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

13.01.2020, 16:00: guest talk: Mahesh Viswanathan, University of Illinois at Urbana-Champaign: Verifying the Privacy and Accuracy ofAlgorithms for Differential Privacy
by Bolke-Hermanns, Helene-Maria 11 Jan '21

11 Jan '21

Happy new year, You are cordially invited to the next UnRAVeL guest talk on Wednesday, 13.01.2021, 16.00: Mahesh Viswanathan, University of Illinois at Urbana-Champaign: Verifying the Privacy and Accuracy of Algorithms for Differential Privacy Differential privacy is a mathematical framework for performing privacy-preserving computations over sensitive data. One important feature of differential privacy algorithms is their ability to achieve provable individual privacy guarantees and at the same time ensure that the outputs are reasonably accurate. Such algorithms compute noisy versions of the right answers to aggregate queries on sensitive data to ensure privacy. Privacy guarantees demand that the algorithm running on "similar" data sets produce responses that are statistically similar; this provides a very strong form of individual privacy. Accuracy, on the other hand, demands that the algorithms output, though noised, be sufficient close to the correct answer for a query. In this talk we will present preliminary results on the algorithmic complexity of checking the privacy and accuracy requirements for a given algorithm. Joint work with Giles Barthe, Rohit Chadha,Vishal Jagannath, Paul Krogmeier, and Prasad Sistla. Based on papers in LICS2020 and POPL 2021. Wednesday, 13.01.2020, 16:00 (!); https://www.unravel.rwth-aachen.de/go/id/kfruu 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 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]

1 0

Einladung: Informatik-Oberseminar Isaak Lim
by Isaak Lim 06 Jan '21

06 Jan '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 21. Januar 2021, 15.00 Uhr Zoom: https://rwth.zoom.us/j/98847090675?pwd=TnA5L3NCTkx0TjBmMWJEZHZnQkx1QT09 Meeting-ID: 988 4709 0675 Kenncode: 904296 Referent: Isaak Lim M.Sc. Lehrstuhl Informatik 8 Thema: Learned Embeddings for Geometric Data Abstract: Solving high-level tasks on 3D shapes such as classification, segmentation, vertex-to-vertex maps or computing the perceived style similarity between shapes requires methods that are able to extract the necessary information from geometric data and describe the appropriate properties. Constructing functions that do this by hand is challenging because it is unclear how and which information to extract for a task. Furthermore, it is difficult to determine how to use the extracted information to provide answers to the questions about shapes that are being asked (e.g. what category a shape belongs to). To this end, we propose to learn functions that map geometric data to an embedding space. The outputs of those maps are compressed encodings of the input geometric data that can be optimized to contain all necessary task-dependent information. These encodings can then be compared directly (e.g. via the Euclidean distance) or by other fairly simple functions to provide answers to the questions being asked. Neural networks can be used to implement such maps and comparison functions. This has the benefit that they offer flexibility and expressiveness. Furthermore, information extraction and comparison can be automated by designing appropriate objective functions that are used to optimize the parameters of the neural networks on geometric data collections with task-related meta information provided by humans. We therefore have to answer two questions. Firstly, given the often irregular nature of representations of 3D shapes, how can geometric data be represented as input to neural networks and how should such networks be constructed? Secondly, how can we design the resulting embedding space provided by neural networks in such a manner that we are able to achieve good results on high-level tasks on 3D shapes? In this talk we provide answers to these two questions. Concretely, de- pending on the availability of the data sources and the task specific requirements we compute encodings from geometric data representations in the form of images, point clouds and triangle meshes. Once we have a suitable way to encode the input, we explore different ways in which to design the learned embedding space by careful construction of appropriate objective functions that extend beyond straightforward cross-entropy minimization based approaches on categorical distributions. We show that these approaches are able to achieve good results in both discriminative as well as generative tasks. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 0

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

1 0

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

1 0

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]

1 0

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]

1 0

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

1 0

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]

1 0

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/

1 0

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]

1 0