Abstract: A server includes a data cache for storing data objects requested by mobile devices, desktop devices, and server devices, each of which may execute a different configuration of an application. When a cache miss occurs, the cache may begin loading portions of a requested data object from various data sources. The cache itself may be divided into multiple partitions, and each of the partitions may be assigned to a specific attribute, such as an application configuration. Portions of the data object may be loaded into corresponding cache partitions based on the attributes of each. Although part of a single cache, each of the partitions may be independently assigned different cache replacement policies. Performance metrics for each of the partitions may be monitored and used to update the cache replacement policy for each partition at runtime without interrupting response traffic.

Abstract: Techniques for efficiently maintaining consistency of data items across storage partitions are disclosed using a hierarchical multi-level hash tree. Copies of a data item may be associated with corresponding attributes that are used to generate hash values for the data item. Hash values of the attributes may then be used to label nodes in a multi-level hash tree. Differences between the replicated copies of a data item may be quickly identified by comparing hash values associated with successively lower peer nodes in corresponding hash trees. Once identified, systems may update versions of a data item that are no longer current.

Abstract: During operation, the system uses N sensors to sample an electromagnetic interference (EMI) signal emitted by a target asset while the target asset is running a periodic workload, wherein each of the N sensors has a sensor sampling frequency f, and wherein the N sensors perform sampling operations in a round-robin ordering with phase offsets between successive samples. During the sampling operations, the system performs phase adjustments among the N sensors to maximize phase offsets between successive sensors in the round-robin ordering. Next, the system combines samples obtained through the N sensors to produce a target EMI signal having an EMI signal sampling frequency F=f×N. The system then generates a target EMI fingerprint from the target EMI signal. Finally, the system compares the target EMI fingerprint against a reference EMI fingerprint for the target asset to determine whether the target asset contains any unwanted electronic components.

Abstract: A manager file system (MFS) runs as a user space file system. The MFS, implemented using an OS process, exposes a mount point as a communication endpoint to the single process. Mounting, unmounting, and changing configuration of individual database file systems (DBFSs) are done by overloading extended attributes on the mount point. The MFS services all DBFSs mounted at different mount points registered to the single process of the MFS and ensures optimal resource utilization among the DBFSs in the single process while guaranteeing resource isolation. Multiple MFSs may be created to manage sets of DBFSs.

Abstract: Systems, methods, and other embodiments associated with migrating key-value data to a relational database. A method comprises query metadata of a key-value store and retrieving at least a namespace, and a kind name associated to the namespace. A subset of entities associated with the kind name in the key-value store are inspected to determine an inspected data type of data values from one or more of property names. A relational table is created in a relational database based on at least the namespace from the metadata, the kind name from the metadata, and the inspected data types. Data from the key-value store is migrated into the relational table by retrieving entities from the key-value store associated with the kind name, and inserting property values from a given entity into a row of the relational table.

Abstract: A computer comprising processors and memory may implement an application execution engine to execute an application including both managed code and unmanaged code, the managed code providing memory safety for accesses to objects in memory and the unmanaged code providing no such safety. Responsive to a request from managed code, the application execution engine allocates memory for an object in a managed pool and the object is accessible to managed code in a managed state. Responsive to a requirement for unmanaged code to access the managed object, the application execution engine transforms the object to be accessible in an unmanaged state, the transformation including allocating memory for the object in an unmanaged pool, copying data from managed memory to unmanaged memory according to a predetermined unmanaged layout, releasing the managed memory and using an address of the unmanaged memory to access the object in the unmanaged state.

Abstract: The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.

Abstract: Systems, methods, and other embodiments associated with computing and generating schedule data structures for items in a display are described. In one embodiment, a method includes accessing a sales data structure corresponding to a store and analyzing sales records for items associated with subcategories to calculate a subcategory profit contribution score for each subcategory. The method may also include selecting a first subcategory from the subcategories as a candidate subcategory of items and analyzing the sales records to calculate an item profit contribution score for each of the items assigned to the candidate subcategory. A first item is selected from the candidate subcategory to be placed on a promotional display space, based upon the item profit contribution score of the first item. A schedule data structure is generated that assigns the first item to the promotional display space.

Abstract: Systems, devices, and methods discussed herein enable the provisioning of one or more virtual resources within a cloud-computing environment based at least in part on user-defined constraints. By way of example, a request to provision one or more virtual resources within the cloud-computing environment may be received. The request may include a set of user-defined constraints. Placement data indicating a mapping of a particular virtual resource to a particular hardware resource may be obtained based at least in part on the set of user-defined constraints. The virtual resource(s) may be provisioned within the cloud-computing environment in accordance with the set of user-defined constraints based at least in part on utilization of the placement data.

