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

[infprof-l] Einladung Informatik-Oberseminar Philipp Weidel, December 15, 2020
by Abigail Morrison 02 Mar '21

02 Mar '21

********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 15. Dezember 2020, 10:00 Uhr Zoom: https://rwth.zoom.us/j/99233095930?pwd=dHhTV253V1ZYUzRtSkk1L3A1REZVUT09 Meeting-ID: 992 3309 5930 Kenncode: 626162 Referent: Philipp Weidel, Dipl. Inform. Thema: Learning and decision making in closed loop simulations of plastic spiking neural networks Abstract: To understand how animals and humans learn, form memories and make decisions is a highly relevant goal both for neuroscience and for fields that take some inspiration from neuroscience, such as machine learning and artificial intelligence. Many models of learning and decision making were developed in the fields of machine learning, artificial intelligence, and computational neuroscience. Although these models aim to describe similar mechanisms, they do not all pursue the same goal. These models can be differentiated between models aiming to reach optimal performance on a specific task (or set of tasks) and models trying to explain how animals and humans learn. Some models of the first class use biologically inspired methods (such as deep learning) but are usually not biologically realistic and are therefore not well suited to explain the function of the brain. Models in the second class focus on being biologically plausible to explain how the brain works, but often demonstrate their capability on too simplistic tasks and yield low performance on well-known tasks from machine learning. This work aims to close the gap between these two types of models. In the first part of this talk, tools are described that allow the combination of biologically plausible neural network models together with powerful toolkits known from machine learning and robotics. To this end, MUSIC, the middleware for spiking neural network simulators such as NEST and NEURON is interfaced with ROS, a middleware for robotic hardware and simulators such as Gazebo. This toolchain is extended with interfaces to reinforcement learning toolkits such as the OpenAI Gym. The second part addresses the question of how the brain can represent its environment in the neural substrate of the cortex and how a realistic model of reinforcement learning can make use of these representations. To this end, a spiking neural network model of unsupervised learning is presented which is able to learn its input projections such that it can detect and represent repeating patterns. By using an actor-critic reinforcement learning architecture driven by a realistic dopamine modulated plasticity rule the model can make use of the representations and learn a range tasks. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Prof. Dr. Abigail Morrison IAS-6 / INM-6 / SimLab Neuroscience Jülich Research Center & Computer Science 3 - Software Engineering RWTH Aachen http://www.fz-juelich.de/inm/inm-6 http://www.fz-juelich.de/ias/jsc/slns http://www.se-rwth.de Office: +49 2461 61-9805 Fax # : +49 2461 61-9460 ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Volker Rieke Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

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Einladung: Informatik-Kolloquium Christina Büsing
by Bolke-Hermanns, Helene-Maria 15 Feb '21

15 Feb '21

********************************************************************** * * * Einladung * * * * Informatik-Kolloquium * * * ********************************************************************** Zeit: Mittwoch, 24. Februar 2021, 16:15-17:00 Uhr Ort: https://rwth.zoom.us/j/98137553896?pwd=WmVnYmhKdXhrM0hjS3NvQy84SjcwUT09 Meeting ID: 981 3755 3896 Passcode: 395762 ********************************************************************** Vortragende: Christina Büsing (RWTH) Titel: Operational Planning for Mobile Medical Units Abstract: Mobile medical units allow for an efficient medical coverage of sparsely populated, spacious areas. Unfortunately, flexibility comes at the price of a highly complex operation planning process. We developed a multi-staged optimization approach for the operation of mobile medical units combining facility location, scheduling and routing problems. To determine our vehicle routes, we have to solve a budgeted matching problem on an edge colored graph, which we refer to as budgeted colored matching problem (BCM). We show the strong NP-hardness of the BCM on bipartite graphs with uniform edge weights, costs and budgets using a reduction from (3,B2)-SAT. On special graph classes, the BCM is solvable in pseudo-polynomial time. Finally, we evaluate the usage of mobile medical units in our model region situated in the northern Eifel. To assess the quality of solutions, we developed an agent-based simulation, which models the interaction between patients and general practitioners. Es laden ein: die Dozentinnen und Dozenten der Informatik **********************************************************************

