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Digital Twins (DTs) have been developed for several pilot-scale membrane capacitive deionization (mCDI) units that are located in remote communities in China and Australia for desalination of brackish water and treated domestic wastewater. These pilot-scale mCDI units have a production capacity ranging from 5 to 50 m3/day and a water recovery rate of up to 85%. The mCDI DTs use Head-mounted Displays (HMDs) to facilitate the visualisation of transient real-time data and historical data from various sensors in the physical plants. The DTs contain device tag and sensor data display functions which greatly enhance the model functionality and user experience. By combining the DTs with Mixed Reality (MR) technology that blends elements of both Virtual Reality (VR) and Augmented Reality (AR), it was possible to use the DTs for remote control and remote operator training in an immersive environment. Our results suggest that more facile remote control and improved training outcomes could be achieved by use of DTs by the water industry compared to those achieved by conventional control and training methods.

Research into Explainable Artificial Intelligence (XAI) has been increasing in recent years as a response to the need for increased transparency and trust in AI. This is particularly important as AI is used in sensitive domains with societal, ethical, and safety implications. Work in XAI has primarily focused on Machine Learning (ML) for classification, decision, or action, with detailed systematic reviews already undertaken. This review looks to explore current approaches and limitations for XAI in the area of Reinforcement Learning (RL). From 520 search results, 25 studies (including 5 snowball sampled) are reviewed, highlighting visualization, query-based explanations, policy summarization, human-in-the-loop collaboration, and verification as trends in this area. Limitations in the studies are presented, particularly a lack of user studies, and the prevalence of toy-examples and difficulties providing understandable explanations. Areas for future study are identified, including immersive visualization, and symbolic representation.

Despite Narrative Visualisation's (NarVis) proliferation in academic circles, a narrative structure designed to suit the individual challenges and needs of the discipline is yet to mature. We present both a structure and tool for NarVis that remediates narrative form and concepts using Rhetorical Structure Theory (RST). Through RST, we develop a dynamic narrative structure that avoids many of the trappings of traditional narratives. Our RST-based structure has been implemented as a node-based editor, itself part of a broader tool for generating dynamic narrative visualisations. We discuss this tool's theoretical foundations, its implementation, and demonstrate it through a narrative about COVID-19. This paper marks an important step towards establishing a clearer view of the narrative elements of NarVis.

Urban modeling facilitates the generation of virtual environments for various scenarios about cities. It requires expertise and consideration, and therefore consumes massive time and computation resources. Nevertheless, related tasks sometimes result in dissatisfaction or even failure. These challenges have received significant attention from researchers in the area of computer graphics. Meanwhile, the burgeoning development of artificial intelligence motivates people to exploit machine learning, and hence improves the conventional solutions. In this paper, we present a review of approaches to urban modeling in computer graphics using machine learning in the literature published between 2010 and 2019. This serves as an overview of the current state of research on urban modeling from a machine learning perspective.

Narrative Visualisation (NarVis) is the pairing of data visualisation with narrative techniques. Due to its interdisciplinary applications and scholarship, NarVis presentations often feature vastly different interpretations of “narrative” and “visualisation”, which is echoed in NarVis authoring tools. To map the morphology of how the narratives of NarVis manifest, we identify three different trajectories for the field. These trajectories are identified through an analysis of selected NarVis presentations and tools, with an emphasis on identifying how traditional narrative techniques are adopted, transposed or indeed challenged by NarVis examples. We then populate our categories with additional examples and tools, providing a foundational point of reference for NarVis scholars, authors and tool developers.

