Keynote Speakers

A robust data engine ensures the availability of high-quality and relevant data, enabling analytics and AI algorithms to learn, adapt, and deliver accurate insights. However, data analytics is often considered in isolation. The attractiveness of the problems that need to be solved, the sophistication of the solutions, and the usefulness of the results are undoubtedly the significant strengths of work on analytics and AI advancements. However, the input data is often too simplistic, or at least there is an assumption that the data is already readily prepared. The process often neglects the fact that preparing such an input data is in many cases, if not all, actually the major work in the data lifecycle. Having the correct input data for the data analytics and any AI algorithms and processes, is critical, as the famous quote “garbage in garbage out” had said. Even when the original data is correct, but when it is presented inaccurately to a data analytics algorithm, it may consequently produce incorrect reasoning. There are also issues surrounding data provenance, data privacy, and data bias or fairness. This talk will present an overview of the current trend in this area and describe a systematic approach to build a data engine for effective analytics.

Keynote Title: Exploring the Data Frontiers: the role of data engine in analytics and AI

Prof Wenny Rahayu is a Professor in Computer Science and currently the Dean of Computing, Engineering, and Mathematical Sciences at La Trobe University in Melbourne Australia. Before taking up this role, she was the Head of Computer Science and IT department. In the last 10 years, she has done substantial work in the area of data engineering, data integration and optimization, knowledge discovery, and big data analytics. So far she has published around 300 papers with more than 8000 citations on the above topics. She has been a chief-investigator of three ARC (Australian Research Council) Industry Linkages, international grants (Open Geospatial Consortium and FAA – USA, Japan JSPS, and Australia Indonesia AIGRP),  industry funding (Airservices Australia and IPL), VPAC (Victoria Partnership for Advanced Computing), the Australian Army (Army Research), the AAS (Australia Academy of Science), and CSIRO (Commonwealth Scientific and Industrial Research Organisation).  She has been appointed as Assessor and/or Lead Assessor of Smart Ideas projects, New Zealand Ministry of Business, Innovation and Employment (MBIE) Endeavour Fund since 2019.

Prof. Dr. Eng. Wisnu Jatmiko was educated at University of Indonesia (BSc in Electrical Engineering, Faculty of Engineering, 1997; MSc in Faculty of Computer Science, 2000) and Nagoya University (Dr in Micro-Nano Systems Engineering, 2007).

He currently works as a lecturer at The Faculty of Computer Science, University of Indonesia. He was then a research manager at The Faculty of Computer Science for two terms, 2008-2012 and 2013-2017. Then from 2017-2021, he served as Chair of The Masters and Doctoral Program in Computer Science, Faculty of Computer Science, University of Indonesia. He is also a professor starting September 2017. He holds the IEEE (The Institute of Electrical and Electronics Engineers) Indonesia Section Chair from 2019-2020 and is a Senior Member of IEEE (SMIEEE). His research interests include Micro Nano Systems, Realtime Traffic Monitoring Systems, Telehealth Information Systems, and Autonomous Robot.

“Sustainable intelligence, situated at the dynamic crossroads of robotics and artificial intelligence (AI), plays a pivotal role in driving continuous and impactful digital innovations. As we navigate the ever-evolving landscape of automation, AI emerges as the transformative force that reshapes industries, economies, and societies.

In this context, sustainable intelligence transcends mere efficiency gains. It encompasses a holistic approach that balances technological advancement with environmental stewardship, social equity, and economic prosperity. Let’s delve into the key facets:

Collaborative Systems: AI-powered robots collaborate seamlessly with humans, augmenting our capabilities. From precision agriculture to disaster response, these systems enhance productivity while minimizing resource consumption.

Nature-Inspired Algorithms: Biomimicry inspires AI algorithms that mimic natural processes. For instance, swarm intelligence algorithms optimize logistics, mirroring the behavior of ant colonies.
Ethical AI: Sustainable intelligence demands ethical AI practices. Fairness, transparency, and accountability are essential. Policymakers and practitioners must collaborate to create guidelines that ensure AI benefits all.

Data-Driven Diplomacy: Diplomacy, too, embraces data analytics. By analyzing global trends, identifying common challenges, and fostering cooperation, data-driven diplomacy promotes peace and stability.
Continual Learning: Just as AI models learn from data, organizations must foster a culture of continual learning. Adaptability, resilience, and agility drive sustainable innovation.
In summary, sustainable intelligence fuels a future where AI and robotics harmonize with humanity and the planet, creating lasting impact.

Hiroshi Murase received the B.Eng, M.Eng, and Ph.D. degrees in Electrical Engineering from Nagoya University, Japan, in 1978, 1980, and 1987, respectively. In 1980 he joined the Nippon Telegraph and Telephone Corporation (NTT). From 1992 to 1993 he was a visiting research scientist at Columbia University, New York. From 2001 to 2003, he was the Executive Manager of NTT Communication Science Laboratories. He has been a Professor at Nagoya University since 2003, and Professor Emeritus since 2021. He was awarded the IEEE CVPR Best Paper Award in 1994, the IEEE ICRA Best Video Award in 1996, the IEEE Transaction on Multimedia Paper Award in 2004, etc. He got a Medal with Purple Ribbon from the Government of Japan in 2012. His research interests include computer vision, pattern recognition, multimedia information recognition, and ITS applications. He is a Life Fellow of the IEEE, and a Fellow of the IAPR, IPSJ, and IEICE.

Keynote Title: Image Recognition for Driving Assistance and Secure Daily Life

Driver assistance systems using image recognition, and camera-based surveillance systems for comfortable secure daily life are becoming increasingly important for sustainable futures. This is due to the increasing rate of traffic accidents involving elderly drivers and the increasing percentage of people living alone in Japan. This may be the same in other countries. First, I will introduce several image recognition methods for driving assistance that we have developed so far, such as the detection of pedestrians which are easily missed by drivers; recognition of weather conditions such as rain and fog; and recognition of consensus between drivers and pedestrians. In addition, I will talk about the human pose estimation using a super-low-resolution infrared sensor array to watch elderly people at home; subjective weight estimation of baggage for carrying assistance when walking; and other research topics.