Omkar2118/datasets huggingface dataset:
CodeGen4Libs Dataset

The dataset is specifically constructed for the library-oriented code generation task, which are constructed in the paper “CodeGen4Libs: A Two-Stage Approach for Library-Oriented Code Generation”.

Introducing paper: CodeGen4Libs: A Two-Stage Approach for Library-Oriented Code Generation
HOST

The heavily occluded scene text (HOST) dataset is a dataset that contains images of text with occlusions. It is used to improve the recognition performance of occluded text in machine vision applications 1. The dataset is composed of 4832 images that are manually occluded in weak or heavy degrees.

Introducing paper: From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network
WOST

The Weakly Occluded Scene Text (WOST) dataset is a public dataset for scene text segmentation. It is used to generate pixel-level annotations in scene text images 1. The dataset is designed to contain weakly annotated images, which means that the images are not fully annotated with pixel-level labels.

Introducing paper: From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network
GePaDe

This dataset encompasses 265 speeches (over 200,000 tokens) from the German Bundestag, primarily from the 19th legislative term (2017-2021), given by 195 distinct speakers representing 6 political parties.

The data was annotated to perform a semantic role labeling task, namely to identify who said what to whom (speaker attribution). Cues (triggers) were annotated that are associated with events of speech, writing, or thought. Additionally, the arguments (roles) of each trigger have been annotated, encompassing the SOURCE, ADDRESSEE, MESSAGE, MEDIUM, TOPIC, and EVIDENCE related to the speech event.

The dataset was introduced in the international GermEval 2023 Shared Task on Speaker Attribution in Newswire and Parliamentary Debates (SpkAtt-2023) to evaluate the quality of systems for automated identification of cues and associated roles.

Reference

Rehbein, I. et al, Overview of the GermEval 2023 Shared Task on Speaker Attribution in Newswire and Parliamentary Debates, https://github.com/umanlp/SpkAtt-2023/blob/master/doc/SpkAtt2023-proceedings.pdf
Google Brain - Ventilator Pressure Prediction

What do doctors do when a patient has trouble breathing? They use a ventilator to pump oxygen into a sedated patient's lungs via a tube in the windpipe. But mechanical ventilation is a clinician-intensive procedure, a limitation that was prominently on display during the early days of the COVID-19 pandemic. At the same time, developing new methods for controlling mechanical ventilators is prohibitively expensive, even before reaching clinical trials. High-quality simulators could reduce this barrier.

Current simulators are trained as an ensemble, where each model simulates a single lung setting. However, lungs and their attributes form a continuous space, so a parametric approach must be explored that would consider the differences in patient lungs.

Partnering with Princeton University, the team at Google Brain aims to grow the community around machine learning for mechanical ventilation control. They believe that neural networks and deep learning can better generalize across lungs with varying characteristics than the current industry standard of PID controllers.

In this competition, you’ll simulate a ventilator connected to a sedated patient's lung. The best submissions will take lung attributes compliance and resistance into account.

If successful, you'll help overcome the cost barrier of developing new methods for controlling mechanical ventilators. This will pave the way for algorithms that adapt to patients and reduce the burden on clinicians during these novel times and beyond. As a result, ventilator treatments may become more widely available to help patients breathe.

Introducing paper: Machine Learning for Mechanical Ventilation Control
Br35H :: Brain Tumor Detection 2020
Full name: Br35H :: Brain Tumor Detection 2020

✔️Abstract
A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System (CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and36 percent for women. Brain Tumors are classified as: Benign Tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients. The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI). A huge amount of image data is generated through the scans. These images are examined by the radiologist. A manual examination can be error-prone due to the level of complexities involved in brain tumors and their properties.
Application of automated classification techniques using Machine Learning (ML) and Artificial Intelligence (AI) has consistently shown higher accuracy than manual classification. Hence, proposing a system performing detection and classification by using Deep Learning Algorithms using Convolution-Neural Network (CNN), Artificial Neural Network (ANN), and Transfer-Learning (TL) would be helpful to doctors all around the world.

✔️ Context
Brain Tumors are complex. There are a lot of abnormalities in the sizes and location of the brain tumor(s). This makes it really difficult for complete understanding of the nature of the tumor. Also, a professional Neurosurgeon is required for MRI analysis. Often times in developing countries the lack of skillful doctors and lack of knowledge about tumors makes it really challenging and time-consuming to generate reports from MRI’. So an automated system on Cloud can solve this problem.

✔️ Definition
To Detect and Classify Brain Tumor using, CNN and TL; as an asset of Deep Learning and to examine the tumor position(segmentation).

✔️ About the data:
The dataset contains 3 folders: yes, no and pred which contains 3060 Brain MRI Images.

Folder	Description
Yes	The folder yes contains 1500 Brain MRI Images that are tumorous
No	The folder no contains 1500 Brain MRI Images that are non-tumorous
By: Ahmed Hamada
WiSARD
Full name: Wilderness Search and Rescue Dataset

WiSARD stands for Wilderness Search and Rescue Dataset (pronounced "wizard").  WiSARD consists of visual and thermal imagery taken from a drone flying over various wilderness environments in Washington, USA. The purpose of the WiSAR Image Dataset is to advance computer vision and deep learning research with a targeted application for wilderness search and rescue.