Abstract: In embodiments of the present disclosure, disclosure is an immunogenicity prediction method comprising the steps of: acquiring information about synthetic long peptides for treating carcinoma in a subject through an immunogenicity prediction device; processing the synthetic long peptides by one processing method of embedding, one-hot encoding, and BLOSUM through the immunogenicity prediction device, and outputting one or more antigen feature values based on the processed data; inputting the cleavage probability vector for each position of the synthetic long peptides through the immunogenicity prediction device to output one or more cleavage feature values; inputting a neoantigen peptide sequence, an HLA class I sequence, an HLA class II sequence within the synthetic long peptides through the immunogenicity prediction device to output one or more neoantigen feature values related to the immunity and binding affinity to the neoantigen peptide sequence; and outputting an immunogenicity score of the neoantigen pe

Abstract: Provided is a method of selecting test stimuli for use in evaluating performance of a video watermarking algorithm, including the steps of: calculating an image complexity rate (ICR) for each of a plurality of test stimulus candidates and classifying them into L (where L is an integer more than or equal to 2) levels based on the calculated ICR values; calculating an energy distribution rate (EDR) for each of the plurality of test stimulus candidates, and classifying them into M (where M is an integer more than or equal to 2) levels based on the calculated EDR values; calculating a motion rate (MR) for each of the plurality of test stimulus candidates and classifying them into N (where N is an integer more than or equal to 2) levels of the calculated MR values; and selecting at least one test stimulus from each of the L levels, M levels, the N levels of the test stimuli, respectively.

Abstract: Provided is a method of selecting test stimuli for use in evaluating performance of a video watermarking algorithm, including the steps of: calculating an image complexity rate (ICR) for each of a plurality of test stimulus candidates and classifying them into L (where L is an integer more than or equal to 2) levels based on the calculated ICR values; calculating an energy distribution rate (EDR) for each of the plurality of test stimulus candidates, and classifying them into M (where M is an integer more than or equal to 2) levels based on the calculated EDR values; calculating a motion rate (MR) for each of the plurality of test stimulus candidates and classifying them into N (where N is an integer more than or equal to 2) levels of the calculated MR values; and selecting at least one test stimulus from each of the L levels, M levels, the N levels of the test stimuli, respectively.