The first systematic review of routine computing synthesizes literature into a taxonomy of temporal, behavioral, cognitive, and variability aspects, outlining applications in health, accessibility, and adaptive support along with persistent challenges.
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LLMs produce stable cognitive distortion labels that improve downstream model performance, paired with a kappa-based framework for dataset-agnostic evaluation in subjective NLP tasks.
LLM-enhanced multi-view gated attention MIL framework using ELB decomposition improves cognitive distortion classification on Korean and English therapy datasets.
citing papers explorer
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Routine Computing: A Systematic Review of Sensing Daily Life Dimensions Towards Human-Centered Goals
The first systematic review of routine computing synthesizes literature into a taxonomy of temporal, behavioral, cognitive, and variability aspects, outlining applications in health, accessibility, and adaptive support along with persistent challenges.
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Towards Consistent Detection of Cognitive Distortions: LLM-Based Annotation and Dataset-Agnostic Evaluation
LLMs produce stable cognitive distortion labels that improve downstream model performance, paired with a kappa-based framework for dataset-agnostic evaluation in subjective NLP tasks.
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Multi-View Attention Multiple-Instance Learning Enhanced by LLM Reasoning for Cognitive Distortion Detection
LLM-enhanced multi-view gated attention MIL framework using ELB decomposition improves cognitive distortion classification on Korean and English therapy datasets.