Split Data Patch

## Overview

Split Data Patch is a dataset for object detection tasks - it contains Patch annotations for 636 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The dataset contains more than 100k code patch pairs extracted from open source projects on GitHub. Each pair comes with the erroneous and the fixed version of the corresponding code snippet. Instead of the whole file, the code snippets are extracted to focus on the problematic region (error line + other lines around it). For each sample, the repository name, the commit id, and the file names are provided so that one can access the complete files in case of interest.

The dataset only has JavaScript programs and the error are detected by the popular static code analyzer ESLint. The dataset can be used in the fields of: program repair, code generation, bug finding, transfer learning and many more fields related to machine learning for code

Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding and requires careful hyperparameter tuning. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. It consists of a set of patches optimized to generalize across different models and applied to ImageNet data after preprocessing them with affine transformations. This process enables an approximate yet faster robustness evaluation, leveraging the transferability of adversarial perturbations.

We release our dataset as a set of folders indicating the patch target label (e.g., banana), each containing 1000 subfolders as the ImageNet output classes.

An example showing how to use the dataset is shown below.

code for testing robustness of a model

import os.path

from torchvision import datasets, transforms, models import torch.utils.data

class ImageFolderWithEmptyDirs(datasets.ImageFolder): """ This is required for handling empty folders from the ImageFolder Class. """

def find_classes(self, directory):
  classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())
  if not classes:
    raise FileNotFoundError(f"Couldn't find any class folder in {directory}.")
  class_to_idx = {cls_name: i for i, cls_name in enumerate(classes) if
          len(os.listdir(os.path.join(directory, cls_name))) > 0}
  return classes, class_to_idx

extract and unzip the dataset, then write top folder here

dataset_folder = 'data/ImageNet-Patch'

available_labels = { 487: 'cellular telephone', 513: 'cornet', 546: 'electric guitar', 585: 'hair spray', 804: 'soap dispenser', 806: 'sock', 878: 'typewriter keyboard', 923: 'plate', 954: 'banana', 968: 'cup' }

select folder with specific target

target_label = 954

dataset_folder = os.path.join(dataset_folder, str(target_label)) normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) transforms = transforms.Compose([ transforms.ToTensor(), normalizer ])

dataset = ImageFolderWithEmptyDirs(dataset_folder, transform=transforms) model = models.resnet50(pretrained=True) loader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=5) model.eval()

batches = 10 correct, attack_success, total = 0, 0, 0 for batch_idx, (images, labels) in enumerate(loader): if batch_idx == batches: break pred = model(images).argmax(dim=1) correct += (pred == labels).sum() attack_success += sum(pred == target_label) total += pred.shape[0]

accuracy = correct / total attack_sr = attack_success / total

print("Robust Accuracy: ", accuracy) print("Attack Success: ", attack_sr)

Patch to assign a classification to otherwise unclassified backbone families which get pooled under the "incertae sedis" kingdom of the GBIF taxonomic backbone.

Intelligent Patch Panel Market Outlook

The global intelligent patch panel market size was estimated at USD 1.2 billion in 2023 and is projected to reach USD 3.8 billion by 2032, growing at a CAGR of 13.8% from 2024 to 2032. The significant growth in this market is driven primarily by the increasing demand for efficient data management solutions and the rising adoption of advanced networking systems across various industries.

One of the primary growth factors for the intelligent patch panel market is the exponential increase in data traffic globally. With the proliferation of IoT devices, cloud computing, and big data analytics, there is an ever-growing need for efficient data management and high-speed network infrastructure. Intelligent patch panels play a crucial role in managing and organizing cables, reducing downtime, and ensuring seamless data transmission, thereby meeting the rising demand for robust and efficient data networks.

Another critical factor contributing to the growth of the intelligent patch panel market is the burgeoning expansion of data centers worldwide. Data centers are the backbone of modern digital infrastructure, hosting a plethora of services ranging from cloud storage to complex computational tasks. Intelligent patch panels enable these data centers to optimize space, enhance performance, and reduce operational costs by streamlining cable management and improving network reliability. As companies and service providers continue to invest heavily in data center expansions, the demand for intelligent patch panels is set to rise significantly.

The increasing focus on network security and compliance is also driving the market for intelligent patch panels. With the rise in cyber threats and stringent regulatory requirements, organizations are seeking advanced solutions to secure their network infrastructure. Intelligent patch panels offer enhanced monitoring and management features, allowing for real-time tracking and automated alerts for any unauthorized access or anomalies in the network. This capability not only enhances security but also ensures compliance with regulatory standards, making intelligent patch panels a preferred choice for enterprises across various sectors.

