Information systems can be considered linguistic artifacts (Stamper 1987, Ortner 1993, Frank 2021). They are constituted through software languages and can only be used if they represent concepts prospective users are familiar with. As a result, the integration of information systems can be considered a semantic issue, too (Frank 2008): Different information systems may utilize various domain concepts in different formats, but still must be enabled to effectively and efficiently communicate with each other.

Integration continues to be an issue for many corporations across various domains and industries, caused, among other reasons, by an increasing number of heterogeneous vendors each of which uses its own domain language. Resulting systems communication issues are addressed by various means, e.g, by boling down all concepts to a “global schema” which the concepts used in another information system must be mapped to. Existing solutions are, however, faced with various insufficiencies and may lead to conceptual redundancy, error-prone semantic reconstruction efforts, and miscommunication between systems. These insufficiencies threathen the integrity of information systems and, with that, their effective and efficient use in organizations.

Existing technical landscapes are often based on so-called two-level software languages, such as Java, C#, Python, UML, or the ERM language (cf. Kühne 2007, Atkinson and Kühne 2008). Two-level languages provide developers with control over two levels of abstraction: a type level and an instance level. In object-oriented development, this corresponds to classes and objects. The dominant two-level development style prohibits the use of further abstraction levels to facilitate the communication between information systems: all communication is restricted to a type and an instance level.

This restriction is alleviated in multi-level software languages, which, among others, allow for the definition of an unbounded number of classification levels. Multi-level software languages have been motivated by limitations of two-level languages in various application scenarios, among the issues of integration with two-level languages outlined above (Frank 2022). However, apart from theoretical discussions about potential prospects of using multi-level languages for the integration of information systems, no detailed conception of how to apply multi-level languages for integration has yet been elaborated. As part of this thesis, you are asked to investigate in detail when and how multi-level languages may aid integration issues, what obstacles arise, and how they might be counteracted. 

The thesis is part of an ongoing research project with Oracle. As part of thesis, students may be granted an internship at Oracle, providing access to Oracle’s huge data sources which may be used to conduct experiments. Proficiency in English is a prequisite for this. 

Application Deadline: Application process will be closed as soon as a suited candidate is found. You can submit your application by sending a short statement of motivation, your current transcript of records, and your CV to pierre.maier (at) uni-due.de AND Sekretariat.IIS (at) icb.uni-due.de. 

• Atkinson C, Kühne T (2008) Reducing Accidental Complexity in Domain Models. Software and Systems Modeling 7:345–359
• Frank U (2008) Integration: Reflections on a Pivotal Concept for Designing and Evaluating Information Systems. Information Systems and e-Business Technologies: 2nd International United Information Systems Conference, UNISCON 2008, Klagenfurt, Austria, April 22-25, 2008, Proceedings, pp 111–122
• Frank U (2021) Language, Change, and Possible Worlds: Philosophical Considerations of the Digital Transformation. In: Siegetsleitner A, Oberprantacher A, Frick M-L, Metschl U (eds). Crisis and Critique: Philosophical Analysis of Current Events, Proceedings of the 42nd International Wittgenstein Symposium. De Gruyter: Berlin, Boston, MA, pp 117–138
• Frank U (2022) Multi-Level Modeling: Cornerstones of a Rationale. Software and Systems Modeling 21:451–480
• Frank U, Töpel D (2020) Contingent Level Classes: Motivation, Conceptualization, Modeling Guidelines, and Implications for Model Management. MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
• Kühne T, Schreiber D (2007) Can Programming be Liberated from the Two-Level Style? Multi-Level Programming with DeepJava. OOPSLA '07: Companion to the 22nd ACM SIGPLAN Conference on Object-oriented Programming Systems and Applications Companion, pp 229–244
• Ortner E (1993) Software-Engineering als Sprachkritik: Die Sprachkritische Methode des Fachlichen Software-Entwurfs. Universitätsverlag Konstanz: Konstanz
• Stamper R (1987) Semantics. In: Boland RJ, Hirschheim R (eds). Critical Issues in Information Systems Research. John Wiley & Sons: Chichester, pp 43–78

Information systems can be considered linguistic artifacts (Stamper 1987, Ortner 1993, Frank 2021). They are constituted through software languages and can only be used if they represent concepts prospective users are familiar with. As a result, the integration of information systems can be considered a semantic issue, too (Frank 2008): Different information systems may utilize various domain concepts in different formats, but still must be enabled to effectively and efficiently communicate with each other.

