Omkar2118/datasets huggingface dataset:
italki NLI
Full name: italki NLI

A large, crowd-sourced dataset for the Native Language Identification (NLI) task. People learning English as a second language write practice Notebooks which can be used to classify the author's native language using word choice, spelling mistakes and other language features.

The dataset has:

- 11 languages (Arabic, Chinese, French, German, Hindi, Italian, Japanese, Korean, Spanish, Telagu, Turkish)
- 111,917 documents

Introducing paper: On the Development of a Large Scale Corpus for Native Language Identification
Toronto NeuroFace Dataset

Toronto NeuroFace Dataset: A New Dataset for Facial Motion Analysis in Individuals with Neurological Disorders

Toronto NeuroFace Dataset is a public dataset with videos of oro-facial gestures performed by individuals with oro-facial impairment due to neurological disorders, such as amyotrophic lateral sclerosis (ALS) and stroke. Perceptual clinical scores from trained clinicians are provided as metadata. Manual annotation of facial landmarks is also provided for a subset of over 3300 frames.
GE852

GE852 is a dataset of 852 game engine repositories mined from GitHub in two languages, namely Java and C++. The dataset contains metadata of all the mined repositories including commits, pull requests, issues and so on. This dataset can lays the foundation for empirical investigation in the area of game engines.

Introducing paper: GE852: A Dataset of 852 Game Engines
VideoSet

**VideoSet** is a large-scale compressed video quality dataset based on just-noticeable-difference (JND) measurement.

The dataset consists of 220 5-second sequences in four resolutions (i.e., 1920×1080, 1280×720, 960×540 and 640×360). Each of the 880 video clips is encoded using the H.264 codec with QP=1,⋯,51 and measure the first three JND points with 30+ subjects. The dataset is called the "VideoSet", which is an acronym for "Video Subject Evaluation Test (SET)".

Introducing paper: VideoSet: A Large-Scale Compressed Video Quality Dataset Based on JND Measurement
Cable TV News

**Cable TV news** is a data set of nearly 24/7 video, audio, and text captions from three U.S. cable TV networks (CNN, FOX, and MSNBC) from January 2010 to July 2019. Using machine learning tools, the authors detect faces in 244,038 hours of video, label each face's presented gender, identify prominent public figures, and align text captions to audio.

Introducing paper: Analyzing Who and What Appears in a Decade of US Cable TV News
OMD
Full name: Oxford Multimotion Dataset

The **Oxford Multimotion Dataset** (OMD) provides a number of multimotion estimation problems of varying complexity. It includes both complex problems that challenge existing algorithms as well as a number of simpler problems to support development. These include observations from both static and dynamic sensors, a varying number of moving bodies, and a variety of different 3D motions. It also provides a number of experiments designed to isolate specific challenges of the multimotion problem, including rotation about the optical axis and occlusion.
In total, the Oxford Multimotion Dataset contains over 110 minutes of multimotion data consisting of stereo and RGB-D camera images, IMU data, and Vicon ground-truth trajectories. The dataset culminates in a complex toy car segment representative of many challenging real-world scenarios.

Introducing paper: The Oxford Multimotion Dataset: Multiple SE(3) Motions with Ground Truth
Cry Wolf

**Cry Wolf** is a dataset for cyber security analysis tasks. It is an open-access dataset of 73 true and false Intrusion Detection System (IDS) alarms derived from real-world examples of "impossible travel" scenarios.

Introducing paper: Cry Wolf: Toward an Experimentation Platform and Dataset for Human Factors in Cyber Security Analysis
IITM-Bandersnatch

**IITM-Bandersnatch** is a dataset to evaluate traffic analysis techniques. The dataset comprises of data points of the form {encrypted traces, ground truth choices}. To collect each data point, we asked the viewer to watch Bandersnatch from the beginning and note down the choices made by them. At the same time, we collected the encrypted network traffic.
As of now, our dataset contains information corresponding to 100 viewers who volunteered for this study.

Introducing paper: White Mirror: Leaking Sensitive Information from Interactive Netflix Movies using Encrypted Traffic Analysis
Hate Counter

This dataset is built from Twitter and contains 1290 hate tweet and counterspeech reply pairs. After the annotation process, the dataset consists of 558 unique hate tweets from 548 user and 1290 counterspeech replies from 1239 users.

Introducing paper: Analyzing the hate and counter speech accounts on Twitter
Alexa Domains

This dataset is composed of the URLs of the top 1 million websites.
The domains are ranked using the Alexa traffic ranking which
is determined using a combination of the browsing behavior
of users on the website, the number of unique visitors, and the number of pageviews. In more detail, unique visitors are the
number of unique users who visit a website on a given day,
and pageviews are the total number of user URL requests for
the website. However, multiple requests for the same website
on the same day are counted as a single pageview. The website
with the highest combination of unique visitors and pageviews
is ranked the highest

Introducing paper: DomainGAN: Generating Adversarial Examples to Attack Domain Generation Algorithm Classifiers