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

Einladung: Informatik-Oberseminar Christian Martin Fiedler
by DSME-Office 12 Mar '25

12 Mar '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 25. März 2025, 14.00 Uhr Ort: Seminarraum 423, 4. OG, Theaterstraße 35-39, 52062 Aachen Referent: Christian Martin Fiedler M.Sc. Institut für Data Science im Maschinenbau (DSME) Thema: Contributions to Kernel Methods in Systems and Control Abstract: Machine learning is increasingly used in systems and control, which is motivated by challenging control, simulation and analysis problems, abundant data and computing resources, as well as impressive theoretical and methodological advances in machine learning. The established class of kernel methods is of particular interest in this context, due to their rich theory, modularity, efficient and reliable algorithms, and indeed kernel methods are frequently used in systems and control. We present two exemplary and complementary contributions to this flourishing field. First, many learning-based control approaches are based on combining uncertainty bounds for Gaussian process (GP) regression with robust control methods. We revisit the foundations of this domain by consolidating, improving, and carefully evaluating the required uncertainty bounds, and use them in learning-enhanced control applications. Furthermore, we discuss a severe practical limitation of these approaches, the a priori knowledge of an upper bound on the reproducing kernel Hilbert space (RKHS) norm of the target function, and propose to combine geometric assumptions together with kernel methods as a promising alternative. Second, we initiate a new research direction by combining kernels with mean field limits as appearing in kinetic theory. Motivated by learning problems on large-scale multiagent systems, we introduce mean field limits of kernels, and provide an extensive theory for the resulting RKHSs. This is used in turn in the analysis of kernel-based statistical learning in the mean field limit, which not only is a novel form of large-scale limit in theoretical machine learning, but provides also a solid foundation for applications in kinetic theory. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung Informatik Oberseminar Lubna Ali
by Bombelka, Angelika 28 Feb '25

28 Feb '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 30. Januar 2024, 14.00 Uhr Ort: 9222, E3, Ahornstr. 55 und hybrid via Zoom (https://rwth.zoom-x.de/j/64937773189?pwd=eGttNUMzSElnQUVkc3FrYzBqK2F4UT09) Referent: Lubna Ali M.Sc. RWTH Lehr- und Forschungsgebiet Informatik 9 (Lerntechnologien) Thema: convOERter: A Technical Assistance Tool to Support Semi-Automatic Conversion of Images in Educational Materials as OER Abstract: Open Educational Resources (OER) are seen as an important element in the process of digitizing higher education teaching and as essential building blocks for openness in education. They can be defined as teaching, learning, and research materials that have been made openly available, shareable, and modifiable. OER include different types of resources such as full courses, textbooks, videos, presentations, tests, and images, which are usually published under the open Creative Commons licences. OER can play an important role in improving education by facilitating access to high quality digital educational materials. Accordingly, there is a steady increase among higher education institutions to participate in the so-called "open movement" in general and in utilizing OER in particular. Nevertheless, there are many challenges that still face the deployment of OER in the educational context. One of the main challenges is the production of new OER materials and converting already existing materials into OER, which could be viable by qualifying educators through training courses and/or supporting them with specific tools. There are many platforms and tools that support the creation of new OER content. However, to our knowledge, there are no tools that perform fully- or semi-automatic conversion of already existing educational materials. This identified gap was the basis for the design and implementation of the OER conversion tool (convOERter). The tool supports the user by semi-automatically converting educational materials containing images into OER-compliant materials. The main functionality of the tool is based on reading a file, extracting all images as well as all possible metadata, and substituting the extracted images with OER elements in a semi-automated way. The retrieved OER images are referenced and licenced properly according to the known TASLL rule. Finally, the entire file is automatically licenced under Creative Commons excluding specific elements from the entire licence such as logos. In order to evaluate the effectiveness of the tool in promoting the use of OER, a comprehensive user study was conducted with educators and OER enthusiastic at different universities. The study was accomplished by offering a series of OER evaluation workshops to compare the conversion efficiency of the tool with manual conversion. The results show that using the conversion tool improves the conversion process in terms of speed, license quality, and total efficiency. These results highlight that the tool can be a valuable addition to the community, especially for users less experienced with OER. As a future work, it is intended to further develop the tool and improve its functionality. Additionally, a long-term study can be conducted to assess the impact of the tool in facilitating and enhancing the production of OER on a larger scale. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Simon Schwitanski
by Simon Schwitanski 21 Jan '25

