Interactive Visualization Research Articles

Beyond Dynamic Drawings: Restoring and Reusing Interactive 3D Visualizations in a Humanities Context

Beyond Dynamic Drawings: Restoring and Reusing Interactive 3D Visualizations in a Humanities Context

Consensus Control of Heterogeneous Uncertain Multiple Autonomous Underwater Vehicle Recovery Systems in Scenarios of Implicit Reduced Visibility

This paper investigates consensus control in heterogeneous and uncertain multiple autonomous underwater vehicle (AUV) systems under implicit reduced visibility conditions. We address challenges such as environmental uncertainties and system nonlinearity by utilizing a unified connectivity approach to model low-visibility interactions and heterogeneous multi-AUV dynamics. Our main contributions include developing a feedback linearization model for heterogeneous multi-AUV systems that accounts for uncertainties, introducing an adaptive consensus controller based on relative positioning that effectively manages implicit visual interaction limitations and validating our strategies through stability analysis and numerical simulations. Our simulations demonstrate approximately a 60% improvement in accuracy compared to previous algorithms, highlighting the practical value of our approach in AUV recovery operations. These advancements provide a robust solution for consensus control in complex underwater environments.

Complexity and scalability of defeasible reasoning in many-valued weighted knowledge bases with typicality

Abstract Weighted knowledge bases for description logics with typicality under a ‘concept-wise’ multi-preferential semantics provide a logical interpretation of MultiLayer Perceptrons. In this context, Answer Set Programming (ASP) has been shown to be suitable for addressing defeasible reasoning in the finitely many-valued case, providing a $\varPi ^{p}_{2}$ upper bound on the complexity of the problem, nonetheless leaving unknown the exact complexity and only providing a proof-of-concept implementation. This paper fulfills the lack by providing a ${P^{NP[log]}}$-completeness result and new ASP encodings that deal with both acyclic and cyclic weighted knowledge bases with large search spaces, as assessed empirically on synthetic test cases. The encodings are used to empower a reasoner for computing solutions and answering queries, possibly interacting with ASP Chef for obtaining an interactive visualization.

Force-Directed Immersive 3D Network Visualization

Network visualization, in mathematics often referred to as graph visualization, has evolved significantly over time, offering various methods to effectively represent complex data structures. New methods and devices advance the possibilities of visualization both from the point of view of the quality of displayed information and of the possibilities of visualizing a larger amount of data. Immersive visualization includes the user directly in presented visual representation but requires a native 3D environment for direct interaction with visualized information. This article describes an approach to creating a force-directed immersive 3D network visualization algorithm available for application in immersive environments, such as a cave automatic virtual environment or virtual reality. The algorithm aims to address the challenge of creating visually appealing and easily interpretable visualizations by utilizing 3D space and the Unity engine. The results show successfully visualized data and developed interactive visualization methods, overcoming limitations of basic force-directed implementations. The main contribution of the presented research is the force-directed algorithm with springs and controlled placement as an immersive visualization technique that combines the use of springs and attractive forces to stabilize a network in a 3D environment.

Direct Visualization of Defect-Controlled Diffusion in van der Waals Gaps.

Diffusion processes govern fundamental phenomena such as phase transformations, doping, and intercalation in van der Waals (vdW) bonded materials. Here, the diffusion dynamics of W atoms by visualizing the motion of individual atoms at three different vdW interfaces: hexagonal boron nitride (BN)/vacuum, BN/BN, and BN/WSe2, by recording scanning transmission electron microscopy movies is quantified. Supported by density functional theory (DFT) calculations, it is inferred that in all cases diffusion is governed by intermittent trapping at electron beam-generated defect sites. This leads to diffusion properties that depend strongly on the number of defects. These results suggest that diffusion and intercalation processes in vdW materials are highly tunable and sensitive to crystal quality. The demonstration of imaging, with high spatial and temporal resolution, of layers and individual atoms inside vdW heterostructures offers possibilities for direct visualization of diffusion and atomic interactions, as well as for experiments exploring atomic structures, their in situ modification, and electrical property measurements of active devices combined with atomic resolutionimaging.

