Back to the Roots: Assessing Mining Techniques for Java Vulnerability-Contributing Commits by Torge Hinrichs (17121607)

“…<p dir="ltr">Vulnerability-contributing commits (VCCs) are changes in code repositories that contribute to the insertion of vulnerabilities. …”

Supporting files for thesis "Deep-learning-based Morphological Modelling: Case Study in Soft Robot Control, Shape Sensing and Deformation" by Yingqi Li (9151304)

“…The algorithm of deep deterministic policy gradient (DDPG) along with domain randomization and offline retraining facilitates fast initialization and stable path following, even under varying tip load, demonstrating its advantages over Jacobian model-based and supervised-learning-based control methods. …”

Fairness in Machine Learning: A Review for Statisticians by Xianwen He (22529252)

“…We organize these fairness-enhancing mechanisms into three categories—pre-processing, in-processing, and post-processing—corresponding to different stages of the machine learning lifecycle and varying levels of access to the underlying algorithm. …”

Design of stiffened panels for stress and buckling via topology optimization: data by Sheng Chu (19655605)

“…To solve the optimization problem, a semi-analytical sensitivity analysis is performed, and the optimization algorithm is outlined. Numerical investigations demonstrate and validate the proposed method.…”

EEG Signals for Robust Cognitive Workload Recognition Under Noisy Conditions.zip by Fariya Bintay Shafi (21692408)

“…</li></ul><h3>File Naming Convention:</h3><p dir="ltr">Each EEG recording is stored in EDF format using the structure:</p><ul><li><code>"SubjectXX_Y.edf"</code></li><li><ul><li><code>XX</code> – Participant number (e.g., 00 to 39)</li><li><code>Y</code> – Task condition:</li><li><ul><li><code>0</code>: No Workload (Resting State)</li><li><code>1</code>: Workload</li></ul></li></ul></li></ul><p dir="ltr">This naming scheme ensures easy identification of each subject’s session and workload state.…”

Data Sheet 1_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl... by Ao Zhang (372387)

“…PIs collectively achieved high-precision predictions (mean 0.42 ≤ R² ≤ 0.94), with the prediction of PH using green leaf index (GLI) in BP algorithm attaining peak accuracy (R² = 0.94). VIs and PIs exhibited comparable predictive capacity for yield, with multi-indicators integrated modeling significantly enhancing performance: VIs achieved R² = 0.87 under RF algorithms, whereas PIs reached R² = 0.81 using BP algorithms. …”

Data Sheet 2_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl... by Ao Zhang (372387)

“…PIs collectively achieved high-precision predictions (mean 0.42 ≤ R² ≤ 0.94), with the prediction of PH using green leaf index (GLI) in BP algorithm attaining peak accuracy (R² = 0.94). VIs and PIs exhibited comparable predictive capacity for yield, with multi-indicators integrated modeling significantly enhancing performance: VIs achieved R² = 0.87 under RF algorithms, whereas PIs reached R² = 0.81 using BP algorithms. …”

CDK6 271123 6Plex TMT.csv by Kathryn Fleming (21585680)

“…</p><p dir="ltr">The raw data files were processed and quantified using Proteome Discoverer software v2.4 (Thermo Scientific) and searched against the UniProt Mouse database (downloaded July 2021: 35859 entries) using the SEQUEST HT algorithm. …”

<b>From street view imagery to the countryside: large-scale perception of rural China using deep learning</b> by Yao Yao (7903457)

“…The project includes both the data and code that support the Pair-CNN model.</p><h3>1. …”

CSPP instance by peixiang wang (19499344)

“…<p dir="ltr">This Python script (<code>instance_generator.py</code>) is a tool designed to <b>programmatically generate synthetic instance data for container stowage and logistics problems.…”

Table 1_Magnetoencephalographic source localization and reconstruction via deep learning.pdf by Stefano Franceschini (488912)

“…Brain signal estimation at source level is a significant challenge in magnetoencephalographic (MEG) data processing. Traditional algorithms offer excellent temporal resolution but are limited in spatial resolution due to the inherent ill-posed nature of the problem. …”

Methodological overview. by Jinming Xiao (12517096)

“…<p>(A) The source reconstruction of TMS-evoked potential of each subject was performed using dSPM method based on MNE software library. The time series of cortical activity were extracted through Schaefer 200 parcellation atlas. …”

Bayesian nonparametric mixture of experts for inverse problems by TrungTin Nguyen (20389663)

“…We establish posterior consistency for the number of mixture components after the merge-truncate-merge algorithm post-processing. Illustrations on simulated data show good results in terms of recovering the true number of experts and the regression function.…”

Fire Lab tree list: A tree-level model of the conterminous United States landscape circa 2014 by Karin L. Riley (19657882)

“…Forest Service’s Forest and Inventory Analysis program (FIA) version 1.7.1 and 2) the landscape target data, which consisted of raster data at 30x30 meter (m) resolution provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE; https://landfire.gov/) FIA plots were imputed to the raster data by the random forests algorithm, providing a tree-level model of all forested areas in the conterminous U.S. …”

Table 1_Image-assisted textural analysis of plagioclase crystals in volcanic rocks: an application to lavas erupted on 2021 at Pacaya volcano, Guatemala.xlsx by Roberto Visalli (8152251)

“…<p>The adoption of semi-automated image processing methods to investigate geo-petrological processes has grown quickly in recent years. …”

Overtuning in Hyperparameter Optimization - Artifacts by Lennart Schneider (20131899)

“…<br></p><p dir="ltr">This data contains the following columns (and some additional ones not explained here which are self-explanatory) where the validation and test performance of each proposed hyperparameter configuration are tracked over time in the form of trajectories</p><ul><li>iteration (iteration of an HPO run)</li><li>valid (validation performance)</li><li>test_retrained (test performance after retraining)</li><li>seed (replication id)</li><li>classifier (learning algorithm)</li><li>data_id (data set id)</li><li>train_valid_size (size of the set used for training and validation)</li><li>resampling (resampling method)</li><li>metric (performance metric)</li><li>method (post selection method and resampling method)</li><li>optimizer (HPO algorithm)</li></ul><p><br></p>…”

Replication Package of "Battling Phish" by Anonymous Author (7372229)

“…</li><li><code>Phishing_Site_URLs_32_Features_Extracted_Data.csv</code> (PSU dataset):<br>Includes phishing and legitimate URLs with 32 extracted lexical features.…”

Alphabet (United States): What does a university look like? by Simon Porter (1364613)

“…Fields of Research with greater than 50% of their output not represented in the network include: Education (65.0%), and Law and Legal Studies (66.67%)</p> <p><strong>Methodology:</strong></p> <p>Graph layout: Batchlayout [1]</p> <p>Clustering: Leiden Algorithm [2]</p> <p>3d Layout: Blender [3]</p> <p>Data: Dimensions [4]</p>…”

Technical University of Munich: What does a university look like? by Simon Porter (1364613)

“…</p> <p><strong>Methodology:</strong></p> <p>Graph layout: Batchlayout [1]</p> <p>Clustering: Leiden Algorithm [2]</p> <p>3d Layout: Blender [3]</p> <p>Data: Dimensions [4]</p>…”

Table 1_An explainable machine learning model for predicting chronic coronary disease and identifying valuable text features.xlsx by Weipeng Gan (22287409)

“…A customized “stop words” list and “custom dictionary” for cardiovascular medicine were created to optimize the processing of text data. Then, ML algorithms were employed to establish CCD prediction models. …”