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  <doc>
    <id>8040</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>25</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>38</volume>
    <type>article</type>
    <publisherName>IGI Global</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-04-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">From Captive Centers to Global Integration</title>
    <abstract language="eng">Contemporary developments in virtual work and global talent management are transforming information technology (IT) nearshoring and offshoring practices, yet research on organizational responses remains limited. Through expert interviews with 33 IT executives (76% chief information officers) from Germany's top 500 companies, this article examines organizational models, strategies, and good practices in IT nearshoring and offshoring. It reveals a shift from traditional captive centers toward fully integrated global teams, enabling high-value task delivery and reflecting increased workforce maturity. Companies are expanding their operations driven by talent needs rather than cost savings, with success factors including employee integration and robust on-site management. This study contributes to theory and practice by providing a framework for understanding organizational models in global IT delivery and offering actionable insights for executives, emphasizing workforce integration over geographical separation.</abstract>
    <parentTitle language="eng">Information Resources Management Journal</parentTitle>
    <subTitle language="eng">Organizational Models and Good Practices in Information Technology Nearshoring and Offshoring</subTitle>
    <identifier type="doi">10.4018/IRMJ.373322</identifier>
    <identifier type="issn">1533-7979</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-80406</identifier>
    <note>Corresponding author der OTH Regensburg: Markus Westner</note>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="CorrespondingAuthor">Westner, Markus</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Markus Westner</author>
    <author>Matthias Reicheneder</author>
    <author>Markus Matschi</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Offshoring</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Informationstechnik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Internationale Arbeitsteilung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Organisationsstruktur</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>IT-Nearshoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>IT-Offshoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Global IT-Teams</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Organizational Models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Distributed Work</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Corresponding author der OTH Regensburg</collection>
    <collection role="funding" number="">Publikationsfonds der OTH Regensburg</collection>
    <collection role="institutes" number="">Labor Regensburg Strategic IT Management (ReSITM)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/8040/Westner_IRMJ_IGI_2025.pdf</file>
  </doc>
  <doc>
    <id>8054</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>416</pageFirst>
    <pageLast>430</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Singapore</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Privacy Challenges in Genomic Data: A Scoping Review of Risks, Mitigation Strategies, and Research Gaps.</title>
    <abstract language="eng">Advances in genomic research have created new privacy challenges. This scoping review analyzes the risks associated with the processing, storage, and sharing of genomic data including epigenetics, and examines current privacy protection strategies. It also attempts to identify research gaps in this area. Using the PRISMA methodology, 37 relevant studies were identified and analyzed. The results of the risk assessment can be grouped into four main themes: Risks posed by processing of functional genomic data, sharing of genomic data, patient (re-)identification, and dividuality, i.e. the extending of privacy risks to blood relatives. The identified risk mitigation strategies were systematically categorized into five classes: pre-release measures, governance, secure data processing and exchange, access restriction and transparency, anonymization and masking. However, there are some important research gaps that still need to be addressed. The current literature neglects to assess the likelihood of potential breaches and tends to focus only on assessing possible scenarios of privacy risks. It also mainly fails to assess the role of contextualized data and the effectiveness of policies and governance systems with respect to privacy risks.</abstract>
    <parentTitle language="eng">Information and Communication Technology: 13th International Symposium, SOICT 2024, Danang, Vietnam, December 13–15, 2024, Proceedings, Part II</parentTitle>
    <identifier type="doi">10.1007/978-981-96-4285-4_34</identifier>
    <enrichment key="opus_doi_flag">false</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="OtherSeries">Communications in Computer and Information Science ; 2351</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Logan Rush</author>
    <author>Marina Schmid</author>
    <author>Georgios Raptis</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="raptisehealth">Raptis, Georgios (Prof. Dr.) - eHealth Lab</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor eHealth (eH)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>8309</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>9</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Taylor &amp; Francis</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-06-05</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Imagining the future beyond the crystal ball: Artificial Human Services here we come?</title>
    <abstract language="eng">This special issue is focused on the topic of artificial intelligence in the field of human services. In addition to the importance and transformative potential of AI, the editorial presents in detail the areas of application, which range from case management, mental health, client support to administrative support and education. The special issue itself features three articles on AI technology in dementia care, AI in psychosocial support during natural disasters and LLM in mental health. The editorial also highlights important challenges related to data management, ethics, human rights and the need for an inclusive, human-centered design. The recommendations and future directions for further research emphasize the importance of interdisciplinary collaboration, ethical standards and stakeholder engagement, human-centered AI and data management to ensure responsible and effective integration and implementation of AI into human services.</abstract>
    <parentTitle language="eng">Journal of Technology in Human Services</parentTitle>
    <identifier type="doi">10.1080/15228835.2025.2502831</identifier>
    <enrichment key="opus.import.date">2025-06-11T06:10:16+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Michael Garkisch</author>
    <author>Joss Steinke</author>
    <author>Olivier Steiner</author>
    <author>Carolin Thiem</author>
    <author>Anica Zeyen</author>
