July 2024 - informatik-vortraege - lists.rwth-aachen.de

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 Hinrikus Wolf
by Cormann, Silke 24 Jul '24

24 Jul '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 08. August 2024, 10:00 - 11:00 Uhr Ort: Raum 9222 (großer Seminarraum, Erweiterungsbau 3, Informatikzentrum, Ahornstraße 55) Referent: Herr Hinrikus Eike Wolf, M. Sc. Lehrstuhl Logik und Theorie diskreter Systeme (Informatik 7) Thema: Learning on Graphs from Theory to Industrial Application in Power Management of Distribution Grids Abstract: Learning on graphs has strong ties to theoretical computer science, as some algorithms used for learning are rooted in graph theory. Furthermore, expressivity of learning methods is analysed with techniques from theoretical computer science. From a practical perspective, graph learning finds application in a wide range of domains, such as biochemistry, social science, and in case of this thesis in power management of electric grids. An illustrative example for graph learning is to predict whether a chemical molecule is toxic or non-toxic. The task behind this example involves predicting properties of the whole graph. Beyond this, graph learning includes also to node level tasks, and link prediction. We propose structural node embeddings motivated from Lovasz' (1967) theory of graph homomorphism counts. These are the number of mappings from Graph H to G such that vertices which are adjacent in H are also adjacent in G. The node embeddings consist of vectors representing homomorphism counts from families of graphs within the graph to be embedded. We showcase that our approach achieves comparable accuracy to other methods on benchmark data, except for recent GNN architectures. We conduct a study of the stability of node embeddings across five prominent methods. Most embedding techniques inherently depend on randomness. We analyse the effects of this randomness on the embeddings themselves and on downstream tasks, uncovering significant instabilities, particularly in individual predictions. This finding is crucial for practitioners in selecting an embedding method that meets the requirements of their tasks. We present a GNN architecture for the AC Power Flow problem, which helps detect congestion in AC (alternating current) grids. The AC Power Flow is a non-linear, complex optimization problem without a closed-form solution and is typically addressed using Newton's iterative method. Experimentally, we demonstrate that our method is able to generalize to unknown grids. While the model is better than previous neural approaches, it is not accurate enough to replace classical solvers. We introduce a deep reinforcement learning architecture that can resolve congestion detected by AC Power Flow computation. Congestions appear more often in electric grids, due to the increasing number of electric vehicles, heatpumps, and photovoltaic systems. As in contemporary grids measuring infrastructure is only sparsely available, our architecture learns from this sparse data to resolve the congestions. We demonstrate the ability of our method by experiments on a real-world low voltage grid. Our approach matches accuracy of state-of-the-art classical solvers, with the distinct advantage of being orders of magnitude faster. Es laden ein: die Dozentinnen und Dozenten der Informatik

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UnRAVeL Survey Lecture - Today!
by Kassing, Jan-Christoph 18 Jul '24

18 Jul '24

Dear all, this is a reminder for Britta Peis's talk with the title "Future Research in Submodular Function Optimization" taking place today (18.07) at 12:30 in the B-IT room 5053.2. Please find the details below --- Abstract --- Submodular function maximisation is a well-studied and fascinating topic which appears in a wide range of research areas, like, e.g., in combinatorial optimization, algorithmic game theory, and machine learning. In the talk, I will survey some results and algorithms for submodular function maximisation with and without various combinatorial constraints, and discuss some open problems. ---------------- Part of the programme of the research training group UnRAVeL is a series of lectures on the topics of UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is given by one of the researchers involved in UnRAVeL. This years topic is "UnRAVeL - New Ideas!". In these lectures, UnRAVeL professors will discuss current research as well as highlight open problems and offer a perspective on potential future directions. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2024 and engage in discussions with researchers and doctoral students. We are looking forward to seeing you at the lecture. Note that this is the last lecture for this year. Many thanks for the great participation in the event! Kind regards, Jan-Christoph for the organisation committee

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UnRAVeL Survey Lecture - Today!
by Kassing, Jan-Christoph 11 Jul '24

11 Jul '24

Dear all, this is a reminder for Christopher Morris's talk with the title "Understanding the Generalization Abilities of Graph Neural Networks: Current Results and Future Directions" taking place today (11.07) at 12:30 in the B-IT room 5053.2. Please find the details below --- Abstract --- Graph neural networks (GNNs) are the dominant approach in machine learning on graphs. Surprisingly, their generalization properties, i.e., their ability to make meaningful predictions outside the training data, are poorly understood. Here, we overview some recent results on GNNs’ generalization abilities and outline some future directions. ---------------- Part of the programme of the research training group UnRAVeL is a series of lectures on the topics of UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is given by one of the researchers involved in UnRAVeL. This years topic is "UnRAVeL - New Ideas!". In these lectures, UnRAVeL professors will discuss current research as well as highlight open problems and offer a perspective on potential future directions. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2024 and engage in discussions with researchers and doctoral students. We are looking forward to seeing you at the lectures. Kind regards, Jan-Christoph for the organisation committee