Abstract: The present embodiments relate to data center outage detection and alert generation. An outage detection service as described herein can process near real-time data from various sources in a datacenter and process the data using a model to determine one or more projected sources of a detected outage. The model as described herein can include one or more machine learning models incorporating a series of rules to process near-real time data and offline data and determine one or more projected sources of an outage. An alert message can be generated to provide the projected sources of the outage and other data relevant to the outage.

Abstract: The present disclosure relates to a method and/or apparatus for versioning cloud network configuration. The method can include receiving base configuration files for a set of devices forming at least a portion of a computing network, pulling current configuration files from each device of the set of devices, for each device, and determining a discrepancy when a base configuration file of the device and a current configuration file of the device do not match. When the discrepancy is determined for a device, generating a patch file based on the determined discrepancy for that device. The method can include extracting a last configuration commit time from that device, allocating a unique snapshot identifier to the patch file and associated with the last configuration commit time, and populating a snapshot database.

Abstract: Embodiments are directed towards employing multiple profiles to track identification of users across multiple domains. Multiple application-domain profiles and multiple web-domain profiles may each include a statistical identifier. A profile enrichment store may be generated from the application-domain profiles and web-domain profiles, where each entry in the profile enrichment store may be based on statistical identifiers. The profile enrichment store may be utilized to determine categories associated with a statistical identifier for a request from a client device. A targeted advertisement may be determined based on the determined category and may be provided to the client device. A client caching identifier may be used in a mobile web domain, in combination with the statistical identifier, to track mobile web users. If a request is associated with a profile that includes an affirmative opt-out flag, categories in the profile enrichment store for a corresponding statistical identifier may be cleaned up.

Abstract: Techniques are provided for authorizations in a virtual computing infrastructure using a federation token service. The techniques may include receiving a request for a launch plan from a user for launching instances in a plurality of sites, determining object permissions required for actions to be performed to launch one or more of the instances at each site of the plurality of sites, contacting an authorization caching service to obtain authorization tokens for each of the determined object permissions required for the actions, receiving the authorization tokens for each of the determined object permissions required for the actions, and forwarding the request to each site of the plurality of sites with an authentication token and the authorization tokens for each of the determined object permissions required for the actions to be performed to launch the one or more of the instances at each site of the plurality of sites.

Abstract: Techniques are described that enable maintaining of session stickiness across authentication and authorization channels in an access management system, through the use an identifier for an access manager from a plurality of access managers. The access manager authenticates a user of a client device based on an authentication request. In response to response to successful authentication of the user, the access manager creates a session. The access manager also generates the identifier and causes the identifier to be stored for the session. The access manager can then receive a second request, which is sent to the access manager based on identifying the access manager using the stored identifier.

Abstract: Described herein is a system and method for providing bounded recursion under bounded concurrency, for use with microservices or reactive programming environments. The system is adapted to determine, for example during evaluation of a chain of Continuations, that the concurrency of onComplete invocations is mutually exclusive with the recursive invocations. The number of concurrently executing threads can be bounded, such that the recursion only needs to operate within a reasonable bound—it does not need to be fully precluded. With these relaxations in mind, the system can enforce a bound on recursion without further synchronization actions.

Abstract: Techniques related to query execution against an in-memory standby database are disclosed. A first database includes PF data stored on persistent storage in a persistent format. The first database is accessible to a first database server that converts the PF data to a mirror format to produce MF data that is stored within volatile memory. The first database server receives, from a second database server, one or more change records indicating one or more transactions performed against a second database. The one or more change records are applied to the PF data, and a reference timestamp is advanced from a first to a second timestamp. The first database server invalidates any MF data that is changed by a subset of the one or more transactions that committed between the first and second timestamps.

Abstract: The present disclosure relates to system and techniques for enabling provisioning of cloud services for a client in an isolated yet scalable manner. In some embodiments, various computing resources are implemented within a cell (a self-sufficient unit). A number of cells are generated for a service or a group of services and distributed across a number of computing devices. Various cells may be generated that each pertain to a different aspect, or particular functionality, of the service. In some embodiments, cells providing various functionality for the service are implemented and distributed across different computing devices.

