Music streaming services increasingly incorporate additional music taxonomies (i.e., mood, activity, and genre) to provide users different ways to browse through music collections. However, these additional taxonomies can distract the user from reaching their music goal, and influence choice satisfaction. We conducted an online user study with an application called "Tune-A-Find," where we measured participants' music taxonomy choice (mood, activity, and genre). Among 297 participants, we found that the chosen taxonomy is related to personality traits. We found that openness to experience increased the choice for browsing music by mood, while conscientiousness increased the choice for browsing music by activity. In addition, those high in neuroticism were most likely to browse for music by activity or genre. Our findings can support music streaming services to further personalize user interfaces. By knowing the user's personality, the user interface can adapt to the user's preferred way of music browsing.

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Music streaming services increasingly incorporate different ways for users to browse for music. Next to the commonly used "genre" taxonomy, nowadays additional taxonomies, such as mood and activities, are often used. As additional taxonomies have shown to be able to distract the user in their search, we looked at how to predict taxonomy preferences in order to counteract this. Additionally, we looked at how the number of categories presented within a taxonomy influences the user experience. We conducted an online user study where participants interacted with an application called "Tune-A-Find". We measured taxonomy choice (i.e., mood, activity, or genre), individual differences (e.g., personality traits and music expertise factors), and different user experience factors (i.e., choice difficulty and satisfaction, perceived system usefulness and quality) when presenting either 6-or 24-categories within the picked taxonomy. Among 297 participants, we found that personality traits are related to music taxonomy preferences. Furthermore, our findings show that the number of categories within a taxonomy influences the user experience in different ways and is moderated by music expertise. Our findings can support personalized user interfaces in music streaming services. By knowing the user's personality and expertise, the user interface can adapt to the user's preferred way of music browsing and thereby mitigate the problems that music listeners are facing while finding their way through the abundance of music choices online nowadays.

Retinal surgery is a bimanual operation in which surgeons operate with an instrument in their dominant hand (more capable hand) and simultaneously hold a light pipe (illuminating pipe) with their non-dominant hand (less capable hand) to provide illumination inside the eye. Manually holding and adjusting the light pipe places an additional burden on the surgeon and increases the overall complexity of the procedure. To overcome these challenges, a robot-assisted automatic light pipe actuating system is proposed. A customized light pipe with force-sensing capability is mounted at the end effector of a follower robot and is actuated through a hybrid force-velocity controller to automatically illuminate the target area on the retinal surface by pivoting about the scleral port (incision on the sclera). Static following-accuracy evaluation and dynamic light tracking experiments are carried out. The results show that the proposed system can successfully illuminate the desired area with negligible offset (the average offset is 2.45 mm with standard deviation of 1.33 mm). The average scleral forces are also below a specified threshold (50 mN). The proposed system not only can allow for increased focus on dominant hand instrument control, but also could be extended to three-arm procedures (two surgical instruments held by surgeon plus a robot-holding light pipe) in retinal surgery, potentially improving surgical efficiency and outcome.