The use of augmented reality technology to support humans with situated visualization in complex tasks such as navigation or assembly has gained increasing importance in research and industrial applications. One impor...
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ISBN:
(纸本)9798350374025;9798350374032
The use of augmented reality technology to support humans with situated visualization in complex tasks such as navigation or assembly has gained increasing importance in research and industrial applications. One important line of research regards supporting and understanding collaborative tasks. Analyzing collaboration patterns is usually done by conducting observations and interviews. To expand these methods, we argue that eye tracking can be used to extract further insights and quantify behavior. To this end, we contribute a study that uses eye tracking to investigate participant strategies for solving collaborative sorting and assembly tasks. We compare participants' visual attention during situated instructions in AR and traditional paper-based instructions as a baseline. By investigating the performance and gaze behavior of the participants, different strategies for solving the provided tasks are revealed. Our results show that with situated visualization, participants focus more on task-relevant areas and require less discussion between collaboration partners to solve the task at hand.
Software visualization concerns itself with the visual depiction of software systems to facilitate their comprehension. Any visualization approach, whether 2D or 3D or immersive, comes with a plethora of configuration...
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ISBN:
(纸本)9798331528492;9798331528485
Software visualization concerns itself with the visual depiction of software systems to facilitate their comprehension. Any visualization approach, whether 2D or 3D or immersive, comes with a plethora of configuration possibilities (e.g., which types of artifacts to visualize and how, which layouts to use). This reflects the complexity of the domain at hand, where manipulating millions of entities pertaining to dozens of different types of artifacts is common. Most visualization tools encode their customizations in the form of view configurations/specifications (in short viewspecs), which are either created declaratively (using DSLs), or through custom user interfaces. In the case of immersive visualization, approaches using such customization facilities are cumbersome, may generate unnecessary context and paradigm switches, and fail to leverage the full potential of modern VR headsets' controllers. We present an approach to interactively manipulate the view specifications by depicting them as 3D objects in the immersive space, supporting definition and configuration with an automatic reflection-based mapping of the software domain model under exploration. IVAR-NI, the tool we developed, incorporates new immersive interaction paradigms (e.g., slot-based selection) and in-object real-time feedback (e.g., preview of the view specification effects) to enhance the usability of this new generation of VR-native interfaces for software visualization customization. https://***/HsWGtrINtHc
To create effective data visualizations, it helps to represent data using visual features in intuitive ways. When visualization designs match observer expectations, visualizations are easier to interpret. Prior work s...
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ISBN:
(纸本)9798350325577
To create effective data visualizations, it helps to represent data using visual features in intuitive ways. When visualization designs match observer expectations, visualizations are easier to interpret. Prior work suggests that several factors influence such expectations. For example, the dark-is-more bias leads observers to infer that darker colors map to larger quantities, and the opaque-is-more bias leads them to infer that regions appearing more opaque (given the background color) map to larger quantities. Previous work suggested that the background color only plays a role if visualizations appear to vary in opacity. The present study challenges this claim. We hypothesized that the background color would modulate inferred mappings for colormaps that should not appear to vary in opacity (by previous measures) if the visualization appeared to have a "hole" that revealed the background behind the map (hole hypothesis). We found that spatial aspects of the map contributed to inferred mappings, though the effects were inconsistent with the hole hypothesis. Our work raises new questions about how spatial distributions of data influence color semantics in colormap data visualizations.
Data stories are a powerful way to present information and data in a way that is easy to understand and engage with. They consist of data, the visual form, and the narrative component. By combining these elements effe...
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ISBN:
(纸本)9798350341614
Data stories are a powerful way to present information and data in a way that is easy to understand and engage with. They consist of data, the visual form, and the narrative component. By combining these elements effectively, data stories can drive behavioral change and lead to a call to action. In two projects dedicated to data stories, we developed an approach that separates the data from possible narratives and adapts it to three audiences of different expertise and age, being inspired by the medieval philosopher Averroes. The method can help to take full advantage of data storytelling and describe complex data in a meaningful way that compels people to act on it.
An appropriate quantum algorithm is needed for each problem to achieve the full performance of a quantum computer. It is necessary to understand the principles of quantum computation to implement quantum algorithms. Q...
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ISBN:
(纸本)9798350341614
An appropriate quantum algorithm is needed for each problem to achieve the full performance of a quantum computer. It is necessary to understand the principles of quantum computation to implement quantum algorithms. Quantum computation simulators can assist in understanding the principles and behavior of quantum computation. Combining this with explanations of quantum algorithms using visualization techniques and interactions can further aid learning. In this study, to support the understanding of quantum computation, a difficult concept for beginners, we develop an interactive system in which diagrams and graphs are added to a quantum computation simulator to support the explanation and understanding of qubits and quantum algorithms. To verify the effectiveness of the interaction in supporting learning, a subject experiment is conducted to compare the amount of knowledge understood by a group of novice quantum computer students who learned using the developed system with a group who learned using paper-based materials.
This paper presents an analysis of the applications and benefits of TCPGraphix, a tool addressing the need for effective visualization within the domain of Test Case Prioritization (TCP). With an intuitive user interf...