Abstract: Embodiments use a hierarchy of machine learning models to predict datacenter behavior at multiple hardware levels of a datacenter without accessing operating system generated hardware utilization information. The accuracy of higher-level models in the hierarchy of models is increased by including, as input to the higher-level models, hardware utilization predictions from lower-level models. The hierarchy of models includes: server utilization models and workload/OS prediction models that produce predictions at a server device-level of a datacenter; and also top-of-rack switch models and backbone switch models that produce predictions at higher levels of the datacenter. These models receive, as input, hardware utilization information from non-OS sources. Based on datacenter-level network utilization predictions from the hierarchy of models, the datacenter automatically configures its hardware to avoid any predicted over-utilization of hardware in the datacenter.

Abstract: Techniques are described for invoking and switching between chatbots of a chatbot system. In some embodiments, the chatbot system is capable of routing an utterance received while a user is already interacting with a first chatbot in the chatbot system. For instance, the chatbot system may identify a second chatbot based on determining that (i) such an utterance is an invalid input to the first chatbot or (ii) that the first chatbot is attempting to route the utterance to a destination associated with the first chatbot. Identifying the second chatbot can involve computing, using a predictive model, separate confidence scores for the first chatbot and the second chatbot, and then determining that a confidence score for the second chatbot satisfies one or more confidence score thresholds. The utterance is then routed to the second chatbot based on the identifying of the second chatbot.

Abstract: A distributed event processing system is disclosed that receives a batch of events via a continuous data stream and performs the serialization of data in the batch of events. In certain embodiments, the system identifies a first data type of a first attribute for each event in a batch of events and determines a first type of data compression to be performed on data values represented by the first attribute. The system determines a first type of data compression to be performed on data values represented by the first attribute based on the first data type of the first attribute. The system then generates a first set of serialized data values for the first attribute. The system processes the first set of serialized data values against a set of one or more continuous queries to generate a first set of output events.

Abstract: Techniques for processing event streams are provided that use streaming analytics to estimate a target user for dynamic assets. An exemplary technique includes receiving a batch of events that have position data for a potential target, determining one or more polygons that comprise the potential target based on the position data for the potential target, determining an approximate target viewership for the one or more polygons based on a profile or taxonomy of the potential target, associating an advertisement with the one or more polygons based on the approximate target viewership, receiving a continuous stream of events that have position data for a dynamic asset, determining that the one or more polygons include the dynamic asset based on the position data for the dynamic asset, obtaining the advertisement associated with the one or more polygons, and providing the advertisement to a sink for display on the dynamic asset.

Abstract: Techniques for expense report submission are disclosed. An expense report submission system receives, via a graphical user interface, a user query that corresponds to requesting whether a particular expense is allowed. The expense report submission system applies the user query to a machine learning model configured to evaluate data associated with expenses against one or more expense policy rules. The expense report submission system generates a response to the user query based at least on a result of applying the user query to the machine learning model. The response to the user query indicates whether the expense is allowed based at least on the expense reporting rule(s). The expense report submission system presents, in the graphical user interface, the response to the user query.

Abstract: “Resource guarantee” refers to a unit of a resource that is guaranteed and therefore designated to a consumer. A multi-phased constraint programming (CP) approach is used to determine assignments of resource guarantees of a set of consumers to a set of hosts in a resource system. Phase I uses CP to segregate non-split consumers from split consumers. Phase II uses CP to assign each cotenant group of non-split consumers to a respective host. Phase III uses CP to assign resource guarantees of the split consumers across the hosts, wherein resource guarantees of a single split consumer may be splits across different hosts. Each phase involves execution of a CP solver based on a different CP data model. A CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

Abstract: Techniques for using machine learning model validated sensor data to generate recommendations for remediating issues in a monitored system are disclosed. A machine learning model is trained to identify correlations among sensors for a monitored system. Upon receiving current sensor data, the machine learning model identifies a subset of the current sensor data that cannot be validated. The system generates estimated values for the sensor data that cannot be validated based on the learned correlations among the sensor values. The system generates the recommendations for remediating the issues in the monitored system based on validated sensor values and the estimated sensor values.

Abstract: Techniques for suppressing notifications are disclosed. An incident may repeatedly flap between various resolved and unresolved states. Furthermore, other incident attributes may flap between various states such as, for example, varying levels of incident severity. Each change in state results in the transmission of a notification. In order to reduce the number of notifications, the system estimates a flapping window for the incident based on the flapping behavior of prior incidents. The system computes a notification suppression window based at least in part on the estimated flapping window. The system suppresses notifications corresponding to changes in incident state that are detected during the notification suppression window. The notification suppression window may be extended in response to extending the estimated flapping window.

