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Native game engines have long been the 3-D development platform of choice for research in mixed and augmented reality. For this reason, they have also been adopted in many immersive visualization and immersive analytics systems and toolkits. However, with the rapid improvements of WebXR and related open technologies, this choice may not always be optimal for future visualization research. In this article, we investigate common assumptions about native game engines versus WebXR and find that while native engines still have an advantage in many areas, WebXR is rapidly catching up and is superior for many immersive analytics applications.
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Internet routing is largely dependent on Border Gateway Protocol (BGP). However, BGP does not have any inherent authentication or integrity mechanisms that help make it secure. Effective security is challenging or infeasible to implement due to high costs, policy employment in these distributed systems, and unique routing behavior. Visualization tools provide an attractive alternative in lieu of traditional security approaches. Several BGP security visualization tools have been developed as a stop-gap in the face of ever-present BGP attacks. Even though the target users, tasks, and domain remain largely consistent across such tools, many diverse visualization designs have been proposed. The purpose of this study is to provide an initial formalization of methods and visualization techniques for BGP cybersecurity analysis. Using PRISMA guidelines, we provide a systematic review and survey of 29 BGP visualization tools with their tasks, implementation techniques, and attacks and anomalies that they were intended for. We focused on BGP visualization tools as the main inclusion criteria to best capture the visualization techniques used in this domain while excluding solely algorithmic solutions and other detection tools that do not involve user interaction or interpretation. We take the unique approach of connecting (1) the actual BGP attacks and anomalies used to validate existing tools with (2) the techniques employed to detect them. In this way, we contribute an analysis of which techniques can be used for each attack type. Furthermore, we can see the evolution of visualization solutions in this domain as new attack types are discovered. This systematic review provides the groundwork for future designers and researchers building visualization tools for providing BGP cybersecurity, including an understanding of the state-of-the-art in this space and an analysis of what techniques are appropriate for each attack type. Our novel security visualization survey methodology-connecting visualization techniques with appropriate attack types-may also assist future researchers conducting systematic reviews of security visualizations. All supplemental materials are available at https://osf.io/tupz6/.
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Immersive analytics has emerged as a promising research area, leveraging advances in immersive display technologies and techniques, such as virtual and augmented reality, to facilitate data exploration and decision-making. This paper presents a systematic literature review of 73 studies published between 2013-2022 on immersive analytics systems and visualizations, aiming to identify and categorize the primary dimensions influencing their design. We identified five key dimensions: Academic Theory and Contribution, Immersive Technology, Data, Spatial Presentation, and Visual Presentation. Academic Theory and Contribution assess the motivations behind the works and their theoretical frameworks. Immersive Technology examines the display and input modalities, while Data dimension focuses on dataset types and generation. Spatial Presentation discusses the environment, space, embodiment, and collaboration aspects in IA, and Visual Presentation explores the visual elements, facet and position, and manipulation of views. By examining each dimension individually and cross-referencing them, this review uncovers trends and relationships that help inform the design of immersive systems visualizations. This analysis provides valuable insights for researchers and practitioners, offering guidance in designing future immersive analytics systems and shaping the trajectory of this rapidly evolving field.
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The dominant markup language for Web visualizations-Scalable Vector Graphics (SVG)-is comparatively easy to learn, and is open, accessible, customizable via CSS, and searchable via the DOM, with easy interaction handling and debugging. Because these attributes allow visualization creators to focus on design on implementation details, tools built on top of SVG, such as D3.js, are essential to the visualization community. However, slow SVG rendering can limit designs by effectively capping the number of on-screen data points, and this can force visualization creators to switch to Canvas or WebGL. These are less flexible (e.g., no search or styling via CSS), and harder to learn. We introduce Scalable Scalable Vector Graphics (SSVG) to reduce these limitations and allow complex and smooth visualizations to be created with SVG. SSVG automatically translates interactive SVG visualizations into a dynamic virtual DOM (VDOM) to bypass the browser's slow 'to specification' rendering by intercepting JavaScript function calls. De-coupling the SVG visualization specification from SVG rendering, and obtaining a dynamic VDOM, creates flexibility and opportunity for visualization system research. SSVG uses this flexibility to free up the main thread for more interactivity and renders the visualization with Canvas or WebGL on a web worker. Together, these concepts create a drop-in JavaScript library which can improve rendering performance by 3-9× with only one line of code added. To demonstrate applicability, we describe the use of SSVG on multiple example visualizations including published visualization research. A free copy of this article, collected data, and source code are available as open science at osf.io/ge8wp.
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Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.
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Tools and interfaces are increasingly expected to be synchronous and distributed to accommodate remote collaboration. Yet, adoption of these techniques for data visualization is low partly because development is difficult: existing collaboration software systems either do not support simultaneous interaction or require expensive redevelopment of existing visualizations. We contribute VisConnect: a web-based synchronous distributed collaborative visualization system that supports most web-based SVG data visualizations, balances system safety with responsiveness, and supports simultaneous interaction from many collaborators. VisConnect works with existing visualization implementations with little-to-no code changes by synchronizing low-level JavaScript events across clients such that visualization updates proceed transparently across clients. This is accomplished via a peer-to-peer system that establishes consensus among clients on the per-element sequence of events, and uses a lock service to grant access over elements to clients. We contribute collaborative extensions of traditional visualization interaction techniques, such as drag, brush, and lasso, and discuss different strategies for collaborative visualization interactions. To demonstrate the utility of VisConnect, we present novel examples of collaborative visualizations in the healthcare domain, remote collaboration with annotation, and show in an education case study for e-learning with 22 participants that students found the ability to remotely collaborate on class activities helpful and enjoyable for understanding concepts. A free copy of this paper and source code are available on OSF at osf.io/ut7e6 and at visconnect.us.
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A brain-computer interface (BCI) platform can be utilized by a user to control an external device without making any overt movements. An EEG-based computer cursor control task is commonly used as a testbed for BCI applications. While traditional computer cursor control schemes are based on sensorimotor rhythm, a new scheme has recently been developed using imagined body kinematics (IBK) to achieve natural cursor movement in a shorter time of training. This article attempts to explore optimal decoding algorithms for an IBK paradigm using EEG signals with application to neural cursor control. The study is based on an offline analysis of 32 healthy subjects' training data. Various machine learning techniques were implemented to predict the kinematics of the computer cursor using EEG signals during the training tasks. Our results showed that a linear regression least squares model yielded the highest goodness-of-fit scores in the cursor kinematics model (70% in horizontal prediction and 40% in vertical prediction using a Theil-Sen regressor). Additionally, the contribution of each EEG channel on the predictability of cursor kinematics was examined for horizontal and vertical directions, separately. A directional classifier was also proposed to classify horizontal versus vertical cursor kinematics using EEG signals. By incorporating features extracted from specific frequency bands, we achieved 80% classification accuracy in differentiating horizontal and vertical cursor movements. The findings of the current study could facilitate a pathway to designing an optimized online neural cursor control.