@thesis{Achter2024, author = {Achter, Lea}, title = {KI-basiert versus klassisch - File Carving in der digitalen Fahrzeugforensik}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:573-45885}, pages = {X, 45, X}, year = {2024}, abstract = {Durch die voranschreitende Entwicklung der Technologie im Bereich der Automobilindustrie k{\"o}nnen immer mehr digitale Spuren in Fahrzeugen festgestellt werden. Das f{\"u}hrt zu einem immer wichtiger werdenden Gebiet, der digitalen Fahrzeugforensik. Dieses besch{\"a}ftigt sich mit dem Auslesen der Fahrzeugspeicher, die meist propriet{\"a}re Dateitypen der Automobilhersteller enthalten. Somit ist der Aufbau dieser Dateien meist unbekannt, was einen Unterschied zur digitalen Forensik darstellt. Hier k{\"o}nnen durch sogenannte File Carver Dateitypen, anhand bekannter Byte Sequenzen, wie Header oder Footer, erkannt werden. Unbekannte propriet{\"a}re Dateien, wie die der Automobilindustrie k{\"o}nnen somit meist nicht gefunden werden. Das Ziel dieser Arbeit ist es zu untersuchen, in wieweit klassische File Carver die spezifischen Dateitypen der Automobilbranche erkennen, und ob KI-basierte Ans{\"a}tze hier m{\"o}glicherweise einen Vorteil bieten k{\"o}nnen. Hierzu wird ein synthetischer Datensatz erstellt, um eine Basis mit relevanten Dateitypen zu schaffen. Die Tests der Softwares auf dem erstellten Datensatz zeigen, dass sich der File Carver Autopsy am Besten f{\"u}r eine Untersuchung in der digitalen Fahrzeugforensik eignet. Jedoch lassen die erzielten Ergebnisse der KI-basierten Methoden auf ein deutliches Entwicklungspotential schließen.}, language = {de} } @inproceedings{KudielkaKlugeShairetal.2015, author = {Kudielka, Guido P. and Kluge, Thomas and Shair, Sultan and Menzel, Marion Irene}, title = {Magnetic Resonance Imaging for 3D Resin Flow and Curing Process Monitoring}, booktitle = {CAMX, the Composites and Advanced Materials Expo, October 26-29, 2015: Conference / October 27-29, 2015: Exhibits, Dallas, Texas USA / Dallas Convention Center}, publisher = {ACMA}, address = {Arlington}, isbn = {978-1-934551-20-2}, url = {https://www.nasampe.org/store/viewproduct.aspx?id=5477796}, pages = {144 -- 154}, year = {2015}, language = {en} } @unpublished{SongLiangCaoetal.2024, author = {Song, Rui and Liang, Chenwei and Cao, Hu and Yan, Zhiran and Zimmer, Walter and Gross, Markus and Festag, Andreas and Knoll, Alois}, title = {Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2402.07635}, year = {2024}, abstract = {Collaborative perception in automated vehicles leverages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However, these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap, we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly, it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features, and (ii) compressed orthogonal attention features shared between vehicles. Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30\%, and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications, showcasing enhanced accuracy and enriched semantic-awareness in road environments.}, language = {en} } @unpublished{PugetGanzOstermaieretal.2024, author = {Puget, Chlo{\´e} and Ganz, Jonathan and Ostermaier, Julian and Konrad, Thomas and Parlak, Eda and Bertram, Christof and Kiupel, Matti and Breininger, Katharina and Aubreville, Marc and Klopfleisch, Robert}, title = {Deep Learning model predicts the c-Kit-11 mutational status of canine cutaneous mast cell tumors by HE stained histological slides}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2401.06169}, year = {2024}, abstract = {Numerous prognostic factors are currently assessed histopathologically in biopsies of canine mast cell tumors to evaluate clinical behavior. In addition, PCR analysis of the c-Kit exon 11 mutational status is often performed to evaluate the potential success of a tyrosine kinase inhibitor therapy. This project aimed at training deep learning models (DLMs) to identify the c-Kit-11 mutational status of MCTs solely based on morphology without additional molecular analysis. HE slides of 195 mutated and 173 non-mutated tumors were stained