Zobrazeno 1 - 10
of 678
pro vyhledávání: '"H.3.1"'
Autor:
Brandizzi, Nicolo', Abdelwahab, Hammam, Bhowmick, Anirban, Helmer, Lennard, Stein, Benny Jörg, Denisov, Pavel, Saleem, Qasid, Fromm, Michael, Ali, Mehdi, Rutmann, Richard, Naderi, Farzad, Agy, Mohamad Saif, Schwirjow, Alexander, Küch, Fabian, Hahn, Luzian, Ostendorff, Malte, Suarez, Pedro Ortiz, Rehm, Georg, Wegener, Dennis, Flores-Herr, Nicolas, Köhler, Joachim, Leveling, Johannes
This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project, a large-scale initiative aimed at creating open and high-performance multilingual large language models (LLMs). The project goal is to
Externí odkaz:
http://arxiv.org/abs/2410.08800
Publikováno v:
in Proceedings of the 5th IEEE International Symposium on the Internet of Sounds (IEEE IS2 2024, https://internetofsounds.net/is2_2024/)
This paper explores a structured application of the One-Class approach and the One-Class-One-Network model for supervised classification tasks, focusing on vowel phonemes classification and speakers recognition for the Automatic Speech Recognition (A
Externí odkaz:
http://arxiv.org/abs/2410.04098
Autor:
Zhou, Dongmei, Tang, Xuri
Domain dependence and annotation subjectivity pose challenges for supervised keyword extraction. Based on the premises that second-order keyness patterns are existent at the community level and learnable from annotated keyword extraction datasets, th
Externí odkaz:
http://arxiv.org/abs/2409.18724
Autor:
Anselmi, Gianluca, Vekaria, Yash, D'Souza, Alexander, Callejo, Patricia, Mandalari, Anna Maria, Shafiq, Zubair
Smart TVs implement a unique tracking approach called Automatic Content Recognition (ACR) to profile viewing activity of their users. ACR is a Shazam-like technology that works by periodically capturing the content displayed on a TV's screen and matc
Externí odkaz:
http://arxiv.org/abs/2409.06203
In data exploration, users need to analyze large data files quickly, aiming to minimize data-to-analysis time. While recent adaptive indexing approaches address this need, they are cases where demonstrate poor performance. Particularly, during the in
Externí odkaz:
http://arxiv.org/abs/2407.18702
Autor:
Niccolucci, Franco, Felicetti, Achille
Publikováno v:
Sensors 2024, 24, 3978
The paper concerns the extension of the Heritage Digital Twin Ontology introduced in previous work to describe the reactivity of digital twins used for cultural heritage documentation by including the semantic description of sensors and activators an
Externí odkaz:
http://arxiv.org/abs/2407.07750
Autor:
Pavlovic, Dusko
Machine-learned language models have transformed everyday life: they steer us when we study, drive, manage money. They have the potential to transform our civilization. But they hallucinate. Their realities are virtual. This note provides a high-leve
Externí odkaz:
http://arxiv.org/abs/2405.14233
Security conferences are important venues at which academics and practitioners share knowledge about new attacks and state-of-the-art defenses. Despite this, researchers have not studied who shares information and about which security topics. To addr
Externí odkaz:
http://arxiv.org/abs/2404.17989
Clustering algorithms or methods for GPS trajectories are in constant evolution due to the interest aroused in part of the scientific community. With the development of clustering algorithms considered traditional, improvements to these algorithms an
Externí odkaz:
http://arxiv.org/abs/2404.17761
Healthcare data contains some of the most sensitive information about an individual, yet sharing this data with healthcare practitioners can significantly enhance patient care and support research efforts. However, current systems for sharing health
Externí odkaz:
http://arxiv.org/abs/2404.11372