Abstract: A machine-learning model may be previously trained with a supervised learning algorithm to identify whether a pair of labels provided as input are similar. A locality sensitive hashing forest (LSH) may be generated for the set of candidate labels. When a user later identifies an input label (e.g., by search query, by interface selection, etc.) the input label may be used to query the LSH forest to identify a subset of the candidate labels. This subset may be used to generate respective pairs comprising the input label, one of the subset candidate labels, and a corresponding feature set generated for the pair. This data may be provided to the model to identify a degree to which the pair of labels are similar. The user may be provided one or more recommendations including similar terms identified from the model's output.

Abstract: In accordance with an embodiment, described are systems and methods for real time data aggregation in a virtual cube in a multidimensional database. A method can provide, at a computer that includes one or more microprocessors, a multidimensional database server executing on the computer, wherein the multidimensional database server supports at least one multidimensional cube, a data source associated with the multidimensional cube, and at least one source of real-time data accessible by the data source. The method can query, by the multidimensional cube, the data source, the query comprising an identification of the at least one source of real-time data. The method can provide, by the data source, on-demand access to the multidimensional cube to the at least one source of real-time data. The method can use targeted data from the at least one source of real-time data within the multidimensional cube without being stored therein.

Abstract: Systems, methods, and other embodiments associated with a REST API parser for test automation are described. In one embodiment, a method for automating REST API testing includes parsing a test case to extract values for parts of a REST API request and a value for at least one validation parameter; automatically composing the REST API request at least in part from the extracted values for the parts of the REST API request; automatically submitting the REST API request for execution by an application; retrieving a value of a result of the execution of the REST API request from the application; determining that the value for the validation parameter(s) and the value of the result are a match; and generating an indication that the REST API is valid for the test case.

Abstract: Techniques are described herein for simulating triggers in a database. A database server receives a query that targets a database managed by the database server. The database server is registered with a plugin that determines whether the query is a particular type of query. If so, plugin sends a message to an external process that includes features of the query. The database server can also execute the query regardless of whether the particular type of query is detected.

Abstract: Fast modern interconnects may be exploited to control when garbage collection is performed on the nodes (e.g., virtual machines, such as JVMs) of a distributed system in which the individual processes communicate with each other and in which the heap memory is not shared. A garbage collection coordination mechanism (a coordinator implemented by a dedicated process on a single node or distributed across the nodes) may obtain or receive state information from each of the nodes and apply one of multiple supported garbage collection coordination policies to reduce the impact of garbage collection pauses, dependent on that information. For example, if the information indicates that a node is about to collect, the coordinator may trigger a collection on all of the other nodes (e.g., synchronizing collection pauses for batch-mode applications where throughput is important) or may steer requests to other nodes (e.g., for interactive applications where request latencies are important).

Abstract: A system and method is described for use with a multidimensional database computing environment to provide support for parallel calculation of multidimensional slices. Users are able to specify a set of slices and a number of parallel threads to employ. The multidimensional database environment generates tasks that include calculations and/or aggregations, which are able to be executed by the system in parallel. Also described herein are mechanisms of utilizing variables within the calculations performed by respective threads, and combining thread variables after execution.

Abstract: Techniques for component-based software engineering, and more particularly, to techniques for enhancing component based development systems by making components self-aware with respect to the applications that they may be consumed in or other peer components within the applications. An exemplary technique includes determining an addition of one or more components to an interface of an application; identifying an asset definition within metadata of a component of the one or more components added to the interface of the application, where the asset definition provides self-wiring or self-configuring instructions for an asset that needs to be created for the component within a predefined scope when the component is added to the interface; and creating the asset for the component based on the asset definition.

Abstract: Permissions on a resource hierarchy of a process are propagated. The resource hierarchy of the process is accessed. The resource hierarchy includes multiple nodes arranged in level and each of the nodes is associated with a separate resource from a plurality of resources. A permission for a user is propagate from a first node to one or more lower nodes arranged below the first node in the resource hierarchy. The user is enabled to interact with each resource associated with the first node and the one or more of the lower nodes based on the propagated permission.

