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478 results for “artifact”
Artifact of 'Unification for Subformula Linking under Quantifiers'
<p>This is the artifact for the CPP '24 paper 'Unification for Subformula Linking under Quantifiers'. It contains formalizations of section 3.3 and section 4, as well as patches for Iris's improved version of iFrame.</p>
Artifact for "Challenges in Empirically Testing Memory Persistency Models"
<p>Presented here are the litmus tests, auxiliary scripts, and output data constituting the artifact accompanying the paper titled 'Challenges in Empirically Testing Memory Persistency Models,' published in ICSE NIER'24. For detailed information, please refer to the README.</p>
Artifacts of the CGO 2024 Paper: EasyTracker: A Python Library for Controlling and Inspecting Program Execution
<p>This is the archive of the artifacts for the CGO 2024 Paper <em>"EasyTracker: A Python Library for Controlling and Inspecting Program Execution"</em></p> <p>The EasyTracker library is an open source project, refer to the Home page and Gitlab repository for up-to-date versions: </p> <ul> <li>Home Page: <a href="https://corse.gitlabpages.inria.fr/easytracker">https://corse.gitlabpages.inria.fr/easytracker</a></li> <li>Repository: <a href="https://gitlab.inria.fr/CORSE/easytracker">https://gitlab.inria.fr/CORSE/easytracker</a></li> </ul> <p>The details of the artifacts generation are described in the paper appendix or in the <code>README.md</code> file included in the main artifacts archive <code>easytracker-artifacts-cgo-2024-v1.2.0.tar.gz</code>.</p> <p>Summary of artifacts construction steps (execution in a Docker container):</p> <ul> <li>ensure Docker is installed with <code>docker --version</code>,</li> <li>download the artifacts archive <code>easytracker-artifacts-cgo-2024-v1.2.0.tar.gz</code>,</li> <li>extract with <code>tar xvzf easytracker-artifacts-cgo-2024-v1.2.0.tar.gz</code>,</li> <li>change dir with <code>cd eastracker-artifacts-cgo-2024</code>,</li> <li>download the EasyTracker sources archive <code>easytracker-archive-dfe8aa888f.tar.gz</code><a href="../api/records/10428215/draft/files/easytracker-archive-dfe8aa888f.tar.gz/content" target="_blank" rel="noopener noreferrer">,</a></li> <li>extract with <code>tar xvzf easytracker-archive-dfe8aa888f.tar.gz</code>,</li> <li>download the docker image <code>docker-image-easytracker-1.2.0.tar</code>,</li> <li>load the Docker image with <code>docker load -i docker-image-easytracker-1.2.0.tar</code>, </li> <li>generate all artifacts with <code>./in-docker.sh ./run-all.sh</code>,</li> <li>all artifacts are generated in <code>figure-*/</code> directories,</li> <li>refer to the artifacts archive <code>README.md</code> file for more details, or refer to the paper artifacts appendix.</li> </ul> <p>Note that this artifacts archive is extracted from the artifacts repository at tag <code>v1.2.0</code>: <a title="Opens in new tab" href="https://gitlab.inria.fr/CORSE/easytracker-artifacts-cgo-2024/-/tree/v1.2.0" target="_blank" rel="noopener">https://gitlab.inria.fr/CORSE/easytracker-artifacts-cgo-2024/-/tree/v1.2.0 </a></p>
Artifact description: CloverLeaf on Intel Multi-Core CPUs: A Case Study in Write-Allocate Evasion
<p>In this paper we analyze the MPI-only version of the CloverLeaf code from the SPEChpc 2021 benchmark suite on recent Intel Xeon "Ice Lake" and "Sapphire Rapids" server CPUs. We observe peculiar breakdowns in performance when the number of processes is prime. Investigating this effect, we create first-principles data traffic models for each of the stencil-like hotspot loops. With application measurements and microbenchmarks to study memory data traffic behavior, we can connect the breakdowns to SpecI2M, a new write-allocate evasion feature in current Intel CPUs. For serial and full-node cases we are able to predict the memory data volume analytically with an error of a few percent. We find that if the number of processes is prime, SpecI2M fails to work properly, which we can attribute to short inner loops emerging from the one-dimensional domain decomposition in this case. We can also rule out other possible causes of the prime number effect, such as breaking layer conditions, MPI communication overhead, and load imbalance.</p>
