Tuesday, December 8
H. Vincent Poor
Princeton University, USA
Biography: H. Vincent Poor is the Michael Henry Strater University Professor at Princeton University. He has also held visiting appointments at a number of other universities, including most recently at Berkeley and Cambridge. His research interests are in wireless networks, smart grid and related areas. Dr. Poor is a Member of the U. S. National Academy of Engineering and the U. S. National Academy of Sciences, and is a Foreign Member of the Royal Society. Recent recognition of his work includes the 2016 John Fritz Medal, the 2017 IEEE Alexander Graham Bell Medal, and honorary doctorates and professorships from several universities.
Executive Yuan, Taiwan
Time: 14:05-14:27 Dec. 8 (Tue.) (local time in Taipei) Live!
Title: The Application of Technology on the Mitigation of COVID-19 in Taiwan
Abstract: In the COVID-19 pandemic in this year, Taiwan fully utilized digital technology to control spreading of disease. In this session, we will briefly share the experience of smart tracing system, from border control to e-fence. Also, we will cover the balance between privacy issue and disease control as well.
Biography: Mr. Jyan is the Director General of Department of Cyber Security, Executive Yuan. His specialty and research include cyber security, electronic government, open data, PKI and electronic certification, government organization reform, and information management.
Mr. Jyan had worked as Technical Specialist, Central Weather Bureau (CWB), Ministry of Transportation and Communications (MOTC); Section Chief, Overseas Compatriot Affairs Commission; Deputy Director, Research, Development, and Evaluation Commission; Director General, National Development Council. He got his Master’s degree from National Chung Cheng University in 1992.
INSA-Lyon / INRIA, France
Time: 14:27-14:49 Dec. 8 (Tue.) (local time in Taipei) Live!
Title: Privacy and Implementation Challenges in Contact Tracing Applications
Abstract: Digital contact tracing (CT) applications have emerged as one of the potential solutions to reduce the spread of the COVID-19 pandemic. Those applications come with several challenges in term of performances but most importantly in term of privacy. We discuss the main challenges associated with the implementation of those application. Covering the basic principle of CT applications, we present the main features of CT systems. A number of privacy risks associated to CT have been identified and two system model have been adopted leading to different trade-off in term of personal data protection. Last but not least, the use of Bluetooth as a mean to estimate proximity of contacts is associated with several issues regarding distance measurement reliability and limitations imposed by mobile operating systems.
Biography: Mathieu Cunche is an associate professor at INSA-Lyon, a member of the CITI Lab and a faculty member of the Inria PRIVATICS team. His research interests include privacy and security in the context of wireless networks, Internet of Things and mobile environments. He is also interested in the analysis of online censorship and surveillance. He conducted several studies on the exposure of personal data from mobile devices, especially with Wi-Fi and Bluetooth. He has been involved in standardization activities, especially at IEEE 802 where he contributed to efforts on privacy protections.
University of South Florida, USA
Time: 14:49-15:11 Dec. 8 (Tue.) (local time in Taipei) Live!
Title: Effective Social Networking for Online Lecture Delivery
Abstract: Social network analysis has been shown valuable to comprehend various collective behavior in complex systems and networks, such as knowledge dissemination, modelling epidemics, and knowledge discovery from the data. Recently, due to the COVID-19 pandemic, people studying in schools and working in companies have to stay at home to remotely study or work utilizing online technology. In spite of continuing education and professional activities, the usual person-to-person interaction dynamic in schools and offices have been substantially impacted and significantly changed. In this paper, we examine the change in human interaction networks and consequent difficulties in forming social networks for inspiring discussion owing to remote interactions. Taking social network analysis, we suggest methods to increase social engagement of individuals affected by the remote technology in classes or work. The resulting network topology offers an insight as to how small-world networks are naturally formed, particularly in an online or online-offline setting.
