- informatik-vortraege - lists.rwth-aachen.de

Einladung: Informatik-Oberseminar Alexander Hermans
by Alexander Hermans 15 Dec '20

15 Dec '20

************************************************************************ * * * Einladung * * * * Informatik-Oberseminar * * * ************************************************************************ Zeit: Montag, 21. Dezember 2020, 13:15-14:15 Uhr Zoom: https://us02web.zoom.us/j/89686061674?pwd=UFUxaWMrdFJtY09JSWRBKzNxUnU0QT09 <https://www.google.com/url?q=https://us02web.zoom.us/j/89686061674?pwd%3DUF…> Meeting-ID: 896 8606 1674 Kenncode: 923078 Referent: Alexander Hermans, M. Sc. Lehrstuhl Informatik 13 Thema: Learning-based Visual Scene and Person Understanding for Mobile Robotics Abstract: We have seen tremendous progress in the computer vision community across the past decades, especially with advancements in deep learning, which is now used as the core machine learning approach for most tasks. However, when deploying computer vision approaches in actual robotic applications, we often find that top-performing methods do not work as well as expected due to hardware constraints and different input data characteristics. We deal with visual scene and person understanding which are highly relevant for robotics applications. Robots need to be able to understand their environment and take special care around persons to ensure a safe navigation and interaction. We specifically deal with three important sub-tasks: semantic segmentation, 2D laser-based object detection, and person re-identification. Semantic segmentation deals with the task of labeling every pixel or point in a scene with a class label. This can in turn be used to extract higher level information about the surrounding scene, which can be used as context for further planning and interaction tasks. While the resulting segmentations provide object labels, they do not contain instance labels, making it hard to detect object instances. However, object detection is an important capability for allowing robots to safely navigate between dynamic objects. Especially the detection of persons is an important task, enabling robots to interact with us. Since many mobile platforms are already equipped with a 2D laser scanner, they are interesting input sensors for object detection, even though the resulting scans only contain sparse data. In addition to person detection, person re-identification is an important task. This can be used to improve tracking, but also allows to gather longer-term statistics and enables person specific interactions. For each of the three tasks we propose state-of-the-art approaches, however, we also consider aspects that are important for their real-world deployability and show applications of our methods within the context of robotics projects. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Frau Can Sun
by Leany Maaßen 09 Dec '20

09 Dec '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 17. Dezember 2020, 15:00-16:00 Uhr Zoom: https://rwth.zoom.us/j/92609933435?pwd=OStsV2w0UUZrK2NmNnE4WDE0Y0pEQT09 Meeting-ID: 926 0993 3435 Kenncode: 584723 Referentin: Frau Can Sun, M. Sc. Lehrstuhl Informatik 5 Thema: Embedding of real-world complexity modeling in adaptive supply chain systems engineering Abstract: Successful supply chain complexity and change management is critical for companies to gain competitive advantages. This thesis aims to build a comprehensive supply chain complexity measurement framework through detailed modeling and apply it in the changeable environment. The research starts with modeling the supply chain and its dynamic change as sociotechnical systems. Then a complexity measurement framework that is decomposed into different levels of complexity drivers is developed, and a decision-making framework for various change scenarios is proposed by highlighting complexity as one of the decisive evaluation criteria. The proposed solutions are validated with a set of real-world case studies from the semiconductor industry. Each case represents one change scenario and addresses the complexity from a particular perspective; the problem is analyzed and modeled with the support of proposed models and frameworks. The results show that complexity can be measured by a set of indicators or formulas, and thus support decision-making by comparing the change-induced complexity. Es laden ein: die Dozentinnen und Dozenten der Informatik _______________________________ Leany Maaßen RWTH Aachen University Lehrstuhl Informatik 5, LuFG Informatik 5 Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke, Prof. Gerhard Lakemeyer Ph.D. Ahornstrasse 55 D-52074 Aachen Tel: 0241-80-21509 Fax: 0241-80-22321 E-Mail: maassen(a)dbis.rwth-aachen.de

