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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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Einladung: Informatik-Oberseminar Herr Mohamed Behery
by Maaßen, Leany 22 Nov '24

22 Nov '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 05.12.2024, 13:30-14:30 Der öffentliche Vortrag findet hybrid statt: Raum: Raum 5053.2 (B-IT-Hörsaal)/Informatikzentrum, Ahornstraße 55 Zoom: https://rwth.zoom-x.de/j/64360669610 Referent: Herr Mohamed Behery, M. Sc. LuFG Informatik 5 (KBSG) Thema: Digital Shadows for Industrial Robotic Agents in the World Wide Lab Abstract: The digital transformation of production has become imperative to increase efficiency, adaptability, and innovation required to face the rapidly changing market requirements, supply chain disruptions, and global competition. In addition to initiatives such as Industry 4.0 and the Industrial Internet of Things (IIoT), the Internet of Production (IoP) aims to establish a world-wide network of research and industrial partners through the World Wide Lab (WWL). Such a network requires standardized communication protocols, which need to remain flexible to accommodate the different standards, use cases, and contexts. To this end, the WWL uses Digital Shadows (DSs) to represent the different entities: parts, products, machines, processes, and companies. DSs are context and purpose-specific representations of entities in the WWL, which enables them to replace the omnipresent Digital Twins by representing different aspects of the denoted entities. The thesis offers a road map for achieving the WWL as an application on top of the IoP. The thesis focuses on industrial robotics processes, which involve teleoperated robots or human-robot collaborative assembly and are not fully integrated in the WWL as they lack a standard DS for task representation. To bridge this gap, the thesis proposes using Behavior Trees (BTs), which offer a flexible, modular, and composable task representation for industrial robotics processes. BTs emerged in the video game industry and were adopted by the roboticists but do not yet fulfil all the requirements to be a standard DS. The presented work offers a plan to fulfill these requirements. The thesis introduces a framework for extracting and creating DSs for robot teleoperation tasks in industrial processes. It starts by collecting data to extract discrete actions. After that, it learns a library of BTs that represent the commonly executed behaviors. This allows a user to compose more complex behaviors using the learned BTs. The different modules of the framework are evaluated qualitatively and quantitatively. The thesis also proposes and demonstrates three BT extensions increasing BT flexibility to accommodate more use cases. They allow a BT to handle highly dynamic production environments, where it interacts with heterogeneous agents such as humans or robots with different capabilities or knowledge. The dissertation concludes with a discussion that evaluates the applicability of the proposed framework and extensions to different industrial scenarios giving recommendations for future work. 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 Dr. Till Hofmann, 27. November 2024,
by Stephanie Jansen 21 Nov '24

21 Nov '24

********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * *********************************************************************** Zeit: Mittwoch, 27. November 2024, 13:00 Uhr Ort: Raum 9222, E3, Informatikzentrum Referent: Dr. Till Hofmann; Lehrstuhl Informatik 6 Thema: Uncertainty in Robotics Abstract: Robotic systems must navigate a world full of unpredictability, facing challenges from dynamic environments, noisy sensors, and imperfect actuators. A robot’s environment is constantly changing, influenced by interactions with other robots and humans. Sensors, with their inherent limitations and noise, provide only an imperfect view of the world. Finally, actuators—susceptible to noise, wear, and mechanical failure—introduce further uncertainty, complicating the robot’s ability to execute precise actions. In this lecture, I will discuss how these challenges can be addressed through probabilistic robotics, where uncertainty is explicitly represented using probability distributions to describe the robot's estimated state of the world. I will first explore how fundamental robotics tasks—such as localization, mapping, and control—are inherently uncertain and demonstrate the benefits of probabilistic methods. In the second part, I will introduce Bayes filters, a general algorithm for belief computation that serves as the theoretical foundation for many robotics algorithms. 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-Kolloquium Nitesh Goyal (Google)
by Martin Henze 20 Nov '24

