Omkar2118/datasets huggingface dataset:
Twitter-HyDrug

Twitter-HyDrug is a real-world hypergraph data that describes the drug trafficking communities on Twitter. We first crawl the metadata (275,884,694 posts and 40,780,721 users) through the official Twitter API from Dec 2020 to Aug 2021. Afterward, we generate a drug keyword list that covers 21 drug types that may cause drug overdose or drug addiction problems to filter the tweets that contain drug-relevant information. Based on the keyword list, we obtain 266,975 filtered drug-relevant posts by 54,680 users. Moreover, we define six types of drug communities, i.e., cannabis, opioid, hallucinogen, stimulant, depressant, and others communities, based on the drug functions. Six researchers spent 62 days annotating these Twitter users into six communities based on the annotation rules discussed in the next section. With the specific criteria, six researchers annotated the filtered metadata separately. For these Twitter users with disagreed labels, we conducted further discussion among annotators for cross-validation. To conclude, we obtained the drug hypergraph including 2,936 user nodes and 33,892 hyperedges.

Introducing paper: Hypergraph Contrastive Learning for Drug Trafficking Community Detection
WRV2
Full name: Wire Removal Video Datasets 2

The WRV2 dataset is meticulously assembled to support developing and evaluating video inpainting algorithms aimed specifically at wire removal. This challenging task is critical for enhancing visual aesthetics in various scenes.

Introducing paper: Raformer: Redundancy-Aware Transformer for Video Wire Inpainting
ENWIDE

This dataset contains LiDAR and IMU data recorded in challenging geometrically degenerate environments. The dataset features 10 sequences recorded in 5 different locations. The data was recorded using an Ouster OS0-128. Ground truth positions were recorded using a Leica MS60 Total Station.

Introducing paper: COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry
MedSecId

The process by which sections in a document are demarcated and labeled is known as section identification. Such sections are helpful to the reader when searching for information and contextualizing specific topics. The goal of this work is to segment the sections of clinical medical domain documentation. The primary contribution of this work is MedSecId, a publicly available set of 2,002 fully annotated medical notes from the MIMIC-III. We include several baselines, source code, a pretrained model and analysis of the data showing a relationship between medical concepts across sections using principal component analysis.

Introducing paper: A New Public Corpus for Clinical Section Identification: MedSecId
Aria Synthetic Environments
Full name: Aria Synthetic Environments

[1]: https://www.projectaria.com/datasets/ase/ ""
[2]: https://facebookresearch.github.io/projectaria_tools/docs/open_datasets/aria_synthetic_environments_dataset ""
[3]: https://www.projectaria.com/research/ ""

**Aria Synthetic Environments** is a large-scale, fully simulated dataset created by Project Aria¹[1]. It consists of procedurally-generated interior layouts filled with 3D objects, simulated with the sensor characteristics of Aria glasses¹[1]. Here are some key features of this dataset:

- **100,000 unique multi-room interior scenes**: These scenes are procedurally generated to produce a diverse set of interior scenes. Each scene has a unique room graph connecting multiple rooms, and unique placement of architectural features, such as windows, doors, and pillars¹[1].
- **Populated with high-quality 3D objects**: Each of the 100,000 unique scenes is filled with objects from a digital library, each with high-quality materials and geometry¹[1].
- **Simulated sensor data per sequence**: This includes 1 x outward-facing RGB camera stream simulated with Aria camera & lens characteristics¹[1].
- **Ground Truth Annotations**: These include 6DoF camera trajectory, 3D floor plan, 2D instance segmentation, and 2D depth map¹[1].
- **Realistic simulated trajectory within each environment**: Device trajectories are simulated within each environment according to a set of rules that mirror how users walk while wearing Project Aria glasses¹[1].

This dataset sets a new precedent for the scale of indoor environment datasets and surfaces exciting new research opportunities for tasks related to 3D scene reconstruction, and object detection and tracking¹[1]. It's designed to provide the wider research community with a dataset large enough to surface new challenges and research opportunities²[2].

(1) Aria Synthetic Environments Dataset | Project Aria. https://www.projectaria.com/datasets/ase/.
(2) Aria Synthetic Environments Dataset | Project Aria Tools. https://facebookresearch.github.io/projectaria_tools/docs/open_datasets/aria_synthetic_environments_dataset.
(3) Project Aria Research | Project Aria. https://www.projectaria.com/research/.

Introducing paper: SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Model
GCN on CORA Dataset
Full name: NeuraChip CORA Experiment

GCN inference on NeuraChip accelerator on Cora dataset.

Introducing paper: NeuraChip: Accelerating GNN Computations with a Hash-based Decoupled Spatial Accelerator
Ultrasound Nerve Segmentation

Identify nerve structures in ultrasound images of the neck
ShipRSImageNet

ShipRSImageNet is a large-scale fine-grainted dataset for ship detection in high-resolution optical remote sensing images. The dataset contains 3,435 images from various sensors, satellite platforms, locations, and seasons. Each image is around 930×930 pixels and contains ships with different scales, orientations, and aspect ratios. The images are annotated by experts in satellite image interpretation, categorized into 50 object categories images. The fully annotated ShipRSImageNet contains 17,573 ship instances. There are five critical contributions of the proposed ShipRSImageNet dataset compared with other existing remote sensing image datasets.
Images are collected from various remote sensors cover- ing multiple ports worldwide and have large variations in size, spatial resolution, image quality, orientation, and environment. Ships are hierarchically classified into four levels and 50 ship categories. The number of images, ship instances, and ship cate- gories is larger than that in other publicly available ship datasets. Besides, the number is still increasing.
We simultaneously use both horizontal and oriented bounding boxes, and polygons to annotate images, providing detailed information about direction, background, sea environment, and location of targets. We have benchmarked several state-of-the-art object detection algorithms on ShipRSImageNet, which can be used as a baseline for future ship detection methods.