Abstract
In InfoVis design, visualizations make use of pre-attentive features to highlight visual artifacts and guide users' perception into relevant information during primitive visual tasks. These are supported by visual marks such as dots, lines, and areas. However, research assumes our pre-attentive processing only allows us to detect specific features in charts. We argue that a visualization can be completely perceived pre-attentively and still convey relevant information. In this work, by combining cognitive perception and psychophysics, we executed a user study with six primitive visual tasks to verify if they could be performed pre-attentively. The tasks were to find: horizontal and vertical positions, length and slope of lines, size of areas, and color luminance intensity. Users were presented with very simple visualizations, with one encoded value at a time, allowing us to assess the accuracy and response time. Our results showed that horizontal position identification is the most accurate and fastest task to do, and the color luminance intensity identification task is the worst. We believe our study is the first step into a fresh field called Incidental Visualizations, where visualizations are meant to be seen at-a-glance, and with little effort.

Abstract
The purpose of this chapter is to identify and analyze the main organizational and technological factors that play a relevant role in a transformational strategic change inherent in changing the business model supported on the internet. An analysis was made of the different business models, as well as their connection to strategic management and how this is accomplished in the organizational and technological plan. This empirical study, based on the qualitative methodology, focuses on a single case study on a Portuguese company that recently changed its business model. The findings allowed the authors to assess the advantages and challenges posed by the transformation of an initial business based on a website that performs a comparison of prices for an integrated model based on a marketplace, in which customers can make their purchases and access all products, from different stores, in one place.

Abstract

Abstract

Abstract
The rapid expansion of Asian hornets poses a high threat for the honey bee survival, as these invaders pray on them. Furthermore, they also pose a threat to people who are allergic, whose sting can lead to death. This study proposes a Decision Support System that uses Computer Vision techniques to automatically detect signs of Vespa velutina through images from GPS equipped camera. The goal of the system is to provide timely information about the presence of these invaders, allowing park managers and beekeepers to act quickly in removing the Vespidae. The proposed methodology obtained an 85% accuracy in the detection of V. velutina using the Mask RCNN architecture, enabling the system to perform detection at 3 FPS. © 2020 MIR Labs.

Abstract