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478 results for “artifact”
Electrodermal Activity artifact correction BEnchmark (EDABE)
<p>"Electrodermal Activity artifact correction BEnchmark" (EDABE) is a dataset for training and testing artifact recognition and correction models to automatically remove major artifacts in electrodermal activity (EDA) signals. It is the first public benchmark to compare methods.</p> <p>EDABE contains a total of 74.46 h of EDA recording affected by hand and body motion artifacts from 43 subjects. It is divided into a training set with 33 subjects (56.27 h), and test set with 10 subjects (18.19 h). The data was collected using a Shimmer3 GSR+ Unit at 128 Hz.</p> <p>The dataset is used to develop a fully automatic pipeline that emulates the manual correction done by the expert, providing a final clean signal. The paper that describe the pipeline is currently in a peer-review process.</p> <p>Each file includes in the filename the user_id and the expert that correct the signal. In addition, the file includes the signal with the following variables:</p> <ul> <li>time: timestamp of the signal.</li> <li>rawdata: raw data obtained by Shimmer3 GSR+ Unit.</li> <li>cleandata: reconstructed clean signal performed by a human expert.</li> <li>binarytarget: label of each sample as artefact or no artifact.</li> <li>signal_automatic: automatic cleaning of the signal performed by the automatic pipeline.</li> <li>predArtifacts: label predicted by the automatic cleaning pipeline.</li> <li>postProcessedPredArtifacts: label predicted by the automatic cleaning pipeline after postprocessing.</li> </ul>
Artifact for "Are LLMs all we need in code comparison tasks?"
<p>This is the artifact for paper "Are LLMs all we need in code comparison tasks?"</p> <p>It includes all the datasets and results.</p>
Clay Artifact 3D Scan
3D photogrammetry scan of a small clay artifact (27mm tall) Source: Objaverse 1.0 / Sketchfab
Apparent RNA bridging between PRC2 and chromatin is an artifact of non-specific chromatin precipitation upon RNA degradation
GEO Series GSE240380. Mus musculus. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
The COAD-Artifact Dataset
Open the record for dataset details and reuse information.
coMPIx Artifact
<p>Artifact Descriptions for coMPIx</p>
Artifact for "Accumulation Analysis"
<p>The artifact for the paper "Accumulation Analysis", submitted to ECOOP 2022. It contains the data for our literature survey of papers that use typestate specifications. See the file `README.md` for details.</p>
Artifact: Are LLMs all we need in code comparison tasks?
<p>The datasets and results for paper "Are LLMs all we need in code comparison tasks?"</p>
Using Simplification to Understand Issues in Comparative Comprehension Artifacts
Open the record for dataset details and reuse information.
PyNose: a test smell detector for Python — sources and artifacts
<p>This archive contains:</p> <ol> <li>The source code of PyNose, a test smell detector for Python.</li> <li>The pre-built version of the tool that can be used as a plugin inside PyCharm.</li> <li>Lists of projects used in our study.</li> <li>A full list of examples of the newly introduced Suboptimal Assert test smell.</li> <li>An example of a change graph used to discover Python-specific test smells.</li> <li>The results of the small-scale mapping study: a full list of papers, a full list of test smells, and the correlation between them.</li> </ol> <p>You can find all the details in README.txt</p>
Dataset related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"
<p>This record contains raw data related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"</p> <p>Abstract</p> <p>Background</p> <p>Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED).</p> <p>Methods</p> <p>In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi<sup>42</sup>(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability.</p> <p>Results</p> <p>In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF − 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s).</p> <p>Conclusions</p> <p>Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.</p>
Artifacts of PEARL