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Einladung: Informatik-Oberseminar Frau Sarah Suleri
by Leany Maaßen 09 Feb '21

09 Feb '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 18. Februar 2021, 11:00-12:00 Uhr Zoom: https://rwth.zoom.us/j/92762889185?pwd=ZVdJV2o1bk13SlJCSDFRanB2TEYwUT09 Meeting-ID: 927 6288 9185 Kenncode: 725212 Referentin: Frau Sarah Suleri, M. Sc. Lehrstuhl Informatik 5 Thema: Impact of Technological Support on the Workload of Software Prototyping Abstract: Prototyping is a broadly utilized iterative technique for brainstorming, communicating, and evaluating user interface (UI) designs. This research aims to analyze this process from three aspects: traditional UI prototyping, rapid prototyping, and prototyping for accessibility. We propose three novel approaches and realize them by introducing three artifacts: 1) Eve, a sketch-based prototyping workbench that supports automation of transforming low fidelity prototypes to higher fidelities, 2) Kiwi, a UI design pattern and guidelines library to support UI design pattern-driven prototyping, 3) Personify, a persona-based UI design guidelines library for accessible UI prototyping. We evaluate the usability of these artifacts, and the results indicate good usability and learnability. Furthermore, we use NASA-TLX to study the impact of using these three novel approaches on the subjective workload experienced by the designers during the software prototyping process. This work aims to extend prior work on UI prototyping and is broadly applicable to understand the impact of using deep learning, UI design patterns, and personas on the workload of UI prototyping. 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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Einladung: Informatik-Oberseminar Herr Gustavo Alejandro Aragón Cabrera
by Leany Maaßen 09 Feb '21

09 Feb '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 24. Februar 2021, 15:00-16:00 Uhr Zoom: <https://rwth.zoom.us/j/95286199371?pwd=M3hCUWk3VER6WnE0RlZzQUFBVFMzdz09> https://rwth.zoom.us/j/95286199371?pwd=M3hCUWk3VER6WnE0RlZzQUFBVFMzdz09 Meeting-ID: 952 8619 9371 Kenncode: 424815 Referent: Herr Gustavo Alejandro Aragón Cabrera, M. Sc. Lehrstuhl Informatik 5 Thema: Extended Model Predictive Control Software Framework for Real-Time Local Management of Complex Energy Systems Abstract: Regarding the requirements of the future electrical grid due to high share of renewable distributed generation, smart energy management systems (EMS) are necessary to integrate the rapid development of ICT technologies and new sensor and actuator technologies. This thesis deals with the challenges of Model Predictive Control (MPC)-based EMS and introduces the concept of extended MPC framework that includes ICT aspects of the integration of new sensor and actuator technologies, forecasting methods, data management, flexible optimization model definition and solvers integration in consideration of user needs and deployment requirements in real systems. The extended MPC framework is validated through its deployment in three different use cases: as discrete and stochastic MPC in real test sites and linked to a power flow simulator. 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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Einladung: Informatik-Oberseminar Anton Pirogov
by Anton Pirogov 02 Feb '21

