2022
Authors
Forero, J; Bernardes, G; Mendes, M;
Publication
MM 2022 - Proceedings of the 30th ACM International Conference on Multimedia
Abstract
Emotional Machines is an interactive installation that builds affective virtual environments through spoken language. In response to the existing limitations of emotion recognition models incorporating computer vision and electrophysiological activity, whose sources are hindered by a head-mounted display, we propose the adoption of speech emotion recognition (from the audio signal) and semantic sentiment analysis. In detail, we use two machine learning models to predict three main emotional categories from high-level semantic and low-level speech features. Output emotions are mapped to audiovisual representation by an end-To-end process. We use a generative model of chord progressions to transfer speech emotion into music and a synthesized image from the text (transcribed from the user's speech). The generated image is used as the style source in the style-Transfer process onto an equirectangular projection image target selected for each emotional category. The installation is an immersive virtual space encapsulating emotions in spheres disposed into a 3D environment. Thus, users can create new affective representations or interact with other previous encoded instances using joysticks. © 2022 Owner/Author.
2022
Authors
Machado, D; Costa, VS; Brandao, P;
Publication
2022 27TH IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (IEEE ISCC 2022)
Abstract
Finger-pricking is the traditional procedure for glycaemia monitoring. It is an invasive method where the person with diabetes is required to prick their finger. In recent years, continuous-glucose monitoring (CGM), a new and more convenient method of glycaemia monitoring, has become prevalent. CGM provides continuous access to glycaemic values without the need of finger-pricking. Data mining can be used to understand glycaemic values, and to ideally warn users of abnormal situations. CGM provides significantly more data than finger-pricking. Thus, the amount and value of CGM data ultimately questions the role of finger-pricking for glycaemic studies. In this work we use the OhioT1DM data set in order to study the importance of finger-prick-based data. We use Random Forest as a classification method, a robust method that tends to obtain quality results. Our results indicate that, although more demanding and scarcer, finger-prick-based glycaemic values have a significant role on diabetes management and on data mining.
2022
Authors
Martins, J; Gonçalves, R; Branco, F;
Publication
UNIVERSAL ACCESS IN THE INFORMATION SOCIETY
Abstract
Even though being perceived as a novel approach, multiple authors claim that the digital transition of all sectors in society started when information and communication technologies (ICT) started to be an integral part of our daily lives. The education sector currently represents one of the contexts where the use of ICT is more promising and allows to reach greater benefits, mostly due to the wide range of tools, applications, and management and methodological approaches that are associated with e-learning. With the above in mind, a bibliometric analysis of the e-learning adoption topic has been performed, aiming on delivering a detailed analysis of the status of the topic. This analysis was carried out by analyzing the scientific literature indexed in the Scopus database that addressed the multiple stages of the e-learning adoption process (i.e., acceptance, adoption, and use). Our study analyzed 896 documents published between 1989 and 2021, of which 98.3% represented papers published in journals and conference proceedings.
2022
Authors
Cujba, GC; Wasim, J; Almeida, F;
Publication
International Journal of Learning and Change
Abstract
2022
Authors
Costa, GD; Petry, MR; Moreira, AP;
Publication
SENSORS
Abstract
With the continuously growing usage of collaborative robots in industry, the need for achieving a seamless human-robot interaction has also increased, considering that it is a key factor towards reaching a more flexible, effective, and efficient production line. As a prominent and prospective tool to support the human operator to understand and interact with robots, Augmented Reality (AR) has been employed in numerous human-robot collaborative and cooperative industrial applications. Therefore, this systematic literature review critically appraises 32 papers' published between 2016 and 2021 to identify the main employed AR technologies, outline the current state of the art of augmented reality for human-robot collaboration and cooperation, and point out future developments for this research field. Results suggest that this is still an expanding research field, especially with the advent of recent advancements regarding head-mounted displays (HMDs). Moreover, projector-based and HMDs developed approaches are showing promising positive influences over operator-related aspects such as performance, task awareness, and safety feeling, even though HMDs need further maturation in ergonomic aspects. Further research should focus on large-scale assessment of the proposed solutions in industrial environments, involving the solution's target audience, and on establishing standards and guidelines for developing AR assistance systems.
2022
Authors
Ferreira, P; Malheiro, B; Silva, M; Borges Guedes, P; Justo, J; Ribeiro, C; Duarte, A;
Publication
EDULEARN Proceedings - EDULEARN22 Proceedings
Abstract
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