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Several species of Polistes paper wasp are well known for their social hierarchies and the ability for individual wasps to modulate their social behaviors based on recognizable facial features of other wasps. For example, wasps that observe an aggressive social interaction between two other wasps will later behave differently toward the winner and loser of that interaction. Being able to alter the physical appearance of wasps~(e.g., with paint) has allowed for testing hypothetical roles of individual recognition in hierarchy formation, which is how researchers know that wasps are attending to faces specifically. However, these physical methods are limited in their scope. Social insects who respond to visual stimuli from other insects have been shown to give the same responses to playbacks of video recordings of those stimuli, which suggests that there may be a role for generative methods in social-insect research. Being able to computationally change the faces of individual wasps in a video recording of wasp social interactions would greatly expand the experimental toolbox of the behavioral researcher. Toward this end, we evaluate the use of an existing annotation-free model for image animation by motion transfer, the thin-plate spline motion model, for creating realistic videos that depict the face of a paper wasp performing behaviors recorded by another. Not needing to pre-define important landmarks is a strength of this method for this application space, but we find that "deep faking wasps" poses unique and non-trivial problems that still need to be solved before off-the-shelf motion transfer models can be used in the insect behavioral laboratory. 

Urban Search and Rescue (USAR) missions continue to benefit from the incorporation of human–robot teams (HRTs). USAR environments can be ambiguous, hazardous, and unstable. The integration of robot teammates into USAR missions has enabled human teammates to access areas of uncertainty, including hazardous locations. For HRTs to be effective, it is pertinent to understand the factors that influence team effectiveness, such as having shared goals, mutual understanding, and efficient communication. The purpose of our research is to determine how to (1) better establish human trust, (2) identify useful levels of robot transparency and robot explanations, (3) ensure situation awareness, and (4) encourage a bipartisan role amongst teammates. By implementing robot transparency and robot explanations, we found that the driving factors for effective HRTs rely on robot explanations that are context-driven and are readily available to the human teammate. 

Ụbụrụ is an executive function computerized rehabilitation application specifically designed for mild Traumatic Brain Injury (mTBI) individuals. Ụbụrụ utilizes serious games to train cognitive flexibility, planning, and organization. This paper explores the rationale and components behind the alpha stage of the application’s development, and its first design iteration. Currently, individuals with a history of mTBI have limited rehabilitation options as a result of lack of knowledge in terms of available services, access, time, or financial and insurance constraints. Due to the invisible nature of mTBIs, perception of injury severity is diminished, individuals are not properly equipped with how to proceed forward with rehabilitation, and awareness of injury can be inadvertently compromised. The intention behind the Ụbụrụ application is to be a computerized cognitive rehabilitation alternative and additive when limitations such as time, finances, or insurance exist.