Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Brett Jefferson"'
Autor:
Song Feng, Emily Heath, Brett Jefferson, Cliff Joslyn, Henry Kvinge, Hugh D. Mitchell, Brenda Praggastis, Amie J. Eisfeld, Amy C. Sims, Larissa B. Thackray, Shufang Fan, Kevin B. Walters, Peter J. Halfmann, Danielle Westhoff-Smith, Qing Tan, Vineet D. Menachery, Timothy P. Sheahan, Adam S. Cockrell, Jacob F. Kocher, Kelly G. Stratton, Natalie C. Heller, Lisa M. Bramer, Michael S. Diamond, Ralph S. Baric, Katrina M. Waters, Yoshihiro Kawaoka, Jason E. McDermott, Emilie Purvine
Publikováno v:
BMC Bioinformatics, Vol 22, Iss 1, Pp 1-21 (2021)
Abstract Background Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way rel
Externí odkaz:
https://doaj.org/article/3730664a2b894f34b1b4e404f44398c0
Autor:
John Wenskovitch, Brett Jefferson, Alexander Anderson, Jessica Baweja, Danielle Ciesielski, Corey Fallon
Publikováno v:
Frontiers in Big Data, Vol 5 (2022)
This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real
Externí odkaz:
https://doaj.org/article/2ac01b3e87204dd69b9ad13ff0da7bd2
Publikováno v:
Frontiers in Robotics and AI, Vol 8 (2021)
Trust calibration for a human–machine team is the process by which a human adjusts their expectations of the automation’s reliability and trustworthiness; adaptive support for trust calibration is needed to engender appropriate reliance on automa
Externí odkaz:
https://doaj.org/article/95bd9f3546c243bdbb8a5785e8466280
Autor:
Sinan Aksoy, Brett Jefferson, Ellyn Ayton, Svitlana Volkova, Dustin Arendt, Karthnik Shrivaram, Joseph Cottam, Emily Saldanha, Maria Glenski
Publikováno v:
Computational and Mathematical Organization Theory. 29:220-241
Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et a
Autor:
John Wenskovitch, Alexander Anderson, Slaven Kincic, Corey Fallon, Danielle Ciesielski, Jessica Baweja, Molly C Mersinger, Brett Jefferson
Publikováno v:
Human Factors in Energy: Oil, Gas, Nuclear and Electric Power.
Introducing machine learning (ML) assistance into any established process comes with adoption barriers, including entrenched procedures, technological and human readiness levels, human-machine trust, and work culture resistance to change. These barri
Publikováno v:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 63:827-832
Automation can be unreliable. This makes appropriate trust and reliance difficult to calibrate. One solution to building appropriate trust is to increase automation transparency by displaying information to the operator about the technology’s under
Publikováno v:
2021 IEEE International Conference on Intelligence and Security Informatics (ISI).
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).
As data grows in size and complexity, finding frameworks which aid in interpretation and analysis has become critical. This is particularly true when data comes from complex systems where extensive structure is available, but must be drawn from perip
Publikováno v:
Frontiers in Robotics and AI
Frontiers in Robotics and AI, Vol 8 (2021)
Frontiers in Robotics and AI, Vol 8 (2021)
Trust calibration for a human–machine team is the process by which a human adjusts their expectations of the automation’s reliability and trustworthiness; adaptive support for trust calibration is needed to engender appropriate reliance on automa