Ettore Ritacco | Giuseppe Manco

Latest

Algorithmic Drift: A simulation framework to study the effects of recommender systems on user preferences
Flexible Generation of Preference Data for Recommendation Analysis
Large language models in the software supply chain: challenges and opportunities
Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences
Balanced Quality Score: Measuring Popularity Debiasing in Recommendation
CAP: Detecting Unauthorized Data Usage in Generative Models via Prompt Generation
Dawn of LLM4Cyber: Current Solutions, Challenges, and New Perspectives in Harnessing LLMs for Cybersecurity
GenRec: A Flexible Data Generator for Recommendations
Robust anomaly detection via adversarial counterfactual generation
Siamese Networks for Unsupervised Failure Detection in Smart Industry
Audio-based anomaly detection on edge devices via self-supervision and spectral analysis
Deep Learning/PUF-based Item Identification for Supply Chain Management in a Distributed Ledger Framework
Fighting Misinformation, Radicalization and Bias in Social Media
Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels
Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels
Neuro-Symbolic techniques for Predictive Maintenance
Siamese Network for Fake Item Detection
A Loosely-coupled Neural-symbolic approach to Compliance of Electric Panels
Generating Synthetic Discrete Datasets with Machine Learning
Machine learning methods for generating high dimensional discrete datasets
A Deep Learning Approach for Unsupervised Failure Detection in Smart Industry (Discussion Paper)
A Factorization Approach for Survival Analysis on Diffusion Networks
Adversarial Regularized Reconstruction for Anomaly Detection and Generation
Unbiasing Collaborative Filtering for Popularity-Aware Recommendation (Discussion Paper)
A Deep Learning Approach for Detecting Security Attacks on Blockchain
Deep Autoencoder Ensembles for Anomaly Detection on Blockchain
Exploiting Temporal Convolution for Activity Prediction in Process Analytics
Using an autoencoder in the design of an anomaly detector for smart manufacturing
Deep Learning
Deep Sequential Modeling for Recommendation
Knowledge Discovery in Databases
Network Models
Network Topology
Predicting Temporal Activation Patterns via Recurrent Neural Networks
Sequential Variational Autoencoders for Collaborative Filtering
Temporal Recurrent Activation Networks
Fault detection and explanation through big data analysis on sensor streams
Survival Factorization on Diffusion Networks
Probabilistic Approaches to Recommendations
Hierarchical clustering of XML documents focused on structural components
Probabilistic topic models for sequence data
Balancing Prediction and Recommendation Accuracy: Hierarchical Latent Factors for Preference Data
Hierarchical Latent Factors for Preference Data
Probabilistic Sequence Modeling for Recommender Systems
A Block Coclustering Model for Pattern Discovering in Users' Preference Data
A Probabilistic Hierarchical Approach for Pattern Discovery in Collaborative Filtering Data
A Probabilistic Hierarchical Approach for Pattern Discovery in Collaborative Filtering Data (Extended Abstract)
Characterizing Relationships through Co-clustering - A Probabilistic Approach
From global to local and viceversa: uses of associative rule learning for classification in imprecise environments
Fast and Effective Hierarchical Clustering of XML Documents by Structure
A Hierarchical Rule-based Framework for Accurate Classification in Imprecise Domains
Rule Learning with Probabilistic Smoothing
DAEDALUS: A knowledge discovery analysis framework for movement data
The DAEDALUS framework: progressive querying and mining of movement data