Abstract: Spot aware print workflow techniques and system are described. In an implementation, a digital document is received for printing that includes a plurality of objects. Spot functionality is detected as corresponding to a respective object based on object properties detected for the respective object. One or more spot planes for are generated based on the spot functionality and a determination is made of color values for the one or more spot planes, respectively, based on context data describing a context, in which, the one or more spot planes are to be printed. The spot planes having the color values are output for printing by a print mechanism.

Abstract: Spot aware print workflow techniques and system are described. In an implementation, a digital document is received for printing that includes a plurality of objects. Spot functionality is detected as corresponding to a respective object based on object properties detected for the respective object. One or more spot planes for are generated based on the spot functionality and a determination is made of color values for the one or more spot planes, respectively, based on context data describing a context, in which, the one or more spot planes are to be printed. The spot planes having the color values are output for printing by a print mechanism.

Abstract: A computer implemented method synchronizes content state on multiple devices that are independently accessing the content. Each device synchronizes their clock and broadcasts an update message that includes an update message timestamp obtained from the clock and a playing state of content being played on the device. Update messages are received from the other devices and the corresponding time stamps are compared to identify the most recent update message and most recent playing state of the content. Each device updates to play the content from the most recent playing state included in the most recent update message.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for implementing content-aware filters to autonomously remove scan marks from digital documents. In particular implementations, the disclosed systems utilize a set of targeted scan mark models in a scan mark removal pipeline. For example, each scan mark model includes a corresponding content-aware filter configured to identify document regions that match a designated class of scan marks to filter. Examples of scan mark models include staple scan mark models, punch hole scan mark models, and page turn scan mark models. In certain embodiments, the disclosed systems then use the scan mark models to generate mark-specific masks based on document input features. Additionally, in some embodiments, the disclosed systems combine the mark-specific masks into a final segmentation mask and apply the final segmentation mask to the digital document for correcting the identified regions with scan marks.

Abstract: A system for generating an architecture diagram includes an input processor, a machine learning processor, and an advice generator. The input processor is configured to receive, from a terminal, entity data associated with a plurality of entities of an architecture and path data associated with a plurality of paths that correspond to interconnections between the plurality of entities. The machine learning processor utilizes a training dataset to assess whether the entities defined by the entity data are correctly interconnected as defined by the path data. The advice generator receives the assessment from the machine learning processor, prepares a recommendation based on the assessment, and communicates the recommendation to the terminal. User feedback is represented in the training data to improve the relevancy of the recommendation.

Abstract: A system for generating an architecture diagram includes an input processor, a machine learning processor, and an advice generator. The input processor is configured to receive, from a terminal, entity data associated with a plurality of entities of an architecture and path data associated with a plurality of paths that correspond to interconnections between the plurality of entities. The machine learning processor utilizes a training dataset to assess whether the entities defined by the entity data are correctly interconnected as defined by the path data. The advice generator receives the assessment from the machine learning processor, prepares a recommendation based on the assessment, and communicates the recommendation to the terminal. User feedback is represented in the training data to improve the relevancy of the recommendation.

Abstract: Learning to, and detecting spam messages using a multi-stage combination of probability calculations based on individual and aggregate training sets of previously identified messages. During a preliminary phase, classifiers are trained, lower and upper limit probabilities, and a combined probability threshold are iteratively determined using a multi-stage combination of probability calculations based on minor and major subsets of messages previously categorized as valid or spam. During a live phase, a first stage classifier uses only a particular subset, and a second stage classifier uses a master set of previously categorized messages. If a newly received message can not be categorized with certainty by the first stage classifier, and a computed first stage probability is within the previously determined lower and upper limits, first and second stage probabilities are combined.

Abstract: Learning to, and detecting spam messages using a multi-stage combination of probability calculations based on individual and aggregate training sets of previously identified messages. During a preliminary phase, classifiers are trained, lower and upper limit probabilities, and a combined probability threshold are iteratively determined using a multi-stage combination of probability calculations based on minor and major subsets of messages previously categorized as valid or spam. During a live phase, a first stage classifier uses only a particular subset, and a second stage classifier uses a master set of previously categorized messages. If a newly received message can not be categorized with certainty by the first stage classifier, and a computed first stage probability is within the previously determined lower and upper limits, first and second stage probabilities are combined.