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Defining the Hund Physics Landscape of Two-Orbital Systems Researchers identify exotic metals in unexpected quantum systems Electrons are ubiquitous among atoms, subatomic tokens of energy that can independently change how a system behaves—but they also can change each other. An international research collaboration found that collectively measuring electrons revealed unique and unanticipated findings. The researchers published their results on May 17 in Physical Review Letters. “It is not feasible to obtain the solution just by tracing the behavior of each individual electron,” said paper author Myung Joon Han, professor of physics at KAIST. “Instead, one should describe or track all the entangled electrons at once. This requires a clever way of treating this entanglement.” Professor Han and the researchers used a recently developed “many-particle” theory to account for the entangled nature of electrons in solids, which approximates how electrons locally interact with one another to predict their global activity. Through this approach, the researchers examined systems with two orbitals — the space in which electrons can inhabit. They found that the electrons locked into parallel arrangements within atom sites in solids. This phenomenon, known as Hund’s coupling, results in a Hund’s metal. This metallic phase, which can give rise to such properties as superconductivity, was thought only to exist in three-orbital systems. “Our finding overturns a conventional viewpoint that at least three orbitals are needed for Hund’s metallicity to emerge,” Professor Han said, noting that two-orbital systems have not been a focus of attention for many physicists. “In addition to this finding of a Hund’s metal, we identified various metallic regimes that can naturally occur in generic, correlated electron materials.” The researchers found four different correlated metals. One stems from the proximity to a Mott insulator, a state of a solid material that should be conductive but actually prevents conduction due to how the electrons interact. The other three metals form as electrons align their magnetic moments — or phases of producing a magnetic field — at various distances from the Mott insulator. Beyond identifying the metal phases, the researchers also suggested classification criteria to define each metal phase in other systems. “This research will help scientists better characterize and understand the deeper nature of so-called ‘strongly correlated materials,’ in which the standard theory of solids breaks down due to the presence of strong Coulomb interactions between electrons,” Professor Han said, referring to the force with which the electrons attract or repel each other. These interactions are not typically present in solid materials but appear in materials with metallic phases. The revelation of metals in two-orbital systems and the ability to determine whole system electron behavior could lead to even more discoveries, according to Professor Han. “This will ultimately enable us to manipulate and control a variety of electron correlation phenomena,” Professor Han said. Co-authors include Siheon Ryee from KAIST and Sangkook Choi from the Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory in the United States. Korea’s National Research Foundation and the U.S. Department of Energy’s (DOE) Office of Science, Basic Energy Sciences, supported this work. -PublicationSiheon Ryee, Myung Joon Han, and SangKook Choi, 2021.Hund Physics Landscape of Two-Orbital Systems, Physical Review Letters, DOI: 10.1103/PhysRevLett.126.206401 -ProfileProfessor Myung Joon HanDepartment of PhysicsCollege of Natural ScienceKAIST
2021.06.17 View 11681
Deep Learning-Based Cough Recognition Model Helps Detect the Location of Coughing Sounds in Real Time The Center for Noise and Vibration Control at KAIST announced that their coughing detection camera recognizes where coughing happens, visualizing the locations. The resulting cough recognition camera can track and record information about the person who coughed, their location, and the number of coughs on a real-time basis. Professor Yong-Hwa Park from the Department of Mechanical Engineering developed a deep learning-based cough recognition model to classify a coughing sound in real time. The coughing event classification model is combined with a sound camera that visualizes their locations in public places. The research team said they achieved a best test accuracy of 87.4 %. Professor Park said that it will be useful medical equipment during epidemics in public places such as schools, offices, and restaurants, and to constantly monitor patients’ conditions in a hospital room. Fever and coughing are the most relevant respiratory disease symptoms, among which fever can be recognized remotely using thermal cameras. This new technology is expected to be very helpful for detecting epidemic transmissions in a non-contact way. The cough event classification model is combined with a sound camera that visualizes the cough event and indicates the location in the video image. To develop a cough recognition model, a supervised learning was conducted with a convolutional neural network (CNN). The model performs binary classification with an input of a one-second sound profile feature, generating output to be either a cough event or something else. In the training and evaluation, various datasets were collected from Audioset, DEMAND, ETSI, and TIMIT. Coughing and others sounds were