Abstract: An anomaly service receives log data from nodes in a computing environment, which includes a sequence of information indicative of log messages produced by the nodes. The anomaly service identifies dominant patterns in the sequence of information that are representative of non-anomalous blocks of the log messages. Having identified the dominant patterns, the service is able to extract the non-anomalous blocks from the log data to reveal anomalous blocks that do not fit the dominant patterns. The service may then generate anomaly vectors based on the anomalous blocks, which can be distributed to the nodes to detect anomalies.

Abstract: The present invention relates to hash join acceleration. In an embodiment, a computer receives a request to join build data rows with probe data rows. Each data row is associated with a respective join key value. From multiple buckets of a hash table, a bucket is selected that contains entries of multiple respective join key values in respective locations within the bucket. Whether or not the bucket contains an entry of the join key value of a build data row is detected. While building, multiple locations within the bucket are concurrently inspected to find an empty location in which to store the entry of the join key value of the build data row. The bucket is concurrently probed to detect whether or not the entries in the bucket include an entry of a join key value of a probe data row. For the join request, a response is sent that is based on the concurrent probing of the bucket.

Abstract: Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

Abstract: The present disclosure relates generally to a data integration system that integrate an object based data integration tool, such a GUI-based data integration tools, with version control systems using a relational database repository for persistence. Examples of distributed version control systems include Git, Mercurial, and Bazaar, and examples of centralized version control systems include Subversion, CVS etc. in centralized or distributed environments.

Abstract: A custom client authenticator is installed on non-server entity. The custom client authenticator authorizes communication between a client application of a client and requested entities of a server. A native authentication technology for the client is different than a native authentication technology for the server. The custom client authenticator is registered in a descriptor file that is accessible by the server. The custom client authenticator generates an authorization token for the client. The authorization token is transmitted as part of a request message for the client application to the server. The custom client authenticator validates the authorization token on behalf of the server. The request message is forwarded to the requested entities executing on the server based on the validating.

Abstract: Information about a set of maintenance tasks and time windows includes a cost value per task per time window. Based on the information, a data model generator generates a data model, including task elements; time elements; cost elements; a total cost element; a constraint that requires each task element be assigned a time window from a respective domain, such that each time element is assigned a task count from a respective domain; a constraint that requires each cost element be assigned a cost value associated with a time window assigned (or to be assigned) to a task element corresponding to the cost element; and a constraint that requires the total cost element be assigned a total cost value that is a sum of the cost values assigned (or to be assigned) to the cost elements. Based on the data model, a CP solver determines a proposed maintenance schedule.

Abstract: The present disclosure relates to system and techniques for prediction of failures in resources deployed in a data plane of a cloud based infrastructure. The resource are selected from a plurality of cloud based resources arranged in a hierarchical manner and allocated to a client device. A predictor employs a first prediction model to obtain a first prediction of a failure of a resource, and a second prediction model to obtain a second prediction of the failure of the resource. Weights are assigned to the first prediction and second prediction based at least in part on a criterion. The predictor computes an overall prediction of the failure of the resource based at least in part on at least one of the first prediction, the second prediction or the respective weights assigned to the predictions. The overall prediction is utilized for restoring the failure of the resource.

Abstract: Implementations generally relate to software testing system and method for automatically generating, deploying, and monitoring software tests employed to test software applications and/or software process flows. In implementations, a system generates software application tests and varies such tests used based on several criteria including monitoring a software application processing defined inputs and outputs to determine which parts of the application are being tested, which parts of the software application are not being tested, user interactions, metadata, type of software application, use computing environment, etc. The system may be configured to adapt testing, either pre or post production, to allow, user interactions of one or more running software applications to be used to provide feedback on which additional tests to add and/or subtract automatically to testing sequences.

Abstract: Techniques described herein provide a handshake mechanism and protocol for notifying an operating system whether system hardware supports persistent cache flushing. System firmware may determine whether the hardware is capable of supporting a full flush of processor caches and volatile memory buffers in the event of a power outage or asynchronous reset. If the hardware is capable, then persistent cache flushing may be selectively enabled and advertised to the operating system. Once persistent cache flushing is enabled, the operating system and applications may treat data committed to volatile processor caches as persistent. If disabled or not supported by system hardware, then the platform may not advertise support for persistent cache flushing to the operating system.

