Abstract: An approach for rule engine, filtering, and rule management is described herein. A computing device can access a rule graph that represents rule sets and includes nodes connected by edges. The computing device can receive record data associated with a record and determine at least one of the rule sets that matches the record by at least traversing the rule graph using the record data. The computing device can generate rule information based on the at least one rule set and associate the rule information with the record.

Abstract: Systems and methods are provided for enforcing symmetric flows of cross-region network traffic through firewalls in multi-region network environments. Enforcement may be configured automatically by analyzing network policy data to identify cross-region traffic that is to be firewalled, and configuring gateway nodes in the various regions to implement symmetric bidirectional flows through any firewalls in the communication path. Beneficially, by enforcing symmetric bi-directional flows of traffic through any firewalls in a communication path, the firewalls may maintain the state of a given communication session even when the communication session is between endpoints in different regions that have different architectures.

Abstract: Techniques for rendering notification and announcement content for read time are described. A notification system provides non-natural language notification content. Sometime thereafter, a user input to output the notification content is received. In response, template-based or natural language generation processing is performed to convert the non-natural language notification content into natural language notification content including updated time information. The natural language notification content is then output to the user. Alternatively, a notification system provides non-natural language announcement content. Sometime thereafter, the template-based or natural language generation processing is performed to convert the non-natural language announcement content into natural language announcement content including updated time information. The natural language announcement content is then proactively output to an intended recipient user or group of users.

Abstract: Disclosed are various embodiments providing user authentication through registered device communications. An authentication request is received from a client device. A user is authenticated for access to a user account based at least in part on the client device providing the authentication token. The authentication token is generated by the client device or by one or more other computing devices and sent to the client device. The client device encrypts the authentication token based at least in part on a user authenticating factor and stores the encrypted authentication token on the client device.

Abstract: During text-to-speech processing, a speech model creates output audio data, including speech, that corresponds to input text data that includes a representation of the speech. A spectrogram estimator estimates a frequency spectrogram of the speech; the corresponding frequency-spectrogram data is used to condition the speech model. A plurality of acoustic features corresponding to different segments of the input text data, such as phonemes, syllable-level features, and/or word-level features, may be separately encoded into context vectors; the spectrogram estimator uses these separate context vectors to create the frequency spectrogram.

Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.

Abstract: Systems, methods, and devices for recognizing a user are disclosed. A speech-controlled device captures a spoken utterance, and sends audio data corresponding thereto to a server. The server determines content sources storing or having access to content responsive to the spoken utterance. The server also determines multiple users associated with a profile of the speech-controlled device. Using the audio data, the server may determine user recognition data with respect to each user indicated in the speech-controlled device's profile. The server may also receive user recognition confidence threshold data from each of the content sources. The server may determine user recognition data associated that satisfies (i.e., meets or exceeds) a most stringent (i.e., highest) of the user recognition confidence threshold data. Thereafter, the server may send data indicating a user associated with the user recognition data to all of the content sources.

Abstract: Changes made to a database table are accumulated, in durable storage, and snapshots of partitions of the table are obtained. For successive snapshots of a partition, the system accesses a previous snapshot, applies changes from the accumulated changes, and stores the updated snapshot to a durable data store. The accumulated changes and the successive partition snapshots are made available to restore the database to any point in time across a continuum between successive snapshots. Although each partition of the table may have a backup snapshot that was generated at a time different from when other partition snapshots were generated, changes from respective change logs may be selectively log-applied to distinct partitions of a table to generate an on-demand backup of the entire table at common point-in-time across partitions. Point-in-time restores of a table may rely upon a similar process to coalesce partition snapshots that are not aligned in time.

Abstract: Techniques are described for providing managed virtual computer networks that have a configured logical network topology with virtual networking devices, such as by a network-accessible configurable network service, with corresponding networking functionality provided for communications between multiple computing nodes of the virtual computer network by emulating functionality that would be provided by the virtual networking devices if they were physically present.

Abstract: Execution status of managed time series processing tasks may be tracked. Status of a time series processing task that operations on different portions of a time series may be respectively captured. A request for the status of one of the portions of the time series with respect to the time series processing task may be received. The status may be identified and returned. For failed tasks, a failure reason may be generated by the time series processing system and included in a response with a failure status.

Abstract: Techniques for presence-detection devices to vary presence-detection sensitivity to detect different types of movements by objects, such as major movements and minor movements, using ultrasonic signals. The devices detect movement of a person in an environment by emitting ultrasonic signals into the environment, and characterizing the change in the frequency, or the Doppler shift, of the reflections of the ultrasonic signals off the person caused by the movement of the person relative to the presence-detection devices. The presence-detection devices may control the presence-detection sensitivity in order to detect major movements, such as a user walking in a room, as well as objects minor movements, such as a user typing at a computer. By adjusting the presence-detection sensitivity, the devices are able to improve the overall accuracy of detecting the presence of users in an environment.

