arXiv subjects

Aaron Quigley

Publications and source records attributed to Aaron Quigley.

17 recordsLinked to original sources

arXiv2026

LEGOUI: Designing with UI-DSL Bricks to Balance Transparency and Controllability

Generative user interface design tools enable rapid prototyping but often operate as black boxes with limited transparency and controllability. When outputs diverge from the designer's intent, users are left tweaking prompts via trial-and-error with little insight into the model's reasoning. We present LegoUI, a staged generative framework that structures the interface design process into sequential, interpretable steps along key design dimensions, capturing each step's result in a UI domain-specific language (UI-DSL) enriched with provenance. This approach exposes the model's intermediate reasoning and enables user intervention and iterative refinement. In a technical evaluation on 40 real-world design prompts, LegoUI's requirement analysis stage captured explicit requirements with over 95% accuracy, near-complete coverage, and zero redundancy. In user studies, participants using LegoUI reported significantly greater transparency, controllability, and alignment with their intent compared to existing one-shot generative UI tools.

cs.HC↗

arXiv2025

SpeechAgent: An End-to-End Mobile Infrastructure for Speech Impairment Assistance

Speech is essential for human communication, yet millions of people face impairments such as dysarthria, stuttering, and aphasia conditions that often lead to social isolation and reduced participation. Despite recent progress in automatic speech recognition (ASR) and text-to-speech (TTS) technologies, accessible web and mobile infrastructures for users with impaired speech remain limited, hindering the practical adoption of these advances in daily communication. To bridge this gap, we present SpeechAgent, a mobile SpeechAgent designed to facilitate people with speech impairments in everyday communication. The system integrates large language model (LLM)- driven reasoning with advanced speech processing modules, providing adaptive support tailored to diverse impairment types. To ensure real-world practicality, we develop a structured deployment pipeline that enables real-time speech processing on mobile and edge devices, achieving imperceptible latency while maintaining high accuracy and speech quality. Evaluation on real-world impaired speech datasets and edge-device latency profiling confirms that SpeechAgent delivers both effective and user-friendly performance, demonstrating its feasibility for personalized, day-to-day assistive communication.

eess.SY↗

arXiv2025

A Case Study of Scalable Content Annotation Using Multi-LLM Consensus and Human Review

Content annotation at scale remains challenging, requiring substantial human expertise and effort. This paper presents a case study in code documentation analysis, where we explore the balance between automation efficiency and annotation accuracy. We present MCHR (Multi-LLM Consensus with Human Review), a novel semi-automated framework that enhances annotation scalability through the systematic integration of multiple LLMs and targeted human review. Our framework introduces a structured consensus-building mechanism among LLMs and an adaptive review protocol that strategically engages human expertise. Through our case study, we demonstrate that MCHR reduces annotation time by 32% to 100% compared to manual annotation while maintaining high accuracy (85.5% to 98%) across different difficulty levels, from basic binary classification to challenging open-set scenarios.

cs.HC↗

arXiv2025

Text-to-Image Generation for Vocabulary Learning Using the Keyword Method

The 'keyword method' is an effective technique for learning vocabulary of a foreign language. It involves creating a memorable visual link between what a word means and what its pronunciation in a foreign language sounds like in the learner's native language. However, these memorable visual links remain implicit in the people's mind and are not easy to remember for a large set of words. To enhance the memorisation and recall of the vocabulary, we developed an application that combines the keyword method with text-to-image generators to externalise the memorable visual links into visuals. These visuals represent additional stimuli during the memorisation process. To explore the effectiveness of this approach we first run a pilot study to investigate how difficult it is to externalise the descriptions of mental visualisations of memorable links, by asking participants to write them down. We used these descriptions as prompts for text-to-image generator (DALL-E2) to convert them into images and asked participants to select their favourites. Next, we compared different text-to-image generators (DALL-E2, Midjourney, Stable and Latent Diffusion) to evaluate the perceived quality of the generated images by each. Despite heterogeneous results, participants mostly preferred images generated by DALL-E2, which was used also for the final study. In this study, we investigated whether providing such images enhances the retention of vocabulary being learned, compared to the keyword method only. Our results indicate that people did not encounter difficulties describing their visualisations of memorable links and that providing corresponding images significantly improves memory retention.

cs.HC↗

arXiv2024

Towards Human-AI Synergy in UI Design: Supporting Iterative Generation with LLMs

In automated UI design generation, a key challenge is the lack of support for iterative processes, as most systems focus solely on end-to-end output. This stems from limited capabilities in interpreting design intent and a lack of transparency for refining intermediate results. To better understand these challenges, we conducted a formative study that identified concrete and actionable requirements for supporting iterative design with Generative Tools. Guided by these findings, we propose PrototypeFlow, a human-centered system for automated UI generation that leverages multi-modal inputs and models. PrototypeFlow takes natural language descriptions and layout preferences as input to generate the high-fidelity UI design. At its core is a theme design module that clarifies implicit design intent through prompt enhancement and orchestrates sub-modules for component-level generation. Designers retain full control over inputs, intermediate results, and final prototypes, enabling flexible and targeted refinement by steering generation and directly editing outputs. Our experiments and user studies confirmed the effectiveness and usefulness of our proposed PrototypeFlow.

cs.HC↗

arXiv2024

Exploring Device-Oriented Video Encryption for Hierarchical Privacy Protection in AR Content Sharing

Content sharing across multiple Augmented Reality (AR) displays is becoming commonplace, enhancing team communication and collaboration through devices like smartphones and AR glasses. However, this practice raises significant privacy concerns, especially concerning the physical environment visible in AR, which may include sensitive personal details like facial features and identifiable information. Our research focuses on protecting privacy within AR environments, particularly the physical backgrounds visible during content sharing across three common AR display methods: projection, smartphone, and AR glasses. We analyze the potential privacy risks associated with each method and employ a Region Of Interest (ROI) video encryption system to hierarchically encrypt the physical backdrop based on its safety rating. This study pioneers the integration of ROI video encryption at the bitstream level within AR contexts, providing a more efficient solution than traditional pixel-level encryption by enhancing encryption speed and reducing the required space. Our adaptive system dynamically adjusts the encryption intensity based on the AR display method, ensuring tailored privacy protection.

cs.HC↗

arXiv2024

DesignRepair: Dual-Stream Design Guideline-Aware Frontend Repair with Large Language Models

The rise of Large Language Models (LLMs) has streamlined frontend interface creation through tools like Vercel's V0, yet surfaced challenges in design quality (e.g., accessibility, and usability). Current solutions, often limited by their focus, generalisability, or data dependency, fall short in addressing these complexities. Moreover, none of them examine the quality of LLM-generated UI design. In this work, we introduce DesignRepair, a novel dual-stream design guideline-aware system to examine and repair the UI design quality issues from both code aspect and rendered page aspect. We utilised the mature and popular Material Design as our knowledge base to guide this process. Specifically, we first constructed a comprehensive knowledge base encoding Google's Material Design principles into low-level component knowledge base and high-level system design knowledge base. After that, DesignRepair employs a LLM for the extraction of key components and utilizes the Playwright tool for precise page analysis, aligning these with the established knowledge bases. Finally, we integrate Retrieval-Augmented Generation with state-of-the-art LLMs like GPT-4 to holistically refine and repair frontend code through a strategic divide and conquer approach. Our extensive evaluations validated the efficacy and utility of our approach, demonstrating significant enhancements in adherence to design guidelines, accessibility, and user experience metrics.

cs.SE↗

arXiv2024

Leafeon: Towards Accurate, Robust and Low-cost Leaf Water Content Sensing Using mmWave Radar

Plant sensing plays an important role in modern smart agriculture and the farming industry. Remote radio sensing allows for monitoring essential indicators of plant health, such as leaf water content. While recent studies have shown the potential of using millimeter-wave (mmWave) radar for plant sensing, many overlook crucial factors such as leaf structure and surface roughness, which can impact the accuracy of the measurements. In this paper, we introduce Leafeon, which leverages mmWave radar to measure leaf water content non-invasively. Utilizing electronic beam steering, multiple leaf perspectives are sent to a custom deep neural network, which discerns unique reflection patterns from subtle antenna variations, ensuring accurate and robust leaf water content estimations. We implement a prototype of Leafeon using a Commercial Off-The-Shelf mmWave radar and evaluate its performance with a variety of different leaf types. Leafeon was trained in-lab using high-resolution destructive leaf measurements, achieving a Mean Absolute Error (MAE) of leaf water content as low as 3.17% for the Avocado leaf, significantly outperforming the state-of-the-art approaches with an MAE reduction of up to 55.7%. Furthermore, we conducted experiments on live plants in both indoor and glasshouse experimental farm environments (see Fig. 1). Our results showed a strong correlation between predicted leaf water content levels and drought events.

eess.SP↗

arXiv2024

Towards Enhanced Context Awareness with Vision-based Multimodal Interfaces

Vision-based Interfaces (VIs) are pivotal in advancing Human-Computer Interaction (HCI), particularly in enhancing context awareness. However, there are significant opportunities for these interfaces due to rapid advancements in multimodal Artificial Intelligence (AI), which promise a future of tight coupling between humans and intelligent systems. AI-driven VIs, when integrated with other modalities, offer a robust solution for effectively capturing and interpreting user intentions and complex environmental information, thereby facilitating seamless and efficient interactions. This PhD study explores three application cases of multimodal interfaces to augment context awareness, respectively focusing on three dimensions of visual modality: scale, depth, and time: a fine-grained analysis of physical surfaces via microscopic image, precise projection of the real world using depth data, and rendering haptic feedback from video background in virtual environments.

cs.HC↗

arXiv2024

MicroCam: Leveraging Smartphone Microscope Camera for Context-Aware Contact Surface Sensing

The primary focus of this research is the discreet and subtle everyday contact interactions between mobile phones and their surrounding surfaces. Such interactions are anticipated to facilitate mobile context awareness, encompassing aspects such as dispensing medication updates, intelligently switching modes (e.g., silent mode), or initiating commands (e.g., deactivating an alarm). We introduce MicroCam, a contact-based sensing system that employs smartphone IMU data to detect the routine state of phone placement and utilizes a built-in microscope camera to capture intricate surface details. In particular, a natural dataset is collected to acquire authentic surface textures in situ for training and testing. Moreover, we optimize the deep neural network component of the algorithm, based on continual learning, to accurately discriminate between object categories (e.g., tables) and material constituents (e.g., wood). Experimental results highlight the superior accuracy, robustness and generalization of the proposed method. Lastly, we conducted a comprehensive discussion centered on our prototype, encompassing topics such as system performance and potential applications and scenarios.

cs.HC↗

arXiv2024

MAxPrototyper: A Multi-Agent Generation System for Interactive User Interface Prototyping

In automated user interactive design, designers face key challenges, including accurate representation of user intent, crafting high-quality components, and ensuring both aesthetic and semantic consistency. Addressing these challenges, we introduce MAxPrototyper, our human-centered, multi-agent system for interactive design generation. The core of MAxPrototyper is a theme design agent. It coordinates with specialized sub-agents, each responsible for generating specific parts of the design. Through an intuitive online interface, users can control the design process by providing text descriptions and layout. Enhanced by improved language and image generation models, MAxPrototyper generates each component with careful detail and contextual understanding. Its multi-agent architecture enables a multi-round interaction capability between the system and users, facilitating precise and customized design adjustments throughout the creation process.

cs.HC↗

arXiv2023

Test-takers have a say: understanding the implications of the use of AI in language tests

Language tests measure a person's ability to use a language in terms of listening, speaking, reading, or writing. Such tests play an integral role in academic, professional, and immigration domains, with entities such as educational institutions, professional accreditation bodies, and governments using them to assess candidate language proficiency. Recent advances in Artificial Intelligence (AI) and the discipline of Natural Language Processing have prompted language test providers to explore AI's potential applicability within language testing, leading to transformative activity patterns surrounding language instruction and learning. However, with concerns over AI's trustworthiness, it is imperative to understand the implications of integrating AI into language testing. This knowledge will enable stakeholders to make well-informed decisions, thus safeguarding community well-being and testing integrity. To understand the concerns and effects of AI usage in language tests, we conducted interviews and surveys with English test-takers. To the best of our knowledge, this is the first empirical study aimed at identifying the implications of AI adoption in language tests from a test-taker perspective. Our study reveals test-taker perceptions and behavioral patterns. Specifically, we identify that AI integration may enhance perceptions of fairness, consistency, and availability. Conversely, it might incite mistrust regarding reliability and interactivity aspects, subsequently influencing the behaviors and well-being of test-takers. These insights provide a better understanding of potential societal implications and assist stakeholders in making informed decisions concerning AI usage in language testing.

cs.CY↗

arXiv2023

Investigating the Design Considerations for Integrating Text-to-Image Generative AI within Augmented Reality Environments

Generative Artificial Intelligence (GenAI) has emerged as a fundamental component of intelligent interactive systems, enabling the automatic generation of multimodal media content. The continuous enhancement in the quality of Artificial Intelligence-Generated Content (AIGC), including but not limited to images and text, is forging new paradigms for its application, particularly within the domain of Augmented Reality (AR). Nevertheless, the application of GenAI within the AR design process remains opaque. This paper aims to articulate a design space encapsulating a series of criteria and a prototypical process to aid practitioners in assessing the aptness of adopting pertinent technologies. The proposed model has been formulated based on a synthesis of design insights garnered from ten experts, obtained through focus group interviews. Leveraging these initial insights, we delineate potential applications of GenAI in AR.

cs.HC↗

arXiv2022

Contactless Haptic Display Through Magnetic Field Control

Haptic rendering enables people to touch, perceive, and manipulate virtual objects in a virtual environment. Using six cascaded identical hollow disk electromagnets and a small permanent magnet attached to an operator's finger, this paper proposes and develops an untethered haptic interface through magnetic field control. The concentric hole inside the six cascaded electromagnets provides the workspace, where the 3D position of the permanent magnet is tracked with a Microsoft Kinect sensor. The driving currents of six cascaded electromagnets are calculated in real-time for generating the desired magnetic force. Offline data from an FEA (finite element analysis) based simulation, determines the relationship between the magnetic force, the driving currents, and the position of the permanent magnet. A set of experiments including the virtual object recognition experiment, the virtual surface identification experiment, and the user perception evaluation experiment were conducted to demonstrate the proposed system, where Microsoft HoloLens holographic glasses are used for visual rendering. The proposed magnetic haptic display leads to an untethered and non-contact interface for natural haptic rendering applications, which overcomes the constraints of mechanical linkages in tool-based traditional haptic devices.

cs.HC↗

arXiv2022

Arigatō: Effects of Adaptive Guidance on Engagement and Performance in Augmented Reality Learning Environments

Experiential learning (ExL) is the process of learning through experience or more specifically "learning through reflection on doing". In this paper, we propose a simulation of these experiences, in Augmented Reality (AR), addressing the problem of language learning. Such systems provide an excellent setting to support "adaptive guidance", in a digital form, within a real environment. Adaptive guidance allows the instructions and learning content to be customised for the individual learner, thus creating a unique learning experience. We developed an adaptive guidance AR system for language learning, we call Arigatō (Augmented Reality Instructional Guidance & Tailored Omniverse), which offers immediate assistance, resources specific to the learner's needs, manipulation of these resources, and relevant feedback. Considering guidance, we employ this prototype to investigate the effect of the amount of guidance (fixed vs. adaptive-amount) and the type of guidance (fixed vs. adaptive-associations) on the engagement and consequently the learning outcomes of language learning in an AR environment. The results for the amount of guidance show that compared to the adaptive-amount, the fixed-amount of guidance group scored better in the immediate and delayed (after 7 days) recall tests. However, this group also invested a significantly higher mental effort to complete the task. The results for the type of guidance show that the adaptive-associations group outperforms the fixed-associations group in the immediate, delayed (after 7 days) recall tests, and learning efficiency. The adaptive-associations group also showed significantly lower mental effort and spent less time to complete the task.

cs.HC↗

arXiv2018

AdaM: Adapting Multi-User Interfaces for Collaborative Environments in Real-Time

Developing cross-device multi-user interfaces (UIs) is a challenging problem. There are numerous ways in which content and interactivity can be distributed. However, good solutions must consider multiple users, their roles, their preferences and access rights, as well as device capabilities. Manual and rule-based solutions are tedious to create and do not scale to larger problems nor do they adapt to dynamic changes, such as users leaving or joining an activity. In this paper, we cast the problem of UI distribution as an assignment problem and propose to solve it using combinatorial optimization. We present a mixed integer programming formulation which allows real-time applications in dynamically changing collaborative settings. It optimizes the allocation of UI elements based on device capabilities, user roles, preferences, and access rights. We present a proof-of-concept designer-in-the-loop tool, allowing for quick solution exploration. Finally, we compare our approach to traditional paper prototyping in a lab study.

cs.HC↗

arXiv2016

Challenges in Mobile Multi-Device Ecosystems

Coordinated multi-display environments from the desktop, second-screen to gigapixel display walls are increasingly common. Personal and intimate mobile and wearable devices such as head-mounted displays, smartwatches, smartphones and tablets are rarely part of such multi-device ecosystems. With this paper, we contribute to a better understanding about factors that impede the creation and use of such mobile multi-device ecosystems. We base our findings on literature research and an expert survey. Specifically, we present grounded challenges relevant for the design, development and use of mobile multi-device environments.

cs.HC↗