arXiv subjects

Zhibang Jiang

Publications and source records attributed to Zhibang Jiang.

5 recordsLinked to original sources

arXiv2026

From Static to Interactive: Authoring Interactive Visualizations via Natural Language

Interactivity is crucial for effective data visualizations. However, it is often challenging to implement interactions for existing static visualizations, since the underlying code and data for existing static visualizations are often not available, and it also takes significant time and effort to enable interactions for them even if the original code and data are available. To fill this gap, we propose Athanor, a novel approach to transform existing static visualizations into interactive ones using multimodal large language models (MLLMs) and natural language instructions. Our approach introduces three key innovations: (1) an action-modification interaction design space that maps visualization interactions into user actions and corresponding adjustments, (2) a multi-agent requirement analyzer that translates natural language instructions into an actionable operational space, and (3) a visualization abstraction transformer that converts static visualizations into flexible and interactive representations regardless of their underlying implementation. Athanor allows users to effortlessly author interactions through natural language instructions, eliminating the need for programming. We conducted two case studies and in-depth interviews with target users to evaluate our approach. The results demonstrate the effectiveness and usability of our approach in allowing users to conveniently enable flexible interactions for static visualizations.

cs.HC↗

arXiv2026

Proteus: Shapeshifting Desktop Visualizations for Mobile via Multi-level Intelligent Adaptation

With the rise of mobile-first consumption, users increasingly engage with data visualizations on mobile devices. However, the vast majority of existing visualizations are originally authored for desktop environments. Due to significant differences in viewport size and interaction paradigms, directly scaling desktop charts often results in illegible text, information loss, and interaction failures. To bridge this gap, we propose an automated framework to adapt desktop-based visualizations for mobile screens. By systematically categorizing the operations involved in the adaptation process, we establish a multi-level design space. This space defines evolution rules spanning from the global topology level, through the reference frame level, down to the visual elements level. Guided by this theoretical framework, we developed Proteus, a large language model-driven multi-agent system that automatically parses online visualizations, predicts optimal transformation strategies within the design space, and generates equivalent, highly readable visualizations for mobile devices. Case studies and an in-depth user study with 12 participants demonstrate the effectiveness and usability of Proteus.

cs.HC↗

arXiv2026

GlyphWeaver: Unlocking Glyph Design Creativity with Uniform Glyph DSL and AI

Expressive glyph visualizations provide a powerful and versatile means to represent complex multivariate data through compact visual encodings, but creating custom glyphs remains challenging due to the gap between design creativity and technical implementation. We present GlyphWeaver, a novel interactive system to enable an easy creation of expressive glyph visualizations. Our system comprises three key components: a glyph domain-specific language (GDSL), a GDSL operation management mechanism, and a multimodal interaction interface. The GDSL is a hierarchical container model, where each container is independent and composable, providing a rigorous yet practical foundation for complex glyph visualizations. The operation management mechanism restricts modifications of the GDSL to atomic operations, making it accessible without requiring direct coding. The multimodal interaction interface enables direct manipulation, natural language commands, and parameter adjustments. A multimodal large language model acts as a translator, converting these inputs into GDSL operations. GlyphWeaver significantly lowers the barrier for designers, who often do not have extensive programming skills, to create sophisticated glyph visualizations.

cs.HC↗

arXiv2025

A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network

Attention-based transformers have played an important role in wireless sensor network (WSN) timing anomaly detection due to their ability to capture long-term dependencies. However, there are several issues that must be addressed, such as the fact that their ability to capture long-term dependencies is not completely reliable, their computational complexity levels are high, and the spatiotemporal features of WSN timing data are not sufficiently extracted for detecting the correlation anomalies of multinode WSN timing data. To address these limitations, this paper proposes a WSN anomaly detection method that integrates frequency-domain features with dynamic graph neural networks (GNN) under a designed self-encoder reconstruction framework. First, the discrete wavelet transform effectively decomposes trend and seasonal components of time series to solve the poor long-term reliability of transformers. Second, a frequency-domain attention mechanism is designed to make full use of the difference between the amplitude distributions of normal data and anomalous data in this domain. Finally, a multimodal fusion-based dynamic graph convolutional network (MFDGCN) is designed by combining an attention mechanism and a graph convolutional network (GCN) to adaptively extract spatial correlation features. A series of experiments conducted on public datasets and their results demonstrate that the anomaly detection method designed in this paper exhibits superior precision and recall than the existing methods do, with an F1 score of 93.5%, representing an improvement of 2.9% over that of the existing models.

eess.SP↗

arXiv2024

AutoLegend: A User Feedback-Driven Adaptive Legend Generator for Visualizations

We propose AutoLegend to generate interactive visualization legends using online learning with user feedback. AutoLegend accurately extracts symbols and channels from visualizations and then generates quality legends. AutoLegend enables a two-way interaction between legends and interactions, including highlighting, filtering, data retrieval, and retargeting. After analyzing visualization legends from IEEE VIS papers over the past 20 years, we summarized the design space and evaluation metrics for legend design in visualizations, particularly charts. The generation process consists of three interrelated components: a legend search agent, a feedback model, and an adversarial loss model. The search agent determines suitable legend solutions by exploring the design space and receives guidance from the feedback model through scalar scores. The feedback model is continuously updated by the adversarial loss model based on user input. The user study revealed that AutoLegend can learn users' preferences through legend editing.

cs.HC↗