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9,152 results for “Networking”
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 9. Results of our facial motion capture system(a,b,c,d)
<p>In test procedures, single video feature vectors consisting of different expressions are given to the neural network and the network produces the corresponding labels for each frame as output. If there is a mode in a video which is not available in the data base, the nearest available mode's label to this mode is produced. For example, in test3 and test6 videos, the surprise expression (that have been showed with number 7) is recognized as open mouth expression. At the end, considering the certain numbers of subsequent similar labels (at least 10 frames, because the minimum number of one modes' frames is related to “rising the eyebrow” mode that takes 10 frames), the expressions are detected, and a 3D show of these expressions are represented. For instance, in test8 videos that have been obtained from unseen face, the “smiling” and “open mouth” expressions are well recognized, but expressions related to rising the eyebrows are not detected properly and all the corresponding frames to this expression are regarded as normal expression. Figure 9 shows example of generated 3D models.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 8. 3D model of some facial expressions
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 6. Proposed feed-forward neural network classifier
<p>After the feature extraction stage, neural network is used for classifying the modes. In this study, the utilized expressions are normal, smiling, open mouth, rising the eyebrows, anger and pursing modes. In fact, they are some selective modes for face movements. It should be noted that the modes can be increased but in this case we work with these six modes. This paper used three layers feed-forward neural network (Figure 6). The proposed neural network includes 800 nodes for the input layer (400 nodes for U matrix and 400 nodes for V matrix), 100 nodes for the hidden layer and 6-nodes for output layer. From the collected data 70% are used for training, 15% for validation and the last 15% are used to evaluate the neural network.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 5. Examples of the circular LBP (Huang et al., 2011)
<p>One limitation of the basic LBP operator is that its small 3x3 neighborhood cannot capture dominant features with large scale structures. To deal with the texture at different scales the operator was later generalized to use neighborhoods of different sizes. A local neighborhood is defined as a set of sampling points evenly spaced on a circle which is centered at the pixel to be labeled. The sampling points that do not fall within the pixels are interpolated using bilinear interpolation, thus allowing for any radius and any number of sampling points in the neighborhood. Figure 5 shows some examples of the extended LBP operator where the notation (P, R) denotes a neighborhood of P sampling points on a circle of radius of R.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB- Figure 4. An example of the uniform LBP operator (Huang et al., 2011)
<p>The original LBP operator labels the pixels of an image by means of decimal numbers called Local Binary Patterns or LBP codes, which encode the local structure around each pixel. It proceeds thus as illustrated in figure 4: Each pixel is compared with its eight neighbors in a 3x3 neighborhood by subtracting the center pixel value. The resulting strictly negative values are encoded with 0 and the others with 1. A binary number is obtained by concatenating all these binary codes in a clockwise direction starting from the top-left one and its corresponding decimal value is used for labeling. The derived binary numbers are referred to as Local Binary Patterns or LBP codes.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 7. Avatar 3D model generation
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 3. Feature vectors of facial expression in database
<p> Figure 3 shows feature vectors of facial expression of our database. Matrices ‘U’ and ‘V’ values that are obtained from this algorithm are used as feature vectors. The ‘U’ matrix represents the position and the ‘V’ matrix represents the change of direction. In the following, the proposed method is combined with some other feature extraction methods (LBP uniform approach and LBP circular approach) and the obtained results will be mentioned.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 2. Facial expression recognition in proposed method
<p>In this stage, a video is prepared using the color data captured from Kinect camera. The face region in each frame is obtained from the video using Viola-Jones algorithm (Figure 2). Because of different distance from the Kinect camera, the obtained images from the face must be re-sized, in order to have the same size. At the end, the colored images are converted to gray-scaled images.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 1. Peak of facial expression in database
<p>In this study, data are obtained from the Kinect camera that benefits from colorful images and depth data. Kinect can record colorful and depth data simultaneously at 30 frames per second. The data are collected from the person who initially pose in front of the camera with normal face mode and then the various modes are represented. It should be noted that data are obtained at different distances from the Kinect camera and in different lighting conditions. Figure 1 shows various facial modes in our database.</p>