02 Feb '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 18. Februar 2021, 10:15 Uhr Ort: Zoom Videokonferenz Link: https://rwth.zoom.us/j/96519556069?pwd=MUVqbXpvZE5XVXBSdDNHZW5LMmJpQT09 Meeting-ID: 965 1955 6069 Kenncode: 482378 Referent: Anton Pirogov, M.Sc. Lehrstuhl Informatik 7 Thema: Determinization and Ambiguity of Classical and Probabilistic Büchi Automata Abstract: Büchi automata can be seen as a straight-forward generalization of ordinary NFA, adapted to handle infinite words. While they were originally introduced for applications in decidability of logics, they became a popular tool for practical applications, e.g. in automata-based approaches to model checking and synthesis problems. Being arguably both the simplest and most well-known variant in the zoo of so-called omega-automata that are considered in this setting, they serve as an intermediate representation of omega-regular specifications of verification or synthesis requirements that are usually expressed in a more declarative fashion, e.g. using linear temporal logic. Unfortunately, nondeterministic automata are not directly suitable for certain applications, whereas deterministic Büchi automata are less expressive. This problem is usually solved by either constructing deterministic automata of a different kind, or by restricting their ambiguity, i.e., the maximal number of accepting runs on some word. In both cases, the transformation is expensive, yielding an exponential blow-up of the state space in the worst case. Therefore, optimized constructions and heuristics for common special cases are useful and in demand for actual practical applications. In this talk we present a new general construction for determinization from nondeterministic Büchi to deterministic parity automata that unifies the dominant branches of previous approaches based on the Safra construction and the Muller-Schupp construction. Additionally, we sketch a number of new heuristics for determinization, some of which exploit properties of our unified construction. Apart from the classical nondeterministic and deterministic variants of automata, it is natural to consider probabilistic automata, i.e., automata that use a probability distribution on the states instead of nondeterminism to decide which state to go to next. It is known that in general, such automata are more expressive than classical automata and many decision problems are undecidable. We present subclasses of probabilistic automata that correspond to certain ambiguity classes and are not more expressive than classical automata. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung Informatik-Oberseminar Konrad Anton Fögen
by Zinner, Marion 26 Jan '21

26 Jan '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 10. Februar 2021, 14.00 Uhr Zoom: https://rwth.zoom.us/j/91716576115?pwd=ZzUwemxXWUJkQURjQjJibmlGb3dYZz09 Meeting ID: 917 1657 6115 Passcode: 496556 Referent: Konrad Anton Fögen M.Sc. Lehr- und Forschungsgebiet Softwarekonstruktion Thema: Combinatorial Robustness Testing based on Error-Constraints Abstract: In this talk, we present an extension to combinatorial testing (CT) which is an effective specification-based test method that is based on an input parameter model (IPM). We argue that robustness is an important property of a software, which must be tested in addition to a software's functionality. This requires invalid values and invalid value combinations to be able to observe a software's reaction to them. However, the effectiveness of CT deteriorates in the presence of invalid values or invalid value combinations. This phenomenon is called invalid input masking effect and is already acknowledged in some research. It led to extensions of CT that we call combinatorial robustness testing (CRT). The objective of CRT is to improve the fault detection by avoiding invalid input masking. This is achieved by separating the testing of valid values and valid value combinations from the testing of invalid values and invalid value combinations. While CRT is a promising extension of CT, it is still insufficiently researched. For instance, in related work, IPMs are extended with additional semantic information to specify invalid values. However, invalid value combinations cannot be specified directly. Therefore, the objective of this work is to further expand the idea of CRT. The aim is to develop a new CRT test method with a modeling approach to specify invalid values and invalid value combinations equally well. This modeling approach should also be incorporated into explicit test adequate criteria and test selection strategies. Furthermore, this modeling approach shall be supported by automated techniques. First, we conduct a controlled experiment to check if CRT is necessary at all or if CT is already appropriate to test robustness. Based on the result, we continue and develop a refined t-factor fault model that incorporates robustness faults and the inherent invalid input masking effect. Next, we develop a new test method for CRT and introduce a new structure that extends the structure of IPMs. It is called robustness input parameter model (RIPM) and contains the concept of error-constraints which is an additional set of logical expressions to describe the validity of values and value combinations. With the refined t-factor fault model and the new RIPM structure, new test adequacy criteria that incorporate the additional semantic information and new test selection strategies that satisfy the test adequacy criteria are developed. The new concept of error-constraints requires additional effort in modeling. Therefore, we develop two techniques to support the modeling of them. First, we develop a technique to identify and repair inconsistencies among error-constraints. Second, we develop a technique to automatically generate error-constraints based on the conformance to another system. Last but not least, all aforementioned concepts and techniques are operationalized and integrated in a test automation framework which includes a process, an architecture, and a Java-based reference implementation. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung Informatik-Oberseminar Oliver Kautz
by Kautz, Oliver 25 Jan '21

