arXiv subjects

Md Rahat-uz- Zaman

Publications and source records attributed to Md Rahat-uz- Zaman.

3 recordsLinked to original sources

arXiv2026

A Case Study in Accessible Redesign of a Wastewater Dashboard

Public health dashboards communicate data that can inform important decisions, but they often raise accessibility challenges. We present a case study redesigning the Utah Wastewater Surveillance System dashboard to improve accessibility and usability across desktop and mobile settings. The redesign was informed by WCAG and developed iteratively with Utah DHHS collaborators, and we collected feedback on the final design from an external blind researcher. Our case study highlights that accessible dashboard design requires aligning accessibility guidelines with user needs, stakeholder workflows, and technical constraints. It also suggests that simple, targeted technical solutions tailored to the existing environment can provide practical value for dashboard redesign in government contexts.

cs.HC↗

arXiv2025

AnnoGram: An Annotative Grammar of Graphics Extension

Annotations are central to effective data communication, yet most visualization tools treat them as secondary constructs -- manually defined, difficult to reuse, and loosely coupled to the underlying visualization grammar. We propose a declarative extension to Wilkinson's Grammar of Graphics that reifies annotations as first-class design elements, enabling structured specification of annotation targets, types, and positioning strategies. To demonstrate the utility of our approach, we develop a prototype extension called Vega-Lite Annotation. Through comparison with eight existing tools, we show that our approach enhances expressiveness, reduces authoring effort, and enables portable, semantically integrated annotation workflows.

cs.HC↗

arXiv2025

ChannelExplorer: Exploring Class Separability Through Activation Channel Visualization

Deep neural networks (DNNs) achieve state-of-the-art performance in many vision tasks, yet understanding their internal behavior remains challenging, particularly how different layers and activation channels contribute to class separability. We introduce ChannelExplorer, an interactive visual analytics tool for analyzing image-based outputs across model layers, emphasizing data-driven insights over architecture analysis for exploring class separability. ChannelExplorer summarizes activations across layers and visualizes them using three primary coordinated views: a Scatterplot View to reveal inter- and intra-class confusion, a Jaccard Similarity View to quantify activation overlap, and a Heatmap View to inspect activation channel patterns. Our technique supports diverse model architectures, including CNNs, GANs, ResNet and Stable Diffusion models. We demonstrate the capabilities of ChannelExplorer through four use-case scenarios: (1) generating class hierarchy in ImageNet, (2) finding mislabeled images, (3) identifying activation channel contributions, and(4) locating latent states' position in Stable Diffusion model. Finally, we evaluate the tool with expert users.

cs.GR↗