Abstract: A parametric constant resolves to different values in different contexts, but a single value within a particular context. An anchor constant is a parametric constant that allows for a degree of parametricity for an API point. The context for the anchor constant is provided by a caller to the API point. The anchor constant resolves to an anchor value that records specialization decisions for the API point within the provided context. Specialization decisions may include type restrictions, memory layout, and/or memory size. The anchor value together with an unspecialized type of the API point result in a specialized type of the API point. A class object representing the specialized type is created. The class object may be accessible to the caller, but the full value of the anchor value is not accessible to the caller. The API point is executed based on the specialization decisions embodied in the anchor value.

Abstract: Techniques are described for enabling concurrent cross-region replications and garbage collection while maintaining consistency and data integrity among file systems. In some embodiments, techniques for garbage collection fencing utilize a system-level garbage fencing key (GC fencing key) and one or more job-level GC fencing keys in a source file system that perform one or more cross-region replications with one or more target file systems, one replication and one job-level GC fencing key per target file system. In some embodiments, one job-level GC fencing key in a source file system and one job-level GC fencing key in a source file system together provide garbage fencing for a cross-region replication. In certain embodiments, the metadata information in a GC fencing key can inform, instruct, or be used to configure garbage collectors to skip garbage collection for a range of snapshots in a file system.

Abstract: Techniques for generating text content arranged in a consistent read order from a source document including text corresponding to different read orders are disclosed. A system parses a binary file representing an electronic document to identify characters and metadata associated with the characters. The system pre-sorts a character order of characters in each line of the electronic document to generate an ordered list of characters arranged according to the right-to-left reading order. The system performs a layout-mirroring operation to change a position of characters within the modified document relative to a right edge of the document and a left edge of the document. Subsequent to performing layout-mirroring, the system identifies native left-to-right reading-order text in-line with the native right-to-left reading-order text.

Abstract: Techniques are disclosed herein for training and deploying a named entity recognition model. The techniques include implementing a nested labeling scheme for named entities within the training data and then training a machine learning model on the training data The techniques further include extracting an entity hierarchy for a predicted class based on a hierarchical template associated with a composite label, where the predicted class is representative of multiple named entity classes comprising at least a parent class and a child class associated with the composite label. The techniques further include increasing the volume of training data via data mining for sequence tags in a language corpus and then training a machine learning model on the training data.

Abstract: Techniques are disclosed for augmenting training data for training a machine learning model to generate database queries. Training data comprising a first training example comprising a first natural language utterance, a logical form for the first natural language utterance, and associated first metadata is obtained. From the first training example, a template utterance is generated. A second natural language utterance is generated by filling slots in the template utterance based on a database schema and database values. Updated metadata is produced based on the first metadata and the second natural language utterance. A second training example is generated, comprising the second natural language utterance, the logical form for the first natural language utterance, and the updated metadata. The training data is augmented by adding the second training example. A machine learning model is trained to generate a database query comprising the database operation using the augmented training data set.

Abstract: Techniques are disclosed herein for using named entity recognition to resolve entity expression while transforming natural language to a meaning representation language. In one aspect, a method includes accessing natural language text, predicting, by a first machine learning model, a class label for a token in the natural language text, predicting, by a second machine-learning model, operators for a meaning representation language and a value or value span for each attribute of the operators, in response to determining that the value or value span for a particular attribute matches the class label, converting a portion of the natural language text for the value or value span into a resolved format, and outputting syntax for the meaning representation language. The syntax comprises the operators with the portion of the natural language text for the value or value span in the resolved format.

Abstract: Techniques are disclosed herein for adaptive training data augmentation to facilitate training named entity recognition (NER) models. Adaptive augmentation techniques are disclosed herein that take into consideration the distribution of different entity types within training data. The adaptive augmentation techniques generate adaptive numbers of augmented examples (e.g., utterances) based on the distribution of entities to make sure enough numbers of examples for minority class entities are generated during augmentation of the training data.

Abstract: Systems and methods identify whether an input utterance is suitable for providing to a machine learning model configured to generate a query for a database. Techniques include generating an input string by concatenating a natural language utterance with a database schema representation for a database; providing the input string to a first machine learning model; based on the input string, generating, by the first machine learning model, a score indicating whether the natural language utterance is translatable to a database query for the database and should be routed to a second machine learning model, the second machine learning model configured to generate a query for the database based on the natural language utterance; comparing the score to a threshold value; and responsive to determining that the score exceeds the threshold value, providing the natural language utterance or the input string to the second machine learning model.

Abstract: Techniques are disclosed herein for converting a natural language utterance to an intermediate database query representation. An input string is generated by concatenating a natural language utterance with a database schema representation for a database. Based on the input string, a first encoder generates one or more embeddings of the natural language utterance and the database schema representation. A second encoder encodes relations between elements in the database schema representation and words in the natural language utterance based on the one or more embeddings. A grammar-based decoder generates an intermediate database query representation based on the encoded relations and the one or more embeddings. Based on the intermediate database query representation and an interface specification, a database query is generated in a database query language.

Abstract: A system for analyzing security threat changes of proposed changes to an infrastructure environment. For example, system and approaches for determining actions to be performed based on security threat changes corresponding to proposed changes to the infrastructure environment is disclosed.

