The simulation code for the paper:

"AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning"

The overall architecture of the MARL framework is shown in the figure.

The figure depicts two algorithms.

Modified MADDPG which trains two critics. The global critic which is trained at the RSU and the local critic exclusive for each agent.

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Medical Symptoms are sometimes very tricky to analyse in real time as it takes time for example, first to detect the symptoms then to perform some tests and finally coming to a solution. This process can be eliminated and lot of time can be saved by introducing the concept of Deep learning. CNNs create a network for extracting the features of a given image in order to evaluate the image based on the conditions required. This property of the CNN is used as a certain advantage in order to detect the symptoms based on the type of X-ray images provided.

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The Objects Mosaic Hyperspectral Database contains 10,666 hyperspectral cubes of size 256x256x29 in the 420-700nm spectral range. This original hyperspectral database of real objects was experimentally acquired as described in the paper "SHS-GAN: Synthetic enhancement of a natural hyperspectral database", by J. Hauser, G. Shtendel, A. Zeligman, A. Averbuch, and M. Nathan, in the IEEE Transactions on Computational Imaging.

In addition, the database contains the SHS-GAN algorithm, which enables to generate synthetic database of hyperspectral images. 

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The proposed dataset, termed PC-Urban (Urban Point Cloud), is captured with an Ouster LiDAR sensor with 64 channels. The sensor is installed on an SUV that drives through the downtown of Perth, Western Australia (WA), Australia. The dataset comprises over 4.3 billion points captured for 66K sensor frames. The labelled data is organized as registered and raw point cloud frames, where the former has a different number of registered consecutive frames. We provide 25 class labels in the dataset covering 23 million points and 5K instances.

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Arbitrarily falling dices were photographed individually and monochromatically inside an Ulbricht sphere from two fixed perspectives. Overall, 11 dices with edge size 16 mm were used for 2133 falling experiments repeatedly. 5 of these dices were modified manually to have the following anomalies: drilled holes, missing dots, sawing gaps and scratches. All pictures in the uploaded pickle containers have a resolution of 400 times 400 pixels with normalized grey scale floating point values of 0 (black) through 1 (white).

Instructions: 

The datasets contain files for training (“x_training.pickle”, w/o anomalies) and testing (“x_test.pickle”, w/ and w/o anomalies). Labels were saved in “y_test.pickle” whereas label zero correspond to non-anomalous data. Because the pose of the falling dice was not constrained the two fixed perspectives had the chance to see anomalies at all in 60 out of 100 experiments. Hence the test dataset contains 60 anomalous samples. Furthermore, data is augmented w.r.t. erased patches, changes in image constituents like brightness, and altered geometry like flipping and rotating.The shapes of the pickles are

  • w/o augmentation, x_train.pickle: (2000, 2, 400, 400)
  • w/o augmentation, x_test.pickle: (133, 2, 400, 400)
  • w/o augmentation, y_test.pickle: (133,)
  • w/ augmentation, x_train.pickle: (4000, 2, 400, 400)
  • w/ augmentation, x_test.pickle: (133, 2, 400, 400)
  • w/ augmentation, y_test.pickle: (133,)
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This dataset supports researchers in the validation process of solutions such as Intrusion Detection Systems (IDS) based on artificial intelligence and machine learning techniques for the detection and categorization of threats in Cyber Physical Systems (CPS). To that aim, data have been acquired from a Secure Water Treatment (SWaT) hardware-in-the-loop testbed which emulates water passage between nine tanks via solenoid-valves, pumps, pressure and flow sensors. The testbed is composed by a real partition which is virtually connected to a simulated one.

Instructions: 

This dataset has related to the paper "A hardware-in-the-loop Secure Water Treatment dataset for cyber-physical security testing".
We provide four different acquisitions:
1) A normal acquisition without attacks ("normal.csv" for network traffic and "dataset_norm.csv" for physical measures)
2) Three acquisitions where different types of attacks and physical faults are reproduced ("attack_1.csv", "attack_2.csv" and "attack_3.csv" for network traffic and "dataset_att_1.csv", "dataset_att_2.csv" and "dataset_att_3.csv" for physical measures)
In addition to .csv files we provide four .pcap files ("attack_1.pcap", "attack_2.pcap", "attack_3.pcap" and "normal.pcap") which refer to network acquisitions for the four previous scenarios.
A README.xlsx file summarizes the key features of the entire dataset.

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This dataset is proposed for human activity recognition tasks. The static activities including sitting, standing, and laying, as well as walking, running, cycling, and walking upstairs/downstairs. Each activity lasts for 2 minutes, 23 subjects were involved in the experiments.

Instructions: 

Twenty-three subjects were involved in 8 activities, including 3 static ones and 5 periodic activities of walking, running, cycling, and walking upstairs/downstairs. Each activity lasts around 2 minutes.

For each column, the sensor signals are organized as " 'wri_Acc_X', 'wri_Acc_Y', 'wri_Acc_Z', 'wri_Gyr_X', 'wri_Gyr_Y', 'wri_Gyr_Z', 'wri_Mag_X', 'wri_Mag_Y', 'wri_Mag_Z', 'ank_Acc_X', 'ank_Acc_Y', 'ank_Acc_Z', 'ank_Gyr_X', 'ank_Gyr_Y', 'ank_Gyr_Z', 'ank_Mag_X', 'ank_Mag_Y','ank_Mag_Z', 'bac_Acc_X', 'bac_Acc_Y', 'bac_Acc_Z', 'bac_Gyr_X', 'bac_Gyr_Y', 'bac_Gyr_Z', 'bac_Mag_X', 'bac_Mag_Y', 'bac_Mag_Z' ".

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Of late, efforts are underway to build computer-assisted diagnostic tools for cancer diagnosis via image processing. Such computer-assisted tools require capturing of images, stain color normalization of images, segmentation of cells of interest, and classification to count malignant versus healthy cells. This dataset is positioned towards robust segmentation of cells which is the first stage to build such a tool for plasma cell cancer, namely, Multiple Myeloma (MM), which is a type of blood cancer. The images are provided after stain color normalization.

Instructions: 

IMPORTANT:

If you use this dataset, please cite below publications-

  1. Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, "GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images," Medical Image Analysis, vol. 65, Oct 2020. DOI: https://doi.org/10.1016/j.media.2020.101788. (2020 IF: 11.148)
  2. Shiv Gehlot, Anubha Gupta and Ritu Gupta, "EDNFC-Net: Convolutional Neural Network with Nested Feature Concatenation for Nuclei-Instance Segmentation," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 1389-1393.
  3. Anubha Gupta, Pramit Mallick, Ojaswa Sharma, Ritu Gupta, and Rahul Duggal, "PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma," PLoS ONE 13(12): e0207908, Dec 2018. DOI: 10.1371/journal.pone.0207908
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Passwords that were leaked or stolen from sites. The Rockyou Dataset is about 14 million passwords.

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We generated attack datasets 1 based on real data from Austin, Texas.

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