---
_id: '64823'
abstract:
- lang: eng
text: "Current legal frameworks enforce that Android developers accurately report
the data their apps collect. However, large codebases can make this reporting
challenging. This paper employs an empirical approach to understand developers'
experience with Google Play Store's Data Safety Section (DSS) form.\r\n\r\nWe
first survey 41 Android developers to understand how they categorize privacy-related
data into DSS categories and how confident they feel when completing the DSS form.
To gain a broader and more detailed view of the challenges developers encounter
during the process, we complement the survey with an analysis of 172 online developer
discussions, capturing the perspectives of 642 additional developers. Together,
these two data sources represent insights from 683 developers.\r\n\r\nOur findings
reveal that developers often manually classify the privacy-related data their
apps collect into the data categories defined by Google-or, in some cases, omit
classification entirely-and rely heavily on existing online resources when completing
the form. Moreover, developers are generally confident in recognizing the data
their apps collect, yet they lack confidence in translating this knowledge into
DSS-compliant disclosures. Key challenges include issues in identifying privacy-relevant
data to complete the form, limited understanding of the form, and concerns about
app rejection due to discrepancies with Google's privacy requirements.\r\nThese
results underscore the need for clearer guidance and more accessible tooling to
support developers in meeting privacy-aware reporting obligations. "
author:
- first_name: Mugdha
full_name: Khedkar, Mugdha
id: '88024'
last_name: Khedkar
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Mohamed Aboubakr Mohamed
full_name: Soliman, Mohamed Aboubakr Mohamed
id: '102489'
last_name: Soliman
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Khedkar M, Schlichtig M, Soliman MAM, Bodden E. Challenges in Android Data
Disclosure: An Empirical Study. In: Proceedings of the IEEE/ACM 13th International
Conference on Mobile Software Engineering and Systems (MOBILESoft ’26). Association
for Computing Machinery, New York, NY, USA, 65–68. ; 2026.'
apa: 'Khedkar, M., Schlichtig, M., Soliman, M. A. M., & Bodden, E. (2026). Challenges
in Android Data Disclosure: An Empirical Study. Proceedings of the IEEE/ACM
13th International Conference on Mobile Software Engineering and Systems (MOBILESoft
’26). Association for Computing Machinery, New York, NY, USA, 65–68. 13th
International Conference on Mobile Software Engineering and Systems 2024, Rio
de Janeiro, Brazil.'
bibtex: '@inproceedings{Khedkar_Schlichtig_Soliman_Bodden_2026, title={Challenges
in Android Data Disclosure: An Empirical Study.}, booktitle={Proceedings of the
IEEE/ACM 13th International Conference on Mobile Software Engineering and Systems
(MOBILESoft ’26). Association for Computing Machinery, New York, NY, USA, 65–68.},
author={Khedkar, Mugdha and Schlichtig, Michael and Soliman, Mohamed Aboubakr
Mohamed and Bodden, Eric}, year={2026} }'
chicago: 'Khedkar, Mugdha, Michael Schlichtig, Mohamed Aboubakr Mohamed Soliman,
and Eric Bodden. “Challenges in Android Data Disclosure: An Empirical Study.”
In Proceedings of the IEEE/ACM 13th International Conference on Mobile Software
Engineering and Systems (MOBILESoft ’26). Association for Computing Machinery,
New York, NY, USA, 65–68., 2026.'
ieee: 'M. Khedkar, M. Schlichtig, M. A. M. Soliman, and E. Bodden, “Challenges in
Android Data Disclosure: An Empirical Study.,” presented at the 13th International
Conference on Mobile Software Engineering and Systems 2024, Rio de Janeiro, Brazil,
2026.'
mla: 'Khedkar, Mugdha, et al. “Challenges in Android Data Disclosure: An Empirical
Study.” Proceedings of the IEEE/ACM 13th International Conference on Mobile
Software Engineering and Systems (MOBILESoft ’26). Association for Computing Machinery,
New York, NY, USA, 65–68., 2026.'
short: 'M. Khedkar, M. Schlichtig, M.A.M. Soliman, E. Bodden, in: Proceedings of
the IEEE/ACM 13th International Conference on Mobile Software Engineering and
Systems (MOBILESoft ’26). Association for Computing Machinery, New York, NY, USA,
65–68., 2026.'
conference:
end_date: 2026-04-18
location: Rio de Janeiro, Brazil
name: 13th International Conference on Mobile Software Engineering and Systems 2024
start_date: 2026-04-12
date_created: 2026-03-04T08:10:43Z
date_updated: 2026-03-13T12:10:10Z
department:
- _id: '76'
external_id:
arxiv:
- '2601.20459'
keyword:
- static analysis
- data collection
- data protection
- privacy-aware reporting
language:
- iso: eng
publication: Proceedings of the IEEE/ACM 13th International Conference on Mobile Software
Engineering and Systems (MOBILESoft '26). Association for Computing Machinery, New
York, NY, USA, 65–68.
status: public
title: 'Challenges in Android Data Disclosure: An Empirical Study.'
type: conference
user_id: '88024'
year: '2026'
...
---
_id: '63754'
abstract:
- lang: eng
text: Data spaces are receiving an emerging interest in Information Systems Research
and industry practice. They are central to many European research initiatives
and shape the data economy in Industry 4.0. Generally, they aim to create secure
environments for cross-organizational data management and sharing. Currently,
there is considerable interest in developing new data spaces in Industry 4.0,
also accelerated through regulatory changes. However, key questions about what
precisely characterizes a data space in Industry 4.0 remain unresolved. Against
this backdrop, we build a taxonomy of data spaces in the Industry 4.0 context.
We identified nine distinctive dimensions and 40 corresponding characteristics
among the 19 data spaces analyzed. The taxonomy enables clearer classification
and nomenclature of data spaces in this context. This short paper will ignite
planned further research on data spaces in Industry 4.0 and contribute to a conceptualization
of a taxonomic theory for interested researchers.
author:
- first_name: Oliver
full_name: Werth, Oliver
last_name: Werth
- first_name: Christian
full_name: Koldewey, Christian
id: '43136'
last_name: Koldewey
orcid: https://orcid.org/0000-0001-7992-6399
- first_name: Mathias
full_name: Uslar, Mathias
last_name: Uslar
- first_name: Julian
full_name: Zerbin, Julian
id: '51711'
last_name: Zerbin
citation:
ama: 'Werth O, Koldewey C, Uslar M, Zerbin J. What Characterizes Data Spaces in
Industry 4.0? Towards a Better Understanding. In: Lecture Notes in Business
Information Processing. Springer Nature Switzerland; 2026. doi:10.1007/978-3-032-14518-5_3'
apa: Werth, O., Koldewey, C., Uslar, M., & Zerbin, J. (2026). What Characterizes
Data Spaces in Industry 4.0? Towards a Better Understanding. Lecture Notes
in Business Information Processing. 16th International Conference on Software
Business (ICSOB 2025), Stuttgart, Germany. https://doi.org/10.1007/978-3-032-14518-5_3
bibtex: '@inproceedings{Werth_Koldewey_Uslar_Zerbin_2026, place={Cham}, title={What
Characterizes Data Spaces in Industry 4.0? Towards a Better Understanding}, DOI={10.1007/978-3-032-14518-5_3},
booktitle={Lecture Notes in Business Information Processing}, publisher={Springer
Nature Switzerland}, author={Werth, Oliver and Koldewey, Christian and Uslar,
Mathias and Zerbin, Julian}, year={2026} }'
chicago: 'Werth, Oliver, Christian Koldewey, Mathias Uslar, and Julian Zerbin. “What
Characterizes Data Spaces in Industry 4.0? Towards a Better Understanding.” In
Lecture Notes in Business Information Processing. Cham: Springer Nature
Switzerland, 2026. https://doi.org/10.1007/978-3-032-14518-5_3.'
ieee: 'O. Werth, C. Koldewey, M. Uslar, and J. Zerbin, “What Characterizes Data
Spaces in Industry 4.0? Towards a Better Understanding,” presented at the 16th
International Conference on Software Business (ICSOB 2025), Stuttgart, Germany,
2026, doi: 10.1007/978-3-032-14518-5_3.'
mla: Werth, Oliver, et al. “What Characterizes Data Spaces in Industry 4.0? Towards
a Better Understanding.” Lecture Notes in Business Information Processing,
Springer Nature Switzerland, 2026, doi:10.1007/978-3-032-14518-5_3.
short: 'O. Werth, C. Koldewey, M. Uslar, J. Zerbin, in: Lecture Notes in Business
Information Processing, Springer Nature Switzerland, Cham, 2026.'
conference:
end_date: 2025-11-26
location: Stuttgart, Germany
name: 16th International Conference on Software Business (ICSOB 2025)
start_date: 2025-11-24
date_created: 2026-01-27T11:56:10Z
date_updated: 2026-03-18T07:12:49Z
department:
- _id: '563'
doi: 10.1007/978-3-032-14518-5_3
keyword:
- Industry 4.0
- Taxonomy
- Data spaces
- Characterization
language:
- iso: eng
place: Cham
publication: Lecture Notes in Business Information Processing
publication_identifier:
isbn:
- '9783032145178'
- '9783032145185'
issn:
- 1865-1348
- 1865-1356
publication_status: published
publisher: Springer Nature Switzerland
quality_controlled: '1'
status: public
title: What Characterizes Data Spaces in Industry 4.0? Towards a Better Understanding
type: conference
user_id: '51711'
year: '2026'
...
