Issue
Dagstuhl Reports, Volume 14, Issue 7
-
Part of:
Volume:
Dagstuhl Reports, Volume 14
Journal: Dagstuhl Reports (DagRep)
Event
Dagstuhl Seminars 24281, 24282, 24291, 24292, 24301, 24302, 24311, 24312Publication Details
- published at: 2025-02-14
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Access Numbers
- Detailed Access Statistics available here
-
Total Document Accesses (updated on a weekly basis):
0PDF Downloads
Documents
No documents found matching your filter selection.
Document
Complete Issue
Dagstuhl Reports, Volume 14, Issue 7, July 2024, Complete Issue
Abstract
Dagstuhl Reports, Volume 14, Issue 7, July 2024, Complete Issue
Cite as
Dagstuhl Reports, Volume 14, Issue 7, pp. 1-209, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{DagRep.14.7,
title = {{Dagstuhl Reports, Volume 14, Issue 7, July 2024, Complete Issue}},
pages = {1--209},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {7},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.7},
URN = {urn:nbn:de:0030-drops-229359},
doi = {10.4230/DagRep.14.7},
annote = {Keywords: Dagstuhl Reports, Volume 14, Issue 7, July 2024, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 14, Issue 7, 2024
Abstract
Dagstuhl Reports, Table of Contents, Volume 14, Issue 7, 2024
Cite as
Dagstuhl Reports, Volume 14, Issue 7, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{DagRep.14.7.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 14, Issue 7, 2024}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {7},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.7.i},
URN = {urn:nbn:de:0030-drops-229267},
doi = {10.4230/DagRep.14.7.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Dynamic Traffic Models in Transportation Science (Dagstuhl Seminar 24281)
Abstract
Traffic assignment models are crucial for traffic planners to be able to predict traffic distributions, especially in light of possible changes in the infrastructure, e.g., road constructions, traffic light controls, etc. There is a trend in the transportation community (science and industry) to base such predictions on complex computer-based simulations capable of resolving many elements of a real transportation system. Moreover, cities worldwide, driven by critical sustainability goals, are developing digital twins of their transportation networks to inform the design and the operations of these intricate networks. On the other hand, the theory of dynamic traffic assignments in terms of equilibrium existence, computability, and efficiency, has not matured to the point matching the model complexity inherent in simulations.
The Dagstuhl Seminar was the fourth in a row on this topic and brought together leading scientists in the areas traffic simulations, algorithmic game theory, and dynamic traffic assignment. In this seminar, we tackled important open research problems that were identified in past seminars. Motivated by the increasing importance, in practice, of developing sustainable, flexible, and on-demand mobility services, this seminar identified a new set of important questions and first results in this field.
Cite as
José Correa, Carolina Osorio, Laura Vargas Koch, David Watling, and Svenja Griesbach. Dynamic Traffic Models in Transportation Science (Dagstuhl Seminar 24281). In Dagstuhl Reports, Volume 14, Issue 7, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{correa_et_al:DagRep.14.7.1,
author = {Correa, Jos\'{e} and Osorio, Carolina and Koch, Laura Vargas and Watling, David and Griesbach, Svenja},
title = {{Dynamic Traffic Models in Transportation Science (Dagstuhl Seminar 24281)}},
pages = {1--16},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {7},
editor = {Correa, Jos\'{e} and Osorio, Carolina and Koch, Laura Vargas and Watling, David and Griesbach, Svenja},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.7.1},
URN = {urn:nbn:de:0030-drops-229340},
doi = {10.4230/DagRep.14.7.1},
annote = {Keywords: Algorithms and Complexity of traffic equilibrium computations, Dynamic traffic assignment models, Simulation and network optimization}
}
Document
Automated Machine Learning For Computational Mechanics (Dagstuhl Seminar 24282)
Abstract
Machine learning (ML) has achieved undeniable success in computational mechanics, an ever-growing discipline that impacts all areas of engineering, from structural and fluid dynamics to solid mechanics and vehicle simulation. Computational mechanics uses numerical models and time- and resource-consuming simulations to reproduce physical phenomena, usually with the goal of optimizing the parameter configuration of the model with respect to the desired properties of the system. ML algorithms enable the construction of surrogate models that approximate the outcome of the simulations, allowing faster identification of well-performing configurations. However, determining the best ML approach for a given task is not straightforward and depends on human experts. Automated machine learning (AutoML) aims to reduce the need for experts to obtain effective ML pipelines. It provides off-the-shelf solutions that can be used without prior knowledge of ML, allowing engineers to spend more time on domain-specific tasks. AutoML is underutilized in computational mechanics; there is almost no communication between the two communities, and engineers spend unnecessary effort selecting and configuring ML algorithms. Our Dagstuhl Seminar aimed to (i) raise awareness of AutoML in the computational mechanics community, (ii) discover strengths and challenges for applying AutoML in practice, and (iii) create a bilateral exchange so that researchers can mutually benefit from their complementary goals and needs.
