Semantic segmentation of the glottal area from high-speed videoendoscopic (HSV) sequences presents a critical challenge in laryngeal imaging. The field faces a significant shortage of high-quality, annotated datasets for training robust segmentation models. Therefore, the development of automatic segmentation technologies is hindered by this limitation and the creation of diagnostic tools such as Facilitative Playbacks…
Graph Neural Networks have emerged as a transformative force in many real-life applications, from corporate finance risk management to local traffic prediction. Thereby, there is no gainsaying that much research has been centered around GNNs for a long time. A significant limitation of the current study, however, is its data dependency—with a focus on supervised…
Neural machine translation (NMT) is a sophisticated branch of natural language processing that automates text conversion between languages using machine learning models. Over the years, it has become an indispensable tool for global communication, with applications spanning diverse areas such as technical document translation and digital content localization. Despite its advancements in translating straightforward text,…
Natural Language Generation (NLG) is a domain of artificial intelligence that seeks to enable machines to produce human-like text. By leveraging advancements in deep learning, researchers aim to develop systems capable of generating contextually relevant and coherent responses. Applications of this technology span diverse areas, including automated customer support, creative writing, and real-time language translation,…
FineWeb2 significantly advances multilingual pretraining datasets, covering over 1000 languages with high-quality data. The dataset uses approximately 8 terabytes of compressed text data and contains nearly 3 trillion words, sourced from 96 CommonCrawl snapshots between 2013 and 2024. Processed using the datatrove library, FineWeb2 demonstrates superior performance compared to established datasets like CC-100, mC4, CulturaX,…
Multimodal reasoning—the ability to process and integrate information from diverse data sources such as text, images, and video—remains a demanding area of research in artificial intelligence (AI). Despite advancements, many models still struggle with contextually accurate and efficient cross-modal understanding. These challenges often stem from limitations in scale, narrowly focused datasets, and restricted access to…
The advancement of artificial intelligence hinges on the availability and quality of training data, particularly as multimodal foundation models grow in prominence. These models rely on diverse datasets spanning text, speech, and video to enable language processing, speech recognition, and video content generation tasks. However, the lack of transparency regarding dataset origins and attributes creates…
Artificial Intelligence (AI) has been making significant advances with an exponentially growing trajectory, incorporating vast amounts of data and building more complex Large Language Models (LLMs). Training these LLMs requires more computational power and resources for memory allocation, power usage, and hardware. Optimizing memory utilization for different types and configurations of GPUs is complex. Deciding…
Graphical User Interfaces (GUIs) play a fundamental role in human-computer interaction, providing the medium through which users accomplish tasks across web, desktop, and mobile platforms. Automation in this field is transformative, potentially drastically improving productivity and enabling seamless task execution without requiring manual intervention. Autonomous agents capable of understanding and interacting with GUIs could revolutionize…
Multi-agent systems (MAS) are pivotal in artificial intelligence, enabling multiple agents to work collaboratively to solve intricate tasks. These systems are designed to function in dynamic and unpredictable environments, addressing data analysis, process automation, and decision-making tasks. By incorporating advanced frameworks and leveraging large language models (LLMs), MAS has increased efficiency and adaptability for various…