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483 results for “SEMANTICS”
Dataset: Language and semantic processing in blind and sighted individuals. A simulation study
<p>-------------------<br> GENERAL INFORMATION<br> -------------------</p> <p>1. Title of Dataset <br> <br> Dataset_SimulationsOutput_BlindvsSightedModels<br> <br> 2. Author Information</p> <p>This dataset is being made public to act as supplementary data for publication. Running title: </p> <p>R. Tomasello, M. Garagnani, T. Wennekers, and F. Pulvermüller, Recruitment of visual cortex for language processing in blind individuals is explained by Hebbian learning.</p> <p> Corresponding author:<br> Name: Rosario Tomasello <br> Institution: Brain Language Laboratory, Freie Universität Berlin<br> Address: Habelschwerdter Allee 45, 14195 Berlin<br> Email: Tomasello.r@fu-berlin.de</p> <p>--------------------------<br> RESEARCH PROJECT & METHODOLOGICAL INFORMATION<br> --------------------------</p> <p>The data were generated by a neurobiologically constrained cortex model of the fronto-temporal-occipital lobes applied to simulate word meaning acquisition of object- and action-related words in action and perception system under undeprived and visually deprived conditions. The purpose of this study was to investigate how and why the visual system is recruited for language processing in blind individuals, as documented by neurocognitive empirical experiments.</p> <p>The files listed above shows the cell assemblies (CA) distributions across the different cortical areas spontaneously emerged as a result of Hebbian learning. For more information about the general features of the model see our previous publications:</p> <p>R. Tomasello, M. Garagnani, T. Wennekers, and F. Pulvermüller, (2018) A Neurobiologically Constrained Cortex Model of Semantic Grounding With Spiking Neurons and Brain-Like Connectivity, Front. Comput. Neurosci., vol. 12, p. 88.</p> <p>M. Garagnani, G. Lucchese, R. Tomasello, T. Wennekers, and F. Pulvermüller, (2017) A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords, Front. Comput. Neurosci., vol. 10, no. January, pp. 1–19.</p> <p><br> ---------------------<br> DATA & FILE OVERVIEW<br> ---------------------</p> <p><br> 1. File List<br> <br> Filename: CA_Structure_Blind VS SightedModel.xlsx (size 150KB) <br> <br> The excel file includes the cell assembly (CA) distributions of the learnt action and object words of both sighted and blind models along with their CA structure comparisons and the related figures. <br> <br> Folders: Raw_Data_SightedModel (size 6KB) & Raw_Data_BlindModel (size 6KB)<br> <br> These folders include the raw data output of the CA structure of the 13 sighted and 13 blind simulated models after word meaning acquisition. </p>
What do package dependencies tell us about semantic versioning?
<p>This is the replication package for our paper on dependency constraints and semantic versioning.</p> <p>This package requires Python 3.5+, and all the dependencies listed in *requirements.txt*. They can be automatically installed using *pip install -r requirements.txt*.</p> <p>The figures and results that are contained in the paper could be obtained by running the corresponding notebooks with Jupyter (*jupyter lab* at the root of this package).</p> <p>Here is a short description of what is contained in this package:</p> <p> * Raw datasets (*data-raw* folder)<br> This folder "should" contain the raw data. Because of their size, and because we provide the required files in the *data* folder, you need to download *Libraries.io-open-data-1.2.0.tar.gz* from libraries.io. Extract *versions.csv* and *dependencies.csv* from this archive, and run the *convert.py* script with Python. The script will extract the data related to the four considered ecosystems into *{ecosystem}-(versions|dependencies).csv.gz*.<br> <br> * Datasets (*data* folder)<br> This folder contains the data that are required for the analyses. They are provided in this replication package, but can be automatically generated from the ones provided in *data-raw* folder by running the *versions.py* and *dependencies.py* scripts. These scripts collect and identify dependency constraints.<br> <br> * Notebooks (*notebooks* folder)<br> This folder contains the notebooks that were used to generate all the results and figures of the paper. File *Constraint differences.ipynb* contains examples of constraints and the corresponding equivalent intervals. The main notebook is *Semver compliance.ipynb* and contains all the necessary material.<br> <br> * Python modules (*constraints* folder)<br> This folder contains the necessary Python modules to run "everything". File *parser.py* contains the 4 dependency constraint parsers. File *versions.py* defines data model. File *constraints.py* contains routines to identify constraint types. The *tool.py* script is a prototype of the tool explained in the paper (and should be executed from the root directory, e.g. *python -m constraints.tool*). All these modules/scripts/tools depend on *python-interval* (see *requirements.txt*). The three remaining files (*test_constraints.py*, *test_parser.py* and *test_versions.py*) contain unit tests. They are expected to be executed with *pytest*.</p>
