arXiv subjects

Shiyi He

Publications and source records attributed to Shiyi He.

7 recordsLinked to original sources

arXiv2026

Trade-offs in Data Color Palette Design Tools

Designing a color palette for data requires designers to balance multiple constraints, including accessibility and aesthetics. Color palette tools support this process through features including direct manipulation, automated palette generation and evaluation, previews, and so on. Despite their prominence, relatively little is known about how these different mechanisms shape design across contexts. We conducted an exploratory think-aloud crowd work study with 40 self-identified designers. Each participant used one of four palette tools selected to span different interaction modalities to complete a series of accessibility- and aesthetics-oriented design tasks. We observed two preliminary patterns. First, tool differences were more pronounced in accessibility-constrained tasks. Second, even when accessibility was not explicitly required, some tools produced more accessibility-friendly palettes and prompted more accessibility-oriented thinking. In this tool genre, then, system design shapes outcomes both via built-in functionality, as well as by directing designers' attention toward particular constraints and design considerations.

cs.HC↗

arXiv2026

How Wrangling Tools Shape Wrangling: A Technical Dimensions Analysis

Wrangling consumes a disproportionate share of the effort associated with any data project. While a variety of tools support it, relatively little is known about how their differing interface forms shape the way people actually wrangle. We conduct a between-subjects (N=40) observational study of data cleaning tasks performed in tools spanning distinct interface paradigms: Jupyter (notebook), Excel (spreadsheet), ChatGPT (conversational AI), and OpenRefine (visual wranglers). We situate our observations within the Technical Dimensions of Programming Systems framework, which we use as a conceptual scaffold for comparing across interface paradigms. Within the context of our study, the results suggest that tool affordances steer user strategies but do not determine outcomes. There is no consistent advantage of any single tool, nor convergence of results within tools observed across our outcome measures. Instead, we identify trade-offs and connect them with observed practice. For example, a key tension is between data- and abstraction-centered interfaces, where data-centered interfaces encourage opportunistic cleaning rather than systematic, planned transformations found in abstraction-focused tools (but come with a cognitive burden). Tool design, beyond mere functionality, plays a structuring role in how data work unfolds.

cs.HC↗

arXiv2026

Reading Between the Curly Braces: On Textual Data Serialization Format Usability

Textual data serialization formats, such as JSON or XML, are ubiquitous, supporting tasks like software configuration and data tabularization. Despite their prominence, little is known about their usability. What makes one good or bad? Is there a best one for cognitive efficiency? We explore these questions via a (N=215) crowd work study and a (N=9) semi-structured interview study. We find that format distinctions (like indentation versus curly braces) do not consistently translate into substantial usability differences. While HJSON and YAML performed better than other formats in certain modification tasks, these advantages disappeared in more realistic settings where task complexity was either trivial or highly demanding. Instead, usability appears driven by sociotechnical ecosystems: the tooling, documentation, and community practices surrounding a format matter more than syntax.

cs.HC↗

arXiv2026

Towards Scaling Qualitative Analysis of Video Data

Scaling qualitative video analysis is difficult as studies grow. This paper presents QualiVision, a design probe examining how an interactive, spreadsheet-backed workspace can support video-based qualitative analysis. By integrating video, transcripts, coding streams, preliminary reports, heuristic visualization and AI support, QualiVision aims to help researchers preserve evidence, compare interpretations, and conduct iterative, reflexive sensemaking as their analysis evolves.

cs.MM↗

arXiv2025

Teaching Critical Visualization: A Field Report

Critical Visualization is gaining popularity and academic focus, yet relatively few academic courses have been offered to support students in this complex area. This experience report describes a recent experimental course on the topic, exploring both what the topic could be as well as an experimental content structure (namely as scavenger hunt). Generally the course was successful, achieving the learning objectives of developing critical thinking skills, improving communication about complex ideas, and developing a knowledge about theories in the area. While improvements can be made, we hope that humanistic notions of criticality are embraced more deeply in visualization pedagogy.

cs.HC↗

arXiv2024

ChARLES: Change-Aware Recovery of Latent Evolution Semantics in Relational Data

Data-driven decision-making is at the core of many modern applications, and understanding the data is critical in supporting trust in these decisions. However, data is dynamic and evolving, just like the real-world entities it represents. Thus, an important component of understanding data is analyzing and drawing insights from the changes it undergoes. Existing methods for exploring data change list differences exhaustively, which are not interpretable by humans and lack salient insights regarding change trends. For example, an explanation that semantically summarizes changes to highlight gender disparities in performance rewards is more human-consumable than a long list of employee salary changes. We demonstrate ChARLES, a system that derives semantic summaries of changes between two snapshots of an evolving database, in an effective, concise, and interpretable way. Our key observation is that, while datasets often evolve through point and other small-batch updates, rich data features can reveal latent semantics that can intuitively summarize the changes. Under the hood, ChARLES compares database versions, infers feasible transformations by fitting multiple regression lines over different data partitions to derive change summaries, and ranks them. ChARLES allows users to customize it to obtain their preferred explanation by navigating the accuracy-interpretability tradeoff, and offers a proof of concept for reasoning about data evolution over real-world datasets.

cs.DB↗

arXiv2018

Robust Student Network Learning

Deep neural networks bring in impressive accuracy in various applications, but the success often relies on the heavy network architecture. Taking well-trained heavy networks as teachers, classical teacher-student learning paradigm aims to learn a student network that is lightweight yet accurate. In this way, a portable student network with significantly fewer parameters can achieve a considerable accuracy which is comparable to that of teacher network. However, beyond accuracy, robustness of the learned student network against perturbation is also essential for practical uses. Existing teacher-student learning frameworks mainly focus on accuracy and compression ratios, but ignore the robustness. In this paper, we make the student network produce more confident predictions with the help of the teacher network, and analyze the lower bound of the perturbation that will destroy the confidence of the student network. Two important objectives regarding prediction scores and gradients of examples are developed to maximize this lower bound, so as to enhance the robustness of the student network without sacrificing the performance. Experiments on benchmark datasets demonstrate the efficiency of the proposed approach to learn robust student networks which have satisfying accuracy and compact sizes.

cs.LG↗