Extended Reality (XR) offers a fascinating capability to transport individuals through time and space. However, are there ways to keep the users grounded in reality while exploring distant landscapes or unfamiliar places? We propose in this paper a Point of Interest portal-based navigation technique that takes full advantage of the available, often limited, physical space of the user while still facilitating natural walking in a virtual environment. This technique can be beneficial for the exploration of Points of Interest (PoIs), which is a recurrent situation in virtual museum visits. It ensures consistency between the physical workspace while a user moves from one PoI to the next, enabling applicability beyond Virtual Reality (VR) and into Mixed Reality (MR) scenarios. The connection of different PoIs in the virtual space is achieved with portals, which are strategically placed in the physical workspace. In order to evaluate the efficiency of this technique, in terms of time, presence and user subjective preference, we conducted an experiment with 19 participants who visited artefacts in various rooms of a fictitious virtual museum. The results indicated that the PoI-based portal technique delivers performance comparable to the most well-known navigation technique (teleportation), while maintaining a balance between immersion in the virtual environment and awareness of the physical space, and offering better spatial consistency. Therefore, it can be considered a viable solution for cross-reality interaction.

Abstract Unlabeled entity deduplication is a relevant task already studied in the recent literature. Most methods can be traced back to the following workflow: entity blocking phase, in-block pairwise comparisons between entities to draw similarity relations, closure of the resulting meshes to create groups of duplicate entities, and merging group entities to remove disambiguation. Such methods are effective but still not good enough whenever a very low false positive rate is required. In this paper, we present an approach for evaluating the correctness of “groups of duplicates”, which can be used to measure the group’s accuracy hence its likelihood of false-positiveness. Our novel approach is based on a Graph Neural Network that exploits and combines the concept of Graph Attention and Long Short Term Memory (LSTM). The accuracy of the proposed approach is verified in the context of Author Name Disambiguation applied to a curated dataset obtained as a subset of the OpenAIRE Graph that includes PubMed publications with at least one ORCID identifier.

In the present study we conducted a systematic review on user studies for Archaeology in eXtended Reality of the last 10 years. After a screening and selection process, 52 articles were selected for an in-depth analysis. Their classification follows different axes: devices, location dependency, type of users, interaction and collaboration. We also organised the existing user studies according to tasks, evaluation measurements, number of participants, and how the study was conducted (pre-test and/or post-test, formative and summative evaluation, quantitative and qualitative data). We found an intertwined relation between Archaeology and Cultural Heritage, which is reflected in the vast presence of applications for museum exhibitions and tours on archaeological sites. Similarities between systems developed for archaeologists and for general public were also investigated. Our purpose was to find a common ground between different user studies that could help designers of the next systems have a base on which they can build their system. We also highlighted which would be the preferred and most suitable evaluation techniques, when they are needed, with the type of users to address. The results show a heterogeneity of measurable variables and possible choices, but some guidelines could be derived.