ACE gene haplotypes and social networks: Using a biocultural framework to investigate blood pressure variation in African Americans
<p>This dataset contains all processed data used in analyses reported in the manuscript, ACE gene haplotypes and social networks: Using a biocultural framework to investigate blood pressure variation in African Americans. This project is part of a larger study focused on investigating the role of stress, discrimination, genetic variants, and other sociocultural factors in hypertension in African Americans.</p> <p> </p> <p> </p> <p>Variable Names and Descriptions:</p> <p>HHID: Participant Identification number</p> <p>sbp-10: Average of two Systolic Blood Pressure readings without 10 point correction for blood pressure medication (mmHg)</p> <p>sbp: Average of two Systolic Blood Pressure readings with 10 point correction for blood pressure medication (mmHg)</p> <p>dbp-5: Average of two Diastolic Blood Pressure readings without 5 point correction for blood pressure medication (mmHg)</p> <p>dbp: Average of two Diastolic Blood Pressure readings with 5 point correction for blood pressure medication (mmHg)</p> <p>ACE.Genotype: ACE genotype (0= Deletion/Deletion, 1= Insertion/Deletion, 2= Insertion/Insertion)</p> <p>WNK1.Genotype: WNK1 Genotype (0= Deletion/Deletion, 1= Insertion/Deletion, 2= Insertion/Insertion)</p> <p>age: age (years)</p> <p>sex: sex (Male =1, Female = 2)</p> <p>bpmedtake: Whether the participant uses blood pressure medication</p> <p>bmi: Body Mass Index (calculated from height and weight)</p> <p>ALTER GENDER|Answer:Male|Value:0|Count: Number of male alters (social network members)</p> <p>Alter Gender Male: Percentage of alters that are male</p> <p>ALTER GENDER|Answer:Female|Value:1|Count: Number of female alters (social network members)</p> <p>Alter Gender Female: Percentage of alters that are female</p> <p>Closeness_Mean: Average closeness centrality of the network</p> <p>Between_Mean: Average between-ness centrality of the network</p> <p>percentage of family in structural percentage: Percentage of central network positions occupied by family members</p> <p>relationship max close family: The most close (centrally) network member is a family member</p> <p>Average distance: Average distance of individuals in a network</p> <p>Hap B: ACE gene haplotypes</p> <p> </p>
Road network graphs for betweenness centrality algorithm
<p>Weighted graph representation of a road network in selected regions. Derived from Open Street Map <a href="https://www.openstreetmap.org/#map=8/49.817/15.478">https://www.openstreetmap.org</a>. The dataset can be used as input for the betweenness centrality algorithm implemented here: <a href="https://code.it4i.cz/ADAS/betweenness">https://code.it4i.cz/ADAS/betweenness</a>.</p> <p><strong>Archive contents</strong><br> The archive contains following folders.</p> <p><strong>CZE</strong><br> Static graphs of three major cities in the Czech Republic (Praha, Brno, Ostrava) and entire Czech road network. Weighted by length of the road segments in metres.</p> <p><strong>PT</strong><br> Static graphs of Lisbon, Porto and entire Portugese road network. Weighted by length of the road segments in metres.</p> <p><strong>Data format</strong><br> Standard UTF-8 encoded CSV files, separated by semicolon with the following columns:<br> <br> <em>id1: (Type: unsigned long) - </em>start node<br> <em>id2: (Type: unsigned long) - </em>end node<br> <em>dist: (Type: unsigned long) - </em>weight of the edge (length in metres, unless described otherwise)<br> <em>edge_id: (Type: unsigned long) - </em>unique edge identifier<br> <br> <br> <br> </p> <p> </p> <p> </p> <p> </p>
Subgraphs of functional brain networks identify dynamical constraints of cognitive control
<p>Post-processed BOLD fMRI functional connectivity data from human subjects performing two distinct cognitive control tasks.</p> <p>See enclosed README file for information regarding data organization and handling.</p>
Community-Based Event Detection in Temporal Networks
<p>This dataset contains daily tweets ids during April 2013. This is provided to facilitate reproducibility of results presented in the following paper:</p> <p>Pablo Moriano, Jorge Finke, and Yong-Yeol Ahn. "Community-Based Event Detection in Temporal Networks." Scientific Reports 9, 4358, 2019. DOI: <a href="https://doi.org/10.1038/s41598-019-40137-0">https://doi.org/10.1038/s41598-019-40137-0</a></p> <p>These data are provided for non-commercial purposes only. If you use this dataset for research, please be sure to cite the above paper.</p>
Trained neural network data for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This archive contains data representing a trained-up neural network suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. The network generates coefficients that can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, networks were trained on a training set of coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a> that is available as <a href="https://doi.org/10.5281/zenodo.1341154">DOI:10.5281/zenodo.1341154</a>. The data were generated using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, were:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The training set was computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each, yielding about 22 million numbers. Training the networks took about 3 hours on an 8-core laptop.</p> <p>For the purposes of <em>neurosynchro</em>, the formats of the files in this package should be regarded as internal implementation details. The <a href="https://pypi.org/project/neurosynchro/">neurosynchro</a> Python package will load up the files in this archive and use them to predict synchrotron coefficients. For specifics, see <a href="https://neurosynchro.readthedocs.io/en/stable/">the neurosynchro documentation</a>.</p>
