In the era of AI, pandemics, and frequent disasters, informatics is no longer a matter of efficiency alone but of survival. We introduce Survival Informatics, a novel academic approach that emphasizes human well-being and societal resilience. Survival Informatics goes beyond traditional informatics by addressing fundamental issues of trust, inclusiveness, and sustainability, and by integrating ethical, legal, social, and economic perspectives into technical development. As one representative field where Survival Informatics can be practically implemented, the analysis of people’s reactions on social media provides a powerful means to capture public perceptions in real time and to link informatics research directly with societal well-being. Motivated by this perspective, we develop a stepwise technical framework that operationalizes the principles of Survival Informatics, highlighting a two-stage clustering approach for large-scale discourse analysis. Through a comprehensive case study of COVID-19 vaccine discourse in Japan, analyzing 32 million tweets, we demonstrate methodological innovations in scalability, reproducibility, and consistency. Finally, we discuss the broader implications of Survival Informatics for social implementation, including real-time public opinion monitoring, misinformation detection, and policy integration.
Skin condition changes throughout the day, yet current skincare does not adapt to these changes. Adaptive skincare requires sensing skin condition and applying appropriate products, ideally in an automatic way even during busy periods. This study proposes a new skincare concept that measures the skin condition and automatically applies lotion to maintain a stable condition. We developed a demo system using a glasses-type sensor device that assesses skin condition and applies a suitable facial mist. In an evaluation experiment, participants used the demo system and completed a questionnaire. Results showed that participants understood and valued the new skincare style, and based on these findings we discussed a possible system design to realize the concept.
While AI-driven financial advisory has become increasingly essential for individuals to navigate volatile markets that offer complex financial products, limited data and privacy concerns pose challenges in its development. This research explores the potential of combining Retrieval-Augmented Generation (RAG) with synthetic data, which imitates real-world data without compromising privacy, in enhancing financial question-answering systems. We propose a framework to generate and incorporate synthetic data into the RAG model with strategies to enhance retrieval processes. The proposed framework is compared with a baseline model without synthetic data to evaluate our approach using the Retrieval-Augmented Generation Assessment (RAGAS). The result shows that the integration of synthetic data can improve recall, precision, and faithfulness of the generated responses. However, relevancy can degrade due to the broader scope of retrieved data. The outcome demonstrates that synthetic data can enhance the accessibility and accuracy of financial data while safeguarding privacy in financial question-answering systems.
The rapid advancement of Large Language Models (LLMs) raises fundamental questions about the limits of AI in software development. This paper investigates whether a complete and playable multi-platform web-based game can be developed using only natural language prompts with an advanced LLM, Gemini 2.5 Pro. To explore this, the study compares two opposing development methodologies without the use of a traditional game engine. The study concludes that creating a playable game with prompts alone is possible, but only through a structured, iterative process. This positions the AI not as an autonomous developer, but as a powerful co-pilot that requires skilled, step-by-step guidance from a human expert.
This study proposes a method for finding museum exhibits that visually unexpected shapes, aiming to enhance visitor engagement and memory in museum. The proposed method measures shape-based unexpectedness by combining shape similarity computation with outlier detection. An exhibit is considered unexpected if it is identified as a shape-based outlier. To compute shape similarity, images are first converted into feature vectors using either Vision Transformer (ViT) or Convolutional Neural Networks (CNN). The study also investigates how converting color images into monochrome or line drawings affects the measurement of shape unexpectedness. For outlier detection, two methods, DBSCAN and PageRank-based approach are evaluated. Experiments were conducted using images of exhibits from the National Museum of Ethnology, Japan. Among all tested combinations, the pairing of ConvNeXt, a type of CNN, and PageRank-based approach achieved the highest performance with an nDCG@3 of 0.794.
