Transformer models with user adapters extract behavioral signals from encrypted network traffic that correlate with stress, loneliness, and sleep issues via sparse features and GEE models, outperforming handcrafted features for within-person changes.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
FinLangNet applies dual-granularity prompting in a sequential model to heterogeneous financial data, reporting 6.3 pp KS improvement and 9.9% bad debt reduction in real-world deployment.
citing papers explorer
-
Learning Behavioral Signals from Encrypted Smartphone Network Traffic
Transformer models with user adapters extract behavioral signals from encrypted network traffic that correlate with stress, loneliness, and sleep issues via sparse features and GEE models, outperforming handcrafted features for within-person changes.
-
A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting
FinLangNet applies dual-granularity prompting in a sequential model to heterogeneous financial data, reporting 6.3 pp KS improvement and 9.9% bad debt reduction in real-world deployment.