October 25, 2022 | 09:00 – 11:00 am (CET) | HS 11 | Patrick Rodler | Alpen-Adria-Universität Klagenfurt

Abstract: In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.

Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.   

In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.

Bio: Patrick Rodler is a postdoctoral researcher at the Department of Artificial Intelligence and Cybersecurity (AICS), University of Klagenfurt. He holds MSc degrees in Technical Mathematics and Computer Science, and received his PhD degree in Computer Science in 2015 from the University of Klagenfurt. As a researcher, he co-authored more than 50 papers, published in prestigious journals such as Web Semantics, Knowledge-Based Systems, Artificial Intelligence, or Information Sciences, and gave 30 talks at renowned venues such as the AAAI Conference on Artificial Intelligence (AAAI), the European Conference on Artificial Intelligence (ECAI), or the Int’l Conference on Knowledge Representation and Reasoning (KR). As a teacher, he was responsible for 26 university courses and lectures, and in 2018 he was awarded a university-wide prize for excellent teaching by the University of Klagenfurt. His research interests include artificial intelligence in general, and model-based diagnosis, intelligent search, heuristic problem solving, as well as knowledge representation and reasoning in particular.

Slides are animated. Please view in Powerpoint presentation mode.

Thursday, October 20, 2022 | 04:00 pm (CET) | Online via Zoom, please register here:

Prof. Dr. Christian Rothenberg | University of Campinas, Brazil

Abstract: As the network softwarization trend started by SDN and NFV keeps evolving, the hardware/software continuum becomes more relevant than ever, offering new offloading/acceleration opportunities at node and network-wide scales. This talk will review evolving transformations behind network softwarization with a special focus on network refactoring and offloading trends leading to “fluid networks planes”, characterized by multiple candidate options for the specific HW/SW embodiment and the location of chained network functions, from the edge to core, from one administrative provider to another, from programmable silicon to portable lightweight virtualized containers. The talk will overview concrete examples from the literature with a special focus on the role of Machine Learning to assist key (automated) decision-making steps.  Lastly, the talk will conclude with a glimpse on ongoing ML work applied to Youtube video QoE prediction in live 5G networks.

Bio: Christian Rothenberg is Associate Professor (tenure-track) and head of the Information & Networking Technologies Research & Innovation Group (INTRIG) at the School of Electrical and Computer Engineering (FEEC) of the University of Campinas (UNICAMP), where he received his Ph.D. in Electrical and Computer Engineering in 2010. From 2010 to 2013, he worked as Senior Research Scientist in the areas of IP systems and networking, leading SDN research at CPQD R&D Center in Telecommunications, Campinas, Brazil. He holds the Telecommunication Engineering degree from the Technical University of Madrid (ETSIT – UPM), Spain, and the M.Sc. (Dipl. Ing.) degree in Electrical Engineering and Information Technology from the Darmstadt University of Technology (TUD), Germany, 2006. Christian has contributed to 07 international patents, co-authored three books, and over 200 scientific publications, including top-tier scientific journals and networking conferences such as SIGCOMM and INFOCOM, altogether featuring 10 000+ citations (h-index: 30+, i10-index: 70+). 

Friday, July 15, 2022 | 02:00 pm (CET) | Room: S.2.42

Dr. Shajulin Benedict | Indian Institute of Information Technology Kottayam

Abstract: IoT-enabled applications increase tremendously in various sectors, such as transportation, healthcare, education, agriculture, and so forth. These applications sense properties using sensors, perform intelligence, and apply the findings using actuators. Instead of submitting sensor data directly to the cloud, intelligence could be performed with the inclusion of several edge/fog nodes. This improves the privacy and computation time of applications. This talk will provide insights on edge intelligence techniques for such IoT-enabled applications. In addition, a few protocols that are involved in such applications are discussed. 

Bio: Dr. Shajulin Benedict graduated in 2001 from Manonmaniam Sunderanar University, India, with Distinction. In 2004, he received M.E Degree in Digital Communication and Computer Networking from A.K.C.E, Anna University, Chennai. He is the University second rank holder for his masters. He did his Ph.D degree in the area of Grid scheduling under Anna University, Chennai (Supervisor – Dr. V. Vasudevan, Director, Software Technologies Group of TIFAC Core in Network Engineering). After his Ph.D award, he joined a research team in Germany to pursue PostDoctorate under the guidance of Prof. Gerndt. He served as Professor at SXCCE Research Centre of Anna University-Chennai. Later, he visited TUM Germany for teaching Cloud Computing as Guest Professor of TUM-Germany.

