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2,025 results for “AIS”
Datasets for experiments in support of "HADA: an Automated Tool for Hardware Dimensioning of AI Applications"
<p>The zip archive contains three datasets used during the experimental phase of the paper:</p> <ul> <li><em>ANTICIPATE_trainDataset.csv</em>: used in order to train the ML models for the ANTICIPATE algorithm (Section 4.1);</li> <li><em>CONTINGENCY_trainDataset.csv</em>: used in order to train the ML models for the CONTINGENCY algorithm (Section 4.1);</li> <li><em>EmpiricalValidationSet.csv</em>: used for validating the EML optimization model (Section 4.2)</li> </ul>
AI HERO Hackathon 2022 - Team H2 Model Parameters
<p>Model parameters for model created by team H2 at AI HERO Hackathon 2022</p>
AI_Anesthesia_WoS
<p>Dataset used for a a<strong> </strong>bibliometric network analysis of publications on artificial intelligence in anesthesia</p>
Hierarhical method and Dynamic Programming methods for AI-SPRINT GPU Scheduler
<p>This repository includes the source code and the datasets of the Hierarchical method and the Dynamic Programming methods used to obtain the results reported in the article "Scheduling Deep Learning Jobs Training in the Cloud: Comparing Multiple Approaches". The two methods constitute a part of the GPU Scheduler developed in the context of the AI-SPRINT project.</p>
Image Dataset for 'AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water'
<p>This dataset is presented in the following publication. Please cite this publication if you use the dataset.</p> <p><em>Jiyoon Yi, Nicharee Wisuthiphaet, Pranav Raja, Nitin Nitin, J. Mason Earles. (2023). AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water. Water Research, 120258. doi: <a href="https://doi.org/10.1016/j.watres.2023.120258">10.1016/j.watres.2023.120258</a></em></p>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Malta
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Czechia
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Austria
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Switzerland
<p>This dataset contains the results of the surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund</li> </ul>
Dataset of the Paper "Architecture Decisions in AI-based Systems Development: An Empirical Study"
<p>This dataset collected from Stack Overflow (SO) and GitHub was used to conduct an empirical study on architecture decisions in AI-based systems development. We provide below a brief description of each file:</p> <p><strong>1. Dataset (SO).xlsx</strong></p> <p>contains the IDs and URLs of the labelled posts which are related to architecture decisions in AI-based systems development from SO, and the data extracted from these related SO posts.</p> <p><strong>2. Dataset (GitHub).xlsx</strong></p> <p>contains the project names, issue IDs, and issue URLs of AI-based projects selected from GitHub, and the data extracted from the relevant issues.</p> <p><strong>3. Extracted Data (SO+GitHub).xlsx</strong></p> <p>provides the final results of data extracted from SO posts and GitHub issues.</p>
Monumento ai Tetrarchi
Monumento ai Tetrarchi - fine III sec d.C. - Venezia Source: Objaverse 1.0 / Sketchfab
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Dataset of AI-generated code created by various versions of GPT model
<p>This is the dataset used for the paper "<span>Human vs AI: Investigation of Security Risks in AI-generated </span><span>Code via Comparison with Human-written Code".</span></p>
Intersectional Analysis of Visual Generative AI
<p>This data set contains the set of 180 images we created and analysed towards creating an intersectional STS analysis of Stable Diffusion.</p>
Paper samples for the SLR "A systematic literature review on the impact of AI models on the security of code generation"
<p>Here we provide the whole list of papers that were queried for the SLR "A systematic literature review on the impact of AI models on the security of code generation" by Negri-Ribalta et al. The dataset provides all the information of all the papers gathered, their database of origin, and if it was accepted/rejected/duplicated. </p> <p>The file is in xls format .</p>
Virtual Furuta pendulum: linear, nonlinear and AI-based controllers implementation (non-perturbed case)
<p><span>This video shows simulations of the control of the virtual prototype of the Furuta pendulum in a MATLAB/Simulink environment controlled by linear, nonlinear, and AI-based controllers in the absence of external disturbance.</span> </p>
Virtual Furuta pendulum: linear, nonlinear and AI-based controllers implementation (perturbed case)
<p><span>This video shows simulations of the control of the virtual prototype of the Furuta pendulum in a MATLAB/Simulink environment controlled by linear, nonlinear, and AI-based controllers in the presence of an external disturbance.</span></p>
Virtual Furuta pendulum: linear, nonlinear and AI-based controllers implementation (perturbed case)
<p>This video shows simulations of the control of the virtual prototype of the Furuta pendulum in a MATLAB/Simulink environment controlled by linear, nonlinear, and AI-based controllers in the presence of an external disturbance.</p>
Replication Package for "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"
<p># Replication Package for the Paper: "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"</p> <p>This replication package includes the raw data, questionnaire answers, and a Python notebook to reproduce the results detailed in the paper "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"</p> <p>## Repository Structure<br>1. **Results:** Contains Excel files with the responses from the 2 human experts and the 5 models and the review of the 3 human reviewers.<br>2. **Tables:** Contains the full Wilcoxon Test Results for H01 and H02 as well as the Anderson Darling test for normality and the Spearmans' Rho.</p> <p>## Replication Process<br>To replicate the results of our study, open the provided Python Notebook in Google Colab and follow the instructions to reproduce the results seamlessly.</p> <p># Instructions for Use<br>To utilize this replicability package, refer to the steps outlined in the notebook file.</p> <p># Remarks<br>If you encounter any issues or have any questions, please get in touch with the paper's authors. We will be glad to assist you!</p>
Stalization of the Furuta pendulum: Linear, nonlinear and AI based controllers (nominal case)
<p><br>Stabilization control of the Furuta pendulum in Matlab/Simulink Simscape environmet in nominla case.</p> <p><br>Linear controllers</p> <ul> <li>LQR</li> <li>PID</li> </ul> <p>Nonlinear controllers</p> <ul> <li>Feedback Linearization</li> <li>SMC</li> </ul> <p>AI-based controllers</p> <ul> <li>Feedback Linearization with adaptive nerual networks</li> <li>Reiforcement Learning</li> <li>Feedback Linearization with Reinforcement Learning compensation</li> </ul> <p> </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.