6 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…
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…
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…
Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring
Fang Wang, Paolo Ceravolo, Ernesto Damiani
Predictive Business Process Monitoring (PBPM) aims to forecast future outcomes of ongoing business processes. However, existing methods often lack flexibility to handle real-world…
HH4AI: A methodological Framework for AI Human Rights impact assessment under the EUAI ACT
Paolo Ceravolo, Ernesto Damiani, Maria Elisa D'Amico +10
This paper introduces the HH4AI Methodology, a structured approach to assessing the impact of AI systems on human rights, focusing on compliance with the EU AI Act and addressing t…
CoSMo: a Framework to Instantiate Conditioned Process Simulation Models
Rafael S. Oyamada, Gabriel M. Tavares, Sylvio Barbon Junior +1
Process simulation is gaining attention for its ability to assess potential performance improvements and risks associated with business process changes. The existing literature pre…