Ústav počítačové grafiky a multimédií

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A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers
(MDPI, 2023-05-22) Zuluaga-Gomez, Juan; Prasad, Amrutha; Nigmatulina, Iuliia; Motlíček, Petr; Kleinert, Matthias
In this paper we propose a novel virtual simulation-pilot engine for speeding up air traffic controller (ATCo) training by integrating different state-of-the-art artificial intelligence (AI)-based tools. The virtual simulation-pilot engine receives spoken communications from ATCo trainees, and it performs automatic speech recognition and understanding. Thus, it goes beyond only transcribing the communication and can also understand its meaning. The output is subsequently sent to a response generator system, which resembles the spoken read-back that pilots give to the ATCo trainees. The overall pipeline is composed of the following submodules: (i) an automatic speech recognition (ASR) system that transforms audio into a sequence of words; (ii) a high-level air traffic control (ATC)-related entity parser that understands the transcribed voice communication; and (iii) a text-to-speech submodule that generates a spoken utterance that resembles a pilot based on the situation of the dialogue. Our system employs state-of-the-art AI-based tools such as Wav2Vec 2.0, Conformer, BERT and Tacotron models. To the best of our knowledge, this is the first work fully based on open-source ATC resources and AI tools. In addition, we develop a robust and modular system with optional submodules that can enhance the system's performance by incorporating real-time surveillance data, metadata related to exercises (such as sectors or runways), or even a deliberate read-back error to train ATCo trainees to identify them. Our ASR system can reach as low as 5.5% and 15.9% absolute word error rates (WER) on high- and low-quality ATC audio. We also demonstrate that adding surveillance data into the ASR can yield a callsign detection accuracy of more than 96%.
Experiences in building a mOSAIC of clouds
(2013-05-24) Petcu, Dana; Di Martino, Beniamino; Venticinque, Salvatore; Rak, Massimiliano; Máhr, Tamás; Lopez, Gorka; Brito, Fabrice; Cossu, Roberto; Stopar, Miho; Šperka, Svatopluk; Stankovski, Vlado
The diversity of Cloud computing services is challenging the application developers as various and non-standard interfaces are provided for these services. Few middleware solutions were developed until now to support the design, deployment and execution of service-independent applications as well as the management of resources from multiple Clouds. This paper focuses on one of these advanced middleware solutions, called mOSAIC. Written after the completion of its development, this paper presents an integrated overview of the mOSAIC approach and the use of its various software prototypes in a Cloud application development process. We are starting from the design concepts and arrive to various applications, as well as to the position versus similar initiatives.
Indoor and Outdoor Backpack Mapping with Calibrated Pair of Velodyne LiDARs
(2019-09-29) Veľas, Martin; Španěl, Michal; Herout, Adam
This paper presents a human-carried mapping backpack based on a pair of Velodyne LiDAR scanners. Our system is a universal solution both for large scale outdoor and also smaller indoor environments. It benefits from a combination of two LiDAR scanners what makes the odometry estimation more precise. The scanners are mounted under different angles, thus larger space around the backpack is scanned. By fusion with GNSS/INS sub-system, the mapping of featureless environments and also the georeferencing of resulting point cloud is possible. By deploying SoA methods for registration and the loop closure optimization it provides sufficient precision for many applications in BIM (Building Information Modeling), inventory check, construction planning, etc. In our indoor experiments, we evaluated our proposed backpack against ZEB-1 solution, using FARO terrestrial scanner as the reference, yielding similar results in terms of precision, while our system provides higher data density, laser intensity readings, and scalability for large environments.
Orbis Pictus: Zpřístupnění netextových dat z digitálních knihoven
(Slovak Centre of Scientific and Technical Information, 2024-10-25) Lehečka, Dalibor; Jebavý, Filip; Kersch, Filip; Pavčík, Filip; Jana, Hrzinová; Fremrová, Květa; Kišš, Martin; Lhoták, Martin; Dvořáková, Martina; Bežová, Michaela; Hradiš, Michal; Žabička, Petr; Jiroušek, Václav
Účel - Projekt "Orbis Pictus - oživení knihy pro kulturní a kreativní odvětví" si klade za cíl zpřístupnit netextový obsah českých digitálních knihoven, který je ve srovnání s textovými daty obtížně dosažitelný a neprohledatelný. Tento článek přináší přehled plánovaných výstupů projektu s důrazem na klíčové výsledky dosažené v prvních dvou letech. Metody - Zpřístupnění netextových objektů v digitalizovaných dokumentech lze rozdělit na tři úlohy: detekci, popis a vyhledání. Identifikaci, lokalizaci a kategorizaci objektů zajistí nástroj AnnoPage, který umožní extrakci popisů objektů a jejich uložení ve standardizovaném formátu. V dalších fázích projektu naváže na AnnoPage nástroj PeopleGator, který identifikuje osoby na fotografiích či kresbách a umožní propojení dokumentů s vyobrazením stejné osoby a vytvoření databáze identifikovaných osob. Projekt bude zakončen softwarovým řešením integrujícím všechny vyvinuté nástroje. Výsledky - V prvních dvou letech projektu byla vytvořena metodika pro zpracování obrazových dokumentů. Ta popisuje způsob detekce netextových objektů, jejich rozdělení do 25 kategorií a zápis informací pomocí mezinárodních standardů, čímž pokládá základ pro nástroj AnnoPage. K detekci objektů je využíván detektor trénovaný na vlastní datové sadě. Detekované objekty jsou popsány pomocí vektorových reprezentací a textových popisů. Originalita/hodnota - Výstupy projektu budou integrovány do České digitální knihovny, což umožní využívání vyvinutých nástrojů širokému spektru knihoven, které platforma agreguje. Orbis Pictus je unikátní projekt v oblasti digital humanities díky rozsáhlému shromáždění netextových dat. Výsledky najdou uplatnění nejen v identifikaci objektů a metadat, ale i ve výzkumu a kulturním a kreativním průmyslu, kde mohou zpřístupněné objekty sloužit jako inspirace pro marketing, vzdělávání, gamifikaci nebo umělou inteligenci.
Exploring the benefits and challenges of AI-driven large language models in gastroenterology: Think out of the box
(PALACKY UNIV, MEDICAL FAC, 2024-12-01) Král, Jan; Hradiš, Michal; Bužga, Marek; Kunovský, Lumír
Artificial Intelligence (AI) has evolved significantly over the past decades, from its early concepts in the 1950s to the present era of deep learning and natural language processing. Advanced large language models (LLMs), such as Chatbot Generative Pre-Trained Transformer (ChatGPT) is trained to generate human-like text responses. This technology has the potential to revolutionize various aspects of gastroenterology, including diagnosis, treatment, education, and The benefits of using LLMs in gastroenterology could include accelerating diagnosis and treatment, providing personalized care, enhancing education and training, assisting in decision-making, and improving communication with patients. However, drawbacks and challenges such as limited AI capability, training on possibly biased data, data errors, security and privacy concerns, and implementation costs must be addressed to ensure the responsible and effective use of this technology. The future of LLMs in gastroenterology relies on the ability to process and analyse large amounts of data, identify patterns, and summarize information and thus assist physicians in creating personalized treatment plans. As AI advances, LLMs will become more accurate and efficient, allowing for faster diagnosis and treatment of gastroenterological conditions. Ensuring effective collaboration between AI developers, healthcare professionals, and regulatory bodies is essential for the responsible and effective use of this technology. By finding the right balance between AI and human expertise and addressing the limitations and risks associated with its use, LLMs can play an increasingly significant role in gastroenterology, contributing to better patient care and supporting doctors in their work.

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