---
_id: '64787'
abstract:
- lang: eng
text: This study proposes a fault diagnostics methodology that addresses the challenges
posed by highly imbalanced datasets typical of railway applications, where faulty
conditions constitute the minority class. Fault diagnostics is performed from
the component level upward, considering each sensor’s proximity to its respective
critical component. Advanced signal analysis, feature engineering, and automated
data-driven model generation techniques were explored to achieve comprehensive
diagnostics, such that the model development process accounts for variations in
the operating conditions and differing levels of information availability. The
proposed methodology is evaluated on datasets from the MONOCAB, for scenarios
with limited faulty instances and on the Beijing 2024 IEEE PHM Conference data
challenge, which focused on fault diagnostics of railway systems under various
fault modes and operating conditions.
article_number: '1'
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Alexander
full_name: Löwen, Alexander
id: '47233'
last_name: Löwen
- first_name: Raphael
full_name: Hanselle, Raphael
last_name: Hanselle
- first_name: Thomas
full_name: Rief, Thomas
last_name: Rief
- first_name: Maximilian
full_name: Beck, Maximilian
last_name: Beck
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Aimiyekagbon OK, Löwen A, Hanselle R, Rief T, Beck M, Sextro W. Multilevel
fault diagnostics for railway applications using limited historical data. In:
PHM Society Asia-Pacific Conference. Vol 5. ; 2025. doi:10.36001/phmap.2025.v5i1.4449'
apa: Aimiyekagbon, O. K., Löwen, A., Hanselle, R., Rief, T., Beck, M., & Sextro,
W. (2025). Multilevel fault diagnostics for railway applications using limited
historical data. PHM Society Asia-Pacific Conference, 5, Article
1. https://doi.org/10.36001/phmap.2025.v5i1.4449
bibtex: '@inproceedings{Aimiyekagbon_Löwen_Hanselle_Rief_Beck_Sextro_2025, title={Multilevel
fault diagnostics for railway applications using limited historical data}, volume={5},
DOI={10.36001/phmap.2025.v5i1.4449},
number={1}, booktitle={PHM Society Asia-Pacific Conference}, author={Aimiyekagbon,
Osarenren Kennedy and Löwen, Alexander and Hanselle, Raphael and Rief, Thomas
and Beck, Maximilian and Sextro, Walter}, year={2025} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Alexander Löwen, Raphael Hanselle, Thomas
Rief, Maximilian Beck, and Walter Sextro. “Multilevel Fault Diagnostics for Railway
Applications Using Limited Historical Data.” In PHM Society Asia-Pacific Conference,
Vol. 5, 2025. https://doi.org/10.36001/phmap.2025.v5i1.4449.
ieee: 'O. K. Aimiyekagbon, A. Löwen, R. Hanselle, T. Rief, M. Beck, and W. Sextro,
“Multilevel fault diagnostics for railway applications using limited historical
data,” in PHM Society Asia-Pacific Conference, 2025, vol. 5, doi: 10.36001/phmap.2025.v5i1.4449.'
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Multilevel Fault Diagnostics for Railway
Applications Using Limited Historical Data.” PHM Society Asia-Pacific Conference,
vol. 5, 1, 2025, doi:10.36001/phmap.2025.v5i1.4449.
short: 'O.K. Aimiyekagbon, A. Löwen, R. Hanselle, T. Rief, M. Beck, W. Sextro, in:
PHM Society Asia-Pacific Conference, 2025.'
date_created: 2026-02-27T20:41:54Z
date_updated: 2026-02-27T20:46:44Z
department:
- _id: '151'
doi: 10.36001/phmap.2025.v5i1.4449
intvolume: ' 5'
keyword:
- MONOCAB
- Beijing Data Challenge
- Diagnostics of railway systems
language:
- iso: eng
project:
- _id: '1355'
name: enableATO – Automatisierter Bahnverkehr als Backbone für eine nachhaltige,
vernetzte Mobilität im ländlichen Raum
publication: PHM Society Asia-Pacific Conference
publication_status: published
quality_controlled: '1'
status: public
title: Multilevel fault diagnostics for railway applications using limited historical
data
type: conference
user_id: '9557'
volume: 5
year: '2025'
...
---
_id: '58650'
abstract:
- lang: eng
text: 'Technical systems are characterized by increasing interdisciplinarity, complexity
and networking. A product and its corresponding production systems require interdisciplinary
multi-objective optimization. Sustainability and recyclability demands increase
said complexity. The efficiency of previously established engineering methods
is reaching its limits, which can only be overcome by systematic integration of
extreme data. The aim of "hybrid decision support" is as follows: Data science
and artificial intelligence should be used to supplement human capabilities in
conjunction with existing heuristics, methods, modeling and simulation to increase
the efficiency of product creation.'
alternative_title:
- Hybride Entscheidungsunterstützung in der Produktentstehung - Mit Data Science und
Künstlicher Intelligenz die Leistungsfähigkeit erhöhen
article_type: original
author:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Jens
full_name: Pottebaum, Jens
id: '405'
last_name: Pottebaum
orcid: http://orcid.org/0000-0001-8778-2989
- first_name: Peter
full_name: Nyhuis, Peter
last_name: Nyhuis
- first_name: Rainer
full_name: Stark, Rainer
last_name: Stark
- first_name: Klaus-Dieter
full_name: Thoben, Klaus-Dieter
last_name: Thoben
- first_name: Petra
full_name: Wiederkehr, Petra
last_name: Wiederkehr
citation:
ama: Gräßler I, Pottebaum J, Nyhuis P, Stark R, Thoben K-D, Wiederkehr P. Hybrid
Decision Support in Product Creation - Improving performance with data science
and artificial intelligence. Industry 40 Science. 2025;2025(1). doi:10.30844/i4sd.25.1.18
apa: Gräßler, I., Pottebaum, J., Nyhuis, P., Stark, R., Thoben, K.-D., & Wiederkehr,
P. (2025). Hybrid Decision Support in Product Creation - Improving performance
with data science and artificial intelligence. Industry 4.0 Science, 2025(1).
https://doi.org/10.30844/i4sd.25.1.18
bibtex: '@article{Gräßler_Pottebaum_Nyhuis_Stark_Thoben_Wiederkehr_2025, title={Hybrid
Decision Support in Product Creation - Improving performance with data science
and artificial intelligence}, volume={2025}, DOI={10.30844/i4sd.25.1.18},
number={1}, journal={Industry 4.0 Science}, publisher={GITO mbH Verlag}, author={Gräßler,
Iris and Pottebaum, Jens and Nyhuis, Peter and Stark, Rainer and Thoben, Klaus-Dieter
and Wiederkehr, Petra}, year={2025} }'
chicago: Gräßler, Iris, Jens Pottebaum, Peter Nyhuis, Rainer Stark, Klaus-Dieter
Thoben, and Petra Wiederkehr. “Hybrid Decision Support in Product Creation - Improving
Performance with Data Science and Artificial Intelligence.” Industry 4.0 Science
2025, no. 1 (2025). https://doi.org/10.30844/i4sd.25.1.18.
ieee: 'I. Gräßler, J. Pottebaum, P. Nyhuis, R. Stark, K.-D. Thoben, and P. Wiederkehr,
“Hybrid Decision Support in Product Creation - Improving performance with data
science and artificial intelligence,” Industry 4.0 Science, vol. 2025,
no. 1, 2025, doi: 10.30844/i4sd.25.1.18.'
mla: Gräßler, Iris, et al. “Hybrid Decision Support in Product Creation - Improving
Performance with Data Science and Artificial Intelligence.” Industry 4.0 Science,
vol. 2025, no. 1, GITO mbH Verlag, 2025, doi:10.30844/i4sd.25.1.18.
short: I. Gräßler, J. Pottebaum, P. Nyhuis, R. Stark, K.-D. Thoben, P. Wiederkehr,
Industry 4.0 Science 2025 (2025).
date_created: 2025-02-15T09:31:30Z
date_updated: 2025-02-15T09:40:52Z
department:
- _id: '152'
doi: 10.30844/i4sd.25.1.18
intvolume: ' 2025'
issue: '1'
keyword:
- AI
- artificial intelligence
- Data Science
- decision support
- extreme data
- Künstliche Intelligenz
- product creation
- product development
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
publication: Industry 4.0 Science
publication_identifier:
issn:
- 2942-6170
publication_status: published
publisher: GITO mbH Verlag
quality_controlled: '1'
status: public
title: Hybrid Decision Support in Product Creation - Improving performance with data
science and artificial intelligence
type: journal_article
user_id: '405'
volume: 2025
year: '2025'
...
---
_id: '60497'
abstract:
- lang: eng
text: Despite the advantages that the virtual knowledge graph paradigm has brought
to many application domains, state-of-the-art systems still do not support popular
graph database management systems like Neo4j. Their query rewriting algorithms
focus on languages like conjunctive queries and their unions, which were developed
for relational data and are poorly suited for graph data. Moreover, they also
limit the expressiveness of the ontology languages that admit rewritings, restricting
them to those that enjoy the so-called FO-rewritability property. Rewritings have
thus focused on the DL-Lite family of Description Logics. In this paper, we propose
a technique for rewriting a family of navigational queries for a suitably tailored
fragment of ELHI. Leveraging navigational features in the target query language,
we can include some widely-used axiom shapes not supported by DL-Lite. We implemented
a proof-of-concept prototype that rewrites into Cypher queries, and tested it
on a real-world cognitive neuroscience use case with promising results.
author:
- first_name: Bianca
full_name: Löhnert, Bianca
last_name: Löhnert
- first_name: Nikolaus
full_name: Augsten, Nikolaus
last_name: Augsten
- first_name: Cem
full_name: Okulmus, Cem
id: '114410'
last_name: Okulmus
orcid: 0000-0002-7742-0439
- first_name: Magdalena
full_name: Ortiz, Magdalena
last_name: Ortiz
citation:
ama: 'Löhnert B, Augsten N, Okulmus C, Ortiz M. Towards Practicable Algorithms for Rewriting
Graph Queries Beyond DL-Lite. In: The Semantic Web - 22nd European Semantic
Web Conference, {ESWC} 2025, Portoroz, Slovenia, June 1-5, 2025, Proceedings,
Part {I}. Vol 15718. Lecture Notes in Computer Science. Springer Nature Switzerland;
2025:342--361. doi:10.1007/978-3-031-94575-5_19'
apa: Löhnert, B., Augsten, N., Okulmus, C., & Ortiz, M. (2025). Towards Practicable
Algorithms for Rewriting Graph Queries Beyond DL-Lite. The Semantic Web - 22nd
European Semantic Web Conference, {ESWC} 2025, Portoroz, Slovenia, June 1-5, 2025,
Proceedings, Part {I}, 15718, 342--361. https://doi.org/10.1007/978-3-031-94575-5_19
bibtex: '@inproceedings{Löhnert_Augsten_Okulmus_Ortiz_2025, series={Lecture Notes
in Computer Science}, title={Towards Practicable Algorithms for Rewriting Graph
Queries Beyond DL-Lite}, volume={15718}, DOI={10.1007/978-3-031-94575-5_19},
booktitle={The Semantic Web - 22nd European Semantic Web Conference, {ESWC} 2025,
Portoroz, Slovenia, June 1-5, 2025, Proceedings, Part {I}}, publisher={Springer
Nature Switzerland}, author={Löhnert, Bianca and Augsten, Nikolaus and Okulmus,
Cem and Ortiz, Magdalena}, year={2025}, pages={342--361}, collection={Lecture
Notes in Computer Science} }'
chicago: Löhnert, Bianca, Nikolaus Augsten, Cem Okulmus, and Magdalena Ortiz. “Towards
Practicable Algorithms for Rewriting Graph Queries Beyond DL-Lite.” In The
Semantic Web - 22nd European Semantic Web Conference, {ESWC} 2025, Portoroz, Slovenia,
June 1-5, 2025, Proceedings, Part {I}, 15718:342--361. Lecture Notes in Computer
Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94575-5_19.