Semantic address matching dataset
<p>Data for our paper <strong>Lin, Y., Kang, M., Wu, Y., Du, Q. and Liu, T. (2019) A deep learning architecture for semantic address matching, <em>International Journal of Geographical Information Science</em>, DOI: 10.1080/13658816.2019.1681431</strong></p> <p>Below is an overview of each file in this dataset.</p> <ul> <li><code>train.txt</code> The training dataset</li> <li><code>train_code_a.txt</code> The index representations of the address elements (i.e., address elements represented by the corresponding indexes in the vocabulary obtained by word2vec) in <em>S<sub>a</sub></em></li> <li><code>train_code_b.txt</code> The index representations of the address elements in <em>S<sub>b</sub></em></li> <li><code>train_lable.txt</code> The labels of address pairs in the training dataset</li> </ul> <p> </p> <ul> <li><code>dev.txt</code> The development dataset</li> <li><code>dev_code_a.txt</code> The index representations of the address elements in <em>S<sub>a</sub></em></li> <li><code>dev_code_b.txt</code> The index representations of the address elements in <em>S<sub>b</sub></em></li> <li><code>dev_lable.txt</code> The labels of address pairs in the development dataset</li> </ul> <p> </p> <ul> <li><code>test.txt</code> The test dataset</li> <li><code>test_code_a.txt</code> The index representations of the address elements in <em>S<sub>a</sub></em></li> <li><code>test_code_b.txt</code> The index representations of the address elements in <em>S<sub>b</sub></em></li> <li><code>test_lable.txt</code> The labels of address pairs in the test dataset</li> </ul>
Electronic Devices Dataset for 2-Class Semantic Segmentation
<p><span>This dataset contains images used in the monograph titled <em>Zastosowanie wybranych metod uczenia głębokiego w wizji komputerowej</em> (Application of Selected Deep Learning Methods in Computer Vision) to build the U-Net model. The full collection consists of 600 image files of resolution 512x512 pixels showing small electronic devices and office accessories (<a title="Electronic Devices Dataset" href="https://drive.google.com/file/d/1CocbDdwcF9hpqniNpkqERjgVHR5O1iqW/view?usp=drive_link" target="_blank" rel="noopener">https://drive.google.com/file/d/1CocbDdwcF9hpqniNpkqERjgVHR5O1iqW/view?usp=drive_link</a>). The set was randomly divided into a training part (50% of the full set), validation and test part (each accounted for 25% of the full set). As a result, the training part contains 300 files, validation part – 150 and test part - 150. The collection was created by augmenting the original set of 100 images with vertical and horizontal flip, random rotation from -45 to 45 degrees, and a combination of both flips and random rotation. The images are labeled with masks representing 2 kind of objects – REMOTES and BATTERIES. Therefore, the dataset can be used to build models for multiclass semantic segmentation.</span></p>
Unsupervised detection of semantic correlations in big data
<p>simulated spin configurations from statistical mechanics models at thermal equilibrium. Further details are in our manuscript with the same title. </p>
Data from: Electrophysiological correlates of semantic dissimilarity reflect the comprehension of natural, narrative speech
People routinely hear and understand speech at rates of 120–200 words per minute [1, 2]. Thus, speech comprehension must involve rapid, online neural mechanisms that process words' meanings in an approximately time-locked fashion. However, in the context of continuous speech, electrophysiological evidence for such time-locked processing has been lacking. Whilst valuable insights into the semantic processing of speech have been provided by the "N400 component" of the event-related potential [3-6], this literature has been dominated by paradigms using incongruous words within specially constructed sentences, and may not accurately reflect natural, narrative speech comprehension. Building on the discovery that cortical activity "tracks" the dynamics of running speech [7-9], and psycholinguistic work both demonstrating [10-12] and modeling [13-15] how context rapidly impacts on word processing, we describe a new approach for deriving an electrophysiological correlate of natural speech comprehension. We used a computational model [16] to quantify the meaning carried by each word based on how semantically dissimilar it was to its preceding context and then regressed this quantity against electroencephalographic (EEG) data recorded from subjects as they listened to narrative speech. This produced a prominent negativity at a time-lag of 200–600 ms on centro-parietal EEG channels, characteristics common to the N400. Applying this approach to EEG datasets involving time-reversed speech, cocktail party attention and audiovisual speech-in-noise demonstrated that this response was very sensitive to whether or not subjects understood the speech they heard. These findings demonstrate that, when successfully comprehending natural speech, the human brain responds to the contextual semantic content of each word in a relatively time-locked fashion.