A Study of Real-world Data Races in Golang (Artifact)
<p>The concurrent programming literature is rich with tools and techniques for data race detection. Less, however, has been known about real-world, industry-scale deployment, experience, and insights about data races. Golang (Go for short) is a modern programming language that makes concurrency a first-class citizen. Go offers both message passing and shared memory for communicating among concurrent threads. Go is gaining popularity in modern microservice-based systems. Data races in Go stand in the face of its emerging popularity.</p> <p>In this paper, using our industrial codebase as an example, we demonstrate that Go developers embrace concurrency and show how the abundance of concurrency alongside language idioms and nuances make Go programs highly susceptible to data races. <br> Google's Go distribution ships with a built-in dynamic data race detector based on ThreadSanitizer. Dynamic race detectors pose scalability and flakiness challenges; we discuss various software engineering trade-offs to scale this detector to work effectively at scale.</p> <p>We have deployed this detector in our 50-million lines of Go codebase hosting 2100 distinct microservices, found over 2000 data races, fixed over 1000 data races, spanning 790 distinct code patches submitted by 210 unique developers over a six-month period. Based on a detailed investigation of these data race patterns in Go, we make seven high-level observations relating to the complex interplay between the Go language paradigm and data races. </p>
Artifact: "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices
<p>Artifact for "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices</p>
EPIC: Annotated epileptic EEG independent components for artifact reduction
<p>Scalp electroencephalogram is a non-invasive multi-channel biosignal that records the brain’s electrical activity. It is highly susceptible to noise that might overshadow important data. Independent component analysis is one of the most used artifact removal methods. Independent component analysis separates data into different components, although it can not automatically reject the noisy ones. Therefore, experts are needed to decide which components must be removed before reconstructing the data. To automate this method, researchers have developed classifiers to identify noisy components. However, to build these classifiers, they need annotated data. Manually classifying independent components is a time-consuming task. Furthermore, few labeled data are publicly available. This dataset is composed of a source of annotated electroencephalogram independent components acquired from patients with epilepsy (EPIC Dataset). This dataset contains 77,426 independent components obtained from approximately 613 hours of electroencephalogram, visually inspected by two experts, which was already successfully utilized to develop independent component classifiers.</p>
Supporting data for the article "Charge-Induced Artifacts in Nonlocal Spin-Transport Measurements: How to Prevent Spurious Voltage Signals"
<p>Supporting data for the article "Charge-Induced Artifacts in Nonlocal Spin-Transport Measurements: How to Prevent Spurious Voltage Signals"</p> <p>If the design files are used to reproduce the current source, we ask to cite the peer-reviewed publication of our work in any publication in which the adjustable virtual ground feature is used for measurements.</p> <p>We provide the following data and design files:</p> <p>1.) Data used to create each figure both in the main manuscript and the supplementary material in the zip-folder "Data presented in figures".<br> The data is provided in two formats:<br> I.) Raw data in freely accessible file formats such as .dat, .txt, or .csv.<br> II.) Graphically processed data (figures as shown in the publication) in the proprietary file format .opju. Used program: OriginPro 2019</p> <p>2.) LTspice models in the zip-folder "LTspice models". The simulations were conducted with LTspice version XVII(x64).</p> <p>3.) Altium Designer files of the current source in the zip-folder "Altium Designer files".</p> <p>4.) Gerber X2 and NC drill files (dimensions in millimeters) for the manufacturing of the PCB that is used in our project in the zip-folder "Fabrication files". See the readme file in the directory for more information on the fabrication process.</p> <p>5.) A bill of materials.</p>
