Abstract: A method, computer program product, and a system where a processor(s), monitors multilingual switches performed on a client on behalf of a given user. Based on the monitoring, the processor(s) identifies switch patterns of the given user to generate a service profile for the user of machine learned multilingual switch patterns for the given user. The processor(s) determines a priority order for languages comprising the voice input streams, for the given user. The processor(s) obtains a new translation request initiated by the client, on behalf of the given user and applies the priority order to identify one or more languages spoken in a voice input stream of the new translation request. The processor(s) transmits indicators of the identified one or more languages to the client, where upon receiving the indicators, the client translates the voice input stream from the identified one or more languages to one or more target languages.

Abstract: Methods and systems for opportunistic multi-party reminders based on sensory data are provided. A system for providing opportunistic multi-party reminders based on sensory data may include a coordination module that coordinates user schedules for a plurality of parties. Also, the system may include a time prediction module that characterizes one or more times in a user schedule in response to gathered context information for one or more parties in the plurality of parties. Further, the system may include a task completion module that identifies at least one time in the one or more times in a user schedule for completing a task in response to the characterization of the one or more times.

Abstract: A method identifies a type of object in a digital image. A user and/or one or more processors selects, from a plurality of partially overlapping sub-images of a digital image, a first sub-image and a second sub-image that overlap one another. The user/processors input the first sub-image into a neural network to create a first inference result that includes an overlapping inference result, for the overlapping area, that recognizes a partial portion of a specific type of object based on the overlapping area. The user/processors infer that the second sub-image creates a second inference result that recognizes a second portion of the specific type of object in the second sub-image based on the second sub-image and the overlapping inference result. The neural network identifies the specific type of object in the digital image based on the first and second sub-images being sub-images of a same type of object.

Abstract: A method optimizes Convolutional Neural Network (CNN) inference time for full resolution images. One or more processors divide a full resolution image into a plurality of partially overlapping sub-images. The processor(s) select, from the plurality of partially overlapping sub-images, a first sub-image and a second sub-image that overlap one another in an overlapping area. The processor(s) feed the first sub-image, including the overlapping area, into a Convolutional Neural Network (CNN) in order to create a first inference result for the first sub-image, where the CNN has been trained at a fine resolution. The processor(s) cache an inference result from the CNN for the overlapping area, and then utilize the cached inference result when inferring the second sub-image in the CNN. The processor(s) then identify a specific object in the full resolution image based on inferring the first sub-image and the second sub-image.

Abstract: A first method includes identifying a potential emoji annotation for a selected context within a text source, validating the potential emoji, determining whether the validated emoji annotation is within an emoji annotation list for the selected context, and adding the validated emoji annotation to the emoji annotation list for the selected context responsive to determining that the validated emoji annotation is not within the emoji annotation list for the selected context. A second method includes adding an emoji annotation written in a first language to an emoji annotation list corresponding to the first language, translating the emoji annotation written in the first language to a second language to produce an emoji annotation written in the second language, and adding the emoji annotation written in the second language to an emoji annotation list corresponding to the second language. Corresponding apparatii, computer program products and computer systems are also disclosed herein.

Abstract: Interactive augmented reality system is provided. A first geographical location and a first environmental characteristic is monitored by a user device. A viewpoint of the user device is determined based, at least in part, on (i) the first geographical location and (ii) the first environmental characteristic. A digital image request is generated based, at least in part, on (i) the first geographical location and (ii) the first environmental characteristic. A digital image is identified from a database based, at least in part, on (i) a second geographical location and (ii) a second environmental characteristic associated with the digital image request. In response to identifying the digital image based, at least in part, on the digital image request populating the user device with the digital image.

Abstract: A processor may initiate a first verification event. The first verification event may be triggered when a mobile device begins generating new data at a first time. The processor may determine, in response to the first verification event, that the mobile device is above a threshold limit of available storage. The processor may analyze one or more applications on the mobile device. The processor may automatically remove a first re-downloadable portion of a first application binary interface associated with a first application from the mobile device. The processor may store the generated new data on the mobile device.

Abstract: A method includes: receiving light at an image sensor of a digital camera; generating an image based on the received light; detecting an artifact in the image; determining a blocking area of the sensor based on the detecting the artifact; blocking the light at the blocking area; determining whether the artifact is present in the image after the blocking; in response to determining that the artifact is present after the blocking, determining a new blocking area and repeating the blocking and the determining using the new blocking area; and in response to determining that the artifact is not present after the blocking, saving the image in memory.

Abstract: Utilizing a computing device to determine and enforce limits on cloud computing containers receiving data over a network. A determination is made of total container time remaining available for a first container to execute in a computing environment. Processor packet receipt time is determined for receiving and processing of a packet or a batch of packets via a network stack associated with the computing device. An updated total container time remaining is calculated for the first container accounting for the processor packet receipt time. The updated total container time remaining is enforced by dropping a subsequent packet or batch of packets received at the network stack if the updated total container time remaining is insufficient.

Abstract: A method accelerates memory access in a network using thread progress based arbitration. A memory controller identifies a prioritized thread from multiple threads in an application. The prioritized thread reaches a synchronization barrier after the other threads due to the thread encountering more events than the other threads before reaching the barrier, where the events are from a group consisting of instruction executions, cache misses, and load/store operations in a core. The memory controller detects a cache miss by the prioritized thread during execution of the prioritized thread after the barrier is reached by the multiple threads. The memory controller then retrieves and returns data from the memory that cures the cache miss for the prioritized thread before retrieving data that cures cache misses for the other threads by applying thread progress based arbitration in the network.

