Special Title: Attention based Classification of Large Images with Small, Focussed Regions Highlighting the Differences
Abstract: We explore the use of Deep learning-based methods for classifying large images where the difference between images can only be observed in small number of pixels, for example, whole-slide images (WSIs) in cancer. Most current methods require extensive annotations at the sub-image (patch) level, which is time consuming. We recently proposed a new framework named annotation-efficient segmentation and attention-based classifier (ANSAC). ANSAC requires only slide-level labels to classify WSIs. ANSAC automatically segments regions relevant to classification, eliminating the need for extensive manual annotations focussed on small number of pixels.

Biodata: Prof Saman Halgamuge, Fellow of IEEE, IET, AAIA and NASSL received the B.Sc. Engineering degree in Electronics and Telecommunication from the University of Moratuwa, Sri Lanka, and the Dipl.-Ing and Ph.D. degrees in data engineering from the Technical University of Darmstadt, Germany. He is currently a Professor of the Department of Mechanical Engineering of the School of Electrical Mechanical and Infrastructure Engineering, The University of Melbourne and the visiting Deputy Vice Chancellor (Research and International) of SLIIT. He is listed as a top 2% most cited researcher for AI and Image Processing in the Stanford database. He was a distinguished Lecturer of IEEE Computational Intelligence Society (2018-21). He supervised 50 PhD students and 16 postdocs on AI and applications in Australia to completion. His research is funded by Australian Research Council, National Health and Medical Research Council, US DoD Biomedical Research program and international industry. His previous leadership roles include Head, School of Engineering at Australian National University and Associate Dean of the Engineering and IT Faculty of University of Melbourne. His publications can be viewed at
https://scholar.google.com.au/citations?hl=en&user=9cafqywAAAAJ&pagesize=80&view_op=list_works&sortby=pubdate

Speech Title: Flutter Programming Learning Assistant System for Multiplatform UI Code Development
Abstract: The Flutter framework with Dart programming allows developers to effortlessly build user interface (UI) applications for both web and mobile from a single codebase. Since utilizing a wide range of widgets in Flutter ensures consistent experiences on various devices for users, it becomes crucial in programming education by providing a unified environment for learning app development while reducing the need for platform-specific knowledge.
In this talk, first, I present a Docker-based environment for Flutter app developments across Windows, Linux, and Mac through Visual Studio Code, ensuring a unified learning experience. To support independent learning for novice students, it is essential to simplify the setup of the Flutter environment by providing user-friendly instructions and automated tools.
Then, I present an image-based UI testing method to automate UI testing by the answer code using the Flask framework. This method produces the UI image by running the answer code and compares it with the image made by the model code for the assignment using ORB and SIFT algorithms in the OpenCV library.
For their evaluations, first, we asked fourth-year bachelor and first-year master engineering students at Okayama University, Japan, to install the Docker-based environment and solve the five Flutter exercise projects of modifying the source codes by following the given instructions. Then, we applied the UI testing method to their answer codes. The results confirm the effectiveness of the proposals.

Biodata: Nobuo Funabiki received the B.S. and Ph.D. degrees in mathematical engineering and information physics from the University of Tokyo, Japan, in 1984 and 1993, respectively. He received the M.S. degree in electrical engineering from Case Western Reserve University, USA, in 1991. From 1984 to 1994, he was with the System Engineering Division, Sumitomo Metal Industries, Ltd., Japan. In 1994, he joined the Department of Information and Computer Sciences at Osaka University, Japan, as an assistant professor, and became an associate professor in 1995. He stayed at University of California, Santa Barbara, in 2000-2001, as a visiting researcher. In 2001, he moved to the Department of Communication Network Engineering (currently, Department of Information and Communication Systems) at Okayama University as a professor. His research interests include computer networks, optimization algorithms, educational technology, and web application systems. He is a member of IEEE, IEICE, and IPSJ. He was the chairman at IEEE Hiroshima section in 2015 and 2016. Currently, he is a vice president at IEEE Consumer Technology Society.
https://scholar.google.com.au/scholar?hl=en&as_sdt=0,5&as_vis=1&q="Nobuo+Funabiki"&scisbd=1