15th International Conference on Brain Inspired Cognitive Systems - BICS 2025

BICS 2025 aims to provide a high-level international forum for scientists, engineers, and educators to present the state of the art of brain inspired cognitive systems research and applications in diverse fields. The conference will feature plenary lectures given by world renowned scholars, regular sessions with broad coverage, and some special sessions focusing on popular and timely topics. All registered and presented BICS papers will be published in Springer LNAl/LNCS proceedings and indexed by El Compendex. Selected papers will be published in special issues of SCI journals, such as Cognitive Computation, Neurocomputing et al.

1st International Workshop on Realistic Robustness and Generalization in Data Mining (RRoG-DM) - ICDM 2025

Robustness and generalization are ubiquitous in modern machine learning and data mining. Challenges arise in terms of robustness when models are faced with unforeseen corrupted data—be these data subject to malicious attacks, termed adversarial data, or those that are non-maliciously compromised, called commonly corrupted data. Simultaneously, there is a pressing question about generalization on how models will fare when applied to the future, standard data that may differ or evolve due to shifts in data, domain and/or categories. The concepts of robustness and generalization, although widely discussed, suffer from ambiguous definitions within the research community and can take on different meanings depending on the context. Some studies even suggest that robustness to adversarial data and the ability to generalize to unseen standard data could be inherently conflicting objectives. Adding to this, the practical measurement of robustness and generalization 'budget' is meant to capture the scope of realistic, noisy disturbances and shifts expected in actual environments. However, the research focus has largely been on exploring synthetic budgets, which does not reflect the real-world complexity (e.g. in autonomous driving or electric arc anomaly detection). This workshop aims to bring researchers and professionals from data mining and machine learning, spotlighting recent endeavors that rise to meet these challenges.

M3RD: Multi-Modal Mathematical Reasoning in Documents - ICDAR 2025

The AI community has been placing significant emphasis on mathematical reasoning as a means to explore the ability of intelligence in large language models (LLMs) and multi-modal large language models (MLLMs), such as OpenAI O1 and DeepSeek R1. As a common information medium, documents consist of text, images, tables, diagrams, charts, mathematical notations, etc. By leveraging multiple elements in documents, multi-modal mathematical reasoning focuses on enabling machines to solve, interpret, and reason about mathematical problems. It combines image and text analysis, symbolic manipulation, numerical computation, and logical inference to address challenges ranging from basic arithmetic to advanced problem-solving in algebra, calculus, and beyond, thus forming an area of growing importance in document intelligence.