chemical and Biomolecular Engineering received the Young Scientist Award from the Korean Academy of Science and Technology.
The Young Scientist Award is presented to a promising young Korean scientist under the age of 40 who shows significant potential, passion, and remarkable achievement.
Professor Kim was lauded for his research of intelligent soft materials. By applying his research, he developed a capsule sensor material that can not only be used for sensors, but also for displays, color aesthetics, anti-counterfeit technology, residual drug detection, and more.
The award ceremony took place on December 14 at the Gwacheon National Science Museum.
The Korean minister of Science and ICT delivered words of encouragement, reminding everyone that “the driving force behind creative performance of scientists is the provision of continuous support.” He added, “Researchers of Korea deserve greater public attention and support.”
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2019.12.21 View 7892 -
Team Geumo Wins Consecutive Victories in K-Cyber Security Challenge
< Professor Sang Kil Cha >
< Masters Candidate Kangsu Kim and Researcher Corentin Soulet >
Team Geumo, led by Professor Sang Kil Cha from the Graduate School of Information Security, won the K-Cyber Security Challenge in the AI-based automatic vulnerability detection division for two consecutive years in 2018 and 2019.
The K-Cyber Security Challenge is an inter-machine hacking competition. Participants develop and operate AI-based systems that are capable of independently identifying software vulnerabilities and gaining operating rights through hacking. The K-Cyber Security Challenge, inspired by the US Cyber Grand Challenge launched by the Defense Advanced Research Projects Agency (DARPA), is hosted by the Ministry of Science and ICT and organized by the Korea Internet and Security Agency.
Researcher Corentin Soulet of the School of Computing and master’s student Kangsu Kim of the Graduate School of Information Security teamed up for the competition. Professor Cha, who has led the research on software and systems security since his days at Carnegie Mellon University, succeeded in establishing a world-class system using domestic technology.
In a recent collaboration with the Cyber Security Research Center, Professor Cha achieved a ten-fold increase in the speed of binary analysis engines, a key component of AI-based hacking systems. For this accomplishment, he received the Best Paper Award at the 2019 Network and Distributed System Security Workshop on Binary Analysis Research (NDSS BAR).
Kangsu Kim said, "It is a great honor to win the competition two years in a row. I will continue to work hard and apply my knowledge to serve society.”
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2019.12.20 View 9697 -
Two Professors Receive Awards from the Korea Robotics Society
< Professor Jee-Hwan Ryu and Professor Ayoung Kim >
The Korea Robotics Society (KROS) conferred awards onto two KAIST professors from the Department of Civil and Environmental Engineering in recognition of their achievements and contributions to the development of the robotics industry in 2019. Professor Jee-Hwan Ryu has been actively engaged in researching the field of teleoperation, and this led him to win the KROS Robotics Innovation (KRI) Award. The KRI Award was newly established in 2019 by the KROS, in order to encourage researchers who have made innovative achievements in robotics. Professor Ryu shared the honor of being the first winner of this award with Professor Jaeheung Park of Seoul National University. Professor Ayoung Kim, from the same department, received the Young Investigator Award presented to emerging robitics researchers under 40 years of age. (END)
2019.12.19 View 10091 -
New IEEE Fellow, Professor Jong Chul Ye
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was named a new fellow of the Institute of Electrical and Electronics Engineers (IEEE). IEEE announced this on December 1 in recognition of Professor Ye’s contributions to the development of signal processing and artificial intelligence (AI) technology in the field of biomedical imaging.
As the world’s largest society in the electrical and electronics field, IEEE names the top 0.1% of their members as fellows based on their research achievements.Professor Ye has published more than 100 research papers in world-leading journals in the biomedical imaging field, including those affiliated with IEEE.
He also gave a keynote talk at the yearly conference of the International Society for Magnetic Resonance Imaging (ISMRM) on medical AI technology. In addition, Professor Ye has been appointed to serve as the next chair of the Computational Imaging Technical Committee of the IEEE Signal Processing Society, and the chair of the IEEE Symposium on Biomedical Imaging (ISBI) 2020 to be held in April in Iowa, USA.
Professor Ye said, “The importance of AI technology is developing in the biomedical imaging field. I feel proud that my contributions have been internationally recognized and allowed me to be named an IEEE fellow.”
