2023
Authors
Pintani, D; Caputo, A; Mendes, D; Giachetti, A;
Publication
Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter, CHItaly 2023, Torino, Italy, September 20-22, 2023
Abstract
Despite significant efforts dedicated to exploring the potential applications of collaborative mixed reality, the focus of the existing works is mostly related to the creation of shared virtual/mixed environments resolving concurrent manipulation issues rather than supporting an effective collaboration strategy for the design procedure. For this reason, we present CIDER, a system for the collaborative editing of 3D augmented scenes allowing two or more users to manipulate the virtual scene elements independently and without unexpected changes. CIDER is based on the use of "layers"encapsulating the state of the environment with private layers that can be edited independently and a global one collaboratively updated with "commit"operations. Using this system, implemented for the HoloLens 2 headsets and supporting multiple users, we performed a user test on a realistic interior design task, evaluating the general usability and comparing two different approaches for the management of the atomic commit: forced (single-phase) and voting (requiring consensus), analyzing the effects of this choice on the collaborative behavior. © 2023 ACM.
2023
Authors
Simões, J; Lourenço, J; Sargo, S; Morais, JC;
Publication
Springer Proceedings in Earth and Environmental Sciences
Abstract
The recent situation of the COVID-19 pandemic has stimulated both the discussion on the use of IT-related teaching tools and the exposure of the student population to vulnerabilities linked to cybersecurity literacy as an integral part of the educational projects of educational institutions and a component of the exercise of citizenship and social sustainability of educational communities. The study presented is based on the assumption that the use of gamification as an element or tool that promotes learning within digital environments may be feasible, and more specifically may function as a teaching element on issues related to cybersecurity for students, especially for higher education students. In order to quantify the openness of students to such a tool path, quantitative methodology was used, and a survey was carried out in two Polytechnic Institutions (PI), achieving a sample of 95 students, and seeking perceptions on positive impacts resulting from the creation of a game scenario for better learning. Results show that students, regardless of their higher education course, clearly understand what gamification is and its goals, and also that students adopt good cybersecurity practices according to their higher education course. This last result goes accordingly with the supposition that gamification can and should be used in cybersecurity literacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
2023
Authors
de Jesus, G;
Publication
ADVANCES IN INFORMATION RETRIEVAL, ECIR 2023, PT III
Abstract
Tetun is one of Timor-Leste's official languages alongside Portuguese. It is a low-resource language with over 932,000 speakers that started developing when Timor-Leste restored its independence in 2002. Newspapers mainly use Tetun and more than ten national online news websites actively broadcast news in Tetun every day. However, since information retrieval-based solutions for Tetun do not exist, finding Tetun information on the internet and digital platforms is challenging. This work aims to investigate and develop solutions that can enable the application of information retrieval techniques to develop search solutions for Tetun using Tetun INL and focus on the ad-hoc text retrieval task. As a result, we expect to have effective search solutions for Tetun and contribute to the innovation in information retrieval for low-resource languages, including making Tetun datasets available for future researchers.
2023
Authors
Torneiro, A; Oliveira, E; Rodrigues, NF;
Publication
2023 IEEE 11TH INTERNATIONAL CONFERENCE ON SERIOUS GAMES AND APPLICATIONS FOR HEALTH, SEGAH
Abstract
Postoperative residual neuromuscular block (PRNB) is still a problem during the surgery procedures resulting in health problems, such as, airway obstruction, hypoxia and pulmonary aspiration. To perform more accurate monitoring of the patient during surgery quantitative neuromuscular blockade monitoring measuring TOF ratio has been recommended by medical institutions. There are some devices available using different techniques, however there are only a few number of clinicians using them, since those devices are costly and have difficult clinical set-up. This paper presents a systematic review of current devices for quantitative neuromuscular monitoring during the surgery procedure following the PRISMA methodology. This study was carried out to list the currently available devices and report the capabilities that are missing in these devices since 2017. The databases used to do the research were PubMed, Cochrane Library, PubMed Central (PMC), Web of Science, IEEE Xplore, ScienceDirect, Directory of Open Access Journals (DOAJ). 17 articles were selected, presenting comparisons between two devices using different techniques. Quantitative monitoring provides the most accurate TOF ratio measurement but still needs to be incentivized.
2023
Authors
Pinto, G; Barroso, B; Rodrigues, N; Guimaraes, M; Oliveira, E;
Publication
2023 IEEE 11TH INTERNATIONAL CONFERENCE ON SERIOUS GAMES AND APPLICATIONS FOR HEALTH, SEGAH
Abstract
Background: Schizophrenia is the most common psychotic illness in the world. The negative and cognitive symptoms of this mental disorder often prevent full reintegration of patients into society, and cannot be effectively addressed with drugs alone, relying on therapy and rehabilitation. Video games as a digital tool for rehabilitation and therapy can help promote accessibility, improve patient engagement and reduce costs to institutions. Methods: A systematic review was conducted from October to November 2022 to analyze the effects of video game based rehabilitation and therapy on negative and cognitive symptoms in schizophrenic patients. The databases used to perform the search were Scopus, PubMed and Web of Science, with the search query: Schizophrenia AND (Video Game OR Serious Game). Results: A total of 228 papers were found, of which 88 duplicates were removed. After reading the titles and abstracts of the remaining 140 papers, 116 were excluded for not meeting the defined eligibility criteria for the review. Of the 24 papers left, 20 were excluded for similar reasons, resulting in the inclusion of four studies in this systematic review Conclusion: The available data for this review was limited, highlighting a need for more research in the field as well as standardization of terms used to describe the digital tools developed and assessment methods used to gather results from these interventions. Nevertheless, statistical data from the four studies included in this review showed that serious games are a promising tool for the rehabilitation and therapy of negative and cognitive symptoms of schizophrenic patients, with significant effects on the patients' performance and motivation.
2023
Authors
Fernandes, N; Oliveira, E; Rodrigues, NF;
Publication
2023 IEEE 11TH INTERNATIONAL CONFERENCE ON SERIOUS GAMES AND APPLICATIONS FOR HEALTH, SEGAH
Abstract
Background-Classification, detection, and segmentation of minimally invasive instruments is an essential component for robotic-assisted surgeries and surgical skill assessments. Methods-Cochrane Library, PubMed, ScienceDirect, and IEEE Xplore databases were searched from January 2018 to May 2022. Selected studies evaluated deep learning (DL) models for image and video analysis of laparoscopic surgery. Comparisons were made of the studies' characteristics such as the dataset source, type of laparoscopic operation, number of images/videos, and types of neural networks (NN) used. Results-22 out of 152 studies identified met the selection criteria. The application with the greatest number of studies was instrument detection (59.1%) and the second was instrument segmentation (40.9%). The most tested procedure was cholecystectomy (72.73%). Conclusions-Although CNN-based algorithms outperform other methods in instrument detection and many have been proposed, there are still challenging conditions where numerous difficulties arise. U-Nets are the dominant force in the field for segmentation, but other models such as Mask R-CNN follow close behind with comparable results. Deep learning holds immense potential in laparoscopic surgery and many improvements are expected as soon as data quality improves.
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