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reminder: today, 10:30, Kuldeep Meel (NUS Singapore): Sparse Hashing for Scalable Approximate Model Counting: When Theory and Practice Finally Meet
by Bolke-Hermanns, Helen 02 Dec '20

02 Dec '20

Dear all, This is a short reminder about the talk of Kuldeep Meel (NUS Singapore)<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Das-Graduiertenkolleg/Gastwi…> today at 10:30. Title: Sparse Hashing for Scalable Approximate Model Counting: When Theory and Practice Finally Meet Given a Boolean formula F, the problem of model counting, also referred to as #SAT, is to compute the number of solutions of F. The hashing-based techniques for approximatecounting have emerged as a dominant approach, promising achievement of both scalability and rigorous theoretical guarantees. The standard construction of strongly 2-universal hash functions employs dense XORs (i.e., involving half of the variables in expectation), which is widely known to cause degradation in the runtime performance of state of the art SAT solvers. Consequently, the past few years have witnessed an intense activity in the design of sparse XORs as hash functions. In this talk, we will first survey the known results, and identify the crucial bottleneck contributing to their lack of ability to provide speedups. We will then formalize a relaxation of universal hashing, called concentrated hashing, and establish a novel and beautiful connection between concentration measures of these hash functions and isoperimetric inequalities on boolean hypercubes. This allows us to obtain tight bounds on variance as well as the dispersion index and show that logarithmically sized XORs suffices for the design of sparse hash functions belonging concentrated hash family. Finally, we use sparse hash functions belonging to this concentrated hash family to develop new approximate counting algorithms. Our comprehensive experimental evaluation of our algorithm on 1896 benchmarks with computational effort of over 20,000 computational hours demonstrates speedup compared to existing approaches. To the best of our knowledge, this work is the first study to demonstrate runtime improvement of approximate model counting algorithms through the usage of sparse hash functions, while still retaining strong theoretical guarantees. (Based on Joint work with S. Akshay, D. Agarwal, and Bhavishya; The corresponding papers were published at SAT-20 and LICS-20) Bio: Kuldeep Meel is Sung Kah Kay Assistant Professor of Computer Science in School of Computing at the National University of Singapore. He received his Ph.D. (2017) and M.S. (2014) degree from Rice University, and B. Tech. (with Honors) degree (2012) in Computer Science and Engineering from Indian Institute of Technology, Bombay. He is a recipient of 2019 NRF Fellowship for AI. His research interests lie at the intersection of Artificial Intelligence and Formal Methods. His work received the 2018 Ralph Budd Award for Best PhD Thesis in Engineering, 2014 Outstanding Masters Thesis Award from Vienna Center of Logic and Algorithms and Best Student Paper Award at CP 201 Wednesday, 2.12.2020, 10:30 https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…> Meeting ID: 920 4794 9381 Password: unravel https://www.unravel.rwth-aachen.de/go/id/kfsgc Best regards Helen Bolke-Hermanns Helen Bolke-Hermanns RTG UnRAVeL - RWTH Aachen University Ahornstr. 55, D-52074 Aachen Building E3, 2nd floor, Room 9218 Telefon: +49 (241) 80-21 004 Fax: +49 (241) 80-22 215 E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de> Web: www.unravel.rwth-aachen.de<http://www.unravel.rwth-aachen.de/> [rwth_unravel_en_rgb]

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02.12.2020, 10:30, Kuldeep Meel (NUS Singapore): Sparse Hashing for Scalable Approximate Model Counting: When Theory and Practice Finally Meet
by Bolke-Hermanns, Helen 27 Nov '20

