Abstract: A speech recognition system utilizing automatic speech recognition techniques such as end-pointing techniques in conjunction with beamforming and/or signal processing to isolate speech from one or more speaking users from multiple received audio signals and to detect the beginning and/or end of the speech based at least in part on the isolation. Audio capture devices such as microphones may be arranged in a beamforming array to receive the multiple audio signals. Multiple audio sources including speech may be identified in different beams and processed.

Abstract: A database stores a document as a plurality of encrypted records, where each record is indicative of an incremental change to the state of the document, and encrypted using a document key. The document key is stored with encryption decryptable using a group key, and the group key is stored with encryption decryptable using a first access key. In response to a request to rotate from the first access key to a second access key, the database decrypts the group key using the first access key, a stores a group key re-encrypted with the second access key.

Abstract: A system, method and processor that mitigates security vulnerabilities using context-dependent address space hiding. In some embodiments, a hardware mechanism allows a more-privileged software component managing multiple less-privileged software components to blind itself against “out-of-context” less-privileged software components. The hardware mechanism can allow the more-privileged software component to dynamically hide a portion of the more-privileged address space related to the “out-of-context” less-privileged software components, based on knowledge of the “in-context” less-privileged software component. A context register is set with a value from which an address range, within the address space of the more-privileged software component, can be determined, where the address range is associated with a first less-privileged software component can be determined.

Abstract: A cable guide can include a first bracket and a second bracket. The first bracket can be configured to be coupled with a reference body The second bracket can be configured to be coupled with a moveable body moveable relative to the reference body. For example, the moveable body may correspond to a slidable server or other appliance receivable in a rack, and the reference body may correspond to a tray in the rack. A conduit structure may extend between the first bracket and the second bracket and include a first conduit laterally joined along a juncture to a second conduit. The first conduit and the second conduit may be separately serviceable. For example, the first conduit and the second conduit may be at least one of releasably separable along the juncture or separately openable along opposite sidewalls of the conduit structure.

Abstract: A quantum computing service may store, in a cache, one or more compiled files of respective quantum functions included in one or more quantum computing programs received one or more customers. When the quantum computing service receives another quantum computing program, from the same or a different customer, the quantum computing service may determine whether the quantum computing program may include one or more of the quantum functions corresponding to the compiled files in the cache. If so, the quantum computing service may use the compiled files in the cache to compile the quantum computing program.

Abstract: Devices and techniques are generally described for a speech processing routing architecture. First input data representing an input request may be received. First data including a semantic interpretation of the input request may be determined. Metadata of the first input data may be determined. The metadata may identify an entity associated with the input request. In some examples, a query may be sent to a first component. The query may include the metadata. In some examples, second data that identifies a first skill associated with the entity may be received from the first component. In various examples, the first skill may be selected for processing the first input data based at least in part on the first data and the second data.

Abstract: Devices and techniques are generally described for learning personalized concepts for natural language processing. In various examples, a first natural language input may be received. In some examples, a determination may be made that the first natural language input comprises non-actionable slot data. A dialog session may be initiated with the user. In some examples, first slot data that is indicated by the user during the dialog session may be determined. In various examples, data representing the first slot data may be stored in a database in association with the first natural language input.

Abstract: Techniques for low latency updating of an electrophoretic panel are described. According to such techniques, pixels may be continuously scanned out instead of buffering and updating at fixed scanning slots. In an example, a controller of a device loads, before receiving update data associated with a pixel of an electrophoretic panel, a LUT into a memory accessible to a LUT hardware module. After the LUT is loaded into the memory, the controller receives the update data indicating a color change to the pixel and associates the update data with the LUT. Based at least in part on the update data and the LUT, the controller determines waveform data corresponding to the color change and causes an output on the electrophoretic panel based at least in part on the waveform data.

Abstract: A resource tagging service allows resource tags to be created and associated with computing resources in a service provider network. The resource tags can be utilized to search, collect, filter, organize and otherwise manage computing resources in the service provider network having matching tags, and/or to perform other types of functionality. A resource groups service allows customers and/or other users of the service provider network to view and access collections of computing resources that share common resource tags and/or other attributes. Resource groups can also be evaluated in order to identify computing resources in a service provider network for which certain types of actions or other functionality is to be performed. Membership of resource groups can also be evaluated over time and inferences can be drawn from the membership and from operational information associated with the member computing resources.

Abstract: Techniques for updating a machine learning (ML) model are described. A device or system may receive input data corresponding to a natural or non-natural language (e.g., gesture) input. Using a first ML model, the device or system may determine the input data corresponds to a data category of a plurality of data categories. Based on the data category, the device or system may select a ML training type from among a plurality of ML training types. Using the input data, the device or system may perform the selected ML training type with respect to a runtime ML model to generate an updated ML model.

Abstract: Techniques for processing input data for a detected user are described. Received image data is processed to identify an indicated user. Based on the user a machine learning model is implemented. The machine learning model is then used to process input data for a user input. An action is performed using the resulting output data.

Abstract: Described is a method for determining when to transition devices between different states based on determined potential activity in an area near the devices. When the potential for activity near an input device is below a threshold, the device is maintained in an inactive state. When the potential for activity near the device exceeds the threshold, the device is transitioned to an active state before the potential activity can occur near the device. Likewise, when the device is in an active state, the implementations described herein provide the ability to determine when to begin and end transmission of data from the device to a remote computing resource for processing. For example, obtained video may be processed locally to determine if an activity is occurring. If an activity is occurring, relevant data is sent to a remote computing resource to determine if an action has been performed during the activity.

Abstract: Dialog acts (e.g., questions) are selected for voice browsing by a model trained to identify a dialog act that is most likely to lead to a desired outcome. Upon receiving an invocation to begin a conversation, a score indicative of a level of confidence that the conversation will have a successful outcome is determined, and a dialog act is selected based on the score. Subsequently, at each turn of the conversation, the score is updated or a new score is calculated, and a dialog act is selected based on the updated or new score. Confidence scores are calculated based on input features that are determined based on the user who uttered the invocation or responses to dialog acts, as well as a context of the conversation, and provided to a linear model or a machine learning model as inputs.

Abstract: Methods, systems, and computer-readable media for automatic archiving of data store log data are disclosed. One or more operation records in a log are selected for archival. The one or more operation records comprise data indicative of operations performed on one or more data objects of a data store. The one or more operation records are selected for archival prior to deletion from the log. The one or more operation records are replicated from the log to an archive. Based at least in part on the replicating, the one or more operation records in the log are marked as archived. Based at least in part on the marking as archived, the deletion of the one or more operation records from the log is permitted.

Abstract: A new type system may be added to a type registry for a data processing service. A request to add the new type system may be received that describes the new type system for a data store. The new type system may be used to perform a data processing job that accesses the data store to obtain or store data as a source or target data store.

Abstract: Disclosed are systems and methods for detecting defects during the handling and processing of items at a material handling facility. Embodiments of the present disclosure can be implemented at material handling to detect quality defects during processing of items in connection with fulfillment operations that may be designed to receive, store, sort, pick, pack, and ship items. Embodiments of the present disclosure provides a plurality of sensors and a trained machine learning model configured to provide real-time acquisition and analysis of item information to facilitate detection and mitigation of processing quality defects.