Neural Networks for Transformation to Spectral Spaces

This work is concerned with mapping between the CMYK colour space and spectral space using Artificial Neural Networks (ANNs). The dimensionality of the spectral space is high (typically 31) leading to a large number of weights (or free parameters) in the network. This paper explores the hypothesis that a computational advantage can be obtained, in these cases, by treating the reflectance at each wavelength as being independent of the reflectance at any other wavelength; the implication of this hypothesis is that instead of using a single large ANN, it is possible to use, for example, 31 separate networks, each of which maps to one dimension of the 31-d spectral space. The results showed that as the number of training samples is reduced the advantage of the population of single-wavelength networks over the standard neural network approach increased

Effects of object colour stimuli on human brain activities and subjective feelings in physical environment and virtual reality

This work explores the potential of Virtual Reality (VR) as a medium to support emotional positivity and well-being. In this study, we investigate and compare the effects of colour first in a physical test room and then in an identical VR environment. We measured ten participants’ physiological responses to different colours of light in both physical and virtual environment with the assistance of an electroencephalogram (EEG) system. We also required all those participants to report on their subjective feelings through a Positive and Negative Affect Schedule (PANAS) questionnaire. In conclusion, our experimental results indicate that human’s subjective experience of emotion and brain activity are affected by the coloured-lightning conditions, and the impacts show different trends between red and blue lightning. Furthermore, such impacts in the physical environment can be generally replicated in VR

A psychophysical analysis of the discernible palette for colour names

A laboratory-based experiment with a colour-calibrated display was used to collect examples of colours that participants associate with each of 9 colour names. The gamut volumes for each of the clusters of colours in CIELAB space were calculated and a computational method was used to estimate how any distinct colours could be placed within each of these volumes. In the case of one of the colour names (pink), an unconstrained web-based experiment was carried out and the gamut volume for pink was similar to the gamut volume derived from the laboratory experiment. It was assumed that colours separated by more than 1 CIELAB unit would be visually distinguishable. The study gave estimates for the number of discernible colours for each of the 9 colour names. The work suggests that although focal colours may exist for each of the colour names used in the study, these colour names are generally not precise communicators of colour and different people might have quite different ideas, for example, about what is being communicated when people use specific colour names

A computational method for predicting color palette discriminability

Automatic analysis of images is increasingly being used to generate color insights and this has led to various methods for generating palettes. Several studies have recently been published that explore methods to predict the visual similarity between pairs of palettes and these methods are often used to evaluate different generative methods. This work is concerned with being able to predict visual similarity between color palettes. Three data sets (two of which were previously published) are used to evaluate two methods for predicting visual similarity between palettes. A novel palette-difference metric (based on the Hungarian algorithm) is compared to the previously published minimum color difference model (MICD) and was found to agree better with the visual data for two of the three data sets. Agreement between models and visual data was also better for CIEDE2000 (1, 2) than for CIELAB metrics

A Comparative Study of Colour Effects on Cognitive Performance in Real-World and VR Environments

This research explores the influence of colour on cognitive performance and intellectual abilities (i.e., logical and lateral thinking abilities and people’s attention to detail) in a conventional laboratory setting and an approximately identical virtual reality (VR) environment. Comparative experiments using psychological methods were carried out in both settings to explore the impact of immersive colour experience. This work builds on earlier studies that suggest that the VR environment enhances user experiences, with results evidencing that a considered approach to colour design can trigger a positive impact on user engagement. The experiments further evaluated the positive effects of immersive colour stimuli in VR by evaluating participants’ logical and lateral thinking abilities, as well as their attention to detail. Their response time and error rate when completing each psychometric test were recorded with different hue backgrounds in both environments. The data collected from participants reveal the differential impacts of colour between the reality setting using standard colour imaging displays and in an approximately identical VR environment. Analysis of the psychometric tests shows the differential influence of colours on logical and lateral thinking abilities and people’s attention to detail between the physical environment and the VR environment. Our findings add to the data demonstrating that a well-designed immersive colour experience in VR can trigger positive user engagement and, as explored in this study, improve cognitive performance. This again positions immersive colour experience as an important design tool to be fully considered in the creation of effective VR research and applications

Characteristics of self-care interventions for patients with a chronic condition: A scoping review