ieee: 'B. Löhnert, N. Augsten, C. Okulmus, and M. Ortiz, “Towards Practicable Algorithms
for Rewriting Graph Queries Beyond DL-Lite,” in The Semantic Web - 22nd European
Semantic Web Conference, {ESWC} 2025, Portoroz, Slovenia, June 1-5, 2025, Proceedings,
Part {I}, Portorož, Slovenia, 2025, vol. 15718, pp. 342--361, doi: 10.1007/978-3-031-94575-5_19.'
mla: Löhnert, Bianca, et al. “Towards Practicable Algorithms for Rewriting Graph
Queries Beyond DL-Lite.” The Semantic Web - 22nd European Semantic Web Conference,
{ESWC} 2025, Portoroz, Slovenia, June 1-5, 2025, Proceedings, Part {I}, vol.
15718, Springer Nature Switzerland, 2025, pp. 342--361, doi:10.1007/978-3-031-94575-5_19.
short: 'B. Löhnert, N. Augsten, C. Okulmus, M. Ortiz, in: The Semantic Web - 22nd
European Semantic Web Conference, {ESWC} 2025, Portoroz, Slovenia, June 1-5, 2025,
Proceedings, Part {I}, Springer Nature Switzerland, 2025, pp. 342--361.'
conference:
end_date: 2025-06-05
location: Portorož, Slovenia
name: 22th European Semantic Web Conference (ESWC 2025)
start_date: 2025-06-01
date_created: 2025-07-02T11:46:06Z
date_updated: 2025-07-02T11:55:19Z
department:
- _id: '888'
doi: 10.1007/978-3-031-94575-5_19
intvolume: ' 15718'
keyword:
- Ontology-based Data Access
- Property Graphs
- Navigational Queries
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2405.18181
oa: '1'
page: 342--361
publication: The Semantic Web - 22nd European Semantic Web Conference, {ESWC} 2025,
Portoroz, Slovenia, June 1-5, 2025, Proceedings, Part {I}
publication_identifier:
isbn:
- '9783031945748'
- '9783031945755'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
quality_controlled: '1'
series_title: Lecture Notes in Computer Science
status: public
title: Towards Practicable Algorithms for Rewriting Graph Queries Beyond DL-Lite
type: conference
user_id: '114410'
volume: 15718
year: '2025'
...
---
_id: '61013'
article_number: '106606'
article_type: original
author:
- first_name: Torben N.
full_name: Rüther, Torben N.
last_name: Rüther
- first_name: David B.
full_name: Rasche, David B.
last_name: Rasche
- first_name: Hans-Joachim
full_name: Schmid, Hans-Joachim
id: '464'
last_name: Schmid
orcid: 000-0001-8590-1921
citation:
ama: Rüther TN, Rasche DB, Schmid H-J. The POCS-Algorithm—An effective tool for
calculating 2D particle property distributions via data inversion of exemplary
CDMA measurement data. Journal of Aerosol Science. 2025;188. doi:10.1016/j.jaerosci.2025.106606
apa: Rüther, T. N., Rasche, D. B., & Schmid, H.-J. (2025). The POCS-Algorithm—An
effective tool for calculating 2D particle property distributions via data inversion
of exemplary CDMA measurement data. Journal of Aerosol Science, 188,
Article 106606. https://doi.org/10.1016/j.jaerosci.2025.106606
bibtex: '@article{Rüther_Rasche_Schmid_2025, title={The POCS-Algorithm—An effective
tool for calculating 2D particle property distributions via data inversion of
exemplary CDMA measurement data}, volume={188}, DOI={10.1016/j.jaerosci.2025.106606},
number={106606}, journal={Journal of Aerosol Science}, publisher={Elsevier BV},
author={Rüther, Torben N. and Rasche, David B. and Schmid, Hans-Joachim}, year={2025}
}'
chicago: Rüther, Torben N., David B. Rasche, and Hans-Joachim Schmid. “The POCS-Algorithm—An
Effective Tool for Calculating 2D Particle Property Distributions via Data Inversion
of Exemplary CDMA Measurement Data.” Journal of Aerosol Science 188 (2025).
https://doi.org/10.1016/j.jaerosci.2025.106606.
ieee: 'T. N. Rüther, D. B. Rasche, and H.-J. Schmid, “The POCS-Algorithm—An effective
tool for calculating 2D particle property distributions via data inversion of
exemplary CDMA measurement data,” Journal of Aerosol Science, vol. 188,
Art. no. 106606, 2025, doi: 10.1016/j.jaerosci.2025.106606.'
mla: Rüther, Torben N., et al. “The POCS-Algorithm—An Effective Tool for Calculating
2D Particle Property Distributions via Data Inversion of Exemplary CDMA Measurement
Data.” Journal of Aerosol Science, vol. 188, 106606, Elsevier BV, 2025,
doi:10.1016/j.jaerosci.2025.106606.
short: T.N. Rüther, D.B. Rasche, H.-J. Schmid, Journal of Aerosol Science 188 (2025).
date_created: 2025-08-25T16:10:18Z
date_updated: 2025-08-25T16:17:49Z
doi: 10.1016/j.jaerosci.2025.106606
funded_apc: '1'
intvolume: ' 188'
keyword:
- POCS
- Projection onto convex sets
- Data inversion
- 2D distribution
- CDMA
- Centrifugal Differential Mobility Analyzer
language:
- iso: eng
publication: Journal of Aerosol Science
publication_identifier:
issn:
- 0021-8502
publication_status: published
publisher: Elsevier BV
quality_controlled: '1'
status: public
title: The POCS-Algorithm—An effective tool for calculating 2D particle property distributions
via data inversion of exemplary CDMA measurement data
type: journal_article
user_id: '464'
volume: 188
year: '2025'
...
---
_id: '62643'
author:
- first_name: Tobias
full_name: Schwabe, Tobias
id: '39217'
last_name: Schwabe
- first_name: Christian
full_name: Kress, Christian
id: '13256'
last_name: Kress
orcid: 0000-0002-4403-2237
- first_name: Stephan
full_name: Kruse, Stephan
id: '38254'
last_name: Kruse
- first_name: Maxim
full_name: Weizel, Maxim
id: '44271'
last_name: Weizel
orcid: 0000-0003-2699-9839
- first_name: Hanjo
full_name: Rhee, Hanjo
last_name: Rhee
- first_name: J. Christoph
full_name: Scheytt, J. Christoph
id: '37144'
last_name: Scheytt
orcid: '0000-0002-5950-6618 '
citation:
ama: Schwabe T, Kress C, Kruse S, Weizel M, Rhee H, Scheytt JC. Forward-Biased Silicon
Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation in
a 250 nm EPIC BiCMOS Technology. Journal of Lightwave Technology. 2025;43(1):255-270.
doi:10.1109/JLT.2024.3450949
apa: Schwabe, T., Kress, C., Kruse, S., Weizel, M., Rhee, H., & Scheytt, J.
C. (2025). Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic
Co-Simulation and Validation in a 250 nm EPIC BiCMOS Technology. Journal of
Lightwave Technology, 43(1), 255–270. https://doi.org/10.1109/JLT.2024.3450949
bibtex: '@article{Schwabe_Kress_Kruse_Weizel_Rhee_Scheytt_2025, title={Forward-Biased
Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation
in a 250 nm EPIC BiCMOS Technology}, volume={43}, DOI={10.1109/JLT.2024.3450949},
number={1}, journal={Journal of Lightwave Technology}, author={Schwabe, Tobias
and Kress, Christian and Kruse, Stephan and Weizel, Maxim and Rhee, Hanjo and
Scheytt, J. Christoph}, year={2025}, pages={255–270} }'
chicago: 'Schwabe, Tobias, Christian Kress, Stephan Kruse, Maxim Weizel, Hanjo Rhee,
and J. Christoph Scheytt. “Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic
Co-Simulation and Validation in a 250 Nm EPIC BiCMOS Technology.” Journal of
Lightwave Technology 43, no. 1 (2025): 255–70. https://doi.org/10.1109/JLT.2024.3450949.'
ieee: 'T. Schwabe, C. Kress, S. Kruse, M. Weizel, H. Rhee, and J. C. Scheytt, “Forward-Biased
Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation
in a 250 nm EPIC BiCMOS Technology,” Journal of Lightwave Technology, vol.
43, no. 1, pp. 255–270, 2025, doi: 10.1109/JLT.2024.3450949.'
mla: Schwabe, Tobias, et al. “Forward-Biased Silicon Phase Shifter Modeling for
Electronic-Photonic Co-Simulation and Validation in a 250 Nm EPIC BiCMOS Technology.”
Journal of Lightwave Technology, vol. 43, no. 1, 2025, pp. 255–70, doi:10.1109/JLT.2024.3450949.
short: T. Schwabe, C. Kress, S. Kruse, M. Weizel, H. Rhee, J.C. Scheytt, Journal
of Lightwave Technology 43 (2025) 255–270.
date_created: 2025-11-27T07:14:34Z
date_updated: 2025-11-27T07:16:01Z
department:
- _id: '58'
doi: 10.1109/JLT.2024.3450949
intvolume: ' 43'
issue: '1'
keyword:
- Integrated circuit modeling
- Capacitance
- Silicon
- Modulation
- Adaptation models
- Semiconductor device modeling
- Bandwidth
- Data communication
- electrooptical transmitter
- equalization
- free-carrier-plasma dispersion effect
- modelling
- optical modulator
- phase shifter
- silicon photonics
language:
- iso: eng
page: 255-270
publication: Journal of Lightwave Technology
status: public
title: Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation
and Validation in a 250 nm EPIC BiCMOS Technology
type: journal_article
user_id: '38254'
volume: 43
year: '2025'
...
