Abstract: A model is configured to determine whether a device is located within a venue. During a baseline time period, the system detects wireless pings from mobile devices. The system obtains device parameters from the wireless pings. The system evaluates the device parameters to determine whether a mobile device entered the venue or remained outside of the venue. The system trains a model on training data corresponding to the baseline time period, the model configured to differentiate between devices that enter the venue and devices that remain outside the venue based on device parameters associated with the device. The system applies the model to future detected devices to determine whether or not the devices enter the venue.

Abstract: A device detection system is configured to determine whether a device is located within a venue. The device detection system provides user identifiers associated with a device to an anonymization server. The anonymization server provides anonymous identifiers to the device detection system corresponding to the user identifiers. A content distribution system provides content items for a campaign associated with the venue to users. The content distribution system provides user identifiers to the anonymization server and receives anonymous identifiers from the anonymization server. The content distribution system provides the anonymous identifiers to the device detection system. The device detection system compares the sets of anonymous identifiers to identify users that received a content item and subsequently visited the venue.

Abstract: A device detection system is configured to determine whether a device is located within a venue. The device detection system provides user identifiers associated with a device to an anonymization server. The anonymization server provides anonymous identifiers to the device detection system corresponding to the user identifiers. A content distribution system provides content items for a campaign associated with the venue to users. The content distribution system provides user identifiers to the anonymization server and receives anonymous identifiers from the anonymization server. The content distribution system provides the anonymous identifiers to the device detection system. The device detection system compares the sets of anonymous identifiers to identify users that received a content item and subsequently visited the venue.

Abstract: A device detection system is configured to determine whether a device is located within a venue. The device detection system provides user identifiers associated with a device to an anonymization server. The anonymization server provides anonymous identifiers to the device detection system corresponding to the user identifiers. A content distribution system provides content items for a campaign associated with the venue to users. The content distribution system provides user identifiers to the anonymization server and receives anonymous identifiers from the anonymization server. The content distribution system provides the anonymous identifiers to the device detection system. The device detection system compares the sets of anonymous identifiers to identify users that received a content item and subsequently visited the venue.

Abstract: A device detection system is configured to determine whether a device is located within a venue. The device detection system provides user identifiers associated with a device to an anonymization server. The anonymization server provides anonymous identifiers to the device detection system corresponding to the user identifiers. A content distribution system provides content items for a campaign associated with the venue to users. The content distribution system provides user identifiers to the anonymization server and receives anonymous identifiers from the anonymization server. The content distribution system provides the anonymous identifiers to the device detection system. The device detection system compares the sets of anonymous identifiers to identify users that received a content item and subsequently visited the venue.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for programmatically generating text strings are provided. An example method may include receiving an input string; generating a first utterance set based at least in part on the input string and a randomness indicator via a generative machine learning model; generating at least one grammar context score for the first utterance set; generating at least one relevance context score for the first utterance set; adjusting the randomness indicator based at least in part on one or more of the at least one grammar context score, a grammar context threshold, the at least one relevance context score, and a relevance context threshold; and generating a second utterance set based at least in part on the input string and the adjusted randomness indicator.

Abstract: A device detection system is configured to determine whether a device is located within a venue. The device detection system provides user identifiers associated with a device to an anonymization server. The anonymization server provides anonymous identifiers to the device detection system corresponding to the user identifiers. A content distribution system provides content items for a campaign associated with the venue to users. The content distribution system provides user identifiers to the anonymization server and receives anonymous identifiers from the anonymization server. The content distribution system provides the anonymous identifiers to the device detection system. The device detection system compares the sets of anonymous identifiers to identify users that received a content item and subsequently visited the venue.

Abstract: A model is configured to determine whether a device is located within a venue. During a baseline time period, the system detects wireless pings from mobile devices. The system obtains device parameters from the wireless pings. The system evaluates the device parameters to determine whether a mobile device entered the venue or remained outside of the venue. The system trains a model on training data corresponding to the baseline time period, the model configured to differentiate between devices that enter the venue and devices that remain outside the venue based on device parameters associated with the device. The system applies the model to future detected devices to determine whether or not the devices enter the venue.