Abstract: Techniques for managing maximum request rates to shared system resources are disclosed. A system applies a machine learning model, such as a long short-term memory (LSTM) recurrent neural network (RNN) type model to historical maximum request rate data to determine a target maximum request rate for a particular client and a particular period of time. The system obtains the historical maximum request rate data from a distributed ledger, such as a blockchain. System clients may record modifications to their maximum request rates in the blockchain. The system modifies the maximum request rates associated with the system clients authorized to access shared resources based on the modified maximum request rates contained in the new blocks added to the blockchain.

Abstract: The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.

Abstract: A computer system may receive one or more requests for access to one or more cloud services and may store the one or more requests in a request log. The computer system may receive one or more access rules applicable to cloud service access rights. The computer system may aggregate the one or more requests of the request log to determine access requirements for a container, the container being configured to store one or more applications. The computer system may generate and store container access policies that define access of a container and the one or more cloud services, the container access policies based at least in part on the aggregated one or more requests and the one or more access rules. The computer system may send the container access policies to a request forwarder of a compute instance in a production environment.

Abstract: A secure, modular multi-tenant machine learning platform is configured to: receive untrusted code supplied by a first tenant; perform a security scan of the untrusted code to determine whether the untrusted code satisfies a set of one or more security requirements; responsive to determining that the untrusted code satisfies the security requirement(s): deploy the untrusted code to a runtime execution environment; deploy a machine learning model associated with the first tenant to the runtime execution environment, the untrusted code being configured to perform one or more functions using the machine learning model; receive a set of untrusted code supplied by a second tenant; perform a security scan of the untrusted code to determine whether the untrusted code satisfies the security requirement(s); and responsive to determining that the untrusted code does not satisfy the security requirement(s): refraining from deploying the untrusted code to a runtime execution environment.

Abstract: The present disclosure relates generally to an integrated machine learning platform. The machine learning platform can convert machine learning models with different schemas into machine learning models that share a common schema, organize the machine learning models into model groups based on certain criteria, and perform pre-deployment evaluation of the machine learning models. The machine learning models in a model group can be evaluated or used individually or as a group. The machine learning platform can be used to deploy a model group and a selector in a production environment, and the selector may learn to dynamically select the model(s) from the model group in the production environment in different contexts or for different input data, based on a score determined using certain scoring metrics, such as certain business goals.

Abstract: Techniques for on-the-fly adaptation of remembered set data structures are disclosed. Operations include initiating execution of an application thread and a garbage collection process for a heap memory including a first plurality of logical partitions, wherein each logical partition of the first plurality of logical partitions is associated with a remembered set data structure. While the application thread and the garbage collection process are executing, the system determines a set of characteristics. Based on the set of characteristics meeting threshold criteria for adjusting a first remembered set data structure corresponding to a first logical partition, the system identifies a first remembered set configuration corresponding to the first remembered set data structure, creates a replacement remembered set data structure based on the first remembered set configuration, and associates the replacement remembered set data structure with the first logical partition.

Abstract: In accordance with an embodiment, described herein is a system and method for delivery of content based on matching of user profiles with content metadata. The system enables delivery of personalized content, without the overhead of managing segment targeting rules, while providing content publishers or marketers with complete control over such personalization. A recommendation service or application program interface, provided by a computer, cloud computing environment, or other type of computer system, enables receipt and processing of requests, from client devices, for personalized content. A recommendation engine delivers content assets in response to a request from a client device. The recommendation engine determines a content channel and a user identity associated with the request, and then delivers content assets based on rules governing the matching of content asset metadata with the user profile. While content classification evolves over time, so also does the personalization of delivered content.

Abstract: Techniques for stitching application programming interfaces (APIs) to communicate between applications are disclosed. Stitching APIs includes transferring data obtained using an API corresponding to a source application and an API corresponding to a destination. A system obtains a mapping file that maps data objects maintained by the source application to data objects maintained by the destination application. When the system receives a data transfer request, the system: (a) identifies the source application and the destination application, (b) identifies the mapping between objects maintained by the source application and objects maintained by the destination application, and (c) identifies the API calls for the source application and the destination application for performing the data transfer. Based on the mapping, the system invokes API calls to a source application and a destination application to transmit the data.