Abstract: Techniques for augmentation and batch balancing of training data to enhance negation and fairness of a machine learning model. In one particular aspect, a method is provided that includes generating a list of demographic words associated with a demographic group, searching an unlabeled corpus of text to identify unlabeled examples in a target domain comprising at least one demographic word from the list of demographic words, rewriting the unlabeled examples to create one or more versions of each of the unlabeled examples and generate a fairness invariance data set, and training the machine learning model using unlabeled examples from the fairness invariance data set.

Abstract: Techniques are disclosed for improved autonomous agents that can provide a recommendation in a non-intrusive, conversational manner. In an aspect, a method determines a first sentiment score for a first utterance and a second sentiment score for a second utterance, each sentiment score indicating an emotion indicated by the respective utterance. The method further identifies that a difference between the first sentiment score and the second sentiment score is greater than a threshold. The method further extracts a noun phrase from the second utterance. The method identifies a text fragment that includes an entity that corresponds to the noun phrase. The method identifies that the text fragment addresses a claim of the second utterance. The method forms a third utterance that includes the a recommendation related to the second utterance and adds the third utterance to the sequence of utterances after the second utterance.

Abstract: Techniques for multi-layer forecasting of computational workloads are disclosed. A system identifies a level of granularity associated with a request to forecast a computational workload for a particular entity. The system obtains attribute data of computational resources at the specified level of granularity. The system determines whether computational resources, not specified in the request, should be included in a workload forecast. The system applies a time-series forecast model to time-series data obtained from computational resources associated with the request. The system presents one or more workload forecasts for computational workloads associated with the request.

Abstract: Techniques for handling data access conflicts are disclosed. In some embodiments, a conflict handling system detects a set of one or more objects that are currently being accessed by an application. The conflict handling system queries a database to determine whether the set of one or more objects have changes pending. Responsive to determining that an object has a change pending, the conflict handling system identifies and loads, at runtime of the application, a set of one or more application-specific rules that are defined for handling data conflicts. The conflict handling system executes the rules to generate a conflict resolution result for the object. The application may then perform an application-specific function based on the conflict resolution result.

Abstract: Example embodiments include software for selectively combining outputs for multiple machine learning modules, so as to generate composite values. The composite values may provide more accurate metrics, e.g., composite scores, that may be used to more reliably predict/estimate, for instance, the likelihood of a given lead converting into a customer within a predetermine time interval. In certain embodiments, a math model selectively combines the scores, and the parameters thereof can be selectively adjusted in real time, based on real time data; so as to maintain accuracy of the composite scores. This can lead to enhanced situational awareness, which can be particularly important for enterprise sales representatives, account managers, and so on.

Abstract: Techniques for augmentation and batch balancing of training data to enhance negation and fairness of a machine learning model. In one particular aspect, a method is provided that includes obtaining a training set of labeled examples for training a machine learning model to classify sentiment, searching the training set of labeled examples or an unlabeled corpus of text on target domains for sentiment examples having negation cues, sentiment laden words, words with sentiment prefixes or suffixes, or a combination thereof, rewriting the sentiment examples to create negated versions thereof and generate a labeled negation pair data set, and training the machine learning model using labeled examples from the labeled negation pair data set.

Abstract: Techniques are disclosed for providing high performant packets processing capabilities in a virtualized cloud environment that enhance the scalability and high availability of the packets processing infrastructure. In certain embodiments disclosed herein, the VNICs functionality performed by network virtualization devices (NVDs) is offloaded from the NVDs to a fleet of computers, referred to as VNIC-as-a-Service System (or VNICaaS system). VNICaaS system is configured to provide Virtual Network Interface Cards (VNICs)-related functionality or service for multiple compute instances belonging to multiple tenants or customers of the CSPI. The VNICaaS system is capable of hosting multiple VNICs to process and transmit traffic in a distributed virtualized cloud networks environment. A single VNIC executed by the VNICaaS system can be used to process packets received from multiple compute instances.

Abstract: Techniques are provided for improved training of a machine-learning model that includes multiple layers and is configured to process textual language input. The machine-learning model includes one or more blocks in which each block includes a multi-head self-attention network, a first connection for providing input to the multi-head self-attention network, and a second (residual) connection for providing the input to a normalization layer, bypassing the multi-head self-attention network. During training, the second connection is dropped out according to a dropout parameter. Additionally, or alternatively, an attention weight matrix is used for dropout by blocking diagonal entries in the attention weight matrix. As a result, the machine-learning model increasingly focuses on contextual information, which provides more accurate language processing results.

