Visualizing Big Data with augmented and virtual reality: challenges and research agenda

This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.publishedVersionPeer reviewe

Current accounts of antimicrobial resistance: stabilisation, individualisation and antibiotics as infrastructure.

Antimicrobial resistance (AMR) is one of the latest issues to galvanise political and financial investment as an emerging global health threat. This paper explores the construction of AMR as a problem, following three lines of analysis. First, an examination of some of the ways in which AMR has become an object for action-through defining, counting and projecting it. Following Lakoff's work on emerging infectious diseases, the paper illustrates that while an 'actuarial' approach to AMR may be challenging to stabilise due to definitional and logistical issues, it has been successfully stabilised through a 'sentinel' approach that emphasises the threat of AMR. Second, the paper draws out a contrast between the way AMR is formulated in terms of a problem of connectedness-a 'One Health' issue-and the frequent solutions to AMR being focused on individual behaviour. The paper suggests that AMR presents an opportunity to take seriously connections, scale and systems but that this effort is undermined by the prevailing tendency to reduce health issues to matters for individual responsibility. Third, the paper takes AMR as a moment of infrastructural inversion (Bowker and Star) when antimicrobials and the work they do are rendered more visible. This leads to the proposal of antibiotics as infrastructure-part of the woodwork that we take for granted, and entangled with our ways of doing life, in particular modern life. These explorations render visible the ways social, economic and political frames continue to define AMR and how it may be acted upon, which opens up possibilities for reconfiguring AMR research and action

Using Evolutionary Computation to Generate Training Set Data for Neural Networks

Most neural networks require a set of training examples in order to attempt to approximate a problem function. For many real-world problems, however, such a set of examples is unavailable. Such a problem involving feedback optimization of a computer network routing system has motivated a general method of generating artificial training sets using evolutionary computation. This paper describes the method and demonstrates its utility by presenting promising results from applying it to an artificial problem similar to a realworld network routing optimization problem

ART neural networks for medical data analysis and fast distributed learning

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number o