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Publikation A flexible, extendable and adaptable model to support AI coaching(Springer, 2023) Duhan, Ritu; Pande, Charuta; Martin, Andreas; Hinkelmann, Knut; López-Pellicer, Francisco J.; Polini, AndreaWe present a model based on coaching definitions, concepts, and theories to support AI coaching. The model represents the evidence-based coaching practice in different coaching domains by identifying the common elements in the coaching process. We then map the elements of the coaching model with Conversational AI design and development strategies to highlight how an AI coach can be instantiated from the model. We showcase the instantiation through an example use case of an HIV coaching chatbot.04B - Beitrag KonferenzschriftPublikation Evaluation of synthetic data generators on complex tabular data(Springer, 2024) Thees, Oscar; Novak, Jiri; Templ, Matthias; Domingo-Ferrer, Josep; Önen, MelekSynthetic data generators are widely utilized to produce synthetic data, serving as a complement or replacement for real data. However, the utility of data is often limited by its complexity. The aim of this paper is to show their performance using a complex data set that includes cluster structures and complex relationships. We compare different synthesizers such as synthpop, Synthetic Data Vault, simPop, Mostly AI, Gretel, Realtabformer, and arf, taking into account their different methodologies with (mostly) default settings, on two properties: syntactical accuracy and statistical accuracy. As a complex and popular data set, we used the European Statistics on Income and Living Conditions data set. Almost all synthesizers resulted in low data utility and low syntactical accuracy. The results indicated that for such complex data, simPop, a computational and methodological framework for simulating complex data based on conditional modeling, emerged as the most effective approach for static tabular data and is superior compared to other conditional or joint modelling approaches.04B - Beitrag KonferenzschriftPublikation Challenges, opportunities and application fields of quantum computing - an introductory overview(Association Information et Management, 2022) Kech, Benjamin; Schneider, Bettina; Gachnang, Phillip; Azan, WilfridThis paper elaborates on the technology of quantum computing. It is aimed at people new to this field and introduces characteristics of quantum computing along with comparisons to classical computing. Furthermore, the paper describes the challenges and opportunities of quantum computing. In addition, applications have been explored where quantum computing could create value for businesses. The findings include three categories. First, quantum computing might make a difference in simulating nature, which was also the initial idea that led to the invention of quantum computing. Second, quantum computing could benefit the category of machine learning. Last, optimization problems will take advantage of quantum computing. It is concluded that quantum computing is still in its early stages and there are many challenges to overcome ‚ in particular the challenge of error correction. To gain in the foreseeable future from the advantages that quantum computing pledges, more advances in research have to be made to have a fault-tolerant system. A fault-tolerant quantum computer is a promising technology that could create significant value for various branches, such as finance.04B - Beitrag Konferenzschrift