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29 results for “Virtual Machine”
Solutions for Reproducibility in Empirical Research: Virtual Machines, Containers, Environment Management Packages, and Cloud Platforms
<p>This image provides a comprehensive overview of various technologies and platforms used to enhance the reproducibility of empirical research. It is divided into several sections:</p> <ol> <li><strong>Virtual Machines (VMs): </strong>the left section of the image illustrates the architecture of VMs with Type 1 and Type 2 hypervisors. <br> - <em>Type 1 Hypervisor </em>runs directly on the hardware, providing high efficiency and performance. Examples include VMware ESXi, <strong>Microsoft Hyper-v</strong>, and Xen Project.<br> - <em>Type 2 Hypervisor</em> runs on an existing operating system, offering flexibility at the cost of some performance. Examples include <strong>Oracle VirtualBox</strong>, VMware Workstation, and Parallels.</li> <li><strong>Containers: </strong>the middle section of the image explains the containerization concept, which shares the host operating system's kernel, making containers more lightweight than VMs. Technologies like <strong>Docker</strong> and <strong>Kubernetes</strong> are shown as popular solutions for container orchestration.</li> <li><strong>Environment Management Packages: </strong>the top right section focuses on tools for managing software dependencies and environments. <strong>renv</strong> (for R) and <strong>Conda</strong> (for Python and other languages) are highlighted as key tools for creating reproducible research environments.</li> <li>Cloud Platforms: the bottom right section features various cloud-based platforms that facilitate reproducible research by providing scalable and shareable computational environments. Platforms include <strong>Google Colab</strong>, <strong>Posit Cloud</strong>, JupyterHub, <strong>Binder</strong>, Nextjournal, OpenShift, and <strong>Code Ocean</strong>.</li> </ol> <p>Together, these solutions provide a robust framework for ensuring that empirical research can be reliably reproduced and validated by others, addressing the challenges of dependency management, environment consistency, and computational resource availability.</p>
Data from: Combining Unity with machine vision to create low latency, flexible, and simple virtual realities
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Replication Package (Virtual Machine) for article "starMC: an automata based CTL* model checker"
<p>This package is a virtual machine (VM) to execute the starMC programs and benchmark. The system setup is described in the research article "starMC: an automata based CTL* model checker", being published at PeerJ Computer Science (currently accepted). The virtual machine contains both tools and data to reproduce a CTL* model checking logic benchmark. CTL* is a formal logic for the verification of temporal properties of programs.</p> <p>The benchmark is a collection of 1018 model instances (encoded as Petri nets), derived from the Model Checking Context benchmark (https://mcc.lip6.fr/), and about 60.000 CTL, LTL and CTL* queries. It is currently the only large-scale CTL* benchmark for Petri nets publicly available.</p> <p>The starMC-benchmark.ova VM is configured to use 4 CPU cores and 16 GB of RAM. The VM was created with VirtualBox version 5, and tested on a host machine with an 8-core Xeon CPU and 32 GB of RAM. The VM uses the Ubuntu 20 OS, and username and password are both "user". The home directory contains a README file with instructions on how to run the tools, where the benchmark data (models, queries, variable orders) are found, and the script to reproduce the plots in the paper.<br> The starMC.ova contains the tools presented in the paper, for user evaluation and reproducibility.</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Complete CoW)
<p>This repository contains the dataset for the complete CoW described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine Learning Guided AQFEP: A Fast & Efficient Absolute Free Energy Perturbation Solution for Virtual Screening
<p>Data to reproduce primary figures in the manuscript titled: Machine Learning Guided AQFEP: A Fast & Efficient Absolute Free Energy Perturbation Solution for Virtual Screening.</p> <p>URL: https://chemrxiv.org/engage/chemrxiv/article-details/6583785e66c1381729ac86f5</p>
Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms: Virtual Machine ID Dataset
<p>The csv files in this dataset contain the virtual machine IDs used in [1] which correspond to the virtual machine traces in the Azure Public Dataset [2]. The file named "vmtable_lifetime_VMoL_1003.csv" holds the IDs used in Section 5 [1] and the file named "vmtable_lifetime_VMoL_55.csv" holds the IDs used in Section 6 [1]. The first column in both files refers to the ID labels in [1], while the second, third, and fourth columns refer to the Virtual Machine IDs, the Subscription IDs, and the Deployment IDs, respectively.</p> <p> </p> <p>[1] Joshua Comden, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. 2019. Online Optimization in<br> Cloud Resource Provisioning: Predictions, Regrets, and Algorithms. Proc. ACM Meas. Anal. Comput. Syst. 3, 1,<br> Article 179 (March 2019).</p> <p>[2] Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. 2017. Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. In Proceedings of SOSP’17. ACM, New York, NY, USA, 15 pages. https://doi.org/10.1145/3132747.3132772 Dataset access: https://github.com/Azure/AzurePublicDataset (August 2018)</p>
Dataset for: Online Virtual Machine Provisioning under Uncertainty: A Robust Approach to Ultimate Resource Utilization
