Abstract: A device including a processor and a memory communicatively coupled to the processor is provided. The memory stores instructions causing the processor, after execution of the instructions by the processor, to: display a reference page number with each displayed page of a digital document having reference page numbers, the digital document corresponding to a reference document having page numbers that correspond to the reference page numbers; and display a fractional page number with each displayed page of the digital document, each fractional page number corresponding to a portion of a page of the reference document.

Abstract: Examples disclosed herein relate to transmitting an authentication mark. For example, a processor creates an authentication mark for a two factor authentication system. The authentication mark is created according to data represented by a data bearing image. The processor transmits via a network the authentication mark to a computing device.

Abstract: Examples associated with identifying documents are disclosed. One example includes identifying at least one document in a corpus of documents that contains at least one token. The token is identified from a search query. Relevance of the search query to each identified document is determined according to a Helmholtz score for each respective identified token.

Abstract: A system that uses biometric data to facilitate learning is provided herein. The system receives at least two data points via a biometric sensor. Information is extracted from the at least two data points and a set of features are identified. Data analysis is then performed on the set of features.

Abstract: Examples associated with keyframe annotation are disclosed. One example includes extracting a set of keyframes from a video presentation. A subset of the keyframes is selected to present to a user based on a user preference. Annotations are generated for the subset of the keyframes. The annotations are personalized to the user. The subset of the keyframes and the annotations are presented to the user.

Abstract: An example method for forming a data-bearing medium in accordance with aspects of the present disclosure includes setting variables associated with the data-bearing medium, the variables comprising a bit length of a codeword, identifying a phase-invariant codeword based on the variables, and arranging rows of the data-bearing medium with the phase-invariant codewords.

Abstract: Provided in one example is an analyte detection package that includes a chamber, a surface-enhanced luminescence analyte stage within the chamber, and a lens integrated as part of the package to focus radiation onto the analyte stage.

Abstract: Examples disclosed herein relate to a multi-tier storage based on data anonymization. A processor may store data in a first data tier. The processor may anonymize the data according to a first anonymization level and store the anonymized data in a second data tier. The processor may associate a first user with the first data tier and associate a second user with the second data tier.

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: An embodiment provides a method for compression of a real-time surveillance signal. This method includes receiving a signal from a monitoring device and analyzing the signal to be monitored to compute spectral content of the signal. This method also includes computing the information content of the signal and determining a count of a number of coefficients to be used to monitor the signal. This method includes deploying a strategy for computing a plurality of coefficients based on the spectral content of the signal and the count of the number of coefficients to be used for monitoring the signal. This method further includes monitoring the signal and resetting the system in the case of above-threshold changes in a selected portion of the plurality of coefficients.

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: 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: Examples disclosed herein relate to classifying paper based on three-dimensional characteristics of the paper. For example, a representation of the three-dimensional characteristics of the paper may be created, and statistical summary information related to the three-dimensional characteristics of the paper may be determined based on the representation. The paper may be classified based on the statistical summary information.

Abstract: An example system to represent data with multi-dimensional cyclic symbols is provided. The system includes a representation engine, a symbol engine, and a translation engine. The representation engine is to provide a plurality of display symbols. Display symbols are selected from a set of multi-dimensional cyclic symbols. The symbol engine is to receive a plurality of received symbols. The translation engine is to convert the plurality of received symbols into a plurality of display symbols.

Abstract: Examples disclosed herein relate to creating a data bearing image related to a compilation and position. In one implementation, a processor tags elements of a compilation with different data bearing images such that the data bearing images include a first information channel related to an identifier associated with the compilation and a second information channel related to relative position information within the compilation of the respective element. The processor may output information related to the compilation and tagged elements.

Abstract: Examples associated with keyframe annotation are disclosed. One example includes extracting a set of keyframes from a video presentation. A subset of the keyframes is selected to present to a user based on a user preference. Annotations are generated for the subset of the keyframes. The annotations are personalized to the user. The subset of the keyframes and the annotations are presented to the user.

Abstract: Topic identification based on functional summarization is disclosed. One example is a system including a plurality of summarization engines, each summarization engine to receive, via a processing system, a document to provide a summary of the document. At least one meta-algorithmic pattern is applied to at least two summaries to provide a meta-summary of the document using the at least two summaries. A content processor identifies, from the meta-summaries, topics associated with the document, maps the identified topics to a collection of topic dimensions, and identifies a representative point based on the identified topics. An evaluator determines distance measures of the representative point from topic dimensions in the collection of topic dimensions, the distance measures indicative of proximity of respective topic dimensions to the representative point. A selector selects a topic dimension to be associated with the document, the selection based on optimizing the distance measures.

Abstract: Examples disclosed herein relate to segmentation based on clustering engines applied to summaries. In one implementation, a processor segments text based on a comparison of the output of multiple clustering engines applied to multiple summarizations of documents associated with the text. The processor outputs information related to the contents of the segments.

Abstract: An example method is described in which a processor receives a first plurality of symbols selected from a first set of symbols, translates the first plurality of symbols into a second plurality of symbols selected from an M-ary cyclic symbol set, and writes the second plurality of symbols to a surface. An additional example method is described in which a processor captures an image of at least a portion of a surface, detects a first plurality of symbols from the image, where the first plurality of symbols comprises symbols selected from an M-ary cyclic symbol set, translates the first plurality of symbols into a second plurality of symbols selected from a second set of symbols, and performs at least one task responsive to the second plurality of symbols.

Abstract: An example apparatus may include a processor and a memory device including computer program code. The memory device and the computer program code may, with the processor, cause the apparatus to receive serialization codes for a first entity in a serialization flow; compare serialization codes for the first entity to serialization codes for a second entity, the second entity being either upstream or downstream of the first entity in the serialization flow; and validating the serialization codes from the first entity by determining if the serialization codes for the first entity are compatible with the serialization codes for the second entity in the serialization flow.