Abstract: A computer program product, system, and computer implemented method for application-level redirect trapping and creation of NAT mapping to work with routing infrastructure for private connectivity in cloud and customer networks. The approach disclosed herein generally comprises a method of leveraging a reverse connection endpoint and IP address mapping controller to capture redirection messages from a private cloud or network (e.g., a service consumer network or a service consumer hybrid cloud). This allows at least the IP address mapping controller to manage a cloud networking infrastructure to provide for a service provider network (e.g., a public cloud) to support applications that overcome the isolation requirements of a private cloud or network to perform useful work. For example, without saddling the private cloud or network user with a heavy pre-configuration burden, the approach disclosed herein supports redirection to dynamically determined IP addresses at the private cloud or network.

Abstract: The embodiments disclosed herein relate to concurrently displaying maps visualizing different subsets of transportation planning data. The concurrently displayed maps are automatically updated based on changes to the transportation planning data. A map locking functionality prevents the locked map from being updated based on changes to the transportation planning data. Other non-locked maps continue to be updated based on the changes to the transportation planning data. The map locking functionality allows for visualizing and comparing different states of the transportation planning data.

Abstract: Load Plan Generator (LPG) is a BIAPPS utility for generating ODI load plans based on desired subset of fact tables for loading BIAPPS Data Warehouse. The tool simplifies the configurations process by minimizing the manual steps and configurations and provides a guided list of configurations steps and checklists. The load plan components can include different sets of load plans that will be stitched together by the load plan generator to create one load plan for loading chosen fact groups in the warehouse sourcing from different transaction systems.

Abstract: Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.

Abstract: A method and apparatus for compression units in an index block is provided. A particular leaf block of a database index for a table is generated. The particular leaf block corresponds to a particular range of key values. A compression scheme is determined for a plurality of index entries with key values within the particular range. A compression unit that includes a plurality of compressed index entries is generated by applying the compression scheme to the plurality of index entries. Compression metadata describing the compression scheme is generated. The compression unit and the compression metadata is stored in the particular leaf block. In response to a request, at least a portion of the compression unit is decompressed to access one or more index entries.

Abstract: Described herein are dialog systems, and techniques for providing such dialog systems, that are suitable for use on standalone computing devices. In some embodiments, a dialog system includes a dialog manager, which takes as input an input logical form, which may be a representation of user input. The dialog manager may include a dialog state tracker, an execution subsystem, a dialog policy subsystem, and a context stack. The dialog state tracker may generate an intermediate logical form from the input logical form combined with a context from the context stack. The context stack may maintain a history of a current dialog, and thus, the intermediate logical form may include contextual information potentially missing from the input logical form. The execution subsystem may execute the intermediate logical form to produce an execution result, and the dialog policy subsystem may generate an output logical form based on the execution result.

Abstract: A cloud manager restores a backup of a distributed environment by comparing a stack of a current distributed environment to a backup stack of the backup of the distributed environment. The cloud manager identifies a difference between a first number of nodes in the first set of nodes specified in the first stack and a second number of nodes in the second set of nodes specified in the second stack. The cloud manager restores the backup of the distributed environment by modifying a number of nodes in the current distributed environment based on the identified difference between the first number of nodes and the number of nodes in the second stack.

Abstract: A method of generating and using a metadata files for integration flows may include analyzing definition files of integration flows to generate metadata files that include summary information for each of the integration flows. The method also includes extracting source-target relationships from the summary information for each of the integration flows and training a model using the plurality of source-target relationships. The method also includes receiving a source element from a current integration flow; providing the source element and characteristics of the current integration flow to the model; and receiving, from the model, recommended target elements to be connected to the source element in the current integration flow.

Abstract: In accordance with an embodiment, systems and methods can provide for load, aggregate, and batch calculation in in a multidimensional database environment. Metadata can be used to identify in which higher dimensions each leaf-level load is used. At such time, the data from the leaf level is aggregated to each of the higher dimensions where it is to be used. Then, the aggregate values are stored in memory until a completed sum (or other calculation) is completed at the higher levels. At such time, the aggregate values can be discarded from memory. The ability of the system to perform a load, aggregate, and batch calculation in one scan is particularly useful in processing very large sets of data, for example in big data environments, where it may not be desirable to load the data for all of the leaf nodes into current memory.

