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.
In this study, we propose a method for generating a comparative table by summarizing product reviews using a Large Language Model (LLM). A comparative table summarizes, for each aspect of two products, the evaluations present in reviews and the number of reviews for each evaluation. By using an LLM to create the table, it becomes possible to generate the table on demand and to change the aspects to be compared depending on the products. The proposed method employs an LLM to extract aspects and their associated evaluations from reviews. These evaluations are summarized for each aspect to create a table mapping evaluations to their respective aspects. We used a review dataset from Rakuten Ichiba and automatically summarized reviews using an LLM to generate comparative tables. We conducted a user study to verify whether the tables are useful for comparing products. Based on the results of the user study, the proposed method was found to be more helpful for comparison than the baseline.
This paper proposes a method for generating expressive and distinctive recipe names by identifying each recipe’s unique features relative to others in the same collection. For example, when most recipes boil pasta in a pot, our method may generate a descriptive recipe name like “One-Pan Carbonara” for a recipe that completes the dish using a single frying pan only. The method detects ingredients, cooking procedures, and utensils that are statistical outliers, either significantly more or less frequent compared to the rest of the recipe set. These distinguishing features are then passed to a fine-tuned large language model, which generates the final recipe name. A user study showed that the proposed method produces accurate and appealing names that effectively highlight the distinctiveness of each recipe.
The global economy's transition towards a Circular Economy (CE) is being accelerated by regulatory instruments like the European Union's Digital Product Passport (DPP), mandated under the Ecodesign for Sustainable Products Regulation (ESPR). While the DPP aims to enhance transparency and enable circular practices, its integration presents significant architectural and operational challenges for businesses, particularly within e-commerce. This paper addresses these challenges by proposing a conceptual architectural framework for a scalable, secure, and federated DPP system. Developed through a qualitative synthesis of regulatory analysis, standardization efforts, and expert workshop feedback, the framework outlines the necessary components for both the EU's registry-centric ecosystem and the corresponding enterprise-level implementation. A key finding is that traditional Product Information Management (PIM) systems are insufficient, requiring a more comprehensive, API-driven, and modular architecture. Furthermore, the paper concludes that AI is not merely an efficiency tool but a critical strategic capability. AI is essential for managing data quality at scale, enhancing security, and transforming the DPP from a compliance burden into a value-creating asset through predictive analytics and personalized consumer engagement. The research posits that successfully operationalizing the DPP marks a paradigm shift towards a product-intelligence economy, demanding a holistic architectural approach to realize its full potential.
In our study we present the basic theoretical background of On-the-Job training of university graduates employed in the IT sector and the results of research conducted in this specific segment of the Slovak labour market, focusing on the respondents’ experiences prior to On-the-Job training.
This paper proposes a method for retrieving diverse real-world user reviews that refer to a specific Aspect Query representing a user’s information need. Given a short Aspect Query, such as “practicality,” the system generates a variety of Sentence Fragment queries, e.g., “*able for da*” to retrieve phrases such as “suitable for daily use” or “comfortable for daytime work.” These Sentence Fragments act as wildcard-like queries and are particularly effective in languages like Japanese, where inflection and agglutinative structures make exact keyword matching challenging. To construct such fragments, we first use a large-scale language model (LLM) to generate a large number of synthetic Aspect Query–review sentence pairs. These pairs are filtered to retain only high-quality examples, which are subsequently used to fine-tune a lightweight local LLM. The fine-tuned model generates synthetic reviews for arbitrary Aspect Queries, from which Sentence Fragments that are frequent in the synthetic reviews but rare in general reviews are extracted and used as expanded queries. A user study on a real-world review dataset demonstrates that our method enables the retrieval of diverse reviews without compromising accuracy, effectively bridging the lexical gap between abstract Aspect Queries and concrete review expressions.
In this paper, we present a collection of 3,503 kanji with complete component hierarchies and representative images. The information is displayed as kanji cards to offer a versatile format that can be easily integrated into Web-based language learning applications. We will make the data publicly available in JSON format together with a simple Web application using templates to access and inspect the kanji cards. As two recent extensions we also introduce an iconic representation of the compositional layout and an evaluation environment focusing on handwritten kanji input by the learner.
This paper explores the potential of Large Language Models (LLMs) to enhance community detection algorithms, with a focus on the SIWO (Strong In, Weak Out) algorithm. By integrating LLMs into the algorithm development process, focusing on their multi-disciplinary knowledge as a potential advantage over human expertise, we demonstrate how LLMs (with the possible oversight of a human expert) can generate innovative algorithm modifications that lead to enhanced performance. Our study reveals substantial reductions in execution times by more than 50
Knowledge bases (KBs) are increasingly used in diverse knowledge-processing tasks. Widely used RDF-style KBs represent knowledge as subject – predicate – object triples, whereas many external sources are described in non-RDF formats. Therefore, applications often need to integrate KBs with such external sources. In our previous work, we proposed an integrated query environment named Knowledge Mediator (KM), in which external sources are accessed via SPARQL magic properties, letting users query them as if they were parts of the KB. KM performs entity linking (EL) to find corresponding KB entities for external objects. However, EL may yield multiple plausible entities, and the results may involve uncertainty. In this study, we propose a model that represents data integration while allowing for the expression of uncertainty in query results caused by EL. Furthermore, we propose an efficient query processing method that retrieves only those results whose likelihoods exceed a user-specified threshold.
