Abstract: Users in a facility may move about and their authentication status may change accordingly. For example, upon entering a facility, a user may present identification data. At that point, the user is considered an authenticated user with persistent profile data. However, the identification of a user may not be presented at entry or may be unreliable, which causes the user to be considered an unauthenticated user with temporary profile data. In other scenarios, the identification of authenticated users may become unreliable based on their location or their proximity to other users. In cases where users are crowded at a location, the profile data for such users may be aggregated and may be based on common settings of the set of possible users in the group. A user interface may be presented to a user based on their designated profile data.

Abstract: Techniques are described for monitoring and analyzing input/output (I/O) messages for patterns indicative of ransomware attacks affecting computer systems of a cloud provider, and for performing various remediation actions to mitigate data loss once a potential ransomware attack is detected. The monitoring of I/O activity for such patterns is performed at least in part by I/O proxy devices coupled to computer systems of a cloud provider network, where an I/O proxy device is interposed in the I/O path between guest operating systems running on a computer system and storage devices to which I/O messages are destined. An I/O proxy device can analyze I/O messages for patterns indicative of potential ransomware attacks by monitoring for anomalous I/O patterns which may, e.g., be indicative of a malicious process attempting to encrypt or otherwise render in accessible a significant portion of one or more storage volumes as part of a ransomware attack.

Abstract: Systems and methods are provided for generating a flat garment pattern and/or 3D mesh representation of a garment from one or more images depicting the garment laid flat or hung up. A system may obtain both a front image depicting a front view of a garment and a back image depicting a back view of the garment. A front and back silhouette of the garment may then be generated, which may include segmenting the garment depiction from background image content. A parametric representation of the garment may then be generated based on the front and back silhouettes, which may be implemented by iteratively optimizing, using differentiable rendering techniques, a garment representation within a parametric garment space previously learned for the particular garment type. A 3D mesh garment representation may then be generated based on the parametric representation, from which a flat sewing pattern may subsequently be generated if desired.

Abstract: Descriptors of machine learning tasks to be used to respond to analysis requests, indicating acceptable categories of runtime environments for the tasks and metrics to be collected from the tasks, are received via programmatic interfaces. In response to an analysis request, an orchestrator receives results from individual tasks as they become available, provides the results to other tasks, and causes a response to the request to be prepared using results from at least a subset of the tasks. Metrics collected from the tasks, and a visual representation of the tasks indicating their runtime environments are presented.

Abstract: Systems and methods are described for execution of multiple tasks associated with a set of code in an on-demand network code execution system. A user may provide a set of code that is associated with the multiple tasks. The system may generate a first virtual machine instance for execution of a first task. The system may determine that a second task is associated with the first task and may identify a location of the first virtual machine instance. The system may further identify a second virtual machine instance for execution of the second task based on the location of the first virtual machine instance. For example, the system may identify the first virtual machine instance from a plurality of pre-generated virtual machine instances and/or may generate the first virtual machine instance.

Abstract: Described technologies generate a data structure corresponding to values sequenced based on a plurality of timestamps associated with the values. The data structure can include a first section identifying a first timestamp associated with the plurality of timestamps and a number representing how many timestamps are associated with the plurality of timestamps, and a second section including at least a value linked to the first timestamp, and an additional value representing an encoding type associated with the second section. The data structure can be stored in computer-implemented storage.

Abstract: A visualization tool for machine learning models obtains metadata from a first training node at which a multi-layer machine learning model is being trained. The metadata includes a parameter of an internal layer of the model. The tool determines a plurality of metrics from the metadata, including respective loss function values corresponding to several training iterations of the model. The tool indicates the loss function values and the internal layer parameter values via a graphical interface.

Abstract: Techniques for evaluating a natural language understanding (NLU) component and determining an action to resolve an issue processing a user input are described. The system determines which component is invoked by a baseline NLU component is processing the user input, and which component is invoked by an updated NLU component. Based on that information, the system selects the action to resolve the updated NLU component generating an undesired response to the user input.