Background: Self-care is a fundamental element of treatment for patients with a chronic condition and a major focus of many interventions. A large body of research exists describing different types of self-care interventions, but these studies have never been compared across conditions. Examination of heterogeneous interventions could provide insights into effective approaches that should be used in diverse patient populations. Objectives: To provide a comprehensive and standardized cross-condition overview of interventions to enhance self-care in patients with a chronic condition. Specific aims were to: 1) identify what self-care concepts and behaviors are evaluated in self-care interventions; 2) classify and quantify heterogeneity in mode and type of delivery; 3) quantify the behavior change techniques used to enhance self-care behavior; and 4) assess the dose of self-care interventions delivered. Design: Scoping review DATA SOURCES: Four electronic databases - PubMed, EMBASE, PsychINFO and CINAHL - were searched from January 2008 through January 2019. Eligibility criteria for study selection: Randomized controlled trials (RCTs) with concealed allocation to the intervention were included if they compared a behavioral or educational self- care intervention to usual care or another self-care intervention and were conducted in adults. Nine common chronic conditions were included: hypertension, coronary artery disease, arthritis, chronic kidney disease, heart failure, stroke, asthma, chronic obstructive lung disease, and type 2 diabetes mellitus. Diagnoses that are psychiatric (e.g. schizophrenia), acute rather than chronic, or benefitting little from self-care (e.g. dementia) were excluded. Studies had to be reported in English with full-text available. Results: 9309 citations were considered and 233 studies were included in the final review. Most studies addressed type 2 diabetes mellitus (n = 85; 36%), hypertension (n = 32; 14%) or heart failure (n = 27; 12%). The majority (97%) focused on healthy behaviors like physical activity (70%), dietary intake (59%), and medication management (52%). Major deficits found in self-care interventions included a lack of attention to the psychological consequences of chronic illness, technology and behavior change techniques were rarely used, few studies focused on helping patients manage signs and symptoms, and the interventions were rarely innovative. Research reporting was generally poor. Conclusions: Major gaps in targeted areas of self-care were identified. Opportunities exist to improve the quality and reporting of future self-care intervention research. Registration: The study was registered in the PROSPERO database (#123,719)

Characterization of complex networks: A survey of measurements

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of measurements for inclusion are welcomed by the author

Identifying networks in social media: The case of #Grexit

We examine the intensity of ‘#Grexit’ usage in Twitter during a period of economic and financial turbulence. Using a frequency-analysis technique, we illustrate that we can extract detailed information from social media data. This allows us to map the networks of interest as it is reflected in Twitter. Our findings identify high-interest in Grexit from Twitter users in key peripheral countries, core Eurozone members as well as core EU member states outside the Eurozone. Overall, our study presents a useful tool for identifying clusters. This is part of a new research agenda utilising the information extracted from big data available via social media channels

I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images

Image dehazing has become an important computational imaging topic in the recent years. However, due to the lack of ground truth images, the comparison of dehazing methods is not straightforward, nor objective. To overcome this issue we introduce a new dataset -named I-HAZE- that contains 35 image pairs of hazy and corresponding haze-free (ground-truth) indoor images. Different from most of the existing dehazing databases, hazy images have been generated using real haze produced by a professional haze machine. For easy color calibration and improved assessment of dehazing algorithms, each scene include a MacBeth color checker. Moreover, since the images are captured in a controlled environment, both haze-free and hazy images are captured under the same illumination conditions. This represents an important advantage of the I-HAZE dataset that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM

An Evaluation Model For Web-based 3D Mass Customization Toolkit Design

The development of geometric modelling technologies and web technologies provides the ability to present a virtual 3D product in a mass customization (MC) toolkit. Compared with 2D graphic toolkits, 3D toolkit design requires better consideration of individual customer needs, consumer and toolkit interaction, and also a means of integrating with the underlying technical infrastructure. However, there is currently no widely accepted model or criteria to regulate and evaluate 3D MC toolkit design. Given these considerations, in this paper we provide an evaluation model for web-based 3D toolkits and a heuristic evaluation of two representative commercial web-based 3D toolkits. The evaluation results indicate the usefulness and effectiveness of the model as a scale for evaluating 3D toolkits. It also reveals that despite a fair amount of effort that has been devoted to theoretical research, current 3D toolkits are still at an early development stage. We therefore conclude this paper by identifying and encouraging further topics and questions as directions for future research