This study critically examines the potential and limitations of AI‑based counseling applications. A review of existing research shows that the structured framework of cognitive behavioral therapy (CBT) aligns well with app‑based interaction and supports short‑term improvements such as reduced anxiety and depression, enhanced self‑understanding, and increased accessibility. At the same time, significant challenges remain, including the absence of interpersonal relational processes, ethical and data‑governance concerns, and limited evidence regarding long‑term effectiveness. By organizing these strengths and limitations, the study identifies key issues that must be addressed for the practical use of AI‑supported counseling in clinical and non‑clinical settings and outlines directions for future development.

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Recommendation systems for tourism typically focus on individual preferences, overlooking cultural contexts. We propose a culturally-aware system integrating gaze tracking with JNTO (Japan National Tourism Organization) data analysis across nine countries. Key findings: dramatic individualization (China 43.8%→96.0%) and regional specialization (Korean hot springs: 22-33% nationally vs. 68.3% in Kagoshima). Our three-layer framework combines gaze-based extraction with hierarchical cultural adaptation. Evaluation metrics balance cultural relevance (60-75%) with discovery (20-35%).

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In response to these challenges, this paper presents the results of a trial educational program that particularly utilized generative AI, with the view that students studying in the social sciences of business administration need basic education about AI. It also discusses new challenges that emerged from the trial. This research has been using generative AI to help students find research topics. However, when the exercises first began, the students seemed to view generative AI as an extension of a search engine and did not necessarily seem to be using it effectively. It was discovered that the reason for this was that they were unable to enter appropriate prompts for the topics they were trying to explore. To solve this problem, we first explained to the students what prompts are from a prompt engineering perspective and attempted to help them understand them. However, while there was some progress compared to the initial exercise results, no significant results were seen. Analyzing the cause of this, we realized that the prompts did not include a specific perspective.

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In recent years, there have been many news reports about quantum computers, some of which suggest that they are nearing the practical stage, such as Google researchers achieving quantum supremacy and IBM’s development of a quantum processor with over 1,000 qubits. In the field of information security, it is believed that if order calculation algorithms based on Shor’s algorithm were put into practical use on quantum computers with a sufficient number of bits, many public key cryptosystems would be at risk. In response to such a situation, quantum communication cryptography systems that utilize the physical properties of quantum have been gaining attention, but Post Quantum Computer Cryptography (PQCC) based on current computer systems has also been proposed. NIST launched a public call for proposals related to PQC (or PQCC) with a deadline of November 2017, and approximately 69 algorithms and systems were proposed in several fields. As of March 2025, five algorithms remain in the fourth round of selection. In this paper, we propose a method using self-dual code based on the idea of one of these, the Classical McEliece algorithm, and provide critical issues in implementing for its implementation.

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We study automatic simplification of Japanese conversational utterances (single-turn spoken-style inputs) into easier Japanese intended to support older adults. Because parallel corpora for spoken-style simplification are scarce, we adopt a two-stage optimization: supervised fine-tuning (SFT) to obtain an initial policy, followed by reinforcement learning with Proximal Policy Optimization (PPO) [2] to better match conversational input distributions. For PPO, we use conversational utterances extracted from the Nagoya University Conversation Corpus (NUCC) [10] and additionally include “difficultified” conversational variants to strengthen robustness to conversational difficulty factors. PPO is trained with a composite reward that balances meaning preservation and naturalness with lexical/syntactic simplicity and penalties for conversational difficulty factors. We evaluate outputs using paired comparisons between SFT and PPO on the same prompts. On valid paired outputs (N = 964), PPO tends to shorten outputs (median Δlength ratio = −0.041; median Δout len = −4 characters), while meaning-related scores decrease on average (median ΔBERTScore Precision = −0.004, Recall = −0.015) [3]. We additionally analyze BERTScore changes by PPO/SFT output-length-ratio bins and observe that Precision can decrease even when PPO outputs are shorter, suggesting sensitivity to surface-form changes. For robustness, we analyze invalid outputs on N = 1000 paired generations; PPO reduces the invalid-output rate from 3.5% to 2.1%. An exact McNemar test indicates the reduction is statistically significant (two-sided p = 5.19×10−4; one-sided improvement p = 2.59 × 10−4).

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This study theoretically reexamines the role of AI-driven accounting information systems (AIS) within ERP environments. Traditionally, AIS has been regarded as suitable for automation due to its numerical processing and rule-based characteristics, leading to expectations that AI could automate accounting judgments. However, accounting judgment is inherently contextual and discretionary, as it is influenced by institutional constraints, managerial decision-making, and relationships with stakeholders. Based on this characteristic, this study reconceptualizes AI-driven AIS not as mechanisms that automate accounting judgment but as information infrastructures that structure judgment conditions and support decision-making. In particular, the study focuses on foundation models (FM) and multimodal foundation models (MFM) and examines their potential to analyze both structured ERP data and unstructured data such as documents and images in an integrated manner. The findings suggest that AI does not replace accounting judgment but can function as a complementary analytical layer that visualizes the decision environment through comparative analysis and deviation detection. At the same time, AI-driven AIS introduces new challenges related to explainability, training data governance, and system governance. This study contributes theoretically by repositioning AI-driven AIS as a governance-oriented decision-support infrastructure.

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Rubrics are used widely to clarify learning goals an d to support feedback. However, their motivational benefits might depend on students’ appraisals of rubric value, on their anticipated difficulties in the absence of rubrics, and on how feedback is enacted. Building on results obtained from our earlier study, we analyzed self reported data from computing related courses at a women ’s junior college in Japan N = 138 students with prior rubric use experience) to examine perceived motivational benefits associated with rubric use . Several linear regression models with robust standard errors were estimated to test associations with perceived usefulness of rubrics, anticipate d difficulties in the absence of rubrics (two factor s core dimensions), assignment return modality (in person vs. Microsoft Teams Microsoft Corp.Corp.))), and age. Across models, perceived usefulness was the most consistent positive predictor of perceived motivational benefits . Return via Microsoft Teams Microsoft Corp .) yielded a negative coefficient, but t he association attenuated after adjustment for perceived usefulness : it was not significant in the final model. Neither the anticipate d difficulty dimensions nor age improved explanatory power significantly . Overall, the findings suggest that students’ subjective value appraisals play a central role in motivational experiences with rubrics.

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Step-frequency radar represents a potential alternative sensor modality for non-invasive heart monitoring, with the aim of developing low-cost and easily portable technology for continuous monitoring of heart activity by sensing the motion of the heart surface as a radar-reflective target. This paper reports on an attempt to account for the deviation of the waveform of the traveling signal from the simple plane-wave approximation typically used for far-field radar, due to the limited distance between the radar source of a chest-mounted sensor and the internal target of the heart surface. Accordingly, this study applied a modification to the discrete Fourier transform converting received frequency-domain data into travel-time-domain data to be used in assessing the distance of reflective targets from the radar via modification of the approximate representation of the radar signal from a plane wave to a spherical wave.

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