Biography: Kwang-Cheng Chen has been a Professor at the Department of Electrical Engineering, University of South Florida, since 2016. From 1987 to 2016, Dr. Chen worked with SSE, Communications Satellite Corp., IBM Thomas J. Watson Research Center, National Tsing Hua University, HP Labs., and National Taiwan University in mobile communications and networks. He visited TU Delft (1998), Aalborg University (2008), Sungkyunkwan University (2013), and Massachusetts Institute of Technology (2012-2013, 2015-2016). He founded a wireless IC design company in 2001, which was acquired by MediaTek Inc. in 2004. He has been actively involving in the organization of various IEEE conferences and serving editorships with a few IEEE journals (most recently as a series editor on Data Science and AI for Communications in the IEEE Communications Magazine), together with various IEEE volunteer services to the IEEE, Communications Society, Vehicular Technology Society, and Signal Processing Society, such as founding the Technical Committee on Social Networks in the IEEE Communications Society. Dr. Chen also has contributed essential technology to various international standards, namely IEEE 802 wireless LANs, Bluetooth, LTE and LTE-A, 5G-NR, and ITU-T FG ML5G. He has authored and co-authored over 300 IEEE publications, 4 books published by Wiley and River (most recently, Artificial Intelligence in Wireless Robotics, 2019), and more than 23 granted US patents. Dr. Chen is an IEEE Fellow and has received a number of awards including 2011 IEEE COMSOC WTC Recognition Award, 2014 IEEE Jack Neubauer Memorial Award, 2014 IEEE COMSOC AP Outstanding Paper Award. Dr. Chen’s current research interests include wireless networks, artificial intelligence and machine learning, IoT/CPS, social networks and data analytics, quantum photonic computing and communications, and cybersecurity.
Scripps Research, USA
Time: 15:11-15:33 Dec. 8 (Tue.) (local time in Taipei) Live!
Title: DETECT: Wearable Sensor Data to Predict COVID-19 and Viral Illnesses
Abstract: Digitalize human beings using biosensors to track our complex physiologic system, process the large amount of data generated with artificial intelligence (AI) and change clinical practice towards individualized medicine: these are the goals of digital medicine. In this talk, we discuss how to tackle the problem of COVID-19, we start with an overview of continuous, passively monitored vital signs from 200,000 individuals wearing a Fitbit wearable device for 2 years. This large study provides the baseline for DETECT, our app-based, nationwide clinical study enrolling individuals who routinely use a smartwatch or other wireless devices to determine if individualized tracking of changes in heart rate, activity and sleep can provide early diagnosis and self-monitoring for COVID-19. We analyze data from more than 36,000 individuals, showing how we can discriminate (on an individual level) between COVID-19 and other types of infections. We discuss how this can impact both the individual and public health, and how the use of AI can be a game changer in this fight against the virus.
Biography: Dr. Giorgio Quer received a Ph.D. degree (2011) in Information Engineering from University of Padova, Italy. In 2007, he was a visiting researcher at the Centre for Wireless Communication at the University of Oulu, Finland. During his Ph.D., he proposed a solution for the distributed compression of wireless sensor networks signals, based on the joint exploitation of Compressive Sensing and Principal Component Analysis. From 2010 to 2016, he was at the Qualcomm Institute, University of California San Diego (UCSD), working on cognitive networks protocols and implementation. At Scripps Research, he is leading the Data Science and Analytics Scripps team involved in the All of Us Research Program (NIH), together with several efforts involving big data and AI in digital medicine, including DETECT, towards the use of wearables to detect COVID-19. He is a Senior Member of the IEEE and a Distinguished Lecturer for the IEEE Communications society. His research interests include wireless sensor networks, probabilistic models, deep convolutional networks, wearable sensors, physiological signal processing, and digital medicine.
SmartAvatar BV, USA
Time: 16:00-16:22 Dec. 8 (Tue.) (local time in Taipei) Live!
Title: Technology & Innovation Trends for the Post COVID-19 Era
Abstract: The global economic downturn due to the pandemic COVID-19 outbreak acted as a catalyst to further amplify the adoption of new technologies and innovations above and beyond the pace we got used to for the last two decades. Few things already seem very clear that platform firms like Amazon, Alibaba, Uber Eats, Zoom, etc. are dominating the markets even more. Companies will further accelerate their investment to conduct their business remotely over the internet to be more resilient to potential future lockdowns. The talk will discuss the technology and innovation trends for the post-COVID-19 era. It will address the role of artificial intelligence, robots, and digital transformation and how it will impact the industries such as healthcare, education, connectivity, e-commerce, media & entertainment, security, and Industrial revolution 4.0.
Biography: Mahbubul is a proven transformative executive, building fruitful partnerships with clients and delivering multimillion-dollar top-line growth. Prior to SmartAvatar, he was CMO and SVP at Trillium Secure Inc. and before that he was CTO and CMO at Movimento Group which was acquired by Aptiv (former Delphi Automotive). Mahbubul held several leadership positions at Cisco and Siemens.
National Central University, Taiwan
Time: 16:22-16:45 Dec. 8 (Tue.) (local time in Taipei) Live!
