@misc{45242,
  author       = {{N., N.}},
  title        = {{{A Scalable and Extensible Architecture for a Crowd-Based Prototype Validation Platform}}},
  year         = {{2022}},
}

@misc{45241,
  author       = {{N., N.}},
  title        = {{{Conception and Implementation of a Situation-specific Design Thinking Tool}}},
  year         = {{2022}},
}

@article{30940,
  abstract     = {{We analyse the two-dimensional Nash bargaining solution (NBS) by deploying
the standard labour market negotiations model of McDonald and Solow (1981).
We show that the two-dimensional bargaining problem can be decomposed into two
one-dimensional problems, such that the two solutions together replicate the solution
of the two-dimensional problem if the NBS is applied.  The axiom of
Independence of Irrelevant Alternatives is shown to be crucial for this type
of decomposability.  This result has significant implications for actual
negotiations because it allows for the decomposition of a multi-dimensional bargaining
problem into one-dimensional problems---and thus helps to facilitate real-world
negotiations.}},
  author       = {{Haake, Claus-Jochen and Upmann, Thorsten and Duman, Papatya}},
  issn         = {{0347-0520}},
  journal      = {{Scandinavian Journal of Economics}},
  keywords     = {{Labour market negotiations, efficient bargains, Nash bargaining solution, sequential bargaining, restricted bargaining games}},
  number       = {{2}},
  pages        = {{403--440}},
  publisher    = {{Wiley}},
  title        = {{{Wage Bargaining and Employment Revisited: Separability and Efficiency in Collective Bargaining}}},
  doi          = {{https://doi.org/10.1111/sjoe.12518}},
  volume       = {{125}},
  year         = {{2022}},
}

@misc{45715,
  author       = {{Tcheussi Ngayap, Vanessa Ingrid}},
  title        = {{{FreeRTOS on a MicroBlaze Soft-Core Processor with Hardware Accelerators}}},
  year         = {{2022}},
}

@misc{45790,
  author       = {{Palushi, Juela}},
  title        = {{{Domain-aware Text Professionalization using Sequence-to-Sequence Neural Networks}}},
  year         = {{2022}},
}

@misc{45789,
  author       = {{Budanurmath, Vinaykumar}},
  title        = {{{Propaganda Technique Detection Using Connotation Frames}}},
  year         = {{2022}},
}

@misc{45914,
  author       = {{Manjunatha, Suraj}},
  publisher    = {{Paderborn University }},
  title        = {{{Dealing With Pre-Processing And Feature Extraction Of Time-Series Data In  Predictive Maintenance}}},
  year         = {{2022}},
}

@misc{45915,
  author       = {{Kaur , Parvinder}},
  title        = {{{Analysis of Time-Series Classification in Conditional Monitoring Systems}}},
  year         = {{2022}},
}

@inproceedings{45248,
  author       = {{Dongol, Brijesh and Schellhorn, Gerhard and Wehrheim, Heike}},
  booktitle    = {{33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland}},
  editor       = {{Klin, Bartek and Lasota, Slawomir and Muscholl, Anca}},
  pages        = {{31:1–31:23}},
  publisher    = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}},
  title        = {{{Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement}}},
  doi          = {{10.4230/LIPIcs.CONCUR.2022.31}},
  volume       = {{243}},
  year         = {{2022}},
}

@article{32095,
  author       = {{Müller, Michelle and Neumann, Jürgen and Kundisch, Dennis}},
  journal      = {{Journal of Management Information Systems}},
  number       = {{3}},
  pages        = {{834--864}},
  title        = {{{Peer-To-Peer Rentals, Regulatory Policies, And Hosts’ Cost Pass-Throughs}}},
  volume       = {{39}},
  year         = {{2022}},
}

@inproceedings{31063,
  author       = {{Grieger, Nicole and Seutter, Janina and Kundisch, Dennis}},
  booktitle    = {{Proceedings of the 28th Americas Conference on Information Systems (AMCIS)}},
  location     = {{Minneapolis, USA}},
  title        = {{{A Rollercoaster of Emotions – A Semantic Analysis of Fundraising Campaigns over the Course of the COVID-19 Pandemic}}},
  year         = {{2022}},
}

@inproceedings{28999,
  author       = {{Grieger, Nicole and Seutter, Janina and Kundisch, Dennis}},
  booktitle    = {{Tagungsband der 17. Internationalen Tagung Wirtschaftsinformatik 2022}},
  location     = {{Nürnberg, Germany}},
  title        = {{{Rollercoaster of Emotions – A Semantic Analysis of Fundraising Campaigns over the Course of the Covid-19 Pandemic}}},
  year         = {{2022}},
}

