Almost all scientific data have uncertainties originating from different sources. Gaussian process regression (GPR) models are a natural way to model data with Gaussian-distributed uncertainties. GPR also has the bene...
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ISBN:
(纸本)9798350393811;9798350393804
Almost all scientific data have uncertainties originating from different sources. Gaussian process regression (GPR) models are a natural way to model data with Gaussian-distributed uncertainties. GPR also has the benefit of reducing I/O bandwidth and storage requirements for large scientific simulations. However, the reconstruction from the GPR models suffers from high computation complexity. To make the situation worse, classic approaches for visualizing the data uncertainties, like probabilistic marching cubes, are also computationally very expensive, especially for data of high resolutions. In this paper, we accelerate the level-crossing probability calculation efficiency on GPR models by subdividing the data spatially into a hierarchical data structure and only reconstructing values adaptively in the regions that have a non-zero probability. For each region, leveraging the known GPR kernel and the saved data observations, we propose a novel approach to efficiently calculate an upper bound for the level-crossing probability inside the region and use this upper bound to make the subdivision and reconstruction decisions. We demonstrate that our value occurrence probability estimation is accurate with a low computation cost by experiments that calculate the level-crossing probability fields on different datasets.
Generative models have received a lot of attention in many areas of academia and the industry. Their capabilities span many areas, from the invention of images given a prompt to the generation of concrete code to solv...
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ISBN:
(纸本)9798350393811;9798350393804
Generative models have received a lot of attention in many areas of academia and the industry. Their capabilities span many areas, from the invention of images given a prompt to the generation of concrete code to solve a certain programming issue. These two paradigmatic cases fall within two distinct categories of requirements, ranging from "creativity" to "precision", as characterized by Bing Chat, which employs ChatGPT-4 as its backbone. visualization practitioners and researchers have wondered to what end one of such systems could accomplish our work in a more efficient way. Several works in the literature have utilized them for the creation of visualizations. And some tools such as Lida, incorporate them as part of their pipeline. Nevertheless, to the authors' knowledge, no systematic approach for testing their capabilities has been published, which includes both extensive and in-depth evaluation. Our goal is to fill that gap with a systematic approach that analyzes three elements: whether Large Language Models are capable of correctly generating a large variety of charts, what libraries they can deal with effectively, and how far we can go to configure individual charts. To achieve this objective, we initially selected a diverse set of charts, which are commonly utilized in data visualization. We then developed a set of generic prompts that could be used to generate them, and analyzed the performance of different LLMs and libraries. The results include both the set of prompts and the data sources, as well as an analysis of the performance with different configurations.
Traditional digital tools for exploring historical data mostly rely on conventional 2D visualizations, which often cannot reveal all relevant interrelationships between historical fragments (e.g., persons or events). ...
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ISBN:
(纸本)9798350328387
Traditional digital tools for exploring historical data mostly rely on conventional 2D visualizations, which often cannot reveal all relevant interrelationships between historical fragments (e.g., persons or events). In this paper, we present a novel interactive exploration tool for historical data in VR, which represents fragments as spheres in a 3D environment and arranges them around the user based on their temporal, geo, categorical and semantic similarity. Quantitative and qualitative results from a user study with 29 participants revealed that most participants considered the virtual space and the abstract fragment representation well-suited to explore historical data and to discover complex interrelationships. These results were particularly underlined by high usability scores in terms of attractiveness, stimulation, and novelty, while researching historical facts with our system did not impose unexpectedly high task loads. Additionally, the insights from our post-study interviews provided valuable suggestions for future developments to further expand the possibilities of our system.
Traditional digital tools for exploring historical data mostly rely on conventional 2D visualizations, which often cannot reveal all relevant interrelationships between historical fragments. We are working on a novel ...
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ISBN:
(纸本)9798350348392
Traditional digital tools for exploring historical data mostly rely on conventional 2D visualizations, which often cannot reveal all relevant interrelationships between historical fragments. We are working on a novel interactive exploration tool for historical data in virtual reality, which arranges fragments in a 3D environment based on their temporal, spatial and categorical proximity to a reference fragment. In this poster, we report on an initial expert review of our approach, giving us valuable insights into the use cases and requirements that inform our further developments.
Spatial information understanding is fundamental to visual perception in Metaverse. Beyond the stereoscopic visual cues naturally carried in Metaverse, the human vision system may use other auxiliary information provi...
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ISBN:
(数字)9781665453653
ISBN:
(纸本)9781665453653
Spatial information understanding is fundamental to visual perception in Metaverse. Beyond the stereoscopic visual cues naturally carried in Metaverse, the human vision system may use other auxiliary information provided by any shadow casting or motion parallax available to perceive the 3D virtual world. However, the combined use of shadows and motion parallax to improve 3D perception have not been fully studied. In particular, when visualizing the combination of volumetric data and associated skeleton models in VR, how to provide the auxiliary visual cues to enhance observers' perception of the structural information is a key yet underexplored topic. This problem is particularly challenging for visualization of data in biomedical research. In this paper, we focus on immersive analytics in neurobiology where the structural information includes the relative position of objects (nuclei / cell body) in the 3D space and the spatial measurement and connectivity of segments (axons and dendrites) in a model. We present a perceptual experiment designed for understanding the consequence of shadow casting and motion parallax in the neuron structures observation and the feedback and analysis of the experiment are reported and discussed.