Integration continues to be an issue for many corporations across various domains and industries, caused, among other reasons, by an increasing number of heterogeneous vendors each of which uses its own domain language. Resulting systems communication issues are addressed by various means, e.g, by boling down all concepts to a “global schema” which the concepts used in another information system must be mapped to. Existing solutions are, however, faced with various insufficiencies and may lead to conceptual redundancy, error-prone semantic reconstruction efforts, and miscommunication between systems. These insufficiencies threathen the integrity of information systems and, with that, their effective and efficient use in organizations.

Existing technical landscapes are often based on so-called two-level software languages, such as Java, C#, Python, UML, or the ERM language (cf. Kühne 2007, Atkinson and Kühne 2008). Two-level languages provide developers with control over two levels of abstraction: a type level and an instance level. In object-oriented development, this corresponds to classes and objects. The dominant two-level development style prohibits the use of further abstraction levels to facilitate the communication between information systems: all communication is restricted to a type and an instance level.

This restriction is alleviated in multi-level software languages, which, among others, allow for the definition of an unbounded number of classification levels. Multi-level software languages have been motivated by limitations of two-level languages in various application scenarios, among the issues of integration with two-level languages outlined above (Frank 2022). However, apart from theoretical discussions about potential prospects of using multi-level languages for the integration of information systems, no detailed conception of how to apply multi-level languages for integration has yet been elaborated. As part of this thesis, you are asked to investigate in detail when and how multi-level languages may aid integration issues, what obstacles arise, and how they might be counteracted. 

The thesis is part of an ongoing research project with Oracle. As part of thesis, students may be granted an internship at Oracle, providing access to Oracle’s huge data sources which may be used to conduct experiments. Proficiency in English is a prequisite for this. 

Application Deadline: Application process will be closed as soon as a suited candidate is found. You can submit your application by sending a short statement of motivation, your current transcript of records, and your CV to pierre.maier (at) uni-due.de AND Sekretariat.IIS (at) icb.uni-due.de. 

• Atkinson C, Kühne T (2008) Reducing Accidental Complexity in Domain Models. Software and Systems Modeling 7:345–359
• Frank U (2008) Integration: Reflections on a Pivotal Concept for Designing and Evaluating Information Systems. Information Systems and e-Business Technologies: 2nd International United Information Systems Conference, UNISCON 2008, Klagenfurt, Austria, April 22-25, 2008, Proceedings, pp 111–122
• Frank U (2021) Language, Change, and Possible Worlds: Philosophical Considerations of the Digital Transformation. In: Siegetsleitner A, Oberprantacher A, Frick M-L, Metschl U (eds). Crisis and Critique: Philosophical Analysis of Current Events, Proceedings of the 42nd International Wittgenstein Symposium. De Gruyter: Berlin, Boston, MA, pp 117–138
• Frank U (2022) Multi-Level Modeling: Cornerstones of a Rationale. Software and Systems Modeling 21:451–480
• Frank U, Töpel D (2020) Contingent Level Classes: Motivation, Conceptualization, Modeling Guidelines, and Implications for Model Management. MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
• Kühne T, Schreiber D (2007) Can Programming be Liberated from the Two-Level Style? Multi-Level Programming with DeepJava. OOPSLA '07: Companion to the 22nd ACM SIGPLAN Conference on Object-oriented Programming Systems and Applications Companion, pp 229–244
• Ortner E (1993) Software-Engineering als Sprachkritik: Die Sprachkritische Methode des Fachlichen Software-Entwurfs. Universitätsverlag Konstanz: Konstanz
• Stamper R (1987) Semantics. In: Boland RJ, Hirschheim R (eds). Critical Issues in Information Systems Research. John Wiley & Sons: Chichester, pp 43–78

