July 2025 - informatik-vortraege - lists.rwth-aachen.de

Survey Lecture - Highlights of UnRAVeL
by Kassing, Jan-Christoph 16 Jul '25

16 Jul '25

Dear all, We invite you to the last UnRAVeL survey lecture talk Tomorrow 12:30 to 14:00, Computer Science Center, Building E2, ground floor, B-IT room 5053.2, given by Christina Büsing about „Dealing with Uncertainty Inspired by Health Care – a Robust Optimization Approach“! -------------------- Titel: Dealing with Uncertainty Inspired by Health Care – a Robust Optimization Approach Abstract: Uncertainty plays a role in nearly every process within health care: patient arrival times are unpredictable, hospital lengths of stay can vary widely, and both treatment paths and resource consumption are often unknown in advance. In addition to these uncertainties in problem parameters, the modeling itself is influenced by subjective judgments — for example, what constitutes a good treatment, or how to define a fair distribution of workload. Over the past years within the UnRAVeL project, we have developed several robust optimization models to address these challenges. In this talk, I will present our results on two such problems: the fair allocation of out-of-hours services among pharmacies and a robust appointment scheduling model for general practitioners. -------------------- 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 "Highlights of UnRAVeL!". As UnRAVeL nears its conclusion, we take this opportunity to reflect on seven years of excellent research. Through nine talks, we invite you to experience the scientific highlights of our RTG, along with its numerous successes, developments, and collaborations. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2025 and engage in discussions with researchers and doctoral students. Kind regards, Jan-Christoph for the organisation committee

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Einladung: Informatik-Oberseminar Birte Heinemann
by Lehr- und Forschungsgebiet Informatik 9 15 Jul '25

15 Jul '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 28. Juli 2025, 15.00 Uhr Ort: Raum 9222, (Informatikzentrum E3, Ahornstraße 55) Referent: Birte Heinemann, M.Sc. Lehr- und Forschungsgebiet Lerntechnologien Thema: Exploring Teaching and Learning in Virtual Reality: Challenges and Opportunities with Multimodal Learning Analytics Abstract: Virtual Reality (VR) in education offers promising opportunities to design immersive and interactive learning environments. As interest in VR grows, research focuses on the conditions under which learning in VR becomes effective. Beyond exploring what can be taught in VR, it is essential to investigate how immersive featuressuch as interactivity, presence, and spatial metaphorsshape learning processes. At the same time, challenges persist in systematically evaluating VR learning scenarios and in developing scalable, reusable infrastructures for educational research. Open key questions include: Which VR features are most beneficial for learning? How can onboarding and usability be optimized for diverse learners? And how can multimodal learning analytics (MMLA) support both learners and educators in making data-informed decisions? This dissertation addresses these questions through the design, implementation, and evaluation of three VR learning projects, each supported by a modular analytics infrastructure: (1) simulation-based teacher training in classroom management using Teach-R, (2) immersive learning of the rendering pipeline with RePiX VR, and (3) interdisciplinary lab-based learning scenarios. Together, these cases demonstrate how MMLA can be meaningfully integrated into VR for both research and educational practice. Methodologically, the work follows an iterative, agile approach, combining design-based research (DBR), human-centred design, and design-make-learn cycles. Empirical studies using pre-test, intervention, and post-test designs were conducted for Teach-R and RePiX VR, while the third case contributed to infrastructure development and conceptual generalization. Key contributions include (1) a reusable, xAPI-based infrastructure for learning analytics in VR; (2) empirical insights into onboarding, presence, and usability; (3) two exemplary VR applications for teacher education and computer graphics; and (4) dashboards and visualizations that support reflection and curriculum development. These contributions are grounded in existing research across educational technology, learning sciences, and human-computer interaction and are validated against state-of-the-art methods and the xAPI specification. By shifting away from traditional media comparison studies, the work avoids pitfalls such as novelty effects and confounding variables, instead focusing on how and when learning in VR works. It shows that VR when paired with multimodal analytics can meaningfully enhance teaching and learning. This dissertation provides pedagogical insights, and technical solutions for scalable, data-informed educational innovation. Es laden ein: die Dozentinnen und Dozenten der Informatik Birte Heinemann lädt Sie zu einem geplanten Zoom-Meeting ein. Thema: Informatik-Oberseminar - Birte Heinemann Zeit: 28.Juli 2025 02:45 PM Amsterdam, Berlin, Rom, Stockholm, Wien An Zoom-Meeting teilnehmen https://rwth.zoom-x.de/j/66245291890?pwd=INE7Ulc0GWCXNB6LawpRehtaXWWdez.1 Meeting-ID: 662 4529 1890 Kenncode: TakePart --- Über SIP beitreten 66245291890(a)lej.zmeu.us <mailto:66245291890@lej.zmeu.us> --- Über H.323 beitreten 159.124.47.249 (Deutschland) Meeting-ID: 662 4529 1890 Kenncode: 31104251

