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7 results for “differencing”
Three-way model differencing and conflict detection
<p>We have further enhanced our practice, DSMCompare, to detect semantic differences and conflicts based on a three-way domain-specific comparison approach.</p> <p>To evaluate our practice, using a novel technique; we first used a reverse engineering method to transform the minimal collection of conflicting files of source codes involved in each commit into a three model for 95 conflicting commits over 13 open source GitHub projects and then calculated a high precision and recall compared to RefConfMiner's result.<br> The repository contains the results of the evaluation including the common ancestor model, left version mode, right version model, and the results of semantic comparison and conflict detection for each commit.</p> <p>The source code and relevant artifacts such as meta-models are available at:<br> https://github.com/geodes-sms/DSMCompare/</p>
TVD differencing on three-dimensional unstructured meshes with monotonicity-preserving correction of mesh skewness (Accompanying data)
<p>This data set contains the data accompanying the article F. Denner and B. van Wachem, TVD differencing on three-dimensional unstructured meshes with monotonicity-preserving correction of mesh skewness, Journal of Computational Physics (2015), http://dx.doi.org/10.1016/j.jcp.2015.06.008.</p>
Replication package for Fine-grained, accurate and scalable source differencing
<p>Replication package for the article <em><strong>Fine-grained, accurate and scalable source differencing</strong></em> accepted at the 46th International Conference on Software Engineering (ICSE 2024).</p> <p>List of files:</p> <ul> <li><strong>dataset</strong>: the folder containing the four datasets used in the paper, including the file-pairs and the script to regenerate them.</li> <li><strong>qualitative_experiment_diffs</strong>: the folder containing the GUI used by the participants of the qualitative experiment on the 100 cases.</li> <li><strong>gumtree</strong>: the folder containing the code of GumTree used to run the experiments.</li> <li><strong>analysis</strong>: the folder containing the CSV files and notebooks for the analysis of the results.</li> </ul>
Arctic Glaciers Mass Balance from satellite gravimetry and DEM differencing
<p>This document describes the LEGOS-Magellium mass balance dataset for Arctic glacier regions based on satellite gravimetry and DEM (Digital Elevation Model) differencing. The total mass balance is evaluated for five regions of the 6th version of the Randolph Glacier Inventory namely the Arctic Canada North, Arctic Canada South, Iceland, Svalbard, and the Russian Arctic. The total mass balance of Arctic glaciers is evaluated mainly based on satellite gravimetry measurements. An ensemble approach updated by Blazquez et al. (2018) is adopted to assess uncertainties associated with the processing and post-processing of GRACE (Gravity Recovery And Climate Experiment) and GRACE-FO (GRACE-Follow On) data. A priori information from DEM differencing (Hugonnet et al., 2021) is used to reduce leakage errors associated with the mislocation of signal sources. Total mass changes expressed in Gt are estimated from April 2002 to September 2022 for five RGI regions. The uncertainty on total mass changes is provided with a confidence interval of 95%. The dataset is provided in the GlaMBIE CSV file format.</p> <p>References:</p> <ul> <li>Blazquez, A., Meyssignac, B., Lemoine, J., Berthier, E., Ribes, A., & Cazenave, A. (2018). Exploring the uncertainty in GRACE estimates of the mass redistributions at the Earth surface : Implications for the global water and sea level budgets. Geophysical Journal International, 215(1), 415‑430. https://doi.org/10.1093/gji/ggy293</li> <li>Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., & Kääb, A. (2021). Accelerated global glacier mass loss in the early twenty-first century. Nature, 592(7856), Article 7856. https://doi.org/10.1038/s41586-021-03436-z</li> </ul> <p>The data product has been developed in collaboration between LEGOS and Magellium within the scope of the hybridization<br>challenge funded by the CNES (R&T Hybrid Spatial Gravimetry 2022/2023). </p>
Data from: An RGB colour image steganography scheme using overlapping block-based pixel-value differencing
This paper presents a steganographic scheme based on the RGB colour cover image. The secret message bits are embedded into each colour pixel sequentially by the pixel-value differencing (PVD) technique. PVD basically works on two consecutive non-overlapping components; as a result, the straightforward conventional PVD technique is not applicable to embed the secret message bits into a colour pixel, since a colour pixel consists of three colour components, i.e. red, green and blue. Hence, in the proposed scheme, initially the three colour components are represented into two overlapping blocks like the combination of red and green colour components, while another one is the combination of green and blue colour components, respectively. Later, the PVD technique is employed on each block independently to embed the secret data. The two overlapping blocks are readjusted to attain the modified three colour components. The notion of overlapping blocks has improved the embedding capacity of the cover image. The scheme has been tested on a set of colour images and satisfactory results have been achieved in terms of embedding capacity and upholding the acceptable visual quality of the stego-image.
Dataset Build Log Differencing
<p>It contains the datasets used for the bachelor's thesis about Build Log Differencing.</p>
Data from: An RGB colour image steganography scheme using overlapping block-based pixel-value differencing
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