Backstory enriches the depth of a character by providing context and history. This online study explored how social robots narrating emotional backstories affect human empathy. Three backstories-happy, neutral (control), and sad- were crafted to mirror believable robot experiences. These narratives, personified as the robot's emotions, were conveyed through multi-modal video recordings of the robot that featured visual and textual elements. A within-subject experiment was conducted through a web-based survey among 60 English-speaking students from a German university. Our results highlight the significant role of backstories in shaping perceptions of robots, with the sad backstory notably increasing empathy towards social robots, diverging from prior Human-Robot Interaction paradigms. Furthermore, gender-based differences in empathetic responses highlighted the impact of sad narratives on female participants, with the robot's voice set as female. Open-ended feedback suggested the need for improved expressiveness in facial expressions and gaze strategies to mitigate the uncanny valley phenomenon.

With cognitive plasticity being a “sluggish” process, regular long-term engagement in interventions like cognitive training is required to realize the potential that such interventions may confer for cognitive enhancement. A significant challenge associated with extended cognitive training studies is poor adherence and attrition. The current qualitative investigation therefore explored 66 older adult participants’ experiences of, and motivations during, a gamified cognitive training program to understand reasons for (non)adherence. Preferences regarding cognitive training were also explored to provide insight into how such programs might be designed and implemented to maximize motivation and long-term engagement. Using a qualitative description design drawing upon thematic analysis, findings revealed that participants were generally motivated by challenge, novelty, and opportunity to focus. In contrast, repetition, external factors, and negative mindsets detracted from the motivation of participants. Suggestions to improve cognitive training programs to promote long-term engagement included the addition of a large selection of games, an incentive system, mini daily challenges, reminder notifications, and social features. These findings may inform the design of cognitive training programs that accommodate the preferences and needs of older adults, which in turn, may promote adherence and improve intervention effectiveness.

Maximizing on k-submodular functions subject to size constraint has received extensive attention recently. In this paper, we investigate a more realistic scenario of this problem that (1) obtaining exact evaluation of an objective function is impractical, instead, its noisy version is acquired; and (2) algorithms are required to take only one single pass over dataset, producing solutions in a timely manner. We propose two novel streaming algorithms, namely DStream and RStream, with their theoretical performance guarantees. We further demonstrate the efficiency of our algorithms in two application, showing that our algorithms can return comparative results to state-of-the-art non-streaming methods while using a much fewer number of queries.