@inproceedings{BergertKoesterKrasnovaetal.2020,
  author    = {Bergert, Cora and K{\"o}ster, Antonia and Krasnova, Hanna and Turel, Ofir},
  title     = {Missing out on life},
  series = {Proceedings of the 15th International Conference on Wirtschaftsinformatik : WI2020 Zentrale Tracks},
  booktitle = {Proceedings of the 15th International Conference on Wirtschaftsinformatik : WI2020 Zentrale Tracks},
  publisher = {GITO Verlag f{\"u}r Industrielle Informationstechnik und Organisation},
  address   = {Berlin},
  isbn      = {978-3-95545-335-0},
  doi       = {10.30844/wi_2020_f1-bergert},
  pages     = {568 -- 583},
  year      = {2020},
  abstract  = {Mobile devices have become an integral part of everyday life due to their portability. As literature shows, technology use is not only beneficial but also has dark sides, such as addiction. Parents face the need to balance perceived benefits and risks of children's exposure to mobile technologies. However, no study has uncovered what kind of benefits and concerns parents consider when implementing technology-related rules. We built on qualitative responses of 300 parents of children aged two to thirteen to explore concerns about, and perceived benefits of children's smartphone and tablet usage, as well as the rules parents have developed regarding technology use. Findings point to concerns regarding children's development, as well as benefits for both children and parents, and ultimately to new insights about mobile technology mediation. These results provide practical guidance for parents, physicians and mobile industry stakeholders, trying to ensure that children are acting responsibly with mobile technology.},
  language  = {en}
}
@article{BornhorstNustedeFudickar2019,
  author    = {Bornhorst, Julia and Nustede, Eike Jannik and Fudickar, Sebastian},
  title     = {Mass Surveilance of C. elegans-Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection},
  series = {Sensors},
  volume    = {19},
  journal   = {Sensors},
  number    = {6},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s19061468},
  pages     = {14},
  year      = {2019},
  abstract  = {The nematode Caenorhabditis elegans (C. elegans) is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and restrictions of the comprehensive use of C. elegans for toxicological trials. With the general applicability of the detection of C. elegans from microscope images via machine learning, as well as of smartphone-based microscopes, this article investigates the suitability of smartphone-based microscopy to detect C. elegans in a complete Petri dish. Thereby, the article introduces a smartphone-based microscope (including optics, lighting, and housing) for monitoring C. elegans and the corresponding classification via a trained Histogram of Oriented Gradients (HOG) feature-based Support Vector Machine for the automatic detection of C. elegans. Evaluation showed classification sensitivity of 0.90 and specificity of 0.85, and thereby confirms the general practicability of the chosen approach.},
  language  = {en}
}
@misc{KliemeTietzMeinel2018,
  author    = {Klieme, Eric and Tietz, Christian and Meinel, Christoph},
  title     = {Beware of SMOMBIES},
  series = {The 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018)/the 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018)},
  journal   = {The 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018)/the 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018)},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-4387-7},
  issn      = {2324-9013},
  doi       = {10.1109/TrustCom/BigDataSE.2018.00096},
  pages     = {651 -- 660},
  year      = {2018},
  abstract  = {Several research evaluated the user's style of walking for the verification of a claimed identity and showed high authentication accuracies in many settings. In this paper we present a system that successfully verifies a user's identity based on many real world smartphone placements and yet not regarded interactions while walking. Our contribution is the distinction of all considered activities into three distinct subsets and a specific one-class Support Vector Machine per subset. Using sensor data of 30 participants collected in a semi-supervised study approach, we prove that unsupervised verification is possible with very low false-acceptance and false-rejection rates. We furthermore show that these subsets can be distinguished with a high accuracy and demonstrate that this system can be deployed on off-the-shelf smartphones.},
  language  = {en}
}