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18,658 results for “impacts”
Figure 6. Interface of FFE program-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Points, Features Extraction and save all input information for the classifier (Features, Ethnic group,<br> Gender and emotion). Figure 6 shows the interface of FFE program.</p>
Figure 7. Samples of MSFDE dataset-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>We performed groups of experiments to study the impact of ethnic group (race) in the<br> accuracy of emotion recognition with three kinds of ethnic groups (Asian, Caucasian as African).<br> So we have three experiments, each experiment has a neural network as a classifier, and each neural<br> network has three layers where there are 16 neurons in the hidden layer except Asian network has<br> 17 neurons (the best result with 17 neurons for Asians).</p>
Figure 3. 46 points are selected on face elements to describe the emotions.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>The number of points and the position of points are not standardized, but it is depending on<br> the features that will be extracted, and used for the classifier. Many researches use various number<br> of points and positions based on their view about the feature to be considered [13] [18] [19]. Figure<br> 3 shows the points we used.</p>
Figure. 2. Examples of Angry from different races. (A,B) African. (C,D) Asian. (E,F) Caucasian.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>We chose 46 points which are distributed over human face image and use these points for<br> features extraction. The choice of these points is to determine the shape of each element of the face<br> (eyes, eyebrows and mouth), because the shape of these elements is changeable for each emotion,<br> but these changes are different for each race as shown in Figure 2.</p>
Figure 4. Distance between eyebrow and eye.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Based on what we stated above, we need to extract 28 features, which describe the distances<br> between certain points explained in the previous stage, these features are classified into six groups,<br> and each group describes the features of one face element. All features are a vertical distances<br> between two points. Group one contains seven features for mouth, groups two and three contains 14<br> features for eyes, groups four and five contain six features for eyebrows, and the last group has one<br> feature only which is the distance between the beginning of the eyebrow and the beginning of the<br> eye in same side, this is significant (from point 23 to 15) because it is used to measure the distance<br> of eyebrow from the eye. This feature is shown in Figure 4 by a line.</p>
Figure 5. ANN Structure 4.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>For classification purpose of the emotions, we use ANN of supervised learning based on<br> backpropagation algorithm. Backpropagation neural network architecture is used with its standards<br> learning function with 28 inputs representing the extracted features and 6 outputs representing 6<br> emotions, happy, sad, angry, fear, shame and disgust. the emotions. We have also a hidden layer<br> with 16 nodes selected after various trails to obtain the best results. The used ANN is depicted in<br> Figure 5.</p>
Figure 3. Impact of the Boom in digital data in learning-Micro Learning: A Modernized Education System-
<p>Figure 3 portrays the research guiding the impact of the boom in digital data in knowledge codification. The current status shows that 80% of the respondents are interested in learning via electronic devices, followed by e-mails at 75%. 72% and 70% of respondents opt for video clips and sound and voice recording. 65% of the respondents selected images followed by graphical display at 61%. 50% of the respondents selected Journals. Further observation from the figure reveals that books and reference volumes had a very insignificant impact, as expected by merely 45% and 30% of the respondents. This directed us towards the necessity for micro learning, and encourages the increase of the usage of electronic devices.</p>
Run 5 video in "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence"
<p>Run 5 presented in the paper "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence" (http://adsabs.harvard.edu/abs/2013MNRAS.428..804S or https://doi.org/10.1093/mnras/sts071).</p>
Data set for the paper "What are the Effects of History Length and Age on Mining Software Change Impact?"