20 Nov '24

+********************************************************************** * * * Einladung * * * * Informatik-Kolloquium * * * +********************************************************************** Zeit: Mittwoch, 27. November 2024, 10.30 Uhr Ort: AH V, Ahornstr. 55 Referent: Nitesh Goyal, PhD Google, New York Thema: Leveraging AI Responsibly in Sensemaking for Successful Human-AI Workflows Abstract: My research vision is to enable expert and non-experts to successfully make sense of complex world problems. As a Human-Computer Interaction researcher, I iteratively focus on studying how sensemaking is performed to identify challenges in collaborative data analytics, design tools using computational techniques that overcome these challenges and evaluate my designs using human participants to inform subsequent designs. In particular, I have focussed on three domains: criminal justice, ML Development and Online Harassment. Solving crimes correctly is a critical and life-altering problem where intelligence analysts are constantly struggling against their biases. ML engineering and development requires constantly struggling with fairness and equity issues related to datasets and algorithmic outcomes. Managing online harassment requires emotional and physical labor by targets of harassment while making sense of who is harassing them and why? There are many similarities across these 3 domains and despite, recurring themes of how AI should be designed responsibly to support these use cases/users, we have barely started to scratch the surface. In this talk, I introduce the notion of Sensemaking Translucence into biases, fairness and equity related challenges. I then provide examples of how AI can support Sensemaking Translucence. My work finally makes the case that it is important to design from a human centered perspective by leveraging AI to support these Human AI Collaboration workflows. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Prof. Dr. Martin Henze Security and Privacy in Industrial Cooperation (SPICe) RWTH Aachen University Im Süsterfeld 9 52072 Aachen, Germany henze(a)spice.rwth-aachen.de https://www.spice.rwth-aachen.de/

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Einladung Informatik-Oberseminar Tim Seppelt
by Tim Seppelt 07 Nov '24

07 Nov '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 29. November 2024, 11.00 Uhr Ort: Raum 9222, Gebäude E3, Informatik-Zentrum Referent: Tim Seppelt, MASt Lehrstuhl Informatik 7 Thema: Homomorphism Indistinguishability Abstract: Two graphs/G/ and/H/ are homomorphism indistinguishable over a class of graphs 𝓕 if, for all graphs/F/ ∊ 𝓕, the number of homomorphisms from/F/ to/G/ is equal to the number of homomorphisms from/F/ to/H/. In 1967, Lovász showed that two graphs are isomorphic if, and only if, they are homomorphism indistinguishable over the class of all graphs. Subsequently, many graph isomorphism relaxations such as quantum isomorphism, spectral, and logical equivalences have been characterised as homomorphism indistinguishability relations over certain graph classes. Thereby, homomorphism indistinguishability connects seemingly disparate fields such as quantum information, finite model theory, and machine learning. This thesis explores three themes: We first review the plenitude of *characterisations* of graph isomorphism relaxations as a homomorphism indistinguishability relation. Focusing on integer programming relaxations for graph isomorphism, we prove that the feasibility of each level of the Sherali–Adams and Lasserre hierarchies is characterised as homomorphism indistinguishability relations. These results, which are derived using (bi)labelled graphs and homomorphism tensors, shed light on the distinguishing power of these hierarchies. In particular, we determine the precise number of Sherali–Adams levels necessary such that their feasibility guarantees the feasibility of a given Lasserre level. Abstracting from the wealth of homomorphism indistinguishability characterisations, we embark on a more principled study of homomorphism indistinguishability investigating the distinguishing power and the complexity of homomorphism indistinguishability relations over minor-closed graph class. The homomorphism distinguishing*closure* cl(𝓕) of a graph class 𝓕 is the central notion for studying the distinguishing power of homomorphism indistinguishability relations. It is defined as the maximal graph class whose homomorphism indistinguishability relation coincides with the one of 𝓕. Roberson conjectured that every minor-closed union-closed graph class 𝓕 is homomorphism distinguishing closed, i.e. cl(𝓕) = 𝓕. We confirm Roberson's conjecture, which is generally wide open, for further graphs classes and prove unconditionally that if 𝓕 is minor-closed then so is cl(𝓕). Lastly, we investigate the*complexity* of deciding whether two graphs are homomorphism indistinguishable over a fixed graph class. For infinite graph classes, this problem is a priori not even decidable. In stark contrast to this, we show that, over every minor-closed graph class of bounded treewidth, homomorphism indistinguishability can be decided in randomised polynomial time. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Jan Tönshoff
by Tönshoff, Jan 15 Oct '24

