Abstract: Systems, devices, and methods are provided for fingerprinting requests, such as transaction records. A transaction record or other suitable request may be parsed to identify a parameter values for a set of fields relevant to fingerprinting. A transaction record representation may be generated based on transaction input data, transaction output data, intermediate data, or combinations thereof. A fingerprint may be generated from the transaction record implementation. Fingerprints may be used to identify various test cases that can be used for regression testing.

Abstract: A technique for remote direct memory access (RDMA) may include receiving a packet that was sent over a network, and determining the packet has metadata used for queue selection. The technique may also include selecting a queue based on the metadata, and writing the data of the packet to an application memory using the datapath associated with the selected queue. Amended metadata can be generated to indicate that the data has been written to the application memory, and the amended metadata can be stored in a software accessible buffer.

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: System and methods are provided for encoding and encrypting DNS labels with content metadata and client metadata. A client computing device transmits a DNS query for the encoded domain name. A DNS server receives the encoded domain name and decrypts and decodes the domain name label. The DNS server uses the decrypted and decoded client and content metadata to make a routing decision to select a particular point of presence (PoP). The DNS server sends, to the client computing device, a DNS reply with the Internet Protocol (IP) address of the selected PoP. The client computing device requests content from the PoP identified by the provided IP address.

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: 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: Multicore fibers can include multiple cores surrounded by a cladding. The cores of the multicore fibers can be coupled with a transition portion to facilitate a fanout into one or more ribbons having multiple single cores.

Abstract: An interactive system may be implemented in part by an audio device located within a user environment, which may accept speech commands from a user and may also interact with the user by means of generated speech. In order to improve performance of the interactive system, a user may use a separate device, such as a personal computer or mobile device, to access a graphical user interface that lists details of historical speech interactions. The graphical user interface may be configured to allow the user to provide feedback and/or corrections regarding the details of specific interactions.

Abstract: Techniques for disambiguating which profile, of multiple profiles, is to be used to respond to a user input are described. A device located in a communal space (e.g., a hotel room or suite of rooms, conference room, hospital room, etc.) may be associated with a device profile and a user profile of a user presently occupying the communal space. When the user inputs a command to the device (either by text or speech), a system associated with the device determines the profiles (e.g., a device profile and a user profile) associated with the device. The system determines one or more policies associated with the device. The one or more policies may correspond to rules for disambiguating which profile to use to execute with respect to the user input. Using the one or more policies, the system determines which profile is to be used, and causes a speechlet component to execute using information specific to the determined profile.

Abstract: Devices and techniques are generally described for three dimensional room modeling. In various examples, 3D mesh data representing a room may be received. Plane data comprising a plurality of planes may be received. Each plane of the plurality of planes may represent a planar surface detected in the room. In some cases, a first plurality of wall candidates for a 3D model of the room may be determined based at least in part on the plane data. A second plurality of wall candidates for the 3D model of the room may be determined by modifying the first plurality of wall candidates based on a comparison of the first plurality of wall candidates to the 3D mesh data. The 3D model of the room may be generated based at least in part on the second plurality of wall candidates.

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: 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: 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 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.

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: Disclosed are systems and methods that determine a likelihood that an item page represents a risky item. For example, a machine learning model may be trained to determine a probability score that an item page corresponds to a topic of a plurality of topics based on item textual descriptions determined from the item page and search queries corresponding to the item page. Still further, one or more of a topic risk score, item risk score, cumulative item risk score, and/or a total item risk score may be determined. Each of the risk scores may be indicative of a likelihood that the item page represents a risky item.

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: 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: 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.