Hochschule für Wirtschaft FHNW

Dauerhafte URI für den Bereichhttps://irf.fhnw.ch/handle/11654/60

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2023 - 20242

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Anfangsdatum: 2023Thema: search.filters.subject.004 - Computer Wissenschaften, Internet

Bereich: Suchergebnisse

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  • Kein Vorschaubild vorhanden
    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, Andrea
    We 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 Konferenzschrift
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    Publikation
    Evaluation of synthetic data generators on complex tabular data
    (Springer, 2024) Thees, Oscar; Novak, Jiri; Templ, Matthias; Domingo-Ferrer, Josep; Önen, Melek
    Synthetic 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 Konferenzschrift