21 Jan '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 5. Februar 2025, 13.00 Uhr Ort: Seminarraum 003, IT Center, Kopernikusstraße 6 Referent: Simon Schwitanski, M.Sc. Lehrstuhl für Hochleistungsrechnen (Informatik 12) Thema: Modeling Synchronization and Consistency for Data Race Detection in Remote Memory Access Programs Abstract: The increasing parallelism in today's supercomputers requires scalable parallel programming methods with efficient communication models. The traditional communication model in scientific computing is message passing: Both the sending and receiving process participate actively in the data exchange via messages. Remote Memory Access (RMA) models provide an alternative communication method where processes can access the memory of other processes directly. RMA models avoid unnecessary synchronization between processes and outperform the traditional message-passing model in modern supercomputers. However, they require users to explicitly ensure the synchronization and consistency of memory accesses through corresponding API calls. Otherwise, concurrent conflicting memory accesses lead to data races with undefined behavior. The non-deterministic nature of data races, commonly known from shared-memory programming, makes them difficult to detect manually. This thesis investigates data races in RMA programs and provides novel scalable methods to detect them at runtime. A classification of data races in the RMA models MPI~RMA, OpenSHMEM, and GASPI shows that synchronization and consistency are the two key properties that a correctness tool must capture to identify RMA races. This thesis provides formal models that allow analyzing both properties in RMA programs. The synchronization model analyzes the happened-before relation of events using a vector clock exchange. It captures the synchronization state of processes at runtime. The consistency model formalizes a relation defining when a remote memory access is guaranteed to be completed. Both models are combined in a generalized on-the-fly race detection model that can detect RMA data races independent of the concrete RMA model used in an application. The developed race detection model is implemented in a tool named RMASanitizer. It combines the shared-memory race detector ThreadSanitizer with the correctness checking tool MUST to detect RMA data races in MPI RMA, OpenSHMEM, and GASPI at runtime. For the evaluation, this thesis provides RMARaceBench, a classification quality benchmark suite designed to quantify the detection accuracy of RMA race detection tools. The evaluation with RMARaceBench shows that RMASanitizer has the highest detection accuracy compared to other state-of-the-art RMA race detectors. An overhead study with RMA proxy applications running with up to 700 processes shows that RMASanitizer is applicable to large-scale workloads. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Herr Sascha Martin Welten
by Maaßen, Leany 06 Jan '25

06 Jan '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 17.01.2025, 14:00-15:00 Uhr Ort: Raum 5053.2 (großer B-IT-Hörsaal)/Informatikzentrum, Ahornstraße 55 Referent: Herr Sascha Martin Welten, M. Sc. Lehrstuhl Informatik 5 Thema: Methods for Practical Data Sharing and Decentralised Analytics: An Integrated Approach Abstract: This dissertation addresses the challenges of sharing data across organisational boundaries, which is crucial for scientific progress, particularly with the rise of AI and data-driven approaches. While data sharing is essential for innovation, concerns around privacy, security, and regulatory compliance often limit access to valuable data. This work introduces an integrated framework that combines privacy-preserving methods with Decentralised Analytics (DA), addressing key requirements such as trust, controlled access, and data heterogeneity. The research develops a DevOps-inspired lifecycle, 'DAOps', for managing shared data analysis, implemented in a novel platform, PADME (Platform for Analytics and Distributed Machine Learning for Enterprises). PADME supports various types of analysis and enables decentralised model training, offering a privacy-preserving alternative to traditional centralised methods. The platforms evaluation in healthcare and hydrology confirms its operational readiness and applicability to real-world research scenarios. Key findings highlight the importance of common data schema standards and balanced data distributions for successful data sharing. Ultimately, this dissertation contributes to the practical realisation of data-sharing concepts, promoting secure, decentralised data analysis and fostering data-driven research collaborations. 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. Gerhard Lakemeyer Ph.D., JunProf. Dr. Sandra Geisler 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 Alexander Kugler
by Kugler, Alexander 18 Dec '24

18 Dec '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 7. Januar 2025, 16.00 Uhr Ort: Lehrstuhl i11, Im Süsterfeld 9, Aachen, Raum R259 im 2. OG Referent: Alexander Kugler M.Sc. Chair of Computer Science 11 - Embedded Software Thema: Test Case Generation from Natural Language Requirements for Embedded Systems with Semantic Role Labeling Abstract: In this talk, the application of Semantic Role Labeling (SRL) for the generation of test cases from natural language requirements for embedded systems is explored. The approach, labelled Test Generation with Semantic Role Labeling (TG-SRL), is composed of five stages and combines machine learning with a rule-based approach. Information extracted via SRL is initially aggregated into logical expressions before being translated into First-Order Logic (FOL) formulae. Test case generation is achieved using Satisfiability Modulo Theory (SMT) solving. By modifying the SMT instance according to defined tactics, a test suite is generated. The thesis concludes with an evaluation of TG-SRL using a mutant-based strength analysis, and a comparison with the Nat2Test approach from Carvalho et al. TG-SRL performs favorably and provides valuable insights into employing Natural Language Processing (NLP) methods, and in particular SRL, in the field of test case generation. The methods and concepts presented in this thesis have been implemented in a publicly available research framework. Es laden ein: die Dozentinnen und Dozenten der Informatik