---
_id: '62644'
author:
- first_name: Tobias
full_name: Schwabe, Tobias
id: '39217'
last_name: Schwabe
- first_name: Christian
full_name: Kress, Christian
id: '13256'
last_name: Kress
orcid: 0000-0002-4403-2237
- first_name: Babak
full_name: Sadiye, Babak
id: '93634'
last_name: Sadiye
- first_name: Stephan
full_name: Kruse, Stephan
id: '38254'
last_name: Kruse
- first_name: J. Christoph
full_name: Scheytt, J. Christoph
id: '37144'
last_name: Scheytt
orcid: '0000-0002-5950-6618 '
citation:
ama: Schwabe T, Kress C, Sadiye B, Kruse S, Scheytt JC. Analysis and Design of Forward
Biased Silicon Photonics Phase Shifter Equalizer Circuits. IEEE Access.
2025;13:192433-192450. doi:10.1109/ACCESS.2025.3629385
apa: Schwabe, T., Kress, C., Sadiye, B., Kruse, S., & Scheytt, J. C. (2025).
Analysis and Design of Forward Biased Silicon Photonics Phase Shifter Equalizer
Circuits. IEEE Access, 13, 192433–192450. https://doi.org/10.1109/ACCESS.2025.3629385
bibtex: '@article{Schwabe_Kress_Sadiye_Kruse_Scheytt_2025, title={Analysis and Design
of Forward Biased Silicon Photonics Phase Shifter Equalizer Circuits}, volume={13},
DOI={10.1109/ACCESS.2025.3629385},
journal={IEEE Access}, author={Schwabe, Tobias and Kress, Christian and Sadiye,
Babak and Kruse, Stephan and Scheytt, J. Christoph}, year={2025}, pages={192433–192450}
}'
chicago: 'Schwabe, Tobias, Christian Kress, Babak Sadiye, Stephan Kruse, and J.
Christoph Scheytt. “Analysis and Design of Forward Biased Silicon Photonics Phase
Shifter Equalizer Circuits.” IEEE Access 13 (2025): 192433–50. https://doi.org/10.1109/ACCESS.2025.3629385.'
ieee: 'T. Schwabe, C. Kress, B. Sadiye, S. Kruse, and J. C. Scheytt, “Analysis and
Design of Forward Biased Silicon Photonics Phase Shifter Equalizer Circuits,”
IEEE Access, vol. 13, pp. 192433–192450, 2025, doi: 10.1109/ACCESS.2025.3629385.'
mla: Schwabe, Tobias, et al. “Analysis and Design of Forward Biased Silicon Photonics
Phase Shifter Equalizer Circuits.” IEEE Access, vol. 13, 2025, pp. 192433–50,
doi:10.1109/ACCESS.2025.3629385.
short: T. Schwabe, C. Kress, B. Sadiye, S. Kruse, J.C. Scheytt, IEEE Access 13 (2025)
192433–192450.
date_created: 2025-11-27T07:14:48Z
date_updated: 2025-11-27T07:16:06Z
department:
- _id: '58'
doi: 10.1109/ACCESS.2025.3629385
intvolume: ' 13'
keyword:
- Optical attenuators
- Equalizers
- Phase shifters
- Optical modulation
- Electro-optic modulators
- Optical amplifiers
- Circuits
- Silicon photonics
- Optical saturation
- Integrated circuit modeling
- Data communication
- equalization
- electro-optical transmitter
- silicon photonics
- phase shifter
- optical modulator
- free-carrier plasma dispersion effect
- driver architectures
- biasing schemes
language:
- iso: eng
page: 192433-192450
publication: IEEE Access
status: public
title: Analysis and Design of Forward Biased Silicon Photonics Phase Shifter Equalizer
Circuits
type: journal_article
user_id: '38254'
volume: 13
year: '2025'
...
---
_id: '62697'
abstract:
- lang: eng
text: "Urged by the European Energy Crisis and the threatening consequences of severe\r\nnatural
gas shortages, energy providers launched gas-saving initiatives incor-\r\nporating
financial incentives to reduce residential natural gas consumption. In\r\ncollaboration
with one of Germany’s largest energy providers, we conducted\r\na natural field
experiment (N = 2,598) to evaluate the effectiveness of a\r\nbehaviorally-guided
co-design of such a gas-saving initiative by implementing\r\ntwo established behavioral
instruments – reminders of gas saving intentions and\r\ndescriptive norm feedback.
Our findings show limited effectiveness of the behav-\r\nioral instruments during
the high-price period. The feedback risks a “boomerang\r\neffect” among households
with above-average initial savings, who reduce their\r\nconservation efforts in
response. The reminder does not significantly enhance sav-\r\nings in our main
specifications, yet, realizes 1 percentage point savings in alternate\r\nmodels
refining for outliers. Potential mechanisms include a significant intention-\r\naction
gap and misperceived effectiveness of energy-saving actions, which are not\r\nalleviated
by the reminder."
author:
- first_name: Vicky
full_name: Tinnefeld, Vicky
last_name: Tinnefeld
- first_name: Martin
full_name: Kesternich, Martin
id: '98922'
last_name: Kesternich
orcid: 0000-0002-0653-7680
- first_name: Madeline
full_name: Werthschulte, Madeline
last_name: Werthschulte
citation:
ama: Tinnefeld V, Kesternich M, Werthschulte M. Do Energy-Saving Nudges Deliver
During High-Price Periods? Field Experimental Evidence From the European Energy
Crisis. ZEW Discussion Paper No. 25-60; 2025.
apa: Tinnefeld, V., Kesternich, M., & Werthschulte, M. (2025). Do Energy-Saving
Nudges Deliver During High-Price Periods? Field Experimental Evidence From the
European Energy Crisis. ZEW Discussion Paper No. 25-60.
bibtex: '@book{Tinnefeld_Kesternich_Werthschulte_2025, title={Do Energy-Saving Nudges
Deliver During High-Price Periods? Field Experimental Evidence From the European
Energy Crisis}, publisher={ZEW Discussion Paper No. 25-60}, author={Tinnefeld,
Vicky and Kesternich, Martin and Werthschulte, Madeline}, year={2025} }'
chicago: Tinnefeld, Vicky, Martin Kesternich, and Madeline Werthschulte. Do Energy-Saving
Nudges Deliver During High-Price Periods? Field Experimental Evidence From the
European Energy Crisis. ZEW Discussion Paper No. 25-60, 2025.
ieee: V. Tinnefeld, M. Kesternich, and M. Werthschulte, Do Energy-Saving Nudges
Deliver During High-Price Periods? Field Experimental Evidence From the European
Energy Crisis. ZEW Discussion Paper No. 25-60, 2025.
mla: Tinnefeld, Vicky, et al. Do Energy-Saving Nudges Deliver During High-Price
Periods? Field Experimental Evidence From the European Energy Crisis. ZEW
Discussion Paper No. 25-60, 2025.
short: V. Tinnefeld, M. Kesternich, M. Werthschulte, Do Energy-Saving Nudges Deliver
During High-Price Periods? Field Experimental Evidence From the European Energy
Crisis, ZEW Discussion Paper No. 25-60, 2025.
date_created: 2025-11-28T12:07:25Z
date_updated: 2025-12-01T10:21:20Z
ddc:
- '330'
file:
- access_level: closed
content_type: application/pdf
creator: mkestern
date_created: 2025-11-28T12:04:11Z
date_updated: 2025-11-28T12:04:11Z
file_id: '62698'
file_name: dp25060.pdf
file_size: 2165853
relation: main_file
success: 1
file_date_updated: 2025-11-28T12:04:11Z
has_accepted_license: '1'
jel:
- C93
- D04
- D91
- Q41
keyword:
- Residential energy savings
- energy crisis
- behavioral interventions
- survey data
- field experiment
language:
- iso: eng
publisher: ZEW Discussion Paper No. 25-60
status: public
title: Do Energy-Saving Nudges Deliver During High-Price Periods? Field Experimental
Evidence From the European Energy Crisis
type: working_paper
user_id: '98922'
year: '2025'
...
---
_id: '61237'
abstract:
- lang: ger
text: In diesem Beitrag wird zunächst die historische Entstehung von Open Science
kurz skizziert und definiert, was unter diesem Begriff zu verstehen ist. Daran
anschließend werden die Open-Science-Praktiken Open Data, Open Access, Open Source,
Open Methodology und Open Peer Review dargestellt und diskutiert, welche Forschungserkenntnisse
zu Open Science vorhanden sind. Im Schluss werden Forschungsdesiderate aufgegriffen
und die Implikationen von Open Science für die Wissenschaft erläutert.
author:
- first_name: Isabel
full_name: Steinhardt, Isabel
id: '90339'
last_name: Steinhardt
orcid: https://orcid.org/0000-0002-2590-6189
- first_name: Ronny
full_name: Röwert, Ronny
last_name: Röwert
citation:
ama: 'Steinhardt I, Röwert R. Open Science. In: Pasternack P, Reinmann G, Schneijderberg
C, eds. Hochschulforschung. Nomos; 2025:487-496. doi:10.5771/9783748943334-487'
apa: Steinhardt, I., & Röwert, R. (2025). Open Science. In P. Pasternack, G.
Reinmann, & C. Schneijderberg (Eds.), Hochschulforschung (pp. 487–496).
Nomos. https://doi.org/10.5771/9783748943334-487
bibtex: '@inbook{Steinhardt_Röwert_2025, place={Baden-Baden}, title={Open Science},
DOI={10.5771/9783748943334-487},
booktitle={Hochschulforschung}, publisher={Nomos}, author={Steinhardt, Isabel
and Röwert, Ronny}, editor={Pasternack, Peer and Reinmann, Gabi and Schneijderberg,
Christian }, year={2025}, pages={487–496} }'
chicago: 'Steinhardt, Isabel, and Ronny Röwert. “Open Science.” In Hochschulforschung,
edited by Peer Pasternack, Gabi Reinmann, and Christian Schneijderberg, 487–96.
