Abstract
Most Convolution Neural Network (CNN) based object detectors, to date, have been optimized for accuracy and/or detection performance on datasets typically comprised of well exposed 8-bits/pixel/channel Standard Dynamic Range (SDR) images. A major existing challenge in this area is to accurately detect objects under extreme/difficult lighting conditions as SDR image trained detectors fail to accurately detect objects under such challenging lighting conditions. In this paper, we address this issue for the first time by introducing High Dynamic Range (HDR) imaging to object detection. HDR imagery can capture and process approximate to 13 orders of magnitude of scene dynamic range similar to the human eye. HDR trained models are therefore able to extract more salient features from extreme lighting conditions leading to more accurate detections. However, introducing HDR also presents multiple new challenges such as the complete absence of resources and previous literature on such an approach. Here, we introduce a methodology to generate a large scale annotated HDR dataset from any existing SDR dataset and validate the quality of the generated dataset via a robust evaluation technique. We also discuss the challenges of training and validating HDR trained models using existing detectors. Finally, we provide a methodology to create an out of distribution (OOD) HDR dataset to test and compare the performance of HDR and SDR trained detectors under difficult lighting condition. Results suggest that using the proposed methodology, HDR trained models are able to achieve 10 - 12% more accuracy compared to SDR trained models on real-world OOD dataset consisting of high-contrast images under extreme lighting conditions.

Abstract
Virtual reality has shown to have great potential as an educational tool when it comes to new learning methods. With the growth and dissemination of this technology, there is a massive opportunity for teachers to add this technology to their methods of teaching a second/foreign language, since students keep showing a growing interest in new technologies. This systematic review of empirical research aims at understanding whether the use of gaming strategies in virtual reality is beneficial for the learning of a second/foreign language or not. Results show that more than half of the articles proved that virtual reality technologies with gaming strategies can be used to learn a foreign language. It was also found that "learning" was the most evaluated dependent variable among the chosen records, augmented reality was the leading technology used, primary education and lower secondary was the most researched school stages, and the most used language to evaluate the use of gamified technology was by far the English language. Given the lack of directed investigation, it is recommended to use these technologies to support second language learning and not entirely replace traditional approaches. A research agenda is also proposed by the authors.

Abstract
Low-cost virtual-reality (VR) head-mounted displays (HMDs) with the integration of smartphones have brought the immersive VR to the masses, and increased the ubiquity of VR. However, these systems are often limited by their poor interactivity. In this paper, we present GestOnHMD, a gesture-based interaction technique and a gesture-classification pipeline that leverages the stereo microphones in a commodity smartphone to detect the tapping and the scratching gestures on the front, the left, and the right surfaces on a mobile VR headset. Taking the Google Cardboard as our focused headset, we first conducted a gesture-elicitation study to generate 150 user-defined gestures with 50 on each surface. We then selected 15, 9, and 9 gestures for the front, the left, and the right surfaces respectively based on user preferences and signal detectability. We constructed a data set containing the acoustic signals of 18 users performing these on-surface gestures, and trained the deep-learning classification pipeline for gesture detection and recognition. Lastly, with the real-time demonstration of GestOnHMD, we conducted a series of online participatory-design sessions to collect a set of user-defined gesture-referent mappings that could potentially benefit from GestOnHMD.

Abstract
Proper evaluation of realism in immersive virtual experiences is crucial to ensure optimisation of resources. This way, we can take better decisions while designing realistic immersive experiences, prioritising factors that have a higher impact on the perceived realism of the virtual experience. This systematic review aims to provide readers with an overview of methodologies used throughout the literature to evaluate realism in immersive virtual, augmented and mixed reality. A total of 79 from 1300 gathered articles met the eligibility criteria and were analysed. Results have shown that virtual reality is by far the platform where realism studies were performed. Head-mounted displays are by far the preferred equipment for such studies. Visual realism is the most researched, followed by audiovisual. The majority of methodologies consisted of subjective, as well as a combination of objective and subjective measures. The most used evaluation instrument is questionnaires where many of which are custom and non-validated. Presence questionnaires are the most used ones and are often used to evaluate the presence, perceived realism and involvement. Cybersickness evaluation is consistently assessed by one self-report questionnaire.

Abstract
Virtual Reality applications have the goal of transporting their users to a given virtual environment (VE). Thus, Presence is a consensual metric for evaluating the VEs' effectiveness. The present study adapts the Presence Questionnaire (PQ) for the Portuguese-speaking population, maintaining the validity of the contents and concepts, to ascertain the psychometric properties of the instrument.The adaptation to Portuguese was achieved through the standard adaptation process of translation and back-translation process. The sample consisted of 451 individuals (268 males and 183 females). Factor reliability ranged from 0.63 to 0.86. Confirmatory factor analysis produced a theoretical model of 21 items distributed among seven factors, where the covariance between some residual item errors was established. The fit indices obtained were , GFI , CFI , RMSEA , P [RMSEA ], MECVI . Results obtained allowed us to consider that the adapted Portuguese version of the PQ, with 21 items, forms a robust and valid questionnaire whose use is recommended to evaluate Presence in virtual reality research programmes, provided that they use samples of the Portuguese language (Europe).

Abstract
Previous works have shown the great potential of Virtual Reality (VR) in the area of Education. This paper studies if users can learn a second language when using a gamified VR application through an English learning test and how learning influences user satisfaction, sense of presence, cybersickness, and quality of experience through questionnaires. For this purpose, the VirtualeaRn game was developed. 20 Portuguese participants were exposed to the application, and the learning test was used before and after using the application. Result analysis shows an increase in learning results after using the VR gamified application, indicating the technology's efficacy in learning a second language. A positive user satisfaction, sense of presence, and quality of experience were also found. Some cases of cybersickness were reported. The outcomes are promising and provide enough information to show the potential of the gamification of VR technology for the area of learning a second language.