Abstract: Implementations are described herein for generating embeddings of source code using both the language and graph domains, and leveraging combinations of these semantically-rich and structurally-informative embeddings for various purposes. In various implementations, tokens of a source code snippet may be applied as input across a sequence-processing machine learning model to generate a plurality of token embeddings. A graph may also be generated based on the source code snippet. A joint representation may be generated based on the graph and the incorporated token embeddings. The joint representation generated from the source code snippet may be compared to one or more other joint representations generated from one or more other source code snippets to make a determination about the source code snippet.

Abstract: An in-ear device includes a housing shaped to hold the in-ear device in an ear of a user, and an audio package, disposed in the housing, to emit augmented sound. A first set of one or more microphones is positioned to receive external sound, and a controller is coupled to the audio package and the first set of one or more microphones. The controller includes a low-latency audio processing path, digital control parameters, and logic that when executed by the controller causes the in-ear device to perform operations. The operations may include receiving the external sound with the first set of one or more microphones to generate a low-latency sound signal; augmenting the low-latency sound signal by passing the low-latency sound signal through the low-latency audio processing path to produce an augmented sound signal; and outputting, with the audio package, the augmented sound based on the augmented sound signal.

Abstract: Implementations are described herein for automatically identifying, recommending, and/or automatically effecting changes to a source code base based on updates previously made to other similar code bases. Intuitively, multiple prior “migrations,” or mass updates, of complex software system code bases may be analyzed to identify changes that were made. More particularly, a particular portion or “snippet” of source code—which may include a whole source code file, a source code function, a portion of source code, or any other semantically-meaningful code unit—may undergo a sequence of edits over time. Techniques described herein leverage this sequence of edits to predict a next edit of the source code snippet. These techniques have a wide variety of applications, including but not limited to automatically updating of source code, source code completion, recommending changes to source code, etc.

Abstract: In an embodiment, an electronic device may be configured to capture still frames during video capture but may capture the still frames in the 4×3 aspect ratio and at higher resolution than the 16×9 aspect ratio video frames. The device may interleave high resolution, 4×3 frames and lower resolution 16×9 frames in the video sequence, and may capture the nearest higher resolution, 4×3 frame when the user indicates the capture of a still frame. Alternatively, the device may display 16×9 frames in the video sequence, and then expand to 4×3 frames when a shutter button is pressed. The device may capture the still frame and return to the 16×9 video frames responsive to a release of the shutter button.

Abstract: Implementations are described herein for automatically identifying, recommending, and/or automatically effecting changes to a source code base based on updates previously made to other similar code bases. Intuitively, multiple prior “migrations,” or mass updates, of complex software system code bases may be analyzed to identify changes that were made. More particularly, a particular portion or “snippet” of source code—which may include a whole source code file, a source code function, a portion of source code, or any other semantically-meaningful code unit—may undergo a sequence of edits over time. Techniques described herein leverage this sequence of edits to predict a next edit of the source code snippet. These techniques have a wide variety of applications, including but not limited to automatically updating of source code, source code completion, recommending changes to source code, etc.

Abstract: Techniques are described herein for translating source code in one programming language to source code in another programming language using machine learning. In various implementations, one or more components of one or more generative adversarial networks, such as a generator machine learning model, may be trained to generate “synthetically-naturalistic” source code that can be used as a translation of source code in an unfamiliar language. In some implementations, a discriminator machine learning model may be employed to aid in training the generator machine learning model, e.g., by being trained to discriminate between human-generated (“genuine”) and machine-generated (“synthetic”) source code.

Abstract: Techniques are described herein for using artificial intelligence to “learn,” statistically, a target programming style that is imposed in and/or evidenced by a code base. Once the target programming style is learned, it can be used for various purposes. In various implementations, one or more generative adversarial networks (“GANs”), each including a generator machine learning model and a discriminator machine learning model, may be trained to facilitate learning and application of target programming style(s). In some implementations, the discriminator(s) and/or generator(s) may operate on graphical input, and may take the form of graph neural networks (“GNNs”), graph attention neural networks (“GANNs”), graph convolutional networks (“GCNs”), etc., although this is not required.

Abstract: Techniques are described for using stenography to protect sensitive information within conversational audio data by generating a pseudo-language representation of conversational audio data. In some implementations, audio data corresponding to an utterance is received. The audio data is classified as likely sensitive audio data. A particular set of sentiments associated with the audio data is determined. Data indicating the particular set of sentiments associated with the audio data is provided to a model. The model is trained to output, for each of different sets of sentiments, desensitized, pseudo-language audio data that exhibits the set of sentiments, and is not classified as likely sensitive audio data. A particular desensitized, pseudo-language audio data is received from the model. The audio data is replaced with the particular desensitized, pseudo-language audio data and stored within an audio data repository.

Abstract: A system to optically measure an ear includes a controller with logic that when executed by the controller causes the system to perform operations. Operations may include capturing the one or more images of the ear using the one or more image sensors, and generating image data from the one or more images. 3D keypoints of the ear are calculated from the image data, and a 3D model of the ear is generated using the 3D keypoints.

Abstract: Implementations are described herein for automatically identifying, recommending, and/or effecting changes to a legacy source code base by leveraging knowledge gained from prior updates made to other similar legacy code bases. In some implementations, data associated with a first version source code snippet may be applied as input across a machine learning model to generate a new source code embedding in a latent space. Reference embedding(s) may be identified in the latent space based on their distance(s) from the new source code embedding in the latent space. The reference embedding(s) may be associated with individual changes made during the prior code base update(s). Based on the identified one or more reference embeddings, change(s) to be made to the first version source code snippet to create a second version source code snippet may be identified, recommended, and/or effected.

