Sentence examples for tasks we employ from inspiring English sources

For the machine learning tasks we employ MATLAB and to solve the optimization problems, we use GAMS (with CPLEX solver).

Even though various classifiers such as HMM and SVM have been fed into speech emotion recognition tasks, we employ the neural network-based classifier used in the facial expression recognition module in order to efficiently handle the fusion process in which the recognition results of two indicators are integrated.

To implement these tasks, we employed the Ci,s function defined in [49].

To accomplish such tasks, we employed logit regression models to control related covariates and observe whether and how the economic resources of the two generations influence living arrangements.

In order to test them in CBIR tasks, we employed the Bag-of-Visual-Words (BOVW) model to calculate vector image representations and went on calculating eight weighted equivalents of those vectors by applying an equal number of weighting schemes.

The effect of learning-by-doing was assessed by comparing the performance of eight subjects (BtSt) who only used the mobile substitution device for the tests, to that of three subjects who, in addition, practiced with it for four hours daily in their normal life (BtSc and BcSc); two subjects who did not use the device at all (BtSn and BcSn) allowed assessment of its use in the tasks we employed.

Alternatively, the absence of significant differences in cortical activation may be due to the tasks we employed.

Since threshold measurements are much faster and more reliable using four-alternative forced choice than two-alternative forced choice tasks, we employed four directions of motion (up, down, left, and right) to be discriminated by the subject.

With respect to the word repetition tasks we employed, we postulate that phonological processing is the main process and that the activations in the angular gyrus arose mainly from phonological familiarity (phonological analysis of novel relative to familiar stimuli), which is relevant to differences in word repetition success rates.

For the sensing task, we employ multiple ceiling view based PIR sensors with compound-eye structured modulation for splitting the object space into multiple subregions.

To accomplish the task, we employed the GeoScaler software developed at the Laboratoire de cartographie numérique et de photogrammétrie (LCNP) of the Quebec division of the Geological Survey of Canada (Version 2009).