25 Jan '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 10. Februar 2021, 15.00 Uhr Zoom: https://rwth.zoom.us/j/96367107600?pwd=c2lMb2M1OXZXSHZFalRySUR2QTExUT09 Referent: Oliver Kautz M.Sc. Lehrstuhl Informatik 3 Thema: Model Analyses Based on Semantic Differencing and Automatic Model Repair Abstract: Models are the primary development artifacts used in model-driven software development. Therefore, models continuously evolve during the design, development, and maintenance of software systems. Thus, model differencing is an important task to understand the syntactic and semantic differences between model versions. Previous work produced general (and thus language-independent) approaches for syntactic model differencing, but only a few language-dependent approaches for semantic model differencing. Approaches combining syntactic with semantic model differencing by relating the syntactic changes of models to their semantic differences rarely exist. Previous work neglected the development of language-independent approaches abstracting from a concrete model property for detecting the syntactic elements of a model, which cause that the model does not satisfy the property. If the property encodes a requirement and the non-satisfaction represents the existence of a bug, then detecting the syntactic model elements causing the non-satisfaction of the property facilitates developers in detecting the syntactic model elements causing the bug. In this talk, we present a framework for precisely defining modeling languages, including syntax, semantics, and model evolution possibilities. We discuss syntactic and semantic model differencing. The framework is instantiated with four concrete modeling languages: Time-synchronous port automata, feature diagrams, sequence diagrams, and activity diagrams. Based on the framework for precisely defining modeling languages, we present a modeling language and property-independent framework for automatic model repairs. The framework facilitates developers in detecting the syntactic elements of a model causing that the model does not satisfy a property. Instantiating the framework with a concrete modeling language and a concrete model property enables the automatic calculation of syntactic changes that transform a model not satisfying the property to a model that satisfies the property. The framework relies on the assumption that it is possible to partition the syntactic changes applicable to each model into finitely many model-specific and property- specific equivalence classes. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung Informatik-Oberseminar Martin Serror
by Martin Serror 20 Jan '21

20 Jan '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 29. Januar 2021, 14.00 Uhr Zoom: https://rwth.zoom.us/j/95719946489?pwd=S0lITm9pcW45b1k4SW5EVis2a1poQT09 Referent: Martin Serror, M.Sc. Lehrstuhl Informatik 4 Thema: On the Benefits of Cooperation for Dependable Wireless Communications Abstract: The emerging Industrial Internet-of-Things (IIoT) improves flexibility, productivity, and costs of industrial processes by connecting sensors, actuators, and controllers to each other and the Internet. On the factory floor, such interconnections increasingly rely on wireless communications, reducing deployment and maintenance costs while supporting the mobility of communication partners. The industrial domain, however, is mainly characterized by safety- and mission-critical Machine-to-Machine communication. Therefore, state-of-the-art wireless communication protocols for home and business environments, such as WLAN and Bluetooth, are not suited for the IIoT. Consequently, the IIoT requires dependable wireless communication, achieving both high reliability and low latency. A promising approach for so-called Ultra-Reliable Low-Latency Communication (URLLC) in the IIoT is cooperative diversity, since the participating stations already collaborate toward a common goal, i. e., keeping the industrial process running. There, a sending station exploits multiple independent transmission paths via cooperating relays to convey a packet to its destination reliably. In contrast to spatial diversity, this approach also works with single-input single-output transceivers. However, when considering relaying for URLLC, it is particularly challenging that all participants have to share the scarce transmission resources. Hence, in this talk, we investigate various mechanisms enabling dependable wireless communication, i. e., increasing communication reliability within a bounded low latency, where we mainly focus on the benefits of cooperative diversity. Therefore, we explore different design options for URLLC and evaluate them, leveraging the advantages of different methodological approaches. This talk thus offers valuable insights into designing communication protocols with challenging requirements. At the example of cooperation, we thoroughly retrace the development process from analysis to prototypical deployment. On the one hand, the achieved results contribute to URLLC for the IIoT; on the other hand, they provide a critical examination of the selected evaluation methodologies. Es laden ein: die Dozentinnen und Dozenten der Informatik

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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]

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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

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