Zobrazeno 1 - 10
of 37 665
pro vyhledávání: '"Aubrey, A."'
Autor:
Nasri, Mahsa, Narayan, Uttkarsh, Sonbudak, Mustafa Feyyaz, Simonson, Aubrey, Chiu, Maria, Donati, Jason, Sivak, Mark, Kosa, Mehmet, Harteveld, Casper
Apprenticeship and training programs in advanced manufacturing frequently encounter safety and accessibility concerns due to using heavy machinery. Virtual Reality (VR) training addresses such constraints while maintaining the spatial and procedural
Externí odkaz:
http://arxiv.org/abs/2411.08859
This paper proposes a novel intelligent human activity recognition (HAR) framework based on a new design of Federated Split Learning (FSL) with Differential Privacy (DP) over edge networks. Our FSL-DP framework leverages both accelerometer and gyrosc
Externí odkaz:
http://arxiv.org/abs/2411.06263
Autor:
Wines, Daniel, Ibrahim, Akram, Gudibandla, Nishwanth, Adel, Tehseen, Abel, Frank M., Jois, Sharadh, Saritas, Kayahan, Krogel, Jaron T., Yin, Li, Berlijn, Tom, Hanbicki, Aubrey T., Stephen, Gregory M., Friedman, Adam L., Krylyuk, Sergiy, Davydov, Albert, Donovan, Brian, Jamer, Michelle E., Walker, Angela R. Hight, Choudhary, Kamal, Tavazza, Francesca, Ataca, Can
Two-dimensional (2D) 1T-VSe$_2$ has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states and the interplay between the two. We employed a combined Diffusion
Externí odkaz:
http://arxiv.org/abs/2409.19082
Autor:
Švábenský, Valdemar, Tkáčik, Kristián, Birdwell, Aubrey, Weiss, Richard, Baker, Ryan S., Čeleda, Pavel, Vykopal, Jan, Mache, Jens, Chattopadhyay, Ankur
This full paper in the research track evaluates the usage of data logged from cybersecurity exercises in order to predict students who are potentially at risk of performing poorly. Hands-on exercises are essential for learning since they enable stude
Externí odkaz:
http://arxiv.org/abs/2408.08531
Autor:
Patton, William, Rhoades, Jeff L., Zouinkhi, Marwan, Ackerman, David G., Malin-Mayor, Caroline, Adjavon, Diane, Heinrich, Larissa, Bennett, Davis, Zubov, Yurii, Team, CellMap Project, Weigel, Aubrey V., Funke, Jan
DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this correspondence, we introduce DaCapo's unique features optimized for t
Externí odkaz:
http://arxiv.org/abs/2408.02834
Conditional forecasts, i.e. projections of a set of variables of interest on the future paths of some other variables, are used routinely by empirical macroeconomists in a number of applied settings. In spite of this, the existing algorithms used to
Externí odkaz:
http://arxiv.org/abs/2407.02262
Machine learning models are only as good as the data to which they are fit. As such, it is always preferable to use as much data as possible in training models. What data can be used for fitting a model depends a lot on the formulation of the task. W
Externí odkaz:
http://arxiv.org/abs/2406.17936
Autor:
Condor, Aubrey
We explore whether the human ratings of open ended responses can be explained with non-content related features, and if such effects vary across different mathematics-related items. When scoring is rigorously defined and rooted in a measurement frame
Externí odkaz:
http://arxiv.org/abs/2405.08574
Autor:
Condor, Aubrey, Pardos, Zachary
We explore the use of deep reinforcement learning to audit an automatic short answer grading (ASAG) model. Automatic grading may decrease the time burden of rating open-ended items for educators, but a lack of robust evaluation methods for these mode
Externí odkaz:
http://arxiv.org/abs/2405.07087
Autor:
Condor, Aubrey, Pardos, Zachary
The use of automatic short answer grading (ASAG) models may help alleviate the time burden of grading while encouraging educators to frequently incorporate open-ended items in their curriculum. However, current state-of-the-art ASAG models are large
Externí odkaz:
http://arxiv.org/abs/2405.00489