    <collection role="institutes" number="FakSoz">Fakultät Sozial- und Gesundheitswissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="2">Geistes- und Sozialwissenschaften</collection>
  </doc>
  <doc>
    <id>7980</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>49</volume>
    <type>article</type>
    <publisherName>Slovenian Association Informatika</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Factors Influencing Cloud Computing Adoption in Small and Medium-Sized Enterprises: A Systematic Review</title>
    <abstract language="eng">This paper investigates the factors influencing cloud computing adoption in small and medium-sized enterprises (SMEs) through a systematic literature review. The analysis identified twelve key factors influencing the adoption of cloud computing in SMEs. Based on the Technology-Organisation-Environment (TOE) model and the Technology Acceptance Model (TAM), a conceptual framework was developed for future research. The most important factors are cost, organisational readiness, compatibility, relative advantage and top management support. Other influential factors include security, perceived usefulness, firm size, government support, perceived ease of use, vendor support and competitive pressure. The majority of studies were conducted in Asian countries, including developing countries, limiting the generalisability of the findings to SMEs in more developed economies. This research highlights the need for cloud computing solutions that not only reduce costs and ensure high levels of security and privacy, but are also easy to use and integrate. Further research is recommended to explore these factors within SMEs in more developed economies.</abstract>
    <parentTitle language="eng">Informatica</parentTitle>
    <identifier type="issn">1854-3871</identifier>
    <identifier type="doi">10.31449/inf.v49i1.6971</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Nathalie Holler</author>
    <author>Markus Westner</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cloud computing adoption</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>small and medium-sized enterprises (SMEs)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>technology-organization-environment (TOE) framework</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>systematic literature review</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>digital transformation</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>8158</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>27</pageNumber>
    <edition/>
    <issue>6</issue>
    <volume>55</volume>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-02-03</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mind the naive forecast! a rigorous evaluation of forecasting models for time series with low predictability</title>
    <abstract language="eng">In the field of time series forecasting, numerous machine learning studies have assessed the performance of new methods on highly volatile data from macroeconomics and finance. Unlike in other domains, where models are also compared to simpler statistical or naive baselines, they mostly compare the performance solely relative to other complex models. This approach may lead to limited conclusions and reduce the practical significance of the results, as it overlooks the unpredictability of some highly volatile time series in the datasets used. We apply state-of-the-art methods from time-series econometrics and machine learning, including autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), Bayesian vector autoregressive model (BVAR), long-short term memory neural networks (LSTM), historical consistent neural networks (HCNN), deep vector autoregressive neural networks (DeepVAR), temporal fusion transformers (TFT), and extreme gradient boosting (XGBoost). Our results demonstrate that no method consistently outperforms the naive (no-change) forecast for highly volatile time series from two popular datasets containing exchange rates and stock prices, rendering comparative analysis between complex models less meaningful. In contrast, when applied to more predictable macroeconomic price indices, many of the methods significantly outperform naive forecasts. We find that the performance of machine learning models deteriorates more than that of statistical models for high-volatility time series. This study highlights the critical importance of using appropriate benchmark models, including cost-effective, simple approaches, on datasets that permit meaningful conclusions.</abstract>
    <parentTitle language="eng">Applied Intelligence</parentTitle>
    <identifier type="doi">10.1007/s10489-025-06268-w</identifier>
    <enrichment key="opus.import.date">2025-06-03T21:32:12+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Nico Beck</author>
    <author>Jonas Dovern</author>
    <author>Stefanie Vogl</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="DFGFachsystematik" number="4">Naturwissenschaften</collection>
  </doc>
  <doc>
    <id>7981</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>preprint</type>
    <publisherName>arxiv</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Deepfake Detection of Face Images based on a Convolutional Neural Network</title>
    <abstract language="eng">Fake News and especially deepfakes (generated, non-real image or video content) have become a serious topic over the last years. With the emergence of machine learning algorithms it is now easier than ever before to generate such fake content, even for private persons. This issue of generated fake images is especially critical in the context of politics and public figures. We want to address this conflict by building a model based on a Convolutions Neural Network in order to detect such generated and fake images showing human portraits. As a basis, we use a pre-trained ResNet-50 model due to its effectiveness in terms of classifying images. We then adopted the base model to our task of classifying a single image as authentic/real or fake by adding an fully connected output layer containing a single neuron indicating the authenticity of an image. We applied fine tuning and transfer learning to develop the model and improve its parameters. For the training process we collected the image data set ’Diverse Face Fake Dataset’ containing a wide range of different image manipulation methods and also diversity in terms of faces visible on the images. With our final model we reached the following outstanding performance metrics: precision = 0.98, recall 0.96, F1-Score = 0.97 and an area-under-curve = 0.99.</abstract>
    <identifier type="doi">10.48550/arXiv.2503.11389</identifier>
    <identifier type="arxiv">2503.11389v1</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Lukas Kroiß</author>
    <author>Johannes Reschke</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ResNet-50</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transfer Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Binary Classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>diverse fake face dataset</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Probability Density Function</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>8168</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>263</pageFirst>
    <pageLast>272</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>SciTePress - Science and Technology Publications</publisherName>
