December 2025 - 28 articles
This work represents the natural continuation of the development of the cognitive architecture developed and named Sophimatics, organically integrating the spatio-temporal processing mechanisms of the Super Time Cognitive Neural Network (STCNN) with...
This paper presents an in-production Rhetorical Role Labeling (RRL) classifier developed for Hungarian judicial decisions. RRL is a sequential classification problem in Natural Language Processing, aiming to assign functional roles (such as facts, ar...
Federated learning has gained popularity in recent years to enhance IoT security because the model allows decentralized devices to collaboratively learn a shared model without exchanging raw data. Despite its privacy advantages, federated learning is...
In the context of remanufacturing, particularly mobile device refurbishing, effective operator training is crucial for accurate defect identification and process inspection efficiency. This study examines the application of Natural Language Processin...
Massive Open Online Courses (MOOCs) have gained increasing popularity in recent years, highlighting the growing importance of effective course recommendation systems (CRS). However, the performance of existing CRS methods is often limited by data spa...
Depression represents a critical global mental health challenge, with social media offering unprecedented opportunities for early detection through computational analysis. We propose a novel BERT–CNN–BiLSTM architecture with attention mec...
As Large Language Models (LLMs) are core components in Retrieval-Augmented Generation (RAG) systems for knowledge-intensive tasks, concerns regarding hallucinations, redundancy, and unverifiable outputs have intensified, particularly in high-stakes d...
As a vital carrier of human intangible culture, dance plays an important role in cultural transmission through digital generation. However, existing dance generation methods rely heavily on high-precision motion capture and manually annotated dataset...
The increasing scale of modern datasets has created a significant computational bottleneck for traditional scientific and statistical algorithms. To address this problem, the current paper describes and validates a high-performance method based on ad...
Pooling strategies are fundamental to convolutional neural networks, shaping the trade-off between accuracy, robustness to spatial variations, and computational efficiency in modern visual recognition systems. In this paper, we present and validate E...
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