IRF: Institutional Repository FHNW

Willkommen auf der Publikations- und Forschungsdatenbank der Fachhochschule Nordwestschweiz FHNW.

Das IRF ist das digitale Repositorium der FHNW. Es enthält Publikationen, studentische Arbeiten und Projekte.

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Publikation
KI als Medium und ›message‹ und die (Un-)Möglichkeit einer queeren Antwort
(Transcript, 2022) Bruder, Johannes; Michael Klipphahn-Karge; Ann-Kathrin Koster; Sara Morais dos Santos Bruss [in: Queere KI. Zum Coming-out smarter Maschinen]
Johannes Bruder untersucht in seinem Beitrag die konstitutiven Ein- und Ausschlüsse von autistischer Subjektivität und Kognition im Kontext von künstlicher Intelligenz. Während autistische Kognition in Fantasien von zukünftiger KI als konstitutives Anderes fungiert, waren und sind autistische Individuen essenzieller Bestandteil der kognitiven Infrastruktur von real existierender KI - ob als Testobjekte, Coder, oder Data Worker. Diese Dynamiken von Ein- und Ausschluss sind nicht neu, sondern gesellschaftlich fest verankert; autistische Aktivist*innen haben dementsprechend Strategien entworfen, sich selektiven Ein- und Ausschlüssen performativ zu entziehen. Im Text versucht Johannes Bruder diese Strategien für eine Antwort auf die Medientheorien zeitgenössischer AI fruchtbar zu machen.
04A - Beitrag Sammelband
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Publikation
How Swiss start-ups deal with business model innovation
(Academic conferences international limited, 2022) Philippi, Stefan; Hinz, Andreas; Kabous, Laila; Sklias, Pantelis; Apostolopoulos, Nikolaos [in: Proceedings of the 17th European conference on innovation and entrepreneurship]
The term business model innovation refers to the introduction of innovations that differ from state-of-the-art business models in the same field. Current research indicates that business model innovations are more resilient (e.g. to imitation) overall and more successful in the long term compared to traditional types of innovation (e.g. product innovations). Working on business model innovation, therefore, can provide valuable insights, particularly for start-ups looking to grow and scale up under conditions of extreme uncertainty. Business model innovation involves the innovation of two of four core elements of a business model: customer, value proposition, value chain and revenue mechanism. A business model can be described using these four elements in a sophisticated and comprehensive manner. Moreover, these elements help us to determine whether a business model innovation exists. However, do start-ups really use the advantages of business model innovation and to what extent? This research paper addresses this issue and examines the role business model innovation plays for start-ups as well as how it has been implemented. To gain these insights, we examine the business plans of 24 finalists of a Swiss innovation competition in 2021 in a multi-stage process. We systematically reviewed and analysed business plans individually using pre-defined innovation criteria for each of these four elements of a business model. The individual analysis allows a robust assessment to be able to make a comprehensible classification. On reviewing the results, we were surprised by how many of the analysed start-ups are pursuing business model innovations, and that they often innovate more than two elements of their business models. According to our findings, start-ups nowadays deal with business model innovation more often than they did in previous research studies. We can also show that business model innovations are often more complex than they were in the past.
04B - Beitrag Konferenzschrift
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Publikation
Graph-based keyword spotting in historical documents using context-aware Hausdorff edit distance
(IEEE, 2018) Stauffer, Michael; Fischer, Andreas; Riesen, Kaspar [in: 13th IAPR International Workshop on Document Analysis Systems. DAS 2018. Proceedings]
Scanned handwritten historical documents are often not well accessible due to the limited feasibility of automatic full transcriptions. Thus, Keyword Spotting (KWS) has been proposed as an alternative to retrieve arbitrary query words from this kind of documents. In the present paper, word images are represented by means of graphs. That is, a graph is used to represent the inherent topological characteristics of handwriting. The actual keyword spotting is then based on matching a query graph with all document graphs. In particular, we make use of a fast graph matching algorithm that considers the contextual substructure of nodes. The motivation for this inclusion of node context is to increase the overall KWS accuracy. In an experimental evaluation on four historical documents, we show that the proposed procedure clearly outperforms diverse other template-based reference systems. Moreover, our novel framework keeps up or even outperforms many state-of-the-art learning-based KWS approaches.
04B - Beitrag Konferenzschrift