SecSAGE: NIST Cybersecurity Framework Visualization on SAGE2

Cybersecurity has been an area of great interest for an organization, given the significance of data and the increasing cybersecurity threats. The National Institute of Standards and Technology (NIST) has developed a cybersecurity framework intended for voluntary utilization by critical infrastructure owners and operators. Its primary purpose is to aid in the effective management of cybersecurity risks. This framework, similar to many other security standards, comprises a substantial volume of textual information that can be challenging to grasp comprehensively in a limited timeframe. In response to this challenge, we designed and developed an interactive visualization of the NIST Cybersecurity Framework using the SAGE2 platform. Our objective is to facilitate a better understanding of the framework. In addition, using SAGE2 enhances collaborative working. In our project, we analyze the content within the NIST document and map the framework's five core functions into a rich visualization workflow. Each function includes categories, sub-categories, and references that users can interactively explore. Our experiments show that our visualization can help participants correctly find the information about the NIST Cybersecurity Framework faster than manually finding the information in the document. For all tasks, participants can complete the tasks around 4.25 times faster than the manual method on average.

PMart Web Application: Marketplace for Interactive Analysis of Panomics Data.

PMart is a web-based tool for reproducible quality control, exploratory data analysis, statistical analysis, and interactive visualization of 'omics data, based on the functionality of the pmartR R package. The newly improved user interface supports more 'omics data types, additional statistical capabilities, and enhanced options for creating downloadable graphics. PMart supports the analysis of label-free and isobaric-labeled (e.g., TMT, iTRAQ) proteomics, nuclear magnetic resonance (NMR) and mass-spectrometry (MS)-based metabolomics, MS-based lipidomics, and ribonucleic acid sequencing (RNA-seq) transcriptomics data. At the end of a PMart session, a report is available that summarizes the processing steps performed and includes the pmartR R package functions used to execute the data processing. In addition, built-in safeguards in the backend code prevent users from utilizing methods that are inappropriate based on omics data type. PMart is a user-friendly interface for conducting exploratory data analysis and statistical comparisons of omics data without programming.

ModDotPlot-rapid and interactive visualization of tandem repeats.

A common method for analyzing genomic repeats is to produce a sequence similarity matrix visualized via a dot plot. Innovative approaches such as StainedGlass have improved upon this classic visualization by rendering dot plots as a heatmap of sequence identity, enabling researchers to better visualize multi-megabase tandem repeat arrays within centromeres and other heterochromatic regions of the genome. However, computing the similarity estimates for heatmaps requires high computational overhead and can suffer from decreasing accuracy. In this work, we introduce ModDotPlot, an interactive and alignment-free dot plot viewer. By approximating average nucleotide identity via a k-mer-based containment index, ModDotPlot produces accurate plots orders of magnitude faster than StainedGlass. We accomplish this through the use of a hierarchical modimizer scheme that can visualize the full 128 Mb genome of Arabidopsis thaliana in under 5 min on a laptop. ModDotPlot is bundled with a graphical user interface supporting real-time interactive navigation of entire chromosomes. ModDotPlot is available at https://github.com/marbl/ModDotPlot.

Interactive Volume Visualization via Multi-Resolution Hash Encoding Based Neural Representation.

Implicit neural networks have demonstrated immense potential in compressing volume data for visualization. However, despite their advantages, the high costs of training and inference have thus far limited their application to offline data processing and non-interactive rendering. In this article, we present a novel solution that leverages modern GPU tensor cores, a well-implemented CUDA machine learning framework, an optimized global-illumination-capable volume rendering algorithm, and a suitable acceleration data structure to enable real-time direct ray tracing of volumetric neural representations. Our approach produces high-fidelity neural representations with a peak signal-to-noise ratio (PSNR) exceeding 30 dB, while reducing their size by up to three orders of magnitude. Remarkably, we show that the entire training step can fit within a rendering loop, bypassing the need for pre-training. Additionally, we introduce an efficient out-of-core training strategy to support extreme-scale volume data, making it possible for our volumetric neural representation training to scale up to terascale on a workstation with an NVIDIA RTX 3090 GPU. Our method significantly outperforms state-of-the-art techniques in terms of training time, reconstruction quality, and rendering performance, making it an ideal choice for applications where fast and accurate visualization of large-scale volume data is paramount.

MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https://github.com/AbimaelGP/MolGC/tree/main .

MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions.

Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

AutoTitle: An Interactive Title Generator for Visualizations.