Abstract: For variable resolution rendering of objects, a processor determines an aggregate familiarity of a user with an object based on at least one of a common familiarity of the object, a content-specific familiarity of the user with the object, and a subjective familiarity of the user with the object. The processor further, in response to the aggregate familiarity satisfying a render policy, reduces a render resolution of the object.

Abstract: Interactive augmented reality system is provided. A first geographical location and a first environmental characteristic is monitored by a user device. A viewpoint of the user device is determined based, at least in part, on (i) the first geographical location and (ii) the first environmental characteristic. A digital image request is generated based, at least in part, on (i) the first geographical location and (ii) the first environmental characteristic. A digital image is identified from a database based, at least in part, on (i) a second geographical location and (ii) a second environmental characteristic associated with the digital image request. In response to identifying the digital image based, at least in part, on the digital image request populating the user device with the digital image.

Abstract: A method enables object label persistence between subsequent images captured by a camera. One or more processors receive a first image, which is captured by an image sensor on a camera, and which includes a depiction of an object. The processor(s) generate a label for the object, and display the first image on a display. The processor(s) subsequently receive movement data that describes a movement of the camera after the image sensor on the camera captures the first image and before the image sensor on the camera captures a second image. The processor(s) receive the second image. The processor(s) display the second image on the display, and then detect a pixel shift between the first image and the second image as displayed on the display. The processor(s) then label the object with the label on the second image as displayed on the display.

Abstract: A method accelerates memory access in a network using thread progress based arbitration. A memory controller identifies a prioritized thread from multiple threads in an application. The prioritized thread reaches a synchronization barrier after the other threads due to the thread encountering more events than the other threads before reaching the barrier, where the events are from a group consisting of instruction executions, cache misses, and load/store operations in a core. The memory controller detects a cache miss by the prioritized thread during execution of the prioritized thread after the barrier is reached by the multiple threads. The memory controller then retrieves and returns data from the memory that cures the cache miss for the prioritized thread before retrieving data that cures cache misses for the other threads by applying thread progress based arbitration in the network.

Abstract: A contemporary image is stored, is analyzed, and an associated data structure is generated. An image data structure may include image meta data, image classification data, image segment data, image caption data, or the like. The storage of the contemporary image is used as a triggering event and its data structure is used as an input to determine contextually relevant ancestral images. The contemporary image data structure is compared to respective data structures of ancestral images. A series of ancestral images are ranked based upon contextual similarly of their data structures to the contemporary image data structure. The most similar ancestral image is simultaneously displayed with the contemporary image. Interactions may be monitored, a prompt may be provided, a next ranked ancestral image may be displayed in place of the previous ancestral image, and/or a prompt to store a new contemporary image may be provided.

Abstract: A method includes: receiving light at an image sensor of a digital camera; generating an image based on the received light; detecting an artifact in the image; determining a blocking area of the sensor based on the detecting the artifact; blocking the light at the blocking area; determining whether the artifact is present in the image after the blocking; in response to determining that the artifact is present after the blocking, determining a new blocking area and repeating the blocking and the determining using the new blocking area; and in response to determining that the artifact is not present after the blocking, saving the image in memory.

Abstract: A method, computer program product, and a system where a processor(s), monitors multilingual switches performed on a client on behalf of a given user. Based on the monitoring, the processor(s) identifies switch patterns of the given user to generate a service profile for the user of machine learned multilingual switch patterns for the given user. The processor(s) determines a priority order for languages comprising the voice input streams, for the given user. The processor(s) obtains a new translation request initiated by the client, on behalf of the given user and applies the priority order to identify one or more languages spoken in a voice input stream of the new translation request. The processor(s) transmits indicators of the identified one or more languages to the client, where upon receiving the indicators, the client translates the voice input stream from the identified one or more languages to one or more target languages.

Abstract: A first method includes identifying a potential emoji annotation for a selected context within a text source, validating the potential emoji, determining whether the validated emoji annotation is within an emoji annotation list for the selected context, and adding the validated emoji annotation to the emoji annotation list for the selected context responsive to determining that the validated emoji annotation is not within the emoji annotation list for the selected context. A second method includes adding an emoji annotation written in a first language to an emoji annotation list corresponding to the first language, translating the emoji annotation written in the first language to a second language to produce an emoji annotation written in the second language, and adding the emoji annotation written in the second language to an emoji annotation list corresponding to the second language. Corresponding apparatii, computer program products and computer systems are also disclosed herein.

Abstract: A method enables object label persistence between subsequent images captured by a camera. One or more processors receive a first image, which is captured by an image sensor on a camera, and which includes a depiction of an object. The processor(s) generate a label for the object, and display the first image on a display. The processor(s) subsequently receive movement data that describes a movement of the camera after the image sensor on the camera captures the first image and before the image sensor on the camera captures a second image. The processor(s) receive the second image. The processor(s) display the second image on the display, and then detect a pixel shift between the first image and the second image as displayed on the display. The processor(s) then label the object with the label on the second image as displayed on the display.

Abstract: A method optimizes Convolutional Neural Network (CNN) inference time for full resolution images. One or more processors divide a full resolution image into a plurality of partially overlapping sub-images. The processor(s) select, from the plurality of partially overlapping sub-images, a first sub-image and a second sub-image that overlap one another in an overlapping area. The processor(s) feed the first sub-image, including the overlapping area, into a Convolutional Neural Network (CNN) in order to create a first inference result for the first sub-image, where the CNN has been trained at a fine resolution. The processor(s) cache an inference result from the CNN for the overlapping area, and then utilize the cached inference result when inferring the second sub-image in the CNN. The processor(s) then identify a specific object in the full resolution image based on inferring the first sub-image and the second sub-image.