In this paper, we describe a technique to determine user preferences concerning in-car micro-gesture interaction. The approach is derived from the theater technique [1], and implies a collaborative adjustment of parameters with the experimenter, until the subject has decided about the final settings. We evaluated three systematically selected gestures (zooming, sweeping, and circling) for controlling four exemplary comfort functions of the car (window lifter, air condition, radio volume, and seat heating). The main result of our study is the geometry of a "sweet spot" for micro-gesture recognition close to the steering wheel, which is independent from the underlying technical recognition approach. Additionally, preferred sizes, angles, and pause times for the investigated gestures are provided. We give an indication, which of the gestures is preferred by the users (the sweeping gesture). Finally, we provide a more detailed view on the interaction between gesture preferences and function.

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The widely used paradigm of faceted browsing is limited by the fact that only one query and result set are displayed at a time. This demonstrator introduces an interaction design for parallel faceted browsing that makes it easy for a user to construct and view the results of multiple interrelated queries. The paradigm offers general benefits for a variety of application areas. Starting Point: Faceted BrowsingFaceted browsing (or faceted search; see, e.g., [6]) is a widely used paradigm for the exploration of a large repository of entities which has been used successfully in types of system ranging from e-commerce sites to applications for personal information management. It presupposes that each entity is described in terms of values of facets (which may be simple attributes or more complexly structured dimensions).

Job interviews are usually high-stakes social situations where professional and behavioral skills are required for a satisfactory outcome. In order to increase the chances of recruitment technological approaches have emerged to generate meaningful feedback for job candidates. We extended an interactive virtual job interview training system with a Generative Adversarial Network (GAN)-based approach that first detects behavioral weaknesses and subsequently generates personalized feedback. To evaluate the usefulness of the generated feedback, we conducted a mixed-methods pilot study using mock-ups from the job interview training system. The overall study results indicate that the GAN-based generated behavioral feedback is helpful. Moreover, participants assessed that the feedback would improve their job interview performance.

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