2022
Authors
Silva, A; Sousa, C; Paulino, D; Sousa, M; Melo, M; Bessa, M; Paredes, H;
Publication
INFORMATION SYSTEMS AND TECHNOLOGIES, WORLDCIST 2022, VOL 2
Abstract
User experience can be affected by the amount and intensity of information presented. Four scenarios were developed to assess the insertion of information elements (chronometer and hint system) and tested with 37 users to find out if they affected the user's sense of presence and symptoms of cybersickness. In order to instruct users and using virtual reality using the Unity 3D game engine, we created a virtual world where the user has the role of exploring the environment and looking for mushrooms, and can consult a description about it. For tests with users, the IPQp and SSQ questionnaires were applied. The results indicate that it is possible to create a virtual world with the addition of informational components without significantly disturbing the user experience.
2022
Authors
de Queiros, RAP; Pinto, M; Simões, A; Portela, CF;
Publication
Research Anthology on Game Design, Development, Usage, and Social Impact
Abstract
2022
Authors
Dantas, M; Leitao, D; Cui, P; Macedo, R; Liu, XL; Xu, WJ; Paulo, J;
Publication
2022 22ND IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING (CCGRID 2022)
Abstract
We present MONARCH, a framework-agnostic storage middleware that transparently employs storage tiering to accelerate Deep Learning (DL) training. It leverages existing storage tiers of modern supercomputers (i.e., compute node's local storage and shared parallel file system (PFS)), while considering the I/O patterns of DL frameworks to improve data placement across tiers. MONARCH aims at accelerating DL training and decreasing the I/O pressure imposed over the PFS. We apply MONARCH to TensorFlow and PyTorch, while validating its performance and applicability under different models and dataset sizes. Results show that, even when the training dataset can only be partially stored at local storage, MONARCH reduces TensorFlow's and PyTorch's training time by up to 28% and 37% for I/O-intensive models, respectively. Furthermore, MONARCH decreases the number of I/O operations submitted to the PFS by up to 56%.
2022
Authors
Ribeiro, J; Tavares, J; Fontes, T;
Publication
INTELLIGENT TRANSPORT SYSTEMS (INTSYS 2021)
Abstract
Geolocation data is fundamental to businesses relying on vehicles such as logistics and transportation. With the advance of the technology, collecting geolocation data become increasingly accessible and affordable, which raised new opportunities for business intelligence. This paper addresses the application of geolocation data for monitoring logistics processes, namely for detecting vehicle-based operations in real time. A stream of geolocation entries is used for inferring stationary events. Data from an international logistics company is used as a case study, in which operations of loading/unloading of goods are not only identified but also quantified. The results of the case study demonstrate the effectiveness of the solution, showing that logistics operations can be inferred from geolocation data. Further meaningful information may be extracted from these inferred operations using process mining techniques.
2022
Authors
Meirinhos, G; Martins, S; Peixoto, B; Monteiro, P; Gonsalves, G; Melo, M; Bessa, M;
Publication
TEM JOURNAL-TECHNOLOGY EDUCATION MANAGEMENT INFORMATICS
Abstract
-This work presents a study on how an immersive virtual environment's level of interaction and fidelity can affect the quality of experience (QOE) in a real estate context. Four versions of the virtual space were created with the level of interaction and the level of fidelity varying between them. The QoE dimensions considered in this work are user satisfaction, lighting quality, interior space quality, and interaction features. The sample comprises 28 participants, of which 21 are men and 7 are women, aged between 18 and 29 years. Results show that, overall, the level of fidelity is more relevant when the level of interaction is low, assuming the movement around the apartment is statistically higher in high-fidelity experiences.
2022
Authors
Brandao, PR; S Mamede, H;
Publication
Journal of Mathematical & Computer Applications
Abstract
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