arXiv subjects

Zainab Aamir

Publications and source records attributed to Zainab Aamir.

3 recordsLinked to original sources

arXiv2026

Scale-Aware Navigation of Astronomical Survey Imagery Data on High Resolution Immersive Displays

Upcoming astronomical surveys produce imagery that spans many orders of magnitude in spatial scale, requiring scientists to reason fluidly between global structure and local detail. Data from the Vera C. Rubin Observatory exemplifies this challenge, as traditional desktop-based workflows often rely on discrete views or static cutouts that fragment context during exploration. This paper presents a design-oriented framework for scale-aware navigation of astronomical survey imagery in high-resolution immersive display environments. We illustrate these principles through representative usage scenarios using Vera Rubin Observatory and Milky Way survey imagery deployed in room-scale immersive environments, including tiled high-resolution displays and curved immersive systems. Our goal is to contribute design insights that inform the development of immersive interaction paradigms for exploratory analysis of extreme-scale scientific imagery.

cs.HC↗

arXiv2025

Explainable XR: Understanding User Behaviors of XR Environments using LLM-assisted Analytics Framework

We present Explainable XR, an end-to-end framework for analyzing user behavior in diverse eXtended Reality (XR) environments by leveraging Large Language Models (LLMs) for data interpretation assistance. Existing XR user analytics frameworks face challenges in handling cross-virtuality - AR, VR, MR - transitions, multi-user collaborative application scenarios, and the complexity of multimodal data. Explainable XR addresses these challenges by providing a virtuality-agnostic solution for the collection, analysis, and visualization of immersive sessions. We propose three main components in our framework: (1) A novel user data recording schema, called User Action Descriptor (UAD), that can capture the users' multimodal actions, along with their intents and the contexts; (2) a platform-agnostic XR session recorder, and (3) a visual analytics interface that offers LLM-assisted insights tailored to the analysts' perspectives, facilitating the exploration and analysis of the recorded XR session data. We demonstrate the versatility of Explainable XR by demonstrating five use-case scenarios, in both individual and collaborative XR applications across virtualities. Our technical evaluation and user studies show that Explainable XR provides a highly usable analytics solution for understanding user actions and delivering multifaceted, actionable insights into user behaviors in immersive environments.

cs.HC↗

arXiv2022

Improving Developers' Understanding of Regex Denial of Service Tools through Anti-Patterns and Fix Strategies

Regular expressions are used for diverse purposes, including input validation and firewalls. Unfortunately, they can also lead to a security vulnerability called ReDoS (Regular Expression Denial of Service), caused by a super-linear worst-case execution time during regex matching. Due to the severity and prevalence of ReDoS, past work proposed automatic tools to detect and fix regexes. Although these tools were evaluated in automatic experiments, their usability has not yet been studied; usability has not been a focus of prior work. Our insight is that the usability of existing tools to detect and fix regexes will improve if we complement them with anti-patterns and fix strategies of vulnerable regexes. We developed novel anti-patterns for vulnerable regexes, and a collection of fix strategies to fix them. We derived our anti-patterns and fix strategies from a novel theory of regex infinite ambiguity - a necessary condition for regexes vulnerable to ReDoS. We proved the soundness and completeness of our theory. We evaluated the effectiveness of our anti-patterns, both in an automatic experiment and when applied manually. Then, we evaluated how much our anti-patterns and fix strategies improve developers' understanding of the outcome of detection and fixing tools. Our evaluation found that our anti-patterns were effective over a large dataset of regexes (N=209,188): 100% precision and 99% recall, improving the state of the art 50% precision and 87% recall. Our anti-patterns were also more effective than the state of the art when applied manually (N=20): 100% developers applied them effectively vs. 50% for the state of the art. Finally, our anti-patterns and fix strategies increased developers' understanding using automatic tools (N=9): from median "Very weakly" to median "Strongly" when detecting vulnerabilities, and from median "Very weakly" to median "Very strongly" when fixing them.

cs.SE↗