Abstract: Systems and methods to generate synthetic lip synchronization may generate source facial keypoints based on a source video, generate target facial keypoints based on a target audio, determine distances between the source facial keypoints and target facial keypoints, and transform or warp the source facial keypoints and associated surfaces to the target facial keypoints. In this manner, target video having synthetic lip synchronization that matches the target audio may be generated, and the target video may substantially preserve or maintain surface textures or features from the source video in the target video, thereby generating natural and believable synthetic lip synchronization corresponding to the target audio.

Abstract: Techniques for configuring device-specific skills as top-level skills are described. When a system receives a user input the system performs NLU processing to determine an intent of the user input. In some instances, the system may identify a device-specific skill associated with the device interacted with by the user. At least partially in parallel to performing NLU processing to determine the intent of the user input, the system may also perform NLU processing to determine a likelihood that the user input corresponds to an intent actionable by the device-specific skill. Once the system has finished NLU processing, the system may implement one or more prioritization rules to determine whether the user input should be sent to the device-specific skill or another skill of the system.

Abstract: An input/output (I/O) device can initiate data migration of a virtual machine (VM) instance from a source device to a target device. The data migration of the VM instance may include migrating the data for the VM instance and tag data associated with the data. The data for the VM instance and the tag data may be stored together in a source memory. A first read request from the I/O device can enable a memory controller in the source device to read the data for the VM instance and the tag data together, store the tag data in a tag data buffer, and transmit the data for the VM instance to the target device. A second read request from the I/O device can read the stored tag data from the tag data buffer and transmit to the target device. The target device can write the data for the VM instance together with the tag data in the target memory.

Abstract: This disclosure describes, in part, devices and methods for deactivating tags. For instance, an electronic device may include antennas that transmit signals and/or fields for deactivating the tags. In some examples, the electronic device includes a first gate that includes a first portion of the antennas and a second gate that includes a second portion of the antennas. To deactivate the tags, the electronic device may detect locations of an object relative to the electronic device. The electronic device may then activate a respective tag and/or a respective group of tags based on the location of the object. For example, the electronic device may activate a first tag and/or a first group of tags when the object is at a first location, activate a second tag and/or a second group of tags when the object is at a second location, and/or so forth.

Abstract: Techniques for detecting regressions with respect to the accuracy of an anomaly detection compute service in detecting anomalies in users' time series data. The techniques include providing an instrumented time series instrumented with a set of one or more anomalies to the anomaly detection service. The anomaly detection service detects a set of one or more anomalies in the instrumented time series. The precision and recall of the detected anomalies with respect to the instrumented anomalies is computed. From the computed precision and recall, an anomaly detection accuracy is computed as an F-score or F-measure. It is then determined whether a regression in anomaly detection accuracy has occurred by comparing the computed accuracy score to a threshold. If a regression has occurred, an alert can be generated or a recent change to the anomaly detection service can be rolled back.

Abstract: Provided herein are techniques for enabling communications between an access point (AP) and a number of stations (STAs) while maximizing transmission quality and/or minimizing power consumption. For example, an STA may be associated with a camera device used to capture video data and transmit the video data to the AP, and the AP may be configured to receive a registration request from the STA, register the STA, receive video data from the STA, and transmit the video data to a backend system such as a storage server. In some cases, the AP and STAs may communicate using a communication bandwidth that is sub-GHz but has a higher bandwidth relative to the low-bandwidth sub-GHz communication protocols discussed above. An example of such a high-bandwidth sub-GHz communication protocol is one that supports data transmission (e.g., video data transmission) using a bandwidth of 900 megahertz (MHz).

Abstract: Techniques for session-based device grouping are described. In an example, a computer system receives first data requesting an output, determines a function to provide content data based at least in part on the first data, determines a session identifier of a session associated with execution of the function, and determines session data associated with the session identifier. The session data includes a first device identifier of a first device, an indication that the first device is outputting the content data, and a second device identifier of a second device. The computer system also causes, based at least in part on the session data including the second device identifier, the second device to output the content data.