Baden-Baden: Nomos, 2025. https://doi.org/10.5771/9783748943334-487.'
ieee: 'I. Steinhardt and R. Röwert, “Open Science,” in Hochschulforschung,
P. Pasternack, G. Reinmann, and C. Schneijderberg, Eds. Baden-Baden: Nomos, 2025,
pp. 487–496.'
mla: Steinhardt, Isabel, and Ronny Röwert. “Open Science.” Hochschulforschung,
edited by Peer Pasternack et al., Nomos, 2025, pp. 487–96, doi:10.5771/9783748943334-487.
short: 'I. Steinhardt, R. Röwert, in: P. Pasternack, G. Reinmann, C. Schneijderberg
(Eds.), Hochschulforschung, Nomos, Baden-Baden, 2025, pp. 487–496.'
date_created: 2025-09-12T06:35:25Z
date_updated: 2025-12-17T09:05:53Z
ddc:
- '300'
department:
- _id: '121'
doi: 10.5771/9783748943334-487
editor:
- first_name: Peer
full_name: Pasternack, Peer
last_name: Pasternack
- first_name: Gabi
full_name: Reinmann, Gabi
last_name: Reinmann
- first_name: 'Christian '
full_name: 'Schneijderberg, Christian '
last_name: Schneijderberg
file:
- access_level: closed
content_type: application/pdf
creator: isste
date_created: 2025-09-12T06:37:04Z
date_updated: 2025-09-12T06:37:04Z
file_id: '61238'
file_name: 2025 Steinhardt & Röwert Open Science.pdf
file_size: 268261
relation: main_file
success: 1
file_date_updated: 2025-09-12T06:37:04Z
has_accepted_license: '1'
keyword:
- Open Data
- Open Access
- Open Source
- Open Methodology
- Open Peer Review
language:
- iso: ger
main_file_link:
- open_access: '1'
url: https://www.nomos-elibrary.de/de/document/view/detail/uuid/cc4f88b8-f9a8-32ef-a706-a9134f224090
oa: '1'
page: 487-496
place: Baden-Baden
publication: Hochschulforschung
publication_identifier:
isbn:
- '9783748943334'
publication_status: published
publisher: Nomos
quality_controlled: '1'
status: public
title: Open Science
type: book_chapter
user_id: '90339'
year: '2025'
...
---
_id: '63498'
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
last_name: Kirchgässner
- first_name: Nikolas
full_name: Förster, Nikolas
last_name: Förster
- first_name: Till
full_name: Piepenbrock, Till
last_name: Piepenbrock
- first_name: Oliver
full_name: Schweins, Oliver
last_name: Schweins
- first_name: Oliver
full_name: Wallscheid, Oliver
last_name: Wallscheid
citation:
ama: 'Kirchgässner W, Förster N, Piepenbrock T, Schweins O, Wallscheid O. HARDCORE:
H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated
Convolutional Neural Networks in Ferrite Cores. IEEE Transactions on Power
Electronics. 2025;40(2):3326-3335. doi:10.1109/TPEL.2024.3488174'
apa: 'Kirchgässner, W., Förster, N., Piepenbrock, T., Schweins, O., & Wallscheid,
O. (2025). HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms
With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. IEEE
Transactions on Power Electronics, 40(2), 3326–3335. https://doi.org/10.1109/TPEL.2024.3488174'
bibtex: '@article{Kirchgässner_Förster_Piepenbrock_Schweins_Wallscheid_2025, title={HARDCORE:
H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated
Convolutional Neural Networks in Ferrite Cores}, volume={40}, DOI={10.1109/TPEL.2024.3488174},
number={2}, journal={IEEE Transactions on Power Electronics}, author={Kirchgässner,
Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid,
Oliver}, year={2025}, pages={3326–3335} }'
chicago: 'Kirchgässner, Wilhelm, Nikolas Förster, Till Piepenbrock, Oliver Schweins,
and Oliver Wallscheid. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary
Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.”
IEEE Transactions on Power Electronics 40, no. 2 (2025): 3326–35. https://doi.org/10.1109/TPEL.2024.3488174.'
ieee: 'W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, and O. Wallscheid,
“HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual,
Dilated Convolutional Neural Networks in Ferrite Cores,” IEEE Transactions
on Power Electronics, vol. 40, no. 2, pp. 3326–3335, 2025, doi: 10.1109/TPEL.2024.3488174.'
mla: 'Kirchgässner, Wilhelm, et al. “HARDCORE: H-Field and Power Loss Estimation
for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in
Ferrite Cores.” IEEE Transactions on Power Electronics, vol. 40, no. 2,
2025, pp. 3326–35, doi:10.1109/TPEL.2024.3488174.'
short: W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, O. Wallscheid,
IEEE Transactions on Power Electronics 40 (2025) 3326–3335.
date_created: 2026-01-06T08:07:13Z
date_updated: 2026-01-06T08:08:01Z
department:
- _id: '52'
doi: 10.1109/TPEL.2024.3488174
intvolume: ' 40'
issue: '2'
keyword:
- Mathematical models
- Estimation
- Data models
- Convolutional neural networks
- Accuracy
- Magnetic hysteresis
- Magnetic cores
- Temperature measurement
- Magnetic domains
- Temperature distribution
- Convolutional neural network (CNN)
- machine learning (ML)
- magnetics
page: 3326-3335
publication: IEEE Transactions on Power Electronics
status: public
title: 'HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual,
Dilated Convolutional Neural Networks in Ferrite Cores'
type: journal_article
user_id: '83383'
volume: 40
year: '2025'
...
---
_id: '64894'
abstract:
- lang: eng
text: "This dataset contains experimental measurements of the radial dynamic and
quasi-static characteristics of four different types of Rubber-Metal Bushings
(RMBs) used in the suspension system of a passenger car under harmonic displacement
excitation. For each bushing type, 2–3 individual specimens were tested.\r\n \r\nQuasi-static
measurements were performed at a constant excitation frequency of 0.05 Hz with
varying displacement amplitudes. Dynamic measurements were conducted with displacement
amplitudes ranging from 0.04 mm to 0.3 mm and excitation frequencies of 2, 5,
10, ..., up to 100 Hz.\r\n\r\nThe data is structured by bushing type, measurement
mode, amplitude, and frequency, and is provided in *.csv and *.hrm format. It
is intended to support further research in modeling rubber-metal bushings and
parameter identification techniques."
author:
- first_name: Jan
full_name: Schütte, Jan
id: '22109'
last_name: Schütte
orcid: 0000-0001-9025-9742
citation:
ama: 'Schütte J. Experimental Dataset: Force and Displacement Measurements of
Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation. LibreCat University; 2025. doi:10.5281/ZENODO.14851317'
apa: 'Schütte, J. (2025). Experimental Dataset: Force and Displacement Measurements
of Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation. LibreCat University. https://doi.org/10.5281/ZENODO.14851317'
bibtex: '@book{Schütte_2025, title={Experimental Dataset: Force and Displacement
Measurements of Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic
Displacement Excitation}, DOI={10.5281/ZENODO.14851317},
publisher={LibreCat University}, author={Schütte, Jan}, year={2025} }'
chicago: 'Schütte, Jan. Experimental Dataset: Force and Displacement Measurements
of Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation. LibreCat University, 2025. https://doi.org/10.5281/ZENODO.14851317.'
ieee: 'J. Schütte, Experimental Dataset: Force and Displacement Measurements
of Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation. LibreCat University, 2025.'
mla: 'Schütte, Jan. Experimental Dataset: Force and Displacement Measurements
of Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation. LibreCat University, 2025, doi:10.5281/ZENODO.14851317.'
short: 'J. Schütte, Experimental Dataset: Force and Displacement Measurements of
Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation, LibreCat University, 2025.'
date_created: 2026-03-11T10:22:01Z
date_updated: 2026-03-11T10:25:23Z
department:
- _id: '151'
doi: 10.5281/ZENODO.14851317
keyword:
- bushing
- experimental data
- rubber-metal-bushing
- Dataset suspension
publisher: LibreCat University
status: public
title: 'Experimental Dataset: Force and Displacement Measurements of Four Rubber-Metal
Bushing Types from a Passenger Car under Harmonic Displacement Excitation'
type: research_data
user_id: '22109'
year: '2025'
...
---
_id: '53793'
abstract:
- lang: eng
text: We utilize extreme learning machines for the prediction of partial differential
equations (PDEs). Our method splits the state space into multiple windows that
are predicted individually using a single model. Despite requiring only few data
points (in some cases, our method can learn from a single full-state snapshot),
it still achieves high accuracy and can predict the flow of PDEs over long time
horizons. Moreover, we show how additional symmetries can be exploited to increase
sample efficiency and to enforce equivariance.
author:
- first_name: Hans
full_name: Harder, Hans
id: '98879'
last_name: Harder
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: Harder H, Peitz S. Predicting PDEs Fast and Efficiently with Equivariant Extreme
Learning Machines.
apa: Harder, H., & Peitz, S. (n.d.). Predicting PDEs Fast and Efficiently
with Equivariant Extreme Learning Machines.
bibtex: '@article{Harder_Peitz, title={Predicting PDEs Fast and Efficiently with
Equivariant Extreme Learning Machines}, author={Harder, Hans and Peitz, Sebastian}
}'
chicago: Harder, Hans, and Sebastian Peitz. “Predicting PDEs Fast and Efficiently
with Equivariant Extreme Learning Machines,” n.d.
ieee: H. Harder and S. Peitz, “Predicting PDEs Fast and Efficiently with Equivariant
Extreme Learning Machines.” .
mla: Harder, Hans, and Sebastian Peitz. Predicting PDEs Fast and Efficiently
with Equivariant Extreme Learning Machines.
short: H. Harder, S. Peitz, (n.d.).
date_created: 2024-04-30T08:43:14Z
date_updated: 2024-04-30T08:45:24Z
keyword:
- extreme learning machines
- partial differential equations
- data-driven prediction
- high-dimensional systems
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2404.18530
oa: '1'
publication_status: unpublished
status: public
title: Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines
type: preprint
user_id: '98879'
year: '2024'
...