Physiological sensing is essential for mental health monitoring, but models often degrade over time due to user behavior changes, sensor noise, and contextual variation. We propose CoRA (Continual and Regularized Adaptation), a lightweight continual learning framework that monitors latent feature drift using class-wise KL divergence and selectively retrains a downstream classifier with Elastic Weight Consolidation (EWC) to prevent forgetting. CoRA operates on top of a pretrained encoder, enabling efficient adaptation without storing raw past samples. In stress detection experiments on LifeSnaps, DAPPER, and WESAD, CoRA improves F1-score by up to 10.4
This study examined the effect of a Learning Analytics Dashboard (LAD) on metacognitive activities in self-directed learning environments. We developed a LAD system that visualizes detailed video clickstream analysis and quiz results to support learners’ self-reflection. Through qualitative analysis using questionnaires and interviews, we conducted a one-month experiment with six first-year university students learning linear algebra, measuring metacognitive activities with the Metacognitive Awareness Inventory (MAI).
Dajare, a form of Japanese pun, utilizes phonetically identical or similar words and phrases with different meanings to create a funny effect. This paper presents a method for generating interactive Dajare, a Japanese pun formed by a response that includes a segment phonetically similar to a part of the original utterance. Our approach involves retrieving candidate words and phrases based on phoneme similarity. We then leverage large language models (LLMs) to generate response sentences for these candidates, which are subsequently ranked according to conversational naturalness to select the most suitable interactive Dajare. Our experiment revealed that the proposed approach, by explicitly providing a phonetically similar segment, makes it easier for annotators to identify interactive Dajare compared to the baseline, which generates them without providing such a segment. This suggests that our method more effectively produces recognizable interactive Dajare. Additionally, the results showed that word selection positively influences the perceived cleverness (the ingenuity of transforming words with similar pronunciations) of interactive Dajare than on its funniness. Furthermore, current LLMs still fall significantly short of human capabilities in generating genuinely funny text.
In this study, we propose a method for retrieving concrete reviews for a product, using an abstract review as a query. However, it is difficult to retrieve concrete reviews from keywords or abstract reviews, as simple sparse or dense retrieval methods cannot account for concreteness. Therefore, we propose a method called Query Rewriting with Retrieved Review Concretization (QR-ReReC), which utilizes retrieved reviews to rewrite the original query. For the experiment, we constructed a dataset to evaluate the effectiveness of QR-ReReC. The results showed that QR-ReReC is more effective for retrieving more concrete reviews than the retrieval methods without query rewriting and pseudo relevance feedback.
On crowdsourcing platforms, the quality of collected data depends on the clarity of instructions, but requesters struggle to create instructions that capture their own implicit criteria. To address this issue, we propose a novel framework that uses two Large Language Models (LLMs) – a Creator and an Evaluator – to automatically explore the space of possible instructions. In this iterative process, the Creator LLM generates diverse instruction candidates, and the Evaluator LLM, acting as a proxy for human workers, assesses their performance on a task, providing a fitness score. Our experiments show that this exploratory approach is effective for discovering high-quality instructions, even if the process does not show monotonic improvement. Using the best-performing instruction created by our method with gemma3, we achieved 5.4
The rapid adoption of electric vehicles (EVs) underscores the growing need for reliable battery health monitoring systems to ensure safety, optimize performance, and extend operational lifespan. In this paper, we introduce BATT2GRAPH, a novel approach that combines a temporal graph-based representation with a CNN-LSTM predictive model for accurate State-of-Health (SOH) estimation and anomaly detection in lithium-ion batteries (LIBs). On one hand, BATT2GRAPH constructs a temporal property graph using Neo4j to store enriched charge-discharge cycles with both raw time-series data and aggregated statistical indicators, enabling interpretable SOH monitoring and anomaly detection through expressive Cypher queries. On the other hand, a hybrid CNN-LSTM model is trained on this data to capture fine-grained variations and long-term degradation trends. Extensive experiments on the Stanford-MIT battery aging dataset demonstrate that our approach consistently outperforms existing baselines across multiple evaluation metrics.
Businesses are depending more and more on small adjustments to their eServices in the face of rapidly accelerated digital transformation in order to stay competitive and satisfy changing client demands. This study looks at how these small-scale developments affect customer experience (CX), pointing out both the advantages and disadvantages. Based on a thorough examination of the literature, the study investigates how usability, personalization, trust, and cultural variations influence how customers react to changes in digital services. The results highlight the importance of user-centered design, open communication, and agile innovation processes by indicating that even minor adjustments can have a big impact on CX. In addition to providing useful implications for matching innovation objectives with customer expectations, the paper adds to the current conversation on digital service management.
