The potential of remote XR experimentation: Defining benefits and limitations through expert survey and case study

Experimentation using extended reality (XR) technology is predominantly conducted in-lab with a co-present researcher. Remote XR experiments, without co-present researchers, have been less common, despite the success of remote approaches for non-XR investigations. In order to understand why remote XR experiments are atypical, this article outlines the perceived limitations, as well as potential benefits, of conducting remote XR experiments, through a thematic analysis of responses to a 30-item survey of 46 XR researchers. These are synthesized into five core research questions for the XR community, and concern types of participant, recruitment processes, potential impacts of remote setup and settings, the data-capture affordances of XR hardware and how remote XR experiment development can be optimized to reduce demands on the researcher. It then explores these questions by running two experiments in a fully “encapsulated” remote XR case study, in which the recruitment and experiment processes is distributed and conducted unsupervised. It discusses the design, experiment, and results from this case study in the context of these core questions

Compressing and Comparing the Generative Spaces of Procedural Content Generators.

The past decade has seen a rapid increase in the level of research interest in procedural content generation (PCG) for digital games, and there are now numerous research avenues focused on new approaches for driving and applying PCG systems. An area in which progress has been comparatively slow is the development of generalisable approaches for comparing alternative PCG systems, especially in terms of their generative spaces. It is to this area that this paper aims to make a contribution, by exploring the utility of data compression algorithms in compressing the generative spaces of PCG systems. We hope that this approach could be the basis for developing useful qualitative tools for comparing PCG systems to help designers better understand and optimize their generators. In this work we assess the efficacy of a selection of algorithms across sets of levels for 2D tile-based games by investigating how much their respective generative space compressions correlate with level behavioral characteristics. We conclude that the approach looks to be a promising one despite some inconsistency in efficacy in alternative domains, and that of the algorithms tested Multiple Correspondence Analysis appears to perform the most effectively

Sensorimotor learning in immersive virtual reality: A scoping literature review

The benefits and drawbacks of using immersive virtual reality (IVR) for learning are increasingly being explored, with growing evidence that a major contributor to IVR learning benefits are the sensorimotor-based affordances of the technology. However, to our knowledge, there have been no reviews of sensorimotor-based IVR learning studies for academic or cognitive learning. In order to provide an overview of the field, we present a scoping review based on a comprehensive search that identified 14 documents reporting on experimental sensorimotor-focused learning studies. The review found universally positive learning outcomes for sensorimotor-led approaches across a variety of topics and approaches, although noted multiple areas of difference and potential issues across studies, including differing measures for learning success, potential common confounding factors, a lack of longitudinal investigations, a lack of a common methods for surveying sensorimotor engagement in IVR, and a disconnect between researchers in this area

Harnessing Phenotypic Diversity towards Multiple Independent Objectives

This work was funded by EPSRC through the Media and Arts Technology Programme, an RCUK Doctoral Training Centre EP/G03723X/1. Computational facilities were provided by the MidPlus Regional Centre of Excellence for Computational Science, Engineering and Mathematics, under EPSRC grant EP/K000128/1

Abnormality detection using graph matching for multi-task dynamics of autonomous systems

Self-learning abilities in autonomous systems are essential to improve their situational awareness and detection of normal/abnormal situations. In this work, we propose a graph matching technique for activity detection in autonomous agents by using the Gromov-Wasserstein framework. A clustering approach is used to discretise continuous agents' states related to a specific task into a set of nodes with similar objectives. Additionally, a probabilistic transition matrix between nodes is used as edges weights to build a graph. In this paper, we extract an abnormal area based on a sub-graph that encodes the differences between coupled of activities. Such sub-graph is obtained by applying a threshold on the optimal transport matrix, which is obtained through the graph matching procedure. The obtained results are evaluated through experiments performed by a robot in a simulated environment and by a real autonomous vehicle moving within a University Campus

Compressing and Comparing the Generative Spaces of Procedural Content Generators

The past decade has seen a rapid increase in the level of research interest in procedural content generation (PCG) for digital games, and there are now numerous research avenues focused on new approaches for driving and applying PCG systems. An area in which progress has been comparatively slow is the development of generalisable approaches for comparing alternative PCG systems, especially in terms of their generative spaces. It is to this area that this paper aims to make a contribution, by exploring the utility of data compression algorithms in compressing the generative spaces of PCG systems. We hope that this approach could be the basis for developing useful qualitative tools for comparing PCG systems to help designers better understand and optimize their generators. In this work we assess the efficacy of a selection of algorithms across sets of levels for 2D tile-based games by investigating how much their respective generative space compressions correlate with level behavioral characteristics. We conclude that the approach looks to be a promising one despite some inconsistency in efficacy in alternative domains, and that of the algorithms tested Multiple Correspondence Analysis appears to perform the most effectively

Micronutrient deficiencies in Manitoba crops

Non-Peer Reviewe