arXiv subjects

Black Sun

Publications and source records attributed to Black Sun.

9 recordsLinked to original sources

arXiv2026

Beyond the Blood Draw: Explainable Machine Learning for Non-Invasive Dysglycemia Risk Screening

Dysglycemia, encompassing both prediabetes and diabetes, affects huge numbers of adults worldwide, yet many of them remain undiagnosed. We developed and validated machine-learning (ML) models for non-invasive screening of dysglycemia risk that require no laboratory tests. Pooling data from the National Health and Nutrition Examination Survey (NHANES) 2017--2023 (n=14,352), we trained six ML models with stratified 5-fold cross-validation and compared them with two established clinical risk scores. LightGBM achieved the highest area under the receiver operating characteristic curve (AUC=0.820, 95% CI: 0.806--0.835), outperforming the Finnish Diabetes Risk Score (0.745) and American Diabetes Association Risk Test (0.783). SHAP analysis identified age, race/ethnicity, and waist-to-height ratio as the most influential predictors. Subgroup analyses confirmed consistent performance across demographic strata (AUC: 0.735--0.832). These results demonstrate the feasibility of explainable, laboratory-free dysglycemia screening for deployment in community settings and self-tracking health applications.

cs.LG↗

arXiv2026

Understanding Older Adults' Experiences of Support, Concerns, and Risks from Kinship-Role AI-Generated Influencers

AI-generated influencers are rapidly gaining popularity on Chinese short-video platforms, often adopting kinship-based roles such as AI grandchildren to attract older adults. Although this trend has raised public concern, little is known about the design strategies behind these influencers, how older adults experience them, and the benefits and risks involved. In this study, we combined social media analysis with interviews to unpack the above questions. Our findings show that influencers use both visual and conversational cues to enact kinship roles, prompting audiences to engage in kinship-based role-play. Interviews further show that these cues arouse emotional resonance, help fulfill older adults' informational and emotional needs, while also raising concerns about emotional displacement and unequal emotional investment. We highlight the complex relationship between virtual avatars and real family ties, shaped by broader sociocultural norms, and discuss how AI might strengthen social support for older adults while mitigating risks within cultural contexts.

cs.HC↗

arXiv2026

MagHeart: Exploring Playful Avatar Co-Creation and Shared Heartbeats for Icebreaking in Hybrid Meetings

Hybrid meetings often begin with social awkwardness and asymmetric participation, particularly for remote attendees who lack access to informal, co-present interaction. We present MagHeart, a multimodal system that explores symmetric icebreaking in hybrid meetings through playful LEGO-based avatar co-creation and a tangible magnetic device that represents a remote participant's heartbeat as an ambient presence cue. By combining creative co-creation with abstract bio-feedback, MagHeart rethinks how remote participants can become materially and perceptually present during meeting openings. We report findings from a scenario-based exploratory study combining quantitative and qualitative data, examining participants' anticipated engagement, perceived social presence, and future-use intentions from both co-located and remote perspectives. Our results highlight opportunities for playful, embodied icebreakers to support early hybrid interaction, while also surfacing tensions around privacy, distraction, and contextual appropriateness. This work contributes design insights and open questions for future hybrid meeting tools that balance playfulness, embodiment, and social sensitivity.

cs.HC↗

arXiv2025

I Prompt, it Generates, we Negotiate. Exploring Text-Image Intertextuality in Human-AI Co-Creation of Visual Narratives with VLMs

Creating meaningful visual narratives through human-AI collaboration requires understanding how text-image intertextuality emerges when textual intentions meet AI-generated visuals. We conducted a three-phase qualitative study with 15 participants using GPT-4o to investigate how novices navigate sequential visual narratives. Our findings show that users develop strategies to harness AI's semantic surplus by recognizing meaningful visual content beyond literal descriptions, iteratively refining prompts, and constructing narrative significance through complementary text-image relationships. We identified four distinct collaboration patterns and, through fsQCA's analysis, discovered three pathways to successful intertextual collaboration: Educational Collaborator, Technical Expert, and Visual Thinker. However, participants faced challenges, including cultural representation gaps, visual consistency issues, and difficulties translating narrative concepts into visual prompts. These findings contribute to HCI research by providing an empirical account of \textit{text-image intertextuality} in human-AI co-creation and proposing design implications for role-based AI assistants that better support iterative, human-led creative processes in visual storytelling.

cs.HC↗

arXiv2025

Ashes or Breath: Exploring Moral Dilemmas of Life and Cultural Legacy through Mixed Reality Gaming

