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Applied Sciences, Vol. 16, Pages 9098: Pairwise Classification as a Unified Framework for Offline Reinforcement Learning and Large-Language-Model Alignment
13+ hour, 10+ min ago (496+ words) Offline reinforcement learning (offline RL) and large-language-model (LLM) alignment are typically studied as independent domains and each has developed its own pairwise comparison technique. Prior studies have validated pairwise classification exclusively within offline RL, leaving open the question of whether it…...
Applied Sciences, Vol. 16, Pages 9093: Security and Safety of Large Language Models—A Use Case for Extended Reality Environments
1+ day, 4+ hour ago (361+ words) This research investigates the security of large language models (LLMs) with the aim of identifying key security and safety threats, control measures, and governance considerations that are relevant to their trustworthy adoption in the Extended Reality (XR) domain. To achieve…...
Applied Sciences, Vol. 16, Pages 9003: A Knowledge Graph-Augmented Large Language Model Framework for Context-Aware Question-Answering and Intelligent Feedback Generation
4+ day, 2+ hour ago (533+ words) This study proposes EQAS (Empowered Question-Answering System), a hybrid framework designed to support context-aware question-answering and intelligent feedback generation in domain-specific knowledge environments. EQAS integrates fine-tuned transformer-based models, instruction-guided large language models, domain-specific knowledge graphs, and LangChain-based vector retrieval to…...
Applied Sciences, Vol. 16, Pages 9001: Agentic AI-Enabled Digital Twins for Intelligent Non-Destructive Testing of 3D-Printed Rehabilitation Equipment—A Narrative Review
4+ day, 3+ hour ago (688+ words) Digital twins (DTs) based on agent-based artificial intelligence (Agentic AI) provide a transformative framework for streamlining nondestructive testing (NDT) of 3D-printed rehabilitation equipment. This study applies a conceptual research methodology based on the integration and analysis of recent advances in…...
Applied Sciences, Vol. 16, Pages 8993: CDTS2: Causal Downstreamer with Causally Disentangled Trend and Seasonality Time-Series Representations
4+ day, 5+ hour ago (411+ words) Time-series representation learning decomposes signals into interpretable factors such as trend and seasonality, and disentangled representation learning assigns these factors to distinct latent dimensions, enhancing interpretability and forecasting accuracy. However, existing methods achieve only statistical disentanglement: an intervention on one…...
Applied Sciences, Vol. 16, Pages 8965: Feature Engineering for Queue Waiting Time Prediction: Temporal and Queue-State Reconstruction on the Theta Supercomputer
5+ day, 2+ hour ago (584+ words) Predicting queue waiting time for batch job schedulers in high-performance computing (HPC) systems is a critical research topic aimed at maximizing resource utilization efficiency and enhancing user experience. However, existing prediction approaches heavily rely on static job characteristics provided by…...