Title: AR/VR Smart Health Service and Technologies for 5G and Beyond
Abstract: Recent developments in Virtual Reality (VR) and Augmented Reality (AR) have gained much interest in their adoption and enabled a new way of working, communications, and entertainment. Since the outbreak of COVID-19, hospital contact visits have been discouraged under social distancing and quarantine practices. As a result, the demand for remote healthcare grows significantly, where AV/VR smart health technology can be a key enabler to it. However, to deliver immersive experiences with high-resolution AR/VR services, it requires intensive computation capability and massive communication bandwidth with ultra-low latency for transmissions, which cannot be handled with current wireless technology. The advent of 5G and beyond (B5G) is expected to significantly boost mobile data rates and address the low latency problem via mobile edge computing (MEC). In this talk, we will present an intelligent B5G AR/VR streaming system for smart health service, which is expected to bring innovative healthcare experience to citizens. The core technologies include: MEC-based streaming, AI-enabled wireless resource management, and AR/VR service design. The developed smart AR/VR healthcare service has been deployed at major hospitals for field trials in Taiwan supported by a 5G/MEC operator and VR technology manufacturers.
Biography: Chih-Wei Huang received the B.S. degree from National Taiwan University, Taipei, in 2001, the M.S. degree from Columbia University, New York, in 2004, and the Ph.D. degree from University of Washington, Seattle, in 2009, all in electrical engineering. He joined the Department of Communication Engineering, National Central University, Taiwan, in 2010. He is currently an Associate Professor heading the Information Processing and Communications (IPC) Laboratory. From 2006 to 2009, he was intern researcher at Siemens Corporate Research and Microsoft Research. He is the author of papers in a broad range of areas, including wireless networking, multimedia communications, digital signal processing, and information retrieval.
National Taiwan University, Taiwan
Time: 17:07-17:30 Dec. 8 (Tue.) (local time in Taipei) Live!
Title: Evaluating Bluetooth-based Decentralized Contact Tracing in Crowds
Abstract: Digital contact tracing is being used by many countries to help contain COVID-19's spread in a post-lockdown world. Among the various available techniques, decentralized contact tracing that uses Bluetooth received signal strength indication (RSSI) to detect proximity is considered less of a privacy risk than approaches that rely on collecting absolute locations via GPS, cellular-tower history, or QR-code scanning. As of October 2020, there have been millions of downloads of such Bluetooth-based contract-tracing apps, as more and more countries officially adopt them. However, the effectiveness of these apps in the real world remains unclear due to a lack of empirical research that includes realistic crowd sizes and densities. This study aims to fill that gap, by empirically investigating the effectiveness of Bluetooth-based contact tracing in crowd environments with between 30 and 50 participants, emulating classrooms, moving lines, and other types of real-world gatherings. The results confirm that Bluetooth RSSI is unreliable for detecting proximity, and that this inaccuracy worsens in environments that are especially crowded. In other words, this technique may be least useful when it is most in need, and that it is fragile when confronted by low-cost jamming. Moreover, technical problems such as high energy consumption and phone overheating caused by the contact-tracing app were found to negatively influence users' willingness to adopt it. On the bright side, however, Bluetooth RSSI may still be useful for detecting coarse-grained contact events, for example, proximity of up to 20m lasting for an hour. Based on our findings, we recommend that existing contact-tracing apps can be re-purposed to focus on coarse-grained proximity detection, and that future ones calibrate distance estimates and adjust broadcast frequencies based on auxiliary information.
Biography: Dr. Hsu-Chun Hsiao is an Associate Professor in the Department of Computer Science and Information Engineering, and the Graduate Institute of Networking and Multimedia at National Taiwan University. She holds an adjunct position in the Center of Information Technology and Innovation at Academia Sinica. Since September 2018, she has also served as a Section Chief in the Information Technology Office, National Taiwan University Hospital. Dr. Hsiao completed her B.S. and M.S. at National Taiwan University in 2006 and 2008, respectively, and Ph.D. at Carnegie Mellon University in 2014. Dr. Hsiao's research interests lie in the field of network and systems security, and her recent work focuses on DDoS defense, IoT security, and automated vulnerability discovery. She is a recipient of the MOST Young Scholar Fellowship, K. T. Li Young Researcher Award, ACM CyberW Early Career Award Honorable Mention, and Ren Min Outstanding Young Chair Professorship.
