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125 results for “Artefact”
Catalogues of Semantic Artefacts - Maturity Dimensions and Features
<p>This dataset contains, in three different formats (XSLX, CSV, and PDF), a description of twelve maturity dimensions identified from the literature that can be used to measure the maturity of the semantic artefacts catalogues (SAC). For each dimension, a number from 2 to 6 features has been added, for a total of 43 features overall.</p>
Dataset of "Towards Artefact Aware Human Motion Capture using Inertial Sensors Integrated into Loose Clothing"
<p>This dataset was used in the publication:<br> <strong>Towards Artefact Aware Human Motion Capture using Inertial Sensors Integrated into Loose Clothing</strong><br> presented at the IEEE International Conference on Robotics and Automation 2022</p> <p><strong>Abstract:</strong><br> Inertial motion capture has become an attractive alternative to optical motion capture for human joint angle estimation outside the laboratory. Usually inertial sensors are assumed to be tightly fixed to the body segments, which can be cumbersome regarding setup-time and ease-of-use. However, integrating the sensors directly into clothing, usually, results in additional clothing motion relative to the motion of the underlying bones that should be captured.<br> In this work we propose the <em>Difference Mapping</em> distributions approach that corrects the segment orientations of a given inertial motion capture system that assumes tightly coupled sensors.<br> The approach allows to reduce the joint angle errors due to clothing artefacts by at least 77.2 percent for people with similar morphology performing a similar task as seen in the training data, including an ergonomic assessments scenario at work places with 10 participants. <br> Moreover, we show that the uncertainty of the distribution can be used to measure the reliability of the predicted map if e.g. the motion is further away from the training data to allow for an artefact aware inertial motion tracking approach.<br> The experimental data for this study is available online</p> <p> </p> <p><strong>Data structure:</strong><br> The data contains trials of 12 subjects for different motions, wearing at the same time a tight setup with inertial sensors and a loose working suit with integrated inertial sensors. It contains the raw IMU data, raw Magnetometer data and the estimated segment orientations using a Sensor Fusion engine provided by Sci-Track.<br> Please note, that in the publication only the first 10 subjects were used and the upper body information was used only. The Sternum sensor of the tight setup of subjects 11, 12 and 13 tilted slowly during the long-term measurements. For this reason only 10 subjects were included in the study. However all remaining sensor of the tight setup were not tilted during recording. In particular the lower body recordings of all subjects are not corrupted.<br> <br> Code samples, a visualizer and further useful information is provided under the following git repository:<br> https://github.com/lorenzcsunikl/Dataset-of-Artefact-Aware-Human-Motion-Capture-using-Inertial-Sensors-Integrated-into-Loose-Clothing</p>
ngungara_backed_artefacts: v1.0.0
<p>The data and R code to accompany Way, A. M., Koungoulos, L., Wyatt-Spratt, S., & Hiscock, P. (2023). Investigating hafting and composite tool repair as factors creating variability in backed artefacts: Evidence from from Ngungara (Weereewa/Lake George), Southeastern Australia. <em>Archaeology in Oceania</em>, 1–18.</p> <p>Code Author: Amy Mosig Way, Loukas Koungoulos, Simon Wyatt-Spratt</p> <p>Abstract: Across the Australian continent, backed artefacts are produced in enormous numbers during the mid-late Holocene. Previous examinations have revealed variation in the average shape of these artefacts, at both continental and regional scales. To better understand the factors creating this variability, we examine a large assemblage of backed artefacts from Ngungara (Weereewa/Lake George), in southeastern Australia. This is one of the few open sites in Australia which has high-resolution evidence for spatially distinct, short-term workshops. Within these well-bounded workshops both locally manufactured and imported backed artefacts are present. However, across this landscape the shape of these artefacts is not uniform; rather similarly shaped backed artefacts are concentrated in different workshop areas. Through the analysis of backed artefacts in different workshops, we suggest that ‘insert copying’ or the replacement of spent inserts with similarly shaped, locally manufactured artefacts creates variability in backed artefact shape.</p> <p>Acknowledgements: The authors would like to thank the traditional landowners for their assistance in undertaking the original PhD study and for sharing language names for the lake.</p>
