2017
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Lupp, Artur; Mirnig, Alexander G; Uhl, Andreas; Tscheligi, Manfred A Study Setup Optimization -- Providing Solutions with Patterns Inproceedings PATTERNS 2017, The Ninth International Conferences on Pervasive Patterns and Applications, pp. 11–16, IARIA, 2017. BibTeX @inproceedings{Lupp2017,
title = {A Study Setup Optimization -- Providing Solutions with Patterns},
author = {Artur Lupp and Alexander G Mirnig and Andreas Uhl and Manfred Tscheligi},
year = {2017},
date = {2017-01-01},
booktitle = {PATTERNS 2017, The Ninth International Conferences on Pervasive Patterns and Applications},
pages = {11--16},
publisher = {IARIA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Meschtscherjakov, Alexander; Trösterer, Sandra; Lupp, Artur; Tscheligi, Manfred Pokémon WALK: Persuasive Effects of Pokémon GO Game-Design Elements Inproceedings de Vries, Peter W; Oinas-Kukkonen, Harri; Siemons, Liseth; Jong, Nienke Beerlage-de; van Gemert-Pijnen, Lisette (Ed.): Persuasive Technology: Development and Implementation of Personalized Technologies to Change Attitudes and Behaviors: 12th International Conference, PERSUASIVE 2017, Amsterdam, The Netherlands, April 4--6, 2017, Proceedings, pp. 241–252, Springer International Publishing, 2017, ISBN: 978-3-319-55134-0. Links | BibTeX @inproceedings{Meschtscherjakov2017c,
title = {Pok\'{e}mon WALK: Persuasive Effects of Pok\'{e}mon GO Game-Design Elements},
author = {Alexander Meschtscherjakov and Sandra Tr\"{o}sterer and Artur Lupp and Manfred Tscheligi},
editor = {Peter W de Vries and Harri Oinas-Kukkonen and Liseth Siemons and Nienke Beerlage-de Jong and Lisette van Gemert-Pijnen},
doi = {10.1007/978-3-319-55134-0_19},
isbn = {978-3-319-55134-0},
year = {2017},
date = {2017-01-01},
booktitle = {Persuasive Technology: Development and Implementation of Personalized Technologies to Change Attitudes and Behaviors: 12th International Conference, PERSUASIVE 2017, Amsterdam, The Netherlands, April 4--6, 2017, Proceedings},
pages = {241--252},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Trösterer, Sandra; Meschtscherjakov, Alexander; Mirnig, Alexander; Lupp, Artur; Gärtner, Magdalena; McGee, Fintan; McCall, Rod; Tscheligi, Manfred; Engel, Thomas What We Can Learn from Pilots for Handovers and (De)Skilling in Semi-Autonomous Driving: An Interview Study Inproceedings Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '17), pp. 173–182, 2017. Abstract | Links | BibTeX @inproceedings{Trosterer2017b,
title = {What We Can Learn from Pilots for Handovers and (De)Skilling in Semi-Autonomous Driving: An Interview Study},
author = {Sandra Tr\"{o}sterer and Alexander Meschtscherjakov and Alexander Mirnig and Artur Lupp and Magdalena G\"{a}rtner and Fintan McGee and Rod McCall and Manfred Tscheligi and Thomas Engel},
doi = {10.1145/3122986.3123020},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '17)},
pages = {173--182},
abstract = {In aviation, pilots interact with autopilots almost on a daily basis. With semi-autonomous vehicles, this is not yet the case. In our work, we aimed at finding out what we can learn from pilots' current experiences for the domain of autonomous driving and what implications can be derived. We conducted three in-depth interviews with pilots to investigate how pilots currently handle handover situations to and from the autopilot, which information is relevant for this transition to be successful, how pilots react in critical situations, how handovers are trained, and how flying and handover skills are maintained. We compare the gained insights with the domain of autonomous driving and reflect on implications for handovers and (de)skilling. Our findings suggest that the AUI community can learn from aviation in areas such as situation awareness, transparency of system status, the need for a primary drive display, calibrated (dis)trust, and driver training.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In aviation, pilots interact with autopilots almost on a daily basis. With semi-autonomous vehicles, this is not yet the case. In our work, we aimed at finding out what we can learn from pilots' current experiences for the domain of autonomous driving and what implications can be derived. We conducted three in-depth interviews with pilots to investigate how pilots currently handle handover situations to and from the autopilot, which information is relevant for this transition to be successful, how pilots react in critical situations, how handovers are trained, and how flying and handover skills are maintained. We compare the gained insights with the domain of autonomous driving and reflect on implications for handovers and (de)skilling. Our findings suggest that the AUI community can learn from aviation in areas such as situation awareness, transparency of system status, the need for a primary drive display, calibrated (dis)trust, and driver training. |
2016
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Mirnig, Alexander; Kaiser, Tim; Lupp, Artur; Perterer, Nicole; Meschtscherjakov, Alexander; Grah, Thomas; Tscheligi, Manfred Automotive User Experience Design Patterns: An Approach and Pattern Examples Journal Article International Journal On Advances in Intelligent Systems, 9 (3&4), 2016. Abstract | BibTeX @article{Mirnig2016,