15 Oct '24

+*********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +*********************************************************************** Zeit: Montag, 4. November 2024, 14:30 Uhr Ort: Raum 9222 (Informatikzentrum E3, Ahornstraße 55) Referent: Jan Martin Tönshoff, M.Sc. Lehrstuhl für Logik und Theorie diskreter Systeme (Informatik 7) Thema: Deep Learning on Graphs: Developing and Understanding Neural Architectures for Structured Data Abstract: Graph Neural Networks (GNNs) have recently emerged as a powerful class of deep learning architectures that can directly learn from graph-structured data. Such data naturally occurs in a wide range of structurally rich learning domains, such as computational chemistry or social network analysis. This thesis aims to study both practical and theoretical aspects of GNNs, with a focus on understanding their capabilities for solving hard learning tasks, exploring new directions for GNN architecture design, and understanding the relative strengths and weaknesses of commonly used models. In this context, we present our main research contributions: Firstly, we propose a GNN-based approach for learning heuristics for Constraint Satisfaction Problems (CSPs) through a general graph representation and neural architecture. Using reinforcement learning, we show that this approach can learn powerful search algorithms, achieving superior performance compared to prior neural methods and being competitive with classical solvers on a range of complex combinatorial problems. Secondly, we examine a novel GNN architecture based on 1D convolutions on sequential features constructed from random walks. We analyze the expressivity of this approach and prove it to be incomparable to the Weisfeiler-Leman hierarchy and many common GNN architectures. The model is further shown to achieve strong empirical performance across various tasks, suggesting a new direction for GNN architecture design beyond traditional message passing. Thirdly, we compare popular GNNs in the context of capturing long-range dependencies in graphs. Through an empirical re-evaluation of commonly used benchmark datasets, we show that standard GNNs based on message passing perform better than previously reported. A theoretical comparison between Graph Transformers and GNNs with Virtual Nodes further shows that neither architecture subsumes the other in terms of uniform expressivity, highlighting the need to consider a wide range of architectures for specific learning tasks. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Oberseminar Sebastian Hueber
by Hueber, Sebastian 15 Oct '24

15 Oct '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 30. Oktober 2024, 14:00 Uhr Ort: Im Süsterfeld 9, 52072 Aachen, Raum 273 Referent: Sebastian Hueber M.Sc. Lehrstuhl Informatik 10 Thema: Using Facial Tracking for Expressive Mobile Device Interactions Abstract: For four decades, user interfaces have been mainly designed for pointing input, either with a mouse or a touchscreen. Pointing input abstracts from the human to a location on the screen. Especially visual cues that make human-to-human communication effective – like eye contact or head nodding – are ignored in this abstraction. Mobile devices, in particular, suffer from the limits of pointing input, as users cannot comfortably reach everything on the screen when using the device one-handedly. We present implicit and explicit usages of facial tracking to make mobile interactions more expressive and ergonomic. We show the advantages of eye tracking using three interaction techniques. First, our Attentive Notifications remove occlusion issues and accidental activations in mobile interfaces. They determine a suitable screen edge for displaying notifications by blocking the area around the user’s gaze at the moment of notification delivery. Second, we show that eye tracking can enhance the perception of content in augmented reality with our User-Aware Rendering. This technique provides enhanced depth perception with good performance in scene exploration. Third, interfaces can exploit that gaze input can reach anything nearby. Our GazeConduits concept fosters collaboration in ad-hoc multi-device environments. This enables users to interact with devices or meeting collaborators by looking at them. However, eye tracking often comes with accuracy issues, especially when people are moving, and suffers from the Midas touch problem. To overcome these challenges, two of our interaction techniques use head tracking instead. We present a Head + Touch controlled cursor that increases the thumb’s reach during one-handed smartphone use. This significantly reduces the overhead of touch-based reachability techniques to under 100 ms. With our Headbang technique, menu selections are also faster than with touch input. Es laden ein: die Dozentinnen und Dozenten der Informatik