The future of work and workplace is very much in flux. A vast amount has been written about artificial intelligence (AI) and its impact on work, with much of it focused on automation and its impact in terms of potential job losses. This review will address one area where AI is being added to creative and design practitioners’ toolbox to enhance their creativity, productivity, and design horizons. A designer’s primary purpose is to create, or generate, the most optimal artifact or prototype, given a set of constraints. We have seen AI encroaching into this space with the advent of generative networks and generative adversarial networks (GANs) in particular. This area has become one of the most active research fields in machine learning over the past number of years, and a number of these techniques, particularly those around plausible image generation, have garnered considerable media attention. We will look beyond automatic techniques and solutions and see how GANs are being incorporated into user pipelines for design practitioners. A systematic review of publications indexed on ScienceDirect, SpringerLink, Web of Science, Scopus, IEEExplore, and ACM DigitalLibrary was conducted from 2015 to 2020. Results are reported according to PRISMA statement. From 317 search results, 34 studies (including two snowball sampled) are reviewed, highlighting key trends in this area. The studies’ limitations are presented, particularly a lack of user studies and the prevalence of toy-examples or implementations that are unlikely to scale. Areas for future study are also identified.

We developed a 3D-Enhanced Facility Management System for Indoors Navigation (3D-EFMS-IN) to assist visually impaired users (VIU). Additionally, the system aims to facilitate the management of estate property and provide support for future scenarios related to emergencies, security, and robotics devices. The system combines four main subsystems: Mapping, Navigation Paths, Indoor Localisation and Navigation, and a Visualisation. An integration of the subsystems has been done and a pretest with one VIU was performed to obtain feedback and tune the critical characteristics of our development. We observed that the system offers an acceptable preliminary user experience for VIU and future tests require to improve the latency of the system and usability. Shortly, we aim to obtain qualitative and quantitative measurements in a significant pool of users once the COVID lockdown ends.

The current state of computer vision methods applied to autism spectrum disorder (ASD) research has not been well established. Increasing evidence suggests that computer vision techniques have a strong impact on autism research. The primary objective of this systematic review is to examine how computer vision analysis has been useful in ASD diagnosis, therapy and autism research in general. A systematic review of publications indexed on PubMed, IEEE Xplore and ACM Digital Library was conducted from 2009 to 2019. Search terms included [‘autis*’ AND (‘computer vision’ OR ‘behavio* imaging’ OR ‘behavio* analysis’ OR ‘affective computing’)]. Results are reported according to PRISMA statement. A total of 94 studies are included in the analysis. Eligible papers are categorised based on the potential biological/behavioural markers quantified in each study. Then, different computer vision approaches that were employed in the included papers are described. Different publicly available datasets are also reviewed in order to rapidly familiarise researchers with datasets applicable to their field and to accelerate both new behavioural and technological work on autism research. Finally, future research directions are outlined. The findings in this review suggest that computer vision analysis is useful for the quantification of behavioural/biological markers which can further lead to a more objective analysis in autism research.

This paper describes a production-grade software toolkit used for shared multi-model visualization systems developed by the Expanded Perception and Interaction Centre. Our High-End Visualization System (HEVS) can be used as a framework to enable content to be run transparently on a wider range of platforms (Figure 2) with fewer compatibility issues and dependencies on commercial software. Content can be transferred more easily from large screens (including cluster-driven systems) such as CAVE-like platforms, hemispherical domes, and projected cylindrical displays through to multi-wall displays and HMDs such as VRR or AR. This common framework is able to provide a unifying approach to visual analytics and visualizations. In addition to supporting multi-modal displays, multiple platforms can be connected to create multi-user collaborative experiences across remotely located labs. We aim to demonstrate multiple projects developed with HEVS that have been deployed to various multi-modal display devices.

This paper discusses a proof-of-concept context-aware container server for exposing multiple VR devices to a single data source. The data source was a real-time streamed reconstruction of a combat simulation generated in NetLogo. The devices included a mobile, tablet, PC, data wall, HMD and dataglove interaction. Each device had its specific requirements and user restrictions. Initial testing of this system suggests it is an efficient method for supporting diverse user needs whilst maintaining data integrity and synchronicity. The overall server architecture is discussed as well as future directions for this research.