Abstract: Techniques for persisting user data across secure shell instances are provided. A method includes receiving a first request from a session manager service to establish a connection to a secure shell instance and restore a user block volume with corresponding backup user data. The method may include reserving an empty block volume. The method may also include transmitting a backup data identifier associated with the corresponding backup user data to a backup service and receiving the corresponding backup user data from the backup service. The method may further include providing the corresponding backup user data to the empty block volume to create a restore volume and transmitting a restore volume identifier corresponding to a data center identifier to the session manager service. The method may include receiving a second request to attach the restore volume to a reserved instance, the second request being received from the session manager service.

Abstract: Techniques are described for generating and applying mini-machine learning variants of machine learning algorithms to save computational resources in tuning and selection of machine learning algorithms. In an embodiment, at least one of the hyper-parameter values for a reference variant is modified to a new hyper-parameter value thereby generating a new variant of machine learning algorithm from the reference variant of machine learning algorithm. A performance score is determined for the new variant of machine learning algorithm using a training dataset, the performance score representing the accuracy of the new machine learning model for the training dataset. By performing training of the new variant of machine learning algorithm with the training data set, a cost metric of the new variant of machine learning algorithm is measured by measuring usage the used computing resources for the training.

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: Embodiments provide automatic representational state transfer application programming interface (“REST API”) document generation for System for Cross-domain Identity Management (“SCIM”) based resources. For each resource, embodiments identify all SCIM based resource type data files and corresponding schema, where each resource type data file includes resource type definition metadata. Embodiments identify available operations based on the resource type definition metadata and apply HyperText Markup Language (“HTML”) operations corresponding to the metadata based on an available Swagger Operation template engine. Embodiments convert SCIM based schema to Swagger schemas by iterating through each available schema and identifying attributes and preparing a Swagger attribute template engine to generate a Swagger based attribute definition to a Swagger template aggregator. Embodiments then generate Swagger JavaScript Object Notation (“JSON”) corresponding to the Swagger schemas.

Abstract: A cloud-based security solution that provides a robust and secure framework for managing and enforcing security policies related to various resources managed in the cloud is disclosed. The cloud-based security solution is implemented by a security zone policy enforcement system in a cloud service provider infrastructure. The system receives a request to perform an operation on a resource and determines a compartment associated with the resource. The system determines that the compartment is associated with a security zone and determines a set of one or more security zone policies applicable to the resource. The system then determines that the operation on the resource is permitted based on the set of one or more security zone policies and responsive to determining that the operation on the resource is permitted, allows the operation to be performed on the resource.

Abstract: Techniques are provided for augmenting training data using gazetteers and perturbations to facilitate training named entity recognition models. The training data can be augmented by generating additional utterances from original utterances in the training data and combining the generated additional utterances with the original utterances to form the augmented training data. The additional utterances can be generated by replacing the named entities in the original utterances with different named entities and/or perturbed versions of the named entities in the original utterances selected from a gazetteer. Gazetteers of named entities can be generated from the training data and expanded by searching a knowledge base and/or perturbing the named entities therein. The named entity recognition model can be trained using the augmented training data.

Abstract: Embodiments described herein include a log management system that detects and addresses excessive log patterns at runtime. In some embodiments, the log management system tracks cumulative attributes associated with patterns that repeat within a set monitoring files, such as a set of log or trace records. The cumulative attributes that are monitored may include the cumulative storage size and/or cumulative count of patterns detected in the monitoring files. The log management system may determine whether a triggering condition is satisfied for initiating a responsive action to reduce the amount of log data that is transmitted and/or stored. If the triggering condition is satisfied, then the log management system may remove one or more instances of the pattern from the monitoring files and/or perform other actions to reduce the overhead of logging operations.

Abstract: When a cloud services provider infrastructure (CSPI) receives a request from an administrator to perform an operation on an appliance, a load balancer may select a first server within the CSPI to process the request. If the first server does not have a connection with the appliance, the first server may generate a redirect response that includes server identification information identifying a particular server having a pre-established connection with the appliance. The first server may send the redirect response to the application that the administrator used to send the request. The application may then generate a second request that includes the server identification information, and send the second request to the CSPI. A load balancer in the CSPI may then forward the second request to the particular server, and the particular server may use the pre-established connection to send a request to the appliance requesting performance of the operation.

