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- 2020 (83) (remove)
Keywords
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Towards an Interaction-Centered and Dynamically Constructed Episodic Memory for Social Robots
(2020)
Computers can help us to trigger our intuition about how to solve a problem. But how does a computer take into account what a user wants and update these triggers? User preferences are hard to model as they are by nature vague, depend on the user’s background and are not always deterministic, changing depending on the context and process under which they were established. We pose that the process of preference discovery should be the object of interest in computer aided design or ideation. The process should be transparent, informative, interactive and intuitive. We formulate Hyper-Pref, a cyclic co-creative process between human and computer, which triggers the user’s intuition about what is possible and is updated according to what the user wants based on their decisions. We combine quality diversity algorithms, a divergent optimization method that can produce many, diverse solutions, with variational autoencoders to both model that diversity as well as the user’s preferences, discovering the preference hypervolume within large search spaces.
Listen to Developers! A Participatory Design Study on Security Warnings for Cryptographic APIs
(2020)
Validierung einer Web-Applikation zum Fern-Monitoring von Belastungs- und Erholungsparametern
(2020)
Simultan zur agilen Entwicklung einer Web-Applikation, die Parameter der Belastungs- und Beanspruchungssteuerung erfasst, wurden die implementierten Belastungs- und Erholungs-parameter an freiwilligen Testern/innen in der Praxis überprüft. Um sowohl die Applikation als auch die z.T. selbst entwickelten Kenngrößen auf ihre externe Validität hin zu bewerten, werden diese regressionsanalytisch bearbeitet.
Autonomous driving enables new mobility concepts such as shared-autonomous services. Although significant re-search has been done on passenger-car interaction, work on passenger interaction with robo-taxis is still rare. In this paper, we tackle the question of how passengers experience robo-taxis as a service in real-life settings to inform the interaction design. We conducted a Wizard of Oz study with an electric vehicle where the driver was hidden from the passenger to simulate the service experience of a robo-taxi. 10 participants had the opportunity to use the simulated shared-autonomous service in real-life situations for one week. By the week's end, 33 rides were completed and recorded on video. Also, we flanked the study conducting interviews before and after with all participants. The findings provided insights into four design themes that could inform the service design of robo-taxis along the different stages including hailing, pick-up, travel, and drop-off.