Business Cases

• Natural language processing for Twitter timelines - technical survey

  Surveyed application cases of natural language processing for Twitter timelines and the use of the processed results.

  Published papers were surveyed and a list of implementers, purpose of implementation, analysis methods, and tools/libraries used was summarized in the report.

  Technologies

  • Twitter
  • Natural Language Processing
  • Python

• EEG measurements - experimental support

  In a project to build a large-scale brain activity database, we provided experimental support for measuring the EEG of a large number of subjects by installing and adjusting electrodes, recording and monitoring EEG data, and detecting and recording the causes of noise generation.

  Electrode placement is the key to EEG measurement. If the electrodes are not placed properly, the measurement data will contain large noise, and accurate analysis will not be possible. Therefore, it is necessary to adjust the placement while checking the measurement data from time to time.

  In addition, because EEG is a very small electrical signal, slight movements of the subject or changes in the environment can result in significant noise. In this case, too, later analysis cannot be performed correctly, so it is necessary to constantly check the measurement data, estimate what kind of noise has been mixed in, and record it.

  In this way, we provided consistent experimental support from measurement preparation to recording in experiments that required experience and expertise in EEG measurement.

  

  Technologies

  • Human electroencephalography

• Face identification method based on a small number of data - algorithm development

  Face recognition by deep learning often requires large amounts of labeled training data, which can be very costly. To overcome this problem, we have developed a method for face identification with a small number of samples in this work.

  This method pre-trains a deep network based on existing labeled data, and then transfers the feature extraction part obtained from the pre-training to the deep network for the data given the label of the person to be recognized. We have confirmed that this method can achieve a high discrimination rate even with a small amount of data.

  Because training deep networks is very time consuming, multiple GPU instances were launched on AWS to evaluate discrimination accuracy for multiple network structures in parallel.

  

  Technologies

  • Python
  • Deep Learning
  • fine-tuning
  • CNN
  • AWS
  • GPGPU

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