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[at]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 and natural language processing in the fields of cybersecurity and fraud detection. Currently I'm working on deep learning models able to capture domain specific relationships within data.
In addition to my affiliation with the Data Science chair, I have been a member of the CLiGS – Computational Literary Genre Stylistics research group, working in the field of Digital Humanities.
Teaching
- Summer term 24: Seminar + Praktikum: Machine Learning for Cyber Security
- Summer term 24: Vorlesung zu Data Science (ehemals Data Mining)
- Winter term 23/24: Vorlesung zu Machine Learning for Time Series and Anomaly Detection
- Summer term 23: Übung zu Music Information Retrieval
- Summer term 23: Seminar: Ausgewählte Themen des Machine Learning (BA and MA)
- Winter term 22/23: Seminar: Ausgewählte Themen des Machine Learning (BA and MA)
- Summer term 21: Praktikum: Musik und Maschinelles Lernen
- Winter term 20/21: Übung zu Grundlagen der Algorithmen und Datenstrukturen
- Winter term 20/21: Seminar: Musik und Maschinelles Lernen
- 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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“Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks”, LWDA’23: Lernen, Wissen, Daten, Analysen. October 09--11, 2023, Marburg, Germany.(2023)
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“Occupational Fraud Detection through Agent-based Data Generation”, The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023 - to appear.(2023)
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“Evaluating feature relevance XAI in network intrusion detection”, The World Conference on eXplainable Artificial Intelligence (xAI 2023) - to appear.(2023)
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“CapsKG: Enabling Continual Knowledge Integration in Language Models for Automatic Knowledge Graph Completion”, International Semantic Web Conference ISWC 2023, to appear.(2023)
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“Enhancing Sequential Next-Item Prediction through Modelling Non-Item Pages”.(2023)
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“Optimizing Medical Service Request Processes through Language Modeling and Semantic Search”, in 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), ICMHI 2023, Kyoto, Japan: Association for Computing Machinery, 136–141, available: https://doi.org/10.1145/3608298.3608324.(2023)
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“Open ERP System Data For Occupational Fraud Detection”, available: http://arxiv.org/abs/2206.04460.(2022)
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“Towards Responsible Medical Diagnostics Recommendation Systems”, CoRR, abs/2209.03760, available: https://doi.org/10.48550/arXiv.2209.03760.(2022)
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Detecting Anomalies in Transaction Data, PhD dissertation, available: https://doi.org/10.25972/OPUS-29856.(2022)
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“Towards Responsible Medical Diagnostics Recommendation Systems”, available: http://arxiv.org/abs/2209.03760.(2022)
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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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Financial Fraud Detection With Improved Neural Arithmetic Logic Units.(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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“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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“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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“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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“A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels”, in DH, ADHO.(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)