Point-of-Interest (POI) recommendation predicts users’ future check-ins based on their historical trajectories and plays a key role in location-based services (LBS). Traditional approaches such as collaborative filtering and matrix factorizatio...
Upstream processes strongly influence downstream analysis in sequential data-processing workflows, particularly in machine learning, where data quality directly affects model performance. Conventional statistical imputations often fail to capture non...
Background: Retrieval-augmented generation (RAG) aims to reduce hallucinations and outdated knowledge by grounding LLM outputs in retrieved evidence, but empirical results are scattered across tasks, systems, and metrics, limiting cumulative insight....
The reliable and early detection of promising research directions is of great practical importance, especially in cases of limited resources. It enables researchers, funding experts, and science authorities to focus their efforts effectively. Althoug...
In wireless communication, information security, and anti-interference technology, modulation recognition of frequency-hopping signals has always been a key technique. Its widespread application in satellite communications, military communications, a...
Stock trading faces significant challenges due to market volatility and the complexity of integrating diverse data sources, such as financial texts and numerical market data. This paper proposes an innovative automated trading system that integrates...
Shipping containers are vital to the transportation industry due to their cost-effectiveness and compatibility with intermodal systems. With the significant increase in container usage since the mid-20th century, manual tracking at port terminals has...
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...
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