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Keynote Lectures

Cloud-based Crowd Monitoring
Maarten van Steen, University of Twente, Netherlands

Blockchain Application Design and Development, and the Case of Programmable Money
Ingo Weber, TU Berlin, Germany

AI Engineering –– Meeting New Challenges in System and Software Development of AI-based Systems
Ivica Crnkovic, Chalmers University of Technology, Sweden

The Science of Systems Benchmarking
Samuel Kounev, University of Würzburg, Germany

 

Cloud-based Crowd Monitoring

Maarten van Steen
University of Twente
Netherlands
 

Brief Bio
Maarten van Steen is professor at the University of Twente in The Netherlands, where he is scientific director of the Digital Society Institute, a virtual institute that bundles all the university's research on digitalization, including AI. He is specialized in large-scale distributed systems, now mainly concentrating on very large wireless distributed systems, notably in the context of crowd monitoring and its associated privacy preservation. Next to Internet-based systems, he has published extensively on distributed protocols, wireless (sensor) networks, and gossiping solutions. Maarten van Steen is associate editor for ACM Transactions on Autonomous and Adaptive Systems, and section editor for Advances in Complex Systems. He authored and co-authored three textbooks, including "Distributed Systems" (with Andrew Tanenbaum), now in its 3rd edition, as well as an introduction to Graph Theory and Complex Networks.


Abstract
In recent years we have witnessed an increasing interest in monitoring crowd flows by capturing the signals from devices such as smartphones. Notably WiFi packet sniffing is popular, in particular by identifying devices by means of their transmitted MAC address. Problematic is the situation that this method infringes upon privacy, even after applying secure one-way encryption techniques, for the simple reason that the European General Data Protection Regulation stipulates that data captured from a device may never be processed if there is any way that this data may lead to the identification of an individual.

Considering the privacy sensitivity of crowd monitoring by capturing signals from smartphones, the question is whether one can design a cloud-based system that is guaranteed to preserve the privacy of individuals while at the same time providing accurate data on crowd flows. In this talk, I will focus on the architecture and design of such a system, which can even withstand malfunctioning cloud-hosting providers.



 

 

Blockchain Application Design and Development, and the Case of Programmable Money

Ingo Weber
TU Berlin
Germany
 

Brief Bio
Ingo Weber is a Full Professor and Head of Chair for Software and Business Engineering at TU Berlin, Germany. He has conducted Blockchain research since the advent of Ethereum, and published about many aspects of Blockchain application design and development, including the book Architecture for Blockchain Applications, Springer, 2019. Ingo served as a reviewer for many prestigious journals, including various IEEE and ACM Transactions like TSE and TOSEM, and as PC member for WWW, BPM (also as PC co-chair), ICSOC, AAAI, ICAPS, IJCAI, and many other conferences and workshops. Previously, he was a Conjoint Associate Professor at the University of New South Wales (UNSW) and an Adjunct Associate Professor at Swinburne University. Prior to TU Berlin, Ingo worked at Data61, CSIRO (formerly NICTA), UNSW in Sydney, Australia, and at SAP Research in Germany. While at SAP, he completed his PhD with the University of Karlsruhe (TH).


Abstract
Blockchain has emerged as a decentralized platform for managing digital assets and executing 'smart contracts', i.e., user-defined code. While blockchain's suitability for a given use case should always be scrutinized, it does have the potential to disrupt many of the connection points between individuals, companies, and government entities.
In this keynote talk, I will provide an overview of what architects and developers need to know in order to build blockchain-based applications, and how it relates to the cloud and software services. Among others, I will cover blockchain-as-a-service concepts, as well as architectural concerns and model-driven engineering for blockchain applications, the latter also in relation to collaborative business processes. To highlight some of the challenges, I will discuss insights from a project on "programmable money", i.e., blockchain-based money for conditional payments where the money itself checks whether it can be spent in a certain way at the point of payment.
Finally, I will touch on insights into current adoption of blockchain.



