Abstract: Described herein are systems and methods for fault tolerance in a network cloud environment. In accordance with various embodiments, the present disclosure provides an improved fault tolerance solution, and improvement in the fault tolerance of systems, by way of failure prediction, or prediction of when an underlying infrastructure will fail, and using the predictions to counteract the failure by spinning up or otherwise providing new component pieces to compensate for the failure.

Abstract: Described herein are systems and methods for a providing Ledger as a Service (LaaS). Blockchain technology helps bring potential solutions to the distributed ledger problem, with a linear record structure to record transaction history. However, there are different types of blockchain techniques (e.g., Hyperledger, Ethereum, Quorum), and users/developers need to know the explicit features of each technique and align with the required APIs. Ledger as a Service can allow users to an develop applications more efficiently, and can allow users to easily migrate applications among different blockchain techniques and platforms (e.g., between Hyperledger and Ethereum). LaaS can also allow for simplified transactions with a blockchain, and can additionally provide simplified communication between blockchains of different types.

Abstract: Techniques for suggesting filters for query terms based on previously selected query results are disclosed. Common characteristics of previously selected query results are presented as a filter. A system trains a machine learning model by obtaining historical data including query characteristics and selected query results. Based on the historical data, the system trains the machine learning model to associate the first filter field with the first search term. The system receives a first query for execution. The system applies the machine learning model to the first query to identify the first filter field as a suggestion. The system: recommends the first field for filtering a first set of search results corresponding to the first query. Responsive to receiving user input selecting a first value for the first filter field, the system filters using the first value to generate a set of filtered search results, and presents the filtered search results.

Abstract: The present is directed to systems, methods, and devices for Zero Touch Provisioning (“ZTP”). The system can include a data center computing network, and at least one server coupled with the data center computing network. The server can receive a trigger signal from a triggering device coupled to a data center hardware network, transmit an executable script to the triggering device, which executable script instructs the triggering device to generate a definition file including a unique identifier of the triggering device, receive the definition file from the triggering device, identify a configuration file associated with the triggering device based at least in part on the unique identifier, and provision the triggering device by delivering the configuration file to the triggering device.

Abstract: In accordance with an embodiment, described herein are systems and methods identifying capabilities and limitations of an orchestration based application integration. The systems and methods described herein can detect and publish/expose such a set of limitations and capabilities of the adapters/connectors to the integration platform. In this way, such metadata can be used, for example, by a user of an integration system in designing an integration of applications, or in selecting an orchestration, based upon, e.g., the published metadata that indicates the capabilities of the individual adapters/connectors, or the orchestration as a whole (or parts thereof). This way, once an orchestration is active for processing requests, then it can also publish a metadata considering the metadata of involved adapters. This metadata can be used in adjusting requests to an integration.

Abstract: Embodiments implement a tool for interrogating heterogeneous computing systems. Environment variables of a computing device including at least an operating system can be detected. Script commands configured using the retrieved environment variables can be built, where the built script commands are customized based on the detected operating system. Structured query level commands configured to retrieve metadata about enterprise elements associated with the computing device can be built. The SQL commands and script commands can be sequentially executed on the computing device, where the execution of the SQL commands and script commands is customized to the computing device such that device specific database execution parameters and application execution parameters are returned. A structured language document organized according to the returned database execution parameters and application execution parameters can be generated.

Abstract: Embodiments implement non-intrusive load monitoring using ensemble machine learning techniques. A first trained machine learning model configured to disaggregate target device energy usage from source location energy usage and a second trained machine learning model configured to detect device energy usage from source location energy usage can be stored, where the first trained machine learning model is trained to predict an amount of energy usage for the target device and the second trained machine learning model is trained to predict when a target device has used energy. Source location energy usage over a period of time can be received, where the source location energy usage includes energy consumed by the target device. An amount of disaggregated target device energy usage over the period of time can be predicted, using the first and second trained machine learning models, based on the received source location energy usage.

Abstract: A system and method for dynamic log management of stream processing in a distributed computing environment, such as, for example, a streaming application or stream analytics system. A streaming application can be deployed or published to a cluster, to execute as a client application. A cluster manager coordinates with worker nodes, to commit tasks associated with the streaming application. If a need arises to generate lower-level log data associated with the streaming application, for example to diagnose an underlying cause of a warning/error message, a configuration job can be committed to the cluster to execute as a separate log-configuration application. The log-configuration application operates with the cluster manager to determine the set of working nodes currently associated with the streaming application, and modify the logger configuration at those nodes, to record or otherwise provide log data according to a modified logging level, for example to provide lower-level log messages.