Abstract: One or more computing devices, systems, and/or methods for generating and/or presenting time-lapse videos and/or live-stream videos are provided. For example, a plurality of video frames may be extracted from a video. A first set of video frames and a second set of video frames may be identified from the plurality of video frames. The first set of video frames may be combined to generate a first time-lapse video frame and the second set of video frames may be combined to generate a second time-lapse video frame. A time-lapse video may be generated based upon the first time-lapse video frame and the second time-lapse video frame. In another example, a time-lapse video may be generated based upon a recorded video associated with a live-stream video. The time-lapse video may be presented. Responsive to a completion of the presenting the time-lapse video, the live-stream video may be presented.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods automatically analyze a live streaming media file, and identify portions of the media that are highlights. The content classified as a highlight can be shared across social media platforms, and indexed for searching respective to attributes of the video content. The streaming and highlight media content is renderable in a novel, modified video player that enables variable playback speeds for how content is classified, and enables on-demand selections of specific content portions and adjustable rendering displays during streaming.

Abstract: A system to optically measure an ear includes a controller with logic that when executed by the controller causes the system to perform operations. Operations may include capturing the one or more images of the ear using the one or more image sensors, and generating image data from the one or more images. 3D keypoints of the ear are calculated from the image data, and a 3D model of the ear is generated using the 3D keypoints.

Abstract: An in-ear device includes a housing shaped to hold the in-ear device in an ear of a user, and an audio package, disposed in the housing, to emit augmented sound. A first set of one or more microphones is positioned to receive external sound, and a controller is coupled to the audio package and the first set of one or more microphones. The controller includes a low-latency audio processing path, digital control parameters, and logic that when executed by the controller causes the in-ear device to perform operations. The operations may include receiving the external sound with the first set of one or more microphones to generate a low-latency sound signal; augmenting the low-latency sound signal by passing the low-latency sound signal through the low-latency audio processing path to produce an augmented sound signal; and outputting, with the audio package, the augmented sound based on the augmented sound signal.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods automatically analyze a live streaming media file, and identify portions of the media that are highlights. The content classified as a highlight can be shared across social media platforms, and indexed for searching respective to attributes of the video content. The streaming and highlight media content is renderable in a novel, modified video player that enables variable playback speeds for how content is classified, and enables on-demand selections of specific content portions and adjustable rendering displays during streaming.

Abstract: Generating notifications comprising text and image data for client devices with limited display screens is disclosed. An image to be included in the notification is resized and reshaped using image processing techniques. The resized image is further analyzed to identify optimal portions for placing the text data. The text data can also be analyzed and shortened for including at the identified portion of resized image to generate a notification. The resulting notification displays the text and image data optimally within the limited screen space of the client device so that a user observing the notification can obtain the information at a glance.

Abstract: In an embodiment, an electronic device may be configured to capture still frames during video capture but may capture the still frames in the 4×3 aspect ratio and at higher resolution than the 16×9 aspect ratio video frames. The device may interleave high resolution, 4×3 frames and lower resolution 16×9 frames in the video sequence, and may capture the nearest higher resolution, 4×3 frame when the user indicates the capture of a still frame. Alternatively, the device may display 16×9 frames in the video sequence, and then expand to 4×3 frames when a shutter button is pressed. The device may capture the still frame and return to the 16×9 video frames responsive to a release of the shutter button.

Abstract: One or more computing devices, systems, and/or methods for emotional reaction sharing are provided. For example, a client device captures video of a user viewing content, such as a live stream video. Landmark points, corresponding to facial features of the user, are identified and provided to a user reaction distribution service that evaluates the landmark points to identify a facial expression of the user, such as a crying facial expression. The facial expression, such as landmark points that can be applied to a three-dimensional model of an avatar to recreate the facial expression, are provided to client devices of users viewing the content, such as a second client device. The second client device applies the landmark points of the facial expression to a bone structure mapping and a muscle movement mapping to create an expressive avatar having the facial expression for display to a second user.

Abstract: In an embodiment, an electronic device may be configured to capture still frames during video capture but may capture the still frames in the 4×3 aspect ratio and at higher resolution than the 16×9 aspect ratio video frames. The device may interleave high resolution, 4×3 frames and lower resolution 16×9 frames in the video sequence, and may capture the nearest higher resolution, 4×3 frame when the user indicates the capture of a still frame. Alternatively, the device may display 16×9 frames in the video sequence, and then expand to 4×3 frames when a shutter button is pressed. The device may capture the still frame and return to the 16×9 video frames responsive to a release of the shutter button.

Abstract: One or more computing devices, systems, and/or methods for generating and/or presenting time-lapse videos and/or live-stream videos are provided. For example, a plurality of video frames may be extracted from a video. A first set of video frames and a second set of video frames may be identified from the plurality of video frames. The first set of video frames may be combined to generate a first time-lapse video frame and the second set of video frames may be combined to generate a second time-lapse video frame. A time-lapse video may be generated based upon the first time-lapse video frame and the second time-lapse video frame. In another example, a time-lapse video may be generated based upon a recorded video associated with a live-stream video. The time-lapse video may be presented. Responsive to a completion of the presenting the time-lapse video, the live-stream video may be presented.