    <publisherPlace>Setúbal, Portugal</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-04-06</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analysis and Design of Smart Components in Digital Energy Twins</title>
    <abstract language="eng">The energy crisis, energy demand growth, and dependence on fossil fuels worldwide have made urgent action necessary for us to seek sustainability in energy production and use. Digital technologies, especially Digital Energy Twins, have immense potential to reduce energy consumption, thereby reducing environmental impacts, particularly in the building sector. This paper presents the development of a digital energy twin that supports sustainable energy consumption analysis and optimization. Our study begins with a comprehensive analysis of the energy consumption data, the weather data, and the building plans as a solid basis for the analysis. We identify key energy consumption trends and patterns across different timescales and device-specific details that could be optimized, such as base load consumption and device-specific inefficiencies. A key part of our work is forecasting energy consumption using time series models, such as the ARIMA model, which promises to be useful in identify ing patterns for improving energy efficiency. Overall, our study provides valuable insights into energy optimization and could form the base for further advances in digital energy twins at OTH Regensburg, helping to contribute to its sustainable development goals and smart campus initiatives.</abstract>
    <parentTitle language="eng">Proceedings of the 10th International Conference on Internet of Things, Big Data and Security., Porto, Portugal April 6-8, 2025</parentTitle>
    <identifier type="isbn">978-989-758-750-4</identifier>
    <identifier type="doi">10.5220/0013289900003944</identifier>
    <enrichment key="opus.import.date">2025-06-03T21:49:53+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Katharina Legler</author>
    <author>Muhammad Sheheryar Jajja</author>
    <author>Klaus Volbert</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data Visualization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital Twins</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Internet of Things</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning Models</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>8192</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>819</pageFirst>
    <pageLast>825</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Gesellschaft für Informatik</publisherName>
    <publisherPlace>Bonn</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Showcase of LLMs in Action: SQL Generation from Natural Language (Demo Paper)</title>
    <abstract language="eng">Today, large language models are a very efficient tool for human-computer interaction using natural language. Chatbots like ChatGPT and their corresponding APIs can be used to solve a large variety of tasks that are provided in human-comprehensible sentences, for example generating SQL queries. Executing spoken SQL queries in a database system poses a challenge, because the various syntactical details of SQL are usually not provided verbally. Here, the LLM can help to augment the recognized raw query with the syntax elements needed for successful execution. Furthermore, the correct spelling of table and column names can be derived from the database schema provided in the LLM prompt. This work showcases four use cases in which LLMs assist in querying database systems: (1) A plugin for phpMyAdmin for voice-query input in natural language, (2) a chart generator, (3) an Alexa skill, and (4) a speech-controlled action game SQL Invaders.</abstract>
    <parentTitle language="eng">Datenbanksysteme für Business, Technologie und Web - Workshopband (BTW 2025)</parentTitle>
    <identifier type="doi">10.18420/BTW2025-47</identifier>
    <note>Datenbanksysteme für Business, Technologie und Web - Workshopband (BTW 2025)</note>
    <enrichment key="opus.import.date">2025-06-03T21:49:53+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="ConferenceStatement">Workshop Data Engineering for Data Science (DE4DS). Bamberg. 3.-7. März 2025</enrichment>
    <enrichment key="OtherSeries">Lecture Notes in Informatics ; P331</enrichment>
    <licence>Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>Johannes Schildgen</author>
    <author>Florian Heinz</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>8195</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>257</pageFirst>
    <pageLast>263</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Gesellschaft für Informatik</publisherName>
    <publisherPlace>Bonn</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">SQLinked - A Hybrid Approach for Local and Database-Remote Program Execution</title>
    <abstract language="eng">When working with today's relational databases, there is usually a clear boundary between the database server and the application, that interfaces with the database system using the query language SQL. The concept of stored procedures allows to move complex parts of the business logic into the database server for various reasons, as, for instance, to reduce the latency of ELT processes that involve several database queries building on each other like distributing records into tables according to their attribute values. Creating and maintaining such stored procedures can be a challenging task, however. The idea pursued in this paper is to create a programming language, as well as a compilation and execution environment that allows the user to mark parts of the application code for being automatically compiled to and later be executed as a stored procedure in the database instead of the execution environment of the actual application. This blurs the border between database and application and provides a natural and maintenance-friendly way for offloading latency sensitive parts of the code to the database system.</abstract>
    <parentTitle language="eng">Datenbanksysteme für Business, Technologie und Web - Workshopband (BTW 2025)</parentTitle>
    <identifier type="doi">10.18420/BTW2025-128</identifier>
    <enrichment key="opus.import.date">2025-06-03T21:49:53+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="ConferenceStatement">Workshop Data Engineering for Data Science (DE4DS). Bamberg. 3.-7. März 2025</enrichment>
    <enrichment key="OtherSeries">Lecture Notes in Informatics ; P331</enrichment>
    <licence>Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>Florian Heinz</author>
    <author>Johannes Schildgen</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>7947</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>128</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>workingpaper</type>
    <publisherName/>
    <publisherPlace>Regensburg</publisherPlace>
    <creatingCorporation>Ostbayerische Technische Hochschule (OTH) Regensburg / Institut für Sozialforschung und Technikfolgenabschätzung (IST)</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="deu">KINiro, Künstliche Intelligenz für Nichtregierungsorganisationen - Bedarf, Akzeptanz und Umsetzungsmöglichkeiten. 4. Arbeitspapier: Künstliche Intelligenz in Nichtregierungsorganisationen: In-depth-Interviews</title>
