Abstract: A response initiation time target based at least in part on a service level agreement is assigned to a task requested from a network-accessible service. A deadline parameter of a thread identified to perform sub-tasks of the task at a service logic implementation node is set based on the target. The thread is scheduled for execution using an operating system thread scheduling algorithm which selects threads for execution based on their deadline parameters. After a first sub-task of thread is completed, a response is sent to the requester of the task.

Abstract: Described implementations monitor potential voltage at a location to determine device usage at the location. The implementations utilize a plug-in energy sensor that is plugged directly into any electrical outlet at the location and measures deviation in voltage at the location. Once plugged into an electrical outlet, the plug-in energy sensor monitors one or more of the lead line and ground, the neutral line and ground, and/or the ground line with respect to a reference for changes in potential voltage at the location. Different devices and different device states of those devices generate different signatures of voltage as they operate, thereby making the signatures unique, or almost unique, to different device types, different devices, and/or different device states. By monitoring for signatures in the voltage, operation of devices in different device states at the location can be determined, along with start times and stop times of those states associated with the various devices.

Abstract: Systems and methods for device integration in augmented reality environments are disclosed. For example, prior to an AR session being initiated, smart devices within a physical environment where the AR session is to be utilized may be identified and selected based on the functionality of the smart devices and the capabilities of an AR application associated with the AR session. Location information about the smart devices may also be determined and this information may be utilized to generate configuration data, which may be used by the AR application to cause device state transitions on the smart devices in response to events that occur in an AR environment of the AR session.

Abstract: Techniques for understanding classification by a machine learning model are described. A system determines a portion of a user input that causes a machine learning model to classify the user input to a particular classification. The system may generate output data according to the determined portion of the user input. In some cases, the system may generate, using the determined portion, output data explaining processing performed with respect to the user input. In other cases, the system may generate output data requesting feedback. In yet other cases, the system may change a domain, an intent or other classification determined for the user input based on the determined portion.

Abstract: Approaches presented herein relate to visibility into network traffic associated with one or more groups of network resources. Approaches provide for automatic deployment and configuration of one or more visibility pods for individual groups of networked resources. A visibility pod may include at least one virtual compute instance to execute at least one analytical tool or application. A copy of network traffic for the monitored resources can be directed to an endpoint for a respective visibility pod, which can extract information (e.g., specific metadata about the traffic) to be analyzed by the analytical tools or applications. The results of the analysis can be written to a log file and/or data repository, and in some instances may be used to generate security alerts or other such notifications. A visibility pod can be automatically deployed and configured for any group of networked resources from which network traffic can be directed.

Abstract: Embodiments describe techniques for controlling picking robots to retrieve items on the floor and return them to an appropriate location. In one embodiment, the picking robots wait until a sensor in a monitored area (e.g., a designated robot area or an area around a pick/stow station) detects a dropped item on the floor. A central control system then dispatches the picking robot to the location to retrieve the dropped item. In another embodiment, when not being dispatched, the picking robots patrol the area looking for dropped items.

Abstract: User enrollment to a biometric identification system begins on selected general input devices (GID) such as smartphones. The user may enter identification data (e.g. name) and use a red-green-blue (RGB) camera of the GID to acquire a first image (e.g. hand). The first image is processed using both a first model to determine a first representation and a second model to determine a second representation. Upon presentation of a hand at a biometric input device, a second image is acquired using a first modality and a third image is acquired using a second modality. The second image is processed using the first model to determine a third representation. The third image is processed using the second model to determine a fourth representation. Given a match between both the first and third representations, as well as the second and fourth representations, enrollment is completed by storing the third and fourth representations.

Abstract: Devices and techniques are generally described for object matching in image data. In various examples, first image data and second image data may be received. A first feature map representing the first image data and a second feature map representing the second image data may be generated. The first feature map and second feature map may be combined and a first and second segmentation mask may be generated using the combined feature map. The first segmentation mask may be used to filter the first feature map to generate a filtered representation. The second segmentation mask may be used to filter the second feature map to generate a filtered representation. A determination may be made that a first object depicted in the first image data corresponds to a second object depicted in the second image data using the filtered representations.

Abstract: Techniques for enabling access in a multi-assistant speech processing system are described, where a first assistant system may use components of a second assistant system as data processing components. Runtime operational data and user input data related to the first assistant may be kept separate from the processing data and input data related to the second assistant by propagating a first account ID, for user inputs directed to the first assistant, through the processing pipeline, and using a second account for user inputs directed to the second assistant. A mapping between the first account ID and the second account ID may be accessible to a select number of system components. Handoffs between the two assistants are handled in a manner where data related to one assistant is not accessible by the other assistant.

