Visual complexity modelling based on image features fusion of multiple kernels

[Abstract] 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.Xunta de Galicia; GRC2014/049Portuguese Foundation for Science and Technology; SBIRC; PTDC/EIA EIA/115667/2009Xunta de Galicia; Ref. XUGA-PGIDIT-10TIC105008-PRMinisterio de Ciencia y Tecnología; TIN2008-06562/TINMinisterio de Ecnomía y Competitividad; FJCI-2015-2607

Editorial: Complex Systems in Aesthetics and Arts

Editorial for a special issue of the journal Complexity on complex systems in aesthetics and the arts

An Investigation of the Correlation Between Mental Workload and Web User’s Interaction

Mental Workload, a psychological concept, was identified as being linked with task’s and system’s performance. In the context of Human-Computer Interaction, recent research has identified Mental Workload as an important measure in the designing and evaluation of web interfaces, and as an additional and supplemental insight to typical usability evaluation methods. Simultaneously, web logs containing data related to web users’ interaction (e.g. scrolling; mouse clicks) have been proved useful in evaluating the usability of web sites by analysing the data tracked for hundreds of users. In order to study if the potential of logs of user interaction can be applied in the study of Mental Workload in Web design, an online experiment with 145 participants was performed. Additionally, the experiment, composed of alternative interfaces, sought to assess the role of Mental Workload in the evaluation of interfaces using interactive Infographics, which were identified by literature as bringing new challenges and concerns in the field of Web Design. The online experiment’s results suggested that correlations between mental demands and users’ interaction can only be observed when taking in consideration the web interface used or the profile of the users. Moreover, the used measurement methods for assessing Mental Workload were not capable of predicting task performance, as previous research suggested (in the context of other types of web interfaces)

An investigation of the correlation between Mental Workload and Web User’s Interaction

Mental Workload, a Psychology concept, was identified as being linked with task’s and system’s performance. In the context of Human-Computer Interaction, recent research has identified Mental Workload as an important measure in the designing and evaluation of web interfaces, and as an additional and supplemental insight to typical Usability evaluation methods. Simultaneously, web logs containing data related to web users’ interaction (e.g. scrolling; mouse clicks) have been proved useful in evaluating the Usability of web sites by levering the data tracked for hundreds of users. In order to study if the potential of logs of user interaction can be applied in the study of Mental Workload in Web design, an online experiment with 145 participations was performed. Additionally, the experiment, composed of alternative interfaces, sought to assess the role of Mental Workload in the evaluation of interfaces using interactive Infographics, which were identified by literature as bringing new challenges and concerns in the field of Web Design. The online experiment’s results suggested that correlations between mental demands and users’ interaction can only be observed when taking in consideration the web interface used or the profile of the users. Moreover, the used measurement methods for assessing Mental Workload were not capable of predicting task performance, as previous research suggested (in the context of other types of web interfaces)

Robotic Training for the Integration of Material Performances in Timber Manufacturing

The research focuses on testing a series of material-sensitive robotic training methods that flexibly extend the range of subtractive manufacturing processes available to designers based on the integration of manufacturing knowledge at an early design stage. In current design practices, the lack of feedback information between the different steps of linear design workflows forces designers to engage with only a limited range of standard materials and manufacturing techniques, leading to wasteful and inefficient solutions. With a specific focus on timber subtractive manufacturing, the work presented in this thesis addresses the main issue hindering the utilisation of non-standard tools and heterogeneous materials in design processes which is the significant deviation between what is prescribed in the digital design environment and the respective fabrication outcome. To begin, it has been demonstrated the extent to which the heterogeneous properties of timber affect the outcome of the robotic carving process beyond the acceptable tolerance thresholds for design purposes. Resting on this premise, the devised strategy to address such a material variance involved capturing, transferring, augmenting and integrating manufacturing knowledge through the collection of real- world fabrication data, both by human experts and robotic sessions, and training of machine learning models (i.e. Artificial Neural Networks) to achieve an accurate simulation of the robotic manufacturing task informed by specific sets of tools affordances and material behaviours. The results of the training process have demonstrated that it is possible to accurately simulate the carving process to a degree sufficient for design applications, anticipating the influence of material and tool properties on the carved geometry. The collaborations with the industry partners of the project, ROK Architects (Zürich) and BIG (Copenhagen), provided the opportunity to assess the different practical uses and related implications of the tools in a real-world scenario following an open-ended and explorative approach based on several iterations of the full design-to-production cycle. The findings have shown that the devised strategy supports decision-making procedures at an early stage of the design process and enables the exploration of novel, previously unavailable, solutions informed by material and tool affordances

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives

Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. Recent advances in deep learning have tremendously advanced predicting posture from videos directly, which quickly impacted neuroscience and biology more broadly. In this primer we review the budding field of motion capture with deep learning. In particular, we will discuss the principles of those novel algorithms, highlight their potential as well as pitfalls for experimentalists, and provide a glimpse into the future.Comment: Review, 21 pages, 8 figures and 5 boxe