Dr. Daniel Schlör

Chair of Data Science (Informatik X)
University of Würzburg
Am Hubland
97074 Würzburg
Germany

Email: daniel.schloer@informatik.uni-wuerzburg.de

Phone: (+49 931) 31 - 84564

Office: Room B103 (Computer Science Building M2)

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

2022[ to top ]

Schlör, D., Hotho, A. (2022) Towards Responsible Medical Diagnostics Recommendation Systems, available: http://arxiv.org/abs/2209.03760.
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The early development and deployment of hospital and healthcare information systems have encouraged the ongoing digitization of processes in hospitals. Many of these processes, which previously required paperwork and telephone arrangements, are now integrated into IT solutions and require physicians and medical staff to interact with appropriate interfaces and tools. Although this shift to digital data management and process support has benefited patient care in many ways, it requires physicians to accurately capture all relevant information digitally for billing and documentation purposes, which takes a lot of time away from actual patient care work. However, systematic collection of healthcare data over a long period of time offers opportunities to improve this process and support medical staff by introducing recommender systems. Based on a practical working example, in this position paper, we will outline the design of a responsible recommender system in the medical context from a technical, application driven perspective and discuss potential design choices and criteria with a specific focus on accountability, safety, and fairness.

@misc{schlor2022towards, abstract = {The early development and deployment of hospital and healthcare information systems have encouraged the ongoing digitization of processes in hospitals. Many of these processes, which previously required paperwork and telephone arrangements, are now integrated into IT solutions and require physicians and medical staff to interact with appropriate interfaces and tools. Although this shift to digital data management and process support has benefited patient care in many ways, it requires physicians to accurately capture all relevant information digitally for billing and documentation purposes, which takes a lot of time away from actual patient care work. However, systematic collection of healthcare data over a long period of time offers opportunities to improve this process and support medical staff by introducing recommender systems. Based on a practical working example, in this position paper, we will outline the design of a responsible recommender system in the medical context from a technical, application driven perspective and discuss potential design choices and criteria with a specific focus on accountability, safety, and fairness.}, author = {Schlör, Daniel and Hotho, Andreas}, keywords = {recsys}, note = {cite arxiv:2209.03760Comment: 4th FAccTRec Workshop on Responsible Recommendation}, title = {Towards Responsible Medical Diagnostics Recommendation Systems}, year = 2022 }
Tritscher, J., Gwinner, F., Schlör, D., Krause, A., Hotho, A. (2022) Open ERP System Data For Occupational Fraud Detection, available: http://arxiv.org/abs/2206.04460.
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Recent estimates report that companies lose 5% of their revenue to occupational fraud. Since most medium-sized and large companies employ Enterprise Resource Planning (ERP) systems to track vast amounts of information regarding their business process, researchers have in the past shown interest in automatically detecting fraud through ERP system data. Current research in this area, however, is hindered by the fact that ERP system data is not publicly available for the development and comparison of fraud detection methods. We therefore endeavour to generate public ERP system data that includes both normal business operation and fraud. We propose a strategy for generating ERP system data through a serious game, model a variety of fraud scenarios in cooperation with auditing experts, and generate data from a simulated make-to-stock production company with multiple research participants. We aggregate the generated data into ready to used datasets for fraud detection in ERP systems, and supply both the raw and aggregated data to the general public to allow for open development and comparison of fraud detection approaches on ERP system data.

@misc{tritscher2022system, abstract = {Recent estimates report that companies lose 5% of their revenue to occupational fraud. Since most medium-sized and large companies employ Enterprise Resource Planning (ERP) systems to track vast amounts of information regarding their business process, researchers have in the past shown interest in automatically detecting fraud through ERP system data. Current research in this area, however, is hindered by the fact that ERP system data is not publicly available for the development and comparison of fraud detection methods. We therefore endeavour to generate public ERP system data that includes both normal business operation and fraud. We propose a strategy for generating ERP system data through a serious game, model a variety of fraud scenarios in cooperation with auditing experts, and generate data from a simulated make-to-stock production company with multiple research participants. We aggregate the generated data into ready to used datasets for fraud detection in ERP systems, and supply both the raw and aggregated data to the general public to allow for open development and comparison of fraud detection approaches on ERP system data.}, author = {Tritscher, Julian and Gwinner, Fabian and Schlör, Daniel and Krause, Anna and Hotho, Andreas}, keywords = {myown}, note = {cite arxiv:2206.04460}, title = {Open ERP System Data For Occupational Fraud Detection}, year = 2022 }
Schlör, D. (2022) Detecting Anomalies in Transaction Data, available: https://doi.org/10.25972/OPUS-29856.
- [ BibTeX ]
@phdthesis{Schloer2022, author = {Schlör, Daniel}, keywords = {myown}, school = {Universität Würzburg}, title = {Detecting Anomalies in Transaction Data}, type = {Doctoral Thesis}, year = 2022 }

