J. Awick, G. Schumann, und J. Marx Gómez, "Exploring Federated Learning for Data Integration: A Structured Literature Review" in Proc. 2023 International Conference on Big Data, Knowledge and Control Systems Engineering (BdKCSE), 2023.
doi: 10.1109/BdKCSE59280.2023.10339771
@INPROCEEDINGS{10339771,
author={Awick, Jan-Philipp and Schumann, Gerrit and Marx G{\'o}mez, Jorge},
booktitle={2023 International Conference on Big Data, Knowledge and Control Systems Engineering (BdKCSE)},
title={Exploring Federated Learning for Data Integration: A Structured Literature Review},
year={2023},
volume={},
number={},
pages={1-8},
doi={10.1109/BdKCSE59280.2023.10339771}
}
G. Schumann und J. Marx Gómez, "Unsupervised Contradiction Detection using Sentence Transformations" in Proc. 2023 5th International Conference on Natural Language Processing (ICNLP), 2023.
doi: 10.1109/ICNLP58431.2023.00065
@INPROCEEDINGS{10236765,
author={Schumann, Gerrit and Marx G{\'o}mez, Jorge},
booktitle={2023 5th International Conference on Natural Language Processing (ICNLP)},
title={Unsupervised Contradiction Detection using Sentence Transformations},
year={2023},
volume={},
number={},
pages={319-324},
doi={10.1109/ICNLP58431.2023.00065}
}
G. Schumann, K. Meyer, und J. Marx Gómez, "Query-Based Retrieval of German Regulatory Documents for Internal Auditing Purposes" in Proc. 2022 5th International Conference on Data Science and Information Technology (DSIT), 2022.
doi: 10.1109/DSIT55514.2022.9943943
@INPROCEEDINGS{9943943,
author={Schumann, Gerrit and Meyer, Katharina and Marx G{\'o}mez, Jorge},
booktitle={2022 5th International Conference on Data Science and Information Technology (DSIT)},
title={Query-Based Retrieval of German Regulatory Documents for Internal Auditing Purposes},
year={2022},
volume={},
number={},
pages={01-10},
doi={10.1109/DSIT55514.2022.9943943}
}
G. Schumann und J. Marx Gómez, "Natural Language Processing in Internal Auditing--a Structured Literature Review."
2021.
@article{schumann2021natural, title={Natural Language Processing in Internal Auditing--a Structured Literature Review},
author={Schumann, Gerrit and Marx G{\'o}mez, Jorge},
year={2021}
}
J. Nonnenmacher, F. Kruse, G. Schumann, und J. Marx Gómez, "Using Autoencoders for Data-Driven Analysis in Internal Auditing" in Proc. Proceedings of the 54th Hawaii International Conference on System Sciences, , p. 5748.
@inproceedings{nonnenmacherusing, title={Using Autoencoders for Data-Driven Analysis in Internal Auditing},
author={Nonnenmacher, Jakob and Kruse, Felix and Schumann, Gerrit and Marx G{\'o}mez, Jorge},
booktitle={Proceedings of the 54th Hawaii International Conference on System Sciences},
pages={5748} year={2021}
}
G. Schumann, F. Kruse, und J. Nonnenmacher, "A Practice-Oriented, Control-Flow-Based Anomaly Detection Approach for Internal Process Audits" in Proc. International Conference on Service-Oriented Computing, 2020.
@inproceedings{schumann2020practice, title={A Practice-Oriented, Control-Flow-Based Anomaly Detection Approach for Internal Process Audits},
author={Schumann, Gerrit and Kruse, Felix and Nonnenmacher, Jakob},
booktitle={International Conference on Service-Oriented Computing},
pages={533--543},
year={2020},
organization={Springer}
}
G. Schumann, J. Awick, und J. Marx Gómez, "Natural Language Processing using Federated Learning: A Structured Literature Review" in Proc. 2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), 2023.
doi: 10.1109/AIBThings58340.2023.10292481
@INPROCEEDINGS{10292481,
author={Schumann, Gerrit and Awick, Jan-Philipp and Marx G{\'o}mez, Jorge},
booktitle={2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)},
title={Natural Language Processing using Federated Learning: A Structured Literature Review},
year={2023},
doi={10.1109/AIBThings58340.2023.10292481},
pages={1-7}
}
M. Beykirch, C. Hilmer, G. Schumann, L. Kölpin, C. Wübbe, M. Mönning, G. Yalcin, K. Lang, T. H. Dam, J. Kathmann, und others, Architektur eines dezentralen, prognosebasierten EnergiehandelsmodellsSpringer.
