Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: A control system for independent alternating-input (“IAI”) devices includes multiple IAI devices and an analog switch component. The control system may also include a bus-generating component. The analog switch component includes multiple switches configured to connect of disconnect input connection points of the analog switch component and voltage input points of the IAI devices. The analog switch component opens or closes switches, responsive to a digital control signal, to provide voltage signals to the voltage input points of the IAI devices. In some cases, the IAI devices activate or deactivate based on the provided voltage signals. In some cases, the bus-generating component provides a first voltage signal to a first voltage input point of an IAI device, and the analog switch component controls the switches to provide a second voltage signal to a second voltage input point of the IAI device.

Abstract: Systems and methods are provided for providing a navigation interface to access or otherwise use electronic content items. In one embodiment, an augmentation application identifies at least one entity referenced in a document. The entity can be referenced in at least two portions of the document by at least two different words or phrases. The augmentation application associates the at least one entity with at least one multimedia asset. The augmentation application generates a layout including at least some content of the document referencing the at least one entity and the at least one multimedia asset associated with the at least one entity. The augmentation application renders the layout for display.

Abstract: Embodiments are disclosed for generating an instant mask from polarized input images.

Abstract: A control system for independent alternating-input (“IAI”) devices includes multiple IAI devices and an analog switch component. The control system may also include a bus-generating component. The analog switch component includes multiple switches configured to connect of disconnect input connection points of the analog switch component and voltage input points of the IAI devices. The analog switch component opens or closes switches, responsive to a digital control signal, to provide voltage signals to the voltage input points of the IAI devices. In some cases, the IAI devices activate or deactivate based on the provided voltage signals. In some cases, the bus-generating component provides a first voltage signal to a first voltage input point of an IAI device, and the analog switch component controls the switches to provide a second voltage signal to a second voltage input point of the IAI device.

Abstract: Embodiments provide traceability of edits to a document, i.e., a verifiable and immutable provenance chain for the document. Systems and methods enable traceability of edits, by encoding, for states of the document, a fingerprint (e.g., a cryptographic hash of the document's contents) and an edit history within a block written to a distributed ledger (e.g., a blockchain). The ledger is maintained via a self-organizing peer-to-peer distributed ledger network. Once added to the ledger, the contents of a block (e.g., the document's fingerprint and edit history) are immutable and the integrity of the edit history encoded in the ledger is secure. The algorithm that generates the fingerprint is sensitive to edits of the document. The non-corruptible fingerprint encoded in the ledger is employable to detect any edits that are not included in the encoded edit history and/or inconsistent with a currently available version of the document.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: Image compensation for an occluding direct-view augmented reality system is described. In one or more embodiments, an augmented reality apparatus includes an emissive display layer for presenting emissive graphics to an eye of a user and an attenuation display layer for presenting attenuation graphics between the emissive display layer and a real-world scene to block light of the real-world scene from the emissive graphics. A light region compensation module dilates an attenuation graphic based on an attribute of an eye of a viewer, such as size of a pupil, to produce an expanded attenuation graphic that blocks additional light to compensate for an unintended light region. A dark region compensation module camouflages an unintended dark region with a replica graphic in the emissive display layer that reproduces an appearance of the real-world scene in the unintended dark region. A camera provides the light data used to generate the replica graphic.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: Embodiments provide traceability of edits to a document, i.e., a verifiable and immutable provenance chain for the document. Systems and methods enable traceability of edits, by encoding, for states of the document, a fingerprint (e.g., a cryptographic hash of the document's contents) and an edit history within a block written to a distributed ledger (e.g., a blockchain). The ledger is maintained via a self-organizing peer-to-peer distributed ledger network. Once added to the ledger, the contents of a block (e.g., the document's fingerprint and edit history) are immutable and the integrity of the edit history encoded in the ledger is secure. The algorithm that generates the fingerprint is sensitive to edits of the document. The non-corruptible fingerprint encoded in the ledger is employable to detect any edits that are not included in the encoded edit history and/or inconsistent with a currently available version of the document.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: Embodiments provide traceability of edits to a document, i.e., a verifiable and immutable provenance chain for the document. Systems and methods enable traceability of edits, by encoding, for states of the document, a fingerprint (e.g., a cryptographic hash of the document's contents) and an edit history within a block written to a distributed ledger (e.g., a blockchain). The ledger is maintained via a self-organizing peer-to-peer distributed ledger network. Once added to the ledger, the contents of a block (e.g., the document's fingerprint and edit history) are immutable and the integrity of the edit history encoded in the ledger is secure. The algorithm that generates the fingerprint is sensitive to edits of the document. The non-corruptible fingerprint encoded in the ledger is employable to detect any edits that are not included in the encoded edit history and/or inconsistent with a currently available version of the document.