Cite as
Elena Raponi, Lars Kotthoff, Hyunsun Alicia Kim, and Marius Lindauer. Automated Machine Learning For Computational Mechanics (Dagstuhl Seminar 24282). In Dagstuhl Reports, Volume 14, Issue 7, pp. 17-34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{raponi_et_al:DagRep.14.7.17,
author = {Raponi, Elena and Kotthoff, Lars and Kim, Hyunsun Alicia and Lindauer, Marius},
title = {{Automated Machine Learning For Computational Mechanics (Dagstuhl Seminar 24282)}},
pages = {17--34},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {7},
editor = {Raponi, Elena and Kotthoff, Lars and Kim, Hyunsun Alicia and Lindauer, Marius},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.7.17},
URN = {urn:nbn:de:0030-drops-229331},
doi = {10.4230/DagRep.14.7.17},
annote = {Keywords: automated algorithm design; computational mechanics; engineering applications of AI; black-box optimization; physics-informed machine learning}
}
Document
Programmable Host Networking (Dagstuhl Seminar 24291)
Abstract
Increasingly communication software is being offloaded to specialized hardware accelerators and into the OS kernel. In most cases this is because offloading is supposed to improve network utilization and reduce costs. However, designing good offloads is challenging, often requiring architectural changes to both software and hardware. But there is little agreement on the form of these changes, and this both increases the true cost of building and deploying offloads and the complexity of doing research on accelerators. This Dagstuhl Seminar aimed to provide a forum to talk about experiences with building and deploying accelerator platforms to address this concern.
Cite as
Gianni Antichi, Katerina Argyraki, Aurojit Panda, and Justine Sherry. Programmable Host Networking (Dagstuhl Seminar 24291). In Dagstuhl Reports, Volume 14, Issue 7, pp. 35-51, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{antichi_et_al:DagRep.14.7.35,
author = {Antichi, Gianni and Argyraki, Katerina and Panda, Aurojit and Sherry, Justine},
title = {{Programmable Host Networking (Dagstuhl Seminar 24291)}},
pages = {35--51},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {7},
editor = {Antichi, Gianni and Argyraki, Katerina and Panda, Aurojit and Sherry, Justine},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.7.35},
URN = {urn:nbn:de:0030-drops-229323},
doi = {10.4230/DagRep.14.7.35},
annote = {Keywords: Networking, Accelerators, Interconnects}
}
Document
Improving Trust between Humans and Software Robots in Robotic Process Automation (Dagstuhl Seminar 24292)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 24292 "Improving Trust between Humans and Software Robots in Robotic Process Automation". The seminar dealt with topics targeted at developing frameworks and guidelines to empower the trust relationship between humans and Software Robots (SW) in Robotic Process Automation (RPA).
RPA is a maturing technology that sits between the fields of Business Process Management (BPM) and Artificial Intelligence (AI). RPA allows organizations to automate high-volume and repetitive tasks – also referred to as routines – performed by human users. The enactment of these routines is emulated by means of a software (SW) robot that works on the applications' user interfaces (UIs) in the same way as the original human operators did. Recent research studies conducted on the effectiveness of RPA within organizations have found that implementation of SW robots does not always lead to the assumed effect, and many SW robots are subsequently withdrawn. In consequence, the human workforce takes over robotized tasks to perform them manually again and, in practice, replaces back SW robots. The fact is that integrating RPA into a human workforce alters the role of human employees and dynamics within the workforce, fueling a lack of trust in RPA technology, an issue deemed increasingly significant given its widespread use in many working domains.
In this direction, this Dagstuhl Seminar aimed to bring together leading experts from industry and academia engaged in diverse communities related to RPA, including BPM and Human-centered AI, intending to reflect on the current RPA principles, which fail to deliver sufficient attention to the interplay between the human workforce and SW robots. The overall goal was to explore the scientific and technological foundations to pioneer new trust-aware RPA solutions that work in partnership with the human workforce, to enhance human capabilities rather than replace human intelligence and break through the barriers to human trust using RPA. The seminar outcomes will serve as a basis to foster joint research efforts and collaborations for charting a roadmap for future RPA research.
