Abstract: Methods, and apparatuses are provided for access limitations to a network in a session using a formatted web token. The method includes: formatting a web token by a schema to create a formatted web token for user access to the network; receiving a log-in request for the user access to the network server via an app hosted by a computing device remotely located to the network server; in response to receipt of a user access request, creating the session by the network server with network limitations for user access to data and resources of the network; passing the formatted web token to a client for enabling user access to the data and resources of the network; decoding payload data of the formatted web token at the client to authenticate the user access; and enabling the client with access limitations based on decoded payload data.

Abstract: Methods and systems are provided for automatically determining a need to invoke functionality of a feature of an application while using a primary application and for automatically invoking that functionality without user action. When input information is input into the primary application via a user interface of a user system, it can be received by an application server from a sender process at the user system, and a pre-fix that occurs before a text string can be identified. The text string that follows the pre-fix can be extracted by performing a matching analysis against a library of keywords and extracting a matching keyword. The text string comprises extracted text from the input information. The matching keyword can be matched to an entry that is mapped to a command that can be executed to generate an action that automatically invokes the functionality of the feature of the application without user action.

Abstract: Verification of discriminative models includes receiving an input; receiving a prediction from a discriminative model for the input; encoding, using an encoder, a latent variable based on the input; decoding, using a decoder, a reconstructed input based on the prediction and the latent variable; and determining, using an anomaly detection module, whether the prediction is reliable based on the input, the reconstructed input, and the latent variable. The encoder and the decoder are jointly trained to maximize an evidence lower bound of the encoder and the decoder. In some embodiments, the encoder and the decoder are further trained using a disentanglement constraint between the prediction and the latent variable. In some embodiments, the encoder and the decoder are further trained without using inputs that are out of a distribution of inputs used to train the discriminative model or that are adversarial to the discriminative model.

Abstract: A method and system uses a test cloud system for monitoring cloud to cloud performance. The method includes initiating a first trust relationship by the test cloud system with a first target cloud system, receiving a success indicator for the trust relationship, in response to the first target cloud verifying the test cloud system is whitelisted, generating a security token using a private key of the test cloud system, and invoking a function of an application programming interface of the first target cloud using the security token to validate functionality of the function of the application programming interface of the first target cloud.

Abstract: Database systems and methods are provided for data migration between source and target computing systems using a configurable transformation service. The configurable transformation service obtains transformation configuration metadata associated with the particular combination of source and target computing systems, obtains a subset of source data in accordance with the transformation configuration metadata and transforms the subset of source data from a format associated with the source computing system to an intermediate, platform-independent format. The service transforms the subset of source data from the intermediate format to a destination format associated with the target computing system in accordance with the transformation configuration metadata and maintains the transformed subset of source data in the destination format in association with the target computing system.

Abstract: Described herein are systems, apparatus, methods and computer program products configured for image detection/recognition of products. The disclosed systems and techniques utilize video data to provide the necessary number of images and view angles needed to train a machine learning product detection/recognition system to recognize a specific product within later provided images. In various embodiments, a user may provide video data and the video data may be transformed in a manner that may aid in training of the machine learning system.

Abstract: A method for providing interoperability between a plurality of security services and target applications by an interoperability service. The method includes receiving a request from one of the plurality of security services to perform a task on a target application, preparing a unified data model for interaction with the target application, determining and organizing data connections to perform the task on the target application, generating a set of requests using the unified data model based on the task and utilizing business logic of the interoperability service for the data connections with the target application, transforming the set of requests into commands and data structures specific to the target application, and sending the set of requests on respective data connections with the target application.

Abstract: Embodiments for training a neural network using sequential tasks are provided. A plurality of sequential tasks are received. For each task in the plurality of tasks a copy of the neural network that includes a plurality of layers is generated. From the copy of the neural network a task specific neural network is generated by performing an architectural search on the plurality of layers in the copy of the neural network. The architectural search identifies a plurality of candidate choices in the layers of the task specific neural network. Parameters in the task specific neural network that correspond to the plurality of candidate choices and that maximize architectural weights at each layer are identified. The parameters are retrained and merged with the neural network. The neural network trained on the plurality of sequential tasks is a trained neural network.

Abstract: Disclosed are some implementations of systems, apparatus, methods and computer program products for implementing a software development tool that enables a set of computer-readable instructions to be requested and obtained in association with an element of a process flow represented in a layout. A set of specifications can be submitted in association with a request, where the set of specifications includes input-output value pairs. In response, the system can automatically generate a first set of computer-readable instructions according to the set of specifications, where the first set of computer-readable instructions implements a mapping between the input-output value pairs. In addition, the system can obtain a second set of computer-readable instructions according to the set of specifications, either from pre-existing software code or from individual(s) (e.g., software developer(s)) to whom the system has sent a request for computer-readable instructions.