 

 

AI Engineering –– Meeting New Challenges in System and Software Development of AI-based Systems

Ivica Crnkovic
Chalmers University of Technology
Sweden
 

Brief Bio
Ivica Crnkovic is a professor of software engineering at Chalmers University, Gothenburg, Sweden. He is the director of Chalmers AI Research Centre (CHAIR). His research interests include, software architecture, software development processes, software engineering for large complex systems, component-based software engineering, and recently Software engineering for AI. Professor Crnkovic is the author of more than 200 refereed publications on software engineering topics, and guest editor of a number of special issues in different journals and magazines, such as IEEE Software, and Elsevier JSS . He was the general chair of 40th International Conference on Software Engineering (ICSE) 2018, held in Gothenburg, 2018. Before Chalmers, Ivica Crnkovic was affiliated with Mälardalen University, Sweden, and before that he was employed at ABB company, Sweden, where he was responsible for software development environments and tools. More information is available on http://www.ivica-crnkovic.net


Abstract
Artificial Intelligence based on Machine Learning, and in particular Deep Learning, is today the fastest growing trend in software development, and literally used in all other research disciplines, with a very high impact on the modern society. However, a wide use of AI in many systems, in particular dependable systems, is still far away. The challenges for managing  AI-based complex and dependable systems through  the entire lifecycle  are enormous. Some aspects of these challenges are based on management of resources, including computational, data storage capacity. Others are related to dependability itself, as the AI has by its nature a probabilistic approach, and dependable systems require justifiable quality assurance. This talk discusses some of these challenges, illustrate a case of Cyber-physical systems, and gives some ideas for new research in software engineering for AI development, i.e. for AI engineering.



 

 

The Science of Systems Benchmarking

Samuel Kounev
University of Würzburg
Germany
 

Brief Bio
Samuel Kounev is a professor and chair of software engineering at the University of Würzburg, Germany. His research interests span the areas of software design, modeling and architecture-based analysis; systems benchmarking and experimental analysis; and autonomic computing. Kounev's research is inspired by the vision of self-aware computing systems, to which he has been one of the major contributors shaping its development. He serves as associate editor for ACM Transactions on Autonomous and Adaptive Systems and is co-founder and chair of the steering committees of the ACM/SPEC International Conference on Performance Engineering (ICPE) and the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). In the area of benchmarking, he founded the SPEC Research Group, a consortium within the Standard Performance Evaluation Corporation (SPEC), providing a platform for collaborative research efforts in the area of quantitative system evaluation and analysis. He recently co-authored the first textbook focussed on systems benchmarking (http://benchmarking-book.com).


Abstract
For a long time benchmarking has been considered a practical discipline driven by industry's interest in marketing their products and services. Benchmarking evolved from the need to be able to compare different systems fairly in order to make an informed purchasing decision. In the past decade, benchmarking has gained increasing interest in the scientific community and its scope has expanded significantly. Systems benchmarking is now considered in a broad sense, including rating tools and research benchmarks (benchmark frameworks), and benchmarks are employed in many fields of testing going beyond competitive system comparisons, for example, regulatory programs, research evaluation, and testing during system design and development. Further, the scope of systems benchmarking has expanded to other system attributes in addition to classical performance aspects, for example, system energy efficiency, reliability, or security. The conception, design, and development of benchmarks requires a thorough understanding of the benchmarking fundamentals including statistics, measurement methodologies, metrics, and relevant workload characteristics. Benchmarking involves deciding what to measure, how to measure it, and how to aggregate the measurement results into meaningful metrics. The aggregation of metrics into scoring systems as well as the design of workloads, including workload characterization and modeling, are further challenging topics.

In this keynote talk, I will first provide an overview of the foundations of benchmarking as a discipline, covering the three fundamental elements of each benchmarking approach: metrics, workloads, and measurement methodology. I will discuss relevant topics and respective literature sources based on extensive experience gained from teaching these topics in the past 15 years at several leading European universities. I will then present several case studies from different fields with a focus on cloud benchmarking. The case studies illustrate the unique challenges that arise in the conception and development of benchmarks for specific systems or subsystems. 




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