extracted from Audioset, and the rest of the datasets were used as background noises for data augmentation so that this model could be generalized for various background noises in public places. The dataset was augmented by mixing coughing sounds and other sounds from Audioset and background noises with the ratio of 0.15 to 0.75, then the overall volume was adjusted to 0.25 to 1.0 times to generalize the model for various distances. The training and evaluation datasets were constructed by dividing the augmented dataset by 9:1, and the test dataset was recorded separately in a real office environment. In the optimization procedure of the network model, training was conducted with various combinations of five acoustic features including spectrogram, Mel-scaled spectrogram and Mel-frequency cepstrum coefficients with seven optimizers. The performance of each combination was compared with the test dataset. The best test accuracy of 87.4% was achieved with Mel-scaled Spectrogram as the acoustic feature and ASGD as the optimizer. The trained cough recognition model was combined with a sound camera. The sound camera is composed of a microphone array and a camera module. A beamforming process is applied to a collected set of acoustic data to find out the direction of incoming sound source. The integrated cough recognition model determines whether the sound is cough or not. If it is, the location of cough is visualized as a contour image with a ‘cough’ label at the location of the coughing sound source in a video image. A pilot test of the cough recognition camera in an office environment shows that it successfully distinguishes cough events and other events even in a noisy environment. In addition, it can track the location of the person who coughed and count the number of coughs in real time. The performance will be improved further with additional training data obtained from other real environments such as hospitals and classrooms. Professor Park said, “In a pandemic situation like we are experiencing with COVID-19, a cough detection camera can contribute to the prevention and early detection of epidemics in public places. Especially when applied to a hospital room, the patient's condition can be tracked 24 hours a day and support more accurate diagnoses while reducing the effort of the medical staff." This study was conducted in collaboration with SM Instruments Inc. Profile: Yong-Hwa Park, Ph.D. Associate Professor yhpark@kaist.ac.kr http://human.kaist.ac.kr/ Human-Machine Interaction Laboratory (HuMaN Lab.) Department of Mechanical Engineering (ME) Korea Advanced Institute of Science and Technology (KAIST) https://www.kaist.ac.kr/en/ Daejeon 34141, Korea Profile: Gyeong Tae Lee PhD Candidate hansaram@kaist.ac.kr HuMaN Lab., ME, KAIST Profile: Seong Hu Kim PhD Candidate tjdgnkim@kaist.ac.kr HuMaN Lab., ME, KAIST Profile: Hyeonuk Nam PhD Candidate frednam@kaist.ac.kr HuMaN Lab., ME, KAIST Profile: Young-Key Kim CEO sales@smins.co.kr http://en.smins.co.kr/ SM Instruments Inc. Daejeon 34109, Korea (END)
2020.08.13 View 19620
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 (END)
2019.10.17 View 29306
Flexible User Interface Distribution for Ubiquitous Multi-Device Interaction < Research Group of Professor Insik Shin (center) > KAIST researchers have developed mobile software platform technology that allows a mobile application (app) to be executed simultaneously and more dynamically on multiple smart devices. Its high flexibility and broad applicability can help accelerate a shift from the current single-device paradigm to a multiple one, which enables users to utilize mobile apps in ways previously unthinkable. Recent trends in mobile and IoT technologies in this era of 5G high-speed wireless communication have been hallmarked by the emergence of new display hardware and smart devices such as dual screens, foldable screens, smart watches, smart TVs, and smart cars. However, the current mobile app ecosystem is still confined to the conventional single-device paradigm in which users can employ only one screen on one device at a time. Due to this limitation, the real potential of multi-device environments has not been fully explored. A KAIST research team led by Professor Insik Shin from the School of Computing, in collaboration with Professor Steve Ko’s group from the State University of New York at Buffalo, has developed mobile software platform technology named FLUID that can flexibly distribute the user interfaces (UIs) of an app to a number of other devices in real time without needing any modifications. The proposed technology provides single-device virtualization, and ensures that the interactions between the distributed UI elements across multiple devices remain intact. This flexible multimodal interaction can be realized in diverse ubiquitous user experiences (UX), such as using live video steaming and chatting apps including YouTube, LiveMe, and AfreecaTV. FLUID can ensure that the video is not obscured by the chat window by distributing and displaying them separately on different devices respectively, which lets users enjoy the chat function while watching the video at the same time. In addition, the UI for the destination input on a navigation app can be migrated into the passenger’s device with the help of FLUID, so that the destination can be easily and safely entered by the passenger while the driver is at the wheel. FLUID can also support 5G multi-view apps – the latest service that allows sports or games to be viewed