Abstract: Systems and methods are provided for dynamic segmentation of users during an experiment based on changes to application data collected during the experiment. Data regarding application interactions and associated application metadata may be collected from users during application experiments that involve testing different variants of a feature or otherwise different user experiences. The data regarding application interactions and associated application metadata may be evaluated to discover segments of users and/or usage patterns (e.g., “cohorts”). During the experiment, the users may be dynamically re-segmented into new/different cohorts based on new application data being collected.

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.

Abstract: An apparatus powered by batteries may include a first processor and a second processor. The first processor may consume more electrical power than the second processor. The second processor acquires data from one or more sensors. At a predetermined time or other trigger, the second processor activates the first processor that is then used to send the acquired data to an external device. For example, items are stowed on the apparatus that includes weight sensors. The second processor awakens hourly, acquires weight data indicative of the weight of items stowed on the apparatus, and returns to a sleep mode. After 24 hours of acquiring weight data, the second processor activates the first processor to send the accumulated weight data to an external device, such as a server, using a network interface. Power consumption is substantially reduced, allowing the apparatus to operate without external power for extended periods of times.

Abstract: At an artificial intelligence system, training iterations of a first machine learning model are implemented. In a particular iteration, a group of data items are selected from an item collection using active learning, and respective labels selected from a set of tags are obtained for at least some of the items of the group. Using feature processing elements of a different machine learning model, a respective feature set corresponding to individual labeled items is generated in the iteration, and the feature sets are included in a training set used to train the first machine learning model. A trained version of the first machine learning model is stored after a training completion criterion is met.

Abstract: Hop counts can be determined in a variety of network segments using packet headers collected at boundaries of the segments. Agents executing on network devices are used to transmit packet header data, including a TTL budget, to a collector server computer. The collector server computer can discern signal (production flows) from noise (traceroutes and probing traffic), detecting packets that are at risk of being dropped due to TTL expiration. Alerts can be generated for packet flows with a dangerously low remaining TTL budget, which are at high risk of expiring due to operational events resulting in traffic temporarily traversing slightly longer paths. Source addresses can be identified for such packet flows and network devices through which the packet flows are traversing can be identified. Hop counts can then be computed for different network segments, such as an Internet segment, a backbone segment and a data center segment.

Abstract: A candidate attribute combination of a first data set is identified, such that the candidate attribute combination meets a data type similarity criterion with respect to a collection of data types of sensitive information for which the first data set is to be analyzed. A collection of input features is generated for a machine learning model from the candidate attribute combination, including at least one feature indicative of a statistical relationship between the values of the candidate attribute combination and a second data set. An indication of a predicted probability of a presence of sensitive information in the first data set is obtained using the machine learning model.

Abstract: A system configured to improve a voice quality during a communication session by performing bandwidth extension on a narrowband speech signal to generate a wideband speech signal with higher audio quality. For example, a system can extend a speech bandwidth from a narrowband signal having a first bandwidth (e.g., 4 kHz) to a wideband signal having a second bandwidth (e.g., 8 kHz or higher). To perform bandwidth extension, the system may include cascaded neural networks, such as two or more sub-pixel convolutional neural networks (CNNs) connected in series. In some examples, a first sub-pixel CNN may extend the speech bandwidth from 4 kHz to 6 kHz and a second sub-pixel CNN may extend the speech bandwidth from 6 kHz to 8 kHz. Alternatively, the system may use three or more cascaded neural networks and/or may extend the speech bandwidth above 8 kHz without departing from the disclosure.

Abstract: Movement of an autonomously motile device may be controlled by a user device. The user device may display image data captured by a camera of the autonomously motile device; a user may provide input, such as a touch gesture on a display screen, indicating a command for the autonomously motile device to move to a location indicated by the input. The autonomously motile device determines a coordinate of the input and a time of the touch input; the autonomously motile device then determines a direction and distance of a corresponding movement.

Abstract: Techniques to reduce overhead in a direct memory access (DMA) engine can include processing descriptors from a descriptor queue to obtain a striding configuration to generate tensorized memory descriptors. The striding configuration can include, for each striding dimension, a stride and a repetition number indicating a number of times to repeat striding in the corresponding striding dimension. One or more sets of tensorized memory descriptors can be generated based on the striding configuration. Data transfers are then performed based on the generated tensorized memory descriptors.