27 Nov '20

Dear members of the Computer Science Department, You are cordially invited to the talk of Kuldeep Meel (NUS Singapore)<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Das-Graduiertenkolleg/Gastwi…> for next week, 2,12., 10:30. Title: Sparse Hashing for Scalable Approximate Model Counting: When Theory and Practice Finally Meet Given a Boolean formula F, the problem of model counting, also referred to as #SAT, is to compute the number of solutions of F. The hashing-based techniques for approximatecounting have emerged as a dominant approach, promising achievement of both scalability and rigorous theoretical guarantees. The standard construction of strongly 2-universal hash functions employs dense XORs (i.e., involving half of the variables in expectation), which is widely known to cause degradation in the runtime performance of state of the art SAT solvers. Consequently, the past few years have witnessed an intense activity in the design of sparse XORs as hash functions. In this talk, we will first survey the known results, and identify the crucial bottleneck contributing to their lack of ability to provide speedups. We will then formalize a relaxation of universal hashing, called concentrated hashing, and establish a novel and beautiful connection between concentration measures of these hash functions and isoperimetric inequalities on boolean hypercubes. This allows us to obtain tight bounds on variance as well as the dispersion index and show that logarithmically sized XORs suffices for the design of sparse hash functions belonging concentrated hash family. Finally, we use sparse hash functions belonging to this concentrated hash family to develop new approximate counting algorithms. Our comprehensive experimental evaluation of our algorithm on 1896 benchmarks with computational effort of over 20,000 computational hours demonstrates speedup compared to existing approaches. To the best of our knowledge, this work is the first study to demonstrate runtime improvement of approximate model counting algorithms through the usage of sparse hash functions, while still retaining strong theoretical guarantees. (Based on Joint work with S. Akshay, D. Agarwal, and Bhavishya; The corresponding papers were published at SAT-20 and LICS-20) Bio: Kuldeep Meel is Sung Kah Kay Assistant Professor of Computer Science in School of Computing at the National University of Singapore. He received his Ph.D. (2017) and M.S. (2014) degree from Rice University, and B. Tech. (with Honors) degree (2012) in Computer Science and Engineering from Indian Institute of Technology, Bombay. He is a recipient of 2019 NRF Fellowship for AI. His research interests lie at the intersection of Artificial Intelligence and Formal Methods. His work received the 2018 Ralph Budd Award for Best PhD Thesis in Engineering, 2014 Outstanding Masters Thesis Award from Vienna Center of Logic and Algorithms and Best Student Paper Award at CP 201 Wednesday, 2.12.2020, 10:30 https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…> Meeting ID: 920 4794 9381 Password: unravel https://www.unravel.rwth-aachen.de/go/id/kfsgc Best regards Helen Bolke-Hermanns Helen Bolke-Hermanns RTG UnRAVeL - RWTH Aachen University Ahornstr. 55, D-52074 Aachen Building E3, 2nd floor, Room 9218 Telefon: +49 (241) 80-21 004 Fax: +49 (241) 80-22 215 E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de> Web: www.unravel.rwth-aachen.de<http://www.unravel.rwth-aachen.de/> [rwth_unravel_en_rgb]

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Kostenloses ONLINE Agile Programming Training für Studierende - 04.01.-08.01.2021
by studenten＠ws4s.de 19 Nov '20

