Submitted to IEEE Journal on Selected Areas in Communications (JSAC); arXiv preprint
QoEReasoner is an end-to-end agentic system for explainable RAN diagnosis. Deterministic tools convert KPIs into evidence, a domain knowledge base constrains causal propagation, and a stateful planner coordinates anomaly detection and root localization.
IEEE Transactions on Networking
DK-Root combines weak operational rules with scarce expert labels for mobile-network QoE diagnosis. Contrastive pretraining mitigates rule noise, conditional diffusion supplies task-faithful augmentation, and expert-guided fine-tuning sharpens the final classifier.
IEEE ICC Workshops
SemiRoot addresses label scarcity through a two-stage semi-supervised framework. Rule-guided contrastive learning structures the representation space before a small set of expert labels calibrates the decision boundaries.
IEEE ACAI
S-PPR augments Personalized PageRank with semantic information for academic collaborator discovery, combining co-authorship frequency with title-embedding similarity in graph transitions and personalization.
Engineering Optimization
This work revises expected improvement to better balance exploration and exploitation in Kriging-based optimization. REGO uses sample-distribution information to avoid premature convergence while retaining efficient global search.
International Journal for Uncertainty Quantification
A sequential Morris framework reduces the cost of sample-based sensitivity analysis. Progressive Latin hypercube sampling preserves space-filling properties while an adaptive stopping rule avoids unnecessary model evaluations.
International Conference on Algorithms, Computing and Artificial Intelligence
This paper develops a data-driven polynomial-chaos Kriging method for systems with correlated inputs. Adaptive Lasso selects a compact polynomial basis before it is embedded in a Kriging model.
IEEE International Conference on System Reliability and Safety
A global sensitivity-analysis workflow is built on arbitrary polynomial chaos expansion for cases where input distributions are only partially known, extending Sobol analysis to more realistic data-limited settings.