Smart camera-based monitoring systems are capable of tracking and monitoring human activities. In contrast to other monitoring systems, cameras have the advantage of being low-cost and unobtrusive. These features make them particularly suitable for applications such as: 

• remote surveillance with an automated alert system for emergency detection in elderly care settings,
• contactless monitoring of vital signs such as heart rate and body temperature, in telemedicine,
• or the assessment of fatigue levels by analysing eye activity to help prevent driving accidents.

Despite the technological capabilities and benefits of these systems, their use in the real world is currently limited. Therefore, the aim of this thesis is to identify the possibilities and limitations of smart camera-based human monitoring systems through a systematic literature review. Then, based on these insights, the most promising approaches will be identified and selected for implementation. 

For more information: sitm.ris.uni-due.de/news/news/master-thesis-on-mapping-and-understanding-stakeholders-in-public-blockchains-24948/

For more information: sitm.ris.uni-due.de/news/news/master-thesis-on-governing-the-decentralized-a-comparative-case-study-of-the-governance-in-tezos-arbitrum-optimism-starknet-polkadot-ethereum-and-bitcoin-24949/

Similar to other predictive tasks, the field of treatment effect prediction (TEP), which attempts to not just predict a singular outcome, but the difference between two or more different counterfactual outcomes, can also benefit from improved performance through deep learning (DL) models. The downside to this manifests in the reduced interpretability of DL models, which can impede the usability of DL-based TEP in high-stakes decision-making contexts like medicine, that require human users to understand the tools they use and be able to detect whether a prediction is based on sound reasoning and thus trustworthy. Although researchers have developed a range of Explainable Artificial Intelligence (XAI) methods, these are subject to various concerns about model faithfulness and their actual usefulness to end users. We intend to specifically address the use case of TEP for the prognosis of diabetes treatment, and explore how visualizations of treatment effects found in the available literature can support user understanding. 

In a previous project, we curated a dataset of visualizations used to represent predictions of treatment effects. In this thesis project, the student will conduct a series of expert interviews with diabetologists and discuss the curated visualizations concerning their helpfulness and accessibility. The interviews must be recorded, transcribed, and analyzed (e.g., via tools like MAXQDA).

While researchers have already proposed a range of methods to explain the behavior of systems using Artificial Intelligence (AI), these methods have posed their own challenges. Questions about the faithfulness and robustness of their outputs have emerged, as well as concerns about these methods being almost as opaque to end users as the original AI system. In response to these challenges, we have approached domain-specific visualizations as explanatory components of medical decision support systems. We intend to research whether more straightforward means of explanation can better fulfill the needs of medical professionals and thus support the adoption of AI in clinical practice.

To develop an improved understanding of the role of visualizations in medical decision support systems, the student conducts a series of interviews with experts on medical decision support systems during the thesis project. The interviews must be recorded, transcribed, and analyzed (e.g., via tools like MAXQDA).

Health tracking with smartwatches or fitness trackers for personalized health management and self-optimization has become increasingly popular. Today, around 260.7 million users track their steps, heart rate, stress levels and other parameters on a daily basis (Statista Health Market Insights, 2024). However, many of these self-tracking solutions rely on invasive devices that require direct skin contact and are often high in cost. A promising alternative is remote health tracking via camera, which could open up new possibilities. For example, a health tracker integrated into the computer camera could be synchronized with a digital calendar, allowing meetings to be scheduled and rescheduled based on the current stress level.