19 Nov '20

Liebe IT-Studierende, alle von euch werden während des Studiums vermutlich schon etwas über agile Methoden erfahren haben. Jetzt habt ihr die Möglichkeit Praxiserfahrung zu sammeln und agile Methoden in der Praxis anzuwenden. Ich würde euch gerne herzlich zum kostenlosen ONLINE Agile Programming Training bei andrena objects einladen: Agile Programming Training - Online (aus Köln) - 04.01.-08.01.2021 Was erwartet euch? - Agile Methoden und andrena objects kennenlernen - Interaktive Scrum-Sprints in Gruppen - Netzwerken und Austausch über Einstiegsmöglichkeiten - Kostenloses Teilnehmerzertifikat für eure Bewerbungsunterlagen Um eine kurze Anmeldung wird gebeten unter: https://ws4s.de/veranstaltungen/online-agile-development-training/ Die Plätze sind begrenzt und in der Regel auch sehr schnell ausgebucht. Sichert euch jetzt einen der begrenzten Plätze! :-) Wer ist andrena objects? andrena objects ist ein mittelständisches Softwareunternehmen und einer der Vorreiter der agilen Softwareentwicklung. Bei diesem besonderen fünfttägigen Training bekommt ihr tiefe Einblicke in die Methoden der agilen Softwareentwicklung und den Arbeitsalltag bei andrena objects. Ihr durchlauft innerhalb dieser Tage in Teams (und mit Unterstützung von Experten) vier beispielhafte Scrum-Sprints und lernt dabei Arbeitspraktiken aus Scrum, Extreme Programming und der Projektpraxis von andrena und SAP kennen. Bei Fragen zögert nicht, mir einfach direkt zu schreiben. Viele Grüße, Marcel vom Workshops4Students-Team _____________________________ Co-Founder Workshops4Students E-Mail: studenten(a)ws4s.de mailto:studenten@ws4s.de Web: http://www.ws4s.de

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reminder: today, 10:30: Prof. Gabriele Kern-Isberner (TU Dortmund): Towards Lifted Inference: Counting Strategies for Relational Maximum Entropy Reasoning
by Bolke-Hermanns, Helen 04 Nov '20

04 Nov '20

This is a short reminder. Prof. Gabriele Kern-Isberner (TU Dortmund) Title: Towards Lifted Inference: Counting Strategies for Relational Maximum Entropy Reasoning Abstract: The principle of maximum entropy (MaxEnt) constitutes a meaningful methodology for drawing non monotonic inferences from probabilistic conditional knowledge as it satisfies some fundamental principles from commonsense reasoning. Similar to alternative approaches to probabilistic reasoning in relational settings, straightforward maximum entropy computations suffer from an exponential dependence from the size of the underlying domain which can lead to intractability in many cases. To overcome this problem, we adopt techniques from weighted first-order model counting (WFOMC) which exploit symmetries and interchangeabilities among the domain elements in order to count models of sentences more efficiently. To meet the requirements of our formalization of knowledge by conditionals, we assign a type to the counted models which captures the three-valued evaluation of the conditionals, i.e. the conditional structure of the models. We present the resulting variant of model counting which we call 'typed model counting' and discuss its benefits by means of some illustrating examples. https://www.informatik.rwth-aachen.de/go/id/jgfze Today, 10:30 https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…> Meeting ID: 920 4794 9381 Password: unravel Helen Bolke-Hermanns Fachgruppe Informatik RWTH Aachen University Ahornstr. 55, D-52074 Aachen Building E3, 2nd floor, Room 9218 Telefon: +49 (241) 80-21-004 Fax: +49 (241) 80-22 215 E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de> [rwth_informatik_bild_rgb]

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Einladung Informatik-Oberseminar Jan Dreier
by Jan Dreier 02 Nov '20

