Browsing by Author "Qu, Huamin"

Now showing 1 - 6 of 6
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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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    Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Lo, Leo Yu-Ho; Gupta, Ayush; Shigyo, Kento; Wu, Aoyu; Bertini, Enrico; Qu, Huamin; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Data visualization is powerful in persuading an audience. However, when it is done poorly or maliciously, a visualization may become misleading or even deceiving. Visualizations give further strength to the dissemination of misinformation on the Internet. The visualization research community has long been aware of visualizations that misinform the audience, mostly associated with the terms ''lie'' and ''deceptive.'' Still, these discussions have focused only on a handful of cases. To better understand the landscape of misleading visualizations, we open-coded over one thousand real-world visualizations that have been reported as misleading. From these examples, we discovered 74 types of issues and formed a taxonomy of misleading elements in visualizations. We found four directions that the research community can follow to widen the discussion on misleading visualizations: (1) informal fallacies in visualizations, (2) exploiting conventions and data literacy, (3) deceptive tricks in uncommon charts, and (4) understanding the designers' dilemma. This work lays the groundwork for these research directions, especially in understanding, detecting, and preventing them.
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    MOOCad: Visual Analysis of Anomalous Learning Activities in Massive Open Online Courses
    (The Eurographics Association, 2019) Mu, Xing; Xu, Ke; Chen, Qing; Du, Fan; Wang, Yun; Qu, Huamin; Johansson, Jimmy and Sadlo, Filip and Marai, G. Elisabeta
    The research on Massive Open Online Course (MOOC) has mushroomed worldwide due to the technical revolution and its unprecedented enrollments. Existing work mainly focuses on performance prediction, content recommendation, and learning behavior summarization. However, finding anomalous learning activities in MOOC data has posed special challenges and requires providing a clear definition of anomalous behavior, analyzing the multifaceted learning sequence data, and interpreting anomalies at different scales. In this paper, we present a novel visual analytics system, MOOCad, for exploring anomalous learning patterns and their clustering in MOOC data. The system integrates an anomaly detection algorithm to cluster learning sequences of MOOC learners into staged-based groups. Moreover, it allows interactive anomaly detection between and within groups on the basis of semantic and interpretable group-wise data summaries. We demonstrate the effectiveness of MOOCad via an in-depth interview with a MOOC lecturer with real-world course data.
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    Oui! Outlier Interpretation on Multi-dimensional Data via Visual Analytics
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zhao, Xun; Cui, Weiwei; Wu, Yanhong; Zhang, Haidong; Qu, Huamin; Zhang, Dongmei; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Outliers, the data instances that do not conform with normal patterns in a dataset, are widely studied in various domains, such as cybersecurity, social analysis, and public health. By detecting and analyzing outliers, users can either gain insights into abnormal patterns or purge the data of errors. However, different domains usually have different considerations with respect to outliers. Understanding the defining characteristics of outliers is essential for users to select and filter appropriate outliers based on their domain requirements. Unfortunately, most existing work focuses on the efficiency and accuracy of outlier detection, neglecting the importance of outlier interpretation. To address these issues, we propose Oui, a visual analytic system that helps users understand, interpret, and select the outliers detected by various algorithms. We also present a usage scenario on a real dataset and a qualitative user study to demonstrate the effectiveness and usefulness of our system.
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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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    SeqDynamics: Visual Analytics for Evaluating Online Problem-solving Dynamics
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Xia, Meng; Xu, Min; Lin, Chuan-en; Cheng, Ta Ying; Qu, Huamin; Ma, Xiaojuan; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Problem-solving dynamics refers to the process of solving a series of problems over time, from which a student's cognitive skills and non-cognitive traits and behaviors can be inferred. For example, we can derive a student's learning curve (an indicator of cognitive skill) from the changes in the difficulty level of problems solved, or derive a student's self-regulation patterns (an example of non-cognitive traits and behaviors) based on the problem-solving frequency over time. Few studies provide an integrated overview of both aspects by unfolding the problem-solving process. In this paper, we present a visual analytics system named SeqDynamics that evaluates students' problem-solving dynamics from both cognitive and non-cognitive perspectives. The system visualizes the chronological sequence of learners' problem-solving behavior through a set of novel visual designs and coordinated contextual views, enabling users to compare and evaluate problem-solving dynamics on multiple scales. We present three scenarios to demonstrate the usefulness of SeqDynamics on a real-world dataset which consists of thousands of problem-solving traces. We also conduct five expert interviews to show that SeqDynamics enhances domain experts' understanding of learning behavior sequences and assists them in completing evaluation tasks efficiently.