Cite as
Adela del Río Ortega, Andrea Marrella, Hajo A. Reijers, and Adriana Wilde. Improving Trust between Humans and Software Robots in Robotic Process Automation (Dagstuhl Seminar 24292). In Dagstuhl Reports, Volume 14, Issue 7, pp. 52-80, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{ortega_et_al:DagRep.14.7.52,
author = {Ortega, Adela del R{\'\i}o and Marrella, Andrea and Reijers, Hajo A. and Wilde, Adriana},
title = {{Improving Trust between Humans and Software Robots in Robotic Process Automation (Dagstuhl Seminar 24292)}},
pages = {52--80},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {7},
editor = {Ortega, Adela del R{\'\i}o and Marrella, Andrea and Reijers, Hajo A. and Wilde, Adriana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.7.52},
URN = {urn:nbn:de:0030-drops-229317},
doi = {10.4230/DagRep.14.7.52},
annote = {Keywords: business process management, human-centered AI, human-computer interaction, robotic process automation, software robots}
}
Document
Art, Visual Illusions, and Data Visualization (Dagstuhl Seminar 24301)
Abstract
This report presents the program and outcomes of Dagstuhl Seminar 24301, titled "Art, Visual Illusions, and Data Visualization." The seminar explored the intersection of art, visual illusions, and data science - three distinct yet interconnected disciplines that share a focus on visual representation and perception. Art serves as a medium for storytelling and complex visual communication, while visual illusions offer insights into cognitive and perceptual mechanisms. Data science complements these fields with advanced methods for analyzing and visualizing complex datasets. The seminar examined historical and contemporary examples of the interplay between these domains, showcasing artists such as M.C. Escher, Bridget Riley, and Yayoi Kusama, as well as modern practitioners like Laurie Frick, Refik Anadol, and Giorgia Lupi. These examples illustrate how visual illusions and data visualization techniques have been used to challenge perceptions, uncover hidden patterns, and foster deeper understanding. By bringing together experts in art, cognitive psychology, and data science, the seminar fostered interdisciplinary dialogue and collaboration. Participants explored innovative approaches to visual storytelling and data communication, emphasizing the potential of integrating artistic methods, perceptual insights, and computational tools to create engaging and intuitive visualizations. The seminar highlighted the rich synergies at the intersection of these fields, advancing both theory and practice in visual representation and perception.
Cite as
Christophe Hurter, Claus-Christian Carbon, Mauro Martino, and Bernice E. Rogowitz. Art, Visual Illusions, and Data Visualization (Dagstuhl Seminar 24301). In Dagstuhl Reports, Volume 14, Issue 7, pp. 81-114, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{hurter_et_al:DagRep.14.7.81,
author = {Hurter, Christophe and Carbon, Claus-Christian and Martino, Mauro and Rogowitz, Bernice E.},
title = {{Art, Visual Illusions, and Data Visualization (Dagstuhl Seminar 24301)}},
pages = {81--114},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {7},
editor = {Hurter, Christophe and Carbon, Claus-Christian and Martino, Mauro and Rogowitz, Bernice E.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.7.81},
URN = {urn:nbn:de:0030-drops-229308},
doi = {10.4230/DagRep.14.7.81},
annote = {Keywords: Data Visualization, Perception, Cognition, Art, Visual Illusions, Generative Art, Artificial Intelligence (AI), Machine Learning (ML), Cognitive Psychology, Human-Computer Interaction, Creativity, Empirical Aesthetics}
}
Document
Learning with Music Signals: Technology Meets Education (Dagstuhl Seminar 24302)
Abstract
Music information retrieval (MIR) is an exciting and challenging research area that aims to develop techniques and tools for organizing, analyzing, retrieving, and presenting music-related data. At the intersection of engineering, social sciences, and humanities, MIR relates to different research disciplines, including signal processing, machine learning, information retrieval, psychology, musicology, and the digital humanities. In Dagstuhl Seminar 24302, we explored advancing technology and education in these fields by examining learning from various angles, using music as a concrete application domain. Typically, learning in computer science brings to mind data-driven techniques like deep learning. While machine learning was crucial to the seminar, we aimed to go beyond a technical perspective, focusing on educational and pedagogical aspects. Specifically, we investigated how music can serve as a vehicle to make learning in signal processing and machine learning interactive and effectively communicated in interdisciplinary research and educational settings. In this report, we give an overview of the various contributions and results of the seminar. We start with an executive summary describing the main topics, goals, and group activities. Then, we give an overview of the participants' stimulus talks and subsequent discussions (listed alphabetically by the main contributor’s last name) and summarize further activities, including group discussions and music sessions.