Abstract: A system is provided for a self-learning policy engine that can be used by various spoken language understanding (SLU) processing components. The system also provides for sharing contextual information from processing performed by an upstream SLU component to a downstream SLU component to facilitate decision making by the downstream SLU component. The system also provides for a SLU component to select from a variety of actions to take. A SLU component may implement an instance of the self-learning policy that is specifically configured for the particular SLU component.

Abstract: A system that provides responses to requests obtains a key that is used to digitally sign the request. The key is derived from information that is shared with a requestor to which the response is sent. The requestor derives, using the shared information, derives a key usable to verify the digital signature of the response, thereby enabling the requestor to operate in accordance with whether the digital signature of the response matches the response.

Abstract: A system configured to perform style-aware listener animation. By representing different listening styles (e.g., facial expressions) using an embedding space, a single model can be trained to generate unique facial animations for a number of distinct listeners. Thus, individual listening styles can be associated with a listener identifier, enabling the system to (i) animate a plurality of different listeners with unique nonverbal behavior and/or (ii) select a particular listener identifier or desired type of listener style with which to animate. This enables the model to be generalized to new listeners to generate additional listener facial responses without needing training data for each new listener. The model may process a listener representation style or listener identifier, along with input data corresponding to a speaker talking, to generate unique facial animation responsive to the speech.

Abstract: A target voice dataset may be augmented using speech prediction. Encoder and decoder models may be trained to encode audio data into encoded speech data, and convert it back to audio. The encoded units may include semantic information (e.g., phonemes and/or words) as well as feature data indicating prosody, timbre, speaker identity, speech style, emotion, etc. of speech. An acoustic/semantic language model (ASLM) may be configured to predict encoded speech data in a manner analogous to a language model predicting words; for example, based on preceding encoded speech data. The models may be used to generate synthesized speech samples having voice characteristics (e.g., feature data) similar to those of the target voice dataset. The augmented dataset may be used to train a text-to-speech (TTS) model to reproduce the target voice characteristics, and may improve performance of the TTS model over training with only the original target voice dataset.

Abstract: Systems and methods are disclosed for automated self-calibration and maintenance for container shuttles. An example system for automated repairs may include a track, a shuttle coupled to a permanent magnet, the shuttle configured to move along the track, and an electromagnet disposed along the track, where the shuttle is propelled via interaction between the electromagnet and the permanent magnet. The system may include a maintenance area accessible via the track, and a set of robotic manipulators disposed about the maintenance area, the set of robotic manipulators configured to perform automated maintenance actions on the shuttle. The system may include a first controller configured to determine that the shuttle is in need of maintenance, and automatically direct the shuttle to the maintenance area.

Abstract: A first extension resource group (ERG) of a provider network, comprising a radio-based application pipeline processing server with a network accelerator card, is configured at an external premise. A first set of network functions of a radio-based application is executed at the card. A second ERG is configured at the premise, and a second set of network functions of the radio-based application is executed at the second ERG.

Abstract: Portions of text data generated from inverse text normalization may be redacted. Text data for redaction may be obtained. One or more inverse text normalization models may be applied to the text data to generate normalized text data. A machine learning model, trained to recognize text for redaction, may be applied to identify portions of the normalized text data for redaction. The identified portions may be redacted and the redacted normalized text provided to a destination.

Abstract: A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.

Abstract: Systems and methods are disclosed for automated container stacking stations. In one embodiment, an example system may include a housing having a first side and a second side, a lifting component configured to lift a first container from a first position to a second position, and to lower the first container from the second position to a third position, such that the first container is stacked on a second container, and a container centering component having a first container engagement member and a second container engagement member, the container centering component configured to align the first container in a predetermined position.

Abstract: Described herein is a system for encoding audio watermarks with frequency extensions to enable enhanced watermark detection. An extended audio watermark may include an existing audio watermark and a duplicate audio watermark, enabling backwards compatibility with existing watermark detection while also enabling enhanced watermark detection with increased accuracy. For example, embedding the extended audio watermark enables (i) limited devices to perform watermark detection to detect the existing audio watermark, and (ii) improved devices to perform enhanced watermark detection to detect the extended audio watermark. As the extended audio watermark includes redundancy in the form of duplicate audio watermark(s), an accuracy of performing enhanced watermark detection is increased relative to detecting the existing audio watermark alone.

Abstract: A future workload may be predicted for a database system using analysis of queries submitted for execution. A feature vector for a query may be determined according to a query plan for the query. If the feature vector has not been previously seen, or has not been sufficiently seen, by the database system, a machine learning inference may be used to predict performance characteristics of the query, the machine learning system trained using previous feature vectors and performance characteristics of executed queries. If the feature vector has been sufficiently seen previously by the database system, a history of performance characteristics of previous queries with similar or the same feature vector may be used to predict the performance characteristics. The predictions may then be used to configure or reconfigure processing cluster(s) of the database system.