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67 results for “Lightweight”
FixMe: An Incremental Lightweight Method for Vulnerability Data Collection for Security Patch Prediction
<div> <div>This repository has the FixMe dataset and the source code for extracting the new dataset. is a lightweight approach for collecting code patches based on analyzing the commits of various version control systems. The practical framework is designed to generate patches across a wide array of programming languages. This open-source tool streamlines the process of gathering vulnerability records from the Common Vulnerabilities and Exposures (CVE) database through an incremental approach. By embracing an incremental methodology, we expedite the acquisition of data, ensuring the inclusion of newly identified vulnerabilities and their corresponding patch pairs. Our methodology involves extracting security issues, obtaining vulnerability-fixing commits, and retrieving relevant source code from various projects. The extracted dataset by the FixMe tool supports for the automated patch prediction, automated program repair, commit classification, vulnerability prediction and more.</div> </div>
Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements
<p>This a dataset set to paper entitled: "Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements".</p> <p>Dataset is one excel file divided in various sheets containing:</p> <ol> <li>Properties of used aggregates</li> <li>Initial properties of concrete</li> <li>Composition of concrete</li> <li>Concrete with fibres</li> <li>Final concrete</li> </ol>
Lightweight Self-adaptive Cloud-IoT Monitoring across Fed4FIRE+ Testbeds (LiSCIo)
<p>Monitoring will be crucial to properly orchestrate next-gen services. Indeed, monitoring’s output can be exploited to choose where to deploy application services for the first time and to decide when and where to migrate them in case their QoS and contextual requirements cannot be satisfied by the current deployment and infrastructure state. However, only a few works have focused so far on the design and prototyping of monitoring tools for next-gen Cloud-IoT computing platforms.</p> <p>In this context, <a href="https://github.com/di-unipi-socc/FogMon">FogMon</a>, described in (Brogi et al., 2019) and (Forti et al., 2021), is an open-source C++ distributed monitoring service targeting heterogeneous infrastructures along the Cloud-IoT continuum, e.g. Fog computing. FogMon monitors hardware and virtualised resources at different Cloud-IoT computing nodes, end-to-end network QoS between such nodes, as well as available IoT devices. Besides, it features a self-organising peer-to-peer overlay topology with self-restructuring mechanisms and differential monitoring updates, which feature scalability, fault-tolerance, and low communication overhead.</p> <p>The LiSCIo project aimed at assessing FogMon over increasing infrastructures from 20 to 40 Cloud and Edge nodes, spanning two testbeds within the Fed4Fire+ federated infrastructure. Particularly, LiSCIo implemented a new version of the service, i.e. <a href="https://github.com/di-unipi-socc/FogMon-LiSCIo/tree/2.0">FogMon 2.0</a>, which was thoroughly fixed and tuned over a large number of experiments carried on Fed4Fire+ facilities. Throughout the project, data have been collected on all the measurements performed by FogMon 1.x and by FogMon 2.0 (viz. node hardware, IoT, latency, bandwidth) to assess their footprint on hardware resources and bandwidth in all settings, and the relative error on its estimates of latency and bandwidth against ground-truth configurations, enforced via GRE tunnels.</p>
Dataset for publication Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718
<p>Dataset consisting of G-code for 3D mortar specimen's printing path and particle size distribution (Origin file) of materials used in the study Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718. <a href="https://doi.org/10.1016/j.jobe.2021.102718">https://doi.org/10.1016/j.jobe.2021.102718</a></p>
Design of experiment (DOE) used in the study: Lightweight design of variable-stiffness imperfection-insensitive cylinders enabled by continuous tow shearing and machine learning
<p>There are five input variables that are changed for this design of experiment (DOE) within the following range:</p> <p><span class="math-tex">\(\begin{eqnarray} 0.05 \leq r_{CTS} \leq 0.20 \nonumber \\ 1 \leq n \leq 12 \nonumber \\ 0 \leq {c_2}_{ratio} \leq 1 \\ 0 \leq \theta_1 \leq 75 \nonumber \\ 0 \leq \theta_2 \leq 75 \nonumber \end{eqnarray}\)</span></p> <p>For the sake of simplicity, these variables are respectively called v1, v2, v3, v4, v5.</p> <p>The DOE consist of 2000 points created with Latin Hyper-cube Sampling, as available in the LHS toolbox (Carnell, R. lhs: Latin Hypercube Samples, 2021. R package version 1.1.3.).</p> <p>The outputs evaluated with this design of experiment (DOE) are the critical buckling load $P_{critical}$, $b_{factor}$ obtained with Koiter's asymptotic approach, and the mass.</p>
Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats
<p>Here we introduce the dataset utilized in our published paper entitled "<a href="https://www.sciencedirect.com/science/article/pii/S2666154324002114">Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats</a>".</p> <p>Contained within the "bcs" folder are all the videos collected for this study. Each video file is named with a format denoting its respective details. The first number signifies the sequence of collection, the second denotes the ear tag, and the final figure represents the body condition score (BCS) value.</p> <p>For example: "1_158734_2.50" indicates the first sampling of an animal with the ear tag "158734" and a BCS value of "2.50".</p> <p>Additionally, we provide two Python scripts in this repository. The first script, "Video2Frame.py", facilitates the splitting of videos into individual frames. The second script, "Frames2npy.py", converts these frames into two numpy-friendly files with the extension ".npy". These files contain both the images ("X_train_bcs300.npy") and their corresponding labels ("Y_train_bcs300.npy").</p> <p>Furthermore, for the convenience of swift experimentation, we have included the desired .npy files within the repository.</p> <p>To load these files into your Python environment, you can use the following code snippet:</p> <div> <div>th4figs = '/content/drive/MyDrive/compag_2023/'</div> <br> <div>path4images = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/X_train_bcs300.npy"</div> <div>Xtrain = np.load(path4images)</div> <br> <div>path4labels = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/Y_train_bcs300.npy"</div> <div>Ytrain = np.load(path4labels).astype(float)</div> <br> <div>print("X train : ", Xtrain.shape)</div> <div>print("Y train : ", Ytrain.shape)</div> <div> <div> <div> <div> <div> <div> <div> </div> </div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <pre>X train : (5332, 300, 300, 3) Y train : (5332,)<br> </pre> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Data sheet for Monitoring of trimming operation for lightweight composite structure
<p>Each file consists of the following data :</p> <p>- Acceleration measurements in the spindle during machining.</p> <p>- Acceleration measurements in the workpiece during machining.</p> <p>- Spindle power consumption during machining.</p> <p> </p>
Lightweight Dynamic Build Batching Algorithms for Continuous Integration
<p>Replication package for Journal publication: "Lightweight Dynamic Build Batching Algorithms for Continuous Integration"</p>
A lightweight backpack harness for tracking hummingbirds
<p>Hummingbirds and other lightweight bird species are challenging to track because they have limited capacity to carry devices for data-logging. We present a simple and customizable three-loop 'backpack' harness for studying hummingbird migration and movement, with step-by-step instructions for harness construction and attachment. The harness has negligible weight and cost (< $0.50 USD/each), is easy for a single person to make and apply in the field, and it requires no complicated setup or equipment. We have field-tested this harness on 74 Giant Hummingbirds (Patagona gigas) with three different types of tracking devices (geolocators, GPS tags, and satellite transmitters) in Chile and Peru from 2017–2020. Based on recaptures to date, we report that harnesses last at least two years, even under high UV-light conditions. We found no evidence of adverse effects of the harnesses on birds after one to two years and apparent survival of geolocator-tracked Giant Hummingbirds was in line with published estimates for other hummingbird species. This harness method is a practical and effective option for mounting ultra-light devices on hummingbirds, and it can be readily modified for other species with short legs, prominent sternal keels, and long wings.</p>
GEOLAB - Transnational Access project LIWEMAT – Deformation characterisation of LIghtWEight foundation MATerials
<p>Proper use of lightweight materials as construction material for various types of infrastructure brings a lot of advantages to European critical infrastructure related to weight reduction and prevention of progressive heating or freezing of its structural elements.</p> <p>Large-scale Triaxial Apparatus was used to perform the loading tests, which enables the characterization of materials at very small strain ranges with a very high accuracy level of load-displacement control. The apparatus has rigid confining aluminum frames with a height of 3 cm and cross-section of 40 x 40 cm, enabling prismatic specimens with a height of up to 80 cm to be tested.</p> <p>This research aims to determine the characteristics of expanded clay and foamed glass aggregates through laboratory testing, performing cyclic loading for the stiffness and damping evaluation, which would be beneficial parameters for numerical simulations.</p> <p>Testing material</p> <p>The foam glass aggregate has a homogeneous microstructure with approximate uniform shape and sizes. Foam glass aggregate is considered one of the best solid isolation materials with several unique properties. It can be widely used in many applications such as basement walls, foundations, floors and roofs, terrace and garden covers, rooftops, and parking areas.</p> <p>The expanded clay aggregate is round shape with different sizes with small, air-filled cavities, with dark brown, reddish, brown-red or gray colors, which depends on the chemical composition of the expanded clay. The lightweight expanded clay aggregate has bulk density from 250 kg/m<sup>3</sup> to 710 kg/m<sup>3</sup>, mostly dependent on the size of the aggregate.</p> <p> </p>
Rapid species discrimination of similar insects using hyperspectral imaging and lightweight edge artificial intelligence
Open the record for dataset details and reuse information.