Integration of Skyline Queries into Spark SQL - Experiment Artifacts
<p>This repository contains the artifacts for the paper "Integration of Skyline Queries into Spark SQL" published at the 2023 EDBT conference. Here, we publish our binaries, benchmark data, queries, and additional scripts used in the experimental evaluation.</p>
Dataset for Code Review Guidelines for GUI-based Testing Artifacts
<p>The Excel file contains meta-data about collected white and gray literature, applied inclusion/exclusion criteria, the code system, and a list of identified guidelines.</p>
Documentation artifacts for conversational SRS in chatbots: a systematic review and a new meta-model proposal and validation
<p>Context: Chatbots are complex applications due to their capacity to engage and maintain a conversation with humans. However, the conversational-related requirements of chatbots are hard to elicit, document, and test. Another challenge is the documentation, since there are not so many directions on how to register and test subjective requirements. </p> <p>Methods: We followed systematic literature review (SLR) guidelines and identified 42 studies that address the artifacts used by practitioners to document conversational-related requirements in literature. We also investigated what conversational requirements are addressed in requirements documentation.</p> <p>Results: The main results indicate that UML diagrams, prototypes, tables of requirements, conversational flows, and scenarios are present in most chatbot documentation. Except for UML diagrams, those artifacts are used to document standard requirements or conversational requirements. In those artifacts, context-dependent behavior, assertivity, error handling, and human-like attitude are the most approached conversational requirements in the studies. In sequence, based on our findings, we proposed the conversational integrated map and validated it by conducting a 2-step questionnaire among software practitioners experience in requirements engineering and chatbot requirement's specification.</p> <p>Conclusion: Future studies should investigate if existing artifacts are enough to address all complex aspects of chatbots' specific conversational requirements or require further adaptation. Future studies should investigate specific SRS needs for different types of softwares.</p>
РИС. 8. Примеры проблем с иЗображением при работе на СЭМ. А, В. Засветка раЗличных частей раковин глохидиев (А. Anodonta anatina (=Colletopterum), оЗ. Красное, ХакасиЯ. В. Inversiunio reinianus, оЗ. Бива, о-в Хонсю, ЯпониЯ). C. РаЗнаЯ скорость сканированиЯ (слева – очень быстраЯ, справа – медленнаЯ) наружной поверхности глохидиЯ (Anodonta cygnea, р. Ялма, МосковскаЯ обл.). D. Артефакты в виде гориЗонтальных полос вследствие накоплениЯ отрицательного ЗарЯда при недостаточном напылении внутренней поверхности глохидиЯ (Nodularia douglasiae, ПетровскаЯ протока, бассейн р. Амур, Хабаровский кр.). МасШтаб 50 мкм (А, В), 2 мкм (С), 5 мкм (D). Микроскопы Zeiss EVO 40 (А, С, D), Zeiss MERLIN (В), напыление углеродом (А, С), хромом (В, D). FIG. 8. Illustration of different problems with SEM images. A, B. Overall illumination of some glochidia shells parts (A. Anodonta anatina (= Colletopterum), Krasnoe Lake, Khakassia. B. Inversiunio reinianus, Biwa Lake, Honshu Island, Japan). C. Different scanning speed (faster on the left and slower on the right) of the exterior glochidia valve (Anodonta cygnea, Yalma River, Moscow Oblast). D. Artifacts as horizontal stripes because of additional accumulation of a negative charge due to insufficient coating of the interior glochidia valve (Nodularia douglasiae, Petrovskaya channel, Amur River basin, Khabarovsk Krai). Scale bars 50 μm (A, B), 2 μm (C), 5 μm (D). Zeiss EVO 40 (A, C, D) and Zeiss MERLIN (B) microscopes, sputter coating with carbon (A, C) and chromium (B, D). in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 8. Примеры проблем с иЗображением