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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
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.
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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
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.
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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
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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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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
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.
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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
Dear all,
this is a reminder for Michael Schaub's talk with the title "How can algebraic topology help with data analysis?" taking place today (27.06) at 12:30 in the B-IT room 5053.2. Please find the details below
--- Abstract ---
Topology is concerned with studying properties of spaces that are preserved under continuous transformation. In particular, using notions from topology we can classify spaces according to certain global properties that are invariant under such transformations. While commonly considered a branch of pure mathematics, the use of topological ideas for data analysis has recently seen a surge of interest under the name "Topological Data Analysis".
Topological Data Analysis (TDA) is typically concerned with high-dimensional point cloud data. TDA aims to extract the "global shape" of this point cloud using computational tools, such as persistent homology, which aim to extract a global topological description of the point cloud. Stated differently, the whole dataset is treated as a single object, which we aim to characterize. This view contrasts somewhat with the standard perspective of unsupervised learning in which the objects of interest are the points (feature vectors of different objects) themselves, and we are interested in characterizing these objects relative to each other.
In this talk, we will provide a brief introduction to topological data analysis and its relation to unsupervised machine learning. We will showcase a few methods that aim to bridge the gap between these two seemingly different viewpoints, and discuss open directions and challenges in this context.
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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
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* Informatik-Oberseminar
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Zeit: Mittwoch, 26. Juni 2024, 13:00 Uhr
Ort: Raum 9222, E3, Informatikzentrum
Referent: Dr. Simon Völker
Lehrstuhl Informatik 10, Studiencenter Informatik
Thema: Beyond Touch: Enriching the Interaction with Mobile Devices
Abstract:
Mobile devices like smartphones and tablets are now the most common digital tools in daily life, aiding in both work and personal tasks. Much of their success stems from their intuitive touchscreen interaction, which allows users to engage with any digital content by simply touching the screen.
However, despite their success, touch input has several limitations and drawbacks. It reduces the extensive input capabilities of our hands to mostly a single touchpoint on a 2D surface, lacking additional information such as the force or orientation of the touch, or extra keys like the multiple modifier buttons found on a mouse or keyboard. These limitations slow down interaction and present numerous challenges, negatively affecting usability. Additional issues such as the fat-finger problem and limited reachability further add to the inconveniences. While the input capabilities are sufficient for simple applications, more complex tasks requiring intensive or intricate input, such as large-scale text editing, are still cumbersome and often avoided.
In this thesis, I aim to present several approaches that explore, design, prototype, and analyze new techniques to enrich and expand the limited interaction methods on mobile devices. The goal is to empower users to utilize their movement and sensing capabilities in a more meaningful way, beyond simply creating basic touch input on a sheet of glass. I achieve this by focusing on three approaches: 1) Improving touch input on the device itself by incorporating additional properties of the user's hand, such as the force of a touchpoint. I utilize this additional input dimension to create new interaction techniques and to address existing issues, such as the reachability on a device or the lack of shortcuts on touchscreens. 2) Combining touch with other input methods, such as the user's head or eye movements, to overcome the inherent limitations of touch input. 3) Utilizing the sensing capabilities of mobile devices to detect their surroundings to enable other objects around the device to serve as additional input devices that enable new use cases for mobile devices.
My habilitation thesis thus contributes a series of new interaction techniques, implemented and evaluated in lab experiments and user studies, that use touch force sensing, head and gaze input, and objects in the surrounding environment, to make mobile touch input more expressive and effective.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Mittwoch, 26. Juni 2024, 10.00 Uhr
Ort: Seminarraum 003, IT Center, Kopernikusstraße 6
Referentin: Andrea Bönsch, M.Sc.
