Abstract: Artificial intelligence (AI) technology can be used process natural language statements to facilitate the automated generation of narratives about data sets that achieve a desired communication goal without any need for a user to directly author computer code. This AI technology permits NLG systems to determine the appropriate content for inclusion in the narrative in a manner that will satisfy the desired communication goal.

Abstract: Implementation(s) for multi-factor network segmentation are described. A plurality of packets at a higher layer of a network stack is processed, where at least one packet of the plurality of packets was previously determined, as part of processing the at least one packet at lower layers of the network stack, to be authorized to be processed by the higher layer. Specifically, responsive to successful authentication of a cryptographic certificate received during the handshake process, a second service is identified from the cryptographic certificate. It is determined, based on a security policy, that the second service is authorized to access the first service. Responsive to the determination, a configuration is caused such that packets sent using the source address are now authorized to be processed by the higher layer.

Abstract: The present disclosure is related to automatically, based on contextual information and without needing explicit input from a user, modifying one or more settings associated with presenting a notification. In examples, settings may include automatically suspending notification presentation or automatically overriding a notification setting that suspends notification presentation. In addition, contextual information may include, among other things, information related to a computing device (e.g., device location or network signal strength), a rate of user interaction or engagement with an application (e.g., rate of information sharing, user reactions, etc.), and/or a calendar or schedule of a user. In examples, the contextual information may be analyzed (e.g., based on comparison to a threshold) to determine whether a condition is met, and based on the analysis, the one or more settings may be modified.

Abstract: Systems, methods, and apparatuses for isolating information from a cloud services provider hosting an enterprise application software, using a distributed ledger. For example, according to one embodiment there is a system having at least a processor and a memory therein executing within a host organization to host an enterprise application software for an enterprise, receive information input from a customer of the enterprise at a user interface for the enterprise application software, transfer the customer-provided information to a distributed ledger accessible to the system, receive information input from the enterprise at an enterprise interface for the enterprise application software, transfer the enterprise-provided information to the distributed ledger accessible to the system, but maintain no copy of the customer-provided information nor copy of the enterprise-provided information in a permanent store accessible to the system and the host organization.

Abstract: Embodiments described herein provide systems and methods for learning representation from unlabeled videos. Specifically, a method may comprise generating a set of strongly-augmented samples and a set of weakly-augmented samples from the unlabeled video samples; generating a set of predictive logits by inputting the set of strongly-augmented samples into a student model and a first teacher model; generating a set of artificial labels by inputting the set of weakly-augmented samples to a second teacher model that operates in parallel to the first teacher model, wherein the second teacher model shares one or more model parameters with the first teacher model; computing a loss objective based on the set of predictive logits and the set of artificial labels; updating student model parameters based on the loss objective via backpropagation; and updating the shared parameters for the first teacher model and the second teacher model based on the updated student model parameters.

Abstract: Methods, computer readable media, and devices to automatically construct kill-chain from security alerts are disclosed. One method may include collecting a plurality of security alerts, receiving a selection of a high severity security alert associated with a node and a user from among the plurality of security alerts, creating a security narrative for the high severity security alert by providing a set of historical security alerts to a deep learning architecture, the set including security alerts selected based on a relation to the node and the user, and identifying a subset of the set of historical security alerts, including security alerts relevant to the high severity security alert, in a reverse time order by the deep learning architecture, and providing the security narrative as part of a response to the high severity security alert.

Abstract: A federation management service may generate a federated application program interface (API) based on multiple data sources. The federation management service may detect changes to one or more of the multiple data sources, which may result in generation of a new version of the federated API. The changes to the data sources may be documented in a change event log. The change event log may be used to recreate a prior version of the federated API by applying changes of the change event log to a federated schema corresponding to the federated API. A snapshotting technique may capture the state of the federated schema at various points in time, and snapshots may be used to regenerate a prior version of the federated API using the change events of the change event log.

Abstract: Systems and methods are provided for receiving a request at a database system that includes a primary node and one or more standby nodes. When the request is determined to be a read-only request and the previous transaction has been replicated to the standby node, the request may be transmitted to one of the one or more standby nodes to perform the read-only operation to retrieve data of the request and transmitting the retrieved data. When the request is determined to be a read-only request and the previous transaction has not been replicated to the standby node, the read-only operation is performed to retrieve data of the request from the primary node and transmitting the retrieved data.

Abstract: Methods, computer readable media, and devices for rapid prototyping of both user experience and technical features in a combined approach. One method may include creating a set of known user experience (UX) components including a user interface element representing at least one of a data element and a data interaction, creating a UX screen including a set of UX elements within a UX prototyping tool, translating the UX screen into a set of standardized UX components, publishing the set of standardized UX components by mapping the set of standardized UX components to synthetic data, and validating a technical aspect of the published set of standardized UX components based on user interaction with the published set of standardized UX components.