from various angles on a single device. With FLUID, the user can watch the event simultaneously from different viewpoints on multiple devices without switching between viewpoints on a single screen. PhD candidate Sangeun Oh, who is the first author, and his team implemented the prototype of FLUID on the leading open-source mobile operating system, Android, and confirmed that it can successfully deliver the new UX to 20 existing legacy apps. “This new technology can be applied to next-generation products from South Korean companies such as LG’s dual screen phone and Samsung’s foldable phone and is expected to embolden their competitiveness by giving them a head-start in the global market.” said Professor Shin. This study will be presented at the 25th Annual International Conference on Mobile Computing and Networking (ACM MobiCom 2019) October 21 through 25 in Los Cabos, Mexico. The research was supported by the National Science Foundation (NSF) (CNS-1350883 (CAREER) and CNS-1618531). Figure 1. Live video streaming and chatting app scenario Figure 2. Navigation app scenario Figure 3. 5G multi-view app scenario Publication: Sangeun Oh, Ahyeon Kim, Sunjae Lee, Kilho Lee, Dae R. Jeong, Steven Y. Ko, and Insik Shin. 2019. FLUID: Flexible User Interface Distribution for Ubiquitous Multi-device Interaction. To be published in Proceedings of the 25th Annual International Conference on Mobile Computing and Networking (ACM MobiCom 2019). ACM, New York, NY, USA. Article Number and DOI Name TBD. Video Material: https://youtu.be/lGO4GwH4enA Profile: Prof. Insik Shin, MS, PhD ishin@kaist.ac.kr https://cps.kaist.ac.kr/~ishin Professor Cyber-Physical Systems (CPS) Lab School of Computing Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon 34141, Korea Profile: Sangeun Oh, PhD Candidate ohsang1213@kaist.ac.kr https://cps.kaist.ac.kr/ PhD Candidate Cyber-Physical Systems (CPS) Lab School of Computing Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon 34141, Korea Profile: Prof. Steve Ko, PhD stevko@buffalo.edu https://nsr.cse.buffalo.edu/?page_id=272 Associate Professor Networked Systems Research Group Department of Computer Science and Engineering State University of New York at Buffalo http://www.buffalo.edu/ Buffalo 14260, USA (END)
2019.07.20 View 42523
Play Games With No Latency One of the most challenging issues for game players looks to be resolved soon with the introduction of a zero-latency gaming environment. A KAIST team developed a technology that helps game players maintain zero-latency performance. The new technology transforms the shapes of game design according to the amount of latency. Latency in human-computer interactions is often caused by various factors related to the environment and performance of the devices, networks, and data processing. The term ‘lag’ is used to refer to any latency during gaming which impacts the user’s performance. Professor Byungjoo Lee at the Graduate School of Culture Technology in collaboration with Aalto University in Finland presented a mathematical model for predicting players' behavior by understanding the effects of latency on players. This cognitive model is capable of predicting the success rate of a user when there is latency in a 'moving target selection' task which requires button input in a time constrained situation. The model predicts the players’ task success rate when latency is added to the gaming environment. Using these predicted success rates, the design elements of the game are geometrically modified to help players maintain similar success rates as they would achieve in a zero-latency environment. In fact, this research succeeded in modifying the pillar heights of the Flappy Bird game, allowing the players to maintain their gaming performance regardless of the added latency. Professor Lee said, "This technique is unique in the sense that it does not interfere with a player's gaming flow, unlike traditional methods which manipulate the game clock by the amount of latency. This study can be extended to various games such as reducing the size of obstacles in the latent computing environment.” This research, in collaboration with Dr. Sunjun Kim from Aalto University and led by PhD candidate Injung Lee, was presented during the 2019 CHI Conference on Human Factors in Computing Systems last month in Glasgow in the UK. This research was supported by the National Research Foundation of Korea (NRF) (2017R1C1B2002101, 2018R1A5A7025409), and the Aalto University Seed Funding Granted to the GamerLab respectively. Figure 1. Overview of Geometric Compensation Publication: Injung Lee, Sunjun Kim, and Byungjoo Lee. 2019. Geometrically Compensating Effect of End-to-End Latency in Moving-Target Selection Games. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI’19) . ACM, New York, NY, USA, Article 560, 12 pages. https://doi.org/10.1145/3290605.3300790 Video Material: https://youtu.be/TTi7dipAKJs 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) http://kaist.ac.kr Daejeon 34141, Korea Profile: Injung Lee, PhD Candidate edndn@kaist.ac.kr PhD Candidate Interactive Media Lab Graduate School of Culture Technology (CT) Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon 34141, Korea Profile: Postdoc. Sunjun Kim, MD, PhD kuaa.net@gmail.com Postdoctoral Researcher User Interfaces Group Aalto University https://www.aalto.fi Espoo 02150, Finland (END)
2019.06.11 View 49191