<p>Data set for the paper What are the Effects of History Length and Age on Mining Software Change Impact?<br> by Leon Moonen, Thomas Rolfsnes, David Binkley and Stefano di Alesio.<br> In Journal of Empirical Software Engineering (EMSE), 2018, Springer. https://doi.org/10.1007/s10664-017-9588-z<br> Available from https://evolveit.bitbucket.io/publications/emse2018/</p> <p>Please cite this work by referring to the corresponding journal publication (a preprint is included in this package).</p> <p>The goal of Software Change Impact Analysis is to identify artifacts (typically source-code files or individual methods therein) potentially affected by a change. Recently, there has been increased interest in <em>mining</em> software change impact based on evolutionary coupling. A particularly promising approach uses association rule mining to uncover potentially affected artifacts from patterns in the system’s change history. Two main considerations when using this approach are the <em>history length</em>, the number of transactions from the change history used to identify the impact of a change, and <em>history age</em>, the number of transactions that have occurred since patterns were last mined from the history. Although history length and age can significantly affect the quality of mining results, few guidelines exist on how to best select appropriate values for these two parameters.</p> <p>In this paper, we empirically investigate the effects of history length and age on the quality of change impact analysis using mined evolutionary coupling. Specifically, we report on a series of systematic experiments using three state-of-the-art mining algorithms that involve the change histories of two large industrial systems and 17 large open source systems. In these experiments, we vary the length and age of the history used to mine software change impact, and assess how this affects precision and applicability. Results from the study are used to derive practical guidelines for choosing history length and age when applying association rule mining to conduct software change impact analysis. </p>
Y-chromosome and mitochondrial data for the article entitled: "The First Horse Herders and the Impact of Early Bronze Age Steppe Expansions into Asia"
<p>Y-chromosome and mitochondrial data for the article entitled: "The First Horse Herders and the Impact of Early Bronze Age Steppe Expansions into Asia"</p>
Artificial Intelligence and the Future of Smart Cities-Figure 7. Q.9.Which of the following job functions will AI impact the most over the next 10 years? (Statistically significant differences by age for 9.3, 9.4, 9.5 and 9.6)
<p>The analysis reveals that people perceive that AI will have a greater impact over the next 10 years on marketing (for example, intelligent customer targeting, planning and executing marketing campaigns) scored significantly higher (M=4.37, SD=.69) than on finance (for example, robotic financial advisors, automated corporate financial analysis) (M=3.87, SD=.60) (Figure 7). For the same question customer services scored significantly higher (M=3.75, SD=.83) than health (e.g. consultation and diagnosis, surgery) (M=3.25, SD=.83). For the same question, the analyses by gender reveals that the majority of female participants scored significantly higher (M=3.40, SD=.81) than male participants (M=3.18, SD=.57) and those aged in the second group.</p>
Tool-supporting Data Protection Impact Assessments with CAIRIS: PLA model
<p>This is the final CAIRIS model associated with the 'Tool-supporting Data Protection Impact Assessments with CAIRIS' ESPRE 2018 paper.</p> <p>To import this model into CAIRIS, select the System/Import Model menu in CAIRIS, check the model type is set to 'model', choose the model file to import, and click on the Import button.</p>
Time-averaged simulation results and in vivo measurements to show the impact of red blood cells on the flow field in the cortical microvasculature
<p>The dataset contains 5 files. 4 of them are time-averaged results of blood flow simulations with discrete red blood cell (RBC) tracking in realistic microvascular networks. The 5th file contains median values of RBC velocity measurements at capillary bifurcations in the somatosensory cortex of the mouse.</p> <p>Further notes on the simulation results:<br> - The realistic microvascular networks are from the mouse parietal cortex and have first been published in Blinder et al., 2013, Nature Neuroscience (<a href="https://doi.org/10.1038/nn.3426">https://doi.org/10.1038/nn.3426</a>).<br> - The numerical model to simulate blood flow in realistic microvascular networks has been described in Schmid et al., 2017, PLOS Computational Biology (<a href="https://doi.org/10.1371/journal.pcbi.1005392">https://doi.org/10.1371/journal.pcbi.1005392</a>).