2019.12.18 View 9662 -
New Members of KAST 2020
< Professor Zong-Tae Bae (Left) and Professor Sang Ouk Kim (Right) >
Professor Zong-Tae Bae from the School of Management Engineering and Professor Sang Ouk Kim from the Department of Materials Science and Engineering became new fellows of the Korean Academy of Science and Technology (KAST) along with 22 other scientists in Korea.
On November 22, KAST announced 24 new members for the year 2020. This includes seven scientists from the field of natural sciences, six from engineering, four from medical sciences, another four from policy research, and three from agriculture and fishery.
The new fellows will begin their term from January next year, and their fellowships wll be conferred during the KAST’s New Year Reception to be held on January 14 in Seoul.
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2019.12.09 View 12348 -
KAIST and Google Jointly Develop AI Curricula
KAIST selected the two professors who will develop AI curriculum under the auspices of the KAIST-Google Partnership for AI Education and Research. The Graduate School of AI announced the two authors among the 20 applicants who will develop the curriculum next year. They will be provided 7,500 USD per subject.
Professor Changho Suh from the School of Electrical Engineering and Professor Yong-Jin Yoon from the Department of Mechanical Engineering will use Google technology such as TensorFlow, Google Cloud, and Android to create the curriculum.
Professor Suh’s “TensorFlow for Information Theory and Convex Optimization “will be used for curriculum in the graduate courses and Professor Yoon’s “AI Convergence Project Based Learning (PBL)” will be used for online courses. Professor Yoon’s course will explore and define problems by utilizing AI and experiencing the process of developing products that use AI through design thinking, which involves product design, production, and verification. Professor Suh’s course will discus“information theory and convergence,” which uses basic sciences and engineering as well as AI, machine learning, and deep learning.
2019.12.04 View 13526 -
AI to Determine When to Intervene with Your Driving
(Professor Uichin Lee (left) and PhD candidate Auk Kim)
Can your AI agent judge when to talk to you while you are driving? According to a KAIST research team, their in-vehicle conservation service technology will judge when it is appropriate to contact you to ensure your safety.
Professor Uichin Lee from the Department of Industrial and Systems Engineering at KAIST and his research team have developed AI technology that automatically detects safe moments for AI agents to provide conversation services to drivers.
Their research focuses on solving the potential problems of distraction created by in-vehicle conversation services. If an AI agent talks to a driver at an inopportune moment, such as while making a turn, a car accident will be more likely to occur.
In-vehicle conversation services need to be convenient as well as safe. However, the cognitive burden of multitasking negatively influences the quality of the service. Users tend to be more distracted during certain traffic conditions. To address this long-standing challenge of the in-vehicle conversation services, the team introduced a composite cognitive model that considers both safe driving and auditory-verbal service performance and used a machine-learning model for all collected data.
The combination of these individual measures is able to determine the appropriate moments for conversation and most appropriate types of conversational services. For instance, in the case of delivering simple-context information, such as a weather forecast, driver safety alone would be the most appropriate consideration. Meanwhile, when delivering information that requires a driver response, such as a “Yes” or “No,” the combination of driver safety and auditory-verbal performance should be considered.
The research team developed a prototype of an in-vehicle conversation service based on a navigation app that can be used in real driving environments. The app was also connected to the vehicle to collect in-vehicle OBD-II/CAN data, such as the steering wheel angle and brake pedal position, and mobility and environmental data such as the distance between successive cars and traffic flow.
Using pseudo-conversation services, the research team collected a real-world driving dataset consisting of 1,388 interactions and sensor data from 29 drivers who interacted with AI conversational agents. Machine learning analysis based on the dataset demonstrated that the opportune moments for driver interruption could be correctly inferred with 87% accuracy.
The safety enhancement technology developed by the team is expected to minimize driver distractions caused by in-vehicle conversation services. This technology can be directly applied to current in-vehicle systems that provide conversation services. It can also be extended and applied to the real-time detection of driver distraction problems caused by the use of a smartphone while driving.
Professor Lee said, “In the near future, cars will proactively deliver various in-vehicle conversation services. This technology will certainly help vehicles interact with their drivers safely as it can fairly accurately determine when to provide conversation services using only basic sensor data generated by cars.”