<p>This repository contain the artifacts of a paper titled "Program Repair as a Code Completion Problem: Addressing the Challenge of Accurately Locating Fine-Grained Buggy Code Elements" under review by ICSE'21.</p> <p>The files <strong>prediction_CoCoNut.csv</strong> and <strong>prediction_ManySStuBs4J.csv</strong> illustrate the prediction results of <strong>BEP</strong> on the two datasets, in which the column <em>element_Rank</em> shows the ranking of buggy element and the column <em>rank</em> is for operation path.</p> <p>The file <strong>Pipeline_Results.zip</strong> contains the results of our repair pipeline on the 111 single-token bugs. In each folder, <em>rem.txt</em> corresponds to the buggy line, <em>add.txt</em> corresponds to the correct line, and <em>context.txt</em> corresponds to the whole buggy method. These three files are required inputs for each prediction. <em>query.txt</em> is the content sent to <em>AnyCodeGen </em>and the returned code fragment is stored in <em>result.txt</em>. </p> <p>We also open the cleaned dataset of ManySStuBs4J which only contains single-token bugs in <strong>Single-token_bugs_in_ManySStuBs4J.zip</strong>. In this file, each bug is corresponding to three lines with the same line number in three different files which are <em>rem.txt</em>, <em>add.txt</em>, and <em>context.txt</em>, respectively representing the buggy line, correct line, and the buggy method. This storage style follows the training set of <a href="https://github.com/lin-tan/CoCoNut-Artifact">CoCoNut</a>. Note that we do not release our pre-processed dataset of CoCoNut sinse it is too large. We encourage researchers to process this dataset based on their own needs. Moreover, we do provide the script we used for selecting single-token bugs which is <strong>single-token_selection.py</strong>.</p> <p>We also note there is no visual interface for the newly added bugs in Defects4J-V2.0. We thus provide our script for selecting single-token bugs (<strong>getSingle-token_bugs_from_D4J.py</strong>) from these bugs, making it easy for others to reproduce our experiment. </p> <p><strong>Case Study:</strong></p> <p><strong>PEARL</strong> does not work well on method name-related fix. For the following bug (Closure-10), our BEP model successfully predicts the oracle operation path as No.2.</p> <pre><code class="language-java">- return allResultsMatch(n, MAY_BE_STRING_PREDICATE); + return anyResultsMatch(n, MAY_BE_STRING_PREDICATE);</code></pre> <p>Nonetheless, we have to change this statement into "<strong>return ??</strong>" when querying <strong>AnyCodeGen</strong> due to the format restriction of it. Unfortunately, <strong>AnyCodeGen</strong> cannot synthesize such a detailed method call, thus leading to the failure of PEARL of repairing this bug.</p> <p> </p> <p> </p> <p>The source code of <strong>PEARL</strong> will be open-access upon acceptance.</p>
The Artifact of the ICSE2023 Submission #582
<p>In this online repository, we release the source code of each of the selected techniques as well as the experiment results from each technique (which are stored in the <em>Results.zip</em> file).</p> <p> </p> <p>The code to calculate the evaluation metrics are reused from <a href="https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans/evaluator/CodeBLEU">CodeBLEU</a>.</p> <p> </p> <p>Below is a piece of code generated by CodeT5. In this case, CodeT5 generates a statement recurrently, which leads to the syntactic error. Despite that, the code itself fulfills certain functionalities, and that is why it can achieve a CodeBLEU of 24.9%.</p> <p> </p> <pre><code class="language-python">def makeMimiLocal(filename): try: with open(filename, 'rb') as f: data = f.read() except IOError: data = b'' data = data.decode('utf-8') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\ </code></pre> <p> </p>
Artifacts for Fuzzing SMT Solvers with Diversified Sub-formulas