This paper investigates how to design an embodied learning experienceof a drumming teacher playing hand drums, to aid higher rhythm understanding and accuracy. By providing novices the first-person perspective of a drumming teacher while learning to play a West-African djembe drum, participants’ learning was measured objectively by their ability to follow the drumming teachers rhythms. Participants subjective learning was assessed through a self assessment questionnaire measuring aspects of flow, user-experience, oneness, and presence. Two test iterations were conducted. In both there was found no significance di erence in participants’ ability to follow the drumming teacher’ s tempo for the experimental group exposed to the first-person perspective of the teacher in a Virtual Reality (VR) drum lesson, versus the control group exposed to a 2D version of the stereoscopic drum lesson. There was sound a significant di erence in the experimental group’s presence scores in the first test iteration, and a significant difference in experimental group’ s oneness scores in the second test iteration. Participants’ subjective feelings indicated enjoyment and motivation to the presented learning technique in both groups.

Over the last few decades, Mixed Reality (MR) interfaces have received great attention from academia and industry. Although a considerable amount of research has already been done to support collaboration between users in MR, there is still no systematic review to determine the current state of collaborative MR applications. In this paper, collaborative MR studies published from 2013 to 2018 were reviewed. A total of 259 papers have been categorised based on their application areas, types of display devices used, collaboration setups, and user interaction and experience aspects. The primary contribution of this paper is to present a high-level overview of collaborative MR influence across several research disciplines. The achievements from each application area are summarised. In addition, remarkable papers in their respective areas are highlighted. Among other things, our study finds that there are three complementary factors to support and enhance collaboration in MR environments: (i) annotation techniques, which provide non-verbal communication cues to users, (ii) cooperative object manipulation techniques, which divide complex 3D object manipulation process into simpler tasks between different users, and (iii) user perception and cognition studies, which aim to lessen cognitive workload for task understanding and completion, and to increase users’ perceptual awareness and presence. Finally, this paper identifies research gaps and future directions that can be useful for researchers who want to explore ways on how to foster collaboration between users and to develop collaborative applications in MR.

© 2019 Elsevier Inc. Visualisation research explores techniques for conveying information in a meaningful way to allow users to make sense of data. This paper aims to review state-of-the-art information visualisation methods and techniques particularly for farmed data in defence simulations. It also discusses some of the key visualisation design challenges and outlines a conceptual framework for future research directions in information visualisation. It further presents a concept demonstrator that has been designed and developed to visualise joint force simulations for the purpose of showcasing some of the key concepts in the proposed information visualisation conceptual framework.

Immersive analytics allows a large amount of data and complex structures to be concurrently investigated. We propose a collaborative analytics system that benefits from new advances in immersive technologies for collaborators working in the early stages of data exploration. We implemented a combination of Star Coordinates and Star Plot visualisation techniques to support the visualisation of multidimensional data and the encoding of datasets using simple and compact visual representations. To support data analytics tasks, we propose tools and interaction techniques for users to build decision trees for visualising and analysing data in a top-down method.

Effective visualisation plays a vital role in generating insights from data. The selection of graph types however, is highly dependent on the analysis tasks and data types at hand. For example, spatio-temporal visualisations encode changes in data over time and space. Although they have the potential of revealing overall tendencies and movement patterns, building effective spatio-temporal visualisations is challenging because it requires encoding all three attributes of spatio-temporal data i.e. thematic (values of attributes), temporal and spatial in a single visualisation. In this application design study, we present PedaViz for representing hour-level spatio-temporal attributes within a single visualisation; a 24-hour radial visual metaphor that encodes hour-level temporal and daily temperature attributes while utilising a thematic map display to present spatial attributes. The design was applied on city planning domain using Melbourne's pedestrian count and temperature data. Results of our preliminary user evaluation suggest that our visualisation is easily understandable by users; and supports users in carrying out selected analysis tasks.

This work presents our strategy and pipeline architecture for visualizingvery large-scale graphs in an immersive environment, usinghigh-performance graphics approach. Innovation lies in utilizingGPUs for real-time cluster-based interactive rendering, but alsointermediate graph representation that utilizes Khronos Group’sGLTF file format, and interaction design.

