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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.

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2995 Views

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.

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167 Views

experimental data

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65 Views

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.

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19999 Views

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

 

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115 Views

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.

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2644 Views

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1341 Views

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.

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182 Views

This dataset is simulation output for our work on model swapping. It represents runs of a simulation where we swapped out the L1 cache model with different simple statistical models. 

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83 Views

Objective: To analyze the effect of electrical pulses (EP) with tomato lipophilic extract (TLE) on human, triple negative breast cancer (TNBC) MDA-MB-231 cells and non-cancerous breast MCF-10A cells and to gain insights into the mechanisms of action. Methods: We prepared TLE using solvent extraction method. We identified the constituents of TLE using LC-MS analysis. TLE with the dose of 250μg/ml was applied with eight, 800V/cm, 100μs pulses to MDA-MB-231 and MCF-10A cells and their viability was studied.

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149 Views

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