Charalampos Z Patrikakis
University of West Attica & COmputer Networks & SErvices Research Team (CONSERT), Greece
Time: 17:30-17:52 Dec. 8 (Tue.) (local time in Taipei) Live!
Title: Social Distancing. Keep Your Distance or Keep Your Personal Data?
Abstract: The presentation will be on proposals for measuring people concentration, and the fears/concerns about privacy. It will discuss the level of information which should be collected and if this information can reveal our identity or habits. Also, it will present solutions which can guarantee privacy protection, involving both scanning for personal devices, or even people concentration through measurable effect (i.e. CO2 levels).
Biography: Dr. Charalampos Z. Patrikakis is an Associate Professor at the Dept. of Electrical and Electronics Engineering of the University of West Attica. He has participated in more than 35 National, European and International programs, in 20 of which he has been involved as technical coordinator or principal researcher. He has more than 150 publications in chapters of books, international journals and conferences, and has 2 contributions in national legislation. He is a member of the editorial committee of more than 50 international journals and conferences, and has acted as editor in the publication of special issues of international journals, conference proceedings volumes and co-edited three books. He is a senior member of IEEE, a member of the Technical Chamber of Greece, and counselor of the IEEE Student Branch of the University of West Attica.
Title: Analyzing Social Distancing and Seasonality of COVID-19 with Mean Field Evolutionary Dynamics
Time: 16:45-17:07 Dec. 8 (Tue.) (local time in Taipei) Live!
Author(s): Hao Gao (University of Houston, USA); Wuchen Li (University of California, Los Angeles, USA); Miao Pan and Zhu Han (University of Houston, USA); H. Vincent Poor (Princeton University, USA)
Abstract: The outbreak of the coronavirus pandemic since the end of 2019 has been declared as a world health emergency by the World Health Organization, which raised the importance of an accurate mathematical epidemic dynamical model to predict the evolution of COVID-19. Replicator dynamics (RDs) are exclusively applied to many epidemic models, but they fail to satisfy the Nash stationarity and can only describe an unidirectional population flow between different states. In this paper, we proposed mean field evolutionary dynamics (MFEDs), inspired by the optimal transport theory and mean field games on graph, to model epidemic dynamics. We have compared the MFEDs with RDs theoretically. In particular, we show efficiency of MFEDs by modeling the evolution of COVID-19 in Wuhan, China. Furthermore, we analyze the effect of one-time social distancing as well as the seasonality of COVID-19 through the post-pandemic period.
University of Houston, USA
Biography: Hao Gao (S’19) received his B.E. degree in Electrical and Information Engineering from Huazhong University of Science and Technology, Wuhan, China, in 2018. He started pursuing his Ph.D. degree in Electrical Engineering in University of Houston, USA, since 2018. His current research interests include mean field game and related applications in wireless communication.
Title: Privacy-Preserving Multi-Operator Contact Tracing for Early Detection of Covid19 Contagions
Time: 11:00-11:22 Dec. 8 (Tue.) (local time in Taipei)
Author(s): Davide Andreoletti (University of Applied Sciences of Southern Switzerland, Switzerland); Omran Ayoub (Politecnico di Milano, Italy); Silvia Giordano (University of Applied Sciences and Arts of Southern Switzerland (SUPSI), Switzerland); Massimo Tornatore (Politecnico di Milano & University of California, Davis, Italy); Giacomo Verticale (Politecnico di Milano, Italy)
Abstract: The outbreak of coronavirus disease 2019 (Covid-19) is imposing a severe worldwide lock-down. Contact tracing based on smartphones' applications (apps) has emerged as a possible solution to trace contagions and enforce a more sustainable selective quarantine. However, a massive adoption of these apps is required to reach the critical mass needed for effective contact tracing. As an alternative, geo-location technologies in next generation networks (e.g., 5G) can enable Mobile Operators (MOs) to perform passive tracing of users' mobility and contacts with a promised accuracy of down to one meter. To effectively detect contagions, the identities of positive individuals, which are known only by a Governmental Authority (GA), are also required. Note that, besides being extremely sensitive, these data might also be critical from a business perspective. Hence, MOs and the GA need to exchange and process users' geo-locations and infection status data in a privacy-preserving manner. In this work, we propose a privacy-preserving protocol that enables multiple MOs and the GA to share and process users' data to make only the final users discover the number of their contacts with positive individuals. The protocol is based on existing privacy-enhancing strategies that guarantee that users' mobility and infection status are only known to their MOs and to the GA, respectively. From extensive simulations, we observe that the cost to guarantee total privacy (evaluated in terms of data overhead introduced by the protocol) is acceptable, and can also be significantly reduced if we accept a negligible compromise in users' privacy.