title = {Automotive User Experience Design Patterns: An Approach and Pattern Examples},
author = {Alexander Mirnig and Tim Kaiser and Artur Lupp and Nicole Perterer and Alexander Meschtscherjakov and Thomas Grah and Manfred Tscheligi},
year = {2016},
date = {2016-01-01},
journal = {International Journal On Advances in Intelligent Systems},
volume = {9},
number = {3&4},
publisher = {IARIA},
abstract = {Patterns are a methodology for capturing best practices and solutions to reoccurring problems in certain fields or disciplines. Applied to automotive interaction design they can combine empirical data, industry knowledge, and experts' experience for state-of-the-art design solutions. In this paper, we present the patterns approach and its application to the automotive interaction domain, together with a newly generated set of eight in-vehicle user experience (UX) design patterns that describe answers to problems in automotive interaction design and engineering. These patterns are part of an ongoing project with the aim of providing a comprehensive, user experience focused, collection of design solutions for contemporary and future automotive designs. We present the pattern approach in general, the specific automotive approach and methodology, the patterns themselves, and finally discuss the benefits, drawbacks, and future work needs of patterns in the automotive domain.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Patterns are a methodology for capturing best practices and solutions to reoccurring problems in certain fields or disciplines. Applied to automotive interaction design they can combine empirical data, industry knowledge, and experts' experience for state-of-the-art design solutions. In this paper, we present the patterns approach and its application to the automotive interaction domain, together with a newly generated set of eight in-vehicle user experience (UX) design patterns that describe answers to problems in automotive interaction design and engineering. These patterns are part of an ongoing project with the aim of providing a comprehensive, user experience focused, collection of design solutions for contemporary and future automotive designs. We present the pattern approach in general, the specific automotive approach and methodology, the patterns themselves, and finally discuss the benefits, drawbacks, and future work needs of patterns in the automotive domain. |
2015
|
Elmer, Peter; Lupp, Artur; Sprenger, Stefan; Thaler, René; Uhl, Andreas Exploring Compression Impact on Face Detection Using Haar-like Features Inproceedings Paulsen, Rasmus R; Pedersen, Kim S (Ed.): Proceedings of Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015, pp. 53–64, Springer International Publishing, 2015, ISBN: 978-3-319-19665-7. Abstract | Links | BibTeX @inproceedings{Elmer2015,
title = {Exploring Compression Impact on Face Detection Using Haar-like Features},
author = {Peter Elmer and Artur Lupp and Stefan Sprenger and Ren\'{e} Thaler and Andreas Uhl},
editor = {Rasmus R Paulsen and Kim S Pedersen},
doi = {10.1007/978-3-319-19665-7_5},
isbn = {978-3-319-19665-7},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015},
volume = {9127},
pages = {53--64},
publisher = {Springer International Publishing},
abstract = {The main goal in our experimental study was to explore the impact of image compression on face detection using Haar-like features. In our setup we used the JPEG, JPEG2000 and JPEG XR compression standards to compress images from selected databases at given compression ratios. We performed the face detection using OpenCV on the reference images from the database as well as on the compressed images. After the detection process we compared the detected areas between the reference and the compressed image gaining the average coverage, false positive and false negative areas. Experimental results comparing JPEG, JPEG2000 and JPEG XR are showing that the average coverage of the detected face area differ between 79,58% in the worst and 99,61% in the best case. The false negative (not covered) areas range between 0,33% and 19,75% and false positive (fallout) areas between 0,38% and 9,45%. We conclude that the JPEG compression standard is performing worse than JPEG2000 and JPEG XR while both latter providing quite equal and good results.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The main goal in our experimental study was to explore the impact of image compression on face detection using Haar-like features. In our setup we used the JPEG, JPEG2000 and JPEG XR compression standards to compress images from selected databases at given compression ratios. We performed the face detection using OpenCV on the reference images from the database as well as on the compressed images. After the detection process we compared the detected areas between the reference and the compressed image gaining the average coverage, false positive and false negative areas. Experimental results comparing JPEG, JPEG2000 and JPEG XR are showing that the average coverage of the detected face area differ between 79,58% in the worst and 99,61% in the best case. The false negative (not covered) areas range between 0,33% and 19,75% and false positive (fallout) areas between 0,38% and 9,45%. We conclude that the JPEG compression standard is performing worse than JPEG2000 and JPEG XR while both latter providing quite equal and good results. |
