Abstract
Visualizations for Streaming Big Data convey high volumes of information in real-time, making it challenging for people to grasp significant data changes. One solution could be having visualizations that change themselves according to the incoming data. However, these changes would need to be effectively conveyed. In this work, we propose a set of transitions between different pairs of visual idioms, aiming to aid users in keeping track of the information in real-time and notice relevant changes. We target transitions between Line charts, Heat maps, and Stream graphs. We conceived seven transitions that modify different properties of the visual elements for each pair of visual idioms, following a novel taxonomy for their conceptualization. To assess the performance of the transitions, we conducted an online user study with 100 participants. Results suggest that animations are indeed better to change between different visualization idioms than abrupt transitions. We also suggest transition techniques for each visualization pair, between those proposed, according to participants' preferences. Lastly, we identify which concepts of our taxonomy were more present in our suggested transitions.

Abstract

Abstract
This work reports on preliminary findings about the learning process of a diverse set of users playing an immersive Virtual Reality serious game based on hand tracking to complement conventional occupational therapy in hand rehabilitation. Two test sessions with a one-day break between them were carried out with seven able-bodied participants. Participants went through twenty minutes guided functional task practice followed by a Semi-structured interview. The System Usability Scale (SUS) scores obtained for the first (75.36) and second (76.43) test sessions positioned the serious game above the average in terms of usability. Insights about the learning process., SUS and the obtained trial results., confirm the potential of the developed solution as a viable complement to conventional hand rehabilitation and highlight possible points for improvement. © 2021 IEEE.

Abstract
Up to one third of the global food production depends on the pollination of honey bees, making them vital. This study defines a methodology to create a bee hive health monitoring system through image processing techniques. The approach consists of two models, where one performs the detection of bees in an image and the other classifies the detected bee's health. The main contribution of the defined methodology is the increased efficacy of the models, whilst maintaining the same efficiency found in the state of the art. Two databases were used to create models based on Convolutional Neural Network (CNN). The best results consist of 95% accuracy for health classification of a bee and 82% accuracy in detecting the presence of bees in an image, higher than those found in the state-of-the-art.

Abstract

Abstract