AI-based remote photoplethysmography algorithms are an innovative approach that enables contactless health monitoring using standard, low-cost cameras. A critical step in this process is the identification of specific areas of the face, known as region of interest (ROI), such as the forehead or cheeks. Stable tracking of the ROI is essential for extracting accurate and reliable heart rate signals. However, influencing factors, such as different lighting conditions, head movements, camera angle and position, make it difficult to obtain reliable measurements in real-world conditions. 

The aim of this thesis is to investigate and compare open source methods for real-time face and ROI recognition. First, common frameworks will be identified through a systematic literature review, and then a prototype will be implemented to evaluate them under selected influencing factors.

Statista Health Market Insights (2024). Statista Health Market Insights. de.statista.com/statistik/daten/studie/1460774/umfrage/nutzer-von-fitnesstrackern-weltweit/. Retrieved 08.05.2025

Among the high-stakes decision-making contexts that use artificial intelligence (AI), finance is one of the fields that sticks out. However, applications such as fraud detection, credit scoring, and stock price forecasting still require insight into the black box of modern deep learning models. Even if poor decisions in finance, for instance, due to bias or poor data quality, do not directly harm people, they can negatively impact human well-being. Hence, AI explanations to foster trust and improve decision making are required. We intend to research explainable AI (XAI) in finance, which involves an analysis of use cases, methods, and previous experimental evaluations. 

To develop a better understanding of XAI in finance, the Bachelor student carries out a systematic literature review (SLR). This SLR is followed by a series of expert interviews to assess the current state of AI and XAI usage in the financial industry. The interviews must be recorded, transcribed, and analyzed (e.g., via tools like MAXQDA).

The literature on artificial intelligence (AI) explanations comprises two primary explanation methods: attribution-based and counterfactual-based. Through the differences in these approaches, two criteria for good explanations are optimized: necessity and sufficiency. Methods looking for counterfactual explanations elicit necessary features, while methods that look at feature attribution focus on sufficient feature values. Mothilal et al. (2021) propose a framework unifying both methods to evaluate the different approaches with respect to those two criteria for good explanations. Research into metrics for evaluating explanations is relevant because, unlike most prediction and classification tasks, there is no ground truth to evaluate the correctness or quality of explanations. Mothilal et al. (2021) rely on three datasets from the credit-scoring domain and a case study on hospital admission to test their framework. We intend to build on this study and examine, whether the results presented by Mothilal et al. (2021) transfer to different datasets and explanation techniques. 

This Master thesis project builds on previous work by reviewing novel methods for attribution-based and counterfactual-based explanations from the literature, applying these to a new selection of datasets from the medical domain, and evaluating whether more recent approaches to AI explainability better fulfill the criteria of necessity and sufficiency. 

Reference:

• Mothilal, R.K., Mahajan, D., Tan, C., & Sharma, A. (2021, July). Towards unifying feature attribution and counterfactual explanations: Different means to the same end. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 652-663).

Artificial neural networks (ANNs) denote a popular class of models used within machine learning. An ANN typically consists of multiple layers of simple processing units, so-called artificial neurons. Most current ANNs involve multiple layers of these processing units, hence the term deep learning is sometimes applied to describe them. Historically, they emerged from a neurophysiological inspiration to express the processing of mammal neurons in mathematical terms (cf. McCulloch and Pitts 1943). There exists a plethora of different approaches to the design of ANNs, some variations include the number of artificial neurons in a layer, the activation function applied, or the connection of artificial neurons between layers. From these variations have emerged several classes of ANN architectures, such as Multi-Layered Perceptrons (MLPs), Generative Adversial Networks (GANs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or more recently Transformers. It is conspicuous that many papers, which discuss a particular ANN architecture, represent them in some diagrammatic form. This diagrammatic representation, however, does not follow any unified structure. This results in two challenges: First, ANNs are not visually comparable through an analysis of their diagrammatic representations. Second, the depicted diagrams of ANNs might lack relevant information, overseen by the original researchers. In short: It appears that the depiction of ANNs lack a conceptual modeling language.

