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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The feature map from convolution neural networks

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This data has related to the paper "Accelerating convolutional neural network by exploiting sparsity on GPUs".

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Mother’s Significant Feature (MSF) Dataset has been designed to provide data to researchers working towards woman and child health betterment. MSF dataset records are collected from the Mumbai metropolitan region in Maharashtra, India. Women were interviewed just after childbirth between February 2018 to March 2021. MSF comprise of 450 records with a total of 130 attributes consisting of mother’s features, father’s features and health outcomes. A detailed dataset is created to understand the mother’s features spread across three phases of her reproductive age i.e.

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We have provided the copy of forms used to collect data for datset and a read me guide to undertand the features provided in dataset along with the content of all the 6 dataset submitted in excel sheet format.

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Expanding our knowledge of small molecules beyond what is known in nature or designed in wet laboratories promises to significantly advance drug discovery, biotechnology, and material science. Computing novel small molecules with specific structural and functional properties is non-trivial, primarily due to the size, dimensionality, and multi-modality of the corresponding search space. Deep generative models that learn directly from data without the need for domain insight are recently providing a way forward.

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This file contains the MAB algorithms for millimeter wave (mmWave) beamforming training (BT) in Indoor environment for both single beam and concurrent beams scenarios. The algorithms were developed using MATLAB software and they are making use of the following data set

 

 

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 This  dataset of 7200 channels is generated at different locations in the room area of 30x15x4 m3, where the locations are separated by 0.25m in both horizontal and vertical directions. Each AP uses 10 dBm TX power and 2D BF. In the concurrent mmWave BT scenario, all APs are operating, while in the single mmWave BT scenario, we consider a single AP fixed on the center of the room’s ceiling

 

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A fundamental building block of any computer-assisted interventions (CAI) is the ability to automatically understand what the surgeons are performing throughout the surgery. In other words, recognizing the surgical activities being performed or the tools being used by the surgeon can be deemed as an essential steps toward CAI. The main motivation for these tasks is to design efficient solutions for surgical workflow analysis. The CATARACTS dataset was proposed in this context. This dataset consists of 50 cataract surgery.

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The dataset consists of 50 videos of cataract surgeries performed in Brest University Hospital. Patients were 61 years old on average (minimum: 23,maximum: 83,standard deviation: 10). Each surgery was recorded in two videos: the microscope video and the surgical tray video. The frame definition was 1920x1080 pixels (full HD resolution) for both types of videos. The frame rate was approximately 30 frames per second for the tool-tissue interaction videos and 50 frames per second for the surgical tray videos. Microscope videos had a duration of 10 minutes and 56 s on average (minimum: 6 minutes 23 s, maximum: 40 minutes 34 s, standard deviation:6 minutes 5 s). Surgical tray videos had a duration of 11 minutes and 3 s on average (minimum: 6 minutes 30 s, maximum: 40 minutes 48 s, standard deviation: 6 minutes 3 s). In total, more than nine hours of surgery (for each video type) have been video recorded. For more details about the dataset and the different tasks proposed, please refer to the links provided in the abstract.

Please note that the evaluation scripts (for the microscope test set) used in the challenges are available now. For CATARACTS 2018, in addition to the videos, we provide the images (images.zip) used in the challenge and the ground truth.

If you use this dataset, please cite the following paper:
Al Hajj, Hassan, et al. "CATARACTS: Challenge on automatic tool annotation for cataRACT surgery." Medical image analysis 52 (2019): 24-41.

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Twitter is one of the most popular social networks for sentiment analysis. This data set of tweets are related to the stock market. We collected 943,672 tweets between April 9 and July 16, 2020, using the S&P 500 tag (#SPX500), the references to the top 25 companies in the S&P 500 index, and the Bloomberg tag (#stocks). 1,300 out of the 943,672 tweets were manually annotated in positive, neutral, or negative classes. A second independent annotator reviewed the manually annotated tweets.

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Twitter RAW data was downloaded using the Twitter REST API search, namely the "Tweepy (version 3.8.0)" Python package, which was created to make the interaction between the REST API and the developers easier. The Twitter REST API only retrieves data from the past seven days and allows to filter tweets by language. The tweets retrieved were filtered out for the English (en) language. Data collection was performed from April 9 to July 16, 2020, using the following Twitter tags as search parameter: #SPX500, #SP500, SPX500, SP500, $SPX, #stocks, $MSFT, $AAPL, $AMZN, $FB, $BBRK.B, $GOOG, $JNJ, $JPM, $V, $PG, $MA, $INTC $UNH, $BAC, $T, $HD, $XOM, $DIS, $VZ, $KO, $MRK, $CMCSA, $CVX, $PEP, $PFE. Due to the large number of data retrieved in the RAW files, it was necessary to store only each tweet's content and creation date.

 

The file tweets_labelled_09042020_16072020.csv consists of 5,000 tweets selected using random sampling out of the 943,672 sampled. Out of those 5,000 tweets, 1,300 were manually annotated and reviewed by a second independent annotator. The file tweets_remaining_09042020_16072020.csv contains the remaining 938,672 tweets.

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This dataset is used to model the zinc concentrate grade monitoring, which includes features of froth video in the last cleaning cell.

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