Abstract User interfaces look different across web, desktop, and mobile platforms — not just in layout, but in how buttons, icons, and text appear. This makes it hard for deep learning models trained on one platform to accurately detect UI components on another. In this paper, we evaluate the cross-domain generalization of three modern…
Abstract This survey reviews how large language models (LLMs) are transforming synthetic training data generation in both natural language and code domains. By producing artificial but task-relevant examples, these models can significantly augment or even substitute for real-world datasets, particularly in scenarios where labeled data is scarce, expensive, or sensitive. This paper surveys recent…
Abstract The complex software systems developed nowadays require assessing their quality and proneness to errors. Reducing code complexity is a never-ending problem, especially in today's fast pace of software systems development. Therefore, the industry needs to find a method to determine the qualities of a software system, the degree of difficulty in developing new…
Abstract Moral stories are a time-tested vehicle for transmitting values, yet modern NLP lacks a large, structured corpus that couples coherent narratives with explicit ethical lessons. We close this gap with TF1-EN-3M, the first open dataset of three million English-language fables generated exclusively by instruction-tuned models no larger than 8B parameters. Each story follows…
Abstract Literary translation has recently gained attention as a distinct and complex task in machine translation research. However, the translation by small open models remains an open problem. We contribute to this ongoing research by introducing TINYFABULIST TRANSLATION FRAMEWORK (TF2), a unified framework for dataset creation, fine tuning, and evaluation in English-Romanian literary translations,…
Abstract Introduction Magnetic resonance imaging (MRI) is conventionally used for the detection and diagnosis of multiple sclerosis (MS), often complemented by lumbar puncture—a highly invasive method—to validate the diagnosis. Additionally, MRI is periodically repeated to monitor disease progression and treatment efficacy. Recent research has focused on the application of artificial intelligence (AI) and radiomics…
Abstract In the past few years, path planning and scheduling became a high-impact research topic due to their real-world applications such as transportation, manufacturing and robotics. This paper focuses on the Multi-robot Path Planning (MPP) problem, which consists of planning the route for a set of robots in a given static environment. The main…
Abstract Extracting essential information including the topological structure of rail-tracks, the position of switches and their current state can increase safety by reducing human error, while also boosting the efficiency of rail transportation. Despite the impressive advancements in the field of autonomous driving, computer vision approaches in the rail domain are still a small…
Abstract Electric vehicles (EVs) are increasingly utilized in logistics and distribution to expedite achieving carbon peaking and neutrality goals, drawing considerable attention to the Electric Vehicle Routing Problem (EVRP). This study investigates the Multi-Depot Half-Open Time-Dependent Electric Vehicle Routing Problem (MDHOTDEVRP) and aims to improve coordination and distribution efficiency among logistics depots. This problem…
Abstract Deep learning became an ubiquitous paradigm due to its extraordinary effectiveness and applicability in numerous domains. However, the approach suffers from the high demand for data required to achieve the potential of this type of model. An ever-increasing subfield of Artificial Intelligence, Image Synthesis, aims to address this limitation through the design of…