Artefact segmentation in digital pathology whole-slide images
<p>Dataset with examples of Artefacts in Digital Pathology.</p> <p>The dataset contains 22 Whole-Slide Images, with H&E or IHC staining, showing various types and levels of defect to the slides. Annotations were made by a biomedical engineer based on examples given by an expert.</p> <p>The dataset is split in different folders:</p> <ul> <li>train <ul> <li>18 whole-slide images (extracted at 1.25x & 2.5x magnification)</li> <li>All from the same Block (colorectal cancer tissue)</li> <li>1/2 with H&E & 1/2 with anti-pan-cytokeratin IHC staining.</li> </ul> </li> <li>validation <ul> <li>3 whole-slide images (1.25x + 2.5x mag)</li> <li>2 from the same Block as the training set (1 IHC, 1 H&E)</li> <li>1 from another Block (IHC anti-pan-cytokerating, gastroesophageal junction lesion)</li> </ul> </li> <li>validation_tiles <ul> <li>patches of varying sizes taken from the 3 validation whole-slide images @1.25x magnification.</li> <li>7 patches from each slide.</li> </ul> </li> <li>test <ul> <li>1 whole-slide image (1.25x + 2.5x mag)</li> <li>From another block: IHC staining (anti-NR2F2), mouth cancer</li> </ul> </li> </ul> <p>For the train, validation and test whole-slide images, each slide has:<br> - The RGB images @1.25x & 2.5x mag<br> - The corresponding background/tissue masks<br> - The corresponding annotation masks containing examples of artefacts (note that a majority of artefacts are not annotated. In total, 918 artefacts are in the train set)</p> <p>For the validation tiles, the following table gives the "patch-level" supervision:</p> <p>tile# Artefact(s)<br> 00 None/Few<br> 01 Tear&Fold<br> 02 Ink<br> 03 None/Few<br> 04 None/Few<br> 05 Tear&Fold<br> 06 Tear&Fold + Blur<br> 07 Knife damage<br> 08 Knife damage<br> 09 Ink<br> 10 None/Few<br> 11 Tear&Fold<br> 12 Tear&Fold<br> 13 None/Few<br> 14 None/Few<br> 15 Knife damage<br> 16 Tear&Fold<br> 17 None/Few<br> 18 None/Few<br> 19 Blur<br> 20 Knife damage</p>
FORTH_ARTEFACTS_09/30/22
Documentation material from the Mastic pilot of the Mingei project
Break the Code? Breaking Changes and Their Impact on Software Evolution (Artefacts)
<p>The artefacts included in this repository accompany the thesis "Break the Code? Breaking Changes and Their Impact on Software Evolution" authored by Lina María Ochoa Venegas and supervised by prof.dr. Jurgen Vinju, prof.dr. Mark van den Brand, and dr.Thomas Degueule. The thesis was developed at Eindhoven University of Technology (TU/e) in Eindhoven, The Netherlands and Centrum Wiskunde & Informatica (CWI) in Amsterdam, The Netherlands. It was submitted to revision in 2022 and defended in 2023.</p> <p> </p> <p><strong>Relevant Links</strong></p> <ul> <li><strong>Maracas:</strong> https://github.com/alien-tools/maracas</li> <li><strong>BreakBot: </strong>https://github.com/alien-tools/breakbot</li> </ul>
Dataset for: Diurnal patterns in solute concentrations measured with in situ UV-Vis sensors: natural fluctuations or artefacts?
<p>This dataset contains high-resolution (10-minute interval) data for nitrate, dissolved organic carbon, precipitation and discharge at four measurement stations (NF, SHA, TTP, OUT) in the South West Mau, Kenya. This data was used for the analysis of diurnal patterns in nitrate and dissolved organic carbon concentrations. The zipped folder contains the following files and data:</p> <ul> <li>Calibration.csv: <ul> <li>site = name of measuring site</li> <li>date = date and time of grab sample (yyyy-mm-dd hh:mm:ss)</li> <li>DOC = dissolved organic carbon concentration in grab sample (mg C/L)</li> <li>nitrate = nitrate concenctration in grab sample (mg N/L)</li> </ul> </li> <li>Files with suffix ".ts.csv" (time series data from 1-11-2014 to 31-10-2019; prefix indicates measuring site): <ul> <li>date = date and time of measurement (yyyy-mm-dd hh:mm:ss)</li> <li>nit.raw = nitrate concentration measured by sensor (mg N/L)</li> <li>nit.flag = indication of validity of nitrate measurement (if NA, measurement is valid; for explanation of flags, see supplement of <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR021592">Jacobs et al. 2018</a>)</li> <li>nit.proc = processed nitrate concentration (mg N/L)</li> <li>nit.bg = background concentration of nitrate (48-h moving median; mg N/L)</li> <li>nit.patt = deviation from background concentration of nitrate (nit.proc minus nit.bg; mg N/L)</li> <li>doc.raw = dissolved organic carbon concentration measured by sensor (mg C/L)</li> <li>doc.flag = indication of validity of dissolved organic carbon measurement (if NA, measurement is valid)</li> <li>doc.proc = processed dissolved organic carbon concentration (mg C/L)</li> <li>doc.bg = background concentration of dissolved organic carbon (48-h moving median; mg C/L)</li> <li>doc.patt = deviation from background concentration of dissolved organic carbon (doc.proc minus doc.bg; mg C/L)</li> <li>p = precipitation (mm/10 mins)</li> <li>q = discharge (m³/s)</li> <li>sensor = serial number of sensor</li> </ul> </li> <li>Files with suffix ".exp.csv" (data for sensor comparison experiment from 5-9-2017 to 1-12-2017; prefix indicates measuring site): <ul> <li>date = date and time of measurement (yyyy-mm-dd hh:mm:ss)</li> <li>prec = precipitation (mm/10 mins)</li> <li>nit.orig = processed nitrate concentration measured by fixed sensor (mg N/L)</li> <li>nit.bg.orig = background concentration of nitrate measured by fixed sensor (48-h moving median; mg N/L)</li> <li>nit.patt.orig = deviation from background concentration of nitrate measured by fixed sensor (nit.orig minus nit.bg.orig; mg N/L)</li> <li>nit.dup = processed nitrate concentration measured by mobile sensor (mg N/L)</li> <li>nit.bg.dup = background concentration of nitrate measured by mobile sensor (48-h moving median; mg N/L)</li> <li>nit.patt.dup = deviation from background concentration of nitrate measured by mobile sensor (nit.dup minus nit.bg.dup; mg N/L)</li> <li>doc.orig = processed dissolved organic carbon concentration measured by fixed sensor (mg C/L)</li> <li>doc.bg.orig = background concentration of dissolved organic carbonmeasured by fixed sensor (48-h moving median; mg C/L)</li> <li>doc.patt.orig = deviation from background concentration of dissolved organic carbonmeasured by fixed sensor (doc.orig minus doc.bg.orig; mg C/L)</li> <li>doc.dup = processed dissolved organic carbonconcentration measured by mobile sensor (mg C/L)</li> <li>doc.bg.dup = background concentration of dissolved organic carbonmeasured by mobile sensor (48-h moving median; mg C/L)</li> <li>doc.patt.dup = deviation from background concentration of dissolved organic carbon measured by mobile sensor (doc.dup minus doc.bg.dup; mg C/L)</li> <li>set = experimental treatment</li> </ul> </li> </ul>
Artefacts for ICSE 2021 technical paper: "RAICC: Revealing Atypical Inter-Component Communication in Android Apps"
<p>This repository represents our artefacts to replicate our paper which is in the proceedings of ICSE 2021: "RAICC: Revealing Atypical Inter-Component Communication in Android Apps"</p>
D2.1: Artefact, Contributor, and Organisation Relationship Data Schema - Appendix A
<p>Comparison of metadata schema for ORCID, DataCite, Dublin Core, CASRAI, MODS and DDI regarding contributors, organizations and artefacts.</p>
Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension (Supporting data)
<p>This data accompanies the paper "Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension", published in Computers & Fluids.</p>
Fig. 17 in Characterizing Late Pleistocene and Holocene Stone Artefact Assemblages from Puritjarra Rock Shelter: A Long Sequence from the Australian Desert
Fig. 17. Retouched artefacts from the Holocene levels of Puritjarra rock shelter. All are from units 1a and 1b except M10/11-1 (unit 1c). Steep-edged scrapers: N11/9-2, N6/5-3, N10/5-3. Notched implements: N9/4-3, QR9/1-9, N10/4-8. Endscraper: M10/11-1.
Fig. 18. Group 2 in Characterizing Late Pleistocene and Holocene Stone Artefact Assemblages from Puritjarra Rock Shelter: A Long Sequence from the Australian Desert
Fig. 18. Group 2 retouched artefacts from the Holocene levels of Puritjarra rock shelter. All are from units 1a and 1b. Geometric microliths: top row (1–8). Thumbnail scrapers: QR9/3-4, N10/3-1, Z10/2-1, N9/3-11, M9/2-14. Tula adze slugs: M10/1-4, M9/2-3, N5/4-1. (M10/1-4 is the largest tula in this assemblage). Burren adze slug: Z9/9-2. Endscrapers: N6/3-2, Z9/5-2. (Z9/5-2 has usepolish and rounding on the distal end, and fine overhang removal scars along the platform edge).