2021[ to top ]

Ring, M., Schlör, D., Wunderlich, S., Landes, D., Hotho, A. (2021) 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.
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Malware is a constant threat and is continuously evolving. Security systems try to keep up with the constant change. One challenge that arises is the large amount of logs generated on an operating system and the need to clarify which information contributes to the detection of possible malware. This work aims at the detection of malware using neural networks based on Windows audit log events. Neural networks can only process continuous data, but Windows audit logs are sequential and textual data. To address these challenges, we extract features out of the audit log events and use LSTMs to capture sequential effects. We create different subsets of features and analyze the effects of additional information. Features describe for example the action-type of windows audit log events, process names or target files that are accessed. Textual features are represented either as one-hot encoding or embedding representation, for which we compare three different approaches for representation learning. Effects of different feature subsets and representations are evaluated on a publicly available data set. Results indicate that using additional information improves the performance of the LSTM-model. While different representations lead to similar classification results, analysis of the latent space shows differences more precisely where FastText seems to be the most promising representation.

@article{RING2021102389, abstract = {Malware is a constant threat and is continuously evolving. Security systems try to keep up with the constant change. One challenge that arises is the large amount of logs generated on an operating system and the need to clarify which information contributes to the detection of possible malware. This work aims at the detection of malware using neural networks based on Windows audit log events. Neural networks can only process continuous data, but Windows audit logs are sequential and textual data. To address these challenges, we extract features out of the audit log events and use LSTMs to capture sequential effects. We create different subsets of features and analyze the effects of additional information. Features describe for example the action-type of windows audit log events, process names or target files that are accessed. Textual features are represented either as one-hot encoding or embedding representation, for which we compare three different approaches for representation learning. Effects of different feature subsets and representations are evaluated on a publicly available data set. Results indicate that using additional information improves the performance of the LSTM-model. While different representations lead to similar classification results, analysis of the latent space shows differences more precisely where FastText seems to be the most promising representation.}, author = {Ring, Markus and Schlör, Daniel and Wunderlich, Sarah and Landes, Dieter and Hotho, Andreas}, journal = {Computers & Security}, keywords = {myown}, pages = 102389, title = {Malware detection on windows audit logs using LSTMs}, volume = 109, year = 2021 }
Tritscher, J., Krause, A., Schlör, D., Gwinner, F., Von Mammen, S., Hotho, A. (2021) 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.
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@inproceedings{Tritscher:2021aa, author = {Tritscher, Julian and Krause, Anna and Schlör, Daniel and Gwinner, Fabian and Von Mammen, Sebastian and Hotho, Andreas}, booktitle = {2021 IEEE Conference on Games (CoG)}, keywords = {myown}, pages = {1-5}, title = {A financial game with opportunities for fraud}, year = 2021 }

2020[ to top ]

Schl{\"o}r, D., Zehe, A., Kobs, K., Veseli, B., Westermeier, F., Br{\"u}bach, L., Roth, D., Latoschik, M.E., Hotho, A. (2020) 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.
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Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.

