In order to bridge interdisciplinary differences in Presence research and to establish connections between Presence and “older” concepts of psychology and communication, a theoretical model of the formation of Spatial Presence is proposed. It is applicable to the exposure to different media and intended to unify the existing efforts to develop a theory of Presence. The model includes assumptions about attention allocation, mental models, and involvement, and considers the role of media factors and user characteristics as well, thus incorporating much previous work. It is argued that a commonly accepted model of Spatial Presence is the only solution to secure further progress within the international, interdisciplinary and multiple-paradigm community of Presence research.

The study of spatial presence is currently receiving increased attention in both media psychology and communication research. The present paper introduces the Spatial Presence Experience Scale (SPES), a short eight-item self-report measure. The SPES is derived from a process model of spatial presence ( Wirth et al., 2007 , Media Psychology, 9, 493–525), and assesses spatial presence as a two-dimensional construct that comprises a user’s self-location and perceived possible actions in a media environment. The SPES is shorter than many other available spatial presence scales, and can be conveniently applied to diverse media settings. Two studies are reported (N₁ = 290, N₂ = 395) that confirm sound psychometric qualities for the SPES.

Accurate and up-to-date information about the building stock is fundamental to better understand and mitigate the impact caused by catastrophic earthquakes, as seen recently in Turkey, Syria, Morocco and Afghanistan. Planning for such events is necessary to increase the resilience of the building stock and to minimize casualties and economic losses. Although in several parts of the world new constructions follow more strict compliance with modern seismic codes, a large proportion of existing building stock still demands a more detailed and automated vulnerability analysis. Hence, this paper proposes the use of computer vision deep learning models to automatically classify buildings and create large scale (city or region) exposure models. Such approach promotes the use of open databases covering most cities in the world (cf. OpenStreetMap, Google Street View, Bing Maps and satellite imagery), Therefore providing valuable geographical, topological and image data that may cheaply be used to extract valuable information to feed exposure models. Our previous work using deep learning models achieved, in line with the results from other projects, high classification accuracy concerning building materials and number of storeys. This paper extends the approach by: (i) implementing four CNN-based models to perform classification of three sets of different/extended buildings’ characteristics; (ii) training and comparing the performance of the four models for each of the sets; (iii) comparing the risk assessment results based on data extracted from the best CNN-based model against the results obtained with traditional ground data. In brief, the best accuracy obtained with the three tested sets of buildings’ characteristics is higher than 80%. Moreover, it is shown that the error resulting from using exposure models fed by automatic classification is not only acceptable, but also far outweighs the time and costs of obtaining a manual and specialised classification of building stocks. Finally, we recognize that automatic assessment of certain complex buildings’ characteristics compares to similar limitations of traditional assessments performed by specialized civil engineers, typically related with the identification of the number of storeys and the construction material. However, the identified limitations do not show worse results when compared against the use of manual buildings’ assessment.

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller skate variant of team hockey, it is of great interest to automatically track player movements, positions, and sticks, and also to make other judgments, such as being able to locate the ball. In this work, we present a real-time pipeline consisting of an object detection model specifically designed for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and fast movements, our deep learning object detection model effectively identifies and tracks important visual elements in real time, such as: ball, players, sticks, referees, crowd, goalkeeper, and goal. Using a curated dataset consisting of a collection of rink hockey videos containing 2525 annotated frames, we trained and evaluated the algorithm’s performance and compared it to state-of-the-art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80% and, according to our results, good performance in terms of accuracy and speed, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected an important event type in rink hockey games, namely, the occurrence of penalties.

Brazil is a pioneer in open government initiatives with legislation aimed at ensuring transparency, open data, and e-government. The objective of this work is to identify the level of openness of data in Brazilian government portals, map initiatives for the organization of information, and compliance with legislation. The clipping comprises the Brazilian government portals, specifically: two maintained by the Federal Government, one by the Government of the Federal District, and the Governments of the twenty-six Brazilian States. We used two instruments to evaluate the portals: the five-star rating scheme for open data and the fourteen principles for open government data. The methodology comprises documentary research, using instruments for analysis and evaluation of the clipping in a qualitative approach with a descriptive purpose. The results showed that only the Brazilian Open Data Portal reached the fourth star. As for the second instrument, the Federal Government and the states of Alagoas and Espirito Santo comply with the largest number of principles. States like Amazonas, Rio de Janeiro, and Parana have the worst results. We have concluded that Brazilian government portals have a low supply of open data, rarely structured and machine-readable, without standardization or interoperability.