U-TAE pre-trained weights on PASTIS for Semantic segmentation
<p>Pre-trained weights of U-TAE for <strong>semantic segmentation.</strong></p> <p>See <a href="https://github.com/VSainteuf/utae-paps">companion GitHub repository</a> and <a href="https://arxiv.org/abs/2107.07933">paper</a> for more information.</p>
Subset of AmazonQA annotated with answerability and semantic & syntactic embedding
<p>3755 Instances taken from the review-question dataset AmazonQA. The file data_answerability contain the 3755 instances annotated with answerability, answer tag (aligned with the passage) and answer text. The file data_annotated contains 1818 answerable instances, annotated with embedding constructions in the answer text. Inventory_phrases and inventory_simple_words contain the expressions of logical operator, implicative and factive predicates that can be used in embedding annotation. annotate.py is the script for embedding annotation. </p>
Semantic competency as a marker of clinical reasoning performance
<p>Purpose: This study sought to explore the relationship between semantic competence (or dyscompetence) displayed during think alouds performed by resident and attending physicians and clinical reasoning performance.</p> <p>Methods: Internal medicine resident physicians and practicing internists participated in "think-alouds" performed after watching videos of typical presentations of common diseases in internal medicine. The think-alouds were evaluated for the presence of semantic competence and dyscompetence and these results were correlated with clinical reasoning performance. </p> <p>Results: We found that the length of think-aloud was negatively correlated with clinical reasoning performance. Beyond this finding, however, we did not find any other significant correlations between semantic competence or dyscompetence and clinical reasoning performance.</p> <p>Conclusion: While this study did not produce the previously hypothesized findings of correlation between semantic competence and clinical reasoning performance, we discuss the possible implications and areas of future study regarding the relationship between semantic competency and clinical reasoning performance.</p>
Abstraction-based Trace Generation to Validate Semantics of Formal Verifiers: Validation Model Suite
<p>Dataset of the Scientific Students’ Association Report titled Abstraction-based Trace Generation to Validate Semantics of Formal Verifiers.</p> <p>These files contain the validation model test suite and the generated traces. The models and traces are in the format of the Gamma modeling tool.</p> <p><em>validation-model-suite/model/package<Letter>/model<Number> </em>contains the files for a given model:<br> - stm.gcd is the statemachine,<br> - default.ggen (and in Package F also abstraction.ggen) is the Gamma script executing trace generation and the generated traces can be found in the default (and abstraction) directories.<br> The report of Theta on possible coverage violation is in the traces directory (report.txt).</p> <p> </p> <p>The prototype implementation of trace generation can be found at: https://github.com/AdamZsofi/gamma/tree/dev-tracegen</p>
Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)
<p><em><strong>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)</strong></em></p> <p>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other)</p> <p><strong>Description</strong></p> <p>3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only</p> <p>These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.</p> <p>Two data sources have been combined</p> <p><strong>Dataset 1</strong></p> <p>* 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571<br> * Labels have been reclassified from 4 classes to 2 classes.<br> * Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.<br> * These images and labels have been made using the Doodleverse software package, Doodler*.</p> <p><strong>Dataset 2</strong></p> <ul> <li>3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******</li> <li>A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water</li> </ul> <p><strong>File descriptions</strong></p> <ul> <li> classes.txt, a file containing the class names</li> <li> images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents</li> <li> labels.zip, a zipped folder containing the 1-band label images</li> <li> nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images</li> <li> swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images</li> <li> overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)</li> <li> resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li> resized_labels.zip, label images resized to 512x512x1 pixels</li> <li> resized_nir.zip, NIR images resized to 512x512x1 pixels</li> <li> resized_swir.zip, SWIR images resized to 512x512x1 pixels</li> </ul> <p>References</p> <p>*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.</p> <p>**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information</p> <p>****Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571</p> <p>*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/</p> <p>******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.</p>
