@article{BierSchellerKlingbeiletal.1993,
  author    = {Bier, Frank Fabian and Scheller, Frieder W. and Klingbeil, Mandy and Oßwald, U.},
  title     = {Biosensoren und Teststreifen f{\"u}r die Umwelt- und Lebensmittelanalytik : eine {\"U}bersicht},
  year      = {1993},
  language  = {de}
}
@misc{BruechnerRenzKlingbeil2019,
  author    = {Bruechner, Dominik and Renz, Jan and Klingbeil, Mandy},
  title     = {Creating a Framework for User-Centered Development and Improvement of Digital Education},
  series = {Scale},
  journal   = {Scale},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  isbn      = {978-1-4503-6804-9},
  doi       = {10.1145/3330430.3333644},
  pages     = {4},
  year      = {2019},
  abstract  = {We investigate how the technology acceptance and learning experience of the digital education platform HPI Schul-Cloud (HPI School Cloud) for German secondary school teachers can be improved by proposing a user-centered research and development framework. We highlight the importance of developing digital learning technologies in a user-centered way to take differences in the requirements of educators and students into account. We suggest applying qualitative and quantitative methods to build a solid understanding of a learning platform's users, their needs, requirements, and their context of use. After concept development and idea generation of features and areas of opportunity based on the user research, we emphasize on the application of a multi-attribute utility analysis decision-making framework to prioritize ideas rationally, taking results of user research into account. Afterward, we recommend applying the principle build-learn-iterate to build prototypes in different resolutions while learning from user tests and improving the selected opportunities. Last but not least, we propose an approach for continuous short- and long-term user experience controlling and monitoring, extending existing web- and learning analytics metrics.},
  language  = {en}
}
@article{ReimannKlingbeilPasewaldtetal.2019,
  author    = {Reimann, Max and Klingbeil, Mandy and Pasewaldt, Sebastian and Semmo, Amir and Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Locally controllable neural style transfer on mobile devices},
  series = {The Visual Computer},
  volume    = {35},
  journal   = {The Visual Computer},
  number    = {11},
  publisher = {Springer},
  address   = {New York},
  issn      = {0178-2789},
  doi       = {10.1007/s00371-019-01654-1},
  pages     = {1531 -- 1547},
  year      = {2019},
  abstract  = {Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. In this work, we first propose a problem characterization of interactive style transfer representing a trade-off between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, we enhance state-of-the-art neural style transfer techniques by mask-based loss terms that can be interactively parameterized by a generalized user interface to facilitate a creative and localized editing process. We report on a usability study and an online survey that demonstrate the ability of our app to transfer styles at improved semantic plausibility.},
  language  = {en}
}
@misc{ReimannKlingbeilPasewaldtetal.2018,
  author    = {Reimann, Max and Klingbeil, Mandy and Pasewaldt, Sebastian and Semmo, Amir and Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {MaeSTrO: A Mobile App for Style Transfer Orchestration using Neural Networks},
  series = {International Conference on Cyberworlds (CW)},
  journal   = {International Conference on Cyberworlds (CW)},
  editor    = {Sourin, A Sourina},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-7315-7},
  doi       = {10.1109/CW.2018.00016},
  pages     = {9 -- 16},
  year      = {2018},
  abstract  = {Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. This work enhances state-of-the-art neural style transfer techniques by a generalized user interface with interactive tools to facilitate a creative and localized editing process. Thereby, we first propose a problem characterization representing trade-offs between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, first user tests indicate different levels of satisfaction for the implemented techniques and interaction design.},
  language  = {en}
}