Dr. Daniel Schlör

Chair of Data Science (Informatik X)
University of Würzburg
Campus Hubland Nord
Emil-Fischer-Straße 50
97074 Würzburg
Germany
Email: daniel.schloer@informatik.uni-wuerzburg.de
Phone: (+49 931) 31 - 84564
Office: Room 50.03.017 (Institutsgebäude Künstliche Intelligenz)
Projects and Research Interests
My main research interests are machine learning, anomaly detection in the fields of cybersecurity and fraud detection and textmining and their application to digital humanities. Currently I'm working on deep learning models able to capture mathematical and other relationships within data.
In addition to my affiliation with the DMIR research group I have been a member of the CLiGS – Computational Literary Genre Stylistics research group, working in the field of Digital Humanities.
Teaching
- Winter term 20/21: Übung zu Grundlagen der Algorithmen und Datenstrukturen
- Winter term 19/20: Übung zu Grundlagen der Algorithmen und Datenstrukturen
- Winter term 18/19: Übung zu Grundlagen der Algorithmen und Datenstrukturen
- Winter term 17/18: Übung zu Grundlagen der Algorithmen und Datenstrukturen
- Winter term 16/17: Sprachverarbeitung und Text Mining
- Winter term 15/16: Sprachverarbeitung und Text Mining
Publications
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“Towards Responsible Medical Diagnostics Recommendation Systems”, available: http://arxiv.org/abs/2209.03760.(2022)
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“Open ERP System Data For Occupational Fraud Detection”, available: http://arxiv.org/abs/2206.04460.(2022)
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Detecting Anomalies in Transaction Data, PhD dissertation, available: https://doi.org/10.25972/OPUS-29856.(2022)
- [ BibTeX ]
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“Malware detection on windows audit logs using LSTMs”, Computers & Security, 109, 102389, available: https://doi.org/https://doi.org/10.1016/j.cose.2021.102389.(2021)
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“A financial game with opportunities for fraud”, in 2021 IEEE Conference on Games (CoG), 1–5, available: https://doi.org/10.1109/CoG52621.2021.9619070.(2021)
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“Improving Sentiment Analysis with Biofeedback Data”, in Proceedings of LREC2020 Workshop ``People in Language, Vision and the mind’’ (ONION2020), Marseille, France: European Language Resources Association (ELRA), 28–33, available: https://www.aclweb.org/anthology/2020.onion-1.5.(2020)
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“Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data.”, in Helic, D., Leitner, G., Stettinger, M., Felfernig, A. and Ras, Z.W., eds., ISMIS, Lecture Notes in Computer Science, Springer, 422–430, available: http://dblp.uni-trier.de/db/conf/ismis/ismis2020.html#TritscherRSHH20.(2020)
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“iNALU: Improved Neural Arithmetic Logic Unit”, Frontiers in Artificial Intelligence, 3, 71, available: https://doi.org/10.3389/frai.2020.00071.(2020)
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Financial Fraud Detection With Improved Neural Arithmetic Logic Units.(2020)
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“Classification of text-types in german novels”, in Digital Humanities 2019: Conference Abstracts, available: https://doi.org/https://doi.org/10.34894/OMLKRN.(2019)
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“Flow-based network traffic generation using Generative Adversarial Networks.”, Comput. Secur., 82, 156–172, available: http://dblp.uni-trier.de/db/journals/compsec/compsec82.html#RingSLH19.(2019)
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“A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels”, in DH, ADHO.(2018)
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“Burrows’ Zeta: Exploring and Evaluating Variants and Parameters”, in DH, 274–277, available: http://dblp.uni-trier.de/db/conf/dihu/dh2018.html#SchochSZG0H18.(2018)
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“Neutralising the Authorial Signal in Delta by Penalization: Stylometric Clustering of Genre in Spanish Novels.”, in Lewis, R., Raynor, C., Forest, D., Sinatra, M. and Sinclair, S., eds., DH, Alliance of Digital Humanities Organizations (ADHO), available: http://dblp.uni-trier.de/db/conf/dihu/dh2017.html#TelloSHS17.(2017)
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“Straight Talk! Automatic Recognition of Direct Speech in Nineteenth-Century French Novels.”, in DH, 346–353.(2016)
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“Extracting Semantics from Unconstrained Navigation on Wikipedia”, KI -- Künstliche Intelligenz, 30(2), 163–168.(2016)