@article{30511,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Many critical codebases are written in C, and most of them use preprocessor directives to encode variability, effectively encoding software product lines. These preprocessor directives, however, challenge any static code analysis. SPLlift, a previously presented approach for analyzing software product lines, is limited to Java programs that use a rather simple feature encoding and to analysis problems with a finite and ideally small domain. Other approaches that allow the analysis of real-world C software product lines use special-purpose analyses, preventing the reuse of existing analysis infrastructures and ignoring the progress made by the static analysis community. This work presents <jats:sc>VarAlyzer</jats:sc>, a novel static analysis approach for software product lines. <jats:sc>VarAlyzer</jats:sc> first transforms preprocessor constructs to plain C while preserving their variability and semantics. It then solves any given distributive analysis problem on transformed product lines in a variability-aware manner. <jats:sc>VarAlyzer</jats:sc> ’s analysis results are annotated with feature constraints that encode in which configurations each result holds. Our experiments with 95 compilation units of OpenSSL show that applying <jats:sc>VarAlyzer</jats:sc> enables one to conduct inter-procedural, flow-, field- and context-sensitive data-flow analyses on entire product lines for the first time, outperforming the product-based approach for highly-configurable systems.</jats:p>}},
  author       = {{Schubert, Philipp and Gazzillo, Paul and Patterson, Zach and Braha, Julian and Schiebel, Fabian Benedikt and Hermann, Ben and Wei, Shiyi and Bodden, Eric}},
  issn         = {{0928-8910}},
  journal      = {{Automated Software Engineering}},
  keywords     = {{inter-procedural static analysis, software product lines, preprocessor, LLVM, C/C++}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Static data-flow analysis for software product lines in C}}},
  doi          = {{10.1007/s10515-022-00333-1}},
  volume       = {{29}},
  year         = {{2022}},
}

@phdthesis{24885,
  author       = {{Neumann, Jürgen}},
  title        = {{{On Biases in Online Reviews and the Moderating Effect of Review System Design}}},
  doi          = {{10.17619/UNIPB/1-1177 }},
  year         = {{2021}},
}

@phdthesis{24886,
  author       = {{van Straaten, Dirk}},
  title        = {{{Inferring Quality with Reputation Systems - Experimental Evidence on Elicitation Mechanisms and Aggregation Metrics}}},
  doi          = {{10.17619/UNIPB/1-1189 }},
  year         = {{2021}},
}

@phdthesis{24887,
  author       = {{Hinnenthal, Kristian}},
  title        = {{{Models and Algorithms for Hybrid Networks and Hybrid Programmable Matter}}},
  doi          = {{10.17619/UNIPB/1-1169 }},
  year         = {{2021}},
}

@inproceedings{25174,
  author       = {{Müller, Michelle and Seutter, Janina and Müller, Stefanie Jutta Marianne and Kundisch, Dennis}},
  booktitle    = {{Proceedings of the 42nd International Conference on Information Systems (ICIS)}},
  title        = {{{Moment or Movement – An Empirical Analysis of the Heterogeneous Impact of Media Attention on Charitable Crowdfunding Campaigns}}},
  year         = {{2021}},
}

@inproceedings{25178,
  author       = {{Poniatowski, Martin and Seutter, Janina and Kundisch, Dennis}},
  booktitle    = {{Proceedings of the 42nd International Conference on Information Systems (ICIS)}},
  title        = {{{"Timing is Everything" — An Empirical Analysis of the Timing of Online Review Elicitation}}},
  year         = {{2021}},
}

@inproceedings{20125,
  abstract     = {{Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load.}},
  author       = {{Hasnain, Asif and Karl, Holger}},
  booktitle    = {{2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)}},
  keywords     = {{Flow scheduling, Deadlines, Reinforcement learning}},
  location     = {{Las Vegas, USA}},
  publisher    = {{IEEE Computer Society}},
  title        = {{{Learning Flow Scheduling}}},
  doi          = {{https://doi.org/10.1109/CCNC49032.2021.9369514}},
  year         = {{2021}},
}

@inproceedings{20244,
  author       = {{Gottschalk, Sebastian and Kirchhoff, Jonas and Engels, Gregor}},
  booktitle    = {{Business Modeling and Software Design}},
  editor       = {{Shishkov, Boris}},
  location     = {{Sofia}},
  title        = {{{Extending Business Model Development Tools with Consolidated Expert Knowledge }}},
  doi          = {{10.1007/978-3-030-79976-2_1}},
  year         = {{2021}},
}