02 Nov '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 16. November 2020, 13:00 Uhr Zoom: https://rwth.zoom.us/j/94172280001?pwd=K2Z6Z1ZHbUVVdDBDbjNSWVBVUTZoZz09 Meeting ID: 941 7228 0001 Passcode: N.gdJ5 Referent: Jan Dreier, M.Sc. Lehr- und Forschungsgebiet Theoretische Informatik Thema: Two New Perspectives on Algorithmic Meta-Theorems Abstract: We present two algorithmic meta-theorems. Our first meta-theorem is an approximation scheme of the parameterized model-checking problem for the first-order counting logic FO({>0}). This logic extends first-order logic with the ability to count the number of satisfying assignments of a variable and compare it to fixed but arbitrarily large numbers. The model-checking scheme runs in linear fpt time (parameterized by formula length) on structures with bounded expansion and either gives the correct answer or says ``I do not know.'' The latter answer may only be given if small perturbations in the number-symbols of the formula could make it both satisfied and unsatisfied. As a consequence, every property expressible in FO({>0}) can be approximated in linear time on bounded expansion graph classes. A restricted set of counting properties can also be solved exactly in linear time, leading for example to an fpt algorithm for partial dominating set on bounded expansion graph classes. These results are complemented by showing that exactly solving the model-checking problem for FO({>0}) is already hard on trees of bounded depth and just slightly increasing the expressiveness of FO({>0}) makes even approximation hard on trees. Our second meta-theorem is a first-order model-checking algorithm for random graphs and complex networks. Roughly speaking, we say a random graph model is alpha-power-law-bounded if it is unclustered and the fraction of vertices with degree k is O(k^-alpha). We solve the parameterized first-order model-checking problem in almost linear fpt time on random graph models satisfying this property with alpha >= 3. This means in particular that one can solve every first-order property in almost linear expected time on these random graph models. This includes many popular models of complex networks such as preferential attachment graphs, Chung–Lu graphs, configuration graphs, and sparse Erdős–Rényi graphs. We present hardness results that show intractability for alpha < 3, assuming we allow an adversary to color the vertices of the input graph. We base our algorithms on a new structural decomposition of alpha-power-law-bounded random graphs and on new concentration bounds for the degree evolution in preferential attachment graphs. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Jan Dreier RWTH Aachen University https://tcs.rwth-aachen.de/~dreier/

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04.11.2020, 10:30: Prof. Gabriele Kern-Isberner (TU Dortmund): Towards Lifted Inference: Counting Strategies for Relational Maximum Entropy Reasoning
by Bolke-Hermanns, Helen 30 Oct '20

30 Oct '20

Dear members of the Computer Science Department, You are cordially invited to the talk of Prof. Gabriele Kern-Isberner (TU Dortmund) for next week, 4.11., 10:30. Title: Towards Lifted Inference: Counting Strategies for Relational Maximum Entropy Reasoning Abstract: The principle of maximum entropy (MaxEnt) constitutes a meaningful methodology for drawing non monotonic inferences from probabilistic conditional knowledge as it satisfies some fundamental principles from commonsense reasoning. Similar to alternative approaches to probabilistic reasoning in relational settings, straightforward maximum entropy computations suffer from an exponential dependence from the size of the underlying domain which can lead to intractability in many cases. To overcome this problem, we adopt techniques from weighted first-order model counting (WFOMC) which exploit symmetries and interchangeabilities among the domain elements in order to count models of sentences more efficiently. To meet the requirements of our formalization of knowledge by conditionals, we assign a type to the counted models which captures the three-valued evaluation of the conditionals, i.e. the conditional structure of the models. We present the resulting variant of model counting which we call 'typed model counting' and discuss its benefits by means of some illustrating examples. https://www.informatik.rwth-aachen.de/go/id/jgfze Wednesday, 04.11.2020, 10:30 https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…> Meeting ID: 920 4794 9381 Password: unravel Best regards Helen Bolke-Hermanns Helen Bolke-Hermanns Fachgruppe Informatik RWTH Aachen University Ahornstr. 55, D-52074 Aachen Building E3, 2nd floor, Room 9218 Telefon: +49 (241) 80-21-004 Fax: +49 (241) 80-22 215 E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de> [rwth_informatik_bild_rgb]

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Ankündigung: Informatik-Oberseminar
by Obster, Mathias 26 Oct '20