Deep Learning Predicts Drug-Drug and Drug-Food Interactions A Korean research team from KAIST developed a computational framework, DeepDDI, that accurately predicts and generates 86 types of drug-drug and drug-food interactions as outputs of human-readable sentences, which allows in-depth understanding of the drug-drug and drug-food interactions. Drug interactions, including drug-drug interactions (DDIs) and drug-food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. However, current prediction methods do not provide sufficient details beyond the chance of DDI occurrence, or require detailed drug information often unavailable for DDI prediction. To tackle this problem, Dr. Jae Yong Ryu, Assistant Professor Hyun Uk Kim and Distinguished Professor Sang Yup Lee, all from the Department of Chemical and Biomolecular Engineering at Korea Advanced Institute of Science and Technology (KAIST), developed a computational framework, named DeepDDI, that accurately predicts 86 DDI types for a given drug pair. The research results were published online in Proceedings of the National Academy of Sciences of the United States of America (PNAS) on April 16, 2018, which is entitled “Deep learning improves prediction of drug-drug and drug-food interactions.” DeepDDI takes structural information and names of two drugs in pair as inputs, and predicts relevant DDI types for the input drug pair. DeepDDI uses deep neural network to predict 86 DDI types with a mean accuracy of 92.4% using the DrugBank gold standard DDI dataset covering 192,284 DDIs contributed by 191,878 drug pairs. Very importantly, DDI types predicted by DeepDDI are generated in the form of human-readable sentences as outputs, which describe changes in pharmacological effects and/or the risk of ADEs as a result of the interaction between two drugs in pair. For example, DeepDDI output sentences describing potential interactions between oxycodone (opioid pain medication) and atazanavir (antiretroviral medication) were generated as follows: “The metabolism of Oxycodone can be decreased when combined with Atazanavir”; and “The risk or severity of adverse effects can be increased when Oxycodone is combined with Atazanavir”. By doing this, DeepDDI can provide more specific information on drug interactions beyond the occurrence chance of DDIs or ADEs typically reported to date. DeepDDI was first used to predict DDI types of 2,329,561 drug pairs from all possible combinations of 2,159 approved drugs, from which DDI types of 487,632 drug pairs were newly predicted. Also, DeepDDI can be used to suggest which drug or food to avoid during medication in order to minimize the chance of adverse drug events or optimize the drug efficacy. To this end, DeepDDI was used to suggest potential causal mechanisms for the reported ADEs of 9,284 drug pairs, and also predict alternative drug candidates for 62,707 drug pairs having negative health effects to keep only the beneficial effects. Furthermore, DeepDDI was applied to 3,288,157 drug-food constituent pairs (2,159 approved drugs and 1,523 well-characterized food constituents) to predict DFIs. The effects of 256 food constituents on pharmacological effects of interacting drugs and bioactivities of 149 food constituents were also finally predicted. All these prediction results can be useful if an individual is taking medications for a specific (chronic) disease such as hypertension or diabetes mellitus type 2. Distinguished Professor Sang Yup Lee said, “We have developed a platform technology DeepDDI that will allow precision medicine in the era of Fourth Industrial Revolution. DeepDDI can serve to provide important information on drug prescription and dietary suggestions while taking certain drugs to maximize health benefits and ultimately help maintain a healthy life in this aging society.” Figure 1. Overall scheme of Deep DDDI and prediction of food constituents that reduce the in vivo concentration of approved drugs
2018.04.18 View 13860
Sangeun Oh Recognized as a 2017 Google Fellow Sangeun Oh, a Ph.D. candidate in the School of Computing was selected as a Google PhD Fellow in 2017. He is one of 47 awardees of the Google PhD Fellowship in the world. The Google PhD Fellowship awards students showing outstanding performance in the field of computer science and related research. Since being established in 2009, the program has provided various benefits, including scholarships worth $10,000 USD and one-to-one research discussion with mentors from Google. His research work on a mobile system that allows interactions among various kinds of smart devices was recognized in the field of mobile computing. He developed a mobile platform that allows smart devices to share diverse functions, including logins, payments, and sensors. This technology provides numerous user experiences that existing mobile platforms could not offer. Through cross-device functionality sharing, users can utilize multiple smart devices in a more convenient manner. The research was presented at The Annual International Conference on Mobile Systems, Applications, and Services (MobiSys) of the Association for Computing Machinery in July, 2017. Oh said, “I would like to express my gratitude to my advisor, the professors in the School of Computing, and my lab colleagues. I will devote myself to carrying out more research in order to contribute to society.” His advisor, Insik Shin, a professor in the School of Computing said, “Being recognized as a Google PhD Fellow is an honor to both the student as well as KAIST. I strongly anticipate and believe that Oh will make the next step by carrying out good quality research.”