<br> - MVN1 and MVN2 stands for microvascular network 1 and 2, respectively.<br> - wRBCs and wpPs stands for 'with red blood cells' and 'with passive particles'. These terms describe two different numerical models: The wRBC-model accounts for all RBC related flow phenomena. The wpP neglects the phase-separation and the Fahraeus-Lindqvist effect, i.e. RBCs and flow are decoupled. Further details are available from Schmid et al. (2019, <a href="https://doi.org/10.1371/journal.pcbi.1007231">https://doi.org/10.1371/journal.pcbi.1007231</a>)</p> <p><strong>File format: </strong>pickle (Python)</p> <p><strong>Files 1 - 4 </strong>(Time-averaged simulation results):<br> Filenames: MVN1_wpPs.tar.bz2, MVN2_wpPs.tar.bz2, MVN1_wRBCs.tar.bz2, MVN2_wRBCs.tar.bz2</p> <p>Each compressed folder contains two files:<br> <br> edgesDict.pkl: dictionary with edge/vessel related data: </p> <ul> <li>flow: Flow rate in vessel [um^3/ms]</li> <li>length: Vessel length [um] (Tortuosity is considered)</li> <li>htt: Tube hematocrit in vessel [-]</li> <li>diameter: Effective vessel diameter [um]</li> <li>connectivity: Vertex indices, e.g. start and end vertex of the corresponding vessel</li> </ul> <p>verticesDict.pkl: dictionary with vertex/bifurcation related data:</p> <ul> <li>index: Index of the current vertex </li> <li>coords: Coordinates to describe the position of the vertex [um]</li> <li>pressure: Pressure at the vertex [mmHg]</li> </ul> <p> </p> <p><strong>File 5</strong> (in vivo RBC velocity measurements):<br> Filename: measurementDict.pkl</p> <p>keys:</p> <ul> <li>divergent_d1: divergent bifurcation, RBC velocity measurement in daughter vessel 1</li> <li>divergent_d2: divergent bifurcation, RBC velocity measurement in daughter vessel 2</li> <li>convergent_m1: convergent bifurcation, RBC velocity measurement in mother vessel 1</li> <li>convergent_m2: convergent bifurcation, RBC velocity measurement in mother vessel 2</li> </ul> <p><br> Data structure: list of list,<br> e.g. daughter vessel 1:<br> [[bif.1 - measure.1, bif.1 - measure.2, bif.1 - measure.3], [bif.2 - measure.1, bif.2 - measure.2, bif.2 - measure.3],...]<br> bif.: bifurcation, measure.: measurement.<br> The order of bifurcations is the same for 'divergent_d1' and 'divergent_d2' (and for 'convergent_m1' and 'convergent_m2'). </p> <p> </p>
The impact of pulsed Electromagnetic Fault Injection on true random number generators
<p>Random number acquisition from HECTOR daughter board and the clock signals of both decimator and Priority Encoder. The acquisition where done with a shift register between the output of Priority Encoder’s output and the scope (the same applies for the decimator’s output).</p> <p>Folder is class by type of faults:</p> <ol> <li>tmp_00_wb means two of Priority Encoder’s bits are stuck at 00.</li> <li>tmp_01_wb means two of Priority Encoder’s bits are stuck at 01.</li> <li>tmp_10_wb means two of Priority Encoder’s bits are stuck at 10.</li> <li>tmp_11_wb means two of Priority Encoder’s bits are stuck at 11.</li> <li>tmp_0_wb means one of Priority Encoder’s bit is stuck at 0.</li> <li>tmp_1_wb means one of Priority Encoder’s bit is stuck at 1.</li> <li>Decim_0_wb means one bit of decimator’s output stuck at 0.</li> <li>Decim_1_wb means one bit of decimator’s output stuck at 1.</li> </ol> <p>Curves are class as follows:</p> <p> (<em>Index</em> means the acquisition number)</p> <ol> <li>CIdecimIndex_0.trc files are the clock signal before decimation, i.e. the clock used to sample Priority encoder’s output.</li> <li>COdecimIndex_0.trc files are the clock signal after decimation, i.e. the clock used to sample decimator’s output.</li> <li>IrandDecim_Index_0.trc is the output of the Priority Encoder.</li> <li>OrandDecim_Index_0.trc is the output of the Priority Encoder.</li> </ol> <p>All trc files are binary files.</p> <p>The file conditions_wb recap all the injection parameter used to obtain the different fault.</p>
Supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation
<p>These are supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation</p> <p>The folowing files are available:</p> <ul> <li>Supplement 1. Map of Mayotte with the sampling sites and the rivers.</li> <li>Supplement 2. <em>rbcL</em> primers, reaction mixture, and conditions used for the PCR of the 312-bp <em>rbcL</em> fragment. The information provided is for a single reaction with a final volume of 25µL.</li> <li>Supplement 3. The 20 fastq files containing the demultiplexed DNA reads.</li> <li>Supplement 4. Number of sequence reads for each sample before and after the trimming procedure.</li> <li>Supplement 5. The 20 OTU lists, corresponding to the 20 SSTs, including the number of DNA reads within the 90 samples and their assigned taxonomy.</li> <li>Supplement 6. Sampling site description with sample codes, names of rivers, year, number of raw DNA reads and GPS coordinates.</li> <li>Supplement 7. Values and summary statistics for the environmental variables.</li> <li>Supplement 8. The script used in Mothur for the bioinformatic analysis from trimming to the used OTU lists.</li> </ul> <p> </p>
Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers
<p>Hydraulic and tracer data used in the Chillan case study presented in "Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers"</p>
Impact of algorithm choice in morphological phylogenetic analysis with inapplicable data
<p>Files to accompany Brazeau <em>et al</em>. (2019), describing the impact of using our new algorithm for handling inapplicable data.</p> <p>Details of the datasets analysed are give at <a href="https://ms609.github.io/ExploreInapplicable/r_dataset_details.html">ms609.github.io/ExploreInapplicable/r_dataset_details.html</a></p>
Interlocked thin-ply reinforcements for improved fracture toughness and compression after impact
<p>This dataset contains data showing the effect of interlocked thin-ply reinforcement units on the fracture toughness and compression after impact strength of CFRP laminates. For both the fracture toughness and the CAI experiments, tests were conducted on non-reinforced baseline specimens, as well as reinforced specimens. These experiments were conducted to test a reinforcement concept, consisting of creating reinforcement units by interlocking two thin-ply prepreg layers with a tab-and-slit geometry. These reinforcement units are then inserted between the plies of a regular composite lay-up.</p> <p>This reinforcement concept was first presented at the 18th European Conference on Composite Materials (2018, Athens, Greece). More information on the concept can be found at: http://wwwf.imperial.ac.uk/aeronautics/research/pinholab/interlock-tabs/. </p> <p>The fracture toughness section of this dataset contains both mode I and mode II data. The mode I data was acquired using DCB specimens, and the mode II data was obtained from 4-point end notch flexure tests. Force, displacement, crack length, and strain energy release rate during the test are provided for all specimens.</p> <p>The CAI section of the dataset contains post-impact C-scans, as well as force, displacement, and strain gauge data from the subsequent compression tests.</p> <p><br> </p>
Supplements to "Digital traces of climate risks: assessing the communication impact of Paris resilience strategy".
<p>These supplements correspond the data used in the PhD thesis : “Digital traces of climate risks: assessing the impact of Paris resilience strategy”, published in 2019 on <a href="http://theses.fr/">theses.fr</a> by Rosa Vicari (HM&Co - École des Ponts ParisTech), under the supervision of Daniel Schertzer (HM&Co - École des Ponts ParisTech).</p> <p>More precisely the data set corresponds to the corpora and term lists that are used in the analysis (based on advanced text mining and graph representation) of press articles and tweets concerning flood events in France and strategic documents released by public authorities.</p> <p>The dataset includes the following files:</p> <p>- Suppl2-2016Parisflood-termlist.csv : the list of terms extracted from the corpus of press articles covering the 2016 Seine River flood.</p> <p>- Suppl6-Cotedazflood-termlist.csv: the list of terms extracted from the corpus of press articles covering the 2015 Côte-d'Azur flood.</p> <p>- Suppl7-Corpus-PA-Strategies.csv : the corpus of strategic documents released by public authorities to cope with flood risk in Paris.</p> <p>- Suppl8-Term-list-PA-Strategies.csv : the list of terms extracted from the corpus of strategic documents released by public authorities to cope with flood risk in Paris (2003 - 2017).</p> <p>The following files are not available and cannot be shared for copyright reasons:</p> <p>- Suppl1-2016Parisflood-corpus.csv : the corpus of press articles covering the 2016 Seine River flood.</p> <p>- Suppl3-2018Parisflood-corpus.csv : the corpus of press articles covering the 2018 Seine River flood.</p> <p>- Suppl5-Cotedazflood-corpus.csv : the corpus of press articles covering the 2015 Côte-d'Azur flood.</p> <p>The following file is not available and cannot be shared for privacy reasons:</p> <p>- Suppl4 - Tweet corpus - 2016 Paris flood.csv : the corpus of tweets covering the 2016 Seine River flood.</p>
The Impact of White Dwarf Luminosity Profiles on Oscillation Frequencies
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018ApJ...867L..30T/abstract">The Impact of White Dwarf Luminosity Profiles on Oscillation Frequencies</a></p> <p> </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.