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2,025 results for “AIS”
Country AI Activity Metrics
<p>This <a href="https://eto.tech" target="_blank" rel="noopener">Emerging Technology Observatory</a> dataset includes national-level metrics for AI-related research, patents, and private-market investment. Metrics are presented for AI as a whole as well as select subfields, such as natural language processing and computer vision. For schemas and a detailed methodological description, visit the <a href="https://eto.tech/dataset-docs/country-ai-activity-metrics" target="_blank" rel="noopener">full documentation</a>. To browse the data visually, visit ETO's <a href="https://cat.eto.tech" target="_blank" rel="noopener">Country Activity Tracker</a>.</p> <p>Research subject classifications are based on work supported in part by the Alfred P. Sloan Foundation under Grant No. G-2023-22358.</p>
Dataset and code for 'AI-based Knowledge Extraction from the Bioprinting Literature for identifying technology trends'
<p>Zip file containing the dataset and code for the paper 'AI-based Knowledge Extraction from the Bioprinting Literature for identifying technology trends'. The dataset is composed of:</p> <ul> <li>A train_data.csv file, containing all annotated keywords used for classifier training.</li> <li>A filt_ls.pkl file, containing the sentences used to train the embeddings model.</li> <li>A train.py file, to train the composite keyword annotation model.</li> </ul> <p>The authors acknowledge the supported by the European Union’s Horizon 2020 research and innovation program under the project GIOTTO: “Giotto: Active ageing and osteoporosis: The next challenge for smart nanobiomaterials and 3D technologies,” grant agreement no. 814410.</p>
A Multimodal Dataset for Automatic Edge-AI Cough Detection
<p>Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. There is a need for wearable devices that employ multimodal sensors to perform accurate, privacy-preserving, automatic cough counting algorithms directly on the device in an edge-AI fashion. To advance this research field, we contribute the first publicly accessible cough counting dataset of multimodal biosignals. The database contains nearly 4 hours of biosignal data, with both acoustic and kinematic modalities, covering 4,300 annotated cough events. Furthermore, several non-cough sounds (i.e. breathing, laughing, and throat clearing), background noises (i.e. music, traffic, bystander coughing) and motion scenarios (i.e. sitting, walking) mimicking daily life activities are also present, which the research community can use to accelerate ML algorithm development.</p> <p>For detailed information about using this dataset to train edge-AI models and example code, please refer to our public Git repository: https://github.com/esl-epfl/edge-ai-cough-count/</p>
List of AI workshops
<p>Links to scrape text data from workshops hosted at AAAI, ICLR, NeurIPS, IJCAI, and ICML.</p>
AI Based Freight Volume Forecasting | White Paper
<p>The services described in this white paper address warehouse collaboration analytics, for which AI-based volume forecasting models were developed and evaluated to determine future amounts of containers arriving at a warehouse and being transported across corridors. Such models aim to help warehouse and transport operators to predict changes in the movement of freight volume and its arrival to discharge ports, warehouses, or distribution centres. This is with the objective of enabling forward planning and timely readiness by reducing the risk of wasting resources, for the required management of the arriving volume.</p>
FIGURE 1 in Harnessing the power of AI language models for taxonomy and systematics: a follow-up to "Can ChatGPT be leveraged for taxonomic investigations? Potential and limitations of a new technology" by Davinack (2023)
FIGURE 1. Python code generated by ChatGPT that allows the preparation of a NEX file format. For Python tutorials, source code and installers, see https://www.python.org.