@inproceedings{schlor-etal-2020-improving, abstract = {Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.}, address = {Marseille, France}, author = {Schl{\"o}r, Daniel and Zehe, Albin and Kobs, Konstantin and Veseli, Blerta and Westermeier, Franziska and Br{\"u}bach, Larissa and Roth, Daniel and Latoschik, Marc Erich and Hotho, Andreas}, booktitle = {Proceedings of LREC2020 Workshop ``People in language, vision and the mind'' (ONION2020)}, keywords = {sentimentanalysis}, month = {may}, pages = {28–33}, publisher = {European Language Resources Association (ELRA)}, title = {Improving Sentiment Analysis with Biofeedback Data}, year = 2020 }
Tritscher, J., Ring, M., Schlör, D., Hettinger, L., Hotho, A. (2020) 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.
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@inproceedings{conf/ismis/TritscherRSHH20, author = {Tritscher, Julian and Ring, Markus and Schlör, Daniel and Hettinger, Lena and Hotho, Andreas}, booktitle = {ISMIS}, crossref = {conf/ismis/2020}, editor = {Helic, Denis and Leitner, Gerhard and Stettinger, Martin and Felfernig, Alexander and Ras, Zbigniew W.}, keywords = {xai}, pages = {422-430}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data.}, volume = 12117, year = 2020 }
Schlör, D., Ring, M., Hotho, A. (2020) iNALU: Improved Neural Arithmetic Logic Unit, Frontiers in Artificial Intelligence, 3, 71, available: https://doi.org/10.3389/frai.2020.00071.
- [ Abstract ]
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Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well. The recently proposed Neural Arithmetic Logic Unit (NALU) is a novel neural architecture which is able to explicitly represent the mathematical relationships by the units of the network to learn operations such as summation, subtraction or multiplication. Although NALUs have been shown to perform well on various downstream tasks, an in-depth analysis reveals practical shortcomings by design, such as the inability to multiply or divide negative input values or training stability issues for deeper networks. We address these issues and propose an improved model architecture. We evaluate our model empirically in various settings from learning basic arithmetic operations to more complex functions. Our experiments indicate that our model solves stability issues and outperforms the original NALU model in means of arithmetic precision and convergence.

@article{10.3389/frai.2020.00071, abstract = {Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well. The recently proposed Neural Arithmetic Logic Unit (NALU) is a novel neural architecture which is able to explicitly represent the mathematical relationships by the units of the network to learn operations such as summation, subtraction or multiplication. Although NALUs have been shown to perform well on various downstream tasks, an in-depth analysis reveals practical shortcomings by design, such as the inability to multiply or divide negative input values or training stability issues for deeper networks. We address these issues and propose an improved model architecture. We evaluate our model empirically in various settings from learning basic arithmetic operations to more complex functions. Our experiments indicate that our model solves stability issues and outperforms the original NALU model in means of arithmetic precision and convergence.}, author = {Schlör, Daniel and Ring, Markus and Hotho, Andreas}, journal = {Frontiers in Artificial Intelligence}, keywords = {networks}, pages = 71, title = {iNALU: Improved Neural Arithmetic Logic Unit}, volume = 3, year = 2020 }
Schlör, D., Ring, M., Krause, A., Hotho, A. (2020) Financial Fraud Detection with Improved Neural Arithmetic Logic Units.
- [ BibTeX ]
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@proceedings{schlr2020financial, author = {Schlör, Daniel and Ring, Markus and Krause, Anna and Hotho, Andreas}, keywords = {myown}, number = 5, title = {Financial Fraud Detection with Improved Neural Arithmetic Logic Units}, volume = {Fifth Workshop on MIning DAta for financial applicationS}, year = 2020 }

2019[ to top ]

Ring, M., Schlör, D., Landes, D., Hotho, A. (2019) 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.
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@article{journals/compsec/RingSLH19, author = {Ring, Markus and Schlör, Daniel and Landes, Dieter and Hotho, Andreas}, journal = {Comput. Secur.}, keywords = {traffic}, pages = {156-172}, title = {Flow-based network traffic generation using Generative Adversarial Networks.}, volume = 82, year = 2019 }

2018[ to top ]

Zehe, A., Schlör, D., Henny-Krahmer, U., Becker, M., Hotho, A. (2018) A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels, in DH, ADHO.
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@inproceedings{zehe2018whitebox, author = {Zehe, Albin and Schlör, Daniel and Henny-Krahmer, Ulrike and Becker, Martin and Hotho, Andreas}, booktitle = {DH}, keywords = {nationality}, organization = {ADHO}, title = {A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels}, year = 2018 }
Schöch, C., Schlör, D., Zehe, A., Gebhard, H., Becker, M., Hotho, A. (2018) Burrows’ Zeta: Exploring and Evaluating Variants and Parameters, in DH, 274–277, available: http://dblp.uni-trier.de/db/conf/dihu/dh2018.html#SchochSZG0H18.
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@inproceedings{schoech2018zeta, author = {Schöch, Christof and Schlör, Daniel and Zehe, Albin and Gebhard, Henning and Becker, Martin and Hotho, Andreas}, booktitle = {DH}, keywords = {zeta}, pages = {274-277}, title = {Burrows’ Zeta: Exploring and Evaluating Variants and Parameters}, year = 2018 }

