Keynote

This talk gives an overview of progress and challenges in learnable image encryption with a secret key for deep neural networks (DNNs). Learnable encryption with a secret key enables us not only to protect visual information on plain images but also to embed unique features controlled with a key into images and models. Various applications of such encryption have been developed by using these properties. In this talk, we first focus privacy-preserving image classification tasks with learnable encryption, and then such encryption is demonstrated to give a new insight to adversarially robust defenses and model protection. Finally, we discuss future prospects for reliable deep leaning. Biography Hitoshi Kiya is a Professor of the Department of Computer Science at Tokyo Metropolitan University, Japan. He received the B.E. and M.E. degrees from the Nagaoka University of Technology, Japan, in 1980 and 1982, respectively, and the Dr.Eng. degree from Tokyo Metropolitan University in 1987. In 1982, he joined Tokyo Metropolitan University, where he became a Full Professor in 2000. From 1995 to 1996, he attended the University of Sydney, Australia, as a Visiting Fellow. He is a Life Fellow of IEEE, and a Fellow of IEICE, ITE and AAIA. He served as the President of APSIPA, and the Regional Director-at-Large for Region 10 of the IEEE Signal Processing Society. He was also the President of the IEICE Engineering Sciences Society. He has organized a lot of international conferences in such roles as the TPC Chair of IEEE ICASSP 2012 and as the General Co-Chair of IEEE ISCAS 2019. He has received numerous awards, including 12 best paper awards. 20 23 1 5t h In te rn at io na l C on fe re nc e on K no w le dg e an d Sm ar t T ec hn ol og y (K ST ) | 9 78 -1 -6 65 477 12 -3 /2 3/ $3 1. 00 © 20 23 IE EE | D O I: 10 .1 10 9/ K ST 57 28 6. 20 23 .1 00 86 84 7 2023 15th International Conference on Knowledge and Smart Technology (KST) __________________________________________________________________________________ XVI Computational Intelligence in Biomedical Engineering Assoc. Prof. Sansanee Auephanwiriyakul, Ph.D. Computer Engineering Department, Faculty of Engineering Biomedical Engineering Institute, Chiang Mai University, Thailand Abstract Computational Intelligence (CI) relies on and combines several algorithms in fuzzy systems, neural networks, evolutionary computation, swarm intelligence, fractals, chaos theory, artificial immune systems, wavelets, etc., to produce an algorithm that is intelligent somehow. CI has been utilized in many applications for several years. One of the areas that CI has an impact on is the area of biomedical engineering, e.g., medical image processing, medical signal processing and biometrics. One of the CI tools used in those mentioned application is classification or sometimes called decision making. The major area in the classification is to develop a classifier, including, feature generation and selection. The fuzzy set theory is one of the main parts in CI that has been utilized in generating features and developing a classifier. In this talk, feature generation methods and classifier methods based on the fuzzy set theory will be presented. We also show those methods on real application, including, medical image diagnosis, medical signal diagnosis, and biometrics. Computational Intelligence (CI) relies on and combines several algorithms in fuzzy systems, neural networks, evolutionary computation, swarm intelligence, fractals, chaos theory, artificial immune systems, wavelets, etc., to produce an algorithm that is intelligent somehow. CI has been utilized in many applications for several years. One of the areas that CI has an impact on is the area of biomedical engineering, e.g., medical image processing, medical signal processing and biometrics. One of the CI tools used in those mentioned application is classification or sometimes called decision making. The major area in the classification is to develop a classifier, including, feature generation and selection. The fuzzy set theory is one of the main parts in CI that has been utilized in generating features and developing a classifier. In this talk, feature generation methods and classifier methods based on the fuzzy set theory will be presented. We also show those methods on real application, including, medical image diagnosis, medical signal diagnosis, and biometrics. Biography B.Eng. (Hons.) degree in electrical engineering from the Chiang Mai University, Thailand (1993), the M.S. degree in electrical and computer engineering and Ph.D. degree in computer engineering and computer science, both from the University of Missouri, Columbia, in 1996, and 2000, respectively. After receiving her Ph.D. degree, she worked as a post-doctoral fellow at the Computational Intelligence Laboratory, University of Missouri-Columbia. She is currently an Associate Professor in the Department of Computer Engineering and a deputy director of the Biomedical Engineering Institute, Chiang Mai University, Thailand. Dr. Auephanwiriyakul is a senior member of the Institute of Electrical and Electronics Engineers (IEEE). She is an Associate Editor of the IEEE Transactions on Fuzzy System, the IEEE Transactions on Neural Networks and Learning Systems, IEEE Computational Intelligence Magazine, IEEE Transactions on Artificial Intelligence, Engineering Applications of Artificial Intelligence, and ECTI Transactions on Computer and Information Technology. She was a general chair of the IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2016). She will be a general chair of the IEEE World Congress on Computational Intelligence (WCCI) 2024 (IEEE International Conference on Fuzzy Systems 2024). She was a Technical Program Chair, Organizing Committee in several major conferences including the IEEE International, Conference Fuzzy Systems. She is also a member of several important IEEE CIS technical committees. 2023 15th International Conference on Knowledge and Smart Technology (KST) __________________________________________________________________________________ XVII Digital transformation of Traditional medicine to meet the era of AI medicine Sang-Hun Lee, M.D. Korea Institute of Oriental Medicine, Daejeon, Republic of Korea Abstract With the development of AI technology, AI is being introduced in many areas such as drug development and imaging diagnosis. However, the reality is that there are far more areas that have not yet been combined compared to the medical area where AI is being combined already. These differences appear depending on how prepared high-quality big data well the medical practice is quantitatively defined and the measurement technology is standard In order to solve these problems, it is necessary to redefine the medical measurement parameters used in diagnosis so that they can be changed into digitized measurement values and to standardize devices that can measure them. When these works are done, we can make medical AI and also can implement AI-Ready Data Practices to Promote Equitable, Machine Readable, and Well-Defined clinical Big Data With the development of AI technology, AI is being introduced in many areas such as drug development and imaging diagnosis. However, the reality is that there are far more areas that have not yet been combined compared to the medical area where AI is being combined already. These differences appear depending on how prepared high-quality big data well the medical practice is quantitatively defined and the measurement technology is standard In order to solve these problems, it is necessary to redefine the medical measurement parameters used in diagnosis so that they can be changed into digitized measurement values and to standardize devices that can measure them. When these works are done, we can make medical AI and also can implement AI-Ready Data Practices to Promote Equitable, Machine Readable, and Well-Defined clinical Big Data Biography Dr. Sanghun Lee is a Principal Researcher at KIOM in Korea and a professor at the University of Science and Technology. After majoring in Korean Traditional Medicine and working as a clinician and university research professor, he joined the Institute of Oriental Medicine in 2009 as a researcher. His primary research areas are standardization and scientificization of Traditional medicine devices, biomarkers, and medical information. He is a member of the Young Korean Academy of Science and Technology, and education director at the Society for Meridian and Acupoint. Currently, he is leading the research project of standard development as a Project leader in International Organization for Standardization Technical Committee 249 (Traditional Chinese Medicine) about Traditional Medicine devices (ISO 22213:2020, ISO 19611:2017, ISO 5227:2022, ISO 24571:2022, ISO 20487:2019)