9 papers
HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data
Fang Wang, Paolo Ceravolo, Ernesto Damiani
We propose HGCN(O), a self-tuning toolkit using Graph Convolutional Network (GCN) models for event sequence prediction. Featuring four GCN architectures (O-GCN, T-GCN, TP-GCN, TE-G…
Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring
Fang Wang, Ernesto Damiani
Structurally constrained event sequence generation remains challenging because generated paths must preserve transition feasibility, temporal order, termination, and attribute cons…
LLM-Enabled NWDAF: A Step Toward AI-Native 6G Network Intelligence
Henok Daniel, Omar Alhussein, Cheng Li +2
The Network Data Analytics Function (NWDAF) is central to enabling zero-touch network management in fifth-generation (5G) networks by supporting real-time analytics and closed-loop…
RedSage: A Cybersecurity Generalist LLM
Naufal Suryanto, Muzammal Naseer, Pengfei Li +5
Cybersecurity operations demand assistant LLMs that support diverse workflows without exposing sensitive data. Existing solutions either rely on proprietary APIs with privacy risks…
Auto-ML Graph Neural Network Hypermodels for Outcome Prediction in Event-Sequence Data
Fang Wang, Lance Kosca, Adrienne Kosca +2
This paper introduces HGNN(O), an AutoML GNN hypermodel framework for outcome prediction on event-sequence data. Building on our earlier work on graph convolutional network hypermo…
Leveraging Duration Pseudo-Embeddings in Multilevel LSTM and GCN Hypermodels for Outcome-Oriented PPM
Fang Wang, Paolo Ceravolo, Ernesto Damiani
Existing deep learning models for Predictive Process Monitoring (PPM) struggle with temporal irregularities, particularly stochastic event durations and overlapping timestamps, lim…