A lightweight backpack harness for tracking hummingbirds
Open the record for dataset details and reuse information.
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
<p>This data repository for MedMNIST v1 is out of date! Please check the <a href="http://medmnist.github.io">latest version</a> of MedMNIST v2. </p> <p> </p> <p><strong>Abstract</strong></p> <p>We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at <a href="https://medmnist.github.io/">https://medmnist.github.io/</a>.</p> <p> </p> <p>Please note that this dataset is <strong>NOT</strong> intended for clinical use.</p> <p> </p> <p>We recommend our official <a href="https://github.com/MedMNIST/MedMNIST">code</a> to download, parse and use the MedMNIST dataset:</p> <blockquote> <pre>pip install medmnist</pre> </blockquote> <p> </p> <p><strong>Citation and Licenses</strong></p> <p>If you find this project useful, please cite our ISBI'21 paper as:<br> <em> Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis," arXiv preprint arXiv:2010.14925, 2020.</em><br> <br> or using bibtex:<br> <em> @article{medmnist,<br> title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},<br> author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},<br> journal={arXiv preprint arXiv:2010.14925},<br> year={2020}<br> }</em></p> <p>Besides, please cite the corresponding paper if you use any subset of MedMNIST. Each subset uses the <strong>same license</strong> as that of the source dataset.</p> <p> </p> <p><strong>PathMNIST</strong></p> <p>Jakob Nikolas Kather, Johannes Krisam, et al., "Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study," PLOS Medicine, vol. 16, no. 1, pp. 1–22, 01 2019.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>ChestMNIST</strong></p> <p>Xiaosong Wang, Yifan Peng, et al., "Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," in CVPR, 2017, pp. 3462–3471.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0</a></em></p> <p> </p> <p><strong>DermaMNIST</strong></p> <p>Philipp Tschandl, Cliff Rosendahl, and Harald Kittler, "The ham10000 dataset, a large collection of multisource dermatoscopic images of common pigmented skin lesions," Scientific data, vol. 5, pp. 180161, 2018.</p> <p>Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, and Allan Halpern: “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)”, 2018; arXiv:1902.03368.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a></em></p> <p> </p> <p><strong>OCTMNIST/PneumoniaMNIST</strong></p> <p>Daniel S. Kermany, Michael Goldbaum, et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning," Cell, vol. 172, no. 5, pp. 1122 – 1131.e9, 2018.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>RetinaMNIST</strong></p> <p>DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD), "The 2nd diabetic retinopathy – grading and image quality estimation challenge," https://isbi.deepdr.org/data.html, 2020.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>BreastMNIST</strong></p> <p>Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy, "Dataset of breast ultrasound images," Data in Brief, vol. 28, pp. 104863, 2020.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>OrganMNIST_{Axial,Coronal,Sagittal}</strong></p> <p>Patrick Bilic, Patrick Ferdinand Christ, et al., "The liver tumor segmentation benchmark (lits)," arXiv preprint arXiv:1901.04056, 2019.</p> <p>Xuanang Xu, Fugen Zhou, et al., "Efficient multiple organ localization in ct image using 3d region proposal network," IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1885–1898, 2019.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <div> <div class="gtx-trans-icon"> </div> </div>
Miniature multihole airflow sensor for lightweight aircraft over wide speed and angular range
<p>This repository contains:</p> <ul> <li>the data collected for the paper "Miniature multihole airflow sensor for lightweight aircraft over wide speed and angular range"</li> <li>the python code to extract the wind tunnel and flight data to reproduce the plots in the paper</li> <li>the exact polynomials used for the calibration</li> <li>the 3D object files of the airflow sensor</li> <li>the schematics of the PCB</li> <li>the video of the flown manoeuvres (available on youtube: https://youtu.be/U3nR1v3fbZg)</li> </ul> <p>The data and the plots are included in the repository, but can be reproduced by running the python scripts in the following order:</p> <ol> <li>Code/reduce_data.py</li> <li>Code/plot_averages.py</li> <li>Code/read_tunnel.py</li> <li>Code/Regress.py</li> <li>Code/Validation_plot.py</li> </ol>
Dataset of the experimental campaign presented in the paper entitled "Shaking table seismic experimental investigation of lightweight rigid bodies"