2019.
@incollection{beykirch2019architektur, title={Architektur eines dezentralen, prognosebasierten Energiehandelsmodells},
author={Beykirch, Marlon and Hilmer, Christin and Schumann, Gerrit and K{\"o}lpin, Lars and W{\"u}bbe, Carolin and M{\"o}nning, Moritz and Yalcin, Guersoy and Lang, Kevin and Dam, Thi Hai and Kathmann, Jonas and others},
booktitle={Smart Cities/Smart Regions--Technische, wirtschaftliche und gesellschaftliche Innovationen},
pages={339--350},
year={2019},
publisher={Springer}
}
G. Schumann und J. Marx Gómez, "Extraction of Numerical Facts from German Texts to Enrich Internal Audit Data" in Proc. Artificial Intelligence Tools and Applications in Embedded and Mobile Systems, Cham, 2024.
doi: 10.1007/978-3-031-56576-2_16
@InProceedings{10.1007/978-3-031-56576-2_16,
author="Schumann, Gerrit and Marx G{\'o}mez, Jorge", editor="Marx G{\'o}mez, Jorge and Elikana Sam, Anael and Godfrey Nyambo, Devotha", title="Extraction of Numerical Facts from German Texts to Enrich Internal Audit Data", booktitle="Artificial Intelligence Tools and Applications in Embedded and Mobile Systems", year="2024", publisher="Springer Nature Switzerland", address="Cham", pages="183--193", abstract="Large-scale automated data processing is usually only possible for internal auditors in the case of structured data. Unstructured data, such as facts contained in texts, on the other hand, are often processed manually and using sampling. This, in turn, can increase the risk of disregarding relevant information during an audit. To address this risk, we present an approach that can be used to extract numerical facts along with their associated entities and relations from German texts and convert them into a format that can be processed by audit tools. The algorithm developed for this purpose follows a rule-based logic and was evaluated using 4637 sentences from 50 German annual reports. The results show that in more than 75{\%} of all cases, the entity and relation of a numeric value within the sentence could be determined correctly.", isbn="978-3-031-56576-2", doi="10.1007/978-3-031-56576-2_16" }
G. Schumann und J. Marx Gómez, "Detection of Contradictions and Inconsistencies in German Regulatory Documents" in Proc. 2024 6th International Conference on Natural Language Processing (ICNLP), 2024.
doi: 10.1109/ICNLP60986.2024.10692679
@INPROCEEDINGS{10692679,
author={Schumann, Gerrit and Marx G{\'o}mez, Jorge},
booktitle={2024 6th International Conference on Natural Language Processing (ICNLP)},
title={Detection of Contradictions and Inconsistencies in German Regulatory Documents},
year={2024},
volume={},
number={},
pages={74-84},
keywords={Accuracy;Annotations;Semantics;Companies;Predictive models;Syntactics;Natural language processing;Prompt engineering;Tuning;Guidelines;Contradiction Detection;Regulatory Documents},
doi={10.1109/ICNLP60986.2024.10692679}
}
G. Schumann und J. Marx Gómez, "Detection of Conflicts, Contradictions and Inconsistencies in Regulatory Documents: A Literature Review" in Proc. 2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA), 2024.
doi: 10.1109/IDSTA62194.2024.10747003
@INPROCEEDINGS{10747003,
author={Schumann, Gerrit and Marx G{\'o}mez, Jorge},
booktitle={2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)},
title={Detection of Conflicts, Contradictions and Inconsistencies in Regulatory Documents: A Literature Review},
year={2024},
volume={},
number={},
pages={81-88},
keywords={Bibliographies;Natural languages;Semantics;Training data;Process control;Machine learning;Logic;Contracts;Guidelines;Formal verification;Conflicts;Contradictions;Inconsistencies;Regulatory Documents;Literature Review},
doi={10.1109/IDSTA62194.2024.10747003}
