Comparing Hand Gestures and a Gamepad Interface for Locomotion in Virtual Environments

Hand gesture is a new and promising interface for locomotion in virtual environments. While several previous studies have proposed different hand gestures for virtual locomotion, little is known about their differences in terms of performance and user preference in virtual locomotion tasks. In the present paper, we presented three different hand gesture interfaces and their algorithms for locomotion, which are called the Finger Distance gesture, the Finger Number gesture and the Finger Tapping gesture. These gestures were inspired by previous studies of gesture-based locomotion interfaces and are typical gestures that people are familiar with in their daily lives. Implementing these hand gesture interfaces in the present study enabled us to systematically compare the differences between these gestures. In addition, to compare the usability of these gestures to locomotion interfaces using gamepads, we also designed and implemented a gamepad interface based on the Xbox One controller. We conducted empirical studies to compare these four interfaces through two virtual locomotion tasks. A desktop setup was used instead of sharing a head-mounted display among participants due to the concern of the Covid-19 situation. Through these tasks, we assessed the performance and user preference of these interfaces on speed control and waypoints navigation. Results showed that user preference and performance of the Finger Distance gesture were close to that of the gamepad interface. The Finger Number gesture also had close performance and user preference to that of the Finger Distance gesture. Our study demonstrates that the Finger Distance gesture and the Finger Number gesture are very promising interfaces for virtual locomotion. We also discuss that the Finger Tapping gesture needs further improvements before it can be used for virtual walking

Eliminating Contextual Bias in Aspect-based Sentiment Analysis.

Pretrained language models (LMs) have made remarkable achievements in aspect-based sentiment analysis (ABSA). However, it is discovered that these models may struggle in some particular cases (e.g., to detect sentiments expressed towards targeted aspects with only implicit or adversarial expressions). Since it is hard for models to align implicit or adversarial expressions with their corresponding aspects, the sentiments of the targeted aspects would largely be impacted by the expressions towards other aspects in the sentence. We name this phenomenon as contextual bias. To tackle the problem, we propose a flexible aspect-oriented debiasing method (Arde) to eliminate the harmful contextual bias without the need of adjusting the underlying LMs. Intuitively, Arde calibrates the prediction towards the targeted aspect by subtracting the bias towards the context. Favorably, Arde can get theoretical support from counterfactual reasoning theory. Experiments are conducted on SemEval benchmark, and the results show that Arde can empirically improve the accuracy on contextually biased aspect sentiments without degrading the accuracy on unbiased ones. Driven by recent success of large language models (LLMs, e.g., ChatGPT), we further uncover that even LLMs can fail to address certain contextual bias, which yet can be effectively tackled by Arde

Neutralizing antibody levels associated with injectable and aerosolized Ad5-nCoV boosters and BA.2 infection

Abstract Background Several COVID-19 vaccines are in widespread use in China. Few data exist on comparative immunogenicity of different COVID-19 vaccines given as booster doses. We aimed to assess neutralizing antibody levels raised by injectable and inhaled aerosolized recombinant adenovirus type 5 (Ad5)-vectored COVID-19 vaccine as a heterologous booster after an inactivated COVID-19 vaccine two-dose primary series. Methods Using an open-label prospective cohort design, we recruited 136 individuals who had received inactivated vaccine primary series followed by either injectable or inhaled Ad5-vectored vaccine and measured neutralizing antibody titers against ancestral SARS-CoV-2 virus and Omicron BA.1 and BA.5 variants. We also measured neutralizing antibody levels in convalescent sera from 39 patients who recovered from Omicron BA.2 infection. Results Six months after primary series vaccination, neutralizing immunity against ancestral SARS-CoV-2 was low and neutralizing immunity against Omicron (B.1.1.529) was lower. Boosting with Ad5-vectored vaccines induced a high immune response against ancestral SARS-CoV-2. Neutralizing responses against Omicron BA.5 were ≥ 80% lower than against ancestral SARS-CoV-2 in sera from prime-boost subjects and in convalescent sera from survivors of Omicron BA.2 infection. Inhaled aerosolized Ad5-vectored vaccine was associated with greater neutralizing titers than injectable Ad5-vectored vaccine against ancestral and Omicron SARS-CoV-2 variants. Conclusions These findings support the current strategy of heterologous boosting with injectable or inhaled Ad5-vectored SARS-CoV-2 vaccination of individuals primed with inactivated COVID-19 vaccine