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Browsing multidimensional visual entities

Publication . Aniceto, Miguel; Moura Pires, João; Datia, Nuno; Afonso, Ana Paula

The field of Information Visualization seeks to identify the general principles of visualization, and makes use of these principles to propose new forms of visualization for specific types of data. Included in these different data types, it was identified a data type which was not thoroughly explored. The notion of entities where each entity is composed by different attributes, and one of these attributes is composed by one picture which invokes a strong feeling of familiarity to the user. The information that we are attempting to visualize is the most basic type of data structure, a table, where the number of entities to visualize should be higher than we can humanely count, yet smaller than a few thousands. In the field of visualization we identified a niche where the main focus is the image, and despite that it has a vast number of applicable scenarios, it hadn't been properly explored. One of the major attempts at doing so, was by Microsoft Live Labs and it demonstrated limitations that will be addressed by our approach. In order to evaluate the proposed forms of visualization they will be applied and evaluated with the Deloitte Portugal organizational case-study.

2016-09-01Conference object

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Heterogeneous personal computing: a case study in materials science

Publication . Oliveira, Nuno; Medeiros, Pedro

The HRTE Platform (Heterogeneous Run-Time Environment) Enables The Construction Of Problem Solving Environments Dedicated To A Specific Area (PSE) That Exploit The Heterogeneous Processing Resources Available In A Desktop Computer (Eg GPU). The HRTE-Enabled PSE Supports The Inter-Operation Between Existing Processing Modules And New Ones (Hmodules), Optimizing The Typical Communication Patterns Of A PSE. Hmodules Can Register Multiple Implementations Allowing HRTE To Select The Target Device At Runtime. The Main Features Of HRTE And The Programming Interface Used To Build Hmodules Are Described. An Application In The Materials Science Area Illustrates The Approach And Allows Us To Show Some Promising Performance Figures.

2017Conference object

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AA-Maps: attenuation and accumulation maps for spatio-temporal event visualisation

Publication . Albino, Catarina; Moura Pires, João; Datia, Nuno; Silva, Ricardo Almeida; Santos, Maribel Yasmina

Some phenomena, such as crimes in a city, fires occurred in a country and road accidents can be interpreted as sets of spatio-temporal events. A spatio-temporal event is described by a geographic location, a time instant and other characterising attributes. The cartographic visualisation of spatio-temporal events remains unresolved, due to the challenges related with portraying multiple dimensions simultaneously: the spatial, the temporal and the semantic (zero or more dimensions) phenomenon's components. In this context, this article presents the Attenuation and Accumulation Maps (AA-Maps). The main idea of this visualisation analytic approach consists in showing in a map, the resulting effect of combining attenuation and accumulation, from a temporal reference of observation, given a spatio-temporal Level of Detail (LoD). Imagine the footprints of people crossing a garden in various directions. They leave different traces that summarize the cumulative effect of the footprints on the grass, which is attenuated as time goes by. AA-Maps support different combinations of attenuation and accumulation functions. In addition, this method also enables analysis with different Levels of Detail (LoD), both spatial and temporal. This allows distinct analytic perspectives of the phenomenon while promoting the search for the most suitable parametrization for its characteristics.

2017-11-16Conference object

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Using deep learning techniques for authentication of Amadeo de Souza Cardoso paintings and drawings

Publication . Chen, Ailin; Jesus, Rui; Villarigues, Márcia

This paper investigates the application of a Convolutional Neural Network (CNN), AlexNet, on the authentication of paintings by different artists, including Portuguese painter Amadeo de Souza Cardoso, Chinese painter Daqian Zhang and Dutch painter Vincent van Gogh. The research is motivated by the studies on the identification of the works by Amadeo based on the painter’s brushstroke implementing Machine Learning algorithms combined with material analysis. The employment of CNN intends to improve the performance of the brushstroke analysis and increase the accuracy while authenticating an artist’ works. The results show that the implementation of AlexNet produces higher accuracies than its counterparts applying previous brushstroke analysis. Notably, when Amadeo drawings are included in the testing based on Amadeo paintings, the accuracies obtained with the original algorithm drop substantially, whilst the counterparts attained with AlexNet improved considerably. However, when other testing sets are introduced, especially the Chinese paintings, the accuracies show a great increase with the original algorithm but a significant decrease with AlexNet. It implies that AlexNet surpasses the traditional computation through learning by examples; it can potentially solve the problem of limited number of artworks by a specific artist for training.

2019Book part

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Visual analytics for spatiotemporal events

Publication . Silva, Ricardo Almeida; Moura Pires, João; Datia, Nuno; Santos, Maribel Yasmina; Martins, Bruno; Birra, Fernando

Crimes, forest fires, accidents, infectious diseases, or human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, as it can highlight useful patterns or insights and enhance the user’ perception of phenomena. For this reason, modeling phenomena at different LoDs is needed to increase the analytical value of the data, as there is no exclusive LOD at which the data can be analyzed. Current practices work mainly on a single LoD of the phenomena, driven by the analysts’ perception, ignoring that identifying the suitable LoDs is a key issue for pointing relevant patterns. This article presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs do interesting patterns emerge, or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process. The use of several synthetic and real datasets supported the evaluation and validation of VAST, suggesting LoDs with different interesting spatiotemporal patterns and pointing the type of expected patterns.

2019-08-16Journal article

Open access