JMLKelinci+: Detecting Semantic Bugs and Covering Branches with Valid Inputs using Coverage-Guided Fuzzing and Runtime Assertion Checking
<p>Testing to detect semantic bugs is essential, especially for critical systems. Coverage-guided fuzzing and runtime assertion checking (RAC) are two well-known approaches for detecting semantic bugs. Coverage-guided fuzzing aims to generate inputs tests with high code coverage. However, while coverage-guided fuzzers are equipped with sanitizers that can detect a fixed set of semantic bugs, they can otherwise only detect bugs that lead to a crash. Thus, the first problem we address is how to help fuzzers detect previously unknown semantic bugs that do not lead to a crash. Moreover, a coverage-guided fuzzer may not necessarily cover all branches with valid inputs, although invalid inputs are useless for detecting semantic bugs. So, the second problem is how to guide a fuzzer to cover all branches in a program using only valid inputs. On the other hand, RAC monitors the expected behavior of a program dynamically and can only detect a semantic bug when a valid input test shows that the program does not satisfy its specification. <br> Thus, the third problem is how to provide high-quality input tests for a RAC that can trigger potential bugs.<br> The combination of a coverage-guided fuzzer and RAC solves these problems and can cover branches with valid inputs and detect semantic bugs effectively. Our study uses RAC to guarantee that only valid inputs reach the program under test using the program's specified preconditions and it also uses RAC to detect semantic bugs using specified postconditions. A prototype tool was developed for this study, named JMLKelinci+. Our results show that combining a coverage-guided fuzzer with RAC will lead to executing the program under test only with valid inputs and that this technique can effectively detect semantic bugs. <br> Also, this idea improves the feedback given to a coverage-guided fuzzer, enabling it to cover all branches faster in programs with non-trivial preconditions.</p>
Artifact For A Large Scale Analysis of Semantic Versioning in NPM
<p>This is the artifact for: A Large Scale Analysis of Semantic Versioning in NPM.</p> <p>The artifact contains:</p> <ul> <li>A full scrape of all metadata from NPM (package / version information, dependencies, etc.) as of October 31, 2022.</li> <li>A copy of our code, which includes the software for scraping metadata and package tarball (code) data, as well as all analysis scripts that are needed to replicate the figures from the paper.</li> </ul>
RESILOC Semantic Layer Open Data
<p>Ontologies implemented for the RESILOC project.</p>
Semantic Census X3ML Mappings
<p>Semantic Census X3ML Mappings files</p>
Materials for "Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning"
<p>This repository contains materials including source code, datasets and results for "Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning"</p>
Asking the Machine: A Literature Review and Research Agenda on Semantic Search
<p><strong>To be submitted to IEEE Access.</strong></p>
Semantic Parameter Matching in Web APIs with Transformer-based Question Answering
<p>This repository contains the evaluation results of our study, as well as datasets and model checkpoints. <br> For a detailed overview regarding the provided materials, please refer to README.md.</p>
Research Data for Beyond Keywords: Intent-Driven Semantic Code Search in Software Ecosystems
<p>This contains the software repository for the intent-enhanced code search engine as well as the baseline code search engine used in the Master Thesis: 'Beyond Keywords: Intent-Driven Semantic Code Search in Software Ecosystems'</p>
Semantic Table Search Dataset
<p>Semantic Table Search Dataset Resource: A Large Scale Test Corpus for Semantic Table Search<br> Check out the <a href="">README</a> for more information.</p>
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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.