consecutively in two different laboratories and scanned with three different slide scanners. This resulted in six different datasets (stain-scanner variations) of whole slide images. DLMs were trained with single and mixed datasets and their performances was assessed under scanner and staining domain shifts. The DLMs correctly classified HE slides according to their c-Kit 11 mutation status in, on average, 87\% of cases for the best-suited stain-scanner variant. A relevant performance drop could be observed when the stain-scanner combination of the training and test dataset differed. Multi-variant datasets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant. In summary, DLM-assisted morphological examination of MCTs can predict c-Kit-exon 11 mutational status of MCTs with high accuracy. However, the recognition performance is impeded by a change of scanner or staining protocol. Larger data sets with higher numbers of scans originating from different laboratories and scanners may lead to more robust DLMs to identify c-Kit mutations in HE slides.}, language = {en} } @inproceedings{PlatzKlanDecker2023, author = {Platz, Melanie and Klan, Friederike and Decker, Alexander}, title = {Developing and promoting search engine literacy in primary education}, booktitle = {Proceedings of 4th International Open Search Symposium \#ossym2022, CERN, Geneva Switzerland, 10-12 October 2022}, editor = {Granitzer, Michael and G{\"u}etl, Christian and Plote, Christine and Voigt, Stefan and Wagner, Andreas}, publisher = {Zenodo}, address = {Genf}, isbn = {978-92-9083-646-9}, doi = {https://doi.org/10.5281/zenodo.8066392}, pages = {43 -- 48}, year = {2023}, language = {en} } @inbook{BaderGassmann2022, author = {Bader, Martin and Gassmann, Oliver}, title = {Patents in the Biomedical Sciences and Industry - The Case of the Swiss Life Science Company Prionics}, booktitle = {Principles of Biomedical Sciences and Industry: Translating Ideas into Treatments}, editor = {Hinder, Markus and Schuhmacher, Alexander and Goldhahn, J{\"o}rg and Hartl, Dominik}, publisher = {Wiley-VCH}, address = {Weinheim}, isbn = {978-3-527-34571-7}, doi = {https://doi.org/10.1002/9783527824014.ch16}, pages = {305 -- 315}, year = {2022}, language = {en} } @inbook{SchuhmacherHinder2022, author = {Schuhmacher, Alexander and Hinder, Markus}, title = {Project, Risk, and Portfolio Management - Managing R \& D Projects Today*}, booktitle = {Principles of Biomedical Sciences and Industry: Translating Ideas into Treatments}, editor = {Hinder, Markus and Schuhmacher, Alexander and Goldhahn, J{\"o}rg and Hartl, Dominik}, publisher = {Wiley-VCH}, address = {Weinheim}, isbn = {978-3-527-34571-7}, doi = {https://doi.org/10.1002/9783527824014.ch14}, pages = {257 -- 279}, year = {2022}, language = {en} } @article{LiscaProdaniucGrauschopfetal.2021, author = {Lisca, Gheorghe and Prodaniuc, Cristian and Grauschopf, Thomas and Axenie, Cristian}, title = {Less Is More: Learning Insights From a Single Motion Sensor for Accurate and Explainable Soccer Goalkeeper Kinematics}, volume = {21}, journal = {IEEE Sensors Journal}, number = {18}, publisher = {IEEE}, address = {Piscataway}, issn = {1530-437X}, doi = {https://doi.org/10.1109/JSEN.2021.3094929}, pages = {20375 -- 20387}, year = {2021}, language = {en} } @book{OPUS4-4576, title = {Principles of Biomedical Sciences and Industry}, subtitle = {Translating Ideas into Treatments}, editor = {Hinder, Markus and Schuhmacher, Alexander and Goldhahn, J{\"o}rg and Hartl, Dominik}, publisher = {Wiley-VCH}, address = {Weinheim}, isbn = {978-3-527-34571-7}, doi = {https://doi.org/10.1002/9783527824014}, pages = {xxxi, 409}, year = {2022}, language = {en} } @article{KoebeSchillingsSchwarz2023, author = {Koebe, Philipp and Schillings, Tobias and Schwarz, Jan Oliver}, title = {How Superintelligence Affects Human Health: A Scenario Analysis}, volume = {28}, journal = {Journal of Futures Studies}, number = {2}, publisher = {Tamkang University}, address = {Taipei}, issn = {1027-6084}, doi = {https://doi.org/10.6531/JFS.202312_28(2).0005}, pages = {79 -- 99}, year = {2023}, language = {en} }