The present thesis should adress this gap. Therefore, it is relevant to expound on the foundations and variations of ANNs as well as to explore the fundamentals of conceptual modeling languages. Based on an analysis of the design, evaluation, and application of ANNs, requirements for a corresponding modeling method should be derived. Thereupon, these insights should be used to specify a conceptual modeling method for ANNs.

Literature:

• Aggarwal CC (2018) Neural Networks and Deep Learning: A Textbook. Springer International Publishing: Cham
• Du K-L, Swamy MNS (2014) Neural Networks and Statistical Learning. Springer-Verlag: London
• Frank U (2013) Domain-Specific Modeling Languages – Requirements Analysis and Design Guidelines. In: Reinhartz-Berger I, Sturm A, Clark T, Wand Y, Cohen S, Bettin J (eds.) Domain Engineering: Product Lines, Conceptual Models, and Languages. Springer: Cham, pp. 133-157
• Kelleher JD (2019) Deep Learning. The MIT Press: Cambridge, MA, London
• McCulloch WS, Pitts W (1943) A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5:115-133

If language shapes our reality, changing our language might lead to a different, potentially preferable reality. This thought is echoed throughout a variety of philosophical schools and can, in different variations, with different assumptions, and with different implications, be found in the writings of Ludwig Wittgenstein, Richard Rorty, Friedrich Nietzsche, Immanuel Kant, or Humberto Maturana. Recently, the discussion has received more widespread attention. Motivated in part from feminist philosophy of the 1990s, philosophers have combined their research efforts towards the improvement of language under the moniker of conceptual engineering and conceptual ethics. The amelioration of concepts and language is faced with several theoretical and practical challenges. What makes a concept “better” than another? How could a new concept be adopted by respective language users?

Information systems development is essentially concerned with language development (clarification and sources per request). Broadly, this poses the question if information systems can support conceptual engineering and, if so, in what regards. Machine learning (ML) might be a fruitful first step to guide this analysis. Contemporary ML approaches are inductive (cf. Rescher 1980): they generate potentially novel generalizations based on a set of observations. Researchers like Rees (2022) therefore suggest that they might guide the development of novel concepts.

This master’s thesis should explore the capabilities of ML to support conceptual engineering. You should identify potential tasks of conceptual engineering and what requirements they face. Then you should investigate how different ML approaches (we can disucss which in our first meetings) can serve to address these requirements.

Literature:

• Burgess A, Cappelen H, Plunkett D (eds) (2020) Conceptual Engineering and Conceptual Ethics. Oxford University Press: Oxford
• Butlin P (2021) Sharing Our Concepts with Machines. Erkenntnis
• Cappelen H, Dever J (2019) Bad Language. Oxford University Press: Oxford
• Haslanger S (2012) Resisting Reality: Social Construction and Social Critique. Oxford University Press: Oxford
• Medin DL, Smith EE (1984) Concepts and Concept Formation. Annual Review of Psychology 35(35):113–138
• Montemayor C (2021) Language and Intelligence. Minds and Machines 31:471–486
• Ontañón S, Dellunde P, Godo L, Plaza E (2012) A Defeasible Reasoning Model of Inductive Concept Learning from Examples and Communication. Artificial Intelligence 193:129–148
• Rees T (2022) Non-Human Words: On GPT-3 as a Philosophical Library. Daedalus 151(2):168–182
• Rescher N (1980) Induction: An Essay on the Justification of Inductive Reasoning. Basil Blackwell: Oxford

Unternehmen setzen zunehmend auf digitale, plattformbasierte Geschäftsmodelle, die Werttransaktionen zwischen komplementären Akteuren, etwa Produzenten und Konsumenten, koordinieren. Diese Verlagerung der Wertschöpfungskette nach außen erfordert eine gezielte Orchestrierung der Anbieter, um das Leistungsversprechen des Ökosystems zu verwirklichen.

