Description
Book Synopsis.- Against the Clock: Lessons Learned by Applying Temporal Planning in
Practice.
.- A Novel Approach for Leveraging Agent-based Experts on Large
Language Models to Enable Data Sharing among Heterogeneous IoT
Devices in Agriculture.
.- An Extensive Empirical Analysis of Macro-Actions for Numeric Planning.
.- Feature selection on contextual embedding pushing the sparseness.
.- Neuro-symbolic Integration for Open Set Recognition in Network
Intrusion Detection.
.- MM-IGLU-IT: Multi-Modal Interactive Grounded Language
Understanding in Italian.
.- IDADA: A Blended Inductive-Deductive Approach for Data Augmentation .
.- HaWANet: Road Scene Understanding with Multi-modal Sensor Data
using Height-Width-driven Attention Network.
.- Hybrid Classification of European Legislation using Sustainable
Development Goals.
.- Supporting Decision-Making for City Management through Automated
Planning and Execution.
.- NutriWell: an Explainable Ontology-Based FoodAI Service for
Nutrition and Health Management.
.- Regular Clocks for Temporal Task Specifications in Reinforcement
Learning.
.- A Real-Time Support with Haptic Feedback for Safer Driving using
Monocular Camera.
.- Relating explanations with the inductive biases of Deep Graph Networks.
.- ntegrating Temporal Planning and Knowledge Representation to
Generate Personalized Touristic Itineraries.
.- ASR Systems Under Acoustic Challenges: A Multilingual Study.
.- Automating Resume Analysis: Knowledge Graphs via Prompt Engineering.
.- Combined Text-Visual Attention Models for Robot Task Learning and
Execution.
.- ICE: An Evaluation Metric to Assess Symbolic Knowledge Quality.
.- Hierarchical Knowledge Extraction from Opaque Machine Learning
Predictors.
.- On Different Symbolic Music Representations for Algorithmic
Composition Approaches based on Neural Sequence Models.
.- DR-Minerva: a Multimodal Language Model based on Minerva for
Diagnostic Information Retrieval .
.- REPAIR platform: Robot-aidEd PersonAlIzed Rehabilitation.
.- Integrating classical planners with GPT-based Planning Policies.
.- Probabilistic Traces in Declarative Process Mining.