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ISBN:
(纸本)9798350344806;9798350344790
This paper presents an analysis of the applications and benefits of TCPGraphix, a tool addressing the need for effective visualization within the domain of Test Case Prioritization (TCP). With an intuitive user interface, TCPGraphix offers an integrated and efficient approach to handle and derive insights from the vast quantity of data produced from TCP processes, specifically TCP processes using Machine Learning (ML). Moreover, it incorporates interactive capabilities, enabling users to navigate and comprehend TCP data easily using a blend of existing and new evaluation metrics. This paper provides a comprehensive overview of TCPGraphix's design, core features, and applications. TCPGraphix effectively tackles critical data analysis hurdles and empowers researchers to extract valuable insights from expansive data sets and their results.
Deep reinforcement learning (DRL) aims to train software agents that can understand environments and learn effective strategies, and has achieved significant breakthroughs in performance and capabilities, particularly...
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ISBN:
(纸本)9798350393811;9798350393804
Deep reinforcement learning (DRL) aims to train software agents that can understand environments and learn effective strategies, and has achieved significant breakthroughs in performance and capabilities, particularly in areas such as Go, Atari games, and autonomous vehicles. Unlike traditional deep learning, the goals of reinforcement learning can be more abstract and require careful modification of reward functions. The training process involves unstructured sequential data, which can be difficult for human experts to analyze and gain insights from. To address this challenge, we propose SampleViz, a visual analytics system that enables flexible interaction between human experts and DRL sequence data, allowing for the extraction of crucial concepts from massive amounts of data and their provision to the agent. SampleViz transforms the tedious task of modifying reward functions and policy debugging into an engaging concept exploration process, allowing for the efficient integration of human expertise with automatic sampling algorithms for effective model improvement. Through case studies and expert feedback, we demonstrate that SampleViz can effectively assist experts in concept extraction and model improvement, and enables the incorporation of interpretability and human-in-the-loop concepts into DRL policy settings.
Electrical Impedance Tomography (EIT), recognized for its visualization and non-invasive characteristics, holds broad application prospects in industrial and biomedical fields. However, its inherent ill-posedness and ...
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ISBN:
(纸本)9798350380903;9798350380910
Electrical Impedance Tomography (EIT), recognized for its visualization and non-invasive characteristics, holds broad application prospects in industrial and biomedical fields. However, its inherent ill-posedness and nonlinearity in the inverse problem led to low spatial resolution, boundary distortion, and inaccurate conductivity representation in current imaging methods. This paper introduces an enhanced U-shaped deep imaging approach, referred to SEC-UNet, which optimizes skip connections using SE attention and Vision Transformer (ViT) to build global information features. Moreover, a cross-attention module is introduced for fusing multi-scale information between encoder and decoder. The experiments show that SEC-UNet achieves a Root Mean Square Error (RMSE) of 2.5067 and a Structural Similarity Index (SSIM) of 0.9282, improving RMSE by 31.01% and SSIM by 1.25% over state-of-the-art Transformer-based and ConvNets-based methods. The visualizations demonstrate better boundary preservation and consistency with actual distributions. The results confirm the enhanced robustness and generalizability, indicating the superiority of the Transformer framework in soft-field imaging over ConvNets.
Microbial volatile organic compounds (MVOCs) can serve as a diagnostic tool for assessing the respiratory status of microorganisms. Localized surface plasmon resonance (LSPR) sensors are noted for their rapid response...
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ISBN:
(纸本)9798350363524;9798350363517
Microbial volatile organic compounds (MVOCs) can serve as a diagnostic tool for assessing the respiratory status of microorganisms. Localized surface plasmon resonance (LSPR) sensors are noted for their rapid response and straightforward operation;however, they often struggle to provide specific information about the detected substances and to differentiate between gas types. In contrast, gas sensors utilizing surface-enhanced Raman spectroscopy (SERS) technology offer excellent selectivity and can generate unique "fingerprint" signals, enabling easy identification and distinction of various gas molecules. Integrating these two technologies promises to enhance the visualization of volatile organic compound distributions both more effectively and expeditiously.
In reinforcement learning, tuning reward weights in the reward function is necessary to align behavior with user preferences. However, current approaches, which use pairwise comparisons for preference elicitation, are...
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ISBN:
(纸本)9798350377712;9798350377705
In reinforcement learning, tuning reward weights in the reward function is necessary to align behavior with user preferences. However, current approaches, which use pairwise comparisons for preference elicitation, are inefficient, because they miss much of the human ability to explore and judge groups of candidate solutions. The paper presents a novel visualization-based approach that better exploits the user's ability to quickly recognize interesting directions for reward tuning. It breaks down the tuning problem by using the visual information-seeking principle: overview first, zoom and filter, then details-on-demand. Following this principle, we built a visualization system comprising two interactively linked views: 1) an embedding view showing a contextual overview of all sampled behaviors and 2) a sample view displaying selected behaviors and visualizations of the detailed time-series data. A user can efficiently explore large sets of samples by iterating between these two views. The paper demonstrates that the proposed approach is capable of tuning rewards for challenging behaviors. The simulation-based evaluation shows that the system can reach optimal solutions with fewer queries relative to baselines.
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