"Found in Translation": An Evolutionary Framework for Auditory-Visual Relationships.

The development of computational artifacts to study cross-modal associations has been a growing research topic, as they allow new degrees of abstraction. In this context, we propose a novel approach to the computational exploration of relationships between music and abstract images, grounded by findings from cognitive sciences (emotion and perception). Due to the problem's high-level nature, we rely on evolutionary programming techniques to evolve this audio-visual dialogue. To articulate the complexity of the problem, we develop a framework with four modules: (i) vocabulary set, (ii) music generator, (iii) image generator, and (iv) evolutionary engine. We test our approach by evolving a given music set to a corresponding set of images, steered by the expression of four emotions (angry, calm, happy, sad). Then, we perform preliminary user tests to evaluate if the user's perception is consistent with the system's expression. Results suggest an agreement between the user's emotional perception of the music-image pairs and the system outcomes, favoring the integration of cognitive science knowledge. We also discuss the benefit of employing evolutionary strategies, such as genetic programming on multi-modal problems of a creative nature. Overall, this research contributes to a better understanding of the foundations of auditory-visual associations mediated by emotions and perception.

EvoDesigner: Evolving Poster Layouts.

Frequently, one of the goals of Graphic Design (gd) is discovering disruptive visual solutions that stand out and attract people's attention. However, due to the increasing democratisation of gd, graphic designers tend to adopt design trends, leading to designs that many times lack innovative and catchy features. EvoDesigner is an evolutionary extension for Adobe InDesign that aims to aid gd processes by automatically evolving layout and style variations of given InDesign pages. The generated pages might be previously created and post-edited by designers, promoting co-creation. As an extension of the study EvoDesigner: Towards Aiding Creativity in Graphic Design, this article begins with a general introduction of EvoDesigner. Then, we review previous experiments on evolving pages towards the page balance of existing target posters. Furthermore, we present new experiments exploring the benefits of using grid systems to position and scale page items along with a user survey made to gather feedback about the impact of grid systems in the generated pages and showcase examples of artefacts created from the collaboration between designers and the system. The findings indicate that the presented techniques can be used to interpret current layouts in different manners, and suggest that grid systems may be a useful tool for promoting the automatic production of layouts with better organisation when compared to applying no organisational constraints. However, a conducted user survey indicates that, depending on the goals of the designers, more organised layouts might not always be synonymous with better results.

Interactive and coordinated visualization approaches for biological data analysis.

The field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only represent various data structures simultaneously but also provide exploratory methods that allow the identification of meaningful relationships that would not be perceptible through data analysis algorithms alone. In this article, we present a survey of visualization approaches applied to the analysis of biological data. We focus on graph-based visualizations and tools that use coordinated multiple views to represent high-dimensional multivariate data, in particular time series gene expression, protein-protein interaction networks and biological pathways. We then discuss how these methods can be used to help solve the current challenges surrounding the visualization of complex biological data sets.

CroP-Coordinated Panel visualization for biological networks analysis.

SUMMARY: CroP is a data visualization application that focuses on the analysis of relational data that changes over time. While it was specifically designed for addressing the preeminent need to interpret large scale time series from gene expression studies, CroP is prepared to analyze datasets from multiple contexts. Multiple datasets can be uploaded simultaneously and viewed through dynamic visualization models, which are contained within flexible panels that allow users to adapt the workspace to their data. Through clustering and the time curve visualization it is possible to quickly identify groups of data points with similar proprieties or behaviors, as well as temporal patterns across all points, such as periodic waves of expression. Additionally, it integrates a public biomedical database for gene annotation. CroP will be of major interest to biologists who seek to extract relations from complex sets of data. AVAILABILITY AND IMPLEMENTATION: CroP is freely available for download as an executable jar at https://cdv.dei.uc.pt/crop/.

An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images.

BACKGROUND: Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. METHODS: In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. RESULTS: The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. CONCLUSIONS: After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes.

PhDs - Portugal has Doctors: A Visualization of Academia Achievements in Portugal from 1970 to 2022.

PhDs - Portugal has Doctors is an interactive installation presenting Portuguese doctoral theses from 1970 to 2022, tracking their historical evolution and distribution across universities and research sectors. This work resulted in an installation that served a dual purpose: to raise awareness and value the work of national doctorates and to reduce the communication gap on this topic, encouraging a public engagement with the subject, fostering discussions beyond the data and prompting reflection on how this lesser-known reality has impacted Portugal with its significantly growing presence. Drawing from recent research on aesthetics and its impact on data perception, we integrated these insights to make the visualization approach accessible to the general public, emphasizing a visually minimalist narrative. The story initially prompts viewers to reflect on the temporal evolution of the academic landscape over the last decades. The visualization then encourages viewers to actively engage with the data, facilitating a more in-depth exploration. The installation garnered positive feedback, provoking amazement and surprise, and revealed an unknown reality, even within the scientific community further justifying the need for this type of dissemination works.

Visual complexity modelling based on image features fusion of multiple kernels.

Humans' perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf's law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans' perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.

Pulsing Blood Vessels: A Figurative Approach to Traffic Visualization.

By using semantic figurative metaphors, a visualization designer invests in a more figurative graphic representation, seeking provocative perspectives on common topics and trying to invoke emotional responses while clearly communicating meaningful data stories. The use of figurative metaphors in visualization, however, involves adding nondata aspects to a visualization. The authors survey this exploratory side of visualization, using a visualization of Lisbon traffic data as a system of pulsing blood vessels as an example, and discuss the strengths and limitations of such as approach.

Ant- and Ant-Colony-Inspired ALife Visual Art.

Ant- and ant-colony-inspired ALife art is characterized by the artistic exploration of the emerging collective behavior of computational agents, developed using ants as a metaphor. We present a chronology that documents the emergence and history of such visual art, contextualize ant- and ant-colony-inspired art within generative art practices, and consider how it relates to other ALife art. We survey many of the algorithms that artists have used in this genre, address some of their aims, and explore the relationships between ant- and ant-colony-inspired art and research on ant and ant colony behavior.

Contiguous Animated Edge-Based Cartograms for Traffic Visualization.

An experimental model animates contiguous computer cartograms by distorting the topological lengths of their edges. Using traffic information for the city of Lisbon, the authors distort a road map to depict traffic velocities. Areas of the city distend when velocities are low and compress when velocities are high. This model is applied to two visualizations: a trajectory visualization of vehicles, creating a temperature map for traffic velocities, and a figurative visualization that portrays Lisbon as a system of pulsing blood vessels. The proposed model can efficiently generate and animate edge-based cartograms with low representation errors.

Computerized measures of visual complexity.

Visual complexity influences people's perception of, preference for, and behaviour toward many classes of objects, from artworks to web pages. The ability to predict people's impression of the complexity of different kinds of visual stimuli holds, therefore, great potential for many domains, basic and applied. Here we use edge detection operations and several image metrics based on image compression error and Zipf's law to estimate the visual complexity of images. The experiments involved 800 images, each previously rated by thirty participants on perceived complexity. In a first set of experiments we analysed the correlation of individual features with the average human response, obtaining correlations up to rs = .771. In a second set of experiments we employed Machine Learning techniques to predict the average visual complexity score attributed by humans to each stimuli. The best configurations obtained a correlation of rs = .832. The average prediction error of the Machine Learning system over the set of all stimuli was .096 in a normalized 0 to 1 interval, showing that it is possible to predict, with high accuracy human responses. Overall, edge density and compression error were the strongest predictors of human complexity ratings.