From a regional perspective, North America holds the largest share in the intelligent patch panel market due to its advanced IT infrastructure and high adoption rate of new technologies. The presence of major market players, coupled with the significant investments in data centers and network security, further propels the market growth in this region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digital transformation, rising investments in data centers, and increasing adoption of smart technologies in countries like China, India, and Japan are driving the market growth in this region.

The Automatic Patch Clamp technology represents a significant advancement in the field of electrophysiology, offering high-throughput capabilities and precision in measuring ion channel activity. This technology is particularly beneficial for pharmaceutical research and drug discovery, as it allows for the rapid screening of potential drug candidates and their effects on ion channels. By automating the traditionally labor-intensive patch clamp technique, researchers can achieve greater efficiency and consistency in their experiments. The integration of Automatic Patch Clamp systems in research facilities is expected to accelerate the pace of scientific discoveries and contribute to the development of new therapeutics.

Component Analysis

The intelligent patch panel market by component is divided into three primary segments: Hardware, Software, and Services. Each segment plays a critical role in the functionality and efficiency of intelligent patch panels, catering to the diverse needs of end-users across various industries.

The hardware segment constitutes the core physical components of an intelligent patch panel, including the modular units, connectors, and cables. This segment is crucial as it forms the foundation upon which the software and services operate. The demand for high-quality, reliable hardware is driven by the need for robust physical infrastructure to support high-speed data transmission and reduce downtime in network operations. Innovations in hardware design, such as modular and scalable components, are enhancing the efficiency and ease of i

Dataset Card for Patch the Planet

  Dataset Description

This data was produced by ThinkOnward for the Patch the Planet Challenge, using a synthetic seismic dataset generator called Synthoseis. This dataset consists of 500 training volumes and 15 test volumes. You will also be provided with a training data generation code in the starter notebook to build the training data. This code allows experimentation with different-sized missing data volumes in the seismic data. The… See the full description on the dataset page: https://huggingface.co/datasets/thinkonward/patch-the-planet.

This dataset is part-2 of the SST patch experiments used in Zhou et al. 2020. Part-1 can be found in https://www.zenodo.org/record/8025843

The code that created this dataset can be seen in https://github.com/nitzanfarhi/SecurityPatchDetection and can be reproduced by running: console python data_collection\create_dataset.py --all -o data_collection\data Notice that this dataset doesn't include the commits' generated data as it is very big. This can be generated by running only : console python data_collection\create_dataset.py --commits -data_collection\data

A repository name is symbolised by $COMPANY_NAME$_$REPOSITORY_NAME$

License

This dataset is publicly available for researchers. If you are using our dataset,
you should cite our related research paper which outlines the details of the dataset and its underlying principles:

@article{farhi2023detecting, title={Detecting Security Patches via Behavioral Data in Code Repositories}, author={Farhi, Nitzan and Koenigstein, Noam and Shavitt, Yuval}, journal={arXiv preprint arXiv:2302.02112}, year={2023} } As well as mentioning gharchive.org, if you use their data as well.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset provides information about the number of properties, residents, and average property values for Patch Reservoir Drive cross streets in Worcester, MA.

41592 Global export shipment records of Patch to United States with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The Watch-n-Patch dataset was created with the focus on modeling human activities, comprising multiple actions in a completely unsupervised setting. It is collected with Microsoft Kinect One sensor for a total length of about 230 minutes, divided in 458 videos. 7 subjects perform human daily activities in 8 offices and 5 kitchens with complex backgrounds. Moreover, skeleton data are provided as ground truth annotations.

327 Global export shipment records of Patch with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

To develop a non-invasive assessment tool using machine learning in supporting a timely, accurate diagnosis in the elderly, we created an annotated dataset of 668 (so far 95) tongue images collected from hospitalized geriatric patients in a tertiary hospital in Shanghai, China. Images were captured via a light-field camera using CIELAB color space (to simulate human visual perception) and then were manually labeled by a panel of subject matter experts after chart reviewing patients’ clinical information documented in the hospital’s information system.

Timeseries data from 'Cane Patch, FL (CANF1)' (gov-nps-ever-canf1)

GCOOS 52North Sensor Observation Service This station provides the following variables: Sea water practical salinity, Sea water temperature

This dataset provides information about the number of properties, residents, and average property values for Patch Road cross streets in Martinsville, OH.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset provides information about the number of properties, residents, and average property values for Patch Court cross streets in Marina, CA.

This dataset provides information about the number of properties, residents, and average property values for Cane Patch cross streets in Webster, NY.