PREFAB applies preference learning grounded in the peak-end rule to let users annotate only key affective change segments while interpolating the rest, reducing workload and improving confidence in a 25-participant study.
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HySecTwin adds semantic modeling and hybrid rule-plus-fuzzy reasoning to digital twins so they can detect and explain cyber threats in cyber-physical systems faster than rule-only methods.
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PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-Annotation
PREFAB applies preference learning grounded in the peak-end rule to let users annotate only key affective change segments while interpolating the rest, reducing workload and improving confidence in a 25-participant study.
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HySecTwin: A Knowledge-Driven Digital Twin Framework Augmented with Hybrid Reasoning for Cyber-Physical Systems
HySecTwin adds semantic modeling and hybrid rule-plus-fuzzy reasoning to digital twins so they can detect and explain cyber threats in cyber-physical systems faster than rule-only methods.