Abstract: Systems and methods for communicating in a network using share signals in accordance with various embodiments of the present disclosure are provided. In one embodiment, a method for communicating in a network may include receiving, from a first client device associated with an A/V recording and communication device, at a backend server in network communication with the A/V recording and communication device, a share signal comprising location data and first image data captured by a first camera of the A/V recording and communication device, the share signal including a command to share the first image data with a network of users; generating a power-up command signal based on the share signal; identifying at least one second camera to power up; and transmitting the power-up command signal to the at least one second camera, by the backend server, using a network interface.

Abstract: On-circuit utilization monitoring may be performed for a systolic array. A current utilization measurement may be determined for processing elements of a systolic array and compared with a prior utilization measurement. Based on the comparison, a throttling recommendation may be provided to a management component to determine whether to perform the throttling recommendation.

Abstract: Generating instructions for programming a processing element array to implement a convolution operation can include determining that the convolution operation under-utilizes the processing element array. The convolution operation involves using the processing element array to perform a series of matrix multiplications between a set of filters and a set of input matrices. Each filter comprises a weight matrix. Each input matrix is assigned to a respective row in the processing element array. Under-utilization can be determined through detecting that less than a threshold number of rows would be used concurrently. In response to determining that the convolution operation under-utilizes the processing element array, instructions can be added for modifying the convolution operation to increase the number of rows used concurrently. The added instructions are executable to cause at least one input matrix to be processed in parallel across more rows compared to processing without modifying the convolution operation.

Abstract: Methods and systems for automatically processing a document may include classifying a document, such as a medical document, as one or more document types based at least in part on one or more machine learning models and one or more tokens extracted from the medical document, determining a token contribution weight of each token towards the classification, modifying the medical document based on the token contribution weights of the one or more tokens, and displaying the modified medical document on a display to a user.

Abstract: Systems, methods, and computer-readable media are disclosed for modular robotic manipulation systems. In one embodiment, an example modular robot assembly may include a housing having a base, and a robotic manipulator coupled to the base, where the housing is configured to provide the robotic manipulator access to a first side, a second side, and a third side of the modular robot assembly. The module robot assembly may include a first camera system, a controller, and an optional display coupled to the housing. The modular robot assembly may be configured to be coupled to other modular robot assemblies, and the housing may be configured to be secured, such that the modular robot assembly can be independently transported.

Abstract: An audio/video (A/V) device includes a first housing and a second housing rotatably coupled to the first housing. The first housing includes a camera oriented in a first direction, one or more microphones oriented in the first direction, one or more first lighting elements oriented in the first direction, one or more second lighting elements oriented in a second direction, one or more third lighting elements oriented in a third direction, and one or more first contacts. The second housing includes a loudspeaker oriented in a fourth direction and one or more second contacts configured to engage with the one or one first contacts. An engagement between the one or more first contacts and the one or more second contacts communicatively couples the loudspeaker to computing components with the first housing.

Abstract: When a query is received by a stateful data processing service, the service determines, for each table scan (and associated operations) of a query, whether to select the table scan for execution by a stateless data processing service. The selected table scans are sent to the stateless data processing service for execution, and results are received by the stateful data processing service. The stateful data processing service may also execute other table scans of the query locally, against a local data cache. If the data is not present in the local data cache, then the stateful data processing service will copy the table data into the local data cache before executing the table scan. A query result based on the remote and/or local table scans may then be returned to the client.

Abstract: Techniques for personalized batch and streaming speech-to-text transcription of audio reduce the error rate of automatic speech recognition (ASR) systems in transcribing rare and out-of-vocabulary words. The techniques achieve personalization of connectionist temporal classification (CT) models by using adaptive boosting to perform biasing at the level of sub-words. In addition to boosting, the techniques encompass a phone alignment network to bias sub-word predictions towards rare long-tail words and out-of-vocabulary words. A technical benefit of the techniques is that the accuracy of speech-to-text transcription of rare and out-of-vocabulary words in a custom vocabulary by automatic speech recognition (ASR) system can be improved without having to train the ASR system on the custom vocabulary. Instead, the techniques allow the same ASR system trained on a base vocabulary to realize the accuracy improvements for different custom vocabularies spanning different domains.

