Alphanumeric text entry is a challenge for Virtual Reality (VR) applications. VR enables new capabilities, impossible in the real world, such as an unobstructed view of the keyboard, without occlusion by the user's physical hands. Several hand representations have been proposed for typing in VR on standard physical keyboards. However, to date, these hand representations have not been compared regarding their performance and effects on presence for VR text entry. Our work addresses this gap by comparing existing hand representations with minimalistic fingertip visualization. We study the effects of four hand representations (no hand representation, inverse kinematic model, fingertip visualization using spheres and video inlay) on typing in VR using a standard physical keyboard with 24 participants. We found that the fingertip visualization and video inlay both resulted in statistically significant lower text entry error rates compared to no hand or inverse kinematic model representations. We found no statistical differences in text entry speed.
@cite_1 investigated the effects of gender on different virtual hand representations. The participants had to conduct various tasks, amongst them a typing task with a single sentence. The authors did not report any text entry metrics. For our experiment on hand representations, we reused their androgynous hand model as a compromise between male and female hand depictions.
@cite_1
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Rendering the user's body in virtual reality increases immersion and presence the illusion of "being there". Recent technology enables determining the pose and position of the hands to render them accordingly while interacting within the virtual environment. Virtual reality applications often use realistic male or female hands, mimic robotic hands, or cartoon hands. However, it is unclear how users perceive different hand styles. We conducted a study with 14 male and 14 female participants in virtual reality to investigate the effect of gender on the perception of six different hands. Quantitative and qualitative results show that women perceive lower levels of presence while using male avatar hands and male perceive lower levels of presence using non-human avatar hands. While women dislike male hands, men accept and feel presence with avatar hands of both genders. Our results highlight the importance of considering the users' diversity when designing virtual reality experiences. We introduce a new benchmark, WinoBias, for coreference resolution focused on gender bias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing coreference benchmark datasets. Our dataset and code are available at this http URL We report a user study which compared four virtual environment travel techniques using a divided attention task. Participants used either real walking, gaze-directed, pointing-directed, or torso-directed travel to follow a target through an environment while simultaneously responding to auditory stimuli. In addition to travel technique, we investigated gender as a between-subjects variable and task difficulty (simple or complex) and task type (single or divided) as within-subjects variables. Real walking allowed superior performance over the pointing-directed technique on measures of navigation task performance and recognition of stimuli presented during navigation. This indicates that participants using real walking may have had more spare cognitive capacity to process and encode stimuli than those using pointing-directed travel. We also found a gender-difficulty interaction where males performed worse and responded slower to the attention task when the spatial task was more difficult, but no differences were observed for females between difficulty levels. While these results may be pertinent for the design of virtual environments, the nature and goal of the virtual environment tasks must be carefully considered to determine whether similar effects on performance can be expected under different conditions. This paper describes an investigation of authorship gender attribution mining from e-mail text documents. We used an extended set of predominantly topic content-free e-mail document features such as style markers, structural characteristics and gender-preferential language features together with a support vector machine learning algorithm. Experiments using a corpus of e-mail documents generated by a large number of authors of both genders gave promising results for author gender categorisation.