Cite as
Meinard Müller, Cynthia Liem, Brian McFee, and Simon Schwär. Learning with Music Signals: Technology Meets Education (Dagstuhl Seminar 24302). In Dagstuhl Reports, Volume 14, Issue 7, pp. 115-152, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{muller_et_al:DagRep.14.7.115,
author = {M\"{u}ller, Meinard and Liem, Cynthia and McFee, Brian and Schw\"{a}r, Simon},
title = {{Learning with Music Signals: Technology Meets Education (Dagstuhl Seminar 24302)}},
pages = {115--152},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {7},
editor = {M\"{u}ller, Meinard and Liem, Cynthia and McFee, Brian and Schw\"{a}r, Simon},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.7.115},
URN = {urn:nbn:de:0030-drops-229298},
doi = {10.4230/DagRep.14.7.115},
annote = {Keywords: Music Information Retrieval, Education, Signal Processing, User Interaction, Deep Learning}
}
Document
Resource-Efficient Machine Learning (Dagstuhl Seminar 24311)
Abstract
Machine learning (ML) enables forecasts, even in real-time, at ever lower cost and better accuracy. Today, data scientists are able to collect more data, access that data faster, and apply more complex data analysis than ever. As a result, ML impacts a variety of fields such as healthcare, finance, and entertainment.
The advances in ML are mainly thanks to the exponential evolution of hardware, the availability of the large datasets, and the emergence of machine learning frameworks, which hide the complexities of the underlying hardware, boosting the productivity of data scientists. On the other hand, the computational need of the powerful ML models has increased several orders of magnitude in the past decade. A state-of-the-art large language processing model can cost of millions dollars to train in the cloud [The AI Index Report, 2024] without accounting for the electricity cost and carbon footprint [Dodge et al, 2022][Wu et al, 2024]. This makes the current rate of increase in model parameters, datasets, and compute budget unsustainable. To achieve a more sustainable progress in ML in the future, it is essential to invest in more resource-/energy-/cost-efficient solutions.
In this Dagstuhl Seminar, our main goal was to reason critically about how we build software and hardware for end-to-end machine learning. The crowd was composed of experts from academia and industry across fields of data management, machine learning, compilers, systems, and computer architecture covering expertise of algorithmic optimizations in machine learning, job scheduling and resource management in distributed computing, parallel computing, and data management and processing.
During the seminar, we explored how to improve ML resource efficiency through a holistic view of the ML landscape, which includes data preparation and loading, continual retraining of models in dynamic data environments, compiling ML on specialized hardware accelerators, hardware/software co-design for ML, and serving models for real-time applications with low-latency requirements and constrained resource environments. We hope that the discussions and the work planned during the seminar will lead to increased awareness for understanding the utilization of modern hardware and kickstart future developments to minimize hardware underutilization while still enabling emerging applications powered by ML.
Cite as
Oana Balmau, Matthias Boehm, Ana Klimovic, Peter Pietzuch, and Pinar Tözün. Resource-Efficient Machine Learning (Dagstuhl Seminar 24311). In Dagstuhl Reports, Volume 14, Issue 7, pp. 153-169, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{balmau_et_al:DagRep.14.7.153,
author = {Balmau, Oana and Boehm, Matthias and Klimovic, Ana and Pietzuch, Peter and T\"{o}z\"{u}n, Pinar},
title = {{Resource-Efficient Machine Learning (Dagstuhl Seminar 24311)}},
pages = {153--169},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {7},
editor = {Balmau, Oana and Boehm, Matthias and Klimovic, Ana and Pietzuch, Peter and T\"{o}z\"{u}n, Pinar},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.7.153},
URN = {urn:nbn:de:0030-drops-229283},
doi = {10.4230/DagRep.14.7.153},
annote = {Keywords: Machine Learning, Modern Hardware, Sustainability, Energy-Efficiency, Benchmarking, Hardware-Software Co-Design, Data Management, Compilation}
}
Document
Security and Privacy of Current and Emerging IoT Devices and Systems (Dagstuhl Seminar 24312)
Abstract
This report summarizes the program of Dagstuhl Seminar 24312 on "Security and Privacy of Current and Emerging IoT Devices and Systems" by providing short overviews over all talks and discussions as well as a list of open probelms and a short outlook to the future.
Cite as
Bruno Crispo, Alexandra Dmitrienko, Gene Tsudik, Wenyuan Xu, and Christoph Sendner. Security and Privacy of Current and Emerging IoT Devices and Systems (Dagstuhl Seminar 24312). In Dagstuhl Reports, Volume 14, Issue 7, pp. 170-207, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{crispo_et_al:DagRep.14.7.170,
author = {Crispo, Bruno and Dmitrienko, Alexandra and Tsudik, Gene and Xu, Wenyuan and Sendner, Christoph},
title = {{Security and Privacy of Current and Emerging IoT Devices and Systems (Dagstuhl Seminar 24312)}},
pages = {170--207},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {7},
editor = {Crispo, Bruno and Dmitrienko, Alexandra and Tsudik, Gene and Xu, Wenyuan and Sendner, Christoph},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.7.170},
URN = {urn:nbn:de:0030-drops-229273},
doi = {10.4230/DagRep.14.7.170},
annote = {Keywords: IoT Security, Trust, Cryptography, Authentication}
}