<p>## Installation:<br> ```<br> virtualenv --python=/usr/bin/python3 virenv<br> source virenv/bin/activate<br> cd octopus<br> python3 setup.py install<br> ```</p> <p>## Usage:</p> <p>```<br> octopus --benchmark=[PATH TO SEED DIRECTORY] --solverbin=[PATH TO SOLVER BIN] --solver=[SOLVER NAME] --theory=[SOLVER THEORY]<br> ```</p> <p>For example:</p> <p>```<br> octopus --benchmark=/home/SMT2021 --solverbin=../z3/build/z3 --solver=z3 --theory=LIA<br> ```</p> <p>To run `n` parallel instances of octopus on `n` cores, use the `--cores` flag. For example:</p> <p>```<br> octopus --benchmark=[PATH TO SEED FILES] --solverbin=[PATH TO SOLVER BIN] --solver=[SOLVER NAME] --theory=LIA --cores=10<br> ```</p> <p><br> ## Refutational soundness bugs detected so far:<br> octopus has detected many new "refutational soundness" bugs in Z3. <br> Here is a list of issues we reported.</p> <p>https://github.com/Z3Prover/z3/issues/5373 `[z3]` `[QF_NRA]` <br><br> https://github.com/Z3Prover/z3/issues/5443 `[z3]` `[QF_BVFP]` <br><br> https://github.com/Z3Prover/z3/issues/5447 `[z3]` `[QF_ABV]` <br><br> https://github.com/Z3Prover/z3/issues/5456 `[z3]` `[QF_IDL]` <br><br> https://github.com/Z3Prover/z3/issues/5457 `[z3]` `[QF_BV]` <br><br> https://github.com/Z3Prover/z3/issues/5460 `[z3]` `[QF_BV]` <br><br> https://github.com/Z3Prover/z3/issues/5468 `[z3]` `[AUFLIRA]` <br><br> https://github.com/Z3Prover/z3/issues/5488 `[z3]` `[QF_BV]` <br><br> https://github.com/Z3Prover/z3/issues/5502 `[z3]` `[QF_NIA]` <br><br> https://github.com/Z3Prover/z3/issues/5508 `[z3]` `[QF_NIA]` <br><br> https://github.com/Z3Prover/z3/issues/5423 `[z3]` `[QF_BVFP]` `Won't fixed` <br><br> </p>
Artifacts for Code Coverage Profiler Validation with Debugging Support
<p><strong>Code Coverage Profiler Validation with Debugging Support</strong></p> <p>Table of Contents</p> <ul> <li>Background</li> <li>Install</li> <li>Usage</li> <li>Experiment</li> <li>Violations</li> <li>Bugs</li> </ul> <p><strong>Background</strong></p> <p><strong>DeCov</strong> is the tool for code coverage profiler validation with debugging support. We totally submit 24 bug reports for gcov and llvm-cov. Of these 24 bug reports, 18 are confirmed/fixed by developers, 2 are marked as duplicate, and the other 4 bug reports are waiting for the developers' confirmation.</p> <p><strong>Install</strong></p> <p>This project uses <a href="https://gcc.gnu.org/">gcc</a>, <a href="http://www.gnu.org/software/gdb/">gdb</a>, <a href="http://llvm.org/">llvm</a> and <a href="http://lldb.llvm.org/">lldb</a>. Go check them out if you don't have them locally installed.</p> <p><code>apt install gcc gdb llvm lldb </code></p> <p><strong>Usage</strong></p> <p>To use DeCov for gcov validation:</p> <p><code>python3 decov-gcov-pexpect.py -s test.c </code></p> <p>or</p> <p><code>python3 decov-gcov-pexpect.py -s testprograms </code></p> <p>To use DeCov for llvm-cov validation:</p> <p><code>python3 decov-llvmcov-pexpect.py -s test.c </code></p> <p>or</p> <p><code>python3 decov-llvmcov-pexpect.py -s testprograms </code></p> <p><strong>Experiment</strong></p> <p>First, download the gcc-10.1.0.tar.xz:</p> <p><code><code>wget http://ftp.gnu.org/gnu/gcc/gcc-10.1.0/gcc-10.1.0.tar.xz </code></code></p> <p>Then, unzip gcc-10.1.0:</p> <p><code>tar -xvf gcc-10.1.0.tar.xz </code></p> <p>Finally, run Decov over the testsuite of gcc-10.1.0:</p> <p><code>python3 decov-gcov-pexpect.py -s gcc-10.1.0/gcc/testsuite/ </code></p> <p>or</p> <p><code>python3 decov-llvmcov-pexpect.py -s gcc-10.1.0/gcc/testsuite/ </code></p> <p>Note that, we recommend to use the parallel option when using our tool if your computer has multi-cores:</p> <p><code>python3 decov-gcov-pexpect.py --parallel -s gcc-10.1.0/gcc/testsuite/ </code></p> <p>or</p> <p><code>python3 decov-llvmcov-pexpect.py --parallel -s gcc-10.1.0/gcc/testsuite/ </code></p> <p><strong>Violations</strong></p> <p>Experimental data are in the Expr folder:</p> <ul> <li>files-DIFFCOV-gcc.txt and files-DIFFCOV-clang.txt list the filenames in the testsuites of GCC-10.1.0 that the coverage statistics from coverage profilers inconsistent with the hit count from debuggers</li> <li>DIFFCOV-Gcov-vs-GDB.txt and DIFFCOV-LLVMcov-vs-LLDB.txt list the filesname and the the respected lines of code that violating <em>HMR</em>, coverage statistics is not consistent with the hit count</li> <li>files-SAMECOV-gcc.txt and files-SAMECOV-clang.txt list the filenames in the testsuites of GCC-10.1.0 that the coverage statistics from coverage profilers consistent with the hit count from debuggers.