Search

Dataverse de l’École de technologie supérieure Logo
NORANDA
Borealis
Voix, Jérémie — 2024-01-10 Audio recordings of industrial noises in a copper refinery, made on January 7, 2000. The A-weighted equivalent level (LAeq,t) for the duration of each recording is also provided in rounded integer dBA value.
Dataverse de l’École de technologie supérieure Logo
Replication Data for: SPLC2022-Towards a Cognitive Model of Feature Model Comprehension: An Exploratory Study using Eye-Tracking
Borealis
Rezaei Sepasi, Elmira; Lopez Herrejon, Roberto Erick — 2022-07-05 This dataset includes a replication package for the paper accepted in SPLC 2022 conference. Title: Towards a Cognitive Model of Feature Model Comprehension: An Exploratory Study using Eye-Tracking. Authors: Elmira Rezaei Sepasi, Kambiz Nezami Balouchi, Julien Mercier, and Roberto Erick Lopez-Herrejon. Abstract:Feature models are pivotal components of Software Product Lines. Therefore, their correct comprehension is crucial for performing adequately all the tasks where they are involved. Despite their importance, to the best of our knowledge, no research has been done on feature model comprehension. As a first step to address this lack, our work contributes an empirical study of feature model comprehension in simple configuration validation tasks. We propose a first cognitive model for this type of tasks that we analyze by measuring eye gaze fixations on the different visual elements involved in the tasks. Our results identified three main components of the cognitive model and their distribution in terms of the cognitive effort for performing these tasks. We argue that further research on feature model comprehension can inform language design and tool development to provide more suitable language structures, user interfaces and support for this kind of models.
Dataverse de l’École de technologie supérieure Logo
JSS An Empirical Eye-Tracker Study of Visualization Techniques for Coverage of Combinatorial Interaction Testing in Software Product Lines
Borealis
Lopez Herrejon, Roberto Erick; Nezami Balouchi, Kambiz — 2024-02-09 Software Product Lines (SPLs) typically provide a large number of configurations to cater to a set of diverse requirements of specific markets. This large number of configurations renders unfeasible to test them all individually. Instead, Combinatorial Interaction Testing (CIT) compute a representative sample according to criteria of the interactions of feature in the configurations. We performed an empirical study using task performance an eye-tracker technologies to analyze the effectiveness of two visualization techniques at conveying the test coverage of ten case studies of varying complexity. Our evaluation considered accuracy, execution time, metacognitive monitoring, and visual attention. The study revealed clear advantages of a visualization technique over the other in three evaluation aspects, with a reverse effect depending on the strength of the coverage and distinct areas of visual attention.
Dataverse de l’École de technologie supérieure Logo
Dataset of deformed ultrasound images and signals under controlled indentation
Borealis
Dahmani, Jawad; Petit, Yvan; Laporte, Catherine — 2024-01-26 Ultrasound imaging data (RF and scan-converted formats) of custom-made elastography phantoms. The data are robotically-acquired image sequences with controlled and gradually increasing phantom indentation. The phantom consists of a background medium traversed by a cylindrical inclusion. The data (images and RF signals) are contained in the 6 folders Acqui1 → Acqui6 corresponding to the 6 acquired image sequences on the phantom. Each Acqui_i folder contains two subfolders: the RF folder containing RF signals in .mat file format (readable in Matlab), and the folder US_Image containing the images of the sequence in PNG format. Both images and RF signals are numbered, with each index corresponding to an indentation level and a force measured by the force sensor as outlined in the Excel file (Tabulation 1). Each RFi.mat file comprises 3152 rows representing the signal along the temporal axis, and 256 columns corresponding to the number of A lines in the image. The Excel file has 5 tabs: -The first tab contains, for each of the 6 acquired image sequences, the frame number in the sequence, the corresponding indentation of the probe (in mm), the recorded voltage value on the force sensor (V), and the corresponding calculated force value (N). Thus, each image in the 6 sequences is identified by a frame number, an indentation, and a force value. -The second tab provide the acquisition parameters of the ultrasound images (frequency, depth, gain, etc.) performed using the SonixTablet ultrasound system from Ultrasonix (now bk medical). -The third tab contains stress-strain curves, the mean, and the standard deviation of the Young's modulus for both the inclusion and the background of the phantom. The Young's modulus is obtained through compression tests conducted by an electromechanical testing machine (Bose, Electroforce 3200) on 10 small cylindrical samples taken from the background and the inclusion. -The fourth tab contains the geometry and dimensions of the phantom. -The fifth tab contains the recipe used to make the gelatin phantom.
Dataverse de l’École de technologie supérieure Logo
Dataset of Assessing Bicycle Helmet Protection for Head and Neck in E-Scooter Falls
Borealis
Bailly, Nicolas; Schäuble, Andreas; Guesneau, Marianne; Wei, Wei; Petit, Yvan — 2024-11-07 This dataset was built from experimental tests of e-scooter crashes against a curb. Six e-scooter forward falls, induced by a curb collision at 20 km/h, were simulated in sled tests using a Hybrid III 50th percentile anthropomorphic test device, with and without a helmet. The curb was positioned either perpendicularly or at a 55° angle to the e-scooter's trajectory. The dataset includes head linear and rotational accelerations, upper neck loads (force and moment), chest and pelvis accelerations, chest deflection, as well as high-speed video of the crash tests and pre- and post-crash photos.

Map search instructions

1.Turn on the map filter by clicking the “Limit by map area” toggle.
2.Move the map to display your area of interest. Holding the shift key and clicking to draw a box allows for zooming in on a specific area. Search results change as the map moves.
3.Access a record by clicking on an item in the search results or by clicking on a location pin and the linked record title.
Note: Clusters are intended to provide a visual preview of data location. Because there is a maximum of 50 records displayed on the map, they may not be a completely accurate reflection of the total number of search results.

(Source Repository: Dataverse de l’École de technologie supérieure)

Limit by:

Clear all

to