Da Komplementäre meist nicht exklusiv an eine einzelne Plattform gebunden sind, müssen Plattformbetreiber strategische Verbindungs- und Koordinierungsmechanismen einsetzen, um das Ökosystem zu gestalten und zu steuern – ohne auf direkte Befehls- und Kontrollmaßnahmen zurückzugreifen. Hierzu implementieren Plattformbetreiber Governance-Mechanismen, die die Ressourcen der Komplementäre gezielt einbinden. Eine unzureichende Governance kann dagegen strukturelle Lücken im Ökosystem verursachen, wodurch das Wertschöpfungspotenzial sinkt und Netzwerkeffekte beeinträchtigt werden könnten.

Trotz der hohen Relevanz der Plattform-Governance ist der wissenschaftliche Diskurs in diesem Bereich noch begrenzt. Die vorliegende Arbeit adressiert diese Forschungslücke, indem sie den aktuellen Forschungsstand durch eine systematische Literaturrecherche aufarbeitet und darauf aufbauend eine Forschungsagenda ableitet.

Forschungsfragen:

• RQ1: Was sind die zentralen Themen der IS-Forschung im Kontext von Plattformen bzw. Ökosystemen und Governance?
• RQ2: Welche Aspekte der Plattform Governance sollten künftig erforscht werden?

Methodisches Vorgehen: Systematische Literaturrecherche in führenden Fachzeitschriften (Basket of Eight) und auf Konferenzen (ICIS, ECIS, PACIS, AMCIS, HICSS).

Zielsetzung: Die Generativität von Plattformen wird maßgeblich durch Governance-Mechanismen beeinflusst. Um diese Zusammenhänge zu verstehen, soll die Arbeit den aktuellen Forschungsstand systematisch aufarbeiten. Dabei sollen die verschiedenen Forschungsströme der inter- und intraorganisationalen Governance erfasst und im wissenschaftlichen Diskurs eingeordnet werden.

Auf Basis der analysierten Literatur soll ein Governance-Framework entwickelt werden, das zentrale Konzepte sowie deren Relationen darstellt. Dabei soll insbesondere berücksichtigt werden, welche Governance-Mechanismen in unterschiedlichen Domänen und Plattformtypen erforscht wurden. Die Ergebnisse dieser Analyse sollen als Grundlage für die Ableitung einer Forschungsagenda dienen.

Startliteratur:

• Hein, Andreas (2020): Digital Platform Ecosystems: Emergence and Value Co-Creation Mechanisms. Technischen Universität München, München.
• Jacobides, Michael G.; Cennamo, Carmelo; Gawer, Annabelle (2018): Towards a theory of ecosystems. In Strat. Mgmt. J. 39 (8), pp. 2255–2276. DOI: 10.1002/smj.2904.
• Tiwana, Amrit (2014): Platform Ecosystems: Elsevier, Kapitel 6
• Hein, Andreas; Schreieck, Maximilian; Wiesche, Manuel; Krcmar, Helmut (2016): Multiple-Case Analysis on Governance Mechanisms of Multi-Sided Platforms. In : Multikonferenz Wirtschaftsinformatik. Ilmenau, Germany.
• Staub, Nicola; Haki, Kazem; Aier, Stephan; Winter, Robert (2022): Governance Mechanisms in Digital Platform Ecosystems: Addressing the Generativity-Control Tension. In Communications of the Association for Information Systems 51 (1), pp. 906–939. DOI: 10.17705/1CAIS.05137.
• Bandara, Wasana; Furtmueller, Elfi; Gorbacheva, Elena; Miskon, Suraya; Beekhuyzen, Jenine (2015): Achieving Rigor in Literature Reviews: Insights from Qualitative Data Analysis and Tool-Support. In CAIS 37. DOI: 10.17705/1CAIS.03708.
• vom Brocke, Jan; Simons, Alexander; Niehaves, Björn; Riemer, Kai; Plattfaut, Ralf; Cleven, Anne (2009): Reconstructing the giant: On the importance of rigour in documenting the literature search process. In : 17th European Conference on Information Systems (ECIS 2009). Verona, Italy, pp. 2206–2217. Available online at aisel.aisnet.org/ecis2009/161.
• Webster, Jane; Watson, Richard T. (2002): Analyzing the Past to Prepare for the Future: Writing a Literature Review. In MIS Quarterly 26 (2), pp. xiii–xxiii.