Artifacts for "FLAG: Finding Line Anomalies (in code) with Generative AI"
<p>Artifacts for our work used to detect defects in code using LLM consistency checking. Please read README.md file in repository to start.</p>
Understanding Explainability during System Design for Safety Critical AI Systems
<p>Our research aims to explore the perspective of internal stakeholders on explainability</p>
New Challenges for Gender Equality in AI: Navigating the Ethical and Social Implications.- 1st SPATIAL podcast episode
<p>Gender bias can penetrate AI systems through multiple avenues, posing a significant challenge in ensuring fair and equitable outcomes. From historical biases ingrained in the data to biased data selection methods, these factors contribute to biased AI systems. Recognizing the implications of gender bias in AI is crucial, as it impacts various domains such as social media advertising, job recruitment, smart devices, facial recognition, and voice recognition. To strive for equity, it is essential to understand the interconnectedness of different social and political identities, emphasizing intersectionality and the need to avoid generalizing experiences.</p> <p>In the first episode of the <a href="https://spatial-h2020.eu/">SPATIAL </a>podcast, we had the privilege of conversing with Marcus Westberg, postdoctoral researcher and project manager at <a href="https://www.tudelft.nl/">TU Delft</a>, the coordinator of the SPATIAL project.</p>
Active Learning Prototypes for Teaching Game AI
<p><strong>Supplementary materials for “Active Learning Prototypes for Teaching Game AI”</strong></p> <p><em>Overview</em></p> <p>This package contains the following files and folders:</p> <ul> <li><code>LICENSE_CODE.txt</code> - The license for the included code.</li> <li><code>LICENSE_DATA.txt</code> - The license for the included data.</li> <li><code>README.md</code> - Package description.</li> <li><code>requirements.txt</code>- Specifies the Python dependencies required for running the included notebook.</li> <li><code>survey_analysis.ipynb</code> - Notebook used for analyzing the survey data.</li> <li><code>survey_data.ods</code>- Fully anonymized survey data.</li> </ul> <p><em>Reproducibility of results</em></p> <p>The results presented in the research paper “Active Learning Prototypes for Teaching Game AI”, published in the Proceedings of the IEEE Conference on Games 2023, and authored by Nuno Fachada, Filipa F. Barreiros, Phil Lopes and Micaela Fonseca, can be reproduced with the Jupyter notebook included in this package.</p> <p><em>Licenses</em></p> <ul> <li>The code in the Jupyter Notebook is made available under the <a href="https://opensource.org/licenses/MIT">MIT</a> license (see <code>LICENSE_CODE.txt</code>).</li> <li>The non-code materials are made available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license (see <code>LICENSE_OTHER.txt</code>).</li> </ul>
Number of works grouped by AI technique employed
<p>Number of works grouped by AI technique employed. Part of the study "What do we mean by GenAI?"</p>
Experimental Results for the PRICAI 2023 Paper ``Detecting AI Planning Modelling Mistakes -- Potential Errors and Benchmark Domains''
<p>This repository contains data related to the following paper:</p> <p>An overview of which domains have been tested on which software, refer to the following paper:<br> @InProceedings{Sleath2023PossibleModelingErrors,<br> author = {Kayleigh Sleath and Pascal Bercher},<br> title = {Detecting AI Planning Modelling Mistakes -- Potential Errors and Benchmark Domains},<br> booktitle = {Proceedings of the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023)},<br> year = {2023},<br> publisher = {Springer}<br> }</p> <p>Specifically, it contains:</p> <p>- the benchmarks used in the paper.<br> - screenshots of the output of call commands to the respective software artifacts<br> (the actual call commands are only contained in some of them)<br> - excel tables collecting the results</p> <p>Please note:</p> <p>(1) These benchmark problems are just copied in here for the sake of completeness and transparency. But if you are actually interested in using them, please use the newest version, which might have corrections and additional test cases. You find it here: https://github.com/ProfDrChaos/flawedPlanningModels</p> <p>(2) Please also note that the screenshots of our tests might not perfectly fit the folder structure of our benchmarks because we made some minor restructurings after the paper submission. Specifically, some test cases that we classified as syntactical in the submission were then changed into a semantic ones for the camera-ready version. (Hence the paths in the screenshots or tables might be slightly different.)</p> <p>(3) Also note that by the time you read this, several errors undetected by these systems at the time of the evaluation might be resolved by now. (We included the version numbers of the tested software.)</p>
Rubin AOS Simulations and Trained AI Model
<p>This deposit saves the simulations of the Rubin Observatory AOS used to train a machine learning model for wavefront estimation. The simulations are in <em>aos_sims_archive.gz</em>. You can use gzip to unpack this archive, and there is a README that describes the format that the data was saved in. This simulated data was generated using the code in this repo: https://github.com/jfcrenshaw/donut-sims</p> <p>The trained ML model is also saved here. This model is defined, trained, etc in the code in this repo: https://github.com/jfcrenshaw/ml-aos</p>
European Survey on Perceptions of AI by IT professionals
<p>Data set from the European survey on Perceptions of AI by IT professionals</p>
Artifacts for "FLAG: Finding Line Anomalies (in code) with Generative AI"
<p>Artifacts for our work used to detect defects in code using Large Language Models. Please read README.md file in repository to start.</p>
Ai Balta
Source: Objaverse 1.0 / Sketchfab
Evaluation of Effectiveness for Connected Network for EMS Comprehensive Technical-support Using Artificial Intelligence (CONNECT-AI) System by Community Intervention
ClinicalTrials.gov study NCT04829279. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluating the Efficacy of AI-Guided (GenAIS TM) vs. Standard Physician-Guided Dietary Supplement Prescriptions for Weight Loss in Obese Patients
ClinicalTrials.gov study NCT06458296. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Multicenter Observational Study of Multimodal AI for Upper GI Mesenchymal Tumor Diagnosis
ClinicalTrials.gov study NCT07078136. IPD Sharing: NO. Countries: 1. Publications: 38.
AI Algorithm for Surveillance of Deep Surgical Site Infections After Elective Colorectal Surgery.
ClinicalTrials.gov study NCT07130656. IPD Sharing: NO. Countries: 1. Publications: 1.
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.