Call for Papers 

Scope and Objectives

Building on 21 successful editions of the CoMoRea (Context and Activity Modeling & Recognition) workshop, CoMoRe-AI represents its next evolutionary step, including the latest advances in AI to redefine context modeling, reasoning, and recognition. The workshop aims to advance the state of the art in context processing and management, identifying key concepts, theories, and methodologies that enhance the design and implementation of context-aware systems. Nowadays, advanced AI methods such as generative and neuro-symbolic AI are reshaping how context is represented, fused, and interpreted. CoMoRe-AI will explore how these advancements improve context reasoning, decision-making, and activity recognition in sensor-rich environments. At the same time, the workshop will highlight key software engineering challenges, including the need for scalable, reusable, and privacy-aware context processing frameworks.

Context-aware applications increasingly act autonomously on behalf of users, adapting their behavior based on both explicit user interactions and the surrounding context. This context spans a diverse range of information, including user activities, environmental factors, and social interactions. While existing context models capture different aspects of this data, fundamental issues remain, such as uncertainty, ensuring interpretability, and managing heterogeneous data sources effectively. A more holistic approach is needed to incorporate multiple types of context information, model their interdependencies, and ensure high-quality, adaptive context awareness.

Human activity recognition (HAR) represents a cornerstone of context-awareness research, with ongoing challenges related to real-world deployments, such as continual learning, data scarcity, privacy concerns, and the discovery of novel activities. While HAR is a primary focus, CoMoRe-AI also welcomes research that addresses broader context-awareness challenges, including new AI techniques for context modeling, novel reasoning paradigms, and practical applications in domains such as healthcare, smart environments, and IoT.

Topics of Interest

We welcome contributions related (but not limited) to:

• Context modeling techniques and domain-specific context models

• AI-driven approaches for context modeling and reasoning

• Ontologies of activities and context

• Hybrid context models and advanced issues in context modeling, including issues of information quality, ambiguity, and provenance

• Context reasoning algorithms, their complexity and accuracy

• Generative AI models (e.g., LLMs) for context-awareness and adaptation

• Neuro-symbolic AI for context reasoning

• Explainable or interpretable context reasoning

• Foundation models for context-aware applications

• Multi-modal sensor fusion techniques for context-awareness

• Distributed context reasoning (e.g., edge AI, federated learning)

• Transfer learning and continual learning for context reasoning

• Human Activity Recognition and Human Behavior Modeling

• Applications of AI-powered context-aware methods in healthcare, smart cities, IoT, autonomous systems, and beyond