    <abstract language="deu">Vor dem Hintergrund der wachsenden Bedeutung Künstlicher Intelligenz (KI) in unterschiedlichen gesellschaftlichen Kontexten untersucht dieses Arbeitspapier aus dem Projekt KINiro den Einsatz, die Bedarfe sowie die Akzeptanz von KI in Nichtregierungsorganisationen (NROs). Im Rahmen des Projekts „KINiro – Bedarf, Akzeptanz und Umsetzungsmöglichkeiten“ wurden zehn vertiefende Expert*innen-Interviews mit Vertreter*innen verschiedener NROs geführt. Ziel war es, ein differenziertes und vertiefendes Verständnis von Wissen, benötigten und vorhandenen Ressourcen, organisatorischen Strukturen, Umsetzungsmöglichkeiten und das Erheben von Akzeptanz und die Gründe für Nicht-Akzeptanz zu erheben. Das Arbeitspapier ist das vierte in dem Projekt und baut auf den Erkenntnissen des Scoping Reviews, der ersten qualitativen Studie und der großen quantitativen Studie auf. Die Ergebnisse zeigen, dass das Interesse an KI in NROs stark zugenommen hat, insbesondere nach der breiten Verfügbarkeit von KI-Anwendungen wie ChatGPT. Die befragten Organisationen experimentieren aktuell mit KI vorwiegend in den Bereichen Textproduktion, Datenanalyse, Wissensmanagement und Prozessoptimierung. Es werden auch spezielle KI-Anwendungen für die Bedarfe der NROs entwickelt und eingesetzt. Zugleich bestehen jedoch große Herausforderungen, insbesondere begrenzte finanzielle und personelle Ressourcen, fehlendes Know-how und der Mangel an Digitalisierung. Datenschutz und ethische Fragestellungen sowie mögliche Verzerrungen (Bias) sind zentrale Bedenken. Dennoch lassen sich erste erfolgreiche Umsetzungsstrategien erkennen, etwa die Bildung interner Arbeitsgruppen, die Unterstützung durch Führungskräfte, auch gezielte Schulungen sowie die Entwicklung von Leitlinien werden positiv bewertet. Diese Ansätze tragen dazu bei, Vorbehalte abzubauen und Akzeptanz zu fördern. Das Arbeitspapier zeigt damit, wie die vorherigen Arbeitspapiere, dass KI im gemeinwohlorientierten Sektor grundsätzlich auf Offenheit trifft, zugleich aber ein breites Spektrum an Voraussetzungen wie Ressourcen, Know-how, rechtliche Rahmenbedingungen und nutzbare Anwendungen benötigt werden, um das volle Potenzial von KI verantwortungsvoll auszunutzen.</abstract>
    <identifier type="doi">10.13140/RG.2.2.25460.46720</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-79471</identifier>
    <enrichment key="Kostentraeger">BMFSFJ, RCHST</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY-NC-SA - Namensnennung - Nicht kommerziell -  Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>Maximilian Schultz</author>
    <author>Franziska Hauer</author>
    <author>Sonja Haug</author>
    <author>Karsten Weber</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>KI</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Nichtregierungsorganisationen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Qualitative Befragung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>NGO</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="FakSoz">Fakultät Sozial- und Gesundheitswissenschaften</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="weberlate">Weber, Karsten (Prof. Dr.) - Labor für Technikfolgenabschätzung und Angewandte Ethik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Institut für Sozialforschung und Technikfolgenabschätzung (IST)</collection>
    <collection role="institutes" number="">Labor Empirische Sozialforschung</collection>
    <collection role="institutes" number="">Labor für Technikfolgenabschätzung und Angewandte Ethik (LaTe)</collection>
    <collection role="persons" number="hauglasofo">Haug, Sonja (Prof. Dr.) - Labor Empirische Sozialforschung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7947/KINiro_WorkingPaper_4_indepth-Interviews.pdf</file>
  </doc>
  <doc>
    <id>8065</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>52</pageFirst>
    <pageLast>57</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IARIA</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-04-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Transformer-Based Framework for Anomaly Detection in Multivariate Time Series</title>
    <abstract language="eng">This paper introduces a comprehensive Transformer-based architecture for anomaly detection in multivariate time series. Using self-attention, the framework efficiently processes high-dimensional sensor data without extensive feature engineering, enabling early detection of unusual patterns to prevent critical system failures. In a subsequent laboratory setup, the framework will be applied using fuzzing techniques to induce anomalies in an Electronic Control Unit, while monitoring side channels, such as temperature, voltage, and Controller Area Network messages. The overall structure of the architecture, as well as the necessary preprocessing steps, such as temporal aggregation and classification&#13;
up to the optimization of the hyperparameters of the model, are presented. The evaluation of the model architecture with the postulated restrictions shows that the model handles anomaly scenarios in the dataset robustly. It is necessary to evaluate the extent to which the model can be used in practical applications in areas, such as cloud environments or the industrial Internet of Things. Overall, the results highlight the potential of Transformer models for the automated and reliable monitoring of complex time series data for deviations.</abstract>
    <parentTitle language="eng">CLOUD COMPUTING 2025, The Sixteenth International Conference on Cloud Computing, GRIDs, and Virtualization, April 06, 2025 to April 10, 2025, Valencia, Spain</parentTitle>
    <identifier type="url">https://www.thinkmind.org/library/CLOUD_COMPUTING/CLOUD_COMPUTING_2025/cloud_computing_2025_1_80_20078.html</identifier>
    <identifier type="isbn">978-1-68558-258-6</identifier>
    <identifier type="issn">2308-4294</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Fabian Folger</author>
    <author>Murad Hachani</author>
    <author>Philipp Fuxen</author>
    <author>Julian Graf</author>
    <author>Sebastian Fischer</author>
    <author>Rudolf Hackenberg</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transformer</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Time Series</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Anomaly Detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Temporal Aggregation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ECU</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>8066</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferencevolume</type>
    <publisherName>IARIA</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-04-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">CLOUD COMPUTING 2025, The Sixteenth International Conference on Cloud Computing, GRIDs, and Virtualization, April 06, 2025 to April 10, 2025, Valencia, Spain</title>
    <identifier type="url">https://www.thinkmind.org/library/CLOUD_COMPUTING/CLOUD_COMPUTING_2025</identifier>
    <identifier type="isbn">978-1-68558-258-6</identifier>
    <identifier type="issn">2308-4294</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>8426</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>40</pageFirst>
    <pageLast>45</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
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    <type>conferenceobject</type>
    <publisherName/>