Abstract: An update basis for updating digital content in a digital medium environment is described. The digital content is updated by incorporating new digital content components from a service provider system, such as a stock content service, to keep the digital content from seeming stale to client device users. The service provider system controls provision of digital content components according to an update basis described in a component request. In part, component requests ask that the service provider system provide digital content components for incorporation with digital content. Component requests also describe a timing basis with which digital content components are to be provided as updates. By way of example, the timing basis may correspond to a time interval (e.g., daily, weekly, monthly, seasonally, times of day, and so on), receiving user input in relation to the digital content (e.g., a navigation input to a web page), and so forth.

Abstract: Image compensation for an occluding direct-view augmented reality system is described. In one or more embodiments, an augmented reality apparatus includes an emissive display layer for presenting emissive graphics to an eye of a user and an attenuation display layer for presenting attenuation graphics between the emissive display layer and a real-world scene to block light of the real-world scene from the emissive graphics. A light region compensation module dilates an attenuation graphic based on an attribute of an eye of a viewer, such as size of a pupil, to produce an expanded attenuation graphic that blocks additional light to compensate for an unintended light region. A dark region compensation module camouflages an unintended dark region with a replica graphic in the emissive display layer that reproduces an appearance of the real-world scene in the unintended dark region. A camera provides the light data used to generate the replica graphic.

Abstract: Image compensation for an occluding direct-view augmented reality system is described. In one or more embodiments, an augmented reality apparatus includes an emissive display layer for presenting emissive graphics to an eye of a user and an attenuation display layer for presenting attenuation graphics between the emissive display layer and a real-world scene to block light of the real-world scene from the emissive graphics. A light region compensation module dilates an attenuation graphic based on an attribute of an eye of a viewer, such as size of a pupil, to produce an expanded attenuation graphic that blocks additional light to compensate for an unintended light region. A dark region compensation module camouflages an unintended dark region with a replica graphic in the emissive display layer that reproduces an appearance of the real-world scene in the unintended dark region. A camera provides the light data used to generate the replica graphic.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: An augmented reality display alternates between displaying color frames and alpha frames to generate composite images for transparency control. A display processor includes two sets of buffers, each set including a color frame buffer and an alpha frame buffer. One set of buffers is used to provide data to the display while new video data is being written into the other set of buffers. The display processor switches which set of buffers is providing data and which set of buffers is having new video data written into them. By switching the alpha and color frame buffers in sets, a pair of color frame buffer and alpha frame buffer are always the current set of buffers to provide data as the output. If a frame drop occurs and the next color frame or alpha frame is not ready, the set of buffers is not switched to avoid disturbing flashing visual artifacts.

Abstract: An augmented reality display alternates between displaying color frames and alpha frames to generate composite images for transparency control. A display processor includes two sets of buffers, each set including a color frame buffer and an alpha frame buffer. One set of buffers is used to provide data to the display while new video data is being written into the other set of buffers. The display processor switches which set of buffers is providing data and which set of buffers is having new video data written into them. By switching the alpha and color frame buffers in sets, a pair of color frame buffer and alpha frame buffer are always the current set of buffers to provide data as the output. If a frame drop occurs and the next color frame or alpha frame is not ready, the set of buffers is not switched to avoid disturbing flashing visual artifacts.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: Image search techniques and systems involving emotions are described. In one or more implementations, a digital medium environment of a content sharing service is described for image search result configuration and control based on a search request that indicates an emotion. The search request is received that includes one or more keywords and specifies an emotion. Images are located that are available for licensing by matching one or more tags associated with the image with the one or more keywords and as corresponding to the emotion. The emotion of the images is identified using one or more models that are trained using machine learning based at least in part on training images having tagged emotions. Output is controlled of a search result having one or more representations of the images that are selectable to license respective images from the content sharing service.