Here are a few use cases for this project:

1. Dental Health Monitoring: The model can be employed by dentists or dental health technologies to monitor the dental health of patients over time, assist in detecting abnormalities, and track the progress of dental treatments or interventions.

2. Dental Education: It can be used as an educational tool in dental schools, allowing students to practice identifying normal and abnormal OPG images and enhancing their diagnostic skills.

3. Dental Insurance Claims: Insurance companies can use the model to verify dental insurance claims, ensuring that billed procedures match the identified abnormalities in the OPG images.

4. Prevention in Oral Health Diseases: The model can be integrated into telemedicine applications, enabling routine check-ups and early detection of any oral health problem for patients, especially during pandemic-related lockdowns or for remote communities.

5. Research Studies: Researchers studying oral health trends and patterns can leverage this AI model to analyze large amounts of dental images, helping to establish correlations between various dental abnormalities and other health factors. They can also use it to evaluate the effectiveness of different dental procedures and treatments.

3DTime: A Large Dataset of Multi-Annotated Multivariate Time-Series for 3D-printing Duration

3DTime is a paper dataset currently under review at the AAAI-26 conference, for the main technical track. We could not find a way to anonymously publish the full 1.2 TB dataset, hence this smaller version.

  Dataset content

The original dataset contains:

9,930 3D models, each sliced 4 times 39,720 annotated G-code files (compressed) 39,720 binary files A total of 12,442,224,222… See the full description on the dataset page: https://huggingface.co/datasets/3DTimeDataset/3DTime.

This anonymized data set consists of one month's (October 2018) web tracking data of 2,148 German users. For each user, the data contains the anonymized URL of the webpage the user visited, the domain of the webpage, category of the domain, which provides 41 distinct categories. In total, these 2,148 users made 9,151,243 URL visits, spanning 49,918 unique domains. For each user in our data set, we have self-reported information (collected via a survey) about their gender and age.

We acknowledge the support of Respondi AG, which provided the web tracking and survey data free of charge for research purposes, with special thanks to François Erner and Luc Kalaora at Respondi for their insights and help with data extraction.

The data set is analyzed in the following paper:

• Kulshrestha, J., Oliveira, M., Karacalik, O., Bonnay, D., Wagner, C. "Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data." Proceedings of the International AAAI Conference on Web and Social Media. 2021. https://arxiv.org/abs/2012.15112.

The code used to analyze the data is also available at https://github.com/gesiscss/web_tracking.

If you use data or code from this repository, please cite the paper above and the Zenodo link.

This dataset contains the results the following data processing steps:1) Rendering of Weather Surveillance Radar data and using it as input to the machine learning model described in Cheng et al. (2020) that is capable of detecting and tracking swallow and martin roost signatures.2) Manual screening of the model's results, labeling each true positive track according to the type and degree of contamination of the roost by other sources of non-biological scattering.3) Grouping of overlapping tracks that can be considered as the same roost dispersal event.5) Calculation of number of birds per detection, assuming a Purple Martin (Progne subis) radar cross-section (following the procedure proposed by Chilson et al 2012), and summarizing counts across all radar sweeps.These procedures have been described in detail in the following publication: Belotti, M.C.T.D., Deng, Y., Zhao, W., Simons, V.F., Cheng, Z., Perez, G., Tielens, E., Maji, S., Sheldon, D., Kelly, J.F. and Horton, K.G. (2023), Long-term analysis of persistence and size of swallow and martin roosts in the US Great Lakes. Remote Sens Ecol Conserv, 9: 469-482. https://doi.org/10.1002/rse2.323Code for the analysis of these data can be found here: https://gitlab.com/mariabelotti/prjct_number_of_birdsReferences:Cheng, Z., Gabriel, S., Bhambhani, P., Sheldon, D., Maji, S., Laughlin, A., & Winkler, D. (2020). Detecting and Tracking Communal Bird Roosts in Weather Radar Data. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 378–385. https://doi.org/10.1609/aaai.v34i01.5373Chilson, P. B., W. F. Frick, P. M. Stepanian, J. R. Shipley, T. H. Kunz, and J. F. Kelly. (2012). Estimating animal densities in the aerosphere using weather radar: To Z or not to Z? Ecosphere, 3(8):72. http://dx.doi.org/10.1890/ES12-00027.1

We create a publicly available dataset of over 3,100 COVID-19 vaccine-related tweets labeled as one of four stance categories: pro-vaxx, anti-vaxx, vaxx-hesitant, or irrelevant.

***

Please use the V2 version.

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We split our dataset into two separate files:

(1) VaccineHesitancy_train_v2.csv (Single + Double annotated)

(2) VaccineHesitancy_test.csv (Double annotated)

We present the details of this dataset here:

VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter (ICWSM 2023)

Our Pre-trained model (GateNLP/covid-vaccine-twitter-bert) : https://huggingface.co/GateNLP/covid-vaccine-twitter-bert

Paper: https://ojs.aaai.org/index.php/ICWSM/article/view/22213/21992

@inproceedings{mu2023vaxxhesitancy,
 title={VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter},
 author={Mu, Yida and Jin, Mali and Grimshaw, Charlie and Scarton, Carolina and Bontcheva, Kalina and Song, Xingyi},
 booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
 volume={17},
 pages={1052--1062},
 year={2023}
}

🚀 SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models

  🎉 Accepted at AAAI-2025 (Long Paper) — Alignment Track

👉 Code
We developed HarmEval, a dataset based on prohibited scenarios listed in OpenAI and Meta’s usage policies. HarmEval categorizes risks into 11 main categories, resulting in approximately ∼550 crafted harmful queries. We employed a two-step verification process for these queries. First, we used GPT-4 to classify the… See the full description on the dataset page: https://huggingface.co/datasets/SoftMINER-Group/HarmEval.

The Lakh Pianoroll Dataset (LPD) is a collection of 174,154 multitrack pianorolls derived from the Lakh MIDI Dataset (LMD). Getting the dataset We provide multiple subsets and versions of the dataset (see here). The dataset is available here. Using LPD The multitrack pianorolls in LPD are stored in a special format for efficient I/O and to save space. We recommend to load the data with Pypianoroll (The dataset is created using Pypianoroll v0.3.0.). See here to learn how the data is stored and how to load the data properly. License Lakh Pianoroll Dataset is a derivative of Lakh MIDI Dataset by Colin Raffel, used under CC BY 4.0. Lakh Pianoroll Dataset is licensed under CC BY 4.0 by Hao-Wen Dong and Wen-Yi Hsiao. Please cite the following papers if you use Lakh Pianoroll Dataset in a published work. Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, and Yi-Hsuan Yang, "MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment," in Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018. Colin Raffel, "Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching," PhD Thesis, 2016. Related projects MuseGAN LeadSheetGAN