<p>This study presents the findings of an extensive shaking table experimental campaign conducted on nine free standing wooden specimens, aiming at providing insights on the rigid body motion of free-standing objects. The specimens, which differ in slenderness and size, are characterized by impairments in their base surface and most likely in their shapes, which also lead to asymmetric responses. The imperfections of the tested objects are an additional source of uncertainty with respect to the intrinsic chaotic character of the rigid body motion, which is a crucial factor that prevents the reproducibility of the tests and induces discrepancies between specimen responses and those of their ideal models. A contactless measurement strategy is employed to assure unaltered data acquisition. The experimental campaign includes free vibration tests, pulse excitation and natural ground motions tests; the dynamic responses of the specimens are organized and rearranged, aiming at providing a comprehensive set of data that could be employed for calibrating numerical models accounting for imperfect conditions. The damping properties of the specimens are discussed, providing a novel estimation of the coefficient of restitution based on the free vibration tests. The limits of the ideal simple rigid model are highlighted, and the roles of size factor and aspect ratio are discussed according to the obtained results.</p>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
Dataset obtained from DoE for the construction of lightweight concrete blocks using expanded polystyrene
<p>The dataset used in this study was generated through a Design of Experiments (DOE) approach to predict the material mixture proportions using machine learning techniques. It contains 36 experiments, where the independent variables include the proportions of cement, sand, gravel, and expanded polystyrene (Styrofoam). The dependent variables are the compressive strength values and water absorption rates, calculated across multiple components and used to classify the mixtures.</p> <p>The dataset columns include:</p> <ul> <li><strong>Cement:</strong> The proportion of cement used in the mixture.</li> <li><strong>Sand:</strong> The proportion of sand used.</li> <li><strong>Gravel:</strong> The proportion of gravel.</li> <li><strong>Styrofoam (Sty):</strong> The proportion of expanded polystyrene.</li> <li><strong>Comp. 1-6:</strong> Compressive strength values for different components.</li> <li><strong>Comp. Mean:</strong> The mean compressive strength value.</li> <li><strong>Abs. 1-3:</strong> Water absorption values for different components.</li> <li><strong>Abs. Mean:</strong> The mean water absorption rate.</li> <li><strong>Classification:</strong> The final classification of the mixture based on the obtained results, indicating if the mixture belongs to categories such as "B," "C," "D," or "No classification."</li> </ul> <p>This dataset was used to train and validate machine learning models, aiming to predict the mixture properties based on the material proportions.</p>
Artifact of the paper: Scheduling with lightweight predictions in power-constrained HPC platforms
<p>Please refer to the <a href="https://zenodo.org/records/13961003/files/artifact-overview.pdf?download=1&preview=1">artifact-overview.pdf</a> file in this dataset for instructions to reproduce the experiments we have conducted for this article, or for more context about the article.</p>
Supplemental data to paper "An Extensible Logic Embedding Tool for Lightweight Non-Classical Reasoning"
<p>These files are supplementary material to the paper<br> An Extensible Logic Embedding Tool for Lightweight Non-Classical Reasoning<br> Alexander Steen<br> accepted at the Eighth Workshop on Practical Aspects of Automated Reasoning (PAAR 2022), 2022.</p> <p>Files ex1.p, ex2.p and ex3.p correspond to Examples 1, 2, and 3 from the paper in section 5.<br> The *.embed.p files are their embedded variants in classical HOL (THF).</p> <p>In a later update, according to the paper [1], the syntax of NXF and NHF was slightly updated to include<br> an "@"-sign between the non-classical operator {...} and its arguments (...), even in NXF. As a consequence,<br> the examples in this archive have been updated accordingly.</p> <p>Alexander Steen, <alexander.steen@uni-greifswald.de></p> <p> </p> <p>[1] Alexander Steen, David Fuenmayor, Tobias Gleißner, Geoff Sutcliffe, Christoph Benzmüller: Automated Reasoning in Non-classical Logics in the TPTP World. PAAR@IJCAR 2022. Available at https://ceur-ws.org/Vol-3201/paper11.pdf</p>
Stress Testing CPS - Lightweight Aircraft dataset
<p>This dataset contains the test traces generated for the Lightweight Aircraft case study of the paper "Stress Testing Control Loops in Cyber Physical Systems". Simply copy the unpacked folder in the code repository of the testing approach matlab implementation and instead of re-executing the tests, the code will use the pre-generated traces.</p>
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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.