We propose AutoTitle, an interactive visualization title generator satisfying multifarious user requirements. Factors making a good title, namely, the feature importance, coverage, preciseness, general information richness, conciseness, and non-technicality, are summarized based on the feedback from user interviews. Visualization authors need to trade off among these factors to fit specific scenarios, resulting in a wide design space of visualization titles. AutoTitle generates various titles through the process of visualization facts traversing, deep learning-based fact-to-title generation, and quantitative evaluation of the six factors. AutoTitle also provides users with an interactive interface to explore the desired titles by filtering the metrics. We conduct a user study to validate the quality of generated titles as well as the rationality and helpfulness of these metrics.

Connecting audiences with climate change: Towards humanised and action-focused data interactions

This paper investigates the relationship between design research and humancomputer interaction (HCI) in the context of climate change communication and engagement. We discuss current practices in climate change communication and the decrease in concern and engagement caused by “crisis fatigue”. Through Research through Design (RtD), we set out to investigate data humanism and how users react to climate change data, testing approaches to improve engagement. With this purpose, we designed and evaluated Finding Arcadia, an interactive data story that uses data humanism to shift the dialogue from crisis-focused to action-focused. One study with the original IMF visualisations (N = 17) and two studies in public spaces (N = 12 and N = 64) point to the contextualization of the data and presenting actionable solutions helping in engaging users with climate change issues; help in creating solution-focused narratives and interpreting and relating with climate data. From these results, we derive insights for designing empowering interactive data visualizations for resilient climate change engagement.

BOPVis: Bridge Monitoring Data Visualization for Operational Performance Mining

Bridges are fundamental facilities in the transportation system, and their operational performance is crucial for economic and social development. Many large bridges are now equipped with structural health monitoring (SHM) systems that collect various types of real-time data. However, our user study found that despite the accumulation of massive amounts of monitoring data, current analysis methods cannot efficiently process large-scale, high-dimensional data. To address this, we have developed BOPVis, a visualization system for bridge monitoring data. BOPVis allows users to intuitively locate sensors and extract corresponding data from a 3D digital model of a bridge. It also provides convenient and flexible interactions for examining trends over time and correlations across hundreds of monitoring channels. A real-world long-span suspension bridge in China is used as a case study to demonstrate the advantages of the BOPVis system for operational performance mining. Through BOPVis, the global temperature deformation behaviors of the bridge are explored and found to align with the physical mechanism documented in the SHM literature. The BOPVis system, with its interactive visualization analysis capabilities, offers a new method for analyzing bridge monitoring data.

Cultural Aspects of Character Education found in Comic "Pelayaran Ke Dusung": An Ecranization through the Application of Augmented Reality

This research examines the cultural aspects of character education portrayed in Firdaus' comic, "Pelayaran Ke Dusung," which was nominated for a festival award. This comic has undergone the process of screen adaptation (ekranisasi) from the Epic I La Galigo with the main character, Sawerigading. To bring this comic closer to readers, it can be accessed through an Augmented Reality (AR) application. This approach aims to preserve the moral character values contained in the Epic I La Galigo. The objectives of this research are 1) to actualize thematic stories with local content that are relevant to actual life, and 2) to preserve the cultural values found in the comic "Pelayaran ke Dusung," which is popular among Gen Z. The results of this research show that comics with reflections or guidance on how the taught values can be implemented in real life provide a strong message, empowering readers to practice the values of Sipakatau, Sipakalebbi, and Sipakainge in their daily lives. In addition to developing AR-based comics, this research also conducts a comprehensive evaluation of the effectiveness of utilizing AR for comics. This not only focuses on technical aspects such as visual quality and interactivity but also on its impact on understanding and internalizing the traditional Bugis character values among the younger generation. The implications of this research are expected to provide empirical evidence regarding the potential of AR in bringing traditional values to life and making it more relevant and engaging for the younger generation.

TME-analyzer: a new interactive and dynamic image analysis tool that identified immune cell distances as predictors for survival of triple negative breast cancer patients

Spatial distribution of intra-tumoral immune cell populations is considered a critical determinant of tumor evolution and response to therapy. The accurate and systemic search for contexture-based predictors would be accelerated by methods that allow interactive visualization and interrogation of tumor micro-environments (TME), independent of image acquisition platforms. To this end, we have developed the TME-Analyzer, a new image analysis tool, which we have benchmarked against 2 software tools regarding densities and networks of immune effector cells using multiplexed immune-fluorescent images of triple negative breast cancer (TNBC). With the TME-Analyzer we have identified a 10-parameter classifier, predominantly featuring cellular distances, that significantly predicted overall survival, and which was validated using multiplexed ion beam time of flight images from an independent cohort. In conclusion, the TME-Analyzer enabled accurate interactive analysis of the spatial immune phenotype from different imaging platforms as well as enhanced utility and aided the discovery of contextual predictors towards the survival of TNBC patients.