Currently, he teaches Internet of Things at the Technical University Munich, Germany; he is affiliated to TUM Germany and to the Indian Institute of Information Technology Kottayam, Kerala, India, an institute of national importance of India. He serves as Director/PI/Representative Officer of AIC-IIITKottayam (Sec.8 Company) for nourishing young entrepreneurs of India. His research interests include IoT Cloud, Performance Analysis of IoT Applications, Cloud Scheduling, Edge Analytics, and so forth.

Friday, July 15, 2022 | 10:00 am (CET) | Room: S.2.42

Prof. (FH) PD Dr. habil. Mario Döller | FH-Rector at the University of Applied Science FH Kufstein Tirol

Abstract: Computer vision and AI methods are percolating many branches nowadays. Also in the research field of real estate rating computer vision and AI methods have lead to very interesting innovations. In this research talk, real estate classification by AI-enabled computer vision techniques is discussed.

The talk will give an overview of recent research efforts in the field and focus on latest findings of our research group. This consists of age or heating demand prediction of real estates by photographs as well as the analysis of satellite images for detecting building footprints.

Bio: Prof. (FH) PD Dr. habil. Mario Döller (male) obtained his PhD from the University of Klagenfurt (Austria) in 2004 and his lecturing qualification in computer science from the University of Passau (Germany) in 2012. Currently, Dr. Döller is full professor for multimedia and web based information systems and FH-Rector at the University of Applied Science FH Kufstein Tirol. Dr. Döller is an active member of the MPEG and JPEG consortium (worked as Session Chair on the standardization of the MPEG Query Format).

Besides, he was invited as scientific expert to the Media Annotation Working Group of W3C. Furthermore, he is in the PC of numerous conferences and participated on the organization committee of EuroPar 2002, MUE 2010, SMPT 2010. Dr. Döller is author or co-author of more than 80 scientific publications and has numerous contributions to standardization bodies. Besides, he holds a patent (RDF DB) and awards (e.g. Best Paper Awards). His research in the area of computer vision (e.g., automated real estate rating) has been awarded by MIT Technology Review in the category Most Thought-Provoking Papers 2018. In 2020, his work in automated mobility has been awarded by the 3rd place in the FFG Galileo Masters challenge.

Friday, July 8, 2022 | 02:00 pm (CET) | Room: B01b.0.203, Lakeside Park

Jun. Prof. Dr. Bernhard Standl | Karlsruhe University of Education

Abstract: When it comes to integrating digital technologies into the classroom in higher education, many teachers face similar challenges. Nevertheless, it is difficult for teachers to share experiences because it is usually not possible to transfer successful teaching scenarios directly from one area to another, as subject-specific characteristics make it difficult to reuse them. To address this problem, instructional scenarios can be described as patterns that have been used previously in educational contexts. Patterns can capture proven teaching strategies and describe instructional scenarios in a consistent structure that can be reused. Because priorities for content, methods, and tools are different in each domain, a consensus-tested taxonomy was first developed with the goal of modeling a domain-independent database to collect digital instructional practices. In addition, this presentation will present preliminary insights into a data-driven approach to identifying effective instructional practices from interdisciplinary data as patterns. A web-based application will be developed for this that can both collect teaching/learning scenarios and individually extract scenarios from patterns for a learning platform.

Bio: Bernhard Standl is a tenure-track professor of Informatics Education at the Karlsruhe University of Education. His research focuses on modeling teaching concepts as pedagogical design patterns and on a data-driven identification of effective teaching-learning scenarios and their reuse in practice.

He received his Ph.D. in informatics education from the University of Vienna and where he was also active as a research assistant in educational projects and in a European Union’s funded project and research associate (post-doc) at the Vienna University of Technology. In addition, he worked as an informatics teacher at a high school in Vienna for more than 10 years. He gained international experience as a Fulbright Visiting Scholar at Missouri State University, Springfield, MO, USA.

