Biography: Prof. Dr. Hironori Washizaki is a Professor and the Associate Dean of the Research Promotion Division at Waseda University in Tokyo, and a Visiting Professor at the National Institute of Informatics. He also works in industry as Outside Directors of SYSTEM INFORMATION and eXmotion. Hironori currently serves as IEEE Computer Society 1st Vice President 2023. He was awarded Golden Core Member and Distinguished Contributor from IEEE CS. He is leading professional and educational activities, including development of the Guide to the Software Engineering Body of Knowledge (SWEBOK), educational courses, and certification programs. He has published more than 200 research papers in refereed international journals and conferences, including IEEE Computer, IEEE IoT-J, TETC, EMSE, SCICO, ICSE, and ASE. He has led many academia-industry joint research and large-funded projects in software design, reuse, traceability, and quality assurance. He is leading a professional IoT/AI education project called SmartSE. Since 2015, he has been the Convenor of ISO/IEC/JTC1/SC7/WG20 to standardize bodies of knowledge and certifications, and leading adoptions of IEEE CS products SWEBOK and Software Engineering Competency Model (SWECOM) into standards. He is currently running for IEEE Computer Society President-Elect 2024. The election will close on 11 September, 2023.

Title of Speech: SWEBOK Guide Evolution and Its Emerging Areas including Integrated Platform for Multi-View Modeling and Machine Learning Pipelines

Abstract: The Guide to the Software Engineering Body of Knowledge (SWEBOK Guide) published by IEEE Computer Society spells out components of the software engineering discipline, promoting a consistent view of SE worldwide. Hironori has led its evolution project to release its newest version 4, which mainly reflects recent developments in SE practice (such as Agile and DevOps) and the growth of BOK, resulting in new knowledge areas (including software architecture, security, and operations) as well as connections to related areas such as AI. This talk firstly overviews the SWEBOK guide and its latest updates, including new knowledge areas and emerging topic areas, including AI SE. Furthermore, as a part of AI SE, the talk explains how recommended SE practices for modeling and DevOps can be incorporated into machine learning (ML)-based application development and operation. The probabilistic nature of ML leads to a more experimentative development approach, which often results in a disparity between the quality of ML models and other aspects such as business, safety, and the overall system architecture. Herein, Hironori introduces a multi-view modeling framework for ML systems as a solution to this problem. An integrated metamodel supports it to ensure the connection and consistency between different models. The framework provides an integrated platform between the modeling environment and the ML training, performance monitoring, and repair pipelines to facilitate the experimentative nature of ML training and monitoring.

Biography: Professor Reynold Cheng is a Professor of the Department of Computer Science in the University of Hong Kong (HKU). His research interests are in data science, big graph analytics and uncertain data management. He was the Assistant Professor in the Department of Computing of the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. He received his BEng (Computer Engineering) in 1998, and MPhil (Computer Science and Information Systems) in 2000 from HKU. He then obtained his MSc and PhD degrees from Department of Computer Science of Purdue University in 2003 and 2005.
Professor Cheng is listed as the World’s Top 2% Scientists by Stanford University in 2022, and is named the 2023 AI 2000 Most Influential Scholar Honorable Mention in Database. He received the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021, and HKU Knowledge Exchange Award (Engineering) 2021. He was granted an Outstanding Young Researcher Award 2011-12 by HKU. He received the Universitas 21 Fellowship in 2011, and two Performance Awards from HKPU Computing in 2006 and 2007. He is an academic advisor to the College of Professional and Continuing Education of HKPU. He is a member of IEEE, ACM, ACM SIGMOD, and UPE. He was a PC co-chair of IEEE ICDE 2021, and has been serving on the program committees and review panels for leading database conferences and journals like SIGMOD, VLDB, ICDE, KDD, IJCAI, AAAI, and TODS. He is on the editorial board of KAIS, IS and DAPD, and was a former editorial board member of TKDE.

Title of Speech: HINCare: Intelligent Volunteering and Timebanking

Abstract: In Hong Kong, the number of elderly citizens is estimated to rise to one third of the population, or 2.37 million, in year 2037. As they age and become more frail, the demand for formal support services (e.g., providing domestic or escort services) will increase significantly in the coming years. However, there is a severe lack of manpower to meet these needs. Some elderly-care homes reported a 70% shortage of employees. There is thus a strong need of voluntary or part-time helpers for taking care of elders.
In this talk, I will introduce HINCare, a software platform that encourages mutual-help and volunteering culture in the community. HINCare uses the HIN (Heterogeneous Information Network) to recommend helpers to elders or other service recipients. The algorithms that use HINs and AI technologies for matching elders and helpers are based on our recent research results. This is the first time that HIN is used to support elderly care.
HINCare is now downloadable in Apple and Google Play Store, and has been serving more than a thousand of elders and helpers in NGOs (e.g., SKH and CSFC). The app is originally designed for elderly users, but has now expanded its services to support the Community Investment and Inclusion Fund (CIIF) and 10 NGOs engaged in teenage and family services. The system won the HKICT Award 2021, two Asia Smart App Awards (2021 and 2020), and the HKU Faculty Knowledge Exchange Awards 2021 HKU.