Abstract: Devices and techniques are generally described for voice-based content attribution for speech processing applications. In some examples, a request for voice-based content may be received from a first speech processing skill. First identifier data associated with the first speech processing skill may be received. A determination may be made that first content that is associated with the request. Voice-based output data describing the first content may be generated. A selection of the first content may be received. Attribution data may be determined based at least in part on the selection of the first content and the first identifier data. The attribution data may be sent to a remote computing device.

Abstract: A quantum computing service may use multiple quantum computers to execute a same quantum computing algorithm to improve the quantum computational accuracy. The quantum computing service may instruct individual ones of the multiple quantum computers to execute the quantum computing algorithm repeatedly for a number of times. The quantum computing service may obtain a plurality of results from the multiple quantum computers. The quantum computing service may aggregate the plurality of results to generate an ensemble result, and provide the ensemble result to a customer as a final result of the quantum computing algorithm.

Abstract: Techniques and systems can obtain a first private key usable with a classical cryptography algorithm and a second private key usable with a post-quantum cryptography algorithm based on classical and post-quantum public keys hosted by a computer-implemented storage of an online service provider. A plurality of keys to perform a cryptography operation on data hosted by the computer-implemented storage can be generated, the plurality of keys generated based on at least the first and second private keys and a cryptography derivation function identified in the computer-implemented storage. The plurality of keys can be used to perform the cryptography operation on the data hosted by the computer-implemented storage.

Abstract: A device including a mount, a first arm coupled to the mount, and a second arm coupled to the mount. The first arm includes first teeth and the second arm includes second teeth. A first light assembly rotationally couples to the first arm, and includes a first coupler that engages with the first teeth. A second light assembly rotationally couples to the second arm, and includes a second coupler that engages with the second teeth. A camera assembly pivotably couples to the mount, and includes a camera, a first passive infrared (PIR) sensor, and a second PIR sensor.

Abstract: Systems and methods are described for implementing a distributed unit in a radio access network that synchronizes its clock with a radio unit. A distributed unit may be deployed in a location where it cannot receive timing information from a satellite or may lack the equipment to obtain and process satellite signals. The clock of the distributed unit may thus drift relative to the clocks of the radio units, which may cause the distributed unit to mistime its transmissions of data to radio units for delivery to user devices. The distributed unit may prevent clock drift by obtaining timing information from the radio units, determining an amount of clock drift that is occurring, and applying a correction factor to keep the distributed unit clock synchronized with the radio unit clock. The distributed unit may determine how often to synchronize based on the severity and variability of the clock drift.

Abstract: A system configured to perform user orientation estimation to determine a direction a user is facing using a deep neural network (DNN). As a directionality of human speech increases with frequency, the DNN may estimate the user orientation by comparing high-frequency components detected by each of the multiple devices. For example, a group of devices may individually generate feature data, which represents audio features and spatial information, and send the feature data to the other devices. Thus, each device in the group receives feature data generated by the other devices and processes this feature data using a DNN to determine an estimate of user orientation. In some examples, the DNN may also generate sound source localization (SSL) data and/or a confidence score associated with the user orientation estimate. A post-processing step may process the individual user orientation estimates generated by the individual devices and determine a final user orientation estimate.

Abstract: This disclosure describes techniques for updating planogram data associated with a facility. The planogram may indicate, for different shelves and other inventory locations within the facility, which items are on which shelves. For example, the planogram data may indicate that a particular item is located on a particular shelf. Therefore, when a system identifies that a user has taken an item from that shelf, the system may update a virtual cart of that user to indicate addition of the particular item. In some instances, however, a new item may be stocked on the example shelf instead of a previous item. The techniques described herein may use sensor data generated in the facility to identify this change and update the planogram data to indicate an association between the shelf and the new item.