при работе на СЭМ. А, В. Засветка раЗличных частей раковин глохидиев (А. Anodonta anatina (=Colletopterum), оЗ. Красное, ХакасиЯ. В. Inversiunio reinianus, оЗ. Бива, о-в Хонсю, ЯпониЯ). C. РаЗнаЯ скорость сканированиЯ (слева – очень быстраЯ, справа – медленнаЯ) наружной поверхности глохидиЯ (Anodonta cygnea, р. Ялма, МосковскаЯ обл.). D. Артефакты в виде гориЗонтальных полос вследствие накоплениЯ отрицательного ЗарЯда при недостаточном напылении внутренней поверхности глохидиЯ (Nodularia douglasiae, ПетровскаЯ протока, бассейн р. Амур, Хабаровский кр.). МасШтаб 50 мкм (А, В), 2 мкм (С), 5 мкм (D). Микроскопы Zeiss EVO 40 (А, С, D), Zeiss MERLIN (В), напыление углеродом (А, С), хромом (В, D). FIG. 8. Illustration of different problems with SEM images. A, B. Overall illumination of some glochidia shells parts (A. Anodonta anatina (= Colletopterum), Krasnoe Lake, Khakassia. B. Inversiunio reinianus, Biwa Lake, Honshu Island, Japan). C. Different scanning speed (faster on the left and slower on the right) of the exterior glochidia valve (Anodonta cygnea, Yalma River, Moscow Oblast). D. Artifacts as horizontal stripes because of additional accumulation of a negative charge due to insufficient coating of the interior glochidia valve (Nodularia douglasiae, Petrovskaya channel, Amur River basin, Khabarovsk Krai). Scale bars 50 μm (A, B), 2 μm (C), 5 μm (D). Zeiss EVO 40 (A, C, D) and Zeiss MERLIN (B) microscopes, sputter coating with carbon (A, C) and chromium (B, D).
Supplementary Material for Documentation artifacts for conversation-related requirements specification in chatbots: a systematic review and a meta-model proposal
<p>This is a supplementary data of the tertiary systematic literature review conducted in the paper "Conversation-related requirements specification in chatbots: a systematic review and a meta-model proposal".</p> <p>Context: Chatbots are complex applications due to their capacity to engage and maintain a conversation with humans. However, the conversational-related requirements of chatbots are hard to elicit, document, and test. Another challenge is the documentation since there are not so many directions on how to register and test subjective requirements.</p> <p>Methods: We followed systematic literature review (SLR) guidelines and identified 42 relevant papers that address the artifacts used by practitioners to document conversational-related requirements in literature. We also investigated what conversational requirements are addressed in requirements documentation.</p> <p>Results: The main results indicate that UML diagrams, prototypes, tables of requirements, conversational flows, and scenarios are present in most chatbot documentation. Except for UML diagrams, those artifacts are used to document standard requirements or conversational requirements. In those artifacts, context-dependent behavior, assertivity, error handling, and human-like attitude are the most approached conversational requirements in the studies. In sequence, based on our findings, we propose the conversational integrated map, a meta-model solution as documentation of conversational requirements.</p>
Recovery of unavailable Requirements Quality Artifacts
<p>We extracted a <a href="http://www.reqfactoront.com/">requirements quality factors ontology</a> from existing requirements quality literature in previous research. This ontology revealed that several artifacts (data sets and tools/implementations) are unavailable, hindering progress in the research domain. In the project based on this replication package, we attempted to recover lost artifacts by requesting authors to disclose their artifacts according to open science principles. This repository contains both the process description, tools for conduction of the recovery, the results, and the evaluation thereof.</p>
Supplemental Material: Research Artifacts for Human-Oriented Experiments in Software Engineering: An ACM Badges–driven Structure Proposal