2017.09.27 View 15186
Professor Jinah Park Received the Prime Minister's Award Professor Jinah Park of the School of Computing received the Prime Minister’s Citation Ribbon on April 21 at a ceremony celebrating the Day of Science and ICT. The awardee was selected by the Ministry of Science, ICT and Future Planning and Korea Communications Commission. Professor Park was recognized for her convergence R&D of a VR simulator for dental treatment with haptic feedback, in addition to her research on understanding 3D interaction behavior in VR environments. Her major academic contributions are in the field of medical imaging, where she developed a computational technique to analyze cardiac motion from tagging data. Professor Park said she was very pleased to see her twenty-plus years of research on ways to converge computing into medical areas finally bear fruit. She also thanked her colleagues and students in her Computer Graphics and CGV Research Lab for working together to make this achievement possible.
2017.04.26 View 12479
KAIST Hosts the Wearable Computer Contest 2015 Deadlines for Prototype Contest by May 30, 2015 and August 15 for Idea Contest KAIST will hold the Wearable Computer Contest 2015 in November, which will be sponsored by Samsung Electronics Co., Ltd. Wearable computers have emerged as next-generation mobile devices, and are gaining more popularity with the growth of the Internet of Things. KAIST has introduced wearable devices such as K-Glass 2, a smart glass with augmented reality embedded. The Glass also works on commands by blinking eyes. This year’s contest with the theme of “Wearable Computers for Internet of Things” is divided into two parts: the Prototype Competition and Idea Contest. With the fusion of information technology (IT) and fashion, contestants are encouraged to submit prototypes of their ideas by May 30, 2015. The ten teams that make it to the finals will receive a wearable computer platform and Human-Computer Interaction (HCI) education, along with a prize of USD 1,000 for prototype production costs. The winner of the Prototype Contest will receive a prize of USD 5,000 and an award from the Minister of Science, ICT and Future Planning (MSIP) of the Republic of Korea. In the Idea Contest, posters containing ideas and concepts of wearable devices should be submitted by August 15, 2015. The teams that make it to the finals will have to display a life-size mockup in the final stage. The winner of the contest will receive a prize of USD 1,000 and an award from the Minister of MSIP. Any undergraduate or graduate student in Korea can enter the Prototype Competition and anyone can participate in the Idea Contest. The chairman of the event, Hoi-Jun Yoo, a professor of the Department of Electrical Engineering at KAIST, noted: “There is a growing interest in wearable computers in the industry. I can easily envisage that there will be a new IT world where wearable computers are integrated into the Internet of Things, healthcare, and smart homes.” More information on the contest can be found online at http://www.ufcom.org. Picture: Finalists in the last year’s contest
2015.05.11 View 9435
Interactions Features KAIST's Human-Computer Interaction Lab Interactions, a bi-monthly magazine published by the Association for Computing Machinery (ACM), the largest educational and scientific computing society in the world, featured an article introducing Human-Computer Interaction (HCI) Lab at KAIST in the March/April 2015 issue (http://interactions.acm.org/archive/toc/march-april-2015). Established in 2002, the HCI Lab (http://hcil.kaist.ac.kr/) is run by Professor Geehyuk Lee of the Computer Science Department at KAIST. The lab conducts various research projects to improve the design and operation of physical user interfaces and develops new interaction techniques for new types of computers. For the article, see the link below: ACM Interactions, March and April 2015 Day in the Lab: Human-Computer Interaction Lab @ KAIST http://interactions.acm.org/archive/view/march-april-2015/human-computer-interaction-lab-kaist
2015.03.02 View 12192
