The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. In this dataset, we present different images and videos for computer vision-based research. The dataset comprises images and videos taken from different sources such as a Drone, a DSLR camera, and a mobile phone camera.
Please find the attached file for complete description
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This dataset is released with our research paper titled “Scene-graph Augmented Data-driven Risk Assessment of Autonomous Vehicle Decisions” (https://arxiv.org/abs/2009.06435). In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers.
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235 Views
As an alternative to classical cryptography, Physical Layer Security (PhySec) provides primitives to achieve fundamental security goals like confidentiality, authentication or key derivation. Through its origins in the field of information theory, these primitives are rigorously analysed and their information theoretic security is proven. Nevertheless, the practical realizations of the different approaches do take certain assumptions about the physical world as granted.
The data is provided as zipped NumPy arrays with custom headers. To load an file the NumPy package is required.
The respective loadz primitive allows for a straight forward loading of the datasets.
To load a file “file.npz” the following code is sufficient:
import numpy as np
measurement = np.load(’file.npz ’, allow pickle =False)
header , data = measurement [’header ’], measurement [’data ’]
The dataset comes with a supplementary script example_script.py illustrating the basic usage of the dataset.
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The Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures followed are consistent with the ethics of handling patients’ data.
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482 ViewsThe Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures followed are consistent with the ethics of handling patients’ data.
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Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images.
In order to load the data, we provide below an example routine working within PyTorch frameworks. We provide two different resolutions, 800 and 7000 um/px.
Within each resolution, we provide .csv files, containing all metadata information for all the included files, comprising:
- image_id;
- label (6 classes - HP, NORM, TA.HG, TA.LG, TVA.HG, TVA.LG);
- type (4 classes - HP, NORM, HG, LG);
- reference WSI;
- reference region of interest in WSI (roi);
- resolution (micron per pixels, mpp);
- coordinates for the patch (x, y, w, h).
Below you can find the dataloader class of UNITOPatho for PyTorch. More examples can be found here.
import torch
import torchvision
import numpy as np
import cv2
import os
class UNITOPatho(torch.utils.data.Dataset):
def __init__(self, df, T, path, target, subsample=-1, gray=False, mock=False):
self.path = path
self.df = df
self.T = T
self.target = target
self.subsample = subsample
self.mock = mock
self.gray = gray
allowed_target = ['type', 'grade', 'top_label']
if target not in allowed_target:
print(f'Target must be in {allowed_target}, got {target}')
exit(1)
print(f'Loaded {len(self.df)} images')
def __len__(self):
return len(self.df)
def __getitem__(self, index):
entry = self.df.iloc[index]
image_id = entry.image_id
image_id = os.path.join(self.path, entry.top_label_name, image_id)
img = None
if self.mock:
C = 1 if self.gray else 3
img = np.random.randint(0, 255, (224, 224, C)).astype(np.uint8)
else:
img = cv2.imread(image_id)
if self.subsample != -1:
w = img.shape[0]
while w//2 > self.subsample:
img = cv2.resize(img, (w//2, w//2))
w = w//2
img = cv2.resize(img, (self.subsample, self.subsample))
if self.gray:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = np.expand_dims(img, axis=2)
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if self.T is not None:
img = self.T(img)
return img, entry[self.target]
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575 Views
Blood pressure and heart rate data set collected from Malaysian population.
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130 Views
The boring and repetitive task of monitoring video feeds makes real-time anomaly detection tasks difficult for humans. Hence, crimes are usually detected hours or days after the occurrence. To mitigate this, the research community proposes the use of a deep learning-based anomaly detection model (ADM) for automating the monitoring process.
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245 Views
This datset contains 2000 images of size 256 X256. The dataset is created by captuirng photos using mobile phone. This dataset is applicable for two classes namely water and wet surface.
This dataset can be used for two classes such as water and wet surface.
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204 Views
This is the dataset provided and collected while "Car Hacking: Attack & Defense Challenge" in 2020. We are the main organizer of the competition along with Culture Makers and Korea Internet & Security Agency. We are very proud of releasing these valuable datasets for all security researchers for free.
The competition aimed to develop attack and detection techniques of Controller Area Network (CAN), a widely used standard of in-vehicle network. The target vehicle of competition was Hyundai Avante CN7.
1. Description
| Round | Type | Description | # Normal | # Attack | # Rows (Total) |
|---|---|---|---|---|---|
| Preliminary | Training | Normal and four types of attacks dataset with class | 3,372,743 | 299,408 | 3,672,151 |
| Submission | Normal and four types of attacks dataset with class (during the competition, without class) | 3,358,210 | 393,836 | 3,752,046 | |
| Final | Submission | Normal and five attacks (4 spoofings, 1 fuzzing) dataset with class (during the competition, without class) | 1,090,312 | 179,998 | 1,270,310 |
Preliminary round contains two status of the vehicle -- S: Stationary, D: Driving.
In final round, only stationary status traffic was collected for safety reason.All csv files have same headers: Timestamp (logging time), Arbitration_ID (CAN identifier), DLC (data length code), Data (CAN data field), Class (Normal or Attack), and SubClass (attack type) of each CAN message.
2. Class
Normal: Normal traffic in CAN bus.
Attack: Attack traffic injected. Four types of attacks are included -- Flooding, Spoofing, Replay, Fuzzing.
Flooding: Flooding attack aims to consume CAN bus bandwidth by sending a massive number of messages.
Spoofing: CAN messages are injected to control certain desired function.
Replay: Replay attack is to extract normal traffic at a specific time and replay (inject) it into the CAN bus.
Fuzzing: Random messages are injected to cause unexpected behavior of the vehicle.
3. Acknowledgement
This work was supported by Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00866, Challenges for next generation security R&D).
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