Main Track Schedule
The conference will take place from 25th to 28th November 2024. Below you find schedule of the Main Track sessions for each day. See also the Schedule Overview, Workshops Schedule, Invited Talks, and Tutorials pages.
Tuesday 26th November
Session 1 – Machine Learning and Deep Learning
10:30 – 12:30
| Hybrid Classification of European Legislation using Sustainable Development Goals Michele Corazza, Franco M. T. Gatti, Salvatore Sapienza and Monica Palmirani |
| On Different Symbolic Music Representations for Algorithmic Composition Approaches based on Neural Sequence Models Felix Schön and Hans Tompits |
| Regular Clocks for Temporal Task Specifications in Reinforcement Learning Giuseppe De Giacomo, Marco Favorito and Fabio Patrizi |
| Combined Text-Visual Attention Models for Robot Task Learning and Execution Giuseppe Rauso, Riccardo Caccavale and Alberto Finzi |
| HaWANet: Road Scene Understanding with Multi-modal Sensor Data using Height-Width-driven Attention Network Soumick Chatterjee, Jiahua Xu, Adarsh Kuzhipathalil and Andreas Nürnberger |
| A Real-Time Support with Haptic Feedback for Safer Driving using Monocular Camera Giorgio De Magistris, Lorenzo Guercio, Francesco Starna, Samuele Russo, Natalia Kryvinska and Christian Napoli |
| IDADA: A Blended Inductive-Deductive Approach for Data Augmentation Pierangela Bruno, Francesco Calimeri, Francesca Filice, Cinzia Marte and Simona Perri |
| Probabilistic Traces in Declarative Process Mining Michela Vespa, Elena Bellodi, Federico Chesani, Daniela Loreti, Paola Mello, Evelina Lamma, Anna Ciampolini, Marco Gavanelli and Riccardo Zese |
Session 2 – NLP and AI Applications
13:30 – 15:30
| MM-IGLU-IT: Multi-Modal Interactive Grounded Language Understanding in Italian Federico Borazio, Claudiu Daniel Hromei, Elisa Passone, Danilo Croce and Roberto Basili |
| Evaluating ASR Performance Under Acoustic Challenges: A Multilingual Analysis Sergei Katkov, Antonio Liotta and Alessandro Vietti |
| Automating Resume Analysis: Knowledge Graphs via Prompt Engineering Giorgio Lazzarinetti, Sara Manzoni and Italo Zoppis |
| Feature selection on contextual embedding pushing the sparseness Stefano Bistarelli and Marco Cuccarini |
| DR-Minerva: a Multimodal Language Model based on Minerva for Diagnostic Information Retrieval Irene Siragusa, Salvatore Contino and Roberto Pirrone |
| A Novel Approach for Leveraging Agent-based Experts on Large Language Models to Enable Data Sharing among Heterogeneous IoT Devices in Agriculture Nur Arifin Akbar, Domenico Tegolo and Biagio Lenzitti |
| REPAIR platform: Robot-aidEd PersonAlIzed Rehabilitation Christian Tamantini, Alessandro Umbrico and Andrea Orlandini |
| Neuro-symbolic Integration for Open Set Recognition in Network Intrusion Detection Alice Bizzarri, Chung-En Yu, Brian Jalaian, Fabrizio Riguzzi and Nathaniel D. Bastian |
Thursday 28th November
Planning and Explainable AI
10:30 – 12:30
| An Extensive Empirical Analysis of Macro-Actions for Numeric Planning Diaeddin Alarnaouti, Francesco Percassi and Mauro Vallati |
| Integrating Temporal Planning and Knowledge Representation to Generate Personalized Touristic Itineraries Silvia Gola, Andrea Orlandini and Alessandro Umbrico |
| Integrating classical planners with GPT-based Planning Policies Massimiliano Tummolo, Nicholas Rossetti, Alfonso Emilio Gerevini, Matteo Olivato, Luca Putelli and Ivan Serina |
| Supporting Decision-Making for City Management through Automated Planning and Execution Riccardo De Benedictis, Gloria Beraldo, Amedeo Cesta and Gabriella Cortellessa |
| ICE: An Evaluation Metric to Assess Symbolic Knowledge Quality Federico Sabbatini and Roberta Calegari |
| Hierarchical Knowledge Extraction from Opaque Machine Learning Predictors Federico Sabbatini and Roberta Calegari |
| NutriWell: an Explainable Ontology-Based FoodAI Service for Nutrition and Health Management Berardina De Carolis, Davide Lofrese, Davide Di Pierro and Stefano Ferilli |
| Relating explanations with the inductive biases of Deep Graph Networks Michele Fontanesi, Alessio Micheli and Marco Podda |