Abstract: When creators generate media content in accordance with media programs, the media content is evaluated to identify any number of violations of policies, and to generate scores representing a level of risk that the creators will violate one or more of the policies in the future. Subsequently, media content of the creators is transmitted to listeners in accordance with the scores. In addition to audio data of creators or transcripts of the audio data, scores may be generated based on images associated with the creators, titles or summaries of media programs, or reports received from listeners. Scores calculated for creators may increase or decrease over time, depending on numbers of violations of policies by such creators, or other factors, and be utilized with a goal of protecting listeners against exposure to harmful content.

Abstract: A spatial search may be performed using representations of geometric shapes stored in a key-value store. A request to perform a spatial search may be received, the request including a geometric shape composed of one or more points. The points of the geometrical shape may be translated into one or more spatial indexes representing spatial cells using a space-filling curve. A key-value store may then be incrementally searched for each spatial index to identify spatial cells intersecting the geometric shape for which other known geometric shapes exist. The key-value store may then be searched to identify the known geometric shapes intersecting the geometric shape included in the search.

Abstract: Systems and methods are provided for handling file operations from a hosted computing instance via a secure compute layer. The secure compute layer is presented to the instance as a virtualized service device that is locally addressable by the instance. Software within the instance can submit file operations to the virtualized service device, which the secure compute layer can translate into calls to a network-accessible storage service. Results from the calls can then be passed back to the instance through the virtualized service device. As a result, the instance can communicate with a variety of different network services, without itself implementing network communications for those services.

Abstract: Systems, methods, and computer-readable media are disclosed for capacitive touch sensing using system-in-package components and batteries. In one embodiment, a device may include a flexible printed circuit, a button cell electrically coupled to the flexible printed circuit, and a system-in-package that is electrically coupled to the flexible printed circuit. The system-in-package may include an electromagnetic interference shield, and a capacitive touch sensor. The capacitive touch sensor may be configured to detect a change in capacitance via a change in electric field at the electromagnetic interference shield.

Abstract: Hyperparameters for tuning a machine learning system may be optimized using Bayesian optimization with constraints. The hyperparameter optimization may be performed for a received training set and received constraints. Respective probabilistic models for the machine learning system and constraint functions may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing entropy search on the respective models, training the machine learning system using the identified values to determine measures of accuracy and constraint metrics, and updating the respective models using the determined measures.

Abstract: An NLQ-SQLQ tool or service of a provider network may receive a natural language query (NLQ) from a client and convert the NLQ to an SQL query using ontological codes and placeholders. For one or more portions of the NLQ, the tool/service determines that the portion is associated with one or more codes of an ontology. The tool/service then assigns, based on criteria, a particular code to the portion. The tool/service replaces portions of the NLQ with different argument placeholders to generate a modified NLQ. A trained model converts the modified NLQ into an initial SQL query that has argument placeholders and subquery placeholders. The tool/service generates a final SQL query based on the initial SQL query, predefined SQL subquery templates associated with the subquery placeholders, and codes associated with the argument placeholders. The tool/service executes the final SQL query and sends results to the client.

Abstract: A system for storing data includes a rack, one or more data storage modules coupled to the rack, and one or more data control modules coupled to the rack. The data storage modules may include a chassis, two or more backplanes coupled to the chassis, and one or more mass storage devices (for example, hard disk drives) coupled to the backplanes. The data control modules may access the mass storage devices in the data storage modules.

Abstract: A system for providing a geofence service is disclosed. The geofence service receives an encrypted geospatial index for a specified geofence based on application of a hash function to respective ones of a plurality of locations for the specified geofence in accordance with a secret key that is unknown to the geofence service. The geofence service stores the encrypted geospatial index to a data store according to a geofence identifier for the specified geofence. The geofence service receives an encrypted device location identifier generated based on application of the hash function to a representation of a current location of a user device in accordance with the secret key. The geofence service determines whether the user device is located in a location of the plurality of locations based on a query of the encrypted geospatial index according to the encrypted device location identifier.