University of Applied Sciences of Southern Switzerland, Switzerland
Biography: Davide Andreoletti received the bachelor's and master's degrees (with honours) in telecommunications engineering from Politecnico di Milano, Milan, Italy, in 2012 and 2015, respectively. He then received, from the same university, the Ph.D. degree in information engineering (with honorus). He is currently a Post Doc researcher the Dipartimento di Tecnologie Innovative, SUPSI University, Lugano, Switzerland. His research interests include Network Neutrality and privacy-preserving strategies within the context of content delivery in Internet. Contact him at email@example.com.
Title: IoT-based Smart Triage of Covid-19 Suspicious Cases in the Emergency Department
Time: 11:22-11:45 (local time in Taipei)
Author(s): Barbara Fyntanidou, Maria Zouka and Aikaterini Apostolopoulou (AHEPA University Hospital, Greece); Panagiotis P. Bamidis, Antonios Billis and Konstantinos Mitsopoulos (Aristotle University of Thessaloniki, Greece); Pantelis Angelidis (Greece); Alexis Fourlis (VIDAVO S.A., Greece)
Abstract: According to scientific reports, the main and most common Covid-19 symptoms are fever and shortness of breath. Therefore, monitoring of vitals such as temperature, breathing and heart rate and blood oxygen saturation is of essence. Our team has designed and developed a wrist-worn wearable device that continuously monitors relevant vital signs with the aim to prioritize and triage Covid-19 patients in the Emergency Department.
VIDAVO S.A., Greece
Biography: Alexis Fourlis, is an Electrical Engineer specializing in microelectronics and embedded systems. Having 8 years of work experience as an embedded systems engineer, designing electronic circuits and developing firmware combining low power communications with edge computing. Currently, he works for Vidavo as Project Manager for Research and Development projects focusing on biosensors, flexible electronics and TinyML.
Title: A Scalable COVID-19 Screening Platform
Time: 11:45-12:07 Dec. 8 (Tue.) (local time in Taipei)
Author(s): Cristian Chilipirea, Stefan-Adrian Toma and Luciana Maria Morogan (Military Technical Academy Ferdinand I, Romania)
Abstract: The COVID-19 pandemic has put a strain on health facilities world-wide. Remote screening can lessen the burden on medical resources. Manual screening cannot be performed due to the large number of people that need to be screened. We are constructing an automated remote screening system for Romania. The system needs to support simultaneous use by many persons (presumably a significant part of the population). Considering the urgency, we propose a light- weight web-based application that can run even on low- resource server infrastructures. We considered using cloud services, however, due to privacy considerations regarding medical data and legal issues we designed the application to be able to run, but to not require cloud services. Some of the decisions that we made were to offload computation to the browser, limit the number of requests and construct a server architecture that uses buffers for writing to the database and that can scale different components.
Military Technical Academy Ferdinand I, Romania
Biography: Cristian Chilipirea is currently a Lecturer at Military Technical Academy "Ferdinand I". He recently received his PhD from University of Twente, The Netherlands and University Politehnica of Bucharest, Romania. His main research for the past few years was focused on crowd mobility as seen from WiFi sensors. He teaches parallel and distributed programming as well as related topics.
Title: Aether - A Novel Method to Eliminate False Positives in Private Automated Contact Tracing
Time: 12:07-12:30 Dec. 8 (Tue.) (local time in Taipei)
Author(s): Satvik Dasari (Westwood High School, USA)
Abstract: Contact tracing has shown promise to mitigate the negative effects of serious infectious diseases, such as COVID-19. Contact tracing can identify people who have potentially been exposed to COVID-19, as well as to enforce quarantine rules. However, many of these methods store location data, and therefore present privacy concerns. Bluetooth technology provides an effective solution to avoid violating fundamental privacy rights. However, Bluetooth technology doesn't have a good way of knowing whether devices are on the same level or different levels. In the case of multistory buildings, false positives can occur between two people who are within close proximity but on different floors. This paper proposes a novel solution in which the built-in barometer in mobile devices is used in combination with Bluetooth to avoid false positives. This paper establishes the viability and effectiveness of the solution through various experiments to make the current Bluetooth contact tracing technology even more accurate, which will ultimately save countless lives.
Westwood High School, USA
Biography: Satvik Dasari is a senior at Westwood High School in Austin, Texas, USA.