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

Einladung zum Informatik-Kolloquium von Prof. Dr. Paula Herber
by Völker, Marcus 30 Aug '24

30 Aug '24

+********************************************************************** * * * Einladung * * * * Informatik-Kolloquium * * * +********************************************************************** Zeit: Dienstag, 10. September 2024, 14.00 Uhr Ort: Raum 9222, Gebäude E3, Ahornstr. 55 Referentin: Prof. Dr. Paula Herber University of Münster Title: Formal Verification of Cyber-physical Systems using Domain-specific Abstractions Abstract: Cyber-physical systems have become ubiquitous in our daily lives, and their complexity continually evolves to unprecedented levels. In addition to their heterogeneity and interaction with a physical environment, we see a tremendous increase in the use of learning to make autonomous decisions in dynamic environments. These developments pose significant challenges for ensuring the safety and reliability of cyber-physical systems. Formal methods have the potential to guarantee crucial safety properties under all circumstances, but are incredibly expensive and severely suffer from scalability issues. In this talk, I will summarize some of our recent efforts towards reusable specification and more scalable verification of cyber-physical systems using domain-specific abstractions. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung Informatik-Oberseminar Yingbo Gao, 28. August 2024
by Stephanie Jansen 22 Aug '24

22 Aug '24

********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * *********************************************************************** Zeit: Mittwoch, 28 August 2024, 15:30 Uhr Ort: Raum 9222, E3, Informatikzentrum Zoom: https://rwth.zoom-x.de/j/66261562709?pwd=2driMW8j0cRJLiYFWNJRqRp4Lya80Z.1 Meeting-ID: 662 6156 2709 Kenncode: 337848 Referent: Yingbo Gao, M.Sc. (Lehrstuhl Informatik 6) Thema: Language Modeling and Machine Translation: Improvements in Training and Modeling Abstract: Substantial improvements in language modeling and machine translation have been achieved since the wide adoption of artificial neural networks. In this talk, we discuss three directions related to training and modeling in neural language modeling and neural machine translation. First, sampling-based training criteria are investigated in order to speed up the training of neural language models with large vocabularies. Second, label smoothing, input smoothing as well as multi-agent training are studied to improve the generalization of neural machine translation models. Finally, a language modeling approach for machine translation is proposed to simplify the architecture of existing translation models. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Stephanie Jansen Faculty of Mathematics, Computer Science and Natural Sciences Chair of Computer Science 6 ML - Machine Learning and Reasoning RWTH Aachen University Theaterstraße 35-39 D-52062 Aachen Tel: +49 241 80-21601 sek(a)ml.rwth-aachen.de www.hltpr.rwth-aachen.de

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Einladung: Informatik-Oberseminar Matthias Naaf
by Matthias Naaf 19 Aug '24

19 Aug '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 30. August 2024, 11:00 Uhr Ort: Raum 9222 (Informatikzentrum E3, Ahornstraße 55) Referent: Matthias Naaf, M.Sc. LuFG Mathematische Grundlagen der Informatik Thema: Logic, Semirings, and Fixed Points Abstract: Why does my SQL query return a particular answer? How does Hanna win an infinite graph game against Simon? These questions can be answered with semiring semantics, which was introduced by Green, Karvounarakis, and Tannen (2007) for database provenance and has since been extended to various logics within and beyond the database setting. In this talk, we study semiring semantics for the fixed-point logic LFP, which (despite its name) features both least and greatest fixed points. The common theme in semiring semantics is the interplay of logic and algebra, such as the compatibility with semiring homomorphisms and the use of freely generated semirings to represent provenance information. This becomes particularly interesting for fixed-point logic, where additional algebraic and order-theoretic assumptions on the semirings must be made to ensure the existence of fixed points. A key question is the algorithmic computation of fixed points in these semirings. Several techniques have been developed for least fixed points, with the Newton iteration (Esparza, Kiefer, and Luttenberger, 2010) emerging as the most general technique, but it was not known how to compute greatest fixed points. We address this issue by providing an efficient closed-form solution for greatest fixed points of polynomial systems over absorptive semirings. Using the example of Büchi games, we show how semiring semantics of LFP can be applied to analyse winning strategies in infinite games. Lastly, we take the perspective of model theory and study how the classical 0-1 laws about the asymptotic behaviour of logic on random structures can be generalised to semiring semantics of first-order logic. By combining our proof with the fixed-point computation, we further extend the 0-1 law to semiring semantics of LFP. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung Informatik-Oberseminar Eva Fluck
by Fluck, Eva 05 Aug '24

