Material Parameters. by Nan Ru (9594384)

Flowchart of Random Aggregate Placement Process. by Nan Ru (9594384)

Splitting Specimen Aggregate Placement Area. by Nan Ru (9594384)

Specimen for the splitting test. by Nan Ru (9594384)

Example Diagram. by Nan Ru (9594384)

Aggregate Measurement Image in IPP. by Nan Ru (9594384)

Internal changes of the specimen of 0.82 to 0.84. by Nan Ru (9594384)

Internal changes of the specimen of 0.86 to 0.88. by Nan Ru (9594384)

Internal changes of the specimen of 0.7 to 0.75. by Nan Ru (9594384)

Internal changes of the specimen of 0.87 to 0.9. by Nan Ru (9594384)

Internal changes of the specimen of 0.74 to 0.76. by Nan Ru (9594384)

Internal changes of the specimen 1.55 to 1.60. by Nan Ru (9594384)

Internal changes of the specimen of 1.70 to 1.75. by Nan Ru (9594384)

Internal changes of the specimen of 0.89 to 1. by Nan Ru (9594384)

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…The <b>innovations</b> and <b>steps</b> in Case 3, including data download, sample generation, and parallel computation optimization, were independently developed and are not dependent on the GeoCube’s code.</p><h2>Requirements</h2><p dir="ltr">The codes use the following dependencies with Python 3.8</p><ul><li>torch==2.0.0</li><li>torch_geometric==2.5.3</li><li>networkx==2.6.3</li><li>pyshp==2.3.1</li><li>tensorrt==8.6.1</li><li>matplotlib==3.7.2</li><li>scipy==1.10.1</li><li>scikit-learn==1.3.0</li><li>geopandas==0.13.2</li></ul><p><br></p>…”

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…The <b>innovations</b> and <b>steps</b> in Case 3, including data download, sample generation, and parallel computation optimization, were independently developed and are not dependent on the GeoCube’s code.</p><h2>Requirements</h2><p dir="ltr">The codes use the following dependencies with Python 3.8</p><ul><li>torch==2.0.0</li><li>torch_geometric==2.5.3</li><li>networkx==2.6.3</li><li>pyshp==2.3.1</li><li>tensorrt==8.6.1</li><li>matplotlib==3.7.2</li><li>scipy==1.10.1</li><li>scikit-learn==1.3.0</li><li>geopandas==0.13.2</li></ul><p><br></p>…”

<b>Code and derived data for</b><b>Training Sample Location Matters: Accuracy Impacts in LULC Classification</b> by Pajtim Zariqi (22155799)

“…</li><li>Python/Kaggle notebooks (<code>.ipynb</code>): reproducibility pipeline for accuracy metrics and statistical analysis.…”

<b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b> by Marly G F Costa (19812192)

“…<p dir="ltr"><b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b></p><p dir="ltr">The code was developed in the Google Collaboratory environment, using Python version 3.7.13, with TensorFlow 2.8.2. …”

Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b> by Zhou (20184816)

“…Attached is the micro-emotion annotation code based on pytorch, which can be used to annotate the Goemotions dataset by yourself, or predict the emotion classification based on the annotation results. …”

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

“…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”