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[sekretariate] Einladung: Informatik-Oberseminar Kevin Batz
by Elke Ohlenforst 11 Dec '24

11 Dec '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 20.12.2024, 13:00 Uhr Ort: Raum 9222 (E3, Informatikzentrum, Ahornstr. 55) Referent: Kevin Batz, M.Sc. Lehrstuhl für Softwaremodellierung und Verifikation (Informatik 2) Thema: Automated Deductive Verification of Probabilistic Programs Abstract: We study both foundational and practical aspects of the automated deductive verification of discrete probabilistic programs. Our special emphasis is on the verification of possibly unbounded loops by quantitative loop invariants. We build upon Kozen's, McIver & Morgan's, and Kaminski's weakest preexpectation calculus for reasoning about expected outcomes such as the probability of terminating in some postcondition or the expected final value of a program variable. The weakest preexpectation calculus replaces predicates from classical program verification by more general expectations - functions mapping program states to numbers instead of truth values. We study a syntax for specifying expectations, providing a foundation for automated deductive probabilistic program verifiers. We prove that our syntax is expressive and hence obtain a relatively complete verification system for reasoning about expected outcomes. We then present three different approaches for automating the verification of bounds on expected outcomes of linear loops, all of which combine weakest preexpectation reasoning with Satisfiability Modulo Theories (SMT) techniques: 1. We revisit two well-established verification techniques for transition systems, k-induction and bounded model checking (BMC), in the more general setting of bounding least fixpoints of functions over complete lattices, yielding latticed k-induction and latticed BMC. Instantiating our latticed techniques with the weakest preexpectation calculus enables the fully automatic verification of linear loops taken from the literature. 2. We present a counterexample-guided inductive synthesis approach for the synthesis of quantitative loop invariants. This enables the fully automatic verification of bounds on both expected outcomes and expected runtimes. Our implementation synthesizes quantitative invariants of various linear loops taken from the literature, can beat state-of-the-art probabilistic model checkers on finite-state programs, and is competitive with modern tools dedicated to invariant synthesis or expected runtime reasoning. 3. We present PrIC3 - the first truly quantitative extension of the state-of-the-art qualitative symbolic model checking algorithm IC3 to symbolic model checking of Markov decision processes. By marrying PrIC3 with the weakest preexpectation calculus, we obtain a symbolic model checking algorithm for probabilistic programs. Alongside, we present an implementation of PrIC3 featuring IC3-specific techniques such as generalization. ------------------------------------------------------------------------ Es laden ein: die Dozentinnen und Dozenten der Informatik _______________________________________________ informatik-sekretariate Mailingliste -- informatik-sekretariate(a)lists.rwth-aachen.de Um den Newsletter abzubestellen, senden Sie bitte eine E-Mail an informatik-sekretariate-leave(a)lists.rwth-aachen.de

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Einladung: Informatik-Oberseminar Patrick Schmidt
by Schmidt, Patrick 10 Dec '24

10 Dec '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 18. Dezember 2024, 13:15 Uhr Ort: Raum 118, Erweiterungsbau 3, Ahornstr. 55 Referent: Patrick Schmidt M.Sc. Lehrstuhl für Informatik 8 Thema: Intrinsic Optimization of Maps Between Surfaces Abstract: This talk presents a series of works focusing on the construction and optimization of high-quality maps between surfaces in 3D. These surface maps establish point-to-point correspondences between multiple 3D models, enabling applications such as attribute transfer, shape interpolation, shape co-analysis, compatible remeshing, and more. We introduce a novel framework to generate surface homeomorphisms—maps that guarantee continuity and bijectivity—while minimizing intrinsic mapping distortion. Along the way, we will explore constructions in spherical and hyperbolic geometry and discuss practical solutions for continuous optimization of geometric problems using automatic differentiation. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Herr Alexander Tobias Neumann
by Maaßen, Leany 04 Dec '24