Abstract: A host device may include an interconnect, a host memory, and a set of processor cores. A processor core may execute a VM assigned to a cryptographic key and may send a request to access a physical address in the host memory toward the interconnect. An enforcer device may receive the request and extract a key identifier from the request. The enforcer device may determine whether to allow the request to access the physical address via the interconnect based on the key identifier and a list of allowed keys stored on the enforcer device. If the enforcer device determines to not allow the request to access, the enforcer device may modify the physical address and/or the key identifier of the request.

Abstract: A virtual network verification service for provider networks that leverages a declarative logic programming language to allow clients to pose queries about their virtual networks as constraint problems; the queries may be resolved using a constraint solver engine. Semantics and logic for networking primitives of virtual networks in the provider network environment may be encoded as a set of rules according to the logic programming language; networking security standards and/or client-defined rules may also be encoded in the rules. A description of a virtual network may be obtained and encoded. A constraint problem expressed by a query may then be resolved for the encoded description according to the encoded rules using the constraint solver engine; the results may be provided to the client.

Abstract: Described herein are approaches for generating a new queue based on an existing queue. This may include receiving a request to transfer the existing queue from a first device to a second device. A set of move criteria may be evaluated using a playback context, a user profile, a configuration associated with the second device, and/or a level of access constraints. Depending on the results of the evaluation, the existing queue may be completely reformulated to define the new queue. The second device may then be instructed to play the new queue.

Abstract: This disclosure is directed to, in part, a processing pipeline for detecting predefined activity using image data, identifying an item-of-interest and a location of the item-of-interest across frames of the image data, determining a trajectory of the item-of-interest, and determining an identifier of the item-of-interest and an action taken with respect to the item-of-interest. The processing pipeline may utilize one or more trained classifiers and, in some instances, additional data to identify items that are placed into or removed from a tote (e.g., basket, cart, or other receptacle) by users in material handling facilities as the users move around the material handling facilities.

Abstract: Enhanced training data representative of possible inputs is used to train a machine learning system. For example, a machine learning system to determine identity based on an image of a human palm may be trained using enhanced training data comprising images. The enhanced training data may comprise source images that have been modified to appear to depict synthetic artifacts that attempt to simulate human palms, augmented images of dirty hands, and so forth. A synthetic artifact image may be produced by selectively removing some data from a source image. An augmented image may be produced by selectively blending the source image with features extracted from sample images. These images may then be used as training data to train the machine learning system.

Abstract: Automatic partitioning of a machine learning model may be performed for training across multiple processing devices. A training job for a machine learning model may specify a number of partitions for a machine learning model. An optimization parameter may be determined for the machine learning model. Different partitions of the machine learning model to be trained across multiple processing devices may be determined based on the specified number of partitions and the optimization parameter. A schedule for executing the training job may be generated according to the respective partitions of the machine learning model. The training job may be executed according to the schedule.

Abstract: Session-specific edit recommendations may be made for editing a code file. After a code editing session is started, code file edits may be captured. A machine learning technique may be applied to learn the code edits and recommend alternative code portions for portions of the code file during the code editing session. The recommendations may be provided and accepted, or not, via an interface of a code editor application.

Abstract: Techniques for generating, from first speaker recognition data corresponding to at least a first word, second speaker recognition data corresponding to at least a second word are described. During a speaker recognition enrollment process, a device receives audio data corresponding to one or more prompted spoken inputs comprising the at least first word. Using the prompted spoken input(s), the first speaker recognition data (specific to that least first word) is generated. Sometime thereafter, a user may indicate that speaker recognition processing is to be performed using at least a second word. Rather than have the user go through the speaker recognition enrollment process a second time, the device (or a system) may apply a transformation model to the first speaker recognition data to generate second speaker recognition data specific to the at least second word.

Abstract: Described herein is a computer-implemented method for extracting and identifying an audio song. An audio file can be accessed by a computing device. A set of audio categories and a set of probabilities associated with the set of audio categories can be determined for a first audio clip. A subset of the set of audio categories can be determined based on a subset of the set of probabilities. Each audio category of the subset of the set of audio categories can correspond to an audio class label. Whether the first audio clip is part of a song can be determined. The song can be defined by combining the first audio clip with other audio clips.