This dataset is a set of eighteen directed networks that represents message exchanges among Twitter accounts during eighteen crisis events. The dataset comprises 645,339 anonymized unique user IDs and 1,396,709 edges that are labeled with respect to Plutchik's basic emotions (anger, fear, sadness, disgust, joy, trust, anticipation, and surprise) or "neutral" (if a tweet conveys no emotion).


This paper concerns static output feedback stabilization of polytopic discrete LTI systems. The previous related studies were mainly based on LMI approaches which are naturally conservative. In this paper, a novel design algorithm is presented that iteratively partitions a primary design space to subspaces. Then, by assessing stabilizability status of each generated subspace, the algorithm determines the total stabilizable parts and removes the undesired parts of the design space.


EmoSurv is a dataset containing keystroke data along with emotion labels. Timing and frequency data is recorded while participants are typing free and fixed texts before and after being induced specific emotions. These emotions are: Anger, Happiness, Calmness, Sadness, and Neutral state.

First, data is collected while the participant is in a neutral state. Then, the participant watches an eliciting video. Once the emotion is induced in the participant, he types another fixed and free text.


The dataset contains 4 .csv files:

  • File 1: Fixed Text Typing Dataset which is collected while a participants it typing a fixed text and it  includes the following features: User Id, Emotion Index, Index, Key Code, key Down, key Up, D1U1, D1U2, D1D2, U1D2, U1U2, D1U3, D1D3, and Answer.

  • File 2: Free Text Typing Dataset which is collected while a participants it typing a free text and it  includes the following features:  User Id, Emotion Index, Index, Key Code, key Down, key Up, D1U1, D1U2, D1D2, U1D2, U1U2, D1U3, D1D3, and Answer.

  • File 3: Frequency Dataset which includes frequency related features like User ID, textIndex, EmotionIndex, DelFreq, LeftFreq, and TotTime.

  • File 4: Participants Information dataset which includes demographics information like UserID, TypeWith, TypistType, PCTimeAverage, AgeRange, gender, status, degree, and country.


  • UserID: each participant is allocated the same ID in the 4 files.

  • Emotion Index: H (for Happy), S (for Sad), A (for Angry), C (for Calm), and N (for Neutral state).

  • Key Code: the key pressed by the participant.

  • Key Down: is the exact timestamp of the key down event. 

  • Key Up: is the exact timestamp of the key up event.

  • TextIndex: the type of text typed being either FI (for Fixed text) or FR (for Free text)

  • D1U1 (DT1): Time between first key down and first key up 1

  • D1U2 (Dig2): Time between first key down and second key up 2

  • D1D2 (Dig1): Time between first key down and second key down 2

  • U1D2 (FT1 / FT2): Time between first key up and second key down 2

  • U1U2 (Dig3): Time between first key up and second key up 2

  • D1U3 (Trig2): Time between first key down and third key up 3

  • D1D3 (Trig1): Time between first key down and third key down 3

  • Answer: Takes “R” (as right answer) if the participant answered correctly the accuracy question and “W” (as wrong answer) if he incorrectly answered it. (The accuracy question is a MCQ related to the video that the participant has watched)

  • DelFreq: Relative frequency of delete key NA

  • LeftFreq: Relative frequency of backspace key NA

  • Typing speed: Number of key pressed in each task the time spent from the first key pressed to the last key released (in the same task). 

  • TypeWith: specifies if the participant types using one hand or two hands

  • TypistType: specifies whether the participant uses one finger, two fingers, or is a touch typist (multiple fingers) to type a text.

  • PCTimeAverage: is the average time a user spends on his/her computer per day.

  • AgeRange: 16-19, 20-29, 30-39, >= 40years old. 

  • Gender: Male, or female

  • Status: Student, or professional

  • Degree: College/University, or High school. 

  • Country: Place of residence.

The figure attached in Documentation section represents how the timing features are calculated.


Grant of License

We grant You a non-exclusive, non-transferable, revocable license to use the EmoSurv  Dataset solely for Your non-commercial, educational, and research purposes only, but without any right to copy or reproduce, publish or otherwise make available to the public or communicate to the public, sell, rent or lend the whole or any constituent part of the Emosurv Dataset thereof. 



A high level of monitoring is necessary for the safety and product quality of the electrical fused magnesia furnace (EFMF). In this paper, a monitoring method based on latent subspace for EFMF is proposed to fully mine the effective information of multi-source heterogeneous data in the process. By minimizing the distance of different types of data in the subspace, the corresponding projection matrix is obtained. Then the data is projected into the obtained subspace to estimate whether fault occurs.In summary, the main contributions of this paper are threefold.


experimental data


Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to erratic writing style of the individuals. Problem becomes more severe when the input image is doctor's prescription. Before feeding such image to the OCR engine, the classification of printed and handwritten texts is a necessity as doctor's prescription contains both handwritten and printed texts which are to be processed separately.


This dataset contains the experimental materials for "Use and Perceptions of Multi-Monitor Workstations".

There are two files:

  1. survey.txt: the survey questions
  2. survey-results.csv: the answers obtained from the 101 respondents tot he survey



The data is straightforward.

A small number of entries are in Hebrew.


Most text-simplification systems require an indicator of the complexity of the words. The prevalent approaches to word difficulty prediction are based on manual feature engineering. Using deep learning based models are largely left unexplored due to their comparatively poor performance. We have explored the use of one of such in predicting the difficulty of words. We have treated the problem as a binary classification problem. We have trained traditional machine learning models and evaluated their performance on the task.


The data is in CSV format. Please check the research paper for obtaining the difficulty label from the I_Z score.


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In this paper, a PID controller is applied in order to control the temperature by a proposed model of Newton’s law of cooling. The control problem considers a model obtained, which the temperature of a body decreases with time is proportional to the difference in temperature between the body and its surroundings.