Abstract: A device detection system is configured to determine whether a device is located within a venue. The device detection system provides user identifiers associated with a device to an anonymization server. The anonymization server provides anonymous identifiers to the device detection system corresponding to the user identifiers. A content distribution system provides content items for a campaign associated with the venue to users. The content distribution system provides user identifiers to the anonymization server and receives anonymous identifiers from the anonymization server. The content distribution system provides the anonymous identifiers to the device detection system. The device detection system compares the sets of anonymous identifiers to identify users that received a content item and subsequently visited the venue.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for programmatically generating text strings are provided. An example method may include receiving an input string; generating a first utterance set based at least in part on the input string and a randomness indicator via a generative machine learning model; generating at least one grammar context score for the first utterance set; generating at least one relevance context score for the first utterance set; adjusting the randomness indicator based at least in part on one or more of the at least one grammar context score, a grammar context threshold, the at least one relevance context score, and a relevance context threshold; and generating a second utterance set based at least in part on the input string and the adjusted randomness indicator.

Abstract: Method and apparatus for converting a document from a fixed-layout format (e.g., Microsoft Office, Adobe PDF) into a non-fixed layout format (e.g., HTML) portable to different platforms (e.g., desktop computers, tablet computer, smart phones) operating different operating systems (e.g., Microsoft Windows, Apple OS X) and different web browsers (e.g., Microsoft Internet Explorer, Apple Safari, Mozilla FireFox). In one stream, fonts are identified, extracted, and processed to enhance compatibility with the portable format. In another stream, textual content is extracted and processed to enhance compatibility and images are taken of non-textual content. These images are used as backgrounds in the output document, over which the textual content is rendered in the appropriate fonts, with sizing, spacing, positioning and/or other characteristics matching or closely approximating that of the original document.

Abstract: Method and apparatus for converting a document from a fixed-layout format (e.g., Microsoft Office, Adobe PDF) into a non-fixed layout format (e.g., HTML) portable to different platforms (e.g., desktop computers, tablet computer, smart phones) operating different operating systems (e.g., Microsoft Windows, Apple OS X) and different web browsers (e.g., Microsoft Internet Explorer, Apple Safari, Mozilla FireFox). In one stream, fonts are identified, extracted, and processed to enhance compatibility with the portable format. In another stream, textual content is extracted and processed to enhance compatibility and images are taken of non-textual content. These images are used as backgrounds in the output document, over which the textual content is rendered in the appropriate fonts, with sizing, spacing, positioning and/or other characteristics matching or closely approximating that of the original document.

Abstract: This invention relates to a paper substrate containing high surface sizing and low internal sizing and having high dimensional stability, as well as methods of making and using the composition.

Abstract: This invention relates to a paper substrate containing high surface sizing and low internal sizing and having high dimensional stability, as well as methods of making and using the composition.

Abstract: This invention relates to a paper substrate containing high surface sizing and low internal sizing and having high dimensional stability, as well as methods of making and using the composition.

Abstract: The invention provides an apparatus and method for testing the tendency of a paper to curl. The paper is cut from a paper making machine and rapidly humidified to standard TAPPI conditions, and is thereafter run through an apparatus having a substantially vertically oriented nip. The paper is measured for curl, all data are tabulated and fed back into a data acquisition or paper machine control system.

Abstract: The invention provides an apparatus and method for testing the tendency of a paper to curl. The paper is cut from a paper making machine and rapidly humidified to standard TAPPI conditions, and is thereafter run through an apparatus having a substantially vertically oriented nip. The paper is measured for curl, all data are tabulated and fed back into a data acquisition or paper machine control system.

Abstract: The invention provides an apparatus and method for testing the tendency of a paper to curl. The paper is cut from a paper making machine and rapidly humidified to standard TAPPI conditions, and is thereafter run through an apparatus having a substantially vertically oriented nip. The paper is measured for curl, all data are tabulated and fed back into a data acquisition or paper machine control system.

Abstract: A paper product suitable for use in making envelopes and the like and having enhanced printability. The enhancement in printability is attained through the use of shoe nip calendering. The final product has the following material properties: (1) a basis weight in the range 16-32 lb./1,300 ft2; (2) a density equal to or less than 0.75 g/cc; (3) a smoothness as measured by the Hagerty/Sheffield test (TAPPI Test Method T 538 om-88) greater than 200 Sheffield units; and (4) a Parker Print Surf roughness as measured by the Parker test (TAPPI Test Method T 555 om-94) less than 5.0 microns (measured using a pressure of 10 kgf/cm2 with a soft backing).