Datasets of Vascular Networks Extracted from Retinal Images of Hypertensive Retinopathy and Glaucoma Patients
<p>This dataset contains high resolution images of blood vessels extracted from Digital Retinal Images for Optic Nerve Segmentation Database (DRIONS-DB). It contains 110 blood vessels of individuals of which 23.1% are patients with chronic glaucoma while the remaining 76.9% are hypertensive retinopathy patients. </p>
gazeNet: End-to-end eye-movement event detection with deep neural networks
<p>This repository contains synthetic eye-movement dataset used to train deep learning based eye-movement event detection algorithm described in Zemblys, R., Niehorster, D.C. & Holmqvist, K. (2018). gazeNet: End-to-end eye-movement event detection with deep neural networks. Behavior research methods, pp 1–25. <a href="https://doi.org/10.3758/s13428-018-1133-5">https://doi.org/10.3758/s13428-018-1133-5</a></p> <p>Code used to train a model can be found on github <a href="https://github.com/r-zemblys/gazeNet">here</a>. Code to generate synthetic eye-movement data can be downloaded from <a href="https://github.com/r-zemblys/gazeGenNet">here</a>.</p>
Revealing Ferroelectric Switching Character Using Deep Recurrent Neural Networks
<p><strong>The ability to manipulate domains and domain walls underpins function in a range of next-generation applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of features of nanoscale ferroelectric switching from multichannel hyperspectral band-excitation piezoresponse force microscopy of tensile-strained PbZr<sub>0.2</sub>Ti<sub>0.8</sub>O<sub>3</sub> with a hierarchical domain structure. Using this approach, we identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we are able to identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in <em>learning</em> features of the physical response of a material from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging multimodal <em>in operando</em> spectroscopies and automated control for the manipulation of nanoscale structures in materials.</strong></p>
Domain-Independent Reviews' Sentiment Polarity Classification using Shallow Word2Seq Convolutional Neural Network
<p>Reviews and comments are perceptions about specific services or products. They are embedded with hidden sentiments the reviewer has towards certain subjects. Business owners use customer reviews to understand customers’ perceptions about specific services or products. The ability to understand reviews’ sentiment from different domains or areas give decision makers and business owners the opportunity to make critical business decisions which can help them to increase profits of their businesses. Previous studies had focussed on classifying sentiment polarity by using traditional machine learning and deep learning methods. However, these suffered from low model generalization ability, causing the models to perform better only on single domain datasets rather than multiple domain datasets. The problem is the inability of the classification model to learn domain-restricted knowledge from multi-domain datasets. Aiming to improve the accuracy of the cross-domain classification, this paper proposes a method which uses Word2Seq Convolutional Neural Network (CNN) to classify reviews’ sentiment across multiple domain datasets (i.e. digital worker, movie, product, hotel and restaurant reviews). The evaluation showed that the proposed method had achieved the state-of-the-art performance. The high classification performance also promoted the reliability and effectiveness of implementing the Word2Seq CNN to classify reviews’ sentiment across different domains and learn domain restricted knowledge while improving the model generalization ability.</p> <p>The uploaded dataset is a sampled dataset with 5000 observations for both training and testing sets.</p>
BPTF-AU1 conformation space network
<p><strong>This dataset contains:</strong></p> <ul> <li><em>allstates.gexf</em>: a graph file describing the nodes and edges of the configuration space network. Nodes are clusters of molecular conformations, and edges are the connections between them, derived from molecular dynamics trajectories. This file also contains properties for each node: minimum ligand-protein distance (<em><strong>mind</strong></em>), ligand solvent accessible surface area (<em><strong>sasa</strong></em>), number of observations from A-initialized simulations (<em><strong>na</strong></em>), number of observations from B-initialized simulations (<em><strong>nb</strong></em>), total number of observations (<em><strong>ntot</strong></em>), free energy prediction (<em><strong>eig_wt_final_sym</strong></em>). This file can be opened in network analysis programs (e.g. Gephi).</li> <li><em>bptf_au1.pdb</em>: a PDB file containing the BPTF protein and the AU1 molecule</li> <li><em>clust90.dcd</em>: a trajectory composed of randomly selected structures from cluster 90, which was predicted to be the most stable cluster according to the free energy</li> <li><em>allclusts.tgz</em>: an archive file containing a (randomly selected) PDB structure for every cluster in the network</li> </ul>
SINS database - Node 4 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</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.