Abstract: Techniques discussed herein relate to implementing a distributed computing cluster (the “cluster”) including a plurality of edge devices (e.g., devices individually configured to selectively execute within an isolated computing environment). One edge device may be configured to operate as a head node of the cluster at a given time. A request for virtual resources of the cluster may be received from a user device and directed to the first edge device of the cluster. The first edge device may determine it is not operating as a head node of the cluster. The first edge device may determine that a second edge device of the cluster is operating as the head node. In response, the first edge device may forward the request to the second edge device, wherein forwarding the request to the second edge device causes the second request to be processed by the cluster.

Abstract: Plugins that are independently written are executed in a collaborative manner to analysis a log. A plugin executing with respect to a particular node of a hierarchical data structure determines values for a set of keys based on information of the particular node and/or any ancestor nodes, and information stored in a shared repository. The plugin stores the values for the keys as additional information of the particular hierarchical node and/or into the shared repository. The plugin does not access information of non-ancestor nodes when executing with respect to the particular hierarchical node. Each plugin writes into and retrieves from the shared repository using the shared naming convention, thereby sharing information. The sequence of execution of the plugins is not dependent on dependencies amongst the plugins. If a dependent plugin requiring an output from a requisite plugin is first executed, the dependent plugin is flagged as pending and subsequently re-executed.

Abstract: A computing device may access visually rich documents comprising an image and metadata. A graph, based on the image or metadata, can be generated for a visually rich document. The graph's nodes can correspond to words from the visually rich document. Features for nodes can be determined by the device. The device may generate model labeled graphs by assigning a pseudo-label to nodes using a pretrained model. The device may generate a plurality of graph labeled graphs by assigning a pseudo-label to nodes by matching a first node from a first graph to at least a second node from a second graph. The device may generate a plurality of updated graphs by cross referencing labels from the model labeled graphs and the graph labeled graphs. Until a change in labels is below a threshold, a model can be trained to perform key-value extraction using the updated graphs.

Abstract: Techniques are disclosed for managing dependencies in an orchestration service. A computer-implemented method can include operations performed by a declarative infrastructure provisioner (DIP). In some embodiments, the DIP parses configuration data associated with a computing system and generates a directed acyclic graph (DAG) for booting a first resource. The DAG may specify a dependency of the first resource on a capability of a second resource. The DIP may traverse the DAG and determine, based at least in part on the traversal, that the dependency has been reached. The DIP may publish, to a scheduling process, an indication that the first resource is awaiting availability of the capability of the second resource. In some embodiments, the DIP receives a subsequent indication that the capability is available, regenerates the DAG, and recommences traversal of the DAG. Additional operations for booting the first resource may be performed in accordance with the recommenced traversal.

Abstract: The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.

Abstract: Systems and methods for improving question-answering recall for complex, multi-sentence, convergent questions. More specifically, an autonomous agent accesses an initial answer that partly answers a question received from a user device. The agent represents the question and the initial answer as discourse trees. From the discourse trees, the agent identifies entities in the question that are not addressed by the answer. The agent forms an additional discourse tree from an additional resource such as a corpus of text. The additional discourse tree rhetorically connects a non-addressed entity with the answer. The agent designates this discourse tree as an imaginary discourse tree. When combined with the initial answer discourse tree, the imaginary discourse tree is used to generate an improved answer relative to existing solutions.

Abstract: An email message delivery system is described that provides a fast and reliable managed email delivery service using MTAs and proxy servers. An email message is selected by an MTA for processing. The MTA determines a sender and an intended recipient associated with the email message. The MTA then identifies a pool of usable source internet protocol addresses for the email message based on the sender and selects a particular source IP address from the pool. The MTA then identifies a particular proxy server that is configured to handle the selected particular source IP address and communicates to the particular proxy server, information including the particular source IP address and the destination IP address. The MTA then transmits the email message to the destination IP address using a connection established by the proxy server between the particular source IP address and the destination IP address.

Abstract: Methods, systems, and computer readable media for limiting network function (NF) repository function (NRF) forwarding are disclosed. One example method for limiting NRF forwarding comprises: at a first NF comprising at least one processor: receiving a request message associated with a consumer NF; determining that the request message cannot be serviced by the first NF; inserting or modifying a forwarding limit value in the request message, wherein the forwarding limit value indicates a number of times that the request message can be forwarded; and forwarding the request message including the forwarding limit value toward a second NF.