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831 results for “automaticity”
Dataset and code for "FK-means: Automatic Atrial Fibrosis Segmentation using Fractal-guided K-means Clustering with Voronoi-Clipping Feature Extraction of Anatomical Structures": FKmeans for fibrosis segmentation
<p>Assessment of left atrial (LA) fibrosis from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) adds to the management of patients with atrial fibrillation (AF). However, accurate assessment of fibrosis in the LA wall remains challenging. Excluding anatomical structures in the LA proximity using clipping techniques can reduce misclassification of LA fibrosis. A novel FK-means approach for combined automatic clipping and automatic fibrosis segmentation was developed. This approach combines a feature-based Voronoi diagram with a hierarchical 3D K-means fractal-based method. The proposed automatic Voronoi clipping method was applied on LGE MRI data and achieved a Dice score of 0.75, similar as the score obtained by a deep learning method (3D UNet) for clipping (0.74). The automatic fibrosis segmentation method, which utilizes the Voronoi clipping method, achieved a Dice score of 0.76. This outperformed a 3D U-Net method for clipping and fibrosis classification, which had a Dice score of 0.69. Moreover, the proposed automatic fibrosis segmentation method achieved a Dice score of 0.90, using manual clipping of anatomical structures. The findings suggest that the automatic FK-means analysis approach enables reliable LA fibrosis segmentation and that clipping of anatomical structures in the atrial proximity can add to the assessment of atrial fibrosis. </p>
Dataset related to article "Automatic imitation in youngsters with Gilles de la Tourette syndrome: A behavioral study"
<p>dataset related to article mentioned at title</p>
ThumbSet - A dataset of partial annotations for Automatic Piano Fingering
<p>Here we introduce ThumbSet, an open dataset created from the collection of MusicXML piano scores published as public domain on the MuseScore website, which include finger label annotations. We relied on music publishers who added partial or full annotations to support piano learning when creating this dataset. It is important to note that these annotations reflect a single editor's expertise and do not represent a global ground truth. The source of the annotations on the MuseScore website is not always clear. Some scores may have incorrectly engraved labels, while others may have finger annotations provided by non-expert users. Consequently, the dataset may contain a significant amount of noise, resulting in lower data quality compared to the PIG dataset.</p> <p>ThumbSet is composed of 2523 music scores, as shown in Table 1. The genres, transcription quality, and fingering quality are highly heterogeneous, and the difficulty level of the pieces tends to lean towards the early years of music education compared to the PIG dataset. However, it is not possible to quantify all these claims with the existing metadata. We can assert that there are more finger labels annotated in the right hand (61%) than in the left hand (39%), although there are more pieces with only left-hand annotations (742) than with only right-hand annotations (153). The proportion of annotated fingers and the window lengths are similar in both hands.</p> <p>To make ThumbSet available for research purposes, we sliced the data into several windows. Each window takes into account the context, other notes, and symbols, ranging from 32 to 64 notes and symbols around each symbol of interest, including other finger label annotations. Additionally, the excerpts are encoded in the PIG encoding, a text format proposed in a previous study, which does not allow reverting to the original score to protect the copyright of the pieces. We distribute ThumbSet as variable-length music windows in the PIG encoding format. This format contains information about pitch, time onset, time offset, and finger label annotations, if they exist for all notes. Access to the data is limited and available upon request through the Zenodo platform. We also provide links to all the MuseScore source pieces used in creating ThumbSet.</p> <p> </p> <p>For citation and more information, please refer to the following Article:</p> <p><code>@inproceedings{ramoneda2022automatic,</code><br><code> title={Automatic Piano Fingering from Partially Annotated Scores using Autoregressive Neural Networks},</code><br><code> author={Pedro Ramoneda and Dasaem Jeong and Eita Nakamura and Xavier Serra and Marius Miron},</code><br><code> booktitle={Proceedings of the 30th ACM International Conference on Multimedia (MM '22)},</code><br><code> year={2022},</code><br><code> month={October 10--14},</code><br><code> location={Lisboa, Portugal}</code><br><code>}</code></p> <p>Disclaimer: Although the name may suggest that there are only thumb fingers annotated on the new dataset, it only indicates the fact that the most typical partial annotations are those suggesting finger crossing, and also that the authors like Tom Thumb story.</p>