---
_id: '55336'
abstract:
- lang: eng
text: "Predicting the remaining useful life of technical \r\nsystems has gained
significant attention in recent years due to \r\nincreasing demands for extending
the lifespan of degrading system \r\ncomponents. Therefore, already used systems
are retrofitted by \r\nintegrating sensors to monitor their performance and \r\nfunctionality,
enabling accurate diagnosis of their condition and \r\nprediction of their remaining
useful life. One of the main \r\nchallenges in this field is identified in the
missing data from the \r\ntime where the retrofitted system has already run but
without \r\nbeing monitored by sensors. In this paper, a novel approach for \r\nthe
combined diagnostics and prognostics of retrofitted systems is \r\nproposed. The
methodology aims to provide an accurate diagnosis \r\nof the system’s health state
and estimation of the remaining useful \r\nlife by a combination of a machine
learning and expert knowledge. \r\nTo evaluate the effectiveness of the proposed
methodology, a case \r\nstudy involving a retrofitted system in an industrial
setting is \r\nselected and applied. It is demonstrated that the approach \r\neffectively
diagnose the current system’s health state and \r\naccurately predict its remaining
useful life, thereby enabling \r\npredictive maintenance and decision-making.
Overall, our \r\nresearch contributes to advancing the field of condition \r\nmonitoring
for retrofitted systems by providing a comprehensive \r\nmethodology that addresses
the challenge of missing data."
author:
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Bender A, Aimiyekagbon OK, Sextro W. Diagnostics and Prognostics for Retrofitted
Systems: A Comprehensive Approach for Enhanced System Health Assessment. In: Proceedings
of the 2024 Prognostics and System Health Management Conference (PHM). IEEE
Computer Society; 2024. doi:10.1109/PHM61473.2024.00038'
apa: 'Bender, A., Aimiyekagbon, O. K., & Sextro, W. (2024). Diagnostics and
Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System
Health Assessment. Proceedings of the 2024 Prognostics and System Health Management
Conference (PHM). 2024 Prognostics and System Health Management Conference
(PHM), Stockholm, Schweden. https://doi.org/10.1109/PHM61473.2024.00038'
bibtex: '@inproceedings{Bender_Aimiyekagbon_Sextro_2024, title={Diagnostics and
Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System
Health Assessment}, DOI={10.1109/PHM61473.2024.00038},
booktitle={Proceedings of the 2024 Prognostics and System Health Management Conference
(PHM)}, publisher={IEEE Computer Society}, author={Bender, Amelie and Aimiyekagbon,
Osarenren Kennedy and Sextro, Walter}, year={2024} }'
chicago: 'Bender, Amelie, Osarenren Kennedy Aimiyekagbon, and Walter Sextro. “Diagnostics
and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced
System Health Assessment.” In Proceedings of the 2024 Prognostics and System
Health Management Conference (PHM). IEEE Computer Society, 2024. https://doi.org/10.1109/PHM61473.2024.00038.'
ieee: 'A. Bender, O. K. Aimiyekagbon, and W. Sextro, “Diagnostics and Prognostics
for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment,”
presented at the 2024 Prognostics and System Health Management Conference (PHM),
Stockholm, Schweden, 2024, doi: 10.1109/PHM61473.2024.00038.'
mla: 'Bender, Amelie, et al. “Diagnostics and Prognostics for Retrofitted Systems:
A Comprehensive Approach for Enhanced System Health Assessment.” Proceedings
of the 2024 Prognostics and System Health Management Conference (PHM), IEEE
Computer Society, 2024, doi:10.1109/PHM61473.2024.00038.'
short: 'A. Bender, O.K. Aimiyekagbon, W. Sextro, in: Proceedings of the 2024 Prognostics
and System Health Management Conference (PHM), IEEE Computer Society, 2024.'
conference:
end_date: 2024-05-31
location: Stockholm, Schweden
name: 2024 Prognostics and System Health Management Conference (PHM)
start_date: 2024-05-28
date_created: 2024-07-22T09:27:57Z
date_updated: 2024-07-22T09:29:26Z
department:
- _id: '151'
doi: 10.1109/PHM61473.2024.00038
keyword:
- retrofit
- diagnosis
- prognostics
- RUL prediction
- missing data
- ball bearings
language:
- iso: eng
publication: Proceedings of the 2024 Prognostics and System Health Management Conference
(PHM)
publication_identifier:
isbn:
- 979-8-3503-6058-5
publisher: IEEE Computer Society
quality_controlled: '1'
status: public
title: 'Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach
for Enhanced System Health Assessment'
type: conference
user_id: '54290'
year: '2024'
...
---
_id: '56166'
abstract:
- lang: eng
text: Developing Intelligent Technical Systems (ITS) involves a complex process
encompassing planning, analysis, design, production, and maintenance. Model-Based
Systems Engineering (MBSE) is a key methodology for systematic systems engineering.
Designing models for ITS requires harmonious interaction of various elements,
posing a challenge in MBSE. Leveraging Generative Artificial Intelligence, we
generated a dataset for modeling, using prompt engineering on large language models.
The generated artifacts can aid engineers in MBSE design or serve as synthetic
training data for AI assistants.
author:
- first_name: Pranav Jayant
full_name: Kulkarni, Pranav Jayant
id: '86782'
last_name: Kulkarni
- first_name: Denis
full_name: Tissen, Denis
id: '44458'
last_name: Tissen
- first_name: Ruslan
full_name: Bernijazov, Ruslan
id: '36312'
last_name: Bernijazov
- first_name: Roman
full_name: Dumitrescu, Roman
id: '16190'
last_name: Dumitrescu
citation:
ama: 'Kulkarni PJ, Tissen D, Bernijazov R, Dumitrescu R. Towards Automated Design:
Automatically Generating Modeling Elements with Prompt Engineering and Generative
Artificial Intelligence. In: Malmqvist J, Candi M, Saemundsson R, Bystrom F, Isaksson
O, eds. DS 130: Proceedings of NordDesign 2024. ; 2024:617-625. doi:10.35199/NORDDESIGN2024.66'
apa: 'Kulkarni, P. J., Tissen, D., Bernijazov, R., & Dumitrescu, R. (2024).
Towards Automated Design: Automatically Generating Modeling Elements with Prompt
Engineering and Generative Artificial Intelligence. In J. Malmqvist, M. Candi,
R. Saemundsson, F. Bystrom, & O. Isaksson (Eds.), DS 130: Proceedings of
NordDesign 2024 (pp. 617–625). https://doi.org/10.35199/NORDDESIGN2024.66'
bibtex: '@inproceedings{Kulkarni_Tissen_Bernijazov_Dumitrescu_2024, title={Towards
Automated Design: Automatically Generating Modeling Elements with Prompt Engineering
and Generative Artificial Intelligence}, DOI={10.35199/NORDDESIGN2024.66},
booktitle={DS 130: Proceedings of NordDesign 2024}, author={Kulkarni, Pranav Jayant
and Tissen, Denis and Bernijazov, Ruslan and Dumitrescu, Roman}, editor={Malmqvist,
J. and Candi, M. and Saemundsson, R. and Bystrom, F. and Isaksson, O.}, year={2024},
pages={617–625} }'
chicago: 'Kulkarni, Pranav Jayant, Denis Tissen, Ruslan Bernijazov, and Roman Dumitrescu.
“Towards Automated Design: Automatically Generating Modeling Elements with Prompt
Engineering and Generative Artificial Intelligence.” In DS 130: Proceedings
of NordDesign 2024, edited by J. Malmqvist, M. Candi, R. Saemundsson, F. Bystrom,
and O. Isaksson, 617–25, 2024. https://doi.org/10.35199/NORDDESIGN2024.66.'
ieee: 'P. J. Kulkarni, D. Tissen, R. Bernijazov, and R. Dumitrescu, “Towards Automated
Design: Automatically Generating Modeling Elements with Prompt Engineering and
Generative Artificial Intelligence,” in DS 130: Proceedings of NordDesign 2024,
Reykjavik, 2024, pp. 617–625, doi: 10.35199/NORDDESIGN2024.66.'
mla: 'Kulkarni, Pranav Jayant, et al. “Towards Automated Design: Automatically Generating
Modeling Elements with Prompt Engineering and Generative Artificial Intelligence.”
DS 130: Proceedings of NordDesign 2024, edited by J. Malmqvist et al.,
2024, pp. 617–25, doi:10.35199/NORDDESIGN2024.66.'
short: 'P.J. Kulkarni, D. Tissen, R. Bernijazov, R. Dumitrescu, in: J. Malmqvist,
M. Candi, R. Saemundsson, F. Bystrom, O. Isaksson (Eds.), DS 130: Proceedings
of NordDesign 2024, 2024, pp. 617–625.'
conference:
end_date: 2024-08-14
location: Reykjavik
name: NordDesign Conference 2024
start_date: 2024-08-12
date_created: 2024-09-17T09:56:43Z
date_updated: 2024-09-17T09:57:07Z
doi: 10.35199/NORDDESIGN2024.66
editor:
- first_name: J.
full_name: Malmqvist, J.
last_name: Malmqvist
- first_name: M.
full_name: Candi, M.
last_name: Candi
- first_name: R.
full_name: Saemundsson, R.
last_name: Saemundsson
- first_name: F.
full_name: Bystrom, F.
last_name: Bystrom
- first_name: O.
full_name: Isaksson, O.
last_name: Isaksson
keyword:
- Data Driven Design
- Design Automation
- Systems Engineering (SE)
- Artificial Intelligence (AI)
language:
- iso: eng
page: 617-625
publication: 'DS 130: Proceedings of NordDesign 2024'
publication_identifier:
unknown:
- 978-1-912254-21-7
publication_status: epub_ahead
related_material:
link:
- relation: confirmation
url: https://www.designsociety.org/publication/47658/Towards+Automated+Design%3A+Automatically+Generating+Modeling+Elements+with+Prompt+Engineering+and+Generative+Artificial+Intelligence
status: public
title: 'Towards Automated Design: Automatically Generating Modeling Elements with
Prompt Engineering and Generative Artificial Intelligence'
type: conference
user_id: '86782'
year: '2024'
...
---
_id: '62078'
abstract:
- lang: eng
text: 'Fiber reinforced plastics (FRP) exhibit strongly non-linear deformation behavior.