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    <creatingCorporation>IARIA</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-04-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">GFDG: a genetic fuzzing method for the Controller Area Network Protocol</title>
    <abstract language="eng">Ensuring the security of modern automotive systems is critical due to their increasing complexity and reliance on interconnected Electronic Control Units. The Controller Area Network still serves as a key communication protocol within these systems, making it a primary target for security testing. Traditional fuzz testing approaches for Controller Area Networks often rely on random or brute-force message generation, not leveraging the system’s feedback to improve the generation process. This paper introduces the Genetic Fuzz Data Generator, a fuzzing method that leverages Genetic Algorithms and side-channel analysis to enhance Controller Area Network security testing. The Genetic Fuzz Data Generator dynamically refines its fuzzing strategy by evaluating system responses through side-channel data, such as processing unit temperatures and power supply variations. By structuring Controller Area Network messages as genetic individuals and applying evolutionary principles—including selection, crossover, and mutation—the Genetic Fuzz Data Generator systematically identifies active Controller Area Network IDs and generates targeted fuzz messages. Experimental validation was conducted on a real automotive electronic control unit within a controlled laboratory setup. The first results demonstrated the approach’s effectiveness, revealing system anomalies, including a Denial of Service vulnerability that disrupted functions of the investigated Electronic Control Unit. The findings highlight the potential of feedback-driven fuzzing for improving the efficiency of black-box security testing in Controller Area Network-based systems. Future research could further optimize fitness functions or explore&#13;
additional side-channel metrics.</abstract>
    <parentTitle language="deu">Cloud Computing 2025 : The Sixteenth International Conference on Cloud Coud Computing, GRIDs, and Virtualization, 06.-10. April 2025, Valencia</parentTitle>
    <identifier type="isbn">978-1-68558-258-6</identifier>
    <identifier type="issn">2308-4294</identifier>
    <identifier type="url">https://www.thinkmind.org/library/CLOUD_COMPUTING/CLOUD_COMPUTING_2025/cloud_computing_2025_1_60_28009.html</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Murad Hachani</author>
    <author>Miguel Stey</author>
    <author>Philipp Fuxen</author>
    <author>Julian Graf</author>
    <author>Rudolf Hackenberg</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Bronze Open Access - frei zugänglich aber ohne Lizenzhinweis</collection>
  </doc>
  <doc>
    <id>8394</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>296</pageFirst>
    <pageLast>303</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>SCITEPRESS - Science and Technology Publications</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">To Model, to Prompt, or to Code? The Choice Is Yours: A Multi-Paradigmatic Approach to Software Development</title>
    <abstract language="eng">This paper considers three fundamental approaches to software development, namely manual coding, model-driven software engineering, and code generation by large language models. All of these approaches have their individual pros and cons, motivating the desire for an integrated approach. We present MoProCo, a technical solution to integrate the three approaches into a single tool chain, allowing the developer to split a software engineering task into modeling, prompting or coding sub-tasks. From a single input file consisting of static model structure, natural language prompts and/or source code fragments, Java source code is generated using a two-stage approach. A case study demonstrates that the MoProCo approach combines the desirable properties of the three development approaches by offering the appropriate level of abstraction, determinism, and dynamism for each specific software engineering sub-task.</abstract>
    <parentTitle language="eng">Proceedings of the 20th International Conference on Software Technologies, 10-12.Juni 2025, Bilbao</parentTitle>
    <identifier type="doi">10.5220/0013557100003964</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Thomas Buchmann</author>
    <author>Felix Schwägerl</author>
    <author>René Peinl</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
  </doc>
  <doc>
    <id>7921</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>91</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace>Regensburg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analysing Learning Behaviour in Moodle: A Heuristic and AI-Driven Approach</title>
    <abstract language="eng">In this thesis, a novel evaluation method to determine the duration of learning sessions and the completion of learning material is introduced. Screen recordings of students’ browser sessions are utilised to capture detailed, low-level learner activity in a blended learning environment.The accumulated recordings are analysed to define a continuous completion state for individual learning material, focusing on the exact time a student spends on the material and their activity during this period. The evaluation method is adapted to the type of material, such as PDF documents, quizzes, or video lectures. The results demonstrate the potential of this approach, based on analysis of two distinct time frames: an exam preparation period and a lecture period. Additionally, an AI-driven approach is outlined to further refine the chosen definition of learning material completion.</abstract>
    <subTitle language="eng">A Thesis</subTitle>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-79210</identifier>
    <identifier type="doi">10.35096/othr/pub-7921</identifier>
    <enrichment key="opus.import.date">2025-01-20T09:16:32+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Ricardo Altweck</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning Analytics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Student Engagement</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Session Replays</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LMS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Blended Learning</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <thesisGrantor>Ostbayerische Technische Hochschule Regensburg</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7921/Masterarbeit_Altweck_Ricardo_CC-BY.pdf</file>
  </doc>
  <doc>
    <id>8025</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>54</pageFirst>
    <pageLast>63</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Exploring the Potential of BIM Models for Deriving Synthetic Training Data for Machine Learning Applications, Montreal</title>
