This study explores the acceptance and perceived effectiveness of AI-powered Augmentative and Alternative Communication (ACC) systems among individuals with speech impairments. Despite increasing advances in inclusive technology, limited research has addressed how people with communication disabilities evaluate such tools regarding usability, privacy, and behavioral intention. An interdisciplinary project involving information technology, rehabilitation medicine, and social work was conducted with 39 participants in Taiwan. Each participant completed pre-assessments, voice recordings to build personal language models, and post-assessments using the Technology Acceptance Model (TAM) framework. Results showed high overall satisfaction and willingness to use the system, particularly among younger and male participants. Although no statistically significant differences in TAM variables were found between subprojects or education levels, male users demonstrated higher behavioral intention, and younger participants exhibited more favorable attitudes toward AI tools. Quality of life (QOL) scores revealed the highest satisfaction in the environmental domain and the lowest in health and life satisfaction. These findings provide a foundational understanding of user experience and acceptance, guiding the future design and deployment of AI-based ACC technologies.
Embedded systems often accumulate complex, monolithic code, making identifying and reusing functionality across projects difficult. This paper presents an automated approach for extracting reusable software features from embedded C code using six pre-trained large language models (CodeBERT, GraphCodeBERT, CodeT5, CodeGPT-small, PLBART, and UniXcoder). The system embeds code-level features (e.g., functions and global variables) and textual artifacts (e.g. comments, control-flow conditions, and README entries) into a shared semantic vector space. Cosine similarity matches each feature with its most relevant textual artifact, and a coherence score is assigned to capture the strongest alignment across models. High-confidence matches are then clustered using unsupervised learning to reveal logical subsystems within the codebase. We evaluate the method on a heating system controller by manually curating 14 feature-requirement description pairs. Each feature is a functional unit selected from the code, and each description summarizes its intended behavior. These pairs serve as the benchmark for assessing extraction performance. Our method successfully traced features with 86% recall, using conservative thresholding that accepts only semantically strong matches. Clustering based on coherence scores produced interpretable groupings corresponding to logical subsystems such as heating control, safety monitoring, and user interface. Compared to single-model baselines (with recall as low as 22%), the multimodel ensemble method significantly improved coverage by leveraging the semantic diversity of different LLM models. These results demonstrate the feasibility of combining diverse pretrained embeddings with multiple textual sources to support feature extraction and modular reuse in embedded software.
Scientific literature contains a vast quantity of validated but fragmented knowledge. Literature-based discovery (LBD) aims to connect this dispersed information to generate new, valuable hypotheses. This paper presents a method for generating new scientific hypotheses by performing symbolic inference over a network of semantic predications-machine-readable scientific claims that are enriched with provenance and contextual metadata. The method constructs inference chains by linking multiple semantic predications from independently published sources. It accounts for specific properties of the predications to improve reliability, including a status of knowledge metric that we define and compute. The approach favors more robust and informative connections when generating hypotheses. To evaluate the quality of the hypotheses produced, we apply a temporal slicing methodology that assesses whether predicted relationships correspond to scientific claims published in the future. This approach enables direct comparison with established LBD methods. In a biomedical use case, our system achieves a Mean Average Precision (MAP) of 0.27 and a Precision@15 of 0.45, indicating the feasibility of interpretable, reusable scientific discovery through symbolic reasoning over structured knowledge.
Large Language Models (LLMs) are increasingly used for decision support in high-stakes domains such as healthcare and law, yet their deployment is limited by output instability and lack of transparency, especially when labeled data is scarce in real-world scenarios. To address these challenges, we propose Med-TAMARA, a trust-aware, reflective multi-agent framework for classifying medical, ethical, and legal risks in patient-doctor conversations. Our system combines self-consistency filtering and few-shot chain-of-thought (CoT) prompting to eliminate unstable predictions, agentic peer review to refine model judgments, trust-weighted aggregation to account for agent reliability, and a confidence scoring mechanism to flag uncertain cases for human oversight. We conducted a proof-of-concept evaluation on the English subset of the NTCIR-18 MedNLP Chat dataset, suggesting that Med-TAMARA may outperform strong baselines in preliminary experiments across standard evaluation metrics. Although our dataset is limited in size, these initial results indicate the potential of the framework as a foundation for a more reliable LLM-assisted risk assessment. The source code for MedTAMARA will be released publicly after further optimization and testing.
Accurate damage assessment following a natural disaster is crucial for guiding effective response and recovery efforts. In this study, we present a deep learning framework that employs multi-temporal fusion of pre- and post-disaster remote sensing imagery, using a shared-weight ResNet50 architecture as the model backbone. To improve both predictive performance and physical realism, we incorporated a physics-informed loss function that integrates texture energy derived from a GrayLevel Co-Occurrence Matrix (GLCM). This additional loss term penalizes outputs that fail to exhibit expected rubble-like texture patterns, thereby encouraging more physically consistent predictions. Model performance was assessed using several evaluation metrics, including confusion matrices, average accuracy, F1 scores, xView2 scores, and multi-class ROC curves. Experimental results show that the inclusion of the GLCM-based physicsinformed loss resulted in measurable improvements in both damage detection and classification accuracy-achieving a 7% gain in average accuracy and a 8.57% increase in xView2 score compared to baseline models without the physics constraint.
