Zobrazeno 1 - 10
of 3 789
pro vyhledávání: '"A P, Rafferty"'
Table extraction from document images is a challenging AI problem, and labelled data for many content domains is difficult to come by. Existing table extraction datasets often focus on scientific tables due to the vast amount of academic articles tha
Externí odkaz:
http://arxiv.org/abs/2412.04262
Autor:
Abdullah, Abdullah, Sandjaja, Fannya Ratana, Majeed, Ayesha Abdul, Wickremasinghe, Gyan, Rafferty, Karen, Sharma, Vishal
This study investigates uncertainty quantification (UQ) using quantum-classical hybrid machine learning (ML) models for applications in complex and dynamic fields, such as attaining resiliency in supply chain digital twins and financial risk assessme
Externí odkaz:
http://arxiv.org/abs/2411.10254
A central goal of both knowledge tracing and traditional assessment is to quantify student knowledge and skills at a given point in time. Deep knowledge tracing flexibly considers a student's response history but does not quantify epistemic uncertain
Externí odkaz:
http://arxiv.org/abs/2407.17427
Autor:
Kumar, Harsh, Xiao, Ruiwei, Lawson, Benjamin, Musabirov, Ilya, Shi, Jiakai, Wang, Xinyuan, Luo, Huayin, Williams, Joseph Jay, Rafferty, Anna, Stamper, John, Liut, Michael
Self-reflection on learning experiences constitutes a fundamental cognitive process, essential for the consolidation of knowledge and the enhancement of learning efficacy. However, traditional methods to facilitate reflection often face challenges in
Externí odkaz:
http://arxiv.org/abs/2406.07571
Industry 4.0 and beyond will rely heavily on sustainable Business Decision Modelling (BDM) that can be accelerated by blockchain and Digital Twin (DT) solutions. BDM is built on models and frameworks refined by key identification factors, data analys
Externí odkaz:
http://arxiv.org/abs/2405.12101
Autor:
Denny, Paul, Gulwani, Sumit, Heffernan, Neil T., Käser, Tanja, Moore, Steven, Rafferty, Anna N., Singla, Adish
This survey article has grown out of the GAIED (pronounced "guide") workshop organized by the authors at the NeurIPS 2023 conference. We organized the GAIED workshop as part of a community-building effort to bring together researchers, educators, and
Externí odkaz:
http://arxiv.org/abs/2402.01580
Autor:
Abdulaziz Alsharifi, Niamh Carter, Akbar Irampaye, Charlotte Stevens, Elisa Mejia, Joerg Steier, Gerrard F. Rafferty
Publikováno v:
Experimental Physiology, Vol 109, Iss 12, Pp 2134-2146 (2024)
Abstract Postural fluid shifts may directly affect respiratory control via a complex interaction of baro‐ and chemo‐reflexes, and cerebral blood flow. Few data exist concerning the steady state ventilatory responses during head‐down tilt. We ex
Externí odkaz:
https://doaj.org/article/adf09d90acdc4be2b56974345bc17d76
Autor:
Joshua Au Yeung, Anthony Shek, Thomas Searle, Zeljko Kraljevic, Vlad Dinu, Mart Ratas, Mohammad Al-Agil, Aleksandra Foy, Barbara Rafferty, Vitaliy Oliynyk, James T. Teo
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Purpose of Review Embedding machine learning workflows into real-world hospital environments is essential to ensure model alignment with clinical workflows and real-world data. Many non-healthcare industries undergoing digital transformation
Externí odkaz:
https://doaj.org/article/8cf525a2371940b88626bd4773b8c30e
Publikováno v:
BMC Health Services Research, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background Allocating healthcare resources to local areas in proportion to need is an important element of many universal health care systems, aiming to provide equal access for equal need. The UK National Health Service allocates resources
Externí odkaz:
https://doaj.org/article/0a54ef9561e248a4aeb3b8ff0ad6e5f2
Autor:
Kumar, Harsh, Li, Tong, Shi, Jiakai, Musabirov, Ilya, Kornfield, Rachel, Meyerhoff, Jonah, Bhattacharjee, Ananya, Karr, Chris, Nguyen, Theresa, Mohr, David, Rafferty, Anna, Villar, Sofia, Deliu, Nina, Williams, Joseph Jay
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence (IAAI) 2024
Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhanc
Externí odkaz:
http://arxiv.org/abs/2310.18326