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[sekretariate] Einladung: Informatik-Oberseminar Bahare Salmani
by Elke Ohlenforst 02 Oct '24

02 Oct '24

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 11. Oktober 2024, 15:30 Uhr Ort: Raum 5053.2 (großer B-IT-Hörsaal), Informatikzentrum, E2, Ahornstr. 55 Referentin: Bahare Salmani Barzoki, M.Sc. Lehrstuhl für Softwaremodellierung und Verifikation (Informatik 2) Thema: Probabilistic Model Checking and Parameter Tuning for Bayesian Networks Abstract: Probabilistic reasoning is a key to handling uncertainties and making decisions based on partial observations. Bayesian networks (BNs) are popular probabilisticgraphical models in probabilistic decision-making and AI and have a wide range of applications, including machine learning, medicine, gene regulatory networks, and robotics. They combine the notions from probability theory and graph theory and enablea succinct representation of joint probability distributions.A Bayesian network is composed of a directed acyclic graph over a set of random variables and a set of conditional probability tables ---CPTs for short. The primary task in Bayesian networks is probabilistic inference, that is, to compute a conditional probability of a joint valuation for a subset of random variables given an observation. Probabilistic model checking is a field in computer science that isfocusedon analyzing stochastic systems with respect toa set of formally defined properties. The stochastic systems are typically Markov models, and the properties of interest are probabilistic extensions of LTL of CTL. The properties are mostly reducedto a pivotal task, computing reachability probabilities: whatis the probability of reaching a set of target states? Recent advancements in the field consider the parametric extensions of the Markov models, where a subset of the probabilities in the model are unknown and target various synthesis problems including/feasibility checking/: is there a satisfying instantiation that satisfies the given constraint?,/region verification/: are all instantiations in a /region/ satisfy the given constraint, and /parameter space partitioning/: split the entire n-dimensional parameter space to sets of satisfying, rejecting, and unknown subregions w.r.t. the given constraint. In this dissertation, we present a new approach based on /probabilistic model checking/ for inference and parameter tuning in Bayesian networks. The dissertation is categorizedinto two main settings: the /non-parametric/ and the /parametric/. In the non-parametric setting,we focus on the classical Bayesian networks, where the model is fully specified.We present mappings from Bayesian networks to discrete-time Markov chains and mathematically reduce performing conditional inference in the BN to computing reachability probabilities in Markov chains. Thisenables the use of state-of-the-art algorithms for computing reachability probabilities and the optimization techniques thereof, e.g., bisimulation minimization for exact inference in BNs. We exploit the explicit and symbolic methods from probabilistic model checking and empirically evaluate our framework for Bayesian inference against the state-of-the-art /weighted model counting. /In the parametric setting,we define /parametric Bayesian networks (pBNs)/, where a subset of CPT entries are unknown polynomials rather than concrete probabilities.We build upon the synthesis techniques for parametric Markov chains and address problems /sensitivity analysis/, /ratio/difference parameter tuning/ from the BN literature, and /parameter space partitioning/from pMC literature. Finally, we focus on the problem /minimal-distanceparameter tunin/g, where the objective is to find the instantiationu for the unknown parameters that satisfy the constraint of interest while minimally deviating from the original instantiation u_0 in an original reference model. The motivation is to minimally disturb the statistical information in the original model. Our detailed experimental evaluations indicate that our parameter synthesis techniques can treat parameter synthesis for Bayesian networks (with hundreds of unknown parameters) that go beyond the capabilities of the existing techniques. We lift the severe restrictions in the literature on the number of unknown parameters, the global dependencies between the parameters, and the form of obtained solutions. ------------------------------------------------------------------------ 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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