If language shapes our reality, changing our language might lead to a different, potentially preferable reality. This thought is echoed throughout a variety of philosophical schools and can, in different variations, with different assumptions, and with different implications, be found in the writings of Ludwig Wittgenstein, Richard Rorty, Friedrich Nietzsche, Immanuel Kant, or Humberto Maturana. Recently, the discussion has received more widespread attention. Motivated in part from feminist philosophy of the 1990s, philosophers have combined their research efforts towards the improvement of language under the moniker of conceptual engineering and conceptual ethics. The amelioration of concepts and language is faced with several theoretical and practical challenges. What makes a concept better than another? How could a new concept be adopted by respective language users?

Information systems development is essentially concerned with language development (clarification and sources per request). Broadly, this poses the question if information systems can support conceptual engineering and, if so, in what regards. Machine learning (ML) might be a fruitful first step to guide this analysis. Contemporary ML approaches are inductive (cf. Rescher 1980): they generate potentially novel generalizations based on a set of observations. Researchers like Rees (2022) therefore suggest that they might guide the development of novel concepts.

This master’s thesis should explore the capabilities of ML to support conceptual engineering. You should identify potential tasks of conceptual engineering and what requirements they face. Then you should investigate how different ML approaches (we can disucss which in our first meetings) can serve to address these requirements.

Literature

• Burgess A, Cappelen H, Plunkett D (eds) (2020) Conceptual Engineering and Conceptual Ethics. Oxford University Press: Oxford
• Butlin P (2021) Sharing Our Concepts with Machines. Erkenntnis
• Cappelen H, Dever J (2019) Bad Language. Oxford University Press: Oxford
• Haslanger S (2012) Resisting Reality: Social Construction and Social Critique. Oxford University Press: Oxford
• Medin DL, Smith EE (1984) Concepts and Concept Formation. Annual Review of Psychology 35(35):113–138
• Montemayor C (2021) Language and Intelligence. Minds and Machines 31:471–486
• Ontañón S, Dellunde P, Godo L, Plaza E (2012) A Defeasible Reasoning Model of Inductive Concept Learning from Examples and Communication. Artificial Intelligence 193:129–148
• Rees T (2022) Non-Human Words: On GPT-3 as a Philosophical Library. Daedalus 151(2):168–182
• Rescher N (1980) Induction: An Essay on the Justification of Inductive Reasoning. Basil Blackwell: Oxford

Artificial neural networks (ANNs) denote a popular class of models used within machine learning. An ANN typically consists of multiple layers of simple processing units, so-called artificial neurons. Most current ANNs involve multiple layers of these processing units, hence the term deep learning is sometimes applied to describe them. Historically, they emerged from a neurophysiological inspiration to express the processing of mammal neurons in mathematical terms (cf. McCulloch and Pitts 1943). There exists a plethora of different approaches to the design of ANNs, some variations include the number of artificial neurons in a layer, the activation function applied, or the connection of artificial neurons between layers. From these variations have emerged several classes of ANN architectures, such as Multi-Layered Perceptrons (MLPs), Generative Adversial Networks (GANs), Convolutional Neural Networks (CNNs), or Recurrent Neural Networks (RNNs). It is conspicuous many papers, which discuss a particular ANN architecture,represent them in some diagrammatic form. This diagrammatic representation, however, does not follow any unified structure. This results in two challenges: First, ANNs are not visually comparable through an analysis of their diagrammatic representations. Second, the depicted diagrams of ANNs might lack relevant information, overseen by the original researchers. In short: It appears that the depiction of ANNs lack a conceptual modeling language.

