Introduction

The proliferation of Artificial Intelligence (AI) and Internet-of-Things (IoT) opens up the possibility of integrating intelligence into various sensors. This creates many smart and efficient applications in various areas such as healthcare, biometrics, self-drive car, human activity recognition, transportation, robots in manufacturing, and risk management to name a few. Since the IoT sensor nodes are constrained in computational and storage resources, it is challenging to port an AI model into such resource-constrained devices. Moreover, the uncertainty issues arise from the sensor measurement in these systems are very important to ensure successful IoT applications. In this workshop, we will review the recent advancements and development of wearables and medical devices. Open set presentation attack, a recent trend in biometric, will be presented, followed by a discussion on the representative techniques to enable deep learning acceleration on constrained platforms. The uncertainty issues introduced by machine learning will be discussed, and the techniques to quantify it will be presented.

Date: 16 and 17 December 2021, 09:00 am -11:00 am, Korea Standard Time (KST) (For time zone conversion, click here.)
Venue: Virtual event

Program Committee Chair:
Prof. Seong Oun Hwang (Gachon University, Korea)

Program Committee:
Prof. Hyung Jin Chang (University of Birmingham, United Kingdom)
Prof. Cheon-won Choi (Dankook University, Korea)
Prof. Markus Westner (OTH Regensburg, Germany)
Prof. Martin Schubert (OTH Regensburg, Germany)
Prof. Ramachandra Achar (Carleton University, Canada)
Prof. Bok-Min Goi (UTAR, Malaysia)
Prof. Haider Abbas (NUST, Pakistan)
Prof. Chin-Chen Chang (Feng Chia University, Taiwan)
Prof. Jin-Min Lin (Feng Chia University, Taiwan)
Dr. Ayesha Khalid (Queen's University Belfast, UK)
Dr. Boon-Yaik Ooi (UTAR, Malaysia)
Dr. Wai Kong Lee (Gachon University, Korea)

Co-host:
IEEE Seoul Section Sensors Council Chapter, Korea
Institute of Electronics and Information Engineers, Korea
Gachon University BK21 FAST Artificial Intelligence Convergence Center, Korea

Sponsor:
IEEE Sensors Council

Program Schedule

16 December 2021

Time	Program	Speaker
09:00 - 10:00 (KST)	Recent Trends in Biometric: Open Set Presentation Attack Detection	Prof. Kevin W. Bowyer
10:00 - 11:00 (KST)	Trends for Wearable and Medical Devices	Prof. Subhas Mukhopadhyay

17 December 2021

Time	Program	Speaker
09:00 - 10:00 (KST)	Quantifying Uncertainty in Machine Learning Based Sensing	Prof. Shervin Shirmohammadi
10:00 - 11:00 (KST)	Efficient Deep Learning at Scale: Hardware and Software	Prof. Yiran Chen

Synopses

16 December 2021

Recent Trends in Biometric: Open Set Presentation Attack Detection

Speaker:Prof. Kevin W. Bowyer, University of Notre Dame, USA

Biometric presentation attack detection is made especially difficult because of its open-set nature in the real world. The current highest-accuracy algorithms for iris presentation attack detection use deep learning approaches.
We show a novel approach to achieve greater accuracy in deep learning from limited training data using human-aided saliency maps.

Related material: https://arxiv.org/pdf/2105.03492.pdf

Trends for Wearable and Medical Devices

Speaker: Prof. Subhas Mukhopadhyay, Macquarie University, Australia

An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano-technologies, and miniaturization makes it possible to develop smart medical systems to monitor activities of human beings continuously.
Wearable sensors monitor physiological parameters continuously along with detect other symptoms such as any abnormal and/or unforeseen situations which need immediate attention. Therefore, necessary help can be provided in times of dire need. This seminar reviews the latest reported systems and the trends on wearable and medical devices to monitor activities of humans and issues to be addressed to tackle the challenges.