Lehr- und Forschungsgebiet Virtuelle Realität und
Immersive Visualisierung (LuFG i12) und IT Center
Thema: Social Wayfinding Strategies to Explore Immersive Virtual
Environments
Abstract:
To create Immersive Virtual Environments (IVEs) representing believable and
appealing urban spaces, high-quality architectural reconstructions alone are
insufficient. Instead, these reconstructions need to be complemented by
embedding Virtual Agents (VAs). These computer-controlled, anthropomorphic
characters breathe life into the scene, either by actively functioning as
direct interaction partners or by passively enlivening the IVEs, e.g., as
virtual pedestrians. Regardless of the VAs' roles, user engagement in
unfamiliar IVEs primarily revolves around scene exploration to facilitate
successful navigation and interaction within the scene. The involved
wayfinding procedure --- encompassing users' understanding of their
surroundings, route planning, and informed decision-making --- is profoundly
grounded in a social context where the presence and behavior of others, in
our case, the embedded VAs, influence the user.
The core contribution of this thesis is, thus, to enhance our understanding
of wayfinding as a social activity in Virtual Reality (VR) applications. We
achieved this through the optimization of user support during scene
exploration by strategically integrating VAs as inherent components within
IVEs. Thereby, we undertake a three-fold approach: Firstly, by adopting a
theoretical approach we develop a user-centered wayfinding taxonomy
categorizing diverse wayfinding strategies in VR. Within this taxonomy, we
introduce VAs as novel wayfinding support, distinguishing between strong
social wayfinding (direct guidance by VAs) and weak social wayfinding
(subtle VA influence on navigation decisions). This conceptual framework
facilitates a deeper understanding of how users interact within IVEs.
Secondly, we enhance the spatial behavior of VAs for improved user
experience during collaborative navigation. Through ecologically valid
VR-based user studies, we uncover nuanced user preferences regarding VA
proximity management, behavior during non-interaction periods, and responses
to inferred user intents. These findings inform the enhancement of VAs'
interactive capabilities displaying responsive and socially compliant
behavior for an improved user experience. Lastly, we enable the efficient
utilization of VAs as social wayfinding support. Through VR-based user
studies, we refine the behavioral design of virtual guides by effectively
balancing guidance and the user's autonomous exploration, fostering optimal
user-agent interaction during strong social wayfinding. Additionally, we
propose a VA design as weak social wayfinding that subtly influences user
navigation within IVEs by shaping pedestrian flows, substantiated through
empirical validation. Comparative analysis of both approaches in terms of
user experience and scene knowledge gain elevates our comprehension of
effective VA utilization in immersive environments. In summation, this
research contributes to the advancement of VAs as advanced human interfaces,
fostering enhanced user acceptance, usability, and perceived social presence
within VR environments.
Es laden ein: die Dozentinnen und Dozenten der Informatik
--
Aleksandra Lukic
RWTH Aachen University
- Visual Computing Institute - Virtual Reality & Immersive Visualization
- Computer Science Department LuFG i12
- IT Center - Computational Science & Engineering
Kopernikusstraße 6, 52074 Aachen, Germany
Phone +49 241 80-29233
Email office(a)vr.rwth-aachen.de
URL www.vr.rwth-aachen.de
Dear all,
this is a reminder for Christina Büsing's talk with the title "Robust Optimization in Health Care" taking place today (13.06) at 12:30 in the B-IT room 5053.2. Please find the details below
--- Abstract ---
Health is one of the most important factors for the prosperity and well-being of a society.
In recent years, this system is challenged by demographic changes: the elderly population
(age 65 and older) will increase, which will result in a higher demand for health care services.
Simultaneously the decrease in the fertility rate results in a shrinking number of trained
medical staff. In order to keep the high standard of the German health care system,
it is crucial to improve on the efficient and effective use of our resources.
Planning and organization in these fields are complicated by the uncertain nature of health care processes,
e.g., the fluctuating demand, arrival time of patients, emergency patients, durations of a single
treatment, length of stay or resource failures. These uncertainties are rarely incorporated in the
optimization process, if optimization and mathematical methods are used at all. In this talk,
I will present, how we approach such kind of optimization problems and show examples how
robust optimization can be used to integrated these uncertainties into the optimization process.
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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