Abstract: Embodiments described herein provide a method for text summarization. The method includes receiving a training dataset having at least an uncompressed text, a compressed text, and one or more information entities accompanying the compressed text. The method also includes generating, using a perturber model, a perturbed text with the one or more information entities being inserted into the compressed text. The method further includes training the perturber model based on a first training objective, and generating, using the trained perturber model, a perturbed summary in response to an input of a reference summary. The method further includes generating, via an editor model, a predicted summary by removing information from the perturbed summary conditioned on a source document of the reference summary, and training the editor model based on a second training objective.

Abstract: A database entry may be stored in a container in a database table corresponding with a partition key. The partition key may be determined by applying one or more partition rules to one or more data values associated with the database entry. The database entry may be an instance of one of a plurality of data object definitions associated with database entries in the database. Each of the data object definitions may identify a respective one or more data fields included within an instance of the data object definition.

Abstract: An application server may identify a set of functions for the source code associated with an uninterruptable software service. In some examples, the subset of the set of functions may include one or more functions having a computational expense greater than a threshold. The application server may create a compilation template to compile a subset of the set of functions to a shared library, and may perform an instrumentation to identify the subset of the set of functions based on the shared library. The application server may create an optimized version of the shared library based on performing the instrumentation. The application server may then change one or more portions of the source code to repoint to the optimized version of the shared library based on creating the optimized version of the shared library.

Abstract: A method by one or more electronic devices to provision an infrastructure for a machine learning application in a multi-tenant on-demand serving infrastructure. The method includes storing a plurality of templates, wherein each of the plurality of templates indicates a scoring interface, a web server, a definition of a continuous integration pipeline, and a definition of a continuous deployment pipeline, receiving a request to provision the infrastructure for the machine learning application using a specified template from the plurality of templates, and provisioning the infrastructure for the machine learning application using the specified template to create a version control system repository, a continuous integration pipeline, and a continuous deployment pipeline.

Abstract: Embodiments described herein provide training a prompt generator for text classification. A first training dataset associated with a first plurality of class labels is received for a first training process. For a first instance of the first training dataset, a set of labels of interest is generated by sampling from a set of possible class labels including the first plurality of class labels. The prompt generator generates a first prompt based on the set of labels of interest. A pretrained language model generates a task output in response to an input of the first instance prepended with the first prompt. A loss objective is generated based on the task output and the set of labels of interest. Parameters of the prompt generator are updated based on the computed loss function via backpropagation while the pretrained language model is frozen.

Abstract: Disclosed herein are system, method, and computer program product embodiments for automatic non-code test suite generation of an application programming language (API) specification. An embodiment operates by receiving a specification of an API, wherein the API comprises a plurality of endpoints. The embodiment generates, using a parser, an abstraction model corresponding to the specification of the API, wherein the abstraction model comprises a plurality of entities corresponding to the plurality of endpoints. The embodiment identifies, based on the abstraction model, an operation that is applicable to an entity of the plurality of entities. The embodiment then generates a functional test based on a use case corresponding to the entity and the operation.

Abstract: Method, apparatus and computer program product for multi-modal user authorization are described herein. For example, the apparatus includes at least one processor and at least one non-transitory memory including program code. The at least one non-transitory memory and the program code are configured to, with the at least one processor, receive a request to enable a first client device associated with a first user profile to access a group-based communication system; determine a first access privilege status for the first user profile; in response to determining that the first access privilege status is a general access privilege status, transmit, to the first client device, first user interface data associated with a general access user interface; and in response to determining that the first access privilege status is a limited access privilege status, transmit, to the first client device, second user interface data associated with a limited access user interface.

Abstract: The technology disclosed provides a quasi-recurrent neural network (QRNN) encoder-decoder model that alternates convolutional layers, which apply in parallel across timesteps, and minimalist recurrent pooling layers that apply in parallel across feature dimensions.

Abstract: Embodiments described herein provide an automated testing pipeline for providing a testing dataset for testing a trained neural network model trained using a first training dataset. A first testing dataset for the trained neural network including a first plurality of user queries is received. A dependency parser is used to filter the first plurality of user queries based on one or more action verbs. A pretrained language model is used to rank the remaining user queries based on respective relationships with queries in the first training dataset. Further, user queries that are classified as keyword matches with the queries in the first training dataset using a bag of words classifier are removed. A second testing dataset is generated using the ranked remaining user queries. Testing outputs are generated, by the trained neural network model, using the second testing dataset.