Crowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots.

Instructions: 

Extract locally the zip files, read the readme file.

Instructions for dataset usage are included in the open access paper: Franzoni, V., Biondi, G., Milani, A., Emotional sounds of crowds: spectrogram-based analysis using deep learning (2020) Multimedia Tools and Applications, 79 (47-48), pp. 36063-36075. https://doi.org/10.1007/s11042-020-09428-x

File are released under Creative Commons Attribution-ShareAlike 4.0 International License

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Segmentation of TC clouds in 2016. The segmentation task was accomplished by an algorithm which takes a time series of brightness temperature images of TCs and uses image processing techniques to acquire segmentation for each image in a semi-supervised manner. 

Instructions: 

2016 TC cloud segmentation animation

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The data include:

  • Demographic data of the participants including: gender, group of participation and number of years in the company.
  • Results of the use of Ethool including: expended time and subjective evaluation of if using a Likert of 5 points. Two different files are available corresponding to each iteration (prototype 1 and prototype 2).
  • Results of the SUS questionnaire for both iterations (prototype 1 and prototype 2).
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Each record in the dataset includes 7 fields:

UserID, CurrentChannel, NextChannel, Date, TimeSection, StartTime, Duration. 

The meanings of them are respectively as follows,

1. UserID : the number of a user, sorted in descending order by the number of channels he/she has switched during the period of time; [1, 13246].

2. CurrentChannel: the channel ID viewed by the user in the current time section.

3. NextChannel: the channel ID which the user would view in the next time section.

Instructions: 

Each record in the dataset includes 7 fields:

 

UserID, CurrentChannel, NextChannel, Date, TimeSection, StartTime, Duration. 

 

The meanings of them are respectively as follows,

1. UserID : the number of a user, sorted in descending order by the number of channels he/she has switched during the period of time; [1, 13246].

2. CurrentChannel: the channel ID viewed by the user in the current time section.

3. NextChannel: the channel ID which the user would view in the next time section.

4. Date : the date of the viewing behavior record, [0, 31], August 1~31, 2014, here 0 denotes July 31, 2014.

5. TimeSection: the number of time section, [1, 144], we divide one day (24 hours) into 144 time sections, each of which is 10 minutes. For example, the number 1 means the record occurs between 00:00 and 00:10 on the day, and number 144 means that the record is between 23:50 and 24:00 on the day.

6.  StartTime: the time when the user starts to watch the current channel, whose value is the cumulative time interval is numbered with a value of 1-86400 in unit of second on the current day; for example, 62990 means 17:29:50.

7.   Duration: the duration the user watches current channel from the start time point, in unit of second, and we have deleted the records whose duration is less than 5 seconds as well as more than 3600*8 seconds. 

Please select your interested data from the dataset for your demand. 

Acknowledgement : We thanks the senior engineer, Mr. Songtao Wu, for the original dataset in GZTV station, Guangdong, China.

Qihu Yuan  and Can Yang

2021-01

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The data are associated with a submitted journal paper.

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Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures.

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The data are associated with a submitted journal paper.

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This dataset contains the data associated with the electrically equivalent model of the IEEE Low Voltage (LV) test feeder for use of the distribution network studies. This dataset is for the letter entitled:" A Reduced Electrically-Equivalent Model of the IEEE European Low Voltage Test Feeder".

Instructions: 

The uploaded data includes a zip file containing the dataset in the form of CSV files for an electrically equivalent reduced model of IEEE LV European feeder. 

  • The test feeder is at the voltage level of 416 V, phase-to-phase.
  • Load shapes with one-minute time resolution over 24 hours are provided for the time-series load flow simulation.
  • Line data and load data of the network are given in seperate CSV files. 
  • The line codes specified by sequence impedances and admittances are available in a seperate CSV file.

 

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This is a large Chinese taxonomic knowledge base, which is translated from Probase by the neural network.

It has 11,292,493 IsA pairs with an accuracy of 86.6%.

 

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This is a large Chinese commonsense knowledge base, which is translated from ConceptNet 5.6, with around 2 million triples and an accuracy of 89.6%.

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