Omkar2118/datasets huggingface dataset:
Labelling for Explosions and Road accidents from UCF-Crime

The whole UCF-Crime dataset consists of real-world 240 × 320 RGB videos with 13 realistic anomaly types such as explosion, road accident, burglary, etc., and normal examples. The CPD specific requires a change in data distribution. We suppose that explosions and road accidents correspond to such a scenario, while most other types correspond to point anomalies. For example, data, obviously, com from a normal regime before the explosion. After it, we can see fire and smoke, which last for some time. Thus, the first moment when an explosion appears is a change point. Along with a volunteer, the authors carefully labelled chosen anomaly types. Their opinions were averaged. We provide the obtained markup, so other researchers can use it to validate their CPD algorithm for video.

Introducing paper: InDiD: Instant Disorder Detection via Representation Learning
Waymo Open Motion Dataset

As autonomous driving systems mature, motion forecasting has received increasing attention as a critical requirement for planning. Of particular importance are interactive situations such as merges, unprotected turns, etc., where predicting individual object motion is not sufficient. Joint predictions of multiple objects are required for effective route planning. There has been a critical need for high-quality motion data that is rich in both interactions and annotation to develop motion planning models. In this work, we introduce the most diverse interactive motion dataset to our knowledge, and provide specific labels for interacting objects suitable for developing joint prediction models. With over 100,000 scenes, each 20 seconds long at 10 Hz, our new dataset contains more than 570 hours of unique data over 1750 km of roadways. It was collected by mining for interesting interactions between vehicles, pedestrians, and cyclists across six cities within the United States. We use a high-accuracy 3D auto-labeling system to generate high quality 3D bounding boxes for each road agent, and provide corresponding high definition 3D maps for each scene. Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models. Finally, we provide strong baseline models for individual-agent prediction and joint-prediction. We hope that this new large-scale interactive motion dataset will provide new opportunities for advancing motion forecasting models.

Introducing paper: Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset
CASIE

### Annotation corpus of cybersecurity event in news articles

The corpus contains 1000 annotation and source files. Our cybersecurity focused on five event types: Databreach, Phishing, Ransom, Discover, and Patch.

More details of the annotation and CASIE's system are in the papers. If you use our data, please cite one of the following papers.

    Taneeya Satyapanich, Francis Ferraro, and Tim Finin, "CASIE: Extracting Cybersecurity Event Information from Text", InProceedings, Proceeding of the 34th AAAI Conference on Artificial Intelligence, February 2020.
    Taneeya Satyapanich, Tim Finin, and Francis Ferraro, "Extracting Rich Semantic Information about Cybersecurity Events", InProceedings, Second Workshop on Big Data for CyberSecurity, held in conjunction with the IEEE Int. Conf. on Big Data, December 2019.

Any problems found, please contact taneeya1@umbc.edu.
Regex101 Regular expressions

This is a dataset of regular expressions collected from regex101.com. It is not made directly available, but can be [crawled from regex101](https://github.com/dataunitylab/semantic-regex).

Introducing paper: Learning from Uncurated Regular Expressions
Boson-nighttime

In order to collect thermal aerial data, we used FLIR's Boson thermal imager (8.7 mm focal length, 640p resolution, and $50^\circ$ horizontal field of view)\footnote{\url{https://www.flir.es/products/boson/}}. The collected images are nadir at approx. 1m/px spatial resolution. We performed six flights from 9:00 PM to 4:00 AM and label this dataset as \textbf{Boson-nighttime}, accordingly. To create a single map, we first run a structure-from-motion (SfM) algorithm to reconstruct the thermal map from multiple views. Subsequently, orthorectification is performed by aligning the photometric satellite maps with thermal maps at the same spatial resolution. The ground area covered by Boson-nighttime measures $33~\text{km}{^2}$ in total. The most prevalent map feature is the desert, with small portions of farms, roads, and buildings.

The Bing satellite map\footnote{\url{https://www.bing.com/maps/aerial}} is cropped in the corresponding area as our satellite reference map. We tile the thermal map into $512\times512$~px thermal image crops with a stride of $35$ px. Each thermal image crop pairs with the corresponding satellite image crop. Areas covered by three flights of Boson-nighttime are used for training and validation. The remaining areas, covered by the other three flights are used for testing. The train/validation/test splits for Boson-nighttime are $10256$/$13011$/$26568$ pairs of satellite and thermal image crops, respectively.

Introducing paper: Long-range UAV Thermal Geo-localization with Satellite Imagery