This paper describes the design and implementation of Sensorland,an immersive environment developed to support an existing policymaking framework. In particular, our aim was to visualise futureapplication scenarios for sensor systems in two different contexts:(1) A highly automated mining environment, and (2) A naturaldisaster response situation. For this purpose, two virtual reality(VR) environments for HTC Vive were created using Unity 3D gameengine. Finally, the opportunities and pitfalls of the VR mediumfor presenting information to support policy making are discussed.We make several recommendations for the future development ofthe content creation process, and consider its applicability in othercontexts.

Understanding complex physiological processes demands the integrationof diverse insights derived from visual and quantitativeanalysis of bio-image data, such as microscopy images. This processis currently constrained by disconnects between methods forinterpreting data, as well as by language barriers that hamper thenecessary cross-disciplinary collaborations. Using immersive analytics,we leveraged bespoke immersive visualizations to integratebio-images and derived quantitative data, enabling deeper comprehensionand seamless interaction with multi-dimensional cellularinformation. We designed and developed a visualization platformthat combines time-lapse confocal microscopy recordings of cancercell motility with image-derived quantitative data spanning 52parameters. The integrated data representations enable rapid, intuitiveinterpretation, bridging the divide between bio-images andquantitative information. Moreover, the immersive visualizationenvironment promotes collaborative data interrogation, supportingvital cross-disciplinary collaborations capable of deriving transformativeinsights from rapidly emerging bio-image big data.

This work presents a narrative story of a Future Mine scenario thatuses Virtual Reality as a medium to replace traditional spreadsheet basedpolicy making framework currently widely used in governmentagencies for decision making process. The scenario presentedenvisions user exploring underground mine, where extraction processeshad been almost fully automated, and environment is constantlymonitored by a variety of modern and futuristic sensors.The use of story-telling using VR is explored to present novel applicationscenarios for sensing technologies and to facilitate betterunderstanding of the context in which they will be used. Furtherthe experience is translated into informed decision making.

Visual Analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It is a multidisciplinary subject that comprises data modelling, information visualisation, knowledge science, and human cognition. It should therefore be considered in a broad interdisciplinary context.This course outlines the fundamentals of Visual Analytics for Big Networks, and illustrates them through applied used cases. It touches on aspects of data integration, visualisation design, simulations, machine learning and combinations of all. We introduce methods that allow exploration and validation of networks through visualisation, statistical models and interactivity to generate new knowledge. The best results can often be achieved through graphical interfaces, which can illustrate patterns and trends in complex networks over time, allowing interactions and re-computations in real-time, acting as a feedback loop for improving Visual Analytics outcomes. However, several elements have to be considered in the design of tools that facilitate Visual Analytics of Big Networks, in order to be effective in extracting and communicating the underlying insights, not only to the scientific community, but also to the public.In this course, we discuss recent approaches towards exploring and analysing Big Networks. Furthermore, we explore the potential of different visualisation modalities, including AR/VR environments. This presents novel opportunities to use the unique properties of immersive environments to facilitate data exploration using combination of machine learning and interactive visualisations to combine the strength of human intuition and power of computation. One example is semi-supervised learning, where the visual presentation is used to determine the best tuning parameters for label propagation.The techniques and elements we focus on include: big graph data integration and representation, multiple views of data, simulations and machine learning, as well as na

Cultural venues, such as libraries, theatres, cinemas and galleries,contribute to a city’s tourism and economy, and enrich the culturallife of the local residents. In this paper, we propose a novelapproach to automatic site selection of cultural venues in an urbanarea, which requires less expertise in urban planning. The two-stageapproach consists of a learning stage for predicting zones as a priorconstraint, and an optimisation stage for determining the numberof cultural venues and their exact locations according to multiplecriteria. Given an input set of urban data, our approach generatesan optimal configuration of two-dimensional locations for culturalvenues that complies with land use policies and provides easy accessfor the public. We implemented the approach using reliablemethods of deep learning and stochastic optimisation, and the resultsdemonstrate the approach’s effectiveness by a comparison totheir real-world counterparts.

An innovative fully interactive and ultra-high resolution navigation tool has been developed to browse and analyze gene expression levels from human cancer cells, acting as a visual microscope on data. The tool uses high-performance visualization and computer graphics technology to enable genome scientists to observe the evolution of regulatory elements across time and gain valuable insights from their dataset as never before.