Traditional approaches to teaching moral dilemmas often rely on abstract, disembodied scenarios that limit emotional engagement and reflective depth. To address this gap, we developed \textit{Ashes or Breath}, a Mixed Reality game delivered via head-mounted displays(MR-HMDs). This places players in an ethical crisis: they must save a living cat or a priceless cultural artifact during a museum fire. Designed through an iterative, values-centered process, the experience leverages embodied interaction and spatial immersion to heighten emotional stakes and provoke ethical reflection. Players face irreversible, emotionally charged choices followed by narrative consequences in a reflective room, exploring diverse perspectives and societal implications. Preliminary evaluations suggest that embedding moral dilemmas into everyday environments via MR-HMDs intensifies empathy, deepens introspection, and encourages users to reconsider their moral assumptions. This work contributes to ethics-based experiential learning in HCI, positioning augmented reality not merely as a medium of interaction but as a stage for ethical encounter.

cs.HC↗

arXiv2025

CTG-Insight: A Multi-Agent Interpretable LLM Framework for Cardiotocography Analysis and Classification

Remote fetal monitoring technologies are becoming increasingly common. Yet, most current systems offer limited interpretability, leaving expectant parents with raw cardiotocography (CTG) data that is difficult to understand. In this work, we present CTG-Insight, a multi-agent LLM system that provides structured interpretations of fetal heart rate (FHR) and uterine contraction (UC) signals. Drawing from established medical guidelines, CTG-Insight decomposes each CTG trace into five medically defined features: baseline, variability, accelerations, decelerations, and sinusoidal pattern, each analyzed by a dedicated agent. A final aggregation agent synthesizes the outputs to deliver a holistic classification of fetal health, accompanied by a natural language explanation. We evaluate CTG-Insight on the NeuroFetalNet Dataset and compare it against deep learning models and the single-agent LLM baseline. Results show that CTG-Insight achieves state-of-the-art accuracy (96.4%) and F1-score (97.8%) while producing transparent and interpretable outputs. This work contributes an interpretable and extensible CTG analysis framework.

cs.LG↗

arXiv2025

Breaking Barriers or Building Dependency? Exploring Team-LLM Collaboration in AI-infused Classroom Debate

Classroom debates are a unique form of collaborative learning characterized by fast-paced, high-intensity interactions that foster critical thinking and teamwork. Despite the recognized importance of debates, the role of AI tools, particularly LLM-based systems, in supporting this dynamic learning environment has been under-explored in HCI. This study addresses this opportunity by investigating the integration of LLM-based AI into real-time classroom debates. Over four weeks, 22 students in a Design History course participated in three rounds of debates with support from ChatGPT. The findings reveal how learners prompted the AI to offer insights, collaboratively processed its outputs, and divided labor in team-AI interactions. The study also surfaces key advantages of AI usage, reducing social anxiety, breaking communication barriers, and providing scaffolding for novices, alongside risks, such as information overload and cognitive dependency, which could limit learners' autonomy. We thereby discuss a set of nuanced implications for future HCI exploration.

cs.HC↗

arXiv2024

Anger Speaks Louder? Exploring the Effects of AI Nonverbal Emotional Cues on Human Decision Certainty in Moral Dilemmas

Exploring moral dilemmas allows individuals to navigate moral complexity, where a reversal in decision certainty, shifting toward the opposite of one's initial choice, could reflect open-mindedness and less rigidity. This study probes how nonverbal emotional cues from conversational agents could influence decision certainty in moral dilemmas. While existing research heavily focused on verbal aspects of human-agent interaction, we investigated the impact of agents expressing anger and sadness towards the moral situations through animated chat balloons. We compared these with a baseline where agents offered same responses without nonverbal cues. Results show that agents displaying anger significantly caused reversal shifts in decision certainty. The interaction between participant gender and agents' nonverbal emotional cues significantly affects participants' perception of AI's influence. These findings reveal that even subtly altering agents' nonverbal cues may impact human moral decisions, presenting both opportunities to leverage these effects for positive outcomes and ethical risks for future human-AI systems.

cs.HC↗

arXiv2024

MultiSurf-GPT: Facilitating Context-Aware Reasoning with Large-Scale Language Models for Multimodal Surface Sensing

Surface sensing is widely employed in health diagnostics, manufacturing and safety monitoring. Advances in mobile sensing affords this potential for context awareness in mobile computing, typically with a single sensing modality. Emerging multimodal large-scale language models offer new opportunities. We propose MultiSurf-GPT, which utilizes the advanced capabilities of GPT-4o to process and interpret diverse modalities (radar, microscope and multispectral data) uniformly based on prompting strategies (zero-shot and few-shot prompting). We preliminarily validated our framework by using MultiSurf-GPT to identify low-level information, and to infer high-level context-aware analytics, demonstrating the capability of augmenting context-aware insights. This framework shows promise as a tool to expedite the development of more complex context-aware applications in the future, providing a faster, more cost-effective, and integrated solution.

cs.HC↗