<p>This Research Artifact contains supplemental material from the study: "Supplemental Material: Research Artifacts for Human-Oriented Experiments in Software Engineering: An ACM Badges–driven Structure Proposal". The supplementary material contains:</p> <ol> <li>The list of the 106 primary studies classified by journals.</li> <li>The list of the 12 research artifacts classified by conferences.</li> <li>The .xlsx file of the dataset used to analyze the RQs.</li> <li>The .xlsx file of the dataset used to analyze the research artifacts problems (Table 3). </li> <li>The list of the figures published in the scientific article.</li> </ol>
OpenAlex Topic Classification v1 Model Artifacts and Training Data
<p>This is all data used to train the topic classification model and also the model artifacts to deploy the model. Please see the github repo for more information:</p> <p>https://github.com/ourresearch/openalex-topic-classification</p>
Linear measurements of Aterian tanged stone artifacts from North African collections
<p>Linear measurements collected from Aterian tanged tools from various museum collections in Morocco and France. Edge lengths were computed using photographs of the artifacts and ImageJ. </p>
Tool Artifact for "Mutation-based Lifted Repair of Software Product Lines"
<p>In this work, we describe the installation, usage, and evaluation results of the tool SPLAllRepair introduced by the paper<br>``Mutation-based Lifted Repair of Software Product Lines''. We provide step-by-step instructions on how to download, run, and compare the tool's outputs to outputs described in the paper. The tool implements a novel lifted repair algorithm for program families (Software Product Lines - SPLs) based on code mutations. The inputs of our algorithm are an erroneous SPL and a specification given in the form of assertions. We use variability encoding to transform the given SPL into a single program, called family simulator, which is translated into a set of SMT formulas whose conjunction is satisfiable iff the simulator (i.e. the input SPL) violates an assertion. We use a predefined set of mutations applied to feature and program expressions of the given SPL.<br>The algorithm repeatedly mutates the erroneous family simulator and checks if it becomes (bounded) correct. The outputs are all minimal repairs in the form of minimal number of (feature and program) expression replacements such that the repaired SPL is (bounded) correct with respect to a given set of assertions. We present the experimental results showing that our approach is able to successfully repair various interesting #ifdef-based C SPLs.</p>
Computational Artifacts for the Paper "Are Noise-resilient Logical Timers useful for Performance Analysis?"
<p>This repository contains computational artifacts for the paper "Are Noise-resilient Logical Timers useful for Performance Analysis?" to be submitted to <a href="https://sc-protools-workshop.github.io/protools24/">ProTools@SC24.</a></p> <p>See also the <a href="https://sc24.supercomputing.org/program/papers/reproducibility-initiative/">SC24 reproducibility initiative.</a></p> <p> </p> <p>Contains</p> <ul> <li>Source code of <a href="https://doi.org/10.5281/zenodo.10822140">Score-P </a>, including implementation of the logical clock algorithm from the paper</li> <li>Software to post-process the Cube files generated by measurements</li> <li>Benchmarks <ul> <li>Source code</li> <li>Configuration skripts</li> <li>Measurement results, including output logs, Cube files</li> <li>Post-processing skripts and results</li> </ul> </li> </ul> <p> </p>
MakeCode and CODAL: Intuitive and Efficient Embedded Systems Programming for Education (Artifact Evaluation)
<p>This artifact allows others to reproduce the results seen in this paper for MakeCode and CODAL, using the BBC micro:bit. The artifact contains an offline build environment for CODAL and MakeCode, allowing evaluators to test and build programs locally. In addition, we also provide espruino and micropython virtual machines to further increase repeatability of our results. Evaluators should download the virtual machine containing all pre-requisite tools, and use an oscilloscope to observe wave forms (used for timing) generated by the micro:bit, and a serial terminal to observe results reported from the micro:bit over serial.</p> <p>Full documentation is available at: https://lancaster-university.github.io/lctes-artefact-evaluation/</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.