2017[ to top ]

Tello, J.C., Schlör, D., Henny-Krahmer, U., Schöch, C. (2017) 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.
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@inproceedings{conf/dihu/TelloSHS17, author = {Tello, José Calvo and Schlör, Daniel and Henny-Krahmer, Ulrike and Schöch, Christof}, booktitle = {DH}, crossref = {conf/dihu/2017}, editor = {Lewis, Rhian and Raynor, Cecily and Forest, Dominic and Sinatra, Michael and Sinclair, Stéfan}, keywords = {stylometry}, publisher = {Alliance of Digital Humanities Organizations (ADHO)}, title = {Neutralising the Authorial Signal in Delta by Penalization: Stylometric Clustering of Genre in Spanish Novels.}, year = 2017 }

2016[ to top ]

Sch{\"o}ch, C., Schl{\"o}r, D., Popp, S., Brunner, A., Henny, U., Tello, J.C. (2016) Straight Talk! Automatic Recognition of Direct Speech in Nineteenth-Century French Novels., in DH, 346–353.
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@inproceedings{schoch2016straight, author = {Sch{\"o}ch, Christof and Schl{\"o}r, Daniel and Popp, Stefanie and Brunner, Annelen and Henny, Ulrike and Tello, Jos{\'e} Calvo}, booktitle = {DH}, keywords = {myown}, pages = {346–353}, title = {Straight Talk! Automatic Recognition of Direct Speech in Nineteenth-Century French Novels.}, year = 2016 }
Niebler, T., Schlör, D., Becker, M., Hotho, A. (2016) Extracting Semantics from Unconstrained Navigation on Wikipedia, KI -- Künstliche Intelligenz, 30(2), 163–168.
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Semantic relatedness between words has been successfully extracted from navigation on Wikipedia pages. However, the navigational data used in the corresponding works are sparse and expected to be biased since they have been collected in the context of games. In this paper, we raise this limitation and explore if semantic relatedness can also be extracted from unconstrained navigation. To this end, we first highlight structural differences between unconstrained navigation and game data. Then, we adapt a state of the art approach to extract semantic relatedness on Wikipedia paths. We apply this approach to transitions derived from two unconstrained navigation datasets as well as transitions from WikiGame and compare the results based on two common gold standards. We confirm expected structural differences when comparing unconstrained navigation with the paths collected by WikiGame. In line with this result, the mentioned state of the art approach for semantic extraction on navigation data does not yield good results for unconstrained navigation. Yet, we are able to derive a relatedness measure that performs well on both unconstrained navigation data as well as game data. Overall, we show that unconstrained navigation data on Wikipedia is suited for extracting semantics.

@article{niebler2016extracting, abstract = {Semantic relatedness between words has been successfully extracted from navigation on Wikipedia pages. However, the navigational data used in the corresponding works are sparse and expected to be biased since they have been collected in the context of games. In this paper, we raise this limitation and explore if semantic relatedness can also be extracted from unconstrained navigation. To this end, we first highlight structural differences between unconstrained navigation and game data. Then, we adapt a state of the art approach to extract semantic relatedness on Wikipedia paths. We apply this approach to transitions derived from two unconstrained navigation datasets as well as transitions from WikiGame and compare the results based on two common gold standards. We confirm expected structural differences when comparing unconstrained navigation with the paths collected by WikiGame. In line with this result, the mentioned state of the art approach for semantic extraction on navigation data does not yield good results for unconstrained navigation. Yet, we are able to derive a relatedness measure that performs well on both unconstrained navigation data as well as game data. Overall, we show that unconstrained navigation data on Wikipedia is suited for extracting semantics.}, author = {Niebler, Thomas and Schlör, Daniel and Becker, Martin and Hotho, Andreas}, journal = {KI – Künstliche Intelligenz}, keywords = {unconstrained}, number = 2, pages = {163–168}, publisher = {Springer}, title = {Extracting Semantics from Unconstrained Navigation on Wikipedia}, volume = 30, year = 2016 }

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