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1 to 10 of 12 Results
Feb 3, 2023
Shekhar, Shashank, 2023, "Response time measures for convolutional neural networks",, Borealis, V1, UNF:6:FdmiVZOfwnZM6L1zcfcSUA== [fileUNF]
Supplemental data for the article "Neural response time analysis: Explainable artificial intelligence using only a stopwatch" including the Neural Response Times from two models - MSDNet and ResNet (E2E, IC only) - and four datasets - ImageNet, ObjectNet, SCEGRAM, and Massive Mem...
Dec 16, 2021
Galloway, Angus; Brunet, Dominique; Valipour, Reza; McCusker, Megan; Biberhofer, Johann; Sobol, Magdalena K.; Moussa, Medhat; Taylor, Graham W., 2021, "Dataset for: Predicting Dreissenid Mussel Abundance in Nearshore Waters using Underwater Imagery and Deep Learning",, Borealis, V2, UNF:6:gsBkPq2oPE0o0UY6/3uuiQ== [fileUNF]
The dataset contains colour images of lakebed sites sampled in Lake Erie and Lake Ontario from a top down view. A pixel-wise segmentation label is provided for each image to indicate the presence of Dreissenid mussels. The data are organized into three sets: for training, validat...
Jun 30, 2020
Veres, Matthew; Cabral, Ian; Moussa, Medhat, 2020, "Incorporating object intrinsic features within Deep Grasp Affordance Prediction",, Borealis, V1
To understand the effects of an object's center-of-mass on grasp affordance prediction. A suction cup gripper with a finite amount of grasp force (60 PSI) is required to grasp objects with an unknown mass / mass distribution using top-down grasps only.
Aug 16, 2019
Kennedy, Maeve; Spachos, Petros; Taylor, Graham W., 2019, "BLE beacon indoor localization dataset",, Borealis, V1
This dataset was created to facilitate research into indoor localization with BLE beacons. Data was collected from September 2018 to May 2019 in two separate locations. Several participants assisted with the experiment each carrying a BLE beacon and a smartphone. This dataset is...
Oct 23, 2018
Freeman, Brian, 2018, "Supplemental data for "Air quality management for coastal urban centres using stochastic and machine learning techniques"",, Borealis, V1
This research addressed specific air management issues faced by regulatory agencies to allow better oversight given constrained budgets and technical staff. Data sets were collected from air monitoring stations in Kuwait to allow development, testing, and validation of stochastic...
Aug 15, 2018
Schneider, Jonathan; Murali, Nihal; Taylor, Graham; Levine, Joel, 2018, "Dataset for: Can Drosophila melanogaster tell who's who?",, Borealis, V1
To investigate whether there is enough visual information to differentiate individual Drosophila melanogaster from each other, and whether flies themselves have the theoretical capacity to use it (simulating a fly visual system via convolutional neural networks based on the Droso...
Mar 6, 2017
Veres, Matthew; Moussa, Medhat; Taylor, Graham, 2017, "Dataset for: An integrated simulator and data set that combines grasping and vision for deep learning",, Borealis, V1
To develop a simulation that collects both visual information, as well as grasp information about different objects using a multi-fingered hand. These sources of data can be used in the future to learn integrated object-action grasp representations.
Feb 27, 2017
Galloway, Angus; Taylor, Graham; Ramsay, Aaron; Moussa, Medhat, 2017, "The Ciona17 dataset for semantic segmentation of invasive species in a marine aquaculture environment",, Borealis, V3
An original dataset for semantic segmentation, Ciona17, is introduced, which to the best of the authors' knowledge, is the first dataset of its kind with pixel-level annotations pertaining to invasive species in a marine environment. Diverse outdoor illumination, a range of objec...
Jul 13, 2016
Taylor, Graham, 2016, "Deep Temporal Models - Gesture recognition using Deep Multimodel Networks",, Borealis, V2
This is a pre-processed version of a publicly available dataset used for human gesture recognition application. Our method won the first prize in the Chalearn Looking at People (2014) challenge and we are sharing our implementation that makes use of this pre-processed data. The D...
Jul 4, 2016
Li, Fan; Taylor, Graham, 2015, "An ice-water classification dataset for learning from label proportions 2015 [Canada]",, Borealis, V3
An ice-water classification dataset for machine learning researchers to test algorithms related to learning from label proportions. The dataset includes patches extracted from 10 RADARSAT-2 dual-polarized scenes, Egg Code polygon (bag) IDs, labels, and the estimated proportion of...
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