@article{KochKatharinaEldar2022, author = {Koch, Christian and Katharina, M{\"u}ller and Eldar, Sultanow}, title = {Which programming languages do hackers use?}, publisher = {arXiv}, doi = {10.48550/arXiv.2203.12466}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:92-opus4-9237}, pages = {3 -- 13}, year = {2022}, abstract = {There are numerous articles about the programming languages most commonly used by hackers. Among them, however, there are hardly any scientific studies. One reason might be that hackers mainly operate anonymously and are difficult to reach. This paper aims to shed light on this interesting and relevant research question. In order to find answers, we conducted a survey among the members of the German Chaos Computer Club. As one of the world's largest organisations for information security and hacking, the club provides a good basis for our study. We examine the question of which programming languages are used by hackers as well as the importance of the programming language for their work. The paper offers first insights into the topic and can provide a starting point for further research.}, subject = {Computersicherheit}, language = {en} } @misc{KochRiehleMueller2024, author = {Koch, Christian and Riehle, Dirk and M{\"u}ller, Katharina}, title = {Schema Inference for Tabular Data with Explainable Artificial Intelligence}, doi = {10.34646/thn/ohmdok-1560}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:92-opus4-15601}, pages = {2 -- 6}, year = {2024}, abstract = {Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference for tabular data with explainable AI (XAI). This third version was derived from two earlier review protocols.}, subject = {Explainable Artificial Intelligence}, language = {en} }