5-7 OctoberOnline

ICUMT 2020

Monday, October 5, 2020, 10:00 a.m. CEST

Distributed Ledger Technology: Modeling Approaches and Infrastructure Requirements

Prof. Mikhail M. Komarov, Department of Business Informatics, National Research University Higher School of Economics, Russia


Interest in applications based on distributed ledger technology (DLT) is on the rise, with corporations worldwide shifting from simply exploring DLT’s potential to creating productive business cases. However, despite the existence of numerous DLT applications, developers are still lacking proper tools and instruments for evaluating system behavior (e.g., performance) of their applications on different distributed ledgers before deployment. Since the behavior of such applications is highly dependent on the characteristics of the distributed ledger they are built upon and changing the distributed ledger after deployment of an application is difficult, selecting the wrong ledger can have severe negative consequences. It is also necessary to consider characteristics of hardware and infrastructure, e.g. Internet of Things characteristics and requirements from the prospective of distributed ledgers and their applications.

Keynote's Bio

Prof. Mikhail M. Komarov is currently a Professor with the Department of Innovations and Business in IT, School of Business Informatics, Faculty of Business and Management, National Research University Higher School of Economics. He is also a specialist in wireless data transmission and IT. He is the Vice-Chair of the Special Interest Group on IoT at the Internet Society.

Tuesday, October 6, 2020, 10:00 a.m. CEST

Machine Learning in Limited Medical Data Sets: Doing Precision Guesswork on Unreliable Data Provided by Those with High Expectations

Dr. Jiri Mekyska, Department of Telecommunications, Brno University of Technology, Czech Republic


ML in limited medical datasets usually means doing precision guesswork on unreliable data provided by those with high expectations. The first part of this talk will focus on issues that data scientists and engineers have to address when working with this kind of data (e.g. unreliable labels, effect of covariates, necessity of clinical interpretability, difficulties with fusing more data sets), then some common ML approaches in this field will be described (yes, sometimes you have to go from deep neural networks to logistic regression) and finally some specific ML-based research studies in the field of neurology and psychology (diagnosis and prediction of motor/non-motor deficits in Parkinson’s disease, assessment of graphomotor disabilities in children with developmental dysgraphia) will be provided.

Keynote's Bio

Jiri Mekyska is the head of the BDALab (Brain Diseases Analysis Laboratory) at the Brno University of Technology, where he leads a group of data scientists and biomedical engineers. He deals especially with the research of non-invasive quantitative analysis of neurodegenerative and neurodevelopmental disorders based on speech and handwriting processing. In cooperation with neurologists/psychologists from different countries, he develops diagnostic/monitoring systems focused on Parkinson’s disease and developmental dysgraphia.

Wednesday, October 7, 2020, 10:00 a.m. CEST

I Want My Car Connected!

Dr. Alessandro Bazzi, University of Bologna, Italy


The automotive industry is undergoing a complete revolution, where cooperative vehicles will be fully connected and automated. The main objective is and must be to achieve the zero vision, which imagines a world with no more people dying on the road. Emissions and the reduction of travel times are also important goals. Overall, it was agreed that connectivity must be ensured by both infrastructure-based and direct communication: the former promises higher data rates and ensures remote management, but the latter seems essential to cope with low-coverage or congested scenarios, or to allow for ultra-low latency communications. Standards are available and the technologies are ready and tested on a large scale. Despite this, only a few vehicles are truly connected, except for non-time-critical applications, and the reasons lie beyond the technical. Furthermore, what is currently available is considered insufficient for the challenges of the future. In this keynote, the focus will be mainly on direct communications, with an overview of the available technologies, of the ongoing activities in the European standardization bodies in which the keynote is involved, and of some research trends that are expected to deserve attention in the coming years.

Keynote's Bio

Alessandro Bazzi is a Senior Researcher at the University of Bologna, Italy, and an associated member of WiLab in the Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT). He received a Laurea degree in Telecommunications Engineering (2002) and a PhD degree (2006) from the University of Bologna. From 2002 to 2019 he was a researcher of the National Research Council of Italy (CNR) and since the academic year 2006/2007 he holds courses at the University of Bologna in the area of wireless systems and networks. His research interests are mainly on medium access control and radio resource management of wireless networks. Particular focus has been placed on connected and autonomous vehicles. He is currently working as project manager in an European-funded project on the validation of connected and automated vehicles, he is acting as consultant with Car2Car regarding the topic of co-channel coexistence at 5.9 GHz and he is part of of the ETSI Specialist Task Force on multi-channel operations (at 5.9 GHz). He won a best paper award at ITSC 2017, he participated to several conferences as tutorial instructor or panelist (IARIA MOBILITY 2012, IEEE ISWCS 2017, IEEE ComSoc Spring School 2019, IEEE PIMRC 2019), and he organized Special Sessions or Workshops at IEEE PIMRC 2018 and IEEE PIMRC 2019. He is a senior member of IEEE. He is currently in the Editorial board of Hindawi Wireless Communications and Mobile Computing and MDPI Vehicles, and Chief Editor of Hindawi Mobile Information Systems.