Abstract
Virtual Reality (VR) has been showing potential in new and diverse areas, notably in education. However, there is a lack of studies in the Foreign Language Teaching and Learning field, particularly in listening comprehension. Therefore, this study investigated the effects of avatar animations and lip synchronization, and user interaction; features deemed relevant in this broader area. A sociodemographic, a quick CEFR-Common European Framework of Reference for Languages-15-minute English test, and questionnaire were used to evaluate the participants' Presence, Quality of Experience, Cybersickness and Knowledge Retention. Results show that, overall, the use of avatars with realistic animations and movements, and featuring lip synchronization have a positive influence on the users' sense of presence, knowledge retention and a more enjoyable overall quality of experience. The same can be said for the use of object interaction and navigation in the cultural representative environment, which had an overall positive impact.

Abstract
Several factors have been identified to contribute to the sense of presence and cybersickness, including the preponderance users have in the virtual environments (VE) and their gender. This work focuses on studying the Role Type and gender in a VE and their impact on the sense of presence and cybersickness when immersive Virtual Reality (VR) setups are used. For this, a set of psychophysical experiments were conducted to evaluate a VR scenario with three Role Types: Viewer, Explorer, and Searcher. Results revealed statistically significant differences in Spatial Presence, Cybersickness, Nausea, Oculomotor Discomfort, and Disorientation for Role Type. In the evaluated scenario, it was observed that a more dominant Role Type on the VE leads to a higher reported spatial presence (sense of physically being present in the VE) and higher cybersickness scores. We conclude that a higher relevance of the Role Type makes the users more sensitive to the stimuli present in the VE regarding the coherency of the interaction/simulation and, consequently, more prone to develop cybersickness symptoms. No differences were found between the genders.

Abstract
Designers often use physical hand-drawn mockups to convey their ideas to stakeholders. Unfortunately, these sketches do not depict the exact final look and feel of web pages, and communication errors will often occur, resulting in prototypes that do not reflect the stakeholder's vision. Multiple suggestions exist to tackle this problem, mainly in the translation of visual mockups to prototypes. Some authors propose end-to-end solutions by directly generating the final code from a single (black-box) Deep Neural Network. Others propose the use of object detectors, providing more control over the acquired elements but missing out on the mockup's layout. Our approach provides a real-time solution that explores: (1) how to achieve a large variety of sketches that would look indistinguishable from something a human would draw, (2) a pipeline that clearly separates the different responsibilities of extracting and constructing the hierarchical structure of a web mockup, (3) a methodology to segment and extract containers from mockups, (4) the usage of in-sketch annotations to provide more flexibility and control over the generated artifacts, and (5) an assessment of the synthetic dataset impact in the ability to recognize diagrams actually drawn by humans. We start by presenting an algorithm that is capable of generating synthetic mockups. We trained our model (N=8400, Epochs=400) and subsequently fine-tuned it (N=74, Epochs=100) using real human-made diagrams. We accomplished a mAP of 95.37%, with 90% of the tests taking less than 430ms on modest commodity hardware (approximate to 2.3fps). We further provide an ablation study with well-known object detectors to evaluate the synthetic dataset in isolation, showing that the generator achieves a mAP score of 95%, approximate to 1.5 x higher than training using hand-drawn mockups alone.

Abstract
Internet-of-Things has reshaped the way people interact with their surroundings and automatize the once manual actions. In a smart home, controlling the Internet-connected lights is as simple as speaking to a nearby conversational assistant. However, specifying interaction rules, such as making the lamp turn on at specific times or when someone enters the space is not a straightforward task. The complexity of doing such increases as the number and variety of devices increases, along with the number of household members. Thus, managing such systems becomes a problem, including finding out why something has happened. This issue lead to the birth of several low-code development solutions that allow users to define rules to their systems, at the cost of discarding the easiness and accessibility of voice interaction. In this paper we extend the previous published work on Jarvis [1], a conversational interface to manage IoT systems that attempts to address these issues by allowing users to specify time-based rules, use contextual awareness for more natural interactions, provide event management and support causality queries. A proof-of-concept is presented, detailing its architecture and natural language processing capabilities. A feasibility experiment was carried with mostly non-technical participants, providing evidence that Jarvis is intuitive enough to be used by common end-users, with participants showcasing an overall preference by conversational assistants over visual low-code solutions.

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