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Survey Lecture - Highlights of UnRAVeL
by Kassing, Jan-Christoph 09 Jul '25

09 Jul '25

Dear all, We invite you to the next UnRAVeL survey lecture talk Tomorrow 12:30 to 14:00, Computer Science Center, Building E2, ground floor, B-IT room 5053.2, given by Christopher Morris about „Exploring the Power of Graph Neural Networks in Solving Optimization Problems“! -------------------- Titel: Exploring the Power of Graph Neural Networks in Solving Optimization Problems Abstract: In recent years, there has been a growing trend toward using machine learning to enhance optimization algorithms by leveraging the structure and distribution of instance data. This lecture will explore recent advances in integrating graph neural networks with exact combinatorial solvers to tackle hard optimization 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 "Highlights of UnRAVeL!". As UnRAVeL nears its conclusion, we take this opportunity to reflect on seven years of excellent research. Through nine talks, we invite you to experience the scientific highlights of our RTG, along with its numerous successes, developments, and collaborations. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2025 and engage in discussions with researchers and doctoral students. The overall remaining schedule is as following: 10.07.2025 - Christopher Morris - Exploring the Power of Graph Neural Networks in Solving Optimization Problems 17.07.2025 - Christina Büsing - Dealing with Uncertainty Inspired by Health Care – a Robust Optimization Approach Kind regards, Jan-Christoph for the organisation committee

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Einladung Informatik Oberseminar Lubna Ali
by Bombelka, Angelika 08 Jul '25

08 Jul '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 Dan Jia
by Jia, Dan 08 Jul '25

08 Jul '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 11. Juli 2025, 15.30 Uhr Ort: Raum 025, Mies-van-der-Rohe Str. 15 (UMIC Gebäude) Referent: Dan Jia, M.Sc. Lehrstuhl Informatik 13 Thema: Efficient Person Detection with LiDAR Sensors for Mobile Robots Abstract: Person detection is a fundamental capability for mobile robots operating in unstructured and dynamic environments, including public spaces, healthcare facilities, and households. While RGB(-D) cameras combined with deep learning-based object detectors have become standard solutions, they face notable limitations such as restricted field of view, reduced depth accuracy at long range, and increased computational complexity in multi-camera setups. These limitations are particularly critical in real-time robotic applications. In contrast, 2D LiDAR sensors—widely deployed for mapping and navigation—offer accurate range measurements, wide field of view, and high frame rates, while also being less intrusive in terms of privacy. This talk introduces DR-SPAAM, a novel and efficient deep neural network designed specifically for detecting persons using sparse 2D LiDAR data. At the core of DR-SPAAM is a Spatial Attention and Auto-Regressive Module that aggregates temporal information across consecutive scans. This architecture addresses the inherent sparsity of 2D LiDAR data while maintaining minimal computational overhead. DR-SPAAM achieves state-of-the-art performance on the DROW benchmark dataset, reaching 70.3% average precision, while operating in real-time on both high-end and embedded GPUs. In addition, DR-SPAAM demonstrates robustness to changes in sensor specifications such as resolution and scan rate, making it adaptable to a wide range of robotic platforms without the need for re-training. To address the scarcity of annotated 2D LiDAR training data, an approach is introduced that automatically generates pseudo-labels using image-based detectors from calibrated cameras. These pseudo-labels enable training or fine-tuning of LiDAR-based detectors during deployment without the need for manual annotation, and performance can be further enhanced through techniques such as mixup regularization and robust loss functions. Finally, the talk provides a comparative analysis of 2D and 3D LiDAR sensors for person detection in terms of accuracy, inference speed, localization performance, and occlusion handling. The results reveal that 2D LiDAR can match 3D LiDAR in detecting nearby, visible persons at a fraction of the cost and computational demand. However, for environments with frequent occlusions or the need for long-range detection, 3D LiDAR remains the preferred choice. Taken together, these findings offer practical guidance for selecting sensor modalities and designing robust, efficient person detection systems for a wide spectrum of robotic applications. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Andreas Klinger
by Andreas Klinger 04 Jul '25

