Factors influencing visual attention switch in multi-display user interfaces: a survey

Multi-display User Interfaces (MDUIs) enable people to take advantage of the different characteristics of different display categories. For example, combining mobile and large displays within the same system enables users to interact with user interface elements locally while simultaneously having a large display space to show data. Although there is a large potential gain in performance and comfort, there is at least one main drawback that can override the benefits of MDUIs: the visual and physical separation between displays requires that users perform visual attention switches between displays. In this paper, we present a survey and analysis of existing data and classifications to identify factors that can affect visual attention switch in MDUIs. Our analysis and taxonomy bring attention to the often ignored implications of visual attention switch and collect existing evidence to facilitate research and implementation of effective MDUIs.Postprin

Opportunistic visualization with iVoLVER

Proposed as 'data analysis anywhere, anytime, from anything', Opportunistic Information Visualization (Opportu-Vis) [1] seeks to provide analytical support in scenarios where the data of interest is not explicitly available and has to be retrieved from digital artifacts that are not traditionally used as data sources. Examples include raster images, web pages, vector files, and photographs. This showpiece presents how iVoLVER, the Interactive Visual Language for Visualization Extraction and Reconstruction, provides support in such settings. We briefly describe the overall construction approach of the tool in scenarios where different digital artifacts are used to compose interactive visuals. All of this becomes possible by using the data extraction capabilities of iVoLVER together with the elements of its visual language.Postprin

VisuaLizations As Intermediate Representations (VLAIR) : an approach for applying deep learning-based computer vision to non-image-based data

We thank the China Scholarship Council (CSC) for financially supporting my PhD study at University of St Andrews, UK, and NSERC Discovery Grant 2020-04401 (Miguel Nacenta).Deep learning algorithms increasingly support automated systems in areas such as human activity recognition and purchase recommendation. We identify a current trend in which data is transformed first into abstract visualizations and then processed by a computer vision deep learning pipeline. We call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental to support accurate recognition in a number of fields while also enhancing humans’ ability to interpret deep learning models for debugging purposes or in personal use. In this paper we describe the potential advantages of this approach and explore various visualization mappings and deep learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity recognition in an apartment) and show that VLAIR attains classification accuracy above classical machine learning algorithms and several other non-image-based deep learning algorithms with several data representations.Publisher PDFPeer reviewe

The cost of display switching : a comparison of mobile, large display and hybrid UI configurations

Attaching a large external display can help a mobile device user view more content at once. This paper reports on a study investigating how different configurations of input and output across displays affect performance, subjective workload and preferences in map, text and photo search tasks. Experimental results show that a hybrid configuration where visual output is distributed across displays is worst or equivalent to worst in all tasks. A mobile device-controlled large display configuration performs best in the map search task and equal to best in text and photo search tasks (tied with a mobile-only configuration). After conducting a detailed analysis of the performance differences across different UI configurations, we give recommendations for the design of distributed user interfaces.Postprin

Computer Vision approaches to solve the screen pose acquisition problem for Perspective Cursor

PespectiveCursor is a new interaction technique for multidisplay environments that significantly improves human interaction with computer systems. Computer Vision has the potential to provide a solution for the implementation of this interaction technique that might be better than the current ones. A survey of the current state-of-the-art in 2D and 3D vision shows that there are many techniques that might be of use in finding the spatial relationships between the point of view of users and the displays in the environment, which is the main requirement for an implementation of PerspectiveCursor

Constructing interactive visualizations with iVoLVER

IVoLVER, the Interactive Visual Language for Visualization Extraction and Reconstruction, is a web-based pen and touch system that graphically supports the construction of interactive visualizations and allows the extraction of data from different types of digital artifacts and photographs. Together, these features enable the creation of visualizations from data that is not structured in traditional formats without the need of textual programming. This demonstration shows how iVoLVER visualizations are constructed and illustrates an interactive example that can be used in teaching and educational contexts.</p