</li> <li>files-CPLERROR-gcc.txt and files-CPLERROR-clang.txt list the filenames in the testsuites of GCC-10.1.0 that cannot be correctly compiled independently by respected compilers</li> </ul> <p><strong>Bugs</strong></p> <p>Bugs reported by <strong>DeCov</strong> for LLVM-cov:</p> <p>llvm-cov 48818 Status History: "NEW"</p> <p>llvm-cov 48795 Status History: "NEW"</p> <p>llvm-cov 48278 Status History: "NEW" -> "DUPLICATE"</p> <p>llvm-cov 48264 Status History: "NEW" -> "INVALID" -> "REOPENED"</p> <p>llvm-cov 48263 Status History: "NEW"</p> <p>llvm-cov 45850 Status History: "New" -> "CONFIRMED"</p> <p>llvm-cov 45849 Status History: "New" -> "FIXED" -> "REOPENED" -> "FIXED"</p> <p>llvm-cov 45846 Status History: "NEW" -> "CONFIRMED"</p> <p>llvm-cov 45845 Status History: "NEW" -> "CONFIRMED"</p> <p>llvm-cov 45844 Status History: "NEW" -> "DUPLICATE"</p> <p>llvm-cov 45821 Status History: "NEW" -> "CONFIRMED"</p> <p>llvm-cov 45678 Status History: "NEW" -> "CONFIRMED"</p> <p>llvm-cov 45595 Status History: "NEW" -> "CONFIRMED"</p> <p>Bugs reported by <strong>DeCov</strong> for Gcov:</p> <p>gcov 97925 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 97924 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 97923 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 97917 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 97910 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 93757 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 93735 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 93726 Status History: "UNCONFIRMED" -> "WAIT"</p> <p>gcov 93725 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 93706 Status History: "UNCONFIRMED" -> "NEW"</p> <p>gcov 93680 Status History: "UNCONFIRMED" -> "NEW"</p>
Artifacts of the paper under review by ESEC/FSE
<p><strong>This repository has been deprecated. Please refer to this <a href="https://github.com/Ringbo/CCT5">link</a> for the latest version.</strong></p> <p>--------------------------------------------------------------------</p> <p>This is the online repository of <strong>CCT5: A Code-Change-Oriented Pre-Trained Model</strong>, a research paper under review by ESEC/FSE. We release the source code and relevant data of CCT5, the data used in our evaluation, as well as the experiment results.</p> <ul> <li>Getting Started</li> </ul> <pre><code>pytorch==1.8.0 cudatoolkit=11.1 datasets==1.18.3 transformers==4.16.2 tensorboard==2.8.0 tree-sitter==0.19.1</code></pre> <ul> <li> <p>Dataset</p> </li> </ul> <p>We provide the datasets of pretraining and three downstream tasks. The datasets should be downloaded and uncompressed in the <strong>data </strong>directory.</p> <p><code>pretraining/CodeChangeNet.tar.lrz</code> contains the dataset used in pretraining, i.e., CodeChangeNet. CodeChangeNet is a collection of over 1000 star projects written in six popular programming languages: Go, Java, JavaScript, PHP, Python, and Ruby.</p> <p><code>finetune/MessageGeneration</code> contains the download and process script of task1 - Commit Message Generation;</p> <p><code>finetune/CommentUpdate</code> contains the dataset of downstream task2 - Just-in-Time Comment Update;</p> <p><code>finetune/JITDefectPrediction</code> contains the dataset of downstream task3 - Just-in-Time Defect Prediction;</p> <ul> <li> <p>Pretrain the model</p> </li> </ul> <pre><code class="language-bash">cd sh bash pretrain.sh</code></pre> <ul> <li> <p>Finetune and evaluate the downstream task</p> </li> </ul> <p>Commit Message Generation</p> <pre><code class="language-bash">cd sh bash finetune_msggen.sh</code></pre> <p>Just-in-Time Comment Update</p> <pre><code class="language-bash">cd sh bash finetune_cup.sh</code></pre> <p>Just-in-Time Defect Prediction</p> <pre><code class="language-bash">cd sh bash finetune_jit.sh</code></pre> <ul> <li>Results</li> </ul> <p>The experiment results of two generation tasks and the ablation study are stored in <strong>results</strong> with the directory <strong>MessageGeneration</strong>, <strong>CommentUpdate</strong>, and <strong>Ablation</strong>, respectively.</p>
Artifacts
<p>Artifacts</p>
Artifact Evaluation
<p>SIGCOMM AE</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.