Speaker: Emilia Cioroaica, Embedded Software Engineering Fraunhofer IESE

Abstract: In the context of Smart Ecosystems, systems engage in dynamic cooperation with
other systems to achieve their goals. Expedient operation is only possible when all
systems cooperate as expected. This requires a level of trust between the components of
the ecosystem. New systems that join the ecosystem therefore first need to build up a
level of trust. Humans derive trust from behavioral reputation in key situations. In
Smart Ecosystems(SES), the reputation of a system or system component can also be
based on observation of its behavior. In this talk, I will present a method
that enable virtual evaluation of decisions at runtime, thereby supporting trust
building within SES.

Abstract.  Applied Machine Learning (ML) is a different field than writing another neural network algorithm. One algorithm is never enough to handle all requirements. A system is usually created by connecting multiple existing systems and multiple ML algorithms. The running system in the cloud brings another level of complexity. Some things can be used out of the box, things that have to be written and a lot of optimizations done so running costs are kept within a reasonable range.

Speaker: Lukáš Grolig,  entrepreneur, software architect, data scientist and Ph.D. student

Abstract. The area of cybersecurity is increasingly crucial in current day interconnected world. This translates into particularly dire demand for qualified experts. These encompass not only highly skilled IT specialists, but also other related professionals with legal, administrative, economic or security studies backgrounds. Despite major employment opportunities in this field, the standard education frameworks nowadays do not produce adequate numbers of qualified workforce required in this field. Furthermore, the dynamic nature of this area required intensive continuous learning mechanisms in place that allow for gradual and systematic improvement in individuals expertise and capabilities. However, establishment of such training programs and qualification validation requires first a thorough analysis of the current and future needs of the field, that would be translated into a comprehensive and complex qualifications framework.

Speaker:  JUDr. Pavel Loutocký, Ph.D., BA (Hons), lawyer and researcher at CERIT-EU, post-doc at Institute of Law and Technology

Abstract. Security is an important requirement for contemporary software systems. However, many systems have security flaws introduced early in design phase of software development process. One of the causes is that software engineers often lack background in cybersecurity. Secure modeling aims to help with designing secure systems while being useable by designer without deep security knowledge. A similar problem is present in designing forensic-ready software systems. The difference is that while security modeling has wide range of methods available, forensic-ready modeling has none. Interestingly, both domains have overlapping concerns, which can be captured by already existing methods. To solve the issue of missing forensic-ready modeling method, some ideas from security modeling can be utilized.


Speaker:  Lukáš Daubner, PhD student FI MU

Abstract. Security is a big subject in recent years. Historically, the detection of malware or phishing attacks relied mainly on the skills and tools used by highly specialised teams such as Threat operations. Their work was tedious and didn't scale with the number of threats present on the Internet nowadays. In this talk, I'm going to speak about how we detect malware and phishing attacks at nearly real-time using a ML-based solution. For each attack, I cover all the necessary steps one has to go through in order to deliver a scalable, high available, ML-based solution. Starting with problem analysis and feature extraction, ending in deployment, scaling and monitoring issues. The talk is going to be enriched by many lessons we have learned the hard way in the past few years.


Speaker:  Ing. Zdenek Letko, PhD., Team lead, Wandera

Abstract. Mobile Edge Computing (MEC) has emerged to place part of the cloud resources closer to the edge of the network to increase performance and provide context-dependent data analysis and storage. However, as MEC infrastructures grow, different parties become involved in delivering services, which poses different trust challenges. MEC infrastructures, and all involved parties need to be subject to identity and provenance checks, in order to increase trust and accountability. In this context, we investigate blockchain-based architectures to track identities and provenance of all orchestration decisions in a MEC infrastructure.


Speaker:  Nabil El Ioini, PhD., Free University of Bozen-Bolzano

Abstract. Consensus algorithms are one of the main differentiators among blockchain technologies and understanding how blockchain consensus is reached is crucial for every developer who wants to work on a given technology. A short taxonomy of consensus algorithms used in various blockchain technologies will be given. We will start with motivations for the consensus in distributed ledger systems, then continue with the well-known proof-of-work used in Bitcoin and show you how it differs from non-Byzantine fault-tolerant consensus algorithms like Raft. We will wrap-up with emerging technologies, like Swirlds and Avalanche.

Speakers:  Ondrej Chaloupka & Vojtech Juranek, Red Hat

Abstract. Modern information systems allow us to track, often in great detail, the execution of processes within companies. Consider for example luggage handling in airports, manufacturing processes of products and goods, or processes related to service provision, all of these processes generate traces of valuable event data. Such event data are typically stored in a company’s information system and describe the execution of the process at hand. In recent years, the field of process mining has emerged. Process mining techniques aim to translate the data captured during the process execution, i.e. the event data, into actionable insights. As such, we identify three main process mining types of analysis, i.e. process discovery, conformance checking and process enhancement. In process discovery, we aim to discover a process model, i.e. a formal behavioral description, which describes the process as captured by the event data. In conformance checking, we aim to assess to what degree the event data is in correspondence with a given reference model, i.e. a model describing how the process ought to be executed. Finally, within process enhancement, the main goal is to improve the view of the process, i.e. by enhancing process models on the basis of facts derived from event data.
 
Recent developments in information technology allow us to capture data at increasing rates, yielding enormous volumes of data, both in terms of size and velocity. In the context of process mining, this relates to the advent of real-time, online, streams of events that result in data sets that are no longer efficiently analyzable by commodity hardware. Such types of data pose both opportunities and challenges. On the one hand, it allows us to get actionable insights into the process, at the moment it is being
executed. On the other hand, conventional process mining techniques do not allow us to gain these insights, as they are not designed to cope with such a new type of data. As a consequence, new methods, techniques and tools are needed to allow us to apply process mining techniques and analyses on streams of event data of arbitrary size.
 

In this presentation, we explore process mining techniques that are able to handle online, streaming event data. The premise of streaming event data, is the fact that we assume the stream of events under consideration to be of infinite size. As such, efficient techniques to temporarily store and use relevant recent subsets of event data are needed. In particular, we focus on a few basic examples of applying process discovery in an online setting. Furthermore, applications and recent breakthroughs in the area of online conformance checking will be discussed as well.

Speaker: Sebastiaan van Zelst, Scientist at Fraunhofer FIT and RWTH Aachen University

Sub-title: with Tooling, AOT compilation, and Embedding

Abstract. GraalVM is a universal virtual machine for running applications written in JVM-based languages like Java, or written in JavaScript, Python, Ruby, R, and LLVM-based languages such as C and C++. GraalVM removes the isolation between programming languages and enables interoperability in a shared runtime. It can run either standalone or in the context of OpenJDK, Node.js, Oracle Database, or MySQL. Multi-lingual tooling (debugger, profiler, etc.) is available for the developers.

In the talk we explore how to do polyglot programming with GraalVM and dive into the technologies and ideas behind it all.

Note: there is possibility of  a paid internship to research and contribute to the GraalVM systems. Find out more at https://www.graalvm.org/community/internship/
The deadline for application is November 30, 2019.

Speaker: Petr Chalupa, Principal Member of Technical Staff, Oracle Labs

Plus: https://prezi.com/view/m/VjYzKln3AmiWvf3t2IPA/