To capture this in simulations, intricate models with a variety of parameters
are typically used. The identification of values for such parameters is highly
challenging and requires in depth understanding of the model itself. Machine learning
(ML) is a promising approach for alleviating this challenge by directly predicting
parameters based on experimental results. So far, this works mostly for purely
artificial data. In this work, two approaches to generalize to experimental data
are investigated: a sequential approach, leveraging understanding of the constitutive
model and a direct, purely data driven approach. This is exemplary carried out
for a highly non-linear strain rate dependent constitutive model for the shear
behavior of FRP.The sequential model is found to work better on both artificial
and experimental data. It is capable of extracting well suited parameters from
the artificial data under realistic conditions. For the experimental data, the
model performance depends on the composition of the experimental curves, varying
between excellently suiting and reasonable predictions. Taking the expert knowledge
into account for ML-model training led to far better results than the purely data
driven approach. Robustifying the model predictions on experimental data promises
further improvement. '
author:
- first_name: Johannes
full_name: Gerritzen, Johannes
id: '105344'
last_name: Gerritzen
orcid: 0000-0002-0169-8602
- first_name: Andreas
full_name: Hornig, Andreas
last_name: Hornig
- first_name: Peter
full_name: Winkler, Peter
last_name: Winkler
- first_name: Maik
full_name: Gude, Maik
last_name: Gude
citation:
ama: 'Gerritzen J, Hornig A, Winkler P, Gude M. Direct parameter identification
for highly nonlinear strain rate dependent constitutive models using machine learning.
In: ECCM21 - Proceedings of the 21st European Conference on Composite Materials.
Vol 3. European Society for Composite Materials (ESCM); 2024:1252–1259. doi:10.60691/yj56-np80'
apa: Gerritzen, J., Hornig, A., Winkler, P., & Gude, M. (2024). Direct parameter
identification for highly nonlinear strain rate dependent constitutive models
using machine learning. ECCM21 - Proceedings of the 21st European Conference
on Composite Materials, 3, 1252–1259. https://doi.org/10.60691/yj56-np80
bibtex: '@inproceedings{Gerritzen_Hornig_Winkler_Gude_2024, title={Direct parameter
identification for highly nonlinear strain rate dependent constitutive models
using machine learning}, volume={3}, DOI={10.60691/yj56-np80},
booktitle={ECCM21 - Proceedings of the 21st European Conference on Composite Materials},
publisher={European Society for Composite Materials (ESCM)}, author={Gerritzen,
Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}, year={2024},
pages={1252–1259} }'
chicago: Gerritzen, Johannes, Andreas Hornig, Peter Winkler, and Maik Gude. “Direct
Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive
Models Using Machine Learning.” In ECCM21 - Proceedings of the 21st European
Conference on Composite Materials, 3:1252–1259. European Society for Composite
Materials (ESCM), 2024. https://doi.org/10.60691/yj56-np80.
ieee: 'J. Gerritzen, A. Hornig, P. Winkler, and M. Gude, “Direct parameter identification
for highly nonlinear strain rate dependent constitutive models using machine learning,”
in ECCM21 - Proceedings of the 21st European Conference on Composite Materials,
2024, vol. 3, pp. 1252–1259, doi: 10.60691/yj56-np80.'
mla: Gerritzen, Johannes, et al. “Direct Parameter Identification for Highly Nonlinear
Strain Rate Dependent Constitutive Models Using Machine Learning.” ECCM21 -
Proceedings of the 21st European Conference on Composite Materials, vol. 3,
European Society for Composite Materials (ESCM), 2024, pp. 1252–1259, doi:10.60691/yj56-np80.
short: 'J. Gerritzen, A. Hornig, P. Winkler, M. Gude, in: ECCM21 - Proceedings of
the 21st European Conference on Composite Materials, European Society for Composite
Materials (ESCM), 2024, pp. 1252–1259.'
date_created: 2025-11-04T12:47:06Z
date_updated: 2026-02-27T06:46:21Z
doi: 10.60691/yj56-np80
intvolume: ' 3'
keyword:
- Direct parameter identification
- Machine learning
- Convolutional neural networks
- Strain rate dependency
- Fiber reinforced plastics
- woven composites
- segmentation
- synthetic training data
- x-ray computed tomography
language:
- iso: eng
page: 1252–1259
project:
- _id: '130'
name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
Prozessketten'
- _id: '137'
name: TRR 285 - Subproject A03
- _id: '131'
name: TRR 285 - Project Area A
publication: ECCM21 - Proceedings of the 21st European Conference on Composite Materials
publication_identifier:
isbn:
- 978-2-912985-01-9
publisher: European Society for Composite Materials (ESCM)
status: public
title: Direct parameter identification for highly nonlinear strain rate dependent
constitutive models using machine learning
type: conference
user_id: '105344'
volume: 3
year: '2024'
...
---
_id: '52235'
abstract:
- lang: eng
text: "Android applications collecting data from users must protect it according
to the current legal frameworks. Such data protection has become even more important
since the European Union rolled out the General Data Protection Regulation (GDPR).
Since app developers are not legal experts, they find it difficult to write privacy-aware
source code. Moreover, they have limited tool support to reason about data protection
throughout their app development process.\r\nThis paper motivates the need for
a static analysis approach to diagnose and explain data protection in Android
apps. The analysis will recognize personal data sources in the source code, and
aims to further examine the data flow originating from these sources. App developers
can then address key questions about data manipulation, derived data, and the
presence of technical measures. Despite challenges, we explore to what extent
one can realize this analysis through static taint analysis, a common method for
identifying security vulnerabilities. This is a first step towards designing a
tool-based approach that aids app developers and assessors in ensuring data protection
in Android apps, based on automated static program analysis. "
author:
- first_name: Mugdha
full_name: Khedkar, Mugdha
id: '88024'
last_name: Khedkar
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Khedkar M, Bodden E. Toward an Android Static Analysis Approach for Data Protection.
In: Proceedings of the IEEE/ACM 11th International Conference on Mobile Software
Engineering and Systems (MOBILESoft ’24). Association for Computing Machinery,
New York, NY, USA, 65–68. ; 2024. doi:10.1145/3647632.3651389'
apa: Khedkar, M., & Bodden, E. (2024). Toward an Android Static Analysis Approach
for Data Protection. Proceedings of the IEEE/ACM 11th International Conference
on Mobile Software Engineering and Systems (MOBILESoft ’24). Association for Computing
Machinery, New York, NY, USA, 65–68. 11th International Conference on Mobile
Software Engineering and Systems 2024, Lisbon, Portugal. https://doi.org/10.1145/3647632.3651389
bibtex: '@inproceedings{Khedkar_Bodden_2024, title={Toward an Android Static Analysis
Approach for Data Protection}, DOI={10.1145/3647632.3651389},
booktitle={Proceedings of the IEEE/ACM 11th International Conference on Mobile
Software Engineering and Systems (MOBILESoft ’24). Association for Computing Machinery,
New York, NY, USA, 65–68.}, author={Khedkar, Mugdha and Bodden, Eric}, year={2024}
}'
chicago: Khedkar, Mugdha, and Eric Bodden. “Toward an Android Static Analysis Approach
for Data Protection.” In Proceedings of the IEEE/ACM 11th International Conference
on Mobile Software Engineering and Systems (MOBILESoft ’24). Association for Computing
Machinery, New York, NY, USA, 65–68., 2024. https://doi.org/10.1145/3647632.3651389.
ieee: 'M. Khedkar and E. Bodden, “Toward an Android Static Analysis Approach for
Data Protection,” presented at the 11th International Conference on Mobile Software
Engineering and Systems 2024, Lisbon, Portugal, 2024, doi: 10.1145/3647632.3651389.'
mla: Khedkar, Mugdha, and Eric Bodden. “Toward an Android Static Analysis Approach
for Data Protection.” Proceedings of the IEEE/ACM 11th International Conference
on Mobile Software Engineering and Systems (MOBILESoft ’24). Association for Computing
Machinery, New York, NY, USA, 65–68., 2024, doi:10.1145/3647632.3651389.
short: 'M. Khedkar, E. Bodden, in: Proceedings of the IEEE/ACM 11th International
Conference on Mobile Software Engineering and Systems (MOBILESoft ’24). Association
for Computing Machinery, New York, NY, USA, 65–68., 2024.'
conference:
end_date: 2024-04-15
location: Lisbon, Portugal
name: 11th International Conference on Mobile Software Engineering and Systems 2024
start_date: 2024-04-14
date_created: 2024-03-03T14:37:53Z
date_updated: 2026-03-04T08:11:48Z
ddc:
- '006'
department:
- _id: '76'
doi: 10.1145/3647632.3651389
external_id:
arxiv:
- '2402.07889'
file:
- access_level: closed
content_type: application/pdf
creator: khedkarm
date_created: 2024-03-03T14:39:08Z
date_updated: 2024-03-03T14:39:08Z
file_id: '52236'
file_name: 2402.07889v1.pdf
file_size: 530812
relation: main_file
success: 1
file_date_updated: 2024-03-03T14:39:08Z
has_accepted_license: '1'
keyword:
- static program analysis
- data protection and privacy
- GDPR compliance
language:
- iso: eng
publication: Proceedings of the IEEE/ACM 11th International Conference on Mobile Software
Engineering and Systems (MOBILESoft '24). Association for Computing Machinery, New
York, NY, USA, 65–68.
status: public
title: Toward an Android Static Analysis Approach for Data Protection
type: conference
user_id: '88024'
year: '2024'
...
---
_id: '57160'
abstract:
- lang: eng
text: Large audio tagging models are usually trained or pre-trained on AudioSet,
a dataset that encompasses a large amount of different sound classes and acoustic
environments. Knowledge distillation has emerged as a method to compress such
models without compromising their effectiveness. There are many different applications
for audio tagging, some of which require a specialization to a narrow domain of
sounds to be classified. For these scenarios, it is beneficial to distill the
large audio tagger with respect to a specific subset of sounds of interest. A
method to prune a general dataset with respect to a target dataset is presented.
