Sentence examples for raw frequency from inspiring English sources

The table shows the number of occurrences of each letter in the text of this article, which approximates the raw frequency distribution for most technical material.

By treating digraphs in the plaintext as units rather than as single letters, the extent to which the raw frequency distribution survives the encryption process can be lessened but not eliminated, as letter pairs are themselves highly correlated.

The dotted curve in the figure indicates that the raw frequency of occurrence pattern is lost when the draft text of this article is encrypted with a random onetime key.

If the key is DECEPTIVE and the message is WE ARE DISCOVERED SAVE YOURSELF, then the resulting cipher will be The graph shows the extent to which the raw frequency of occurrence pattern is obscured by encrypting the text of this article using the repeating key DECEPTIVE.

It is possible to conceal information about raw frequency of occurrence by providing multiple cipher symbols for each plaintext letter in proportion to the relative frequency of occurrence of the letter i.e., twice as many symbols for E as for S, and so on.

If the key is DECEPTIVE and the message is WE ARE DISCOVERED SAVE YOURSELF, then the resulting cipher will be The graph shows the extent to which the raw frequency of occurrence pattern is obscured by encrypting the text of an article using the repeating key DECEPTIVE.

The first column shows the raw frequencies in each corpus and the second, the normalised frequencies per 10,000 words.

Note that the raw frequencies are visualized here, contrary to Figure 4, which shows the terms deemed to be most important according to the models.

Although the raw frequencies are not high (seven was the highest count), they were arrived at from responses to questions that did not prompt students to discuss positive collaborative activities.

We highlight the selected key concepts by labeling them in the structural space, and we add another visual dimension by coloring the nodes based on their raw frequencies of occurrence in the text data.

During his presentation, co-founder Jack Nolan introduced the audience to Alice, an AI that learned to listen to and understand music purely based on its sound (the raw frequencies that make up the song).