As the volume of content and the connectivity of social media have grown, snackable content has increasingly become an enjoyable and engaging way to share content. Snackable content is a shortened form of original content focusing on a single theme or motif for entertainment and quick understanding of a video moment. For content owners with a large library of long-form content (movies, television series, documentaries, etc.), one challenge in accommodating snackable content in social media uses is the correct identification and cutting of interesting regions. Related problems have been studied for algorithmic discovery of content for movie trailers, short-duration meme content, and medium duration news stories, but none of these approaches included user preferences as explicit drivers for cuts. This paper analyzes both human and automatic methods for creating snackable clips across different categories of content with two comprehensive user studies. Contrary to initial expectations, findings amongst the surveyed population indicate a preference for slightly longer snackable clips (60-90 seconds) and those that began or ended with a human character.

Billions of robocalls annually have undermined the public's trust in the entire phone system. New functionality, called STIR/SHAKEN (S/S), hopes to help fix this issue by detecting whether a call is coming from the number it says it is. However, due to the nature of the system, at first only a portion of calls would go through the S/S system. This led us to question whether presenting this information would confuse users more than help. In this paper, we detail the results of online surveys, in-person interviews, and a lab-based simulation. The research recommends "Valid Number" for the label on the display and found that even with only 30% of calls being validated, S/S increased trust, answer frequency and consumer satisfaction. Based on these results, the launch of S/S could positively affect the current phone system and re-establish consumer trust.

To be effective team members, it is important for robots to understand the high-level behaviors of collocated humans. This is a challenging perceptual task when both the robots and people are in motion. In this paper, we describe an event-based model for multiple robots to automatically measure synchronous joint action of a group while both the robots and co-present humans are moving. We validated our model through an experiment where two people marched both synchronously and asynchronously, while being followed by two mobile robots. Our results suggest that our model accurately identifies synchronous motion, which can enable more adept human-robot collaboration.