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Validierung von TSP Untersuchungen zum Ablöseverhalten am Beispiel eines NACA-0012 Profils mittels LDV
No full textIn diesem Beitrag werden TSP-Messungen zur Visualisierung von Ablösevorgängen und dem Übergang von der laminaren zur turbulenten Umströmung einer ebenen Platte und eines NACA 0012 Profils [1] vorgestellt. Der Strömungszustand wird durch LDV-Messungen
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User Errors in Augmented Reality Assisted Preoperative Planning. In: Sangeun J., Jeong H.K., Yong-Ku K., Jaehyun P., Myung H.Y. (Eds.). Proceedings of the 22nd Congress of the International Ergonomics Association, Volume 4. Better Life Ergonomics for Future Humans (IEA 2024).
No full textDer Zusammenhang zwischen dem organisationalen Gesundheitsklima und der Arbeitsleistung: Identifikation mit der Organisation, psychologisches Kapital und Aufblühen am Arbeitsplatz als Mediatoren
No full textLifestyle Medicine as a Pathway to Longevity: The Impact of the Healthy Lifestyle Community Program (Cohort 2, Hlcp-2) on Oxidative Stress Biomarkers in the Adult Rural German Population
No full textHand and wrist complaints in dialysis nurses in Germany: a survey of prevalence, severity, and occupational associations
No full textTowards Automated and Robust Forensic Event Reconstruction
No full textReconstructing past events in IT systems is a critical
bottleneck in forensic investigations, consuming valuable time
from investigators. It requires meticulous analysis of complex
digital traces in an environment where attackers may try to erase
traces. For example, deletion of digital artifacts is an anti-forensic
technique used to jeopardize the success of forensic investigations.
To address these challenges, we introduce Investigator Copilot,
a novel framework that automates post-mortem event reconstruc-
tion using explainable machine learning. To overcome the general
scarcity of datasets, Investigator Copilot replays realistic events on
virtual machines, and creates datasets by extracting, normalizing
and labeling traces from corresponding hard disks. Using these
datasets, Investigator Copilot trains human-interpretable decision
tree stumps that evaluate digital evidence and combines these
binary classifiers in Forensic Forests. Forensic Forests utilize an
adjusted voting scheme to provide robust event reconstruction
even when faced with deleted evidence.
We evaluate our approach by executing 2100 events on 50
virtual machines, training Forensic Forests and measuring their
event reconstruction performance on previously unseen data. Our
results demonstrate that tree-based classifiers perform exceedingly
well in event reconstruction. When measuring reconstruction
performance on manipulated evidence, we observe that Forensic
Forests significantly outperform the state-of-the-art, which po-
sitions them as a valuable tool for investigators. Our findings
indicate that automated frameworks such as Investigator Copilot
can contribute to the efficiency and robustness of forensic analyses,
and may save scarce resources of human investigators
