Abstract
Virtual Reality (VR) has been evolving over the years, becoming more and more accessible, in a wide area of applications. One of these areas where VR can have a major impact is training and certification. Hydrogen vehicles are becoming a reality and first responders still lack proper tools and resources to train emergency responses for the purpose. VR can play here a crucial role in ensuring a proper hydrogen emergency response training due to the advantages associated with VR training programs such as resource optimization, repeatability, and replicability. This paper proposes using VR for hydrogen emergency response training by developing a solution composed of three components: tutorial mode, training mode, and certification mode. A usability study is further conducted to evaluate its usability and user satisfaction. The results show that the use of this application regards usability and user satisfaction were extremely positive.

Abstract
Innovations in virtual reality have boosted their use for business purposes and personal consumption. The main objective of this technology is to transport the user to virtual environments. The use of avatars to replace the user's body in these environments increases the levels of presence and embodiment of the user. Through the inverse kinematics it is possible to animate the avatars according to the data obtained in sensors scattered around the user's body, replicating the movements in a avatar. These sensors can vary in number offering different levels of fidelity in the tracking of the human body. In this paper, we study the impact of the number of sensors used in the presence of the user and in the embodiment of the avatar, using three, five and six tracking points. The results show that there is no statistically significant differences in presence nor in any of its sub-scale, however one can observe a positive trend in the 6-points conditions. As for embodiment, the results show that there is statistically signifficant differences in some of presence sub-scales, namelly tactile sensation, response and in the embodiment as a whole.

Abstract
Multiple factors can affect presence in virtual environments, such as the number of human senses engaged in a given experience or the extent to which the virtual experience is credible. The purpose of the present work is to study how the inclusion of credible multisensory stimuli affects the sense of presence, namely, through the use of wind, passive haptics, vibration, and scent. Our sample consisted of 37 participants (27 men and 10 women) whose ages ranged from 17 to 44 years old and were mostly students. The participants were divided randomly into 3 groups: Control Scenario (visual and auditory - N = 12), Passive Haptic Scenario (visual, auditory, and passive haptic - N = 13) and Multisensory Scenario (visual, auditory, wind, passive haptic, vibration, and scent - N = 12). The results indicated a significant increase in the involvement subscale when all multisensory stimuli were delivered. We found a trend where the use of passive haptics by itself has a positive impact on presence, which should be the subject of further work.

Abstract
Promotional 360 degrees videos are now widely used to promote touristic sites, giving consumers a more immersive glimpse of what they can expect from those places. However, these 360 degrees videos often comprise so much information that it overloads the users, not allowing them to benefit from such a rich multimedia experience. To overcome this issue, we propose and evaluate a novel method that allows the experience of immersive 360 degrees promotional videos to be more interactive and informative without overloading users. The evaluation study focuses on how the proposed interaction method performs versus the non-interactive method in terms of user satisfaction, presence, and cybersickness in both a low-immersive (computer monitor) setup and an immersive platform (head-mounted display (HMD)). Our sample (N = 50) was randomly divided into four groups: 360 degrees (computer monitor without interaction), 360 degrees Interaction (computer monitor with interaction), IVR360 degrees (HMD without interaction) and IVR360 degrees Interaction (HMD with interaction). The results show that the novel proposed method is preferred by users over the non-interactive approach regardless of the setup (low-immersive or immersive). For cybersickness, there were no differences across all the experimental scenarios. We conclude that our method has the potential to bring added value to touristic promotion when compared to conventional promotional approaches.

Abstract
The goal of this study is to examine the effects of the sense of presence and immersive tendencies on learning outcomes while comparing different media formats (Interactive VR, Non-interactive VR and Video). An experiment was conducted with 36 students that watched a Biology lesson about the human cells. Contrary to expected, the results demonstrate that the Non-interactive VR was the most successful format. Sense of presence and immersive tendencies did not have an effect on learning gain, and the latter was not a critical factor to experience the sense of presence. The findings provide empirical evidence to help understand the influence of these variables on learning in VR. © 2020, Springer Nature Switzerland AG.

Abstract
Convolution Neural Network (CNN)-based object detection models have achieved unprecedented accuracy in challenging detection tasks. However, existing detection models (detection heads) trained on 8-bits/pixel/channel low dynamic range (LDR) images are unable to detect relevant objects under lighting conditions where a portion of the image is either under-exposed or over-exposed. Although this issue can be addressed by introducing High Dynamic Range (HDR) content and training existing detection heads on HDR content, there are several major challenges, such as the lack of real-life annotated HDR dataset(s) and extensive computational resources required for training and the hyper-parameter search. In this paper, we introduce an alternative backwards-compatible methodology to detect objects in challenging lighting conditions using existing CNN-based detection heads. This approach facilitates the use of HDR imaging without the immediate need for creating annotated HDR datasets and the associated expensive retraining procedure. The proposed approach uses HDR imaging to capture relevant details in high contrast scenarios. Subsequently, the scene dynamic range and wider colour gamut are compressed using HDR to LDR mapping techniques such that the salient highlight, shadow, and chroma details are preserved. The mapped LDR image can then be used by existing pre-trained models to extract relevant features required to detect objects in both the under-exposed and over-exposed regions of a scene. In addition, we also conduct an evaluation to study the feasibility of using existing HDR to LDR mapping techniques with existing detection heads trained on standard detection datasets such as PASCAL VOC and MSCOCO. Results show that the images obtained from the mapping techniques are suitable for object detection, and some of them can significantly outperform traditional LDR images.