The present thesis should adress this gap. Therefore, it is relevant to expound on the foundations and variations of ANNs as well as to explore the fundamentals of conceptual modeling languages. Based on an analysis of the design, evaluation, and application of ANNs, requirements for a corresponding modeling method should be derived. Thereupon, these insights should be used to specify a conceptual modeling method for ANNs.

Introductory Literature:

• Aggarwal CC (2018) Neural Networks and Deep Learning: A Textbook. Springer International Publishing: Cham
• Du K-L, Swamy MNS (2014) Neural Networks and Statistical Learning. Springer-Verlag: London
• Frank U (2013) Domain-Specific Modeling Languages – Requirements Analysis and Design Guidelines. In: Reinhartz-Berger I, Sturm A, Clark T, Wand Y, Cohen S, Bettin J (eds.) Domain Engineering: Product Lines, Conceptual Models, and Languages. Springer: Cham, pp. 133-157
• Kelleher JD (2019) Deep Learning. The MIT Press: Cambridge, MA, London
• McCulloch WS, Pitts W (1943) A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5:115-133

Vacant master thesis seeks to investigate and assess how municipal enterprises’ innovation culture influences the effectiveness of digital innovation activity

Against the backdrop of climate change and digitalization, cities all over the world are facing the need for a radical transformation towards “smartness” (Gimpel et al., 2021). To meet the increasing amount of customer expectations that cities are facing, municipal enterprises – such as electricity suppliers or waste management services – are continuously working on modernizing their digital service offerings and business models (Hosseini et al., 2018; Mora et al., 2019). Sometimes those offerings represent the replacement of analog tasks with digital tasks, for example, online appointment scheduling or the application of IoT sensors to enhance processes or estimate waiting times[1]. Such novel digital services are often the result of digital innovation activities (Hjalmarsson & Rudmark, 2012). Those innovation activities may be internally and externally driven, and in light of the smart city context, the complexity of the innovation process is increasing (Hjalmarsson & Rudmark, 2012).

This is among the reasons why digital innovations are increasingly critical to the success of municipal enterprises. Yet, the municipal sector could be characterized as rather non-innovative and reluctant to change (Hawlitschek, 2021). While the need for digital innovation is widely acknowledged, implementing the right measures (e.g., competence building, structural adjustments, new processes, and new forms of collaboration) is still a challenge to municipal enterprises. Further, measuring innovativeness is a challenging task (Hinings et al., 2018; Van Looy, 2021).

All those challenges as well as the rapid environmental developments are creating a very demanding situation for municipal companies, which are often characterized by highly bureaucratic processes, a strict matrix organization, and using static workflow processes that remain unchanged possibly even for decades. To this end, research finds that the innovation culture significantly impacts the degree of organizations’ innovativeness (Dobni, 2008; Dodge et al., 2017). However, less attention has been devoted to grasp the influence of municipal enterprises’ innovation culture on (digital) innovativeness. To address those challenges, municipal enterprises may benefit from a systematic approach to evaluate their innovation culture’s maturity level as well as degree of digital innovativeness and compare their maturity level to similar organizations.

To address this issue, we are looking for an engaged student who will address this topic within the scope of a master thesis. First, the student is expected to conduct a profound literature review and gather relevant findings from academia and practice. Further, those findings are to be extended by conducting interviews with representatives from German municipal enterprises to define and uncover the nature and relationships of municipal enterprises’ innovation culture and digital innovativeness. Subsequently, the student is expected to develop a measurement instrument (i.e., survey) that later allows measuring municipal enterprises’ innovation culture, its maturity level as well as its impact on the effectiveness of digital innovation activity.

References

Dobni, C. B. (2008). Measuring innovation culture in organizations: The development of a generalized innovation culture construct using exploratory factor analysis. European journal of innovation management.

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[1] Example: www.wbd-innovativ.de/projekte/intelligenter-recyclinghof