    <abstract language="eng">To increase the efficiency and quality of design and construction tasks, the use of Artificial Intelligence (AI) and Machine Learning (ML) offers a way to automate both repetitive and complex tasks. Many of these ML models rely heavily on large amounts of suitable, machine-readable, and labeled training data. Therefore, a variety of conceivable use cases for ML in the Architecture, Engineering and Construction (AEC) industry are difficult to implement due to a lack of freely and directly usable training data. The process of manually structuring and labeling existing data is time-consuming and needs in some cases skilled personnel to ensure the quality of the labeled data. Due to these factors, approaches for utilizing artificially generated data, referred to as synthetic data, are becoming more prevalent. Since Building Information Models contain a large amount of information, deriving training data from these models presents an obvious route for generation of this data. There are many ML applications whose implementation is inhibited due to a lack of training data, for which model-based synthetic data offer a possible solution approach. The Industry Foundation Classes (IFC) standard provides a powerful exchange format for models independently of their authoring software. Parametric and generative approaches to model creation enable the generation of numerous different building models within a short period of time and with low effort. This paper presents a workflow for automated derivation of synthetic training data from rule-based or parametrically generated models combined with existing IFC datasets as a multimodal data repository. The method is validated by testing automated synthetically labeled image data for a plan detection task, which is carried out with the Object Detection Framework YOLOv8. The suggested workflow has the potential to enhance data accessibility, thereby contributing to the implementation of ML applications in the AEC industry.</abstract>
    <parentTitle language="eng">Advances in Information Technology in Civil and Building Engineering, Proceedings of ICCCBE 2024, Volume 2, Simulation and Automation</parentTitle>
    <identifier type="isbn">9783031873638</identifier>
    <identifier type="issn">2366-2557</identifier>
    <identifier type="doi">10.1007/978-3-031-87364-5_5</identifier>
    <enrichment key="OtherSeries">Lecture Notes in Civil Engineering ; 629</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="ConferenceStatement">20th International Conference on Computing in Civil and Building Engineering, Montreal, Canada, August 25-28, 2024</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Simon Konrad Hoeng</author>
    <author>Friedrich Eder</author>
    <author>Marc Schmailzl</author>
    <author>Mathias Obergrießer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>KI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>BIM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Synthetic Data</value>
    </subject>
    <collection role="institutes" number="FakBau">Fakultät Bauingenieurwesen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>7962</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>185</pageFirst>
    <pageLast>190</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Fachmedien Wiesbaden</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Data-proximal Neural Networks for Limited-view CT</title>
    <abstract language="eng">Limited-angle computed tomography (CT) requires solving an inverse problem that is both ill-conditioned and underdetermined. In recent years, learned reconstruction methods have proven highly effective in addressing this challenge. Most of these methods follow a two-step process: first, an initial reconstruction method is applied to the data to generate an auxiliary reconstruction; second, a neural network is used to map the auxiliary reconstruction closer to the ground truth images. However, when applied to unseen data, there are no guarantees that the network’s output will remain consistent with the available measurement data. To address this, we recently introduced a data-proximal network architecture. In this paper, we implement this approach for limited-angle CT and compare its performance with a standard residual network and a null space network.</abstract>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2025 : Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025</parentTitle>
    <identifier type="isbn">978-3-658-47421-8</identifier>
    <identifier type="doi">10.1007/978-3-658-47422-5_41</identifier>
    <enrichment key="opus.import.date">2025-03-17T20:19:32+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Simon Göppel</author>
    <author>Jürgen Frikel</author>
    <author>Markus Haltmeier</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="4">Naturwissenschaften</collection>
  </doc>
  <doc>
    <id>8189</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>286</pageFirst>
    <pageLast>291</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer VS</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-03-02</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Two-stage Approach for Low-dose and Sparse-angle CT Reconstruction using Backprojection</title>
    <abstract language="eng">This paper presents a novel two-stage approach for computed tomography (CT) reconstruction, focusing on sparse-angle and low-dose setups to minimize radiation exposure while maintaining high image quality. Two-stage approaches consist of an initial reconstruction followed by a neural network for image refinement. In the initial reconstruction, we apply the backprojection (BP) instead of the traditional filtered backprojection (FBP). This enhances computational speed and offers potential advantages for more complex geometries, such as fan-beam and cone-beam CT. Additionally, BP addresses noise and artifacts in sparse-angle CT by leveraging its inherent noise-smoothing effect, which reduces streaking artifacts common in FBP reconstructions. For the second stage, we fine-tune the DRUNet proposed by Zhang et al. to further improve reconstruction quality. We call our method BP-DRUNet and evaluate its performance on a synthetically generated ellipsoid dataset alongside thewell-established LoDoPaBCT dataset. Our results show that BP-DRUNet produces competetive results in terms of PSNR and SSIM metrics compared to the FBP-based counterpart, FBPDRUNet, and delivers visually competitive results across all tested angular setups.</abstract>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2025 (BVM 2025): Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025</parentTitle>
    <identifier type="isbn">978-3-658-47421-8</identifier>
    <identifier type="doi">10.1007/978-3-658-47422-5_67</identifier>
    <enrichment key="opus.import.date">2025-06-03T21:49:53+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="OtherSeries">Informatik aktuell</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Tim Selig</author>
    <author>Patrick Bauer</author>
    <author>Jürgen Frikel</author>
    <author>Thomas März</author>
    <author>Martin Storath</author>
    <author>Andreas Weinmann</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="DFGFachsystematik" number="4">Naturwissenschaften</collection>
  </doc>
  <doc>
    <id>7958</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>19</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">IT-Near- und Offshoring in deutschen Großunternehmen: Aktueller Stand, Motive und Auswahlkriterien</title>