Comparison of EEVEE and Cycles Rendering Performance in Blender 3.5 in the Context of Interactive Visuals for 3D Animation

This research aims to compare the performance of two rendering engines, namely EEVEE and Cycles, available in Blender 3.5, in the context of developing interactive visual 3D animation. The rendering engine is a key element in 3D animation production, and the right choice between EEVEE and Cycles can have a significant impact on the final animation result. In this research, we conducted a series of experiments and analyzes to evaluate rendering speed, the quality of the resulting images, and the ability to achieve the visual effects desired by the animator. The results of this research provide deep insight into the strengths and limitations of each rendering engine in interactive 3D animation scenarios. These findings can help animators, game developers, and similar creative professionals make more informed choices when choosing a rendering engine that suits their project needs. Thus, this research contributes to the development of rendering techniques in the growing 3D animation industry.

Media Pembelajaran Kimia Berbasis AR

The integration of augmented reality (AR) technology in education is advancing, as seen in a project at Atisa Dipamkara School. This project developed a chemistry learning application using Unity, Android XR Plugin, and ARCore to address laboratory limitations. The app uses interactive 3D visualizations to help students grasp complex chemistry concepts. Blackbox testing showed that all main modules work well, except for a bug in the compound reset module. The app effectively improves students' understanding, interest, and motivation in chemistry, making abstract concepts more tangible. This application is both a learning aid and an educational innovation, promoting a more interactive and enjoyable learning experience.

How to enhance consumers’ purchase intention in live commerce? An affordance perspective and the moderating role of age

Live commerce is a novel form of social commerce characterized by real-time interaction and synchronous visualization. Drawing upon affordance theory and the affective-cognitive framework, we propose a comprehensive research model that integrates three key dimensions of affordances of live commerce technology, namely utilitarian (visibility), social (social presence, interactivity, self-presentation), and hedonic affordances (entertainment), together with consumers’ cognitive and affective states (perceived diagnosticity and psychological distance). Furthermore, we explore age as an important contingency factor in understanding consumers’ purchase intention in live commerce. We collect data from 353 consumers who have live shopping experiences in China and analyze the research model with the structural equation model approach. Results indicate that visibility, social presence, and interactivity positively contribute to perceived diagnosticity, whereas entertainment and the three social affordances reduce psychological distance. Notably, the influence of perceived diagnosticity on purchase intention is more pronounced among older consumers, whereas the impact of psychological distance on purchase intention is stronger among younger consumers. This study not only advances the understanding of affordance theory within live commerce contexts but also offers practical insights for streamers and live commerce platforms.

Analysis of ZARA's Social Media Marketing Strategy in China

The rapid iteration of the fashion industry and the influx of numerous niche emerging fast fashion brands into the market have driven continuous innovation in social media marketing strategies, with new promotional methods constantly emerging. This article, utilizing literature analysis and observational methods, examines ZARA's social media marketing strategy in China. This analysis is beneficial for companies in the fast fashion industry, particularly those looking to expand their influence in the Chinese market. After a detailed analysis of the characteristics and marketing methods of Chinese social media platforms, the impact of external factors on Chinese consumers, and ZARA's social media presence, the article provides detailed recommendations for social media marketing strategies. ZARA, as a pioneer in the fast fashion industry, has established a certain market presence on Chinese social media platforms. However, its promotional strategy still shows potential for improvement. On platforms like Weibo and Little Red Book, ZARA's content strategy lacks specificity and diversity. In contrast, its live streaming marketing on platforms like TikTok demonstrates excellent visual effects and interactivity. This article provides several specific recommendations for ZARA's social media marketing strategy in China, such as strengthening brand storytelling and product showcases on Little Red Book, hiring celebrity endorsements, and regularly hosting styling challenges on TikTok. In summary, by implementing these strategies, ZARA is poised to achieve more effective brand building and market expansion in China's social media landscape.