AutoMESC: Automatic Framework for Mining and Classifying Ethereum Smart Contract Vulnerabilities and Their Fixes
<p>AutoMESC Dataset: Automatic Framework for Mining and Classifying Ethereum Smart Contract Vulnerabilities and Their<br> Fixes<br> <br> </p>
Testing Scratch Programs Automatically
<p># Replication Package</p> <p>This is the replication package for our work on<br> "Testing Scratch Programs Automatically".</p> <p>The package contains our raw results and scripts for generating<br> the plots of the paper from the raw data.</p> <p>## Abstract</p> <p>Block-based programming environments like Scratch foster engagement<br> with computer programming and are used by millions of young learners.<br> Scratch allows learners to quickly create entertaining programs and<br> games, while eliminating syntactical program errors that could<br> interfere with progress.</p> <p>However, functional programming errors may still lead to incorrect<br> programs, and learners and their teachers need to identify and<br> understand these errors. This is currently an entirely manual process.</p> <p>In this paper, we introduce a formal testing framework that describes<br> the problem of Scratch testing in detail. We instantiate this formal<br> framework with the Whisker tool, which provides automated and<br> property-based testing functionality for Scratch programs.</p> <p>Empirical evaluation on real student and teacher programs<br> demonstrates that Whisker can successfully test Scratch programs,<br> and automatically achieves an average of 95.25% code coverage.</p> <p>Although well-known testing problems such as test flakiness also<br> exist in the scenario of Scratch testing, we show that automated and<br> property-based testing can accurately reproduce and replace the<br> manually and laboriously produced grading efforts of a teacher, and<br> opens up new possibilities to support learners of programming in<br> their struggles.</p> <p>## Contents</p> <p>The replication package is structured into two main directories:</p> <p>* 'data/':<br> raw data and scripts that have been used for collecting the data</p> <p>* 'scripts/':<br> scripts for generating the plots that are presented in the paper</p> <p>### RAW data</p> <p>* 'data/teacher-data/'<br> data from the scratch workshop: sample solution and scores for student solutions</p> <p>* 'data/code-club-stats/'<br> block counts and input methods of the used Code Club projects</p> <p>* 'data/coverage`<br> code for measuring the coverage of automated input generation</p> <p>* 'data/coverage-results/'<br> coverage measurements on the Code Club projects</p> <p>* 'data/test/'<br> test suites for the projects of the Scratch workshop</p> <p>* 'data/test-results/'<br> test results from the test suites in 'data/test/'</p> <p>* 'data/time/'<br> Scratch programs for time measurement (10x the sample solution from 'data/teacher-data/`)</p> <p>* 'data/time-results/'<br> time measurements on the projects in 'data/time/'</p> <p>## Reproducing the Plots</p> <p>### Prerequisites</p> <p>We describe the process based on:</p> <p> * the R statistics package in version 3.5<br> * an Unix environment (Linux or MacOSX)</p> <p>Following R packages are required:</p> <p> * ggplot2<br> * dplyr<br> * viridis</p> <p>The package can be installed with the R command "install.packages".</p> <p>### Generating the Plots</p> <p>Coverage (Figure 10)</p> <p> ./scripts/coverage.R</p> <p> The result is a set of "coverage-*.pdf" files</p> <p>Inconsistency (Figure 9)</p> <p> ./scripts/consistency.R</p> <p> The result is a set of "consistency-*.pdf" files</p> <p>Scatter Plots (Figure 8, Figure 11)</p> <p> ./scripts/scatter.R</p> <p> The result is a set of "scatter-*.pdf" files</p>
Sample Dataset for YOLO-Behaviour: A simple, flexible framework to automatically quantify animal behaviours from videos
Open the record for dataset details and reuse information.