Abstract: A method is provided for processing a query. In some embodiments, the method includes creating a historical schema defining an ordered set of fields based on a current schema of an object and a field modification of the current schema. The method includes inserting, based on the field modification, a new field at an end of the historical schema and archiving records into a second storage device. The method includes receiving a query, which includes a command specifying a subset of fields and which indicates a non-current field. The method includes searching in the second storage device for archive records having a second schema including the subset of fields and the non-current field. The first and second schemas follow the field order in accordance with the historical schema. The method further includes accessing the subset of fields and the non-current field in accordance with the command.

Abstract: Systems and methods are provided for near-zero-cost (NZC) query framework or approach for differentially private deep learning. To protect the privacy of training data during learning, the near-zero-cost query framework transfers knowledge from an ensemble of teacher models trained on partitions of the data to a student model. Privacy guarantees may be understood intuitively and expressed rigorously in terms of differential privacy. Other features are also provided.

Abstract: Embodiments described herein provide systems and methods for an Explicit Memory Tracker (EMT) that tracks each rule sentence to perform decision making and to generate follow-up clarifying questions. Specifically, the EMT first segments the regulation text into several rule sentences and allocates the segmented rule sentences into memory modules, and then feeds information regarding the user scenario and dialogue history into the EMT sequentially to update each memory module separately. At each dialogue turn, the EMT makes a decision among based on current memory status of the memory modules whether further clarification is needed to come up with an answer to a user question. The EMT determines that further clarification is needed by identifying an underspecified rule sentence span by modulating token-level span distributions with sentence-level selection scores. The EMT extracts the underspecified rule sentence span and rephrases the underspecified rule sentence span to generate a follow-up question.

Abstract: Systems and methods are described for applying a plurality of data points of a time series data set representing values of a metric measuring performance of a cloud computing service to a machine learning model to predict a forecast of a most likely value of the metric at a selected future time. The method includes determining whether the plurality of data points of the time series data set are anomalies according to the machine learning model and the forecast and generating a collective anomaly from the anomalies when the plurality of data points is determined to be anomalies. The method further includes determining whether the collective anomaly does not meet one or more cloud computing service level objective (SLO) threshold requirements and sending an alert when the collective anomaly does not meet one or more cloud computing SLO threshold requirements.

Abstract: Methods and systems are provided for generating an interactive simulation representing one or more assets based on one or more asset records. Based on information from asset records stored at a database system of a cloud-based computing system, an asset simulator module, executed at a cloud-based computing system, can generate one or more simulated representations of the assets. A simulator application executed at the cloud-based computing system can augment the simulated representations of the assets with (at least) additional information from the asset records stored in the database system, and generate a user interface that presents an interactive simulation of the assets. The user interface can include the simulated representations of the assets with the additional information from the asset records stored in the database system.

Abstract: A self-healing build pipeline architecture for a software application build job across a distributed computer platform comprises a public API that receives configuration data describing the build job, stores the configuration data in a decentralized database, serves requests to/from a pipeline tracker API, and stores states of build pipelines during the build job. The decentralized database stores the configuration data and a project identifier for the build, and metadata regarding states of the build pipelines collected across the distributed computer platform. The pipeline tracker API runs local to the build environment in the distributed computer platform and sends a build status to public API for updating the decentralized database. For any failures in any of the build pipelines, the state is retrieved from the decentralize database and a new build pipeline is triggered locally that resumes from the failed state to provide a self-healing build pipeline architecture.

Abstract: System and methods are described for automated recovery from errors occurring during replication of a database. The method includes getting text from one or more log files generated during database replication processing in a cloud computing environment, transforming the text into a structured language form represented by vectors, and identifying patterns in the vectors. The method further includes classifying one or more errors based on the identified patterns using supervised learning as either a recoverable error or an unrecoverable error, analyzing the one or more errors to determine one or more recovery jobs associated with database replication processing in the cloud computing environment for each of the recoverable errors, and invoking the one or more recovery jobs.

Abstract: Techniques and structures to prevent exhaustion of a database connection pool, including receiving a plurality of events from a plurality of application servers operating as a database connection pool, categorizing each of the plurality of events, including generating a categorization message for each of the plurality events, wherein each categorization message includes one or more fields having contextual information to identify an event type to which an event is associated, analyzing the categorization messages to generate an event record; and generating a report based on the event record including one or more performance measurements to indicate an effectiveness of management of the database connection pool.

Abstract: Disclosed herein are system, method, and computer program product embodiments for facilitating access to and incorporation of APIs within an application during development, while ensuring that the API satisfies quality constraints. These quality constraints are controlled by the application of rules, which result in a maturity score for the API, in accordance with embodiments. These rules may be provided by a variety of sources for use in scoring the API at various stages. For example, a developer group may control access to developers within the group by applying a ruleset that restricts APIs for applications written by the developer group to a threshold score. This threshold score can limit or restrict APIs that are offered to a developer by an API exchange. Additionally, a ruleset may be applied to enforce a minimum score for submission of an API for inclusion within an API exchange.