17 December 2021

Quantifying Uncertainty in Machine Learning Based Sensing

Speaker:Prof. Shervin Shirmohammadi, Ottawa University, Canada

Like any science and engineering field, Instrumentation and Measurement (I&M) including sensors are currently experiencing the impact of the recent rise of Applied AI and in particular Machine Learning (ML). In fact the relationship between I&M and ML has reached new levels: sensors are used to measure and collect data, which is used to train an ML model, which is then used in a sensor system or application. Uncertainty is accumulated at every stage, and quantifying it is crucial. But I&M and ML use terminology that sometimes sound or look similar, though they might only have a marginal relationship or even be false friends. Therefore, understanding the terminology used by both communities is of crucial importance to understand the influences of ML and I&M in each other.
In this talk, we will give an overview of ML’s contribution to a sensor’s measurement error, and how to avoid confusion with the said terminology in order to better understand the application of ML in sensor measurements. We then use that understanding and terminology to show how to quantify the uncertainty introduced by ML, specifically Deep learning (DL), in DL-based sensor systems and applications.

Efficient Deep Learning at Scale: Hardware and Software

Speaker:Prof. Yiran Chen, Duke University, USA

The rapid growth of modern neural network models’ scale generates ever-increasing demands for high computing power of Artificial Intelligence (AI) systems. Many specialized computing devices have been also deployed in the AI systems, forming a truly application-driven heterogeneous computing platform. This talk discusses the importance of hardware/software co-design in the development of AI computing systems.
We first use resistive memory based Neural Network (NN) accelerators to illustrate the design philosophy of heterogeneous AI computing systems, and then present several hardware-friendly neural network model compression techniques.
We also extend our discussions to distributed systems and briefly introduce the automation of the co-design flow, e.g., neural architecture search. A research roadmap of our relevant research is given at the end of the talk.

Speakers

Prof. Kevin W. Bowyer

University of Notre Dame, USA

Kevin Bowyer is the Schubmehl-Prein Family Professor of Computer Science and Engineering at the University of Notre Dame. He has served as EIC of the IEEE Transactions on Biometrics, Behavior, and Identity Science and the IEEE Transactions on Pattern Analysis and Machine Intelligence, as well as General Chair or Program Chair of conferences such as Computer Vision and Pattern Recognition (CVPR), Winter Conference on Applications of Computer Vision (WACV), and Face and Gesture Recognition (FG), Biometrics Theory, Applications and Systems (BTAS) and International Joint Conference on Biometrics (IJCB). Professor Bowyer is a Fellow of the IAPR, IEEE and AAAS.

Prof. Yiran Chen

Duke University, USA

Yiran Chen received B.S (1998) and M.S. (2001) from Tsinghua University and Ph.D. (2005) from Purdue University. After five years in industry, he joined University of Pittsburgh in 2010 as Assistant Professor and then was promoted to Associate Professor with tenure in 2014, holding Bicentennial Alumni Faculty Fellow. He is now the Professor of the Department of Electrical and Computer Engineering at Duke University and serving as the director of the NSF AI Institute for Edge Computing Leveraging the Next-generation Networks (Athena) and the NSF Industry-University Cooperative Research Center (IUCRC) for Alternative Sustainable and Intelligent Computing (ASIC), and the co-director of Duke Center for Computational Evolutionary Intelligence (CEI). His group focuses on the research of new memory and storage systems, machine learning and neuromorphic computing, and mobile computing systems. Dr. Chen has published 1 book and about 500 technical publications and has been granted 96 US patents. He has served as the associate editor of a dozen international academic transactions/journals and served on the technical and organization committees of more than 60 international conferences. He is now serving as the Editor-in-Chief of the IEEE Circuits and Systems Magazine. He received seven best paper awards, one best poster award, and fifteen best paper nominations from international conferences and workshops. He received many professional awards and is the distinguished lecturer of IEEE CEDA (2018-2021). He is a Fellow of the ACM and IEEE and now serves as the chair of ACM SIGDA.