Thursday, July 7, 2022 | 02:00 pm (CET) | Room: B4.1.114, Lakeside Park B04b, level 1

Professor Dr. rer. nat. Marc Timme | Strategic Professor & Chair for Network Dynamics TU Dresden, Germany

Abstract: The dynamics of networks enables the function of a variety of systems we rely on every day, from gene regulation and metabolism in the cell to the distribution of electric power and communication of information. Understanding, steering and predicting the function of interacting nonlinear dynamical systems, in particular if they are externally driven out of equilibrium, relies on obtaining and evaluating suitable models, posing at least two major challenges. First, how can we extract key structural system features of networks if only time series data provide information about the dynamics of (some) units?  Second, how can we characterize nonlinear responses of nonlinear multi-dimensional systems externally driven by fluctuations, and consequently, predict tipping points at which normal operational states may be lost? Here we report recent progress on nonlinear response theory extended to predict tipping points and on model-free inference of network structural features from observed dynamics.

This is work with Jose Casadiego, Mor Nitzan, Hauke Haehne, Georg Boerner, Moritz Thuemler and others.

[1] Topical Review: Marc Timme & Jose Casadiego,  J. Phys. A 47:343001 (2014).

[2] Casadiego et al., Nature Comm. 8:2192 (2017).

[3] Nitzan et al., Science Adv.:e1600396 (2017).

[4] Haehne et al., Phys. Rev. Lett. 122:158301 (2019).

[5] Moritz Thuemler et al., submitted (2022).

Bio: Marc Timme studied Physics and Mathematics in Würzburg, Stony Brook (USA) and Göttingen. After work as a postdoctoral research at the Max Planck Institute for Flow Research and as a Research Scholar at Cornell University (USA), he was selected to head a broadly cross-disciplinary Max Planck Research Group on Network Dynamics at the Max Planck Institute for Dynamics and Self-Organization. Marc held  a Visiting Professorship at TU Darmstadt and was visiting faculty at ETH Zurich. He is now Strategic Professor and heads the Chair for Network Dynamics at the Cluster of Excellence Center for Advancing Electronics Dresden (cfaed) and the Institute for Theoretical Physics at TU Dresden. He is also Co-Chair of the Division of Socio-Economic Physics of the German Physical Society (DPG) and since 2018 Honorary Member of Lakeside Labs, Klagenfurt.

With collaborator teams he develops insights about collective nonlinear dynamics of complex systems and their applications in fields of energy and sustainability, mobility, as well as biological and bio-inspired information processing.

Thursday, June 30^th, 2022 | 10:00 (CET) | Room: S.2.69

Dr. Dragi Kimovski, MSc

Abstract: The advent of fog and edge computing has prompted predictions that they will take over the traditional cloud for information processing and knowledge extraction in Internet of Things (IoT) systems. Notwithstanding the fact that fog and edge computing have undoubtedly large potential, these predictions are probably oversimplified and wrongly portray the relations between cloud, fog and edge computing. 

Concretely, fog and edge computing have been introduced as an extension of the cloud services towards the data sources, thus forming the computing continuum. The computing continuum enables the creation of a new type of services, spanning across distributed infrastructures, supporting various IoT applications. These applications have a large spectrum of requirements, burdensome to meet with „distant“ cloud data centers. However, the introduction of the computing continuum raises multiple challenges for management, deployment and orchestration of complex distributed applications, such as: increased network heterogeneity, limited resource capacity of edge devices, fragmented storage management, high mobility of edge devices and limited support of native monolithic applications. These challenges primarily concern the complexity and the large diversity of the devices, managed by different entities (cloud providers, universities, private institutions), which range from single-board computers such as Raspberry Pis to powerful multi-processor servers.

Therefore, in this talk, we will discuss novel algorithms for low latency, scalable, and sustainable computing over heterogeneous resources for information processing and reasoning, thus enabling transparent integration of IoT applications. We will tackle the heterogeneity challenge of dynamically changing topologies of the computing infrastructure and present a novel concept for sustainable processing at scale.

CV: Dragi Kimovski is a  postdoctoral researcher at the Institute of Information Technology (ITEC), University of Klagenfurt, Austria. He earned his doctoral degree in 2013 from the Technical University in Sofia, Bulgaria. He was an assistant professor at  Ohrid University, N. Macedonia, and a senior researcher at the University of Innsbruck, Austria.

He coauthored more than 50 articles in international conferences and journals. His research interests include parallel and distributed computing and multi-objective optimization. He is a work-package leader and scientific coordinator in dozen Horizon 2020 projects (DataCloud, ENTICE, and ASPIDE) and participated in multiple national projects. 

Thursday, June 23rd, 2022 | 14:00 (CET) | Room: Seminarraum Lakeside Labs B4.1.114

Priv.Doz. Tamer Khatib, MSc. PhD.

