Abstract: An example system includes a stage identification portion to determine a stage in a progression of a progressive identifier, and an identifier encoding portion to encode or read the progressive identifier. A number or percentage of bits of the progressive identifier encoded, or read as encoded, by the identifier encoding portion is uniquely associated with the stage in the progression.

Abstract: An example system includes a stage identification portion to determine a stage in a progression of a progressive identifier, and an identifier encoding portion to encode or read the progressive identifier. A number or percentage of bits of the progressive identifier encoded, or read as encoded, by the identifier encoding portion is uniquely associated with the stage in the progression.

Abstract: Examples associated with printer encryption are described. One example printer includes a data store to store a one-time pad. An encryption module may encrypt a message using the one-time pad. The encryption module also transmits the encrypted message to a trusted device that stores a copy of the one-time pad. A decryption module uses the one-time pad to decrypt a received message form the trusted device. The decryption module also controls the printer to perform an action based on the received message. A refresh module replaces the one-time pad during a service event.

Abstract: One example is a system including a plurality of summarization engines, an evaluator, and a selector. Each of the plurality of summarization engines receives content to provide a summary of the content. The evaluator determines a value of each summary for a selected task. The selector selects a summarization engine for the selected task based on the assessed value of each summary.

Abstract: Examples disclosed herein relate to item identifier assignment. For example, a processor may create a mass serialization scheme to provide a list of item identifiers to allow a subset of the identifiers to be knocked out. Each item identifier may associated with multiple containers, and each container may include two item identifiers relating to the sequence of item identifiers included within the container.

Abstract: Functional summarization of non-textual content based on a meta-algorithmic pattern is disclosed. One example is a system including a converter, a plurality of summarization engines and/or meta-algorithmic patterns, an extractor, and an evaluator. The converter converts the non-textual content into a plurality of tokens. Combinations of summarization engines and/or meta-algorithm patterns are applied to the plurality of tokens to provide a meta-summary. The extractor extracts at least one summarization term from the meta-summary, and at least one class term for each given class of a plurality of classes of non-textual content. The evaluator determines similarity values of the non-textual content over each given class, each similarity value indicative of a similarity between the at least one summarization term and the at least one class term for each given class. The selector selects a class of the plurality of classes, the selecting based on the determined similarity values.

Abstract: One example is a system including a plurality of combinations of summarization engines and/or meta-algorithmic patterns used to combine a plurality of summarizers, an extractor, an evaluator, and a selector. Each of the plurality of combinations of summarization engines and/or meta-algorithmic patterns receives content to provide a meta-summary of the content. The extractor generates a collection of search queries based on the content. The evaluator determines a similarity value of each combination of summarization engines and/or meta-algorithmic patterns for the collection of search queries. The selector selects an optimal combination of summarization engines and/or meta-algorithmic patterns based on the similarity value.

Abstract: Examples associated with printer encryption are described. One example printer includes a data store to store a one-time pad. An encryption module may encrypt a message using the one-time pad. The encryption module also transmits the encrypted message to a trusted device that stores a copy of the one-time pad. A decryption module uses the one-time pad to decrypt a received message form the trusted device. The decryption module also controls the printer to perform an action based on the received message. A refresh module replaces the one-time pad during a service event.

Abstract: Examples herein disclose multiple engines to produce output representative of a summary of document produced by each of the multiple engines. The examples apply a weighting mechanism to the output specific to that engine to obtain a value corresponding to that output. The examples identify specialized language if the value corresponding to that output reaches at least a particular threshold.

Abstract: A counterfeit identification performance attribute (SIPA) sensitivity to changes in resolution of the image for features of an image is determined. The CIPA sensitivity for the features is used to choose at least one feature to determine whether the image on a sample is a counterfeit.

Abstract: A method and apparatus to select from an image (30) of a first sample (28), at least one region (44) digitally captured at a first resolution based upon how a counterfeit identification performance attribute of each region (44) digitally captured at the first resolution correlate to the counterfeit identification performance attribute of the region (44) digitally captured at a second resolution higher than the first resolution. The selected region (44) is used to determine whether the image (30) on a second sample is a counterfeit.