A Combined Deep Learning System for Automatic Detection of "Bovine" Aortic Arch on Computed Tomography Scans
<p>A Combined Deep Learning System for Automatic Detection of “Bovine” Aortic Arch on Computed Tomography Scans</p> <p><em>Appl. Sci.</em> <strong>2022</strong>, <em>12</em>(4), 2056; <a href="https://doi.org/10.3390/app12042056">https://doi.org/10.3390/app12042056</a></p> <p>Abstract</p> <p>The “bovine” aortic arch is an anatomic variant consisting in a common origin of the innominate and left carotid artery (CILCA), associated with a greater risk of thoracic aortic diseases (aneurysms and dissections), stroke, and complications after endovascular procedures. CILCA can be detected by visual assessment of computed tomography (CT) chest scans, but it is rarely reported. We developed a deep learning (DL) segmentation-plus-classification system to automatically detect CILCA based on 302 CT studies acquired at 2 centers. One model (3D U-Net) was trained from scratch (supervised by manual segmentation), validated, and tested for the automatic segmentation of the aortic arch and supra-aortic vessels. Three DL architectures (ResNet50, DenseNet-201, and SqueezeNet), pre-trained over millions of common images, were trained, validated, and tested for the automatic classification of CILCA versus non-CILCA, supervised by radiologist’s classification. The 3D U-Net-plus-DenseNet-201 was found to be the best system (Dice index 0.912); its classification performance obtained from internal, independent testing on 126 patients gave a receiver operating characteristic area under the curve of 87.0%, sensitivity 66.7%, specificity 90.5%, positive predictive value 87.5%, negative predictive value 73.1%, positive likelihood ratio 7.0, and negative likelihood ratio 0.4. In conclusion, a combined DL system applied to chest CT scans was developed and proven to be an effective tool to detect individuals with “bovine” aortic arch with a low rate of false-positive findings.</p>
Automatic extraction of road boundary and land marking data
<p>This video demonstrates the application of our method in a software tool for automatically constructing road boundaries and lane markings data.</p>
Dataset for "Accurate automatic detection of densely distributed cell nuclei in 3D space", 1 of 2
<p>Data and code associated with "Accurate automatic detection of densely distributed cell nuclei in 3D space" manuscript.</p> <p>This upload contains the data and code used in the manuscript except for 4D image movie of Data2. The other part of the data will be published as another upload.</p> <p>"Software" includes code and executable of the image analysis pipeline. "Data" includes microscopy images and grand truth used int the manuscript. The files in Data1.zip and Data1_aligned.zip are the part of the "Data" but were compressed separately because of file size limit. Please decompress and locate them in appropriate directory according to the instruction in "Data".</p> <p> </p>
Raw data for "Automatic Selection of Control Features for Electroencephalography-Based Brain-Computer Interface Assisted Motor Rehabilitation: The GUIDER Algorithm": unpublished figure and source data.
<p>Classification Performances for each stroke participant according to the GUIDER and the MANUAL procedure. </p>
Figures - Automatic quantification method of eosinophilic degranulation in tissues: application for the study of eosinophilic disorders
<p>Figure 2: (A) Anti-EPX staining of histological samples was manually and automatically quantified in 2 fields (F1 and F2) in 3 sections of BP biopsies (BP 1 to 3). (B) Comparison of degranulation surfaces between manual quantification by 2 independent experts and automatic quantification by the EoJ method of study areas (n=6) (B). Expert 1 (green) vs. Expert 2 (red) vs. EoJ (blue), p>0.05, paired Friedman test and Dunn’s multiple comparison test. NS: nonsignificant. (C) Correlation matrix as appropriate, p<0.05 for all correlations.</p> <p>Figure 3: (A) Representative skin biopsies of BP, psoriasis and healthy skin (anti-EPX staining, high-power field x200). (B) Comparison of Eo density (cell/mm<sup>2</sup>, left), isolated granule density (granules/mm<sup>2</sup>, middle) and degranulation ratio (right) between the 3 groups (BP n=7, psoriasis n=7 and healthy skin n=7; *p<0.05, **p<0.01, ***p<0.001, ns: nonsignificant) according to unpaired nonparametric Kruskal‒Wallis test and Dunn’s multiple comparison test. For the comparison, the degranulation surfaces are expressed as a ratio of quantified degranulation surfaces to total biopsy surface. BP: bullous pemphigoid, Pso: psoriasis and HS: healthy skin.</p> <p>Figure 5: Representative biopsies of EoE, BP and NP with low-density infiltrate (A, green) with intact cells and high-density infiltrate of Eo aggregates (B, red). Graphical representation of high heterogenous infiltration by Eos in tissues concerning intact cell density, isolated granule density (per mm<sup>2</sup>) and ratio of degranulation surfaces to total EPX surfaces (C). The highest intact Eo density in the series is graphically represented by red dots, and the lowest infiltration is represented by green dots: the highest intact Eo infiltrate is not necessarily associated with the highest degranulation ratio.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.