Abstract: Techniques for generating formatting tags for textual content obtained from a source electronic document are disclosed. A system parses a digital file to obtain information about characters in an electronic document. The system applies tags to text generated based on the textual content of the electronic document by creating segments of textually-consecutive characters and applying corresponding text formatting style tags to the segments. The system further identifies segments of text overlapping bounding boxes in the electronic document. The system generates textual content including a segment of text and a corresponding hyperlink associated with the segment of text. The system further generates textual content by selectively applying line breaks from the source electronic document in the textual content.

Abstract: Techniques are disclosed herein for calibrating confidence scores of a machine learning model trained to translate natural language to a meaning representation language. The techniques include obtaining one or more raw beam scores generated from one or more beam levels of a decoder of a machine learning model trained to translate natural language to a logical form, where each of the one or more raw beam scores is a conditional probability of a sub-tree determined by a heuristic search algorithm of the decoder at one of the one or more beam levels, classifying, by a calibration model, a logical form output by the machine learning model as correct or incorrect based on the one or more raw beam scores, and providing the logical form with a confidence score that is determined based on the classifying of the logical form.

Abstract: Techniques for automatically preventing execution plan regressions are provided. In one technique, in a first user database session, in response to receiving a first database statement, a first execution plan is generated and, while executing the first execution plan, first performance data that indicates one or more first performance metrics of executing the first execution plan is recorded. In response to receiving a second database statement, where the first execution plan may be used to generate a result for the second database statement, a second execution plan is generated and second performance data that indicates one or more second performance metrics of executing the second execution plan is recorded. A comparison between the first performance data and the second performance data is performed. Based on the comparison, it is determined whether the second execution plan will be stored for future use to process a database statement.

Abstract: Upon receiving a query at a local database server from a client directed to a LOB stored at a remote database server, a remote LOB locator is created comprising a local LOB locator indicating the LOB's location and a database link associated with the remote database server. The remote LOB locator is communicated to the client, which submits a request to the local database server to perform an operation directed to the LOB. The request is forwarded to the remote database server using the remote LOB locator, where the operation is performed. This mechanism is extended to work with sharded databases. Character set conversions are minimized when returning data to the client from the remote database server. Remote LOB locators may serve as bind variables for DML statements directed to local or remote tables. Local LOB locators may serve as bind variables for DML statements directed to remote tables.

Abstract: Systems, methods, and other embodiments associated with performing maintenance of a hardware node and relocating (live migrating) virtual machine (VM) instances in a multi-tenant, hyper scale, public Cloud service are described. In one embodiment, the method includes finding a suitable hardware node for hosting one or more VM instances from a hardware node selected for maintenance, preparing it for hosting VMs, and moving (live migrating) all virtual machines from the hardware node to the hosting hardware node.

Abstract: Example embodiments facilitate creation of efficient tree structures (and strategic modification of and manipulation of existing tree structures), such that the resulting tree structures enable rapid computing operations, including, but not limited to, rapid relational set operations (e.g., joining trees, comparing tree structures, determining structural similarities of trees, adding tree nodes, removing or replacing tree nodes, accessing tree data, updating tree data, and so on). An example method relates to a method and/or system for creating and manipulating tree data structures (also simply called trees herein) as, for example, in relational databases. One embodiment uses compact bit-wise path encoding that stores structural data related to tree branches to which a current node belongs.

Abstract: Disclosed herein are techniques for using a generative adversarial network (GAN) to train a semantic parser of a dialog system. A method described herein involves accessing seed data that includes seed tuples. Each seed tuple includes a respective seed utterance and a respective seed logical form corresponding to the respective seed utterance. The method further includes training a semantic parser and a discriminator in a GAN. The semantic parser learns to map utterances to logical forms based on output from the discriminator, and the discriminator learns to recognize authentic logical forms based on output from the semantic parser. The semantic parser may then be integrated into a dialog system.

Abstract: A method for providing updated network slice information to a network slice selection function (NSSF includes registering, by a network slice management function (NSMF) with a network function (NF) repository function (NRF), an NF profile corresponding to the NSMF and subscribing, by the NSMF with the NRF, for status updates corresponding to NF instances belonging to network slice instances created by the NSMF. The method further includes receiving, by the NSMF from the NRF, a notification message including one or more network traffic load level updates related to at least one of the NF instances, processing the one or more network traffic load level updates to generate network slice instance configuration information for at least one of the network slice instances, and providing, by the NSMF, the network slice instance configuration information to a NSSF managing the at least one of the network slice instances.

Abstract: A feedback button is configured to receive a feedback expression characterizing a user experience and submit information in a user interface with a single action. When the user hovers an input cursor near the feedback button, icons/text may be displayed that indicate different types of feedback expressions that may be submitted. As the user moves the cursor back and forth over the button, the icons for the different feedback expressions may be emphasized indicating locations where the button may be clicked to submit specific feedback. When the user is satisfied with the displayed feedback expression, the user may click the feedback button at the current location to submit the feedback expression and perform the final submit action for other data in the user interface.

Abstract: In an embodiment, multiple computers cooperate to retrieve content from tables in a relational database. Each table contains respective rows. Each row contains a vertex of a graph. Many high-degree vertices are identified. Each high-degree vertex is connected to respective edges in the graph. A count of the edges of each high-degree vertex exceeds a degree threshold. A central computer detects that all vertices in a high-degree subset of tables are high-degree vertices. Based on detecting the high-degree subset of tables, multiple vertices of the graph that are not in the high-degree subset of tables are replicated. Within local storage capacity limits of the computers, this degree-based replication may be supplemented with other vertex replication strategies that are schema based, content based, or workload based. This intelligent selective replication maximizes system throughput by minimizing graph data access latency based on data locality.