<div> <div>In cloud resource scheduling and management, efficiency and quality are two vital yet conflicting objectives. Cloud providers, such as Huawei, prioritize quality by avoiding hotspots and aim to optimize and increase the utilization efficiency of PM resources without compromising quality. The typical online VM scheduling problem in cloud practice can be stated as follows: given a fixed number of PMs and a queue of arriving VMs, the goal is to place as many VMs as possible onto the PMs while ensuring that no hotspots occur.</div> <br> <div>The trace consists of a total of 297 instances, where each instance is represented by a JSON file named in the format X-1.json, X-2.json, and X-3.json. Here, X represents the number of PMs capable of hosting the arriving VMs, ranging from 2 to 100. For example, 50-1.json indicates that there are 50 available PMs to host the arriving VMs. For each value of X, there are three replicas denoted by the suffixes 1, 2, and 3.</div> <br> <div>There are multiple flavors of PM available in our cloud service provider. Readers can refer to our online paper, which we will provide the link for below, to learn about the specific flavor we used for academic and testing purposes. However, they are also free to use other typical flavors as per their requirements.</div> <br> <div>Each JSON file contains information about the VMs waiting to be assigned to PMs, and the fields for these VMs are as follows (each line represents a VM):</div> <br> <div> <ul> <li><strong>Created_at_point</strong>: the timestamp when the VM arrives. The list is already sorted in ascending order based on the arrival times.</li> </ul> </div> <ul> <li><strong>memory</strong>: the memory capacity of the VM defined by its flavor, measured in gigabytes (GB).</li> <li><strong>duration_point</strong>: the timestamps indicating the duration of the VM's usage. Each timestamp represents a 5-minute interval in practice.</li> <li><strong>vm_util</strong>: the real utilized capacity of the VM at each timestamp, with a similar meaning as the Hotspot Resolution trace. The length of the list is equal to the value of "duration_point".</li> </ul> </div> <div> </div>
Use of Machine Learning in virtual learning environments: a bibliometric review
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Ubuntu18.04 virtual machine for nuc3d
<p>Contains compiled version of nuc3d. For source code see https://github.com/tjs23/nuc_3d</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACoA)
<p>This repository contains the dataset for the Missing ACoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database:Missing PCoA)
<p>This repository contains the dataset for the Missing PCoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoA and PCA P1)
<p>This repository contains the dataset for the Missing PCoA and PCA P1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACA A1)
<p>This repository contains the dataset for the Missing ACA A1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoAs)
<p>This repository contains the dataset for the Missing PCoAs described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
SLUBStick Virtual Machine Image
<p>This is a Virtual Machine (VM) image containing all relevant programs for the artifact evalution of <a href="https://github.com/IAIK/SLUBStick.git">SLUBStick</a>. This VM image is the default Ubuntu 22.04 image running the Linux kernel v6.2. The username to log in is <code>lmaar</code>, and the password is <code>asdf</code>. This user is in the <code>sudo</code> group and, thereby, can gain root privileges via <code>sudo su</code>.</p>
PMX NN Repair: Datasets, Networks and Virtual Machine
<p>Supplementary Material for the Paper "Can we trust neural networks in pharmacometrics? Yes, wec an, but we need guarantees!" by David Boetius, Dominic Stefan Bräm, Marc Pfister, Stefan Leue, and Gilbert Koch.</p>
Combining Virtual Reality and Machine Learning for Enhancing the Resiliency of Transportation Infrastructure in Extreme Events
<p>Corresponding data set for Tran-SET Project No. 18ITSLSU09. Abstract of the final report is stated below for reference:</p> <p>"Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, extreme conditions, etc. As a result, the model’s predictions are made at an aggregate level and for a fixed set of contextual factors. There is a clear need to develop traffic models that take into account local contexts and are closer to ground reality to provide government agencies the ability to make well-informed model-based decisions/policies.</p> <p>In this project: (1) used Immersive Virtual Environment (IVE) tools for generating context-aware and high-fidelity data related to drivers’ route choice behavior, (2) developed a novel approach for developing high-fidelity route choice models with increased predictive power by augmenting existing aggregate level baseline models with information on drivers' responses to contextual factors obtained from stated choice experiments carried out in an IVE through the use of knowledge distillation. To this end, the study used a virtual driving environment designed based on I-10 in Baton Rouge, LA. Five alternate routes were introduced to the participant. Ten experimental scenarios were conducted to produce initial data about drivers’ dynamic route choice behavior, given emerging contextual factors. Experimental results have demonstrated that the predictions of the augmented models produced by our approach are much closer to reality than that of the baseline. Our study demonstrates that existing route choice models based on econometric theories cannot accurately predict behavior in real world scenarios. For high-fidelity route choice models, one needs to combine existing route choice models with information about contextual factors gleaned from SCEs."</p>
A Comprehensive Study of Bugs in WebAssembly Virtual Machines
<p>This contains the dataset and results of our analysis.</p>
Are We Benchmarking the Java Virtual Machine Right?
<p><strong>This project contains data and code we used in our master thesis project in computer science performed at LTH, Lund University. </strong></p>
A Comprehensive Study of Bugs in Embedded WebAssembly Virtual Machines
<p>This contains all the experimental code, data sets, and result files for our experiments.</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.