Fig. 16 in Characterizing Late Pleistocene and Holocene Stone Artefact Assemblages from Puritjarra Rock Shelter: A Long Sequence from the Australian Desert
Fig. 16. Large flake implements from late Pleistocene levels of Puritjarra rock shelter. All are from unit 2a, except N13/20-1 (unit 2b). Steep-edged scrapers: N11/19-1, N11/22-2, QR9/8-11, N12/14-3. Amorphous retouched artefacts: N5/15-12, M11/18-1, N11/19-3. Notched implements: N13/20-1, N11/21-2. Saws: N10/9-1, N5/19-1.
Fig. 15 in Characterizing Late Pleistocene and Holocene Stone Artefact Assemblages from Puritjarra Rock Shelter: A Long Sequence from the Australian Desert
Fig. 15. Large flake implements from late Pleistocene levels of Puritjarra rock shelter. All are from unit 2a. Steep-edged scrapers: QR9/8-2, N5/15-11. Amorphous retouched implement: M10/22-2. QR9/8-1 is a large formal implement with extensive shallow invasive flaking and a thin convex working edge.
Fig. 13 in Characterizing Late Pleistocene and Holocene Stone Artefact Assemblages from Puritjarra Rock Shelter: A Long Sequence from the Australian Desert
Fig. 13. Artefacts from the palaeosurface at c. 32,000 B.P. N13/24-1 is a large sandstone flake typical of the larger component of the flake assemblage. N13/25-3 shows a sandstone flake detached from a rotated core. Bottom two rows show small finely-made silcrete flakes. N12/26-1, M11/ 27-5 and M11/27-6 are made on exotic silcrete. M11/27-2 (2) is a chalcedony flake with a short length of retouch or edge damage. M11/27-4 (4) is a trimming flake detached from the retouched edge of a chalcedony implement.
Fig. 2 in Characterizing Late Pleistocene and Holocene Stone Artefact Assemblages from Puritjarra Rock Shelter: A Long Sequence from the Australian Desert
Fig. 2. Plan of Puritjarra rock shelter showing layout of excavation trenches. Also shown are spot heights (m below
Fig. 9 in Characterizing Late Pleistocene and Holocene Stone Artefact Assemblages from Puritjarra Rock Shelter: A Long Sequence from the Australian Desert
Fig. 9. Flake size and shape by analytical unit. Data are length/ breadth measurements for a representative sample of 769 complete flakes. (A) Comparison of units 1a–b (solid grey circles), and unit 2a (open squares). Late Holocene flakes are smaller and less variable in size than early Holocene/terminal Pleistocene flakes, but have similar proportions. (B) Comparison of units 2b–d (solid grey circles) and unit 2a (open squares). Late Pleistocene flakes are smaller than those in the early Holocene/terminal Pleistocene, but have similar variability and proportions.
Fig. 7 in Characterizing Late Pleistocene and Holocene Stone Artefact Assemblages from Puritjarra Rock Shelter: A Long Sequence from the Australian Desert
Fig. 7. Large flakes from late Pleistocene levels of Puritjarra rock shelter. All are from unit 2a except M11/26-1 (unit 2b). M10/16-2 is an ironstone flake struck from a horsehoof core, and has fine overhang-removal flaking along the platform edge. M10/20-3 is chert flake with evidence of a prior platform, showing that the core was rotated before this flake was detached.
Fig. 6 in Characterizing Late Pleistocene and Holocene Stone Artefact Assemblages from Puritjarra Rock Shelter: A Long Sequence from the Australian Desert
Fig. 6. Small flakes from late Pleistocene levels of Puritjarra rock shelter. From unit 2c: N10/11-1. From unit 2b: N12/21-8, N12/21-15, N12/23-1, N18/13-1, M11/25-2. Remainder are from unit 2a. N12/21-8 and N12/17-4 each have a series of fine flakes scars along the platform edge, showing trimming of an overhang prior to detachment of the flake. N12/19- 5 exhibits a facetted platform. N11/22-5 is a sandstone flake struck from a bifacial core.
Fig. 1 in Characterizing Late Pleistocene and Holocene Stone Artefact Assemblages from Puritjarra Rock Shelter: A Long Sequence from the Australian Desert
Fig. 1. The western part of central Australia showing the location of Puritjarra rock shelter and regional topography
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