The development and widespread adoption of immersive XR applications has led to a renewed interest in representations that are capable of reproducing real-world objects and scenes with high fidelity. Among such representations, point clouds have attracted the interest of industry and academia alike, and new compression solutions have been developed to facilitate their adoption in mainstream applications. To ensure the best quality of experience for the end-user in limited bandwidth scenarios, new full-reference objective quality metrics have been proposed, promoting features designed specifically for point cloud contents. However, the performance of such features to predict the quality of point cloud contents when the reference is not available is largely unexplored. In this paper, we evaluate the performance of features commonly used to model point cloud distortions in a no-reference framework. The obtained features are integrated into a quality value through a support vector regression model. Results demonstrate the potential of full-reference features for no-reference assessment.

In popular crowdsourcing marketplaces like Amazon Mechanical Turk, crowd workers complete tasks posted by requesters in return for monetary rewards. Task requesters are solely responsible for deciding whether to accept or reject submitted work. Rejecting work can directly affect the monetary reward of corresponding workers, and indirectly influence worker qualifications and their future work opportunities in the marketplace. Unexpected or unwarranted rejections therefore result in negative emotions and reactions among workers. Despite the high prevalence of rejections in crowdsourcing marketplaces, little research has explored ways to mitigate the negative emotional repercussions of rejections on crowd workers. Addressing this important research gap, we investigate whether introducing self-reflection at different stages after task execution can alleviate the emotional toll of rejection decisions on crowd workers. Our work is inspired by prior studies in psychology that have shown that self-reflection on negative personal experiences can positively affect one's emotion. To this end, we carried out an experimental study investigating the impact of explicit self-reflection on the emotions of rejected crowd workers. Results show that allowing workers to self-reflect on their delivered work, especially before receiving a rejection, has a significantly positive impact on their self-reported emotions in terms of valence and dominance. Our findings reveal that introducing a self-reflection stage before workers receive acceptance or rejection decisions on submitted work, can help in positively influencing the emotions of a worker. These findings have important design implications towards fostering a healthier requester-worker relationship and contributing towards the sustainability of the crowdsourcing marketplace.

Music is often both personally and affectively meaningful to human listeners. However, little work has been done to create music recommender systems that take this into account. In this demo proposal, we present Spotivibes: a first prototype for a new color-based tagging and music recommender system. This innovative tagging system is designed to take the users' personal experience of music into account and allows them to tag their favorite songs in a non-intrusive way, which can be generalized to their entire library. The goal of Spotivibes is twofold: to help users better tag their playlists to get better playlists and to provide research data on implicit grouping mechanisms in personal music collections. The system was tested with a user study on 34 Spotify users.