Browsing by Author "Wang, Yong"

Now showing 1 - 4 of 4
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    Don't Peek at My Chart: Privacy-preserving Visualization for Mobile Devices
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Zhang, Songheng; Ma, Dong; Wang, Yong; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
    Data visualizations have been widely used on mobile devices like smartphones for various tasks (e.g., visualizing personal health and financial data), making it convenient for people to view such data anytime and anywhere. However, others nearby can also easily peek at the visualizations, resulting in personal data disclosure. In this paper, we propose a perception-driven approach to transform mobile data visualizations into privacy-preserving ones. Specifically, based on human visual perception, we develop a masking scheme to adjust the spatial frequency and luminance contrast of colored visualizations. The resulting visualization retains its original information in close proximity but reduces visibility when viewed from a certain distance or farther away. We conducted two user studies to inform the design of our approach (N=16) and systematically evaluate its performance (N=18), respectively. The results demonstrate the effectiveness of our approach in terms of privacy preservation for mobile data visualizations.
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    iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Yue, Xuanwu; Gu, Qiao; Wang, Deyun; Qu, Huamin; Wang, Yong; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    The model-based investing using financial factors is evolving as a principal method for quantitative investment. The main challenge lies in the selection of effective factors towards excess market returns. Existing approaches, either hand-picking factors or applying feature selection algorithms, do not orchestrate both human knowledge and computational power. This paper presents iQUANT, an interactive quantitative investment system that assists equity traders to quickly spot promising financial factors from initial recommendations suggested by algorithmic models, and conduct a joint refinement of factors and stocks for investment portfolio composition. We work closely with professional traders to assemble empirical characteristics of ''good'' factors and propose effective visualization designs to illustrate the collective performance of financial factors, stock portfolios, and their interactions. We evaluate iQUANT through a formal user study, two case studies, and expert interviews, using a real stock market dataset consisting of 3000 stocks x 6000 days x 56 factors.
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    RankBooster: Visual Analysis of Ranking Predictions
    (The Eurographics Association, 2020) Puri, Abishek; Ku, Bon Kyung; Wang, Yong; Qu, Huamin; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
    Ranking is a natural and ubiquitous way to facilitate decision-making in various applications. However, different rankings are often used for the same set of entities, with each ranking method placing emphasis on different factors. These factors can also be multi-dimensional in nature, compounding the problem. This complexity can make it challenging for an entity which is being ranked to understand what they can do to improve their rankings, and to analyze the effect of changes in various factors to their overall rank. In this paper, we present RankBooster, a novel visual analytics system to help users conveniently investigate ranking predictions.We take university rankings as an example and focus on helping universities to better explore their rankings, where they can compare themselves to their rivals in key areas as well as overall. Novel visualizations are proposed to enable efficient analysis of rankings, including a Scenario Analysis View to show a high-level summary of different ranking scenarios, a Relationship View to visualize the influence of each attribute on different indicators and a Rival View to compare the ranking of a university and those of its rivals. A case study demonstrates the usefulness and effectiveness of RankBooster in facilitating the visual analysis of ranking predictions and helping users better understand their current situation.
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    VENUS: A Geometrical Representation for Quantum State Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Ruan, Shaolun; Yuan, Ribo; Guan, Qiang; Lin, Yanna; Mao, Ying; Jiang, Weiwen; Wang, Zhepeng; Xu, Wei; Wang, Yong; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
    Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well-designed case studies and an in-depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits.