Biomedical data harmonization is essential for enabling exploratory analyses and meta-studies, but the process of schema matching-identifying semantic correspondences between elements of disparate datasets (schemas)-remains a labor-intensive and error-prone task. Even state-of-the-art automated methods often yield low accuracy when applied to biomedical schemas due to the large number of attributes and nuanced semantic differences between them. We present BDIViz, a novel visual analytics system designed to streamline the schema matching process for biomedical data. Through formative studies with domain experts, we identified key requirements for an effective solution and developed interactive visualization techniques that address both scalability challenges and semantic ambiguity. BDIViz employs an ensemble approach that combines multiple matching methods with LLM-based validation, summarizes matches through interactive heatmaps, and provides coordinated views that enable users to quickly compare attributes and their values. Our method-agnostic design allows the system to integrate various schema matching algorithms and adapt to application-specific needs. Through two biomedical case studies and a within-subject user study with domain experts, we demonstrate that BDIViz significantly improves matching accuracy while reducing cognitive load and curation time compared to baseline approaches.

Large language models (LLMs) have exhibited impressive abilities for multimodal content comprehension and reasoning with proper prompting in zero- or few-shot settings. Despite the proliferation of interactive systems developed to support prompt engineering for LLMs across various tasks, most have primarily focused on textual or visual inputs, thus neglecting the complex interplay between modalities in multimodal inputs. This oversight hinders the development of effective prompts that guide models’ multimodal reasoning processes by fully exploiting the rich context provided by multiple modalities. In this paper, we present POEM, a visual analytics system to facilitate efficient prompt engineering for steering the multimodal reasoning performance of LLMs. The system enables users to explore the interaction patterns across modalities at varying levels of detail for a comprehensive understanding of the multimodal knowledge elicited by various prompts. Through diverse recommendations of demonstration examples and instructional principles, POEM supports users in iteratively crafting and refining prompts to better align and enhance model knowledge with human insights. The effectiveness and efficiency of our system are validated through quantitative and qualitative evaluations with experts.

Pilots operating modern cockpits often face high cognitive demands due to complex interfaces and multitasking requirements, which can lead to overload and decreased performance. This study introduces AdaptiveCoPilot, a neuroadaptive guidance system that adapts visual, auditory, and textual cues in real time based on the pilot’s cognitive workload, measured via functional Near-Infrared Spectroscopy (fNIRS). A formative study with expert pilots (N=3) identified adaptive rules for modality switching and information load adjustments during preflight tasks. These insights informed the design of AdaptiveCoPilot, which integrates cognitive state assessments, behavioral data, and adaptive strategies within a context-aware Large Language Model (LLM). The system was evaluated in a virtual reality (VR) simulated cockpit with licensed pilots (N=8), comparing its performance against baseline and random feedback conditions. The results indicate that the pilots using AdaptiveCoPilot exhibited higher rates of optimal cognitive load states on the facets of working memory and perception, along with reduced task completion times. Based on the formative study, experimental findings, qualitative interviews, we propose a set of strategies for future development of neuroadaptive pilot guidance systems and highlight the potential of neuroadaptive systems to enhance pilot performance and safety in aviation environments.

The concept of an intelligent augmented reality (AR) assistant has significant, wide-ranging applications, with potential uses in medicine, military, and mechanics domains. Such an assistant must be able to perceive the environment and actions, reason about the environment state in relation to a given task, and seamlessly interact with the task performer. These interactions typically involve an AR headset equipped with sensors which capture video, audio, and haptic feedback. Previous works have sought to facilitate the development of intelligent AR assistants by visualizing these sensor data streams in conjunction with the assistant's perception and reasoning model outputs. However, existing visual analytics systems do not focus on user modeling or include biometric data, and are only capable of visualizing a single task session for a single performer at a time. Moreover, they typically assume a task involves linear progression from one step to the next. We propose a visual analytics system that allows users to compare performance during multiple task sessions, focusing on non-linear tasks where different step sequences can lead to success. In particular, we design visualizations for understanding user behavior through functional near-infrared spectroscopy (fNIRS) data as a proxy for perception, attention, and memory as well as corresponding motion data (acceleration, angular velocity, and gaze). We distill these insights into embedding representations that allow users to easily select groups of sessions with similar behaviors. We provide two case studies that demonstrate how to use these visualizations to gain insights about task performance using data collected during helicopter copilot training tasks. Finally, we evaluate our approach through an in-depth examination of a think-aloud experiment with five domain experts.

Social stigma negatively impacts the well-being of neurodivergent individuals. Specifically for autistic people, the social isolation and pressure to conform to normative ways of being can take a tremendous toll; such as a thwarted sense of belonging to the point of higher rates of suicide. Yet, few technologies are directly targeting the problem of stigma. Socio-technical systems have tremendous potential to shift public perception of traditionally marginalized populations. However, these systems are not consistently designed to reflect the values and needs of neurodivergent individuals. This work explores the use of design sprints to envision CT– a spectrum of technology that could reduce social stigma by increasing the public’s awareness, accommodations, acceptance, advocacy, and appreciation. This work reports on two design sprints across 25 HCI community members with varying lived experiences with neurodiversity and knowledge of design practice. The resulting design concepts were discussed in the groups and then analyzed to reflect on how they might combat stigma. Results reveal designs that support the freedom to be oneself via (1) safe spaces (2) public understanding, and (3) authentic expression of strengths, challenges, and needs.

Text presented in augmented reality provides in-situ, real-time information for users. However, this content can be challenging to apprehend quickly when engaging in cognitively demanding AR tasks, especially when it is presented on a head-mounted display. We propose ARTiST, an automatic text simplification system that uses a few-shot prompt and GPT-3 models to specifically optimize the text length and semantic content for augmented reality. Developed out of a formative study that included seven users and three experts, our system combines a customized error calibration model with a few-shot prompt to integrate the syntactic, lexical, elaborative, and content simplification techniques, and generate simplified AR text for head-worn displays. Results from a 16-user empirical study showed that ARTiST lightens the cognitive load and improves performance significantly over both unmodified text and text modified via traditional methods. Our work constitutes a step towards automating the optimization of batch text data for readability and performance in augmented reality.

The concept of augmented reality (AR) assistants has captured the human imagination for decades, becoming a staple of modern science fiction. To pursue this goal, it is necessary to develop artificial intelligence (AI)-based methods that simultaneously perceive the 3D environment, reason about physical tasks, and model the performer, all in real-time. Within this framework, a wide variety of sensors are needed to generate data across different modalities, such as audio, video, depth, speech, and time-of-flight. The required sensors are typically part of the AR headset, providing performer sensing and interaction through visual, audio, and haptic feedback. AI assistants not only record the performer as they perform activities, but also require machine learning (ML) models to understand and assist the performer as they interact with the physical world. Therefore, developing such assistants is a challenging task. We propose ARGUS, a visual analytics system to support the development of intelligent AR assistants. Our system was designed as part of a multi-year-long collaboration between visualization researchers and ML and AR experts. This co-design process has led to advances in the visualization of ML in AR. Our system allows for online visualization of object, action, and step detection as well as offline analysis of previously recorded AR sessions. It visualizes not only the multimodal sensor data streams but also the output of the ML models. This allows developers to gain insights into the performer activities as well as the ML models, helping them troubleshoot, improve, and fine-tune the components of the AR assistant.

Graph or network data are widely studied in both data mining and visualization communities to review the relationship among different entities and groups. The data facts derived from graph visual analysis are important to help understand the social structures of complex data, especially for data journalism. However, it is challenging for data journalists to discover graph data facts and manually organize correlated facts around a meaningful topic due to the complexity of graph data and the difficulty to interpret graph narratives. Therefore, we present an automatic graph facts generation system, Calliope-Net, which consists of a fact discovery module, a fact organization module, and a visualization module. It creates annotated node-link diagrams with facts automatically discovered and organized from network data. A novel layout algorithm is designed to present meaningful and visually appealing annotated graphs. We evaluate the proposed system with two case studies and an in-lab user study. The results show that Calliope-Net can benefit users in discovering and understanding graph data facts with visually pleasing annotated visualizations.

Visual data stories can effectively convey insights from data, yet their creation often necessitates intricate data exploration, insight discovery, narrative organization, and customization to meet the communication objectives of the storyteller. Existing automated data storytelling techniques, however, tend to overlook the importance of user customization during the data story authoring process, limiting the system's ability to create tailored narratives that reflect the user's intentions. We present a novel data story generation workflow that leverages adaptive machine-guided elicitation of user feedback to customize the story. Our approach employs an adaptive plug-in module for existing story generation systems, which incorporates user feedback through interactive questioning based on the conversation history and dataset. This adaptability refines the system's understanding of the user's intentions, ensuring the final narrative aligns with their goals. We demonstrate the feasibility of our approach through the implementation of an interactive prototype: Socrates. Through a quantitative user study with 18 participants that compares our method to a state-of-the-art data story generation algorithm, we show that Socrates produces more relevant stories with a larger overlap of insights compared to human-generated stories. We also demonstrate the usability of Socrates via interviews with three data analysts and highlight areas of future work.

The target of automatic video summarization is to create a short skim of the original long video while preserving the major content/events. There is a growing interest in the integration of user queries into video summarization or query-driven video summarization. This video summarization method predicts a concise synopsis of the original video based on the user query, which is commonly represented by the input text. However, two inherent problems exist in this query-driven way. First, the text query might not be enough to describe the exact and diverse needs of the user. Second, the user cannot edit once the summaries are produced, while we assume the needs of the user should be subtle and need to be adjusted interactively. To solve these two problems, we propose IntentVizor, an interactive video summarization framework guided by generic multi-modality queries. The input query that describes the user's needs are not limited to text but also the video snippets. We further represent these multi-modality finer-grained queries as user ‘intent’, which is interpretable, interactable, editable, and can better quantify the user's needs. In this paper, we use a set of the proposed intents to represent the user query and design a new interactive visual analytic interface. Users can interactively control and adjust these mixed-initiative intents to obtain a more satisfying summary through the interface. Also, to improve the summarization quality via video understanding, a novel Granularity-Scalable Ego-Graph Convolutional Networks (GSE-GCN) is proposed. We conduct our experiments on two benchmark datasets. Comparisons with the state-of-the-art methods verify the effectiveness of the proposed framework. Code and dataset are available at https://github.com/jnzs1836/intentvizor.