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code implementation » model implementation (Expand Search), time implementation (Expand Search), world implementation (Expand Search)
python model » action model (Expand Search), motion model (Expand Search)
code implementation » model implementation (Expand Search), time implementation (Expand Search), world implementation (Expand Search)
python model » action model (Expand Search), motion model (Expand Search)
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121
Voice recognition workflow.
Published 2025“…</p><p>Method</p><p>The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. …”
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122
Memory monitoring recognition test main screen.
Published 2025“…</p><p>Method</p><p>The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. …”
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123
Task descriptions.
Published 2025“…</p><p>Method</p><p>The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. …”
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124
A game of life with dormancy - Code
Published 2024“…</p><ul><li>To run an animated simulation, use `python simulation.py'.</li><li>The implementation of Spore Life can be found in gol.py.…”
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125
<b>Code and derived data for</b><b>Training Sample Location Matters: Accuracy Impacts in LULC Classification</b>
Published 2025“…</li><li>Python/Kaggle notebooks (<code>.ipynb</code>): reproducibility pipeline for accuracy metrics and statistical analysis.…”
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126
The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation"
Published 2025“…</li><li>The <b>CIPrediction</b> folder contains model training code.</li><li>The <b>ParallelComputation</b> folder contains geographic computation code.…”
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127
The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation"
Published 2025“…</li><li>The <b>CIPrediction</b> folder contains model training code.</li><li>The <b>ParallelComputation</b> folder contains geographic computation code.…”
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128
Data and code for: Automatic fish scale analysis
Published 2025“…</p><h3>Includeed in this repository:</h3><ul><li><b>Raw data files:</b></li><li><code>comparison_all_scales.csv</code> – comparison_all_scales.csv - manually verified vs. automated measurements of 1095 coregonid scales</li></ul><ul><li><ul><li><code>Validation_data.csv</code> – manually measured scale data under binocular</li><li><code>Parameter_correction_numeric.csv</code> – calibration data (scale radius vs. fish length/weight)</li></ul></li><li><b>Statistical results:</b></li><li><ul><li><code>comparison_stats_core_variables.csv</code> – verification statistics (bias, relative error, limits of agreement)</li><li><code>Validation_statistics.csv</code> – summary statistics and model fits (manual vs. automated)</li></ul></li><li><b>Executable script (not GUI):</b></li><li><ul><li><code>Algorithm.py</code> – core processing module for scale feature extraction<br>→ <i>Note: The complete Coregon Analyzer application (incl. …”
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129
Testing Code for JcvPCA and JsvCRP.
Published 2025“…<p>This file contains the code that implements both metrics in python and apply them on a simulated dataset.…”
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130
A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me...
Published 2025“…<p><b>(a)</b> UMAP, t-SNE, PCA, and Diffmap were first generated using the Python libraries Scikit-learn, UMAP, and PyDiffmap within Jupyter to generate static graphs as a starting point. …”
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131
Workflow of a typical Epydemix run.
Published 2025“…<div><p>We present Epydemix, an open-source Python package for the development and calibration of stochastic compartmental epidemic models. …”
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132
Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b>
Published 2025“…</p><p dir="ltr">GPU:NVIDIA GeForce RTX 3090 GPU</p><p dir="ltr">Bert-base-cased pre-trained model: https://huggingface.co/google-bert/bert-base-cased</p><p dir="ltr">python=3.7,pytorch=1.9.0,cudatoolkit=11.3.1,cudnn=8.9.7.29.…”
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134
Design and Implementation of a Browser-Based Toolfor Protecting Gaming Assets from UnauthorizedAccess
Published 2025“…<p dir="ltr">The project <b>“Design and Implementation of a Browser-Based Tool for Protecting Gaming Assets from Unauthorized Access”</b> focuses on developing a security-oriented software solution that safeguards digital game assets within browser environments.…”
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136
Comparison data 7 for <i>Lamprologus ocellatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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137
Sample data for <i>Neolamprologus multifasciatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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138
Sample data for <i>Lamprologus ocellatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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139
Comparison data 3 for <i>Lamprologus ocellatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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140
Sample data for <i>Telmatochromis temporalis</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”