Sentence examples similar to gram set from inspiring English sources

21 Grams set out to chop itself up into "digital bits" so as to challenge viewers and keep them on the edge of their seat.

N-gram set is obtained by moving a window of n tokens length through an entire sentence.

The unique n-gram set includes two columns - the n-gram and the genome in which it is present.

This n-gram set represents all possible n-grams in the reference genome set that includes a number of distinct strains of the same genus and species.

On the other hand the common n-gram set includes four columns - the n-gram, frequency of its occurrence in the entire dataset, its weight assigned by the scoring function and the genomes in which it is present.

(b) A stores the resulting q-gram set in a Bloom filter bf i of length l using the k keyed hash functions with the key K. 3. A stores the resulting n a Bloom filters and a randomly generated unique ID number id a in a list BF a. 4. A removes any identifier in DB a, replacing them by id a. 5.

(b) B stores the resulting q-gram set in a Bloom filter bf j of length l using the k keyed hash functions with the key K. 7. B stores the resulting n b Bloom filters and a randomly generated unique ID number id b in a list BF b. 8. B removes any identifier in DB b, replacing them by id b. 9. B sends DB b to D. 10.

Table 3 Distribution of n-gram sizes, chosen by each feature selection method, for the two n-grams sets that consist of varying n-grams sizes.

For this we used three OpCode n-grams sets on which the three feature selection methods were applied with four top-selections (50, 100, 200 or 300): This option refers to the 6 OpCode n-grams sets that were used in the previous experiments, in which the n-grams in each set are of the same size (1, 2, 3, 4, 5 and 6).

The distribution of n-grams sizes for the two n-grams sets that consist of varying n-gram sizes is presented in Table 3. From the table we can see, as expected, that the DF feature selection method favors short n-grams which appear in a larger number of files.

The third party C observes the Bloom filters of the data holders, but the encoding of the names is irreversible since the data holders use one-way hash functions to store the q-gram sets in the Bloom filters.