Situational visual impairments (SVIs) hinder mobile readability, causing discomfort and limiting information access. Building on prior work in adaptive typography and accessibility, this paper presents SituFont, a context-aware and human-in-the-loop adaptive typography adjustment approach that enhances smartphone mobile readability by dynamically adjusting font parameters based on real-time contextual changes. Using smartphone sensors and a human-in-the-loop approach, SituFont personalizes text presentation to accommodate personal factors (e.g., fatigue, distraction) and environmental conditions (e.g., lighting, motion, location). To inform its design, we conducted formative interviews (N=15) to identify key SVI factors and controlled experiments (N=18) to quantify their impact on optimal text parameters. A comparative user study (N=12) across eight simulated SVI scenarios demonstrated SituFont’s effectiveness in improving smartphone mobile readability in terms of improved efficiency and reduced workload compared with a non-trivial manual adjustment baseline.

Space exploration has advanced rapidly, but the emotional needs of astronauts on long-duration missions remain underexplored. We present ReHome Earth, a dual-component design approach addressing space homesickness: 1) a future-oriented installation concept integrating transparent OLED displays with spaceship windows for real-time Earth connectivity, and 2) a functional VR prototype simulating astronaut isolation for testing AI-generated content effectiveness. Since accessing astronauts during missions is impossible, we conducted concept validation with terrestrial participants experiencing geographic displacement. Through evaluation with 84 proxy participants and 6 HCI experts, we demonstrate strong emotional resonance and validate three design implications: emotional pacing mechanisms, explainable biophysical feedback systems, and evolution from individual tools to collective affective infrastructure. Our contributions include a technically feasible space installation concept, a functional VR prototype for space HCI research, and empirical insights into the design of AI-driven emotional support systems for extreme isolation environments.

Alive Yi 2.0 combines cultural heritage, design innovation, and artificial intelligence (AI) to preserve and reimagine Yi minority embroidery patterns.Using a curated database of traditional Yi embroidery patterns, we implemented LoRA-based AI models to generate new designs that maintain cultural authenticity while enabling contemporary interpretations.This work transforms traditional patterns into modern variations through fine-tuned stable diffusion models, creating designs that respect cultural elements while appealing to younger generations.Our approach demonstrates the potential of AI-assisted design in cultural heritage preservation and provides a framework for using computational creativity to revitalize traditional heritage in the digital era.

Visual narrative methodologies provide a more comprehensive and intuitive framework for articulating the multifaceted interactions between human users and computational systems.This workshop guides participants through five segments: image-based storytelling, figure sketching, narrative development, practical exercises, and collaborative critique.Participants learn to translate complex interactive systems into clear visual narratives using both analog and digital techniques.Through structured activities and provided C&C '25, June 23-25, 2025, Virtual, United Kingdom Guo and Gao et al.resources, they develop skills to effectively communicate user experiences, system behaviors, and design concepts across stakeholder groups.The workshop equips both new and experienced practitioners with tools to enhance design communication and cross-cultural collaboration in Human-Computer Interaction (HCI).

Edible intangible cultural heritage, such as sugar painting, is eph-emeral and difficult to preserve. This work presents an AI-driven Mixed Reality (MR) pipeline that captures, semantically analyzes, and dynamically animates static sugar paintings using Meta Quest 3. The system employs OpenAI’s vision capabilities to classify traditional motifs into five culturally-appropriate animation categories (Fly, Swim, Walk, Jump, Grow), enabling gesture-based interaction with floating animated artworks. Evaluation with eight participants demonstrates strong cultural authenticity (M=4.50), high AI trustworthiness (M=4.88), and enhanced artistic engagement (M=4.75). Unlike traditional static documentation, this approach reactivates finished artworks as interactive cultural interfaces, transforming ephemeral heritage into persistent, spatially-aware digital experiences that maintain semantic fidelity while enabling cultural transmission and creative reinterpretation.

Effectively mitigating online misinformation requires understanding of their mechanisms and learning of practical skills for identification and counteraction. Serious games may serve as tools for combating misinformation, teaching players to recognize common misinformation tactics, and improving their skills of discernment. However, current interventions are designed as single-player, choice-based games, which present players with limited predefined choices. Such restrictions reduce replayability and may lead to an overly simplistic understanding of misinformation and how to debunk them. This study seeks to empower people to understand opinion-influencing and misinformation-debunking processes. We created a Player vs. Player (PvP) game in which participants attempt to generate or debunk misinformation to convince the public opinion represented by LLM. Using a within-subjects mixed-methods study design (N=47), we found that this game significantly raised participants' media literacy and improved their ability to identify misinformation. Qualitative analyses revealed how participants' use of debunking and content creation strategies deepened their understanding of misinformation. This work shows the potential for illuminating contrasting viewpoints of social issues by LLM-based mechanics in PvP games.

Anomaly detection has been widely studied and used in diverse applications. Building an effective anomaly detection system requires researchers and developers to learn complex structure from noisy data, identify dynamic anomaly patterns, and detect anomalies with limited labels. Recent advancements in deep learning techniques have greatly improved anomaly detection performance, in comparison with classical approaches, and have extended anomaly detection to a wide variety of applications. This tutorial will help the audience gain a comprehensive understanding of deep learning based anomaly detection techniques in various application domains. First, we give an overview of the anomaly detection problem, introducing the approaches taken before the deep model era and listing out the challenges they faced. Then we survey the state-of-the-art deep learning models that range from building block neural network structures such as MLP, CNN, and LSTM, to more complex structures such as autoencoder, generative models (VAE, GAN, Flow-based models), to deep one-class detection models, etc. In addition, we illustrate how techniques such as transfer learning and reinforcement learning can help amend the label sparsity issue in anomaly detection problems and how to collect and make the best use of user labels in practice. Second to last, we discuss real world use cases coming from and outside LinkedIn. The tutorial concludes with a discussion of future trends.

Anomaly detection has been widely studied and used in diverse applications. Building an effective anomaly detection system requires the researchers/developers to learn the complex structure from noisy data, identify the dynamic anomaly patterns and detect anomalies while lacking sufficient labels. Recent advancement in deep learning techniques has made it possible to largely improve anomaly detection performance compared to the classical approaches. This tutorial will help the audience gain a comprehensive understanding of deep learning-based anomaly detection techniques in various application domains. First, it introduces what is the anomaly detection problem, the approaches taken before the deep model era and the challenges it faced. Then it surveys the state-of-the-art deep learning models extensively and discusses the techniques used to overcome the limitations from traditional algorithms. Second to last, it studies deep model anomaly detection techniques in real world examples from LinkedIn production systems. The tutorial concludes with a discussion of future trends.