    <abstract language="deu">Die vorliegende Studie untersucht die aktuelle IT-Near- und Offshoring-Praxis in deutschen Großunternehmen. Dafür wurden 33 CIOs und IT-Führungskräfte mittels Interviews befragt. Haupttreiber der geografischen Verlagerung von IT-Services ins Ausland ist die Ressourcenverfügbarkeit in anderen Ländern, bedingt durch den Fachkräftemangel in Deutschland. Dies trifft auch auf die Auswahl geeigneter IT-Services für das Near- und Offshoring zu. Während IT-Near- und Offshoring historisch primär aufgrund von Kosteneinsparungen initiiert wurde, spielt dieser Aspekt heute eine untergeordnete Rolle. Die Interviewteilnehmer und -teilnehmerinnen sehen in Kosteneinsparungen zwar immer noch einen positiven Effekt, erwarten aber, dass dieser in Zukunft aufgrund der Angleichung des Kostenniveaus schwinden wird. Die Unternehmen verlagern hauptsächlich Applikationsbetrieb und -entwicklung, erkennen jedoch zunehmend das Potenzial, auch höherwertige Aufgaben, z. B. Projektmanagement und Unternehmensarchitekturmanagement, aus geografisch entfernteren Ländern erbringen zu lassen. Unternehmen, die ausschließlich IT-Nearshoring betreiben, erbringen weniger als ein Drittel ihrer IT-Services aus nahegelegenen Ländern. Firmen mit IT-Offshoring-Präsenzen weisen einen höheren IT-Near‑/Offshoringgrad auf und verlagern knapp die Hälfte der eigenen IT-Serviceerbringung ins Ausland. Die beliebtesten Länder sind Portugal für das Nearshoring und Indien für das Offshoring.</abstract>
    <parentTitle language="deu">HMD Praxis der Wirtschaftsinformatik</parentTitle>
    <identifier type="issn">1436-3011</identifier>
    <identifier type="doi">10.1365/s40702-025-01150-2</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-79582</identifier>
    <note>Corresponding author der OTH Regensbrg: Markus Westner</note>
    <enrichment key="CorrespondingAuthor">Markus Westner</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Matthias Reicheneder</author>
    <author>Markus Westner</author>
    <author>Markus Matschi</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nearshoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Offshoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>IT Services</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Drivers</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Selection Criteria</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Large Companies</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Study</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Motive</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Auswahlkriterien</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Großunternehmen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Studie</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="oaweg" number="">Corresponding author der OTH Regensburg</collection>
    <collection role="funding" number="">DEAL Springer Nature</collection>
    <collection role="institutes" number="">Labor Regensburg Strategic IT Management (ReSITM)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7958/Westner_HMD_2025.pdf</file>
  </doc>
  <doc>
    <id>8170</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>645</pageFirst>
    <pageLast>656</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-03-04</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Does the Tool Matter? Exploring Some Causes of Threats to Validity in Mining Software Repositories</title>
    <abstract language="eng">Software repositories are an essential source of information for software engineering research on topics such as project evolution and developer collaboration. Appropriate mining tools and analysis pipelines are therefore an indispensable precondition for many research activities. Ideally, valid results should not depend on technical details of data collection and processing. It is, however, widely acknowledged that mining pipelines are complex, with a multitude of implementation decisions made by tool authors based on their interests and assumptions. This raises the questions if (and to what extent) tools agree on their results and are interchangeable. In this study, we use two tools to extract and analyse ten large software projects, quantitatively and qualitatively comparing results and derived data to better understand this concern. We analyse discrepancies from a technical point of view, and adjust code and parametrisation to minimise replication differences. Our results indicate that despite similar trends, even simple metrics such as the numbers of commits and developers may differ by up to 500%. We find that such substantial differences are often caused by minor technical details. We show how tool-level and data post-processing changes can overcome these issues, but find they may require considerable efforts. We summarise identified causes in our lessons learned to help researchers and practitioners avoid common pitfalls, and reflect on implementation decisions and their influence in ensuring obtained data meets explicit and implicit expectations. Our findings lead us to hypothesise that similar uncertainties exist in other analysis tools, which may limit the validity of conclusions drawn in tool-centric research.</abstract>
    <parentTitle language="eng">2025 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Montreal, QC, Canada, March, 4-7, 2025</parentTitle>
    <identifier type="isbn">979-8-3315-3510-0</identifier>
    <identifier type="doi">10.1109/SANER64311.2025.00067</identifier>
    <enrichment key="opus.import.date">2025-06-03T21:49:53+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Nicole Hoess</author>
    <author>Carlos Paradis</author>
    <author>Rick Kazman</author>
    <author>Wolfgang Mauerer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Codes</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Collaboration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data collection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data mining</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Developer Networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Empirical Software Engineering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Market research</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Measurement</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mining Software Repositories</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pipelines</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Research Software</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Software engineering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Uncertainty</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor für Digitalisierung (LFD)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>8304</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>106</pageFirst>
    <pageLast>114</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-06-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Eye Movement Modelling Examples on Usability Heuristics</title>
    <abstract language="eng">The user interface (UI) and user experience (UX) design is of crucial importance for human-computer interaction (HCI), particularly in the context of web applications. In light of the high expectations of users and the competitive nature of the market, it is imperative to employ usability measurement techniques to avoid losing users. Heuristic evaluation (HE) is a cost- and resource-efficient method for evaluating the usability of websites in which evaluators are guided by heuristics. However, the level of expertise of the evaluators has a significant impact on the results, with experts identifying up to 50% more usability issues than novices. To address this gap, this paper proposes Eye Movement Modeling Examples (EMMEs) to demonstrate Jakob Nielsen’s ten usability heuristics in an easy-to-understand format for all levels of experience while also incorporating expert knowledge. In particular, the eye movements and verbal feedback of a usability expert are recorded as the expert analyses the usability of a simple website application in terms of Jakob Nielsen’s ten usability heuristics. This reveals the strategies and cognitive processes of the expert when assessing the usability of a website and makes them more tangible for non-experts or novices. The findings of a questionnaire-based assessment indicate that EMMEs are perceived as beneficial and supportive during the learning process. Ultimately, this comprehensive analysis not only enables a deeper understanding of heuristics for usability novices, but could also lead to EMMEs being applied more efficiently in diverse domains.</abstract>