Abstract: Techniques are disclosed for identifying hypocrisy in text. A computer system creates, from fragments of text, a syntactic tree that represents syntactic relationships between words in the fragments. The system identifies, in the syntactic tree, a first entity and a second entity. The system further determines that the first entity is opposite to the second entity. The system further determines a first sentiment score for a first fragment comprising the first entity and a second sentiment score for a second fragment comprising the second entity. The system, responsive to determining that the first sentiment score and the second sentiment score indicate opposite emotions, identifies the text as comprising hypocrisy and providing the text to an external device.

Abstract: Techniques for named entity bias detection and mitigation for sentence sentiment analysis. In one particular aspect, a method is provided that includes obtaining a training set of labeled examples for training a machine learning model to classify sentiment, preparing a list of named entities using one or more data sources, for each example in the training set of labeled examples with a named entity, replacing the named entity with a corresponding entity type tag to generate a labeled template data set, executing a sampling process for each entity type t within the labeled template data set to generate a augmented invariance data set comprising one or more invariance groups having labeled examples for each entity type t, and training the machine learning model using labeled examples from the augmented invariance data set.

Abstract: A method may include maintaining first data structure with records organized in a first hierarchy, and maintaining a second data structure with records organized in a second hierarchy. The method may additionally include receiving a change request for the value stored in the first record. The change request may be received from a parent in the second data structure of the first record. The method may further include sending a notification to the parent in the first data structure that the parent in the second data structure is attempting to change the first record.

Abstract: A method for dynamically updating domain name system (DNS) records from network function (NF) profile information includes, at an NF repository function (NRF) including at least one processor, receiving a message relating to an NF profile of an NF. The method further includes constructing, based on NF service availability information in the NF profile and local configuration of the NRF, a DNS resource record for the NF. The method further includes utilizing a dynamic DNS update procedure to dynamically update a DNS resource record for the NF with a DNS server.

Abstract: Techniques for stop word data augmentation for training chatbot systems in natural language processing. In one particular aspect, a computer-implemented method includes receiving a training set of utterances for training an intent classifier to identify one or more intents for one or more utterances; augmenting the training set of utterances with stop words to generate an augmented training set of out-of-domain utterances for an unresolved intent category corresponding to an unresolved intent; and training the intent classifier using the training set of utterances and the augmented training set of out-of-domain utterances. The augmenting includes: selecting one or more utterances from the training set of utterances, and for each selected utterance, preserving existing stop words within the utterance and replacing at least one non-stop word within the utterance with a stop word or stop word phrase selected from a list of stop words to generate an out-of-domain utterance.

Abstract: Disclosed is a system, method, and computer program product to efficiently process multi-set operations in a database system. An approach is described to perform a group-by operation with a counter to efficiently process such queries. Techniques are described to optimize multi-set operations into regular-set operations.

Abstract: Disclosed is a method, a computer program product, and a computer system for implementing automatic connection load balancing of database connections between instances of a cluster. Load imbalances are detected globally in the cluster, and can be corrected autonomously by the local instances. The local controller/orchestrator detects the imbalance, computes a target for the instance, and makes any necessary adjustments on the instance, where a server marks the connections to redistribute, and then implements draining and/or planned failover to move sessions.

Abstract: Embodiments operate a multi-tenant cloud system. At a first data center, embodiments authenticate a first client corresponding to a first tenant ID and store resources that correspond to the first client, the first data center in communication with a second data center that is configured to authenticate the first client and replicate the resources. The first data center receives an Application Programming Interface (“API”) request for the first client corresponding to a change to the resources, and generates a change log and corresponding change event message in response to the API request. Embodiments compute a first hash corresponding to the first tenant ID of the change log to determine a first partition of a first queue at the first data center. The first data center pushes the change event message to the second data center via an API call.

Abstract: A method for dynamic optimized NF discovery includes querying an NF repository function (NRF), receiving at least one response to the querying, and building, from the at least one response, an NF discovery data digest of NF profile attributes and corresponding attribute values usable as query parameters in NF discovery requests. The method further includes determining that a consumer NF has a need for a service, and, in response, formulating, using the NF discovery data digest, an NF discovery request message including at least one of the NF profile attribute values as a query parameter. The method further includes transmitting the NF discovery request message to the NRF. The method further includes receiving an NF discovery response from the NRF including at least one NF profile. The method further includes using the at least one NF to facilitate providing of the service to the consumer NF.