26 Oct '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 10. November 2020, 14:00 Uhr Ort: Videokonferenz (Zoom-Meeting, Informationen siehe unten) Referent: Mathias Obster, M.Sc. RWTH Informatik 11 - Embedded Software Thema: Unterstützung der SPS-Programmierung durch Statische Analyse während der Programmeingabe Abstract: Durch abstrakte Interpretation und genauer durch Wertemengenanalyse lassen sich vollautomatisch Fehler in Programmcode finden, ohne diesen auszuführen. Bei Programmen für Speicherprogrammierbare Steuerungen (SPSen) ist dies für die Fehlervermeidung von besonderem Interesse, da sie im industriellen Umfeld zum Einsatz kommen. In diesem Vortrag wird ein Ansatz vorgestellt, mit dem diese Methoden bereits während der Programmeingabe, also während der Entwicklung eines SPS-Programms, zur Fehlererkennung und -vermeidung beitragen kann. Dafür wurde das Analyseframework ARCADE.PLC erweitert, sodass es Analyseergebnisse in einer Entwicklungsumgebung darstellen kann, die auch in der Industrie zum Einsatz kommt. So können dort sowohl Warnungen vor potenziell fehlerhaftem Programmverhalten als auch mögliche Variablenwerte dargestellt werden. Ein neu eingeführter inkrementeller Ansatz kann darüber hinaus den Berechnungsaufwand verringern, der sonst durch die häufige Ausführung der Analysen entsteht. Dabei wird ausgenutzt, dass sich während der Entwicklung in kurzen Zeitintervallen üblicherweise nur kleine Änderungen für das Gesamtprogramm ergeben. Für die Bereitstellung einer integrierten Lösung für den Anwender wurden die Konzepte und eine Erweiterung für eine Entwicklungsumgebung für SPS-Programme prototypisch umgesetzt und im Rahmen einer kleinen Nutzerstudie evaluiert. Dabei stand die Frage im Mittelpunkt, ob Programmierer während der Eingabe und Bearbeitung eines SPS-Programms von Ergebnissen der Statischen Analyse profitieren können. Es laden ein: die Dozentinnen und Dozenten der Informatik ******************************** Thema: [Promotion Mathias Obster] Vortrag Uhrzeit: 10.Nov.2020 02:00 PM Amsterdam, Berlin, Rom, Stockholm, Wien Zoom-Meeting beitreten https://rwth.zoom.us/j/98589897540?pwd=OWZidEpuU1N5bjA5UFIxcFhNUEJidz09 Meeting-ID: 985 8989 7540 Kenncode: 111012

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21.10., 10.00: Prof. Michael Schaub, RWTH Aachen: Learning from signals on graphs with unobserved edges
by Bolke-Hermanns, Helen 18 Oct '20

18 Oct '20

Dear members of the Computer Science Department, You are cordially invited to the talk of Prof. Michael Schaub, RWTH Aachen. Title: Learning from signals on graphs with unobserved edges Abstract: In many applications we are confronted with the following system identification scenario: we observe a dynamical process that describes the state of a system at particular times. Based on these observations we want to infer the (dynamical) interactions between the entities we observe. In the context of a distributed system, this typically corresponds to a "network identification" task: find the (weighted) edges of the graph of interconnections. However, often the number of samples we can obtain from such a process are far too few to identify the edges of the network exactly. Can we still reliably infer some aspects of the underlying system? Motivated by this question we consider the following identification problem: instead of trying to infer the exact network, we aim to recover a (low-dimensional) statistical model of the network based on the observed signals on the nodes. More concretely, here we focus on observations that consist of snapshots of a diffusive process that evolves over the unknown network. We model the (unobserved) network as generated from an independent draw from a latent stochastic blockmodel (SBM), and our goal is to infer both the partition of the nodes into blocks, as well as the parameters of this SBM. We present simple spectral algorithms that provably solve the partition and parameter inference problems with high-accuracy. We further discuss some possible variations and extensions of this problem setup. https://www.informatik.rwth-aachen.de/go/id/jgfsd Wednesday, 21.10.2020, 10:00 Join Zoom Meeting https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…> Meeting ID: 920 4794 9381 Password: unravel Best regards Helen Bolke-Hermanns Helen Bolke-Hermanns Fachgruppe Informatik RWTH Aachen University Ahornstr. 55, D-52074 Aachen Building E3, 2nd floor, Room 9218 Telefon: +49 (241) 80-21-004 Fax: +49 (241) 80-22 215 E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de> [rwth_informatik_bild_rgb]

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