Many problems in application domains deal with graphs. Graphs are often visualized into node-link diagrams for better understanding of them. Traditionally, graphs are visualized to meet some pre-defined quality metrics, or aesthetics. It is commonly agreed that graphs that are drawn based on aesthetics are more likely to be not only beautiful but also comprehensible. However, those aesthetics were originally proposed by researchers based on their intuitions from a designer's perspective. Although some of them have been empirically validated, to produce truly user cognitively friendly visualizations,well-grounded cognitive theories and design guidelines are needed. In this paper, we attempt to explore a cognitive approach that follows a user-centered process in visualizing graphs. We begin by proposing a graph visualization model, which is further conceptualized into a two-stage assessment cycle. It is argued that to provide effective cognitive support, it is essential to understand the cognitive process of a particular task in association with the attributes of the end user and the visual representation, and that to develop a set of design principles, what cognitive processes or graph reading strategies are involved should be part of evaluation. We materialized the model by reviewing the literature and providing relevant examples in the context of providing cognitive support and understanding cognitive process. We introduce theories from other disciplines such as perceptual and cognitive psychology, summarize methodologies that have been used in prior research, and discuss their implications for producing user-centered visualizations.

Audio recording is a convenient and important method for large-scale terrestrial environmental monitoring. However, it is impossible to listen and make sense of all the data collected. Attempts to generalise automated analysis tasks have not been successful due to the unconstrained nature of long-term environmental recording. Our approach to this big-data challenge is to facilitate visualisation of long-term audio recording, to keep ecologists in the loop. The content of long-duration audio recordings are visualised by calculating acoustic indices. Our interface facilitates the customised visualisation and navigation of long-term audio recording by ecologists. Two case studies, one in Australia and one in Bhutan, are presented as examples.

Acoustic recordings play an increasingly important role in monitoring terrestrial environments. However, due to rapid advances in technology, ecologists are accumulating more audio than they can listen to. Our approach to this big-data challenge is to visualize the content of long-duration audio recordings by calculating acoustic indices. These are statistics which describe the temporal-spectral distribution of acoustic energy and reflect content of ecological interest. We combine spectral indices to produce false-color spectrogram images. These not only reveal acoustic content but also facilitate navigation. An additional analytic challenge is to find appropriate descriptors to summarize the content of 24-hour recordings, so that it becomes possible to monitor long-term changes in the acoustic environment at a single location and to compare the acoustic environments of different locations. We describe a 24-hour ‘acoustic-fingerprint’ which shows some preliminary promise.

This research draws on theories of emergence to inform the creation of an artistic and direct visualization. This is an interactive artwork and drawing tool for creative participant experiences. As is discussed, emergence is characteristically creative. It is also debated across and within disciplines, resulting in a range of understandings as well as models. This paper shows how one field’s understanding of emergence (complexity theory) can be used to facilitate emergence in another domain (design research) and, importantly provide the opportunity for someone to act creatively. This paper begins with a brief review of some theories of emergence to show how they interrelate and can effect the perception of emergent structures in an observer, and, correspondingly, the design for creative experience. This is subsequently demonstrated in the second section of the paper where an interactive artwork and drawing application, Of me with me, is presented. This artwork by the author was created during collaboration with community artists from Cerebral Palsy League. The discussion covers the application of emergence theories to create this visualization in order facilitate the perception of structures and creative behaviours in a participant and to facilitate self-efficacy in the community artist user group.

In this paper we describe the design of DNA Jewellery, which is a wearable tangible data representation of personal DNA profile data. An iterative design process was followed to develop a 3D form-language that could be mapped to standard DNA profile data, with the aim of retaining readability of data while also producing an aesthetically pleasing and unique result in the area of personalized design. The work explores design issues with the production of data tangibles, contributes to a growing body of research exploring tangible representations of data and highlights the importance of approaches that move between technology, art and design.

We describe an investigation into how Massey University's Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University's pollen reference collection (2890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set. In addition to the Classifynder's native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples. © 2013 AIP Publishing LLC.