Prof. Shervin Shirmohammadi

University of Ottawa, Canada

Shervin Shirmohammadi received his Ph.D. in Electrical Engineering in 2000 from the University of Ottawa, Canada, where he is currently a Professor with the School of Electrical Engineering and Computer Science. He is the Director of the DISCOVER Lab, doing research in measurement methods and Applied AI for multimedia and networking systems. The results of his research, funded by more than $26 million from public and private sectors, include 400 publications, 3 Best Paper awards, over 70 researchers trained at the postdoctoral, PhD, and Master’s levels, 30 patents and technology transfers to the private sector, and a number of awards. He is the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement, and was also the Associate Editor-in-Chief of IEEE Instrumentation and Measurement Magazine in 2014 and 2015, and is currently on its editorial board.
He has been an IEEE Instrumentation and Measurement Society AdCom member since 2014, served as the Vice President of its Membership Development Committee from 2014 to 2017, and was a member of the IEEE I²MTC Board of Directors from 2014 to 2016.
Dr. Shirmohammadi is an IEEE Fellow for contributions to multimedia systems and network measurements, winner of the 2019 George S. Glinski Award for Excellence in Research, a Senior Member of the ACM, a University of Ottawa Gold Medalist, and a licensed Professional Engineer in Ontario.

Prof. Subhas Mukhopadhyay

Macquarie University, Australia

Subhas Mukhopadhyay (M’97, SM’02, F’11) holds a B.E.E. (gold medallist), M.E.E., Ph.D. (India) and Doctor of Engineering (Japan). He has over 31 years of teaching, industrial and research experience.
Currently he is working as a Professor of Mechanical/Electronics Engineering, Macquarie University, Australia and is the Discipline Leader of the Mechatronics Engineering Degree Programme. He is also the Director of International Engagement for the School of Engineering of Macquarie University. His fields of interest include Smart Sensors and sensing technology, instrumentation techniques, wireless sensors and network (WSN), Internet of Things (IoT), wearable sensors and medical devices. He has supervised over 40 postgraduate students and over 100 Honours students. He has examined over 70 postgraduate theses.
He has published over 450 papers in different international journals and conference proceedings, written ten books and fifty two book chapters and edited eighteen conference proceedings. He has also edited thirty five books with Springer-Verlag and thirty two journal special issues. He has been cited so far 13933 times and has a h-index of 58. He has received various awards, most notably: the Australian Research Field Leader in Engineering and Computer Science 2020; Distinguished Lecturer, IEEE Sensors Council 2020-2022; Outstanding Volunteer by IEEE R10, 2019; World Famous Professor by Government of Indonesia, 2018; Certificate of Distinction from IEEE Sensors Council, 2017; IETE R.S. Khandpur Award-India, 2016; Best Performing Topical Editor of IEEE Sensors Journal from 2013 to 2018, six years consecutively. He has organized over 20 international conferences as either General Chairs/co-chairs or Technical Programme Chair. He has delivered 389 presentations including keynote, invited, tutorial and special lectures.
He is a Fellow of IEEE (USA), a Fellow of IET (UK), a Fellow of IETE (India), a Topical Editor of IEEE Sensors journal, an associate editor of IEEE Transactions on Instrumentation and Measurements, and IEEE Review of Biomedical Engineering. He is the Editor-in-Chief of the International Journal on Smart Sensing and Intelligent Systems and Springer Natura on Computer Science. He is a Distinguished Lecturer of the IEEE Sensors Council from 2017 to 2022. He is the Founding Chair of the IEEE Sensors Council New South Wales Chapter.
More details can be available at:
https://researchers.mq.edu.au/en/persons/subhas-mukhopadhyay
https://scholar.google.com.au/citations?hl=en&user=8p-BvWIAAAAJ
https://orcid.org/0000-0002-8600-5907

Registration (Free of Charge)

All participants need to pre-register no later than 13 December 2021.

Webex sign-in details will be sharted with the registered participants using the email address provided in the registration form.
For further enquiries, please contact Prof. Seong Oun Hwang (sohwang at gachon dot ac dot kr, https://ai-security.github.io/index_e.htm)

IEEE Seoul Section Sensors Council Chapter International Workshop 2021 (Virtual,Free)