04 Dec '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 13.12.2024, 12:30-13:30 Uhr Ort: Raum 5053.2 (großer B-IT-Hörsaal)/Informatikzentrum, Ahornstraße 55 Referent: Herr Alexander Tobias Neumann, M. Sc. Lehrstuhl Informatik 5 Thema: Chatbots as Professional Companions in Large-Scale Community Information Systems: Integrating Chatbots in Educational Ecosystems Abstract: Online communities are fundamental for knowledge sharing, collaboration, and professional development in our digital age. However, challenges like information overload and low engagement hinder their effectiveness. New members often struggle to find accurate information from multiple sources. Chatbots with natural language understanding and automation capabilities can solve these challenges. By providing personalized guidance, improving efficiency, and integrating with external knowledge bases, chatbots enhance the user experience in online communities. Unfortunately, community members often lack the necessary resources or expertise to create bots that optimize their practices. This dissertation follows a design science approach that empowers communities to create and maintain customized chatbots collaboratively. We introduce a model-driven Social Bot Framework that allows community members to participate in bot creation and evaluation. This framework incorporates process mining techniques and leverages Large Language Models (LLMs) to enhance bot performance. The dissertation also presents a catalog of success factors for bots, providing a multi-dimensional model for assessing chatbot effectiveness within communities of practice. Through a co-design process involving real-world use cases, we have produced, demonstrated, and evaluated chatbots as artifacts, showcasing their impact and usefulness. Our research explores the application of chatbots in Technology Enhanced Learning (TEL). We developed a series of mentoring bots that address various aspects of the educational process. These include systems for collaborative knowledge building, providing personalized feedback on writing tasks, and facilitating quiz-based learning. We also investigated the integration of gamification elements to enhance user engagement and motivation in educational contexts. Furthermore, we leveraged LLMs to provide more sophisticated, personalized learning support. The evaluations, conducted through user studies, surveys, and performance metrics, demonstrate the effectiveness and user acceptance of our developed chatbots. Our research promotes the democratization of bot-building, enabling communities to leverage chatbot potential. This represents a significant advancement in the self-governance and development of online communities in response to changing needs and opportunities. Ultimately, this work pushes the boundaries of current community practices, opening up new horizons where bots are professional companions supporting the success of online communities. 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., JunProf. Dr. Sandra Geisler 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
by Mokhtarian, Armin 02 Dec '24

02 Dec '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 6. Dezember 2024, 16.00 Uhr Ort: Raum 259, Im Süsterfeld 9, 52072 Aachen Referent: Armin Mokhtarian M.Sc. Lehrstuhl Informatik 11 Thema: CPM Remote: A Remote Access to a Scaled Experimental Testbed for Connected and Automated Vehicles Abstract: Small-scale testbeds are used in research and development to provide a controlled setting for testing new concepts. They are affordable and can be duplicated, making them useful for both education and research purposes. To address the limitations of small-scale testbeds, particularly their accessibility, we introduce CPM Remote, a web-based platform that provides real-time access to the Cyber-Physical Mobility (CPM) Lab, a scaled testbed at RWTH Aachen University. This innovative approach eliminates the need for users to travel, purchase, or reconstruct physical testbeds. CPM Remote incorporates a built-in editor, a simulation environment, and a real-time interface to the CPM Lab, extending accessibility to a global audience. Users have the flexibility to conduct their own experiments or choose from two ready-to-use applications, enabling them to actively engage with the field of Connected and Automated Vehicles (CAVs). The educational application, CPM Academy, offers students an immersive learning experience by presenting a package delivery service scenario to develop algorithms within the context of CAVs. For research purposes, we introduce CPM Olympics, an annual competition that offers participants real-world scenarios to benchmark their motion planning algorithms. In our evaluation of the platform and its applications, we carried out multiple user studies and a technology acceptance analysis to pinpoint potential areas for enhancement. The feedback from each study was considered during the development of CPM Remote to enhance its feature set and usability. Based on the successfully conducted iterations of the CPM Academy and CPM Olympics, we demonstrate how CPM Remote bridges the gap between simulations and costly real-world tests, democratizing access to experimental research infrastructure.

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Einladung: Informatik-Oberseminar Alexander
by DSME-Office 29 Nov '24

29 Nov '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 3. Dezember 2024, 12.00 Uhr Ort: Technologiezentrum Aachen, Seminarraum Halle F1 Referent: Alexander von Rohr M.Sc. Institut für Data Science im Maschinenbau (DSME) Thema: Probabilistic Optimization for the Control of Dynamical Systems Abstract: Controlling dynamical systems is a key aspect of automation technology. However, designing resource-efficient controllers for each system is challenging and costly. Machine learning can help automate this process, but learning-based control requires data-efficient and robust methods. We frame learning-based control as a probabilistic optimization problem, proposing novel methods for decision-making under uncertainty and learning-based control algorithms with formal guarantees. First, we address controller tuning using Bayesian optimization to efficiently improve candidate controllers, validated on synthetic problems, simulations, and hardware experiments. Second, we propose three probabilistically robust control methods for uncertain, data-driven models, addressing probabilistic stability, aleatoric uncertainty, and event-triggered learning for time-varying dynamics. Es laden ein: die Dozentinnen und Dozenten der Informatik

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