    <parentTitle language="eng">Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025</parentTitle>
    <identifier type="isbn">9798400712821</identifier>
    <identifier type="doi">10.1145/3723010.3723035</identifier>
    <enrichment key="opus.import.date">2025-06-11T06:10:16+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Dominik Bittner</author>
    <author>Florian Hauser</author>
    <author>Fabian Engl</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>7679</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition>1. Aufl</edition>
    <issue/>
    <volume>2025</volume>
    <type>bookpart</type>
    <publisherName>Ernst &amp; Sohn</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-09-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Von der Handarbeit zur Hochtechnologie im Reallabor: Die Möglichkeit der BIM-basierten Planung und effizienten Produktion von Ziegelwänden durch Roboter</title>
    <parentTitle language="deu">Mauerwerk-Kalender 2025</parentTitle>
    <identifier type="isbn">978-3-433-03445-3</identifier>
    <identifier type="doi">10.1002/9783433612019.ch7</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Stefan Biersack</author>
    <author>Marc Schmailzl</author>
    <author>Thomas Linner</author>
    <author>Friedrich Eder</author>
    <author>Mathias Obergrießer</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mauerwerk</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mauerwerksbau</value>
    </subject>
    <collection role="institutes" number="FakBau">Fakultät Bauingenieurwesen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>8306</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>40</pageFirst>
    <pageLast>48</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-06-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">ML based Evaluation Methodology for Learning Path Recommender Systems</title>
    <abstract language="eng">In education, recommender systems can provide students with personalized learning materials based on their preferences. When comparing various recommendation algorithms, the main question is, which algorithm provides the most suitable recommendations for each student. Answering this question requires a quantitative evaluation methodology (i.e. a concrete metric) for ranking the results of (even non-deterministic) recommender systems. While there is already literature on this topic, the uniqueness of our approach lies in the application of machine learning: we deploy a likelihood based analysis via Hidden Markov Models named Aiakos. With this strategy, we aim to provide data-driven insights about accuracy and stability of recommendations towards a more reasonable selection of the appropriate recommender system. The training data for the Hidden Markov Models is collected from 80 students. Data from another 26 students is then used to discuss the behavior of our evaluation procedure considering a single recommendation as well as the results from 100 recommendations. Furthermore, the proposed concept allows to be applied to other domains as well.</abstract>
    <parentTitle language="eng">Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025</parentTitle>
    <identifier type="isbn">9798400712821</identifier>
    <identifier type="doi">10.1145/3723010.3723022</identifier>
    <enrichment key="opus.import.date">2025-06-11T06:10:16+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Flemming Bugert</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Dominik Bittner</author>
    <author>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Robert Maier</author>
    <author>Simon Röhrl</author>
    <author>Susanne Staufer</author>
    <author>Florian Hauser</author>
    <author>Jürgen Mottok</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>8061</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>123</pageFirst>
    <pageLast>128</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IOS Press</publisherName>
    <publisherPlace>Wien</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-04-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Acceptance and Usage of AI Applications in Health-Focused NGOs</title>
    <abstract language="eng">Background: AI applications promise to be a valuable tool for healthfocused NGOs. While often operating with limited resources, these organizations recognize the potential of AI to streamline processes and support workflows through automation. However, challenges such as data privacy concerns—especially regarding personal medical data—and the low prioritization of implementation hinder progress. Objectives: This study examines the extent to which German health-focused NGOs can currently benefit from the application of AI. It evaluates demands, available resources, and use cases. Methods: A health-focused subset of data from project KINiro was analyzed. This research utilized a mixed-methods approach, combining two rounds of qualitative interviews with a quantitative survey. Results: The study revealed that while health-focused NGOs are in the early stages of AI implementation, there are hurdles such as ethical concerns and a lack of resources. Conclusion: AI has the potential to support health-focused NGOs in their work, if the challenges like resources, ethics, and data privacy are effectively addressed.</abstract>
    <parentTitle language="eng">dHealth 2025, Proceedings of the 19th Health Informatics Meets Digital Health Conference</parentTitle>
    <identifier type="isbn">978-1-64368-592-2</identifier>
    <identifier type="doi">10.3233/SHTI250172</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-80613</identifier>
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    <title language="eng">Towards AI-enhanced facade planning : integrating human expertise with machine learning-driven parametric modeling</title>
    <abstract language="eng">Planning modern facade systems is complex, requiring optimization across multiple domains.This paper proposes an AI-enhanced workflow for facade planning, harnessing computer vision and human input via a Large Language Model.A generative AI system then guides a parametric model to produce 3D facade designs. Automated checks provide feedback to a Reinforcement Learning system, to iteratively determine optimal solutions.These solutions are verified and finalized by human expertise, ensuring improved outcomes with reduce planning time and effort.The approach illustrates how combining advanced AI methods with human expertise can address the multifactorial challenges of facade design within current industry practices.</abstract>
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