Abstract
Hands are the most important tool to interact with virtual environments, and they should be available to perform the most critical tasks. For example, a surgeon in VR should keep his/her hands on the instruments and be able to do secondary tasks without performing a disruptive event to the operative task. In this common scenario, one can observe that hands are not available for interaction. The goal of this systematic review is to survey the literature and identify which hands-free interfaces are used, the performed interaction tasks, what metrics are used for interface evaluation, and the results of such evaluations. From 79 studies that met the eligibility criteria, the voice is the most studied interface, followed by the eye and head gaze. Some novel interfaces were brain interfaces and face expressions. System control and selection represent most of the interaction tasks studied and most studies evaluate interfaces for usability. Despite the best interface depending on the task and study, the voice was found to be versatile and showed good results amongst the studies. More research is recommended to improve the practical use of the interfaces and to evaluate the interfaces more formally.

Abstract
Immersion is a proven method of learning a foreign language and using Virtual Reality to achieve that immersion has high potential educational benefits. However, there are no recent systematic reviews that combine both foreign language education and immersive Virtual Reality. This systematic review aims to identify features, educational methods, technologies, and gaps of immersive virtual reality for foreign and second language education. PRISMA method was followed to carry out the systematic review. From the analysis of the results, two main conclusions were drawn. Firstly, the relation between immersive Virtual Reality and foreign language learning is quite positive, particularly when compared with conventional pedagogical practices. Not only that, the connection between immersive Virtual Reality and the user's motivation and satisfaction is also quite positive. Lastly, limitations such as the low sample of studies and gaps in the literature are addressed, and directions for future work and the area's progress are pointed out.

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
This paper aims to explore the impact of sense of presence and cybersickness on the users' expectancy and perceived similarity between virtual and the corresponding real environments. Two virtual reality setups were tested (non-immersive and immersive) to achieve further conclusions. This research encompassed a quantitative analysis using data collection based on questionnaires, applied to a sample of 45 participants. A virtual experience was conducted (to explore users' cybersickness and sense of presence), followed by a visit to the actual real sites (to determine the degree of perceived similarity between the virtual and the corresponding real environment and if their expectations were fulfilled). Our results show a positive correlation between the global sense of presence and perceived similarity and users' expectancy for the non-immersive VR setup. A positive correlation was also found between the global cybersickness on both perceived similarity and users' expectancy for the immersive VR setup. Implications of such results for virtual tourism are discussed.

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.