Abstract: East-west oriented photovoltaic power system is a new trend in orienting photovoltaic system. This lecture presents an evaluation of east–west oriented photovoltaic power system. A comparison between east–west oriented photovoltaic system and south oriented photovoltaic system in terms of cost of energy and technical requirement is conducted is presented in this lecture. In addition to that, the benefits of using east–west oriented photovoltaic system are discussed in this paper. By this lecture the following issues will be realized,

• East–west oriented photovoltaic system requires less land area.
• East–west oriented photovoltaic system requires less cost for mounting piles and steel structure, and less costs of the interfacing power substation
• South oriented photovoltaic system produces more energy than east–west oriented photovoltaic system.
• No significant difference between the costs of energy for both systems.
• Grid interfacing east–west oriented PV system can provide smoother power injection to the grid with fewer harmonic and less reverse power.
• South oriented photovoltaic system is preferred when high power injection is required.

Bio: Tamer is researcher in photovoltaic power systems. He holds a B.Sc. degree in electrical engineering from An-Najah National University (ANNU), as well as a M.Sc. degree and a Ph.D degree in electrical, electronic and systems engineering from National University of Malaysia (UKM). In addition he holds Habilitation degree in renewable and sustainable energy from Alpen Adria Universitat (AAU). Currently he is an Associate professor of renewable energy and Director of Scientific Centers at ANNU. In addition to that, he is the director of An-Najah Company for Consultancy and Technical Studies (sister research company of ANNU).

So far, he has 2 patents, 4 books and 140 research articles, while his current h-index is 40. He has supervised 4 Ph.D researches, 22 master researches and 60 bachelor researches.

Wednesday, June 8th, 2022 | 17:00 (CET) | Room: Z.1.09

Univ.-Prof. i. R. Dipl-Ing. Dr. techn. Hannes Werthner

Abstract: We live in a “digital” world, the separation between physical and virtual makes (almost) no sense anymore. Here, the Corona pandemic has also acted as an accelerator/magnifier demonstrating that the future of our digital society is here with all its possibilities, but also shortcomings.
In his talk, Hannes Werthner will briefly reflect on the history of computer science, and then discuss the need for an interdisciplinary response to these shortcomings. Such an answer is the Digital Humanism, which looks at this interplay of technology and humankind, it analyzes, and, most importantly, tries to influence the complex interplay of technology and humankind, for a better society and life. In the second part he will discuss this approach, and show what was achieved since its first workshop in 2019, and what lies ahead.

Bio: Hannes Werthner is a retired Professor for E-Commerce at the Faculty of Informatics, TU Wien. Prior to joining TU Wien, he had several professorships at Austrian and international Universities. His research is in fields such as Decision Support Systems, E-Commerce and E-Tourism, Recommender Systems, and lately in Network Analysis and Text Mining.

Besides research and teaching he is active in starting new initiatives, such as the Vienna PhD School of Informatics and the i2c (Informatics Innovation Center). In the area of E-Tourism, the International Federation for IT and Tourism (IFITT) grants the “Hannes Werthner Tourism and Technology Lifetime Achievement Award” to outstanding academics and/or professionals in the field. He is one of the key persons of the Digital Humanism Initiative and the Vienna Manifesto on Digital Humanism (dighum.ec.tuwien.ac.at).

Wednesday, June 1^st, 2022 | 10:00 am (CET) | Room: V.1.07

Univ.-Prof. DI Dr. Peter M. Roth | Prof. at Vetmeduni Wien

Abstract: When talking about new developments in Machine Learning, we typically think about new algorithms, better optimization techniques, or optimized hyperparameters. However, one important aspect is often neglected: the quality and the structure of training data: measurement noise, label noise, and correct but ambiguous labels. In this talk, we address the latter problem, trying to deal with high intra-class and small inter-class variability in the data, following two different strategies. First, we consider the problem of metric learning, showing that by selecting/learning a better metric for a specific problem, better results can be obtained: using the same learning method and the same data. Second, focusing on neural networks, we analyze the influence of specific hyperparameters, namely the activation functions. For both directions, we show that the quality of the finally learned model is highly dependent on the data. To illustrate these aspects, we will further discuss a visualization technique, namely information planes, providing better insights into the current state of the learning system.

Bio: He has been a professor at Vetmeduni Vienna since January 2022. Research interests include Data Science and Machine Learning.