05 Aug '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 16.08.2024, 10:00 Uhr Ort: Informatikzentrum der RWTH Aachen University, Gebäude E3, 2. Etage, Raum 9222 Referent: Eva Fluck, M.Sc. Lehrstuhl für Logik und Theorie diskreter Systeme Thema: Graph decompositions. A study on decompositions of graphs and their application to logic and data science Abstract: We study decompositions of graphs and their application to the expressiveness of first-order logic with counting quantifiers and clustering data. Besides the decompositions themselves we also consider graph parameters that arise from these decompositions as well as obstructions to the existence of good decompositions. The graph class T_q^k are all graphs which have a bounded tree-depth decomposition that also bounds the width. Its homomorphism indistinguishability relation enjoys a rich connection to the expressiveness of counting logic, as shown by Dawar, Jakl and Reggio (2021). Building upon ideas by Dvořák (2010), we reprove this connection. Our proof strategy also enables to construct a graph class which characterizes equivalence under guarded counting logic. A graph theoretic analysis of T_q^k allows us separate it from the intersection of TW_k−1 , the class of graphs of tree-width at most k−1, and TD_q the graphs of tree-depth at most q, if q is sufficiently larger than k. This also yields a new characterization of tree-depth via treedecompositions and a characterization of T_q^k via a Cops-and- Robber game. To lift this separation to the respective equivalence relations, we show that this game is monotone, that is Robber can never reach a vertex that was cleared in some earlier round. This also give a novel proof of the monotonicity of the Cops-and-Robber game for tree- width without the use of any dual object, like brambles. Another part of the separation of the equivalence classes is to prove that T_q^k and TD_q are homomorphism distinguishing closed, which was conjectured by Roberson (2022). Tangles are a concept to formalize regions of high connectivity in graphs in an unambiguous way. It can be translated to more abstract connectivity systems, where they describe highly connected regions in the underling universe. Recently they emerged as a new approach to clustering data. We find that the non-trivial clusters resulting from the well known single linkage hierarchical clustering algorithm are exactly the tangles of an appropriately chosen connectivity function on the data points. We generalize this connection to a one-to-one correspondence between connectivity functions that are maximum- submodular and dendograms which are a way to represent the result of an arbitrary hierarchical clustering algorithm. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Malte Breuer
by Malte Breuer 01 Aug '24

01 Aug '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 16. August 2024, 10:00 Uhr Ort: UMIC_025 (2165|025), Mies-van-der-Rohe-Str. 15, EG Referent: Malte Breuer M.Sc. Lehr- und Forschungsgebiet IT-Sicherheit Thema: Privacy-Preserving Kidney Exchange Abstract: Chronic kidney disease has become one of the most common causes of natural death in our modern society. The preferred treatment for chronic kidney disease is the transplant of a kidney from a living donor, who is typically a close friend or relative of the patient. An impediment that prevents such a living donation is that the found living donor is sometimes not medically compatible with the patient. Kidney exchange enables a patient to still receive a kidney transplant in such a situation by exchanging the living donor with other patients. Nowadays, many countries have centralized systems that organize kidney exchange, often on a nationwide scale. Hospitals can register their associated pairs of patients and medically incompatible donors with a central platform, which then tries to find potential exchanges among all registered pairs of patients and donors. Such a centralized kidney exchange system, however, harbors severe security risks that make the central platform susceptible to manipulation and corruption. The core issue is that the operator of the platform alone is responsible for the entire computation of the exchanges. This, for example, allows the platform operator to manipulate the computation such that a particular patient is treated with priority. This does not only make the platform operator susceptible to corruption but it also makes the platform a prime target for high impact attacks aimed at manipulating the computation of exchanges. The central platform becomes an even more attractive target for attackers as it stores the sensitive data of many patients and donors. Thus, any attack that leads to a data breach has a direct impact on the privacy of the sensitive data of many individuals. The main research goal of this thesis is to develop an alternative approach for kidney exchange that is resistant to manipulation and corruption, and protects the sensitive data of the involved patients and donors. To this end, we propose the model of a privacy-preserving kidney exchange system that follows a decentralized approach, where the computation of exchanges is distributed among a set of so-called computing peers. This model ensures that a computing peer is neither able to manipulate the computation of the exchanges nor to learn any information on the sensitive data of the involved patients and donors. We achieve this by using a cryptographic technique called secure multi-party computation. This allows a set of parties to compute a functionality on their private inputs such that each party only learns its own input and output and what can be deduced from both. The core contribution of this thesis is then the development of secure multi-party computation protocols that enable the computing peers to efficiently compute the exchanges for a set of patients and their associated medically incompatible donors. We evaluate the run time of all our protocols and show that our most efficient protocol scales for the large numbers of patients and donors that are to be expected in practice. Thereupon, we simulate the use of our most efficient protocols in our model of a privacy-preserving kidney exchange system over time using real-world data. Our simulations show that the number of transplants achieved over time in our privacy-preserving model is comparable to the number of transplants achieved in the model that is implemented by the existing kidney exchange systems that are susceptible to manipulation and corruption. Thus, our model allows for the replacement of most existing kidney exchange systems at a small or sometimes even negligible impact on the number of transplants over time, while significantly increasing the security guarantees compared to the existing systems. 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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