A KAIST Student Team Wins the ACM UIST 2014 Student Innovation Contest A KAIST team consisted of students from the Departments of Industrial Design and Computer Science participated in the ACM UIST 2014 Student Innovation Contest and received 1st Prize in the category of People’s Choice. The Association for Computing Machinery (ACM) Symposium on User Interface Software and Technology (UIST) is an international forum to promote innovations in human-computer interfaces, which takes place annually and is sponsored by ACM Special Interest Groups on Computer-Human Interaction (SIGCHI) and Computer Graphics (SIGGRAPH). The ACM UIST conference brings together professionals in the fields of graphical and web-user interfaces, tangible and ubiquitous computing, virtual and augmented reality, multimedia, and input and output devices. The Student Innovation Contest has been held during the UIST conference since 2009 to innovate new interactions on state-of-the-art hardware. The participating students were given with the hardware platform to build on—this year, it was Kinoma Create, a JavaScript-powered construction kit that allows makers, professional product designers, and web developers to create personal projects, consumer electronics, and "Internet of Things" prototypes. Contestants demonstrated their creations on household interfaces, and two winners in each of three categories -- Most Creative, Most Useful, and the People’s Choice -- were awarded. Utilizing Kinoma Create, which came with a built-in touchscreen, WiFi, Bluetooth, a front-facing sensor connector, and a 50-pin rear sensor dock, the KAIST team developed a “smart mop,” transforming the irksome task of cleaning into a fun game. The smart mop identifies target dirt and shows its location on the display built in the rod of a mop. If the user turns on a game mode, then winning scores are gained wherever the target dirt is cleaned. The People’s Choice award was decided by conference attendees, and they voted the smart mop as their most favorite project. Professor Tek-Jin Nam of the Department of Industrial Design at KAIST, who advised the students, said, "A total of 24 teams from such prestigious universities as Carnegie Mellon University, Georgia Institute of Technology, and the University of Tokyo joined the contest, and we are pleased with the good results. Many people, in fact, praised the integration of creativity and technical excellence our have shown through the smart mop.” Team KAIST: pictured from right to left, Sun-Jun Kim, Se-Jin Kim, and Han-Jong Kim The Smart Mop can clean the floor and offer users a fun game.
2014.11.12 View 13489
KAIST develops TransWall, a transparent touchable display wall At a busy shopping mall, shoppers walk by store windows to find attractive items to purchase. Through the windows, shoppers can see the products displayed, but may have a hard time imagining doing something beyond just looking, such as touching the displayed items or communicating with sales assistants inside the store. With TransWall, however, window shopping could become more fun and real than ever before. Woohun Lee, a professor of Industrial Design at KAIST, and his research team have recently developed TransWall, a two-sided, touchable, and transparent display wall that greatly enhances users' interpersonal experiences. With an incorporated surface transducer, TransWall offers audio and vibrotactile feedback to the users. As a result, people can collaborate via a shared see-through display and communicate with one another by talking or even touching one another through the wall. A holographic screen film is inserted between the sheets of plexiglass, and beam projectors installed on each side of the wall project images that are reflected. TransWall is touch-sensitive on both sides. Two users standing face-to-face on each side of the wall can touch the same spot at the same time without any physical interference. When this happens, TransWall provides the users with specific visual, acoustic, and vibrotactile experiences, allowing them to feel as if they are touching one another. Professor Lee said, "TransWall concept enables people to see, hear, or even touch others through the wall while enjoying gaming and interpersonal communication. TransWall can be installed inside buildings, such as shopping centers, museums, and theme parks, for people to have an opportunity to collaborate even with strangers in a natural way." He further added that "TransWall will be useful in places that require physical isolation for high security and safety, germ-free rooms in hospitals, for example." TransWall will allow patients to interact with family and friends without compromising medical safety. TransWall was exhibited at the 2014 Conference on Computer-Human Interaction (CHI) held from April 26, 2014 to May 1, 2014 in Toronto, Canada. YouTube Link: http://www.youtube.com/watch?v=1QdYC_kOQ_w&list=PLXmuftxI6pTXuyjjrGFlcN5YFTKZinDhK
2014.07.15 View 8759