By distilling with such a specialized pruned dataset, we obtain a compressed model
with better classification accuracy in the specific target domain than with target-agnostic
distillation.
author:
- first_name: Alexander
full_name: Werning, Alexander
id: '62152'
last_name: Werning
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Werning A, Haeb-Umbach R. Target-Specific Dataset Pruning for Compression
of Audio Tagging Models. In: 32nd European Signal Processing Conference (EUSIPCO
2024). ; 2024.'
apa: Werning, A., & Haeb-Umbach, R. (2024). Target-Specific Dataset Pruning
for Compression of Audio Tagging Models. 32nd European Signal Processing Conference
(EUSIPCO 2024). 32nd European Signal Processing Conference, Lyon.
bibtex: '@inproceedings{Werning_Haeb-Umbach_2024, title={Target-Specific Dataset
Pruning for Compression of Audio Tagging Models}, booktitle={32nd European Signal
Processing Conference (EUSIPCO 2024)}, author={Werning, Alexander and Haeb-Umbach,
Reinhold}, year={2024} }'
chicago: Werning, Alexander, and Reinhold Haeb-Umbach. “Target-Specific Dataset
Pruning for Compression of Audio Tagging Models.” In 32nd European Signal Processing
Conference (EUSIPCO 2024), 2024.
ieee: A. Werning and R. Haeb-Umbach, “Target-Specific Dataset Pruning for Compression
of Audio Tagging Models,” presented at the 32nd European Signal Processing Conference,
Lyon, 2024.
mla: Werning, Alexander, and Reinhold Haeb-Umbach. “Target-Specific Dataset Pruning
for Compression of Audio Tagging Models.” 32nd European Signal Processing Conference
(EUSIPCO 2024), 2024.
short: 'A. Werning, R. Haeb-Umbach, in: 32nd European Signal Processing Conference
(EUSIPCO 2024), 2024.'
conference:
location: Lyon
name: 32nd European Signal Processing Conference
date_created: 2024-11-18T09:29:16Z
date_updated: 2025-11-28T13:22:00Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: closed
content_type: application/pdf
creator: awerning
date_created: 2024-11-18T12:10:09Z
date_updated: 2024-11-18T12:10:09Z
file_id: '57200'
file_name: Eusipco__Target_specific_Dataset_Pruning_for_Compression_of_Audio_Tagging_Models.pdf
file_size: 183539
relation: main_file
success: 1
file_date_updated: 2024-11-18T12:10:09Z
has_accepted_license: '1'
keyword:
- data pruning
- knowledge distillation
- audio tagging
language:
- iso: eng
project:
- _id: '512'
name: WestAI - AI Service Center West
publication: 32nd European Signal Processing Conference (EUSIPCO 2024)
quality_controlled: '1'
status: public
title: Target-Specific Dataset Pruning for Compression of Audio Tagging Models
type: conference
user_id: '62152'
year: '2024'
...
---
_id: '50118'
abstract:
- lang: eng
text: Despite the widespread use of machine learning algorithms, their effectiveness
is limited by a phenomenon known as algorithm aversion. Recent research concluded
that unobserved variables can cause algorithm aversion. However, the impact of
an unobserved variable on algorithm aversion remains unclear. Previous studies
focused on situations where humans had more variables available than algorithms.
We extend this research by conducting an online experiment with 94 participants,
systematically varying the number of observable variables to the advisor and the
advisor type. Surprisingly, our results did not confirm that an unobserved variable
had a negative effect on advice-taking. Instead, we found a positive impact in
an algorithm appreciation scenario. This study provides new insights into the
paradoxical behavior in which people weigh advice more despite having fewer variables,
as they correct for the advisor's errors. Practitioners should consider this behavior
when designing algorithms and account for user correction behavior.
author:
- first_name: Dirk
full_name: Leffrang, Dirk
id: '51271'
last_name: Leffrang
orcid: 0000-0001-9004-2391
citation:
ama: 'Leffrang D. The Broken Leg of Algorithm Appreciation: An Experimental Study
on the Effect of Unobserved Variables on Advice Utilization. In: Wirtschaftsinformatik
Conference. ; 2023.'
apa: 'Leffrang, D. (2023). The Broken Leg of Algorithm Appreciation: An Experimental
Study on the Effect of Unobserved Variables on Advice Utilization. Wirtschaftsinformatik
Conference, 19.'
bibtex: '@inproceedings{Leffrang_2023, title={The Broken Leg of Algorithm Appreciation:
An Experimental Study on the Effect of Unobserved Variables on Advice Utilization},
number={19}, booktitle={Wirtschaftsinformatik Conference}, author={Leffrang, Dirk},
year={2023} }'
chicago: 'Leffrang, Dirk. “The Broken Leg of Algorithm Appreciation: An Experimental
Study on the Effect of Unobserved Variables on Advice Utilization.” In Wirtschaftsinformatik
Conference, 2023.'
ieee: 'D. Leffrang, “The Broken Leg of Algorithm Appreciation: An Experimental Study
on the Effect of Unobserved Variables on Advice Utilization,” in Wirtschaftsinformatik
Conference, Paderborn, 2023, no. 19.'
mla: 'Leffrang, Dirk. “The Broken Leg of Algorithm Appreciation: An Experimental
Study on the Effect of Unobserved Variables on Advice Utilization.” Wirtschaftsinformatik
Conference, no. 19, 2023.'
short: 'D. Leffrang, in: Wirtschaftsinformatik Conference, 2023.'
conference:
location: Paderborn
name: Wirtschaftsinformatik
date_created: 2024-01-03T09:50:06Z
date_updated: 2024-01-10T09:53:24Z
department:
- _id: '196'
issue: '19'
keyword:
- Algorithm aversion
- Data
- Decision-making
- Advice-taking
- Human-Computer Interaction
language:
- iso: eng
main_file_link:
- url: 'https://aisel.aisnet.org/wi2023/19 '
publication: Wirtschaftsinformatik Conference
status: public
title: 'The Broken Leg of Algorithm Appreciation: An Experimental Study on the Effect
of Unobserved Variables on Advice Utilization'
type: conference
user_id: '51271'
year: '2023'
...
---
_id: '52369'
abstract:
- lang: eng
text: Megatrends, such as digitization or sustainability, are confronting the product
management of manufacturing companies with a variety of challenges regarding the
design of future products, but also the management of the actual products. To
successfully position their products in the market, product managers need to gather
and analyze comprehensive information about customers, developments in the products’
environment, product usage, and more. The digitization of all aspects of life
is making data on these topics increasingly available – via social media, documents,
or the internet of things from the products themselves. The systematic collection
and analysis of these data enable the exploitation of new potentials for the adaption
of existing products and the creation of the products of tomorrow. However, there
are still no insights into the main concepts and cause-effect relationships in
exploiting data-driven approaches for product management. Therefore, this paper
aims to identify the main concepts and advantages of data-driven product management.
To answer the corresponding research questions a comprehensive systematic literature
review is conducted. From its results, a detailed description of the main concepts
of data-driven product management is derived. Furthermore, a taxonomy for the
advantages of data-driven product management is presented. The main concepts and
the taxonomy allow for a deeper understanding of the topic while highlighting
necessary future actions and research needs.
author:
- first_name: Timm
full_name: Fichtler, Timm
id: '66731'
last_name: Fichtler
orcid: https://orcid.org/0000-0001-6034-4399
- first_name: Khoren
full_name: Grigoryan, Khoren
last_name: Grigoryan
- first_name: Christian
full_name: Koldewey, Christian
id: '43136'
last_name: Koldewey
orcid: https://orcid.org/0000-0001-7992-6399
- first_name: Roman
full_name: Dumitrescu, Roman
id: '16190'
last_name: Dumitrescu
citation:
ama: 'Fichtler T, Grigoryan K, Koldewey C, Dumitrescu R. Towards a Data-Driven Product
Management – Concepts, Advantages, and Future Research. In: 2023 IEEE International
Conference on Technology Management, Operations and Decisions (ICTMOD). IEEE;
2023. doi:10.1109/ictmod59086.2023.10438135'
apa: Fichtler, T., Grigoryan, K., Koldewey, C., & Dumitrescu, R. (2023). Towards
a Data-Driven Product Management – Concepts, Advantages, and Future Research.
2023 IEEE International Conference on Technology Management, Operations and
Decisions (ICTMOD). IEEE International Conference on Technology Management,
Operations and Decisions (ICTMOD), Rabat, Morocco. https://doi.org/10.1109/ictmod59086.2023.10438135
bibtex: '@inproceedings{Fichtler_Grigoryan_Koldewey_Dumitrescu_2023, title={Towards
a Data-Driven Product Management – Concepts, Advantages, and Future Research},
DOI={10.1109/ictmod59086.2023.10438135},
booktitle={2023 IEEE International Conference on Technology Management, Operations
and Decisions (ICTMOD)}, publisher={IEEE}, author={Fichtler, Timm and Grigoryan,
Khoren and Koldewey, Christian and Dumitrescu, Roman}, year={2023} }'
chicago: Fichtler, Timm, Khoren Grigoryan, Christian Koldewey, and Roman Dumitrescu.
“Towards a Data-Driven Product Management – Concepts, Advantages, and Future Research.”
In 2023 IEEE International Conference on Technology Management, Operations
and Decisions (ICTMOD). IEEE, 2023. https://doi.org/10.1109/ictmod59086.2023.10438135.
ieee: 'T. Fichtler, K. Grigoryan, C. Koldewey, and R. Dumitrescu, “Towards a Data-Driven
Product Management – Concepts, Advantages, and Future Research,” presented at
the IEEE International Conference on Technology Management, Operations and Decisions
(ICTMOD), Rabat, Morocco, 2023, doi: 10.1109/ictmod59086.2023.10438135.'
mla: Fichtler, Timm, et al. “Towards a Data-Driven Product Management – Concepts,
Advantages, and Future Research.” 2023 IEEE International Conference on Technology
Management, Operations and Decisions (ICTMOD), IEEE, 2023, doi:10.1109/ictmod59086.2023.10438135.
short: 'T. Fichtler, K. Grigoryan, C. Koldewey, R. Dumitrescu, in: 2023 IEEE International
Conference on Technology Management, Operations and Decisions (ICTMOD), IEEE,
2023.'
conference:
end_date: 2023-11-24
location: Rabat, Morocco
name: IEEE International Conference on Technology Management, Operations and Decisions
(ICTMOD)
start_date: 2023-11-22
date_created: 2024-03-07T18:13:47Z
date_updated: 2024-03-07T18:17:34Z
department:
- _id: '563'
doi: 10.1109/ictmod59086.2023.10438135
keyword:
- Product Lifecyle Management (PLM)
- Data Analytics
- Data-driven Design
- Engineering Management
- Lifecycle Data
language:
- iso: eng
publication: 2023 IEEE International Conference on Technology Management, Operations
and Decisions (ICTMOD)
publication_status: published
publisher: IEEE
status: public
title: Towards a Data-Driven Product Management – Concepts, Advantages, and Future
Research
type: conference
user_id: '66731'
year: '2023'
...