2018 World Automation Congress (WAC) 2018
DOI: 10.23919/wac.2018.8430483
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Automatic Text Summarization Using Customizable Fuzzy Features and Attention on the Context and Vocabulary

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Cited by 31 publications
(15 citation statements)
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“…If there is uncertainty in the source data, indicator (8) cannot be used explicitly, since it does not provide for working with fuzzy values [2,[4][5]11,13,15,18]. Complete mismatch can be expressed as follows:…”
Section: Fig 1 Methods Of Fuzzy Multiparametric Selection Immentioning
confidence: 99%
“…If there is uncertainty in the source data, indicator (8) cannot be used explicitly, since it does not provide for working with fuzzy values [2,[4][5]11,13,15,18]. Complete mismatch can be expressed as follows:…”
Section: Fig 1 Methods Of Fuzzy Multiparametric Selection Immentioning
confidence: 99%
“…Inspired by Liu's (2016) Spatio-Temporal LSTM model, we apply a similar model that is equipped with a spatio-temporal mechanism to recognize the performed action and learn the exclusive motion patterns of the action. In other words, our inspired LSTM model utilizes two attention mechanisms [56]: attention over the time frames, and attention over various keypoint coordinates. Such spatio-temporal attention helps the model to understand an action despite variation among individuals preforming the same action with a certain intensity index, such as walking fast or punching hard.…”
Section: Lstm With Spatio-temporal Attentionmentioning
confidence: 99%
“…Updating the position of wolves during the hunting process using equations (9-11), does not guarantee that the value of every direction does not exceed the bounds of the problem. As a result, after attaining the discrete values of each direction through rounding toward zero the updated values of positions obtained from equation (9)(10)(11), an additional step is needed to be considered to return back the exceeding values into the boundaries of the problem. In the case of SCOS problem, upper bound denoted by ub , equals to the number of available candidates for each subtask, and lower bound, denoted by lb , is equal to 1.…”
Section: Range Checkingmentioning
confidence: 99%
“…Step5: Update the position of wolves through hunting process using Equations (9)(10)(11) Step6: Discretize the elements of position vectors obtained in step5 ( 1Xt ) using the floor function Step7: Perform range checking for values obtained in Step6 and map out of bound values (if any) into the allowed space according to Equation 12Step8: Select c Pn  number of parents for crossover operation and m Pn  number of parents for mutation operator according to Equation 13, where c P and m P are probability of crossover and mutation, respectively Step9: Perform the two-point crossover on the selected parents Step10: Perform the swap mutation operator on the selected individuals for mutation Step11: Merge the populations created in Steps 9 and 10 with the original population obtained after Step7…”
Section: Mutation Operatormentioning
confidence: 99%
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