The researchers presented their findings at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp’19) in London, UK. This research was supported in part by Hyundai NGV and by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.
(Figure: Visual description of safe enhancement technology for in-vehicle conversation services)
2019.11.13 View 17068 -
Tungsten Suboxide Improves the Efficiency of Platinum in Hydrogen Production
< PhD Candidate Jinkyu Park and Professor Jinwoo Lee >
Researchers presented a new strategy for enhancing catalytic activity using tungsten suboxide as a single-atom catalyst (SAC). This strategy, which significantly improves hydrogen evolution reaction (HER) in metal platinum (pt) by 16.3 times, sheds light on the development of new electrochemical catalyst technologies.
Hydrogen has been touted as a promising alternative to fossil fuels. However, most of the conventional industrial hydrogen production methods come with environmental issues, releasing significant amounts of carbon dioxide and greenhouse gases.
Electrochemical water splitting is considered a potential approach for clean hydrogen production. Pt is one of the most commonly used catalysts to improve HER performance in electrochemical water splitting, but the high cost and scarcity of Pt remain key obstacles to mass commercial applications.
SACs, where all metal species are individually dispersed on a desired support material, have been identified as one way to reduce the amount of Pt usage, as they offer the maximum number of surface exposed Pt atoms.
Inspired by earlier studies, which mainly focused on SACs supported by carbon-based materials, a KAIST research team led by Professor Jinwoo Lee from the Department of Chemical and Biomolecular Engineering investigated the influence of support materials on the performance of SACs.
Professor Lee and his researchers suggested mesoporous tungsten suboxide as a new support material for atomically dispersed Pt, as this was expected to provide high electronic conductivity and have a synergetic effect with Pt.
They compared the performance of single-atom Pt supported by carbon and tungsten suboxide respectively. The results revealed that the support effect occurred with tungsten suboxide, in which the mass activity of a single-atom Pt supported by tungsten suboxide was 2.1 times greater than that of single-atom Pt supported by carbon, and 16.3 times higher than that of Pt nanoparticles supported by carbon.
The team indicated a change in the electronic structure of Pt via charge transfer from tungsten suboxide to Pt. This phenomenon was reported as a result of strong metal-support interaction between Pt and tungsten suboxide.
HER performance can be improved not only by changing the electronic structure of the supported metal, but also by inducing another support effect, the spillover effect, the research group reported. Hydrogen spillover is a phenomenon where adsorbed hydrogen migrates from one surface to another, and it occurs more easily as the Pt size becomes smaller.
The researchers compared the performance of single-atom Pt and Pt nanoparticles supported by tungsten suboxide. The single-atom Pt supported by tungsten suboxide exhibited a higher degree of hydrogen spillover phenomenon, which enhanced the Pt mass activity for hydrogen evolution up to 10.7 times compared to Pt nanoparticles supported by tungsten suboxide.
Professor Lee said, “Choosing the right support material is important for improving electrocatalysis in hydrogen production. The tungsten suboxide catalyst we used to support Pt in our study implies that interactions between the well-matched metal and support can drastically enhance the efficiency of the process.”
This research was supported by the Ministry of Science and ICT and introduced in the International Edition of the German journal Angewandte Chemie.
Figure. Schematic representation of hydrogen evolution reaction (HER) of pseudo single-atom Pt supported by tungsten suboxide
-Publication
Jinkyu Park, Dr. Seonggyu Lee, Hee-Eun Kim, Ara Cho, Seongbeen Kim, Dr. Youngjin Ye, Prof. Jeong Woo Han, Prof. Hyunjoo Lee, Dr. Jong Hyun Jang, and Prof. Jinwoo Lee. 2019. Investigation of the Support Effect in Atomically Dispersed Pt on WO3−x for Utilization of Pt in the Hydrogen Evolution Reaction. International Edition of Angewandte Chemie. Volume No. 58. Issue No. 45. 6 pages. https://doi.org/10.1002/anie.201908122
-ProfileProfessor Jinwoo LeeConvergence of Energy and Nano Science Laboratoryhttp://cens.kaist.ac.kr
Department of Chemical and Biomolecular EngineeringKAIST
2019.10.28 View 20876 -
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
byungjoo.lee@kaist.ac.kr
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.
yoone3422@kaist.ac.kr
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
sl2820@kaist.ac.kr
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
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2019.10.17 View 25171 -
Two Professors Recognized for the National R&D Excellence 100
< Professor Haeng-Ki Lee (left) and Professor Jeong-Ho Lee (right) >
Two KAIST professors were listed among the 2019 National R&D Excellence 100 announced by the Ministry of Science and ICT and the Korea Institute of S&T Evaluation and Planning.