04 Jul '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 18. Juli 2025, 10:00 Uhr Ort: UMIC_025 (2165|025), Mies-van-der-Rohe-Str. 15, EG Referent: Andreas Klinger M.Sc. Lehr- und Forschungsgebiet IT-Sicherheit Thema: Variations of Secure Multi-Party Computation Abstract: Secure multi-party computation (SMPC) enables multiple parties to evaluate functions over their private inputs such that none of the parties can learn anything about the other parties’ private inputs and outputs but what can be deduced from their own input and output of the function evaluation. In classical SMPC it is assumed that the set of parties evaluating the function is fixed and a priori known. However, there are also online functionalities, in which parties arrive and leave over time, e. g., when providing possible dating recommendations, or finding other people for ride-sharing on the fly, where the personal preferences or start and destination should be kept private. Furthermore, in some applications participants may not only want to hide their private inputs and outputs, but may also want to hide the fact that they are participating in a given function evaluation in the first place, e. g., when checking the (private) DNA for a disease, then the participation in the check itself can already leak sensitive information. On the practical side of SMPC, the performance and applicability of SMPC protocols is typically evaluated with extensive benchmarking in varying settings, e. g., different number of parties, problem sizes, and network settings. However, evaluating the performance of SMPC protocols can be very time and resource consuming, as SMPC protocols typically introduce a considerable performance overhead compared to non-privacy-preserving computations. In this work we will extend the classical notions of SMPC to allow the secure evaluation of online functionalities in a distributed fashion. We present the notion of an online trusted third party that allows to prove the security of SMPC protocols implementing online functionalities. We also show in a general feasibility result that online functionalities can be implemented in a privacy-preserving fashion. In addition, we present two privacy-preserving online SMPC protocols for the online problem “fully online matching with deadlines”. In this problem an (a priori unknown) set of parties arrives with their inputs over time and can then be matched with other parties until they leave when their individual deadline is reached. We explore the performance of our protocols in an extensive evaluation. Furthermore, we present an anonymous system for SMPC that allows parties to anonymously evaluate an (offline) functionality in a fully distributed and secure fashion. The overall system provides robustness with penalty against a dishonest majority in the presence of a malicious adversary. Finally, we present an approach to estimating the runtime and global network traffic of SMPC protocols. We implemented this approach in the tool XENON, and show its estimation capabilities in an extensive evaluation. In addition, we present a second tool called NEON that simplifies the benchmarking of SMPC protocols, which might be of independent interest. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Survey Lecture - Highlights of UnRAVeL
by Kassing, Jan-Christoph 03 Jul '25

03 Jul '25

Dear all, We invite you to the next UnRAVeL survey lecture talk today (03.07.) 12:30 to 14:00, Computer Science Center, Building E2, ground floor, B-IT room 5053.2, given by Sebastian Trimpe about "Uncertainty in Learning and Control“! -------------------- Titel: Uncertainty in Learning and Control Abstract: In the research at DSME, we combine feedback control and machine learning to develop algorithms for physical systems such as robots, autonomous vehicles, or production. A central theme across our research is uncertainty. In feedback control, uncertainty is the prime reason for using feedback in the first place. When we add learning to the loop, we make decisions based on components learned from data, which are inherently imperfect. This makes uncertainty quantification and management even more critical. In this talk, I will discuss several of our approaches in learning-based control, emphasizing the role of uncertainty in each. In particular, I will focus on methods in deep reinforcement learning, imitation learning from optimal control, and auto-tuning via Bayesian optimization. I will highlight recent results and describe different mechanisms we use to address and leverage uncertainty. Throughout, I will connect these methods to real-world applications, including hardware experiments with unicycle robots, quadcopters, and cars. -------------------- 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 "Highlights of UnRAVeL!". As UnRAVeL nears its conclusion, we take this opportunity to reflect on seven years of excellent research. Through nine talks, we invite you to experience the scientific highlights of our RTG, along with its numerous successes, developments, and collaborations. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2025 and engage in discussions with researchers and doctoral students. The overall remaining schedule is as following: 03.07.2025 - Sebastian Trimpe - Uncertainty in Learning and Control 10.07.2025 - Christopher Morris - Exploring the Power of Graph Neural Networks in Solving Optimization Problems 17.07.2025 - Christina Büsing - Dealing with Uncertainty Inspired by Health Care – a Robust Optimization Approach Kind regards, Jan-Christoph for the organisation committee

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