We often encounter many situations where we manually interact with others collaboratively. This allows us to solve tasks that we would otherwise not be able to do on our own. Handover tasks are an everyday interaction...
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ISBN:
(数字)9781665453653
ISBN:
(纸本)9781665453653
We often encounter many situations where we manually interact with others collaboratively. This allows us to solve tasks that we would otherwise not be able to do on our own. Handover tasks are an everyday interaction that we perform with others without thinking about it. However, these tasks are complex coordinated actions between two actors based on sensory feedback that are performed in a very short time. In this work, an overview of handover tasks will be provided from a perspective of the interdisciplinary fields of human-robot interaction (HRI), neuroscience, cognitive science and human-computer interaction (HCI). For this purpose, the structure and process of a handover interaction will be examined and the current state of research will be reflected. The ability to represent realistic manual interactions in an immersive collaborative scenario enables the simulation and training of complex workflows in a realistic context. The study design presented in this paper is intended to improve the understanding and design of handover interaction techniques in virtual environments. Furthermore possibilities to substitute missing haptic feedback with other sensory stimuli will be evaluated.
Feral data visualization is a posture offering theories and practices for designing data visualizations for collaborative survival in the midst of ecological and social turmoil. Feral data visualization demands an emb...
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ISBN:
(纸本)9781665413664
Feral data visualization is a posture offering theories and practices for designing data visualizations for collaborative survival in the midst of ecological and social turmoil. Feral data visualization demands an embrace of situated, embodied, vegetized, and feral forms of visual pattern making that might allow us to take up the task of building livability in our surrounding ecologies and communities. We demonstrate how these imperatives might unfold in practice through Tsuga Convictio, a research through design project. Feral data visualization calls designers to action, outlining three paths for crafting data visualizations for the feminine, vegetal, and embodied.
An approach to analyzing Streaming Big Data as it comes in while maintaining the proper context of past events is to employ contiguous visualizations with an increasingly aggressive aggregation degree. This allows for...
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ISBN:
(纸本)9781728180144
An approach to analyzing Streaming Big Data as it comes in while maintaining the proper context of past events is to employ contiguous visualizations with an increasingly aggressive aggregation degree. This allows for the most recent data to be displayed in detail, while older data is shown in an aggregated form according to how long ago it was received. However, the transitions applied between visualizations with different aggregations must not compromise the understandability of the data flow. Particularly, new data should be perceived considering the context established by older data, and the visualizations should not be perceived as independent or unconnected. In this paper, we present the first study on transitions between two contiguous visualizations, focusing on time series data. We developed several animated transitions between a scatter plot, where all data points are individually represented as they arrive, and other visualizations where data is displayed in an aggregated form. We then conducted a user evaluation to assess the most appealing and effective transitions that allow for the best comprehension of the displayed data for each visualization pair.
Augmented reality (AR) systems support complex tasks like assembly by overlaying task-related content onto the real world. In recent years, the effort of designing and developing assembly task support systems in AR de...
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ISBN:
(纸本)9781728185088
Augmented reality (AR) systems support complex tasks like assembly by overlaying task-related content onto the real world. In recent years, the effort of designing and developing assembly task support systems in AR decreased with the availability of high potential head-mounted displays and provision of integrated development environments. Nevertheless, problems still arise when companies craft an effective AR task support system, particularly in the difficulty of selecting appropriate techniques and information-presentation methods, and the requirements that vary with each use case. In this study, we formulated a corresponding guideline, developed a selection aid tool that incorporates filtering based on the categorization of sub tasks and the degree of freedom of available tracking, and evaluated their effectiveness in two experiments. First, to confirm effects on system design, we asked 18 participants to perform the design action of the AR system with the guideline for two tasks (PC assembly and rope work). Consequently, to verify the quality of the designed AR systems from Experiment 1, we asked another set of 20 participants to perform the same tasks with those systems. The results confirm that using the guideline can considerably lower efforts creating media and alleviate the error for a specific process. We envision our guideline and tool to he accessible as an online web page, assisting AR assembly task support system designer/developers worldwide.
Online social networks are prone to be targeted by various frauds and attacks, which are difficult to detect due to their complexity and variations. The challenge is to make sense of all information with suitable expl...
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ISBN:
(纸本)9781728138763
Online social networks are prone to be targeted by various frauds and attacks, which are difficult to detect due to their complexity and variations. The challenge is to make sense of all information with suitable exploration tools for different groups of users. This project focuses on an explainable visualization approach to study collaborative behaviors of vandal users on Wikipedia. Our approach creates visualization with commonly used techniques from cartography and statistical graphics that are familiar to the general public for effectiveness and explainability. We have built a large-scale visualization system which supports an illustrative interface with multiple data query, filtering, analysis, and interactive exploration functions. Examples and case studies are provided to demonstrate that our approach can be used effectively for a set of Wikipedia behavior analysis tasks.
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