Abstract: A combinatorial summarizer includes a plurality of summarization engines, a processor in selective communication with each summarization engine, and computer readable instructions executable by the processor and embodied on a tangible, non-transitory computer readable medium. Each summarization engine is to select a respective plurality of sentences, and generate a relative rank and an associated weight for each sentence of the respective plurality of sentences. The computer readable instructions include instructions to determine a combined weight for each sentence of each respective plurality of sentences. The combined weight is based upon the respective associated weight and a respective relative human rank for each sentence in a set of sentences, including all sentences of each respective plurality of sentences.

Abstract: Personalized learning based on functional summarization is disclosed. One example is a system including a content processor, a plurality of summarization engines, at least one meta-algorithmic pattern, an evaluator, and a selector. The content processor provides course material to be learned, the course material selected from a corpus of educational content, and identifies retained material indicative of a portion of the course material retained by user. Each of the plurality of summarization engines provides a differential summary indicative of differences between the course material and the retained material. The at least one meta-algorithmic pattern is applied to at least two differential summaries to provide a meta-summary using the at least two differential summaries. The evaluator determines a value of each differential summary and meta-summary.

Abstract: Examples herein disclose multiple engines to produce output representative of a summary of document produced by each of the multiple engines. The examples apply a weighting mechanism to the output specific to that engine to obtain a value corresponding to that output. The examples identify specialized language if the value corresponding to that output reaches at least a particular threshold.

Abstract: One example is a system including a plurality of combinations of summarization engines and/or meta-algorithmic patterns used to combine a plurality of summarizers, an extractor, an evaluator, and a selector. Each of the plurality of combinations of summarization engines and/or meta-algorithmic patterns receives content to provide a meta-summary of the content. The extractor generates a collection of search queries based on the content. The evaluator determines a similarity value of each combination of summarization engines and/or meta-algorithmic patterns for the collection of search queries. The selector selects an optimal combination of summarization engines and/or meta-algorithmic patterns based on the similarity value.

Abstract: Functional summarization of non-textual content based on a meta-algorithmic pattern is disclosed. One example is a system including a converter, a plurality of summarization engines and/or meta-algorithmic patterns, an extractor, and an evaluator. The converter converts the non-textual content into a plurality of tokens. Combinations of summarization engines and/or meta-algorithm patterns are applied to the plurality of tokens to provide a meta-summary. The extractor extracts at least one summarization term from the meta-summary, and at least one class term for each given class of a plurality of classes of non-textual content. The evaluator determines similarity values of the non-textual content over each given class, each similarity value indicative of a similarity between the at least one summarization term and the at least one class term for each given class. The selector selects a class of the plurality of classes, the selecting based on the determined similarity values.

Abstract: Examples disclosed herein relate to item identifier assignment. For example, a processor may create a mass serialization scheme to provide a first of item identifiers to allow a subset of the identifiers to be knocked out. Each item identifier may associated with multiple containers, and each container may include two item identifiers relating to the sequence of item identifiers included within the container.

Abstract: One example is a system including a plurality of summarization engines, an evaluator, and a selector. Each of the plurality of summarization engines receives content to provide a summary of the content. The evaluator determines a value of each summary for a selected task. The selector selects a summarization engine for the selected task based on the assessed value of each summary.

Abstract: A method and apparatus to select from an image (30) of a first sample (28), at least one region (44) digitally captured at a first resolution based upon how a counterfeit identification performance attribute of each region (44) digitally captured at the first resolution correlate to the counterfeit identification performance attribute of the region (44) digitally captured at a second resolution higher than the first resolution.

Abstract: Techniques related to data stream traffic control are disclosed herein. A bit equivalent entropy of an anonymized data stream is computed. Traffic of the data stream is controlled based on the value of the bit equivalent entropy.