Abstract: Techniques are described for communications in an L2 virtual network. In an example, the L2 virtual network includes a plurality of L2 compute instances hosted on a set of host machines and a plurality of L2 virtual network interfaces and L2 virtual switches hosted on a set of network virtualization devices. An L2 virtual network interface emulates an L2 port of the L2 virtual network. IGMP configuration is distributed to the L2 virtual switches.

Abstract: Embodiments implement multifactor authentication without a user footprint. An application programming interface call from a client application can be received that includes a messaging identifier. A transaction identifier can be transmitted to the client application and stored. Using a shared secret, a temporary password can be generated, where the shared secret can be associated with the transaction identifier and can be stored. The temporary password can be transmitted to the messaging identifier. A second application programming interface call can be received that includes a reference transaction identifier and input, where a user provides the input to the client application. The user can be authenticated when the reference transaction identifier matches a transaction identifier stored and the input matches an expected password that is based on a stored shared secret associated with the matching stored transaction identifier.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for obtaining data items for input to a chatbot. In certain embodiments, a chatbot system includes a component that can be invoked by a chatbot in the chatbot system to obtain data items needed by the chatbot. The component can be invoked based on a reference to the component in a dialog flow definition configured for the chatbot. The reference to the component can indicate a composite entity that the component will use to determine how the data items are obtained from a user. The composite entity acts as a container for the data items and may be configured separately from the dialog flow definition of the chatbot. The data items can be obtained based on rules specified in a composite entity definition configured for the composite entity.

Abstract: Systems, methods, and other embodiments for ML-Based automated below-the-line threshold tuning include, in one embodiment, training an ML model to predict probabilities that an event is fraudulent on a set of events (i) sampled from a set of historic events labeled by an alerting engine as either above-the-line events or below-the-line events on either side of a threshold line indicating that an event is suspicious, and (ii) confirmed to be either fraudulent or not fraudulent; determining that the alerting engine should be tuned based on differences between probability values predicted for the events by the trained machine learning model and the labels applied to the events; generating a tuned threshold value for the threshold line based at least in part on the probability values predicted by the machine learning model; and tuning the alerting engine by replacing a threshold value with the tuned threshold value to adjust the threshold line.

Abstract: Systems, methods, and other embodiments associated with configuring related links and controlling of a display are described. In one embodiment, in response to a user activating a data page on a user interface, wherein the data page includes a plurality of input fields for receiving input values: monitoring the plurality of input fields for inputted values. When a first value is input into a first input field of the plurality of input fields, the system (i) identifies related links assigned to the first input field, (ii) counts a number of the related links that are assigned to the first input field, and (iii) generates and displays, on a display, a graphical badge that includes the number of the related links to inform the user that related links are available for the first input field.

Abstract: Systems, devices, and methods discussed herein are directed to modifying aspects of a compute instance. A user may request a change to the compute instance. The system may derive a state object indicating a future state of the compute instance were the change to be applied. A hash of a subset of the state object's attributes may be computed and provided to the requesting computing component. The system may subsequently proceed with applying the change. A current state object indicating a current state of the compute instance may be derived based on applying the change. An additional hash of the subset of the current state object's attributes may be computed and provided to the requesting computing component. The two hashes may be configured to enable the requesting computing component to verify the change to the compute instance has been implemented.

Abstract: A method for supporting configurable producer network function (NF) Internet protocol (IP) address mappings includes, at an NF repository function (NRF), receiving, from a requesting node, a request message for network address and/or service information of a producer NF. The method further includes determining, from the request message, at least one consumer NF parameter. The method further includes using the at least one consumer NF parameter, a producer NF IP address mapping rule. The method further includes, in response to locating the producer NF IP address mapping rule, determining, using the producer NF IP address mapping rule, an IP address to return to the requesting node. The method further includes generating a response message including the IP address and transmitting the response message to the requesting node.

Abstract: Embodiments determine an optimized maintenance schedule for a maintenance program that includes multiple levels, each level including at least one asset (i.e., asset type) and at least one of the levels including a plurality of assets. Embodiments receive historical failure data for each of the assets, the historical failure data generated at least in part by a sensor network. For each asset, embodiments generate a probability density function (“PDF”) using kernel density estimation (“KDE”). For each asset, based on a reliability rate threshold, embodiments determine a cumulative density function (“CDF”) using the PDF. For each asset, embodiments determine an optimized time to failure (“TTF”) using the CDF. Embodiments then create the schedule for each level that includes a minimum TTF for the assets at each level.