Professor Haeng-Ki Lee from the Department of Civil and Environmental Engineering was recognized in the field of mechanics and materials for his research on developing new construction materials through the convergence of nano- and biotechnologies.
In the field of life and marine science, Professor Jeong-Ho Lee from the Graduate School of Medical Science and Engineering was lauded for his research of diagnostic tools and therapies for glioblastoma and pediatric brain tumors.
A certificate from the Minister of Ministry of Science and ICT will be conferred to these two professors, and their names will be inscribed on a special 2019 National R&D Excellence 100 plaque to celebrate their achievements. The professors will also be given privileges during the process of new R&D project selection.
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2019.10.15 View 11712 -
Sungjoon Park Named Google PhD Fellow
PhD candidate Sungjoon Park from the School of Computing was named a 2019 Google PhD Fellow in the field of natural language processing. The Google PhD fellowship program has recognized and supported outstanding graduate students in computer science and related fields since 2009. Park is one of three Korean students chosen as the recipients of Google Fellowships this year. A total of 54 students across the world in 12 fields were awarded this fellowship.
Park’s research on computational psychotherapy using natural language processing (NLP) powered by machine learning earned him this year’s fellowship. He presented of learning distributed representations in Korean and their interpretations during the 2017 Annual Conference of the Association for Computational Linguistics and the 2018 Conference on Empirical Methods in Natural Language Processing. He also applied machine learning-based natural language processing into computational psychotherapy so that a trained machine learning model could categorize client's verbal responses in a counseling dialogue. This was presented at the Annual Conference of the North American Chapter of the Association for Computational Linguistics.
More recently, he has been developing on neural response generation model and the prediction and extraction of complex emotion in text, and computational psychotherapy applications.
2019.09.17 View 8675 -
Researchers Describe a Mechanism Inducing Self-Killing of Cancer Cells
(Professor Kim (left) and lead author Lee)
Researchers have described a new mechanism which induces the self-killing of cancer cells by perturbing ion homeostasis. A research team from the Department of Biochemical Engineering has developed helical polypeptide potassium ionophores that lead to the onset of programmed cell death. The ionophores increase the active oxygen concentration to stress endoplasmic reticulum to the point of cellular death.
The electrochemical gradient between extracellular and intracellular conditions plays an important role in cell growth and metabolism. When a cell’s ion homeostasis is disturbed, critical functions accelerating the activation of apoptosis are inhibited in the cell.
Although ionophores have been intensively used as an ion homeostasis disturber, the mechanisms of cell death have been unclear and the bio-applicability has been limited. In the study featured at Advanced Science, the team presented an alpha helical peptide-based anticancer agent that is capable of transporting potassium ions with water solubility. The cationic, hydrophilic, and potassium ionic groups were combined at the end of the peptide side chain to provide both ion transport and hydrophilic properties.
These peptide-based ionophores reduce the intracellular potassium concentration and at the same time increase the intracellular calcium concentration. Increased intracellular calcium concentrations produce intracellular reactive oxygen species, causing endoplasmic reticulum stress, and ultimately leading to apoptosis.
Anticancer effects were evaluated using tumor-bearing mice to confirm the therapeutic effect, even in animal models. It was found that tumor growth was strongly inhibited by endoplasmic stress-mediated apoptosis.
Lead author Dr. Dae-Yong Lee said, “A peptide-based ionophore is more effective than conventional chemotherapeutic agents because it induces apoptosis via elevated reactive oxygen species levels. Professor Yeu-Chun Kim said he expects this new mechanism to be widely used as a new chemotherapeutic strategy. This research was funded by the National Research Foundation.
2019.08.28 View 20130