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Einladung: Informatik-Oberseminar Herr Felix Schwinger
by Maaßen, Leany 05 Jul '24

05 Jul '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 17.07.2024, 15:00-16:00 Uhr Ort: Raum 5053.2 (großer B-IT-Hörsaal)/Informatikzentrum, Ahornstraße 55 Referent: Herr Felix Schwinger, M. Sc. Lehrstuhl Informatik 5 Thema: Ride-Sharing and Micromobility in Intermodal Transportation: Data-Driven Integration, Assessment, and Facilitation of Mobility as a Service Abstract: Information and communication technology has led to a wide range of smartphone-based mobility services, including car-, bike-, scooter-, and ride-sharing. Combining these services into intermodal journeys promises more flexibility and customizability to travelers. However, this combination is challenging and requires improvements at several levels: The lack of a technical ability for mobility providers to share information hinders their cooperation, the impact of intermodal transportation networks is poorly understood and inhibits an intelligent distribution of available transportation resources, and the increasingly heterogeneous nature of intermodal journeys easily overwhelms travelers. Meanwhile, the status quo of car-centric transportation systems is inadequate, as climate change and urbanization have accelerated the need for rapid decarbonization and increased efficiency in the transportation sector. Thus, Mobility as a Service (MaaS) has been proposed as a solution that addresses these challenges by seamlessly integrating mobility services across different providers into a single platform. Hence, MaaS-driven intermodal journeys promise to leverage the benefits of each mobility mode to fill the gap between individual car and public transportation journeys. To study MaaS adoption, we employed the design science research paradigm, revealing three main problem areas: i) The integration among mobility providers; ii) the assessment of the impact of MaaS on the transportation network; and iii) the facilitation of user-interaction concepts for travel information systems. For each problem area, we produce, demonstrate, and evaluate artifacts of real-world use cases, illustrating the advantages for different stakeholders to support the creation of a seamless intermodal transportation network. For the lack of integration, we show that MaaS applications have yet to accommodate all mobility modes. Since a seamless integration requires highly accurate data, we propose approaches to improve the providers' data forecasts. As for the second problem area, we observe that a lack of historical data impedes the analysis of MaaS. We tackled the issue by developing an inference approach for the necessary data from openly available sources, thereby supporting the investigation of intermodal transportation networks. Finally, as MaaS changes how people conduct their daily mobility, we design two natural language interfaces that complement traditional travel information systems, thereby reducing the burden of intermodal journey planning. Our research supports the management of the impact of the digital transformation as follows: i) The integration of autonomous ride-sharing into MaaS requires a strict adherence to the holistic mobility service chain, thus demonstrating its extensive applicability; ii) the novel forecasting algorithms serve as an improved information base for intermodal journey planning, thereby increasing the resilience of transfers; iii) our micromobility assessment compares the travel characteristics of micromobility with those of public transportation in a data-driven manner, which allowed us to find evidence for their complementary use; and iv) the proactive and context-overarching natural language interfaces support users in exploring mobility offers and complement traditional travel information systems. Overall, our research contributes significantly to the vision of enabling a seamless intermodal transportation network. 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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UnRAVeL Survey Lecture - Today!
by Kassing, Jan-Christoph 04 Jul '24

04 Jul '24

Dear all, this is a reminder for Gerhard Lakemeyer's talk with the title "Challenges in Cognitive Robotics" taking place now! (04.07) at 12:30 in the B-IT room 5053.2. Please find the details below --- Abstract --- It is generally accepted that cognitive robots need to be able to represent and reason about the relevant aspects of the environment they operate in. Complications arise because sensors are noisy and the effects of actions are often uncertain. In this lecture I will briefly review a few milestones in the development of cognitive robots and then address some of the many research challenges that still need to be addressed in order to arrive at truly cognitive robots. ---------------- Part of the programme of the research training group UnRAVeL is a series of lectures on the topics of UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is given by one of the researchers involved in UnRAVeL. This years topic is "UnRAVeL - New Ideas!". In these lectures, UnRAVeL professors will discuss current research as well as highlight open problems and offer a perspective on potential future directions. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2024 and engage in discussions with researchers and doctoral students. We are looking forward to seeing you at the lectures. Kind regards, Jan-Christoph for the organisation committee

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