Zobrazeno 1 - 10
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pro vyhledávání: '"Belkadi A"'
Publikováno v:
Слобожанський науково-спортивний вісник, Vol 27, Iss 3, Pp 118-127 (2023)
Background and Study Aim This study aimed to investigate the physiological and neuromuscular aspects influencing the performance of highly trained judo athletes across different weight categories. Material and Methods: A total of twenty-one male j
Externí odkaz:
https://doaj.org/article/3b583e155f3446a68893fc9ddc41ea73
Publikováno v:
REM: International Engineering Journal, Vol 76, Iss 3, Pp 229-237 (2023)
Abstract This article evaluates the impact of different fiber types on the flexural creep of self-compacting concretes (SCC). The study focuses on the effects of vegetable fibers (Hemp, [H] and Dis [D]) and synthetic fibers (Polypropylene [P]) on SCC
Externí odkaz:
https://doaj.org/article/b0b1130649db4427b87ad26d66d87d1b
Autor:
Belkadi, Samuel
The problem of safety for robotic systems has been extensively studied. However, little attention has been given to security issues for three-dimensional systems, such as quadrotors. Malicious adversaries can compromise robot sensors and communicatio
Externí odkaz:
http://arxiv.org/abs/2409.11897
In this paper, we present a system that generates synthetic free-text medical records, such as discharge summaries, admission notes and doctor correspondences, using Masked Language Modeling (MLM). Our system is designed to preserve the critical info
Externí odkaz:
http://arxiv.org/abs/2409.09831
Since clinical letters contain sensitive information, clinical-related datasets can not be widely applied in model training, medical research, and teaching. This work aims to generate reliable, various, and de-identified synthetic clinical letters. T
Externí odkaz:
http://arxiv.org/abs/2409.09501
Autor:
Li, Zihao, Belkadi, Samuel, Micheletti, Nicolo, Han, Lifeng, Shardlow, Matthew, Nenadic, Goran
In this system report, we describe the models and methods we used for our participation in the PLABA2023 task on biomedical abstract simplification, part of the TAC 2023 tracks. The system outputs we submitted come from the following three categories
Externí odkaz:
http://arxiv.org/abs/2408.03871
Large Language Models (LLMs) have revolutionised the field of Natural Language Processing (NLP) and have achieved state-of-the-art performance in practically every task in this field. However, the prevalent approach used in text generation, Causal La
Externí odkaz:
http://arxiv.org/abs/2405.12630
Access to real-world medication prescriptions is essential for medical research and healthcare quality improvement. However, access to real medication prescriptions is often limited due to the sensitive nature of the information expressed. Additional
Externí odkaz:
http://arxiv.org/abs/2310.19727
Autor:
Li, Zihao, Belkadi, Samuel, Micheletti, Nicolo, Han, Lifeng, Shardlow, Matthew, Nenadic, Goran
Biomedical literature often uses complex language and inaccessible professional terminologies. That is why simplification plays an important role in improving public health literacy. Applying Natural Language Processing (NLP) models to automate such
Externí odkaz:
http://arxiv.org/abs/2309.13202
The practice of fine-tuning Pre-trained Language Models (PLMs) from general or domain-specific data to a specific task with limited resources, has gained popularity within the field of natural language processing (NLP). In this work, we re-visit this
Externí odkaz:
http://arxiv.org/abs/2210.12770