Zobrazeno 1 - 10
of 5 010
pro vyhledávání: '"Rafferty, P."'
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
The rapidly advancing field of Explainable Artificial Intelligence (XAI) aims to tackle the issue of trust regarding the use of complex black-box deep learning models in real-world applications. Existing post-hoc XAI techniques have recently been sho
Externí odkaz:
http://arxiv.org/abs/2403.19444
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:
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
Autor:
Li, ZhaoBin, Yee, Luna, Sauerberg, Nathaniel, Sakson, Irene, Williams, Joseph Jay, Rafferty, Anna N.
Digital educational technologies offer the potential to customize students' experiences and learn what works for which students, enhancing the technology as more students interact with it. We consider whether and when attempting to discover how to pe
Externí odkaz:
http://arxiv.org/abs/2309.02856
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:
Robert J. Graham, Reshma Amin, Nadir Demirel, Lisa Edel, Charlotte Lilien, Victoria MacBean, Gerrard F. Rafferty, Hemant Sawnani, Carola Schön, Barbara K. Smith, Faiza Syed, Micaela Sarazen, Suyash Prasad, Salvador Rico, Geovanny F. Perez
Publikováno v:
Respiratory Research, Vol 25, Iss 1, Pp 1-10 (2024)
Abstract X-linked myotubular myopathy (XLMTM) is a rare, life-threatening congenital myopathy. Most (80%) children with XLMTM have profound muscle weakness and hypotonia at birth resulting in severe respiratory insufficiency, the inability to sit up,
Externí odkaz:
https://doaj.org/article/eb06dadc6d0a4658906411a1a624f6c3