Abstract: The fail-over computer cluster enables multiple computing devices to operate using adaptive quorum rules to dictate which nodes are in the fail-over cluster at any given time. The adaptive quorum rules provide requirements for communications between nodes and connections with voting file systems. The adaptive quorum rules include particular recovery rules for unplanned changes in node configuration, such as due to a disruptive event. Such recovery quorum rules enable the fail-over cluster to continuing to operate with various changed configurations of its node members as a result of the disruptive event. In the changed configuration, access to voting file systems may not be required for a majority-group subset of nodes. If no majority-group subset remains, nodes may need direct or indirect access to voting file systems.

Abstract: Techniques for management of data storage in distributed storage systems are provided. A method may include receiving, by a computer system, a request to write data to a volume. The method may include identifying, by the computer system, a zone segment mapped to the volume. The zone segment may include a plurality of zones. The method may include identifying, by the computer system, a segment pointer indicating a write location in a zone of the zone segment. The method may include writing, by the computer system, the data to one or more zones of the plurality of zones of the zone segment, starting at the write location. The method may also include updating, by the computer system, the segment pointer according to a data endpoint of the data in the zone segment.

Abstract: Disclosed techniques relate to automatically instrumenting a web application. In an aspect, a method identifies that a web application includes an event that is triggered by a user interaction. The method associates the event with a tracer that is configured to log tracing information based on an execution of a first set of operations caused by the event and to obtain a first measurement of performance of a first span. The method identifies, in the code, that the execution of a first set of operations causes a request to be made to a server. The method associates the request with the tracer. The tracer is configured to log tracing information based on an execution of a second set of operations caused by the request and to obtain a second measurement of performance of a second span that is a child span of the first span.

Abstract: Techniques for migrating worker nodes within clusters to a new manager instance. One technique includes receiving a request to migrate or update a configuration of a cluster within a container system, where the migration or update includes switching from a first communication pathway to a second communication pathway between worker nodes and a manager instance; creating a component and associated IP address for the second communication pathway; communicating a pod specification that includes the IP address for the second communication pathway to the manager instance, where the pod specification will cause a container tool to update each of the worker nodes with the IP address for the second communication pathway; receiving a notification that all worker nodes have been updated with the IP address; and removing a component and associated IP address for the first communication pathway from the cluster.

Abstract: A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.

Abstract: Discussed herein is a routing mechanism for graphical processing units (GPUs) that are hosted on several host machines in a cloud environment. For a packet transmitted by a GPU of a host machine and received by a network device, the network device determines an incoming port-link of the network device on which the packet was received. The network device obtains a flow information associated with the packet, and computes, based on the flow information, an outgoing port-link of the network device in accordance with a hashing algorithm. The hashing algorithm is configured to hash packets received on a particular incoming port-link of the network device to be transmitted on a same outgoing port-link of the network device. The network device forwards the packet on the outgoing port-link of the network device.

Abstract: Techniques are disclosed for automatic scene detection and character extraction. In one example, audiovisual content with video frames, an audio recording, and timing information is received. A score, based on the frame's visual characteristics, is determined for a first frame and subsequent frames. The first frame's score and subsequent frame's scores are compared to determine if the difference between the scores is above a threshold. When the difference in scores is above a threshold, the subsequent frame is classified as a new scene. The audiovisual content is segmented into scenes and textual characters are identified in at least one frame from each scene. The characters are stored and indexed in a searchable database with the timing information for the scene where the characters were identified. The audio recording is transcribed and the transcribed words are stored and indexed in the searchable database with timing information.

Abstract: A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.

Abstract: A method, a non-transitory computer readable medium, and an apparatus are provided for switching between a synchronous data entry processing and an asynchronous data entry processing for processing input into a data entry field of the GUI based on a latency threshold for validation and a latency threshold for conversion; and for determining which input from a series of inputs entered into the data field during the asynchronous data entry processing based on a criteria.

Abstract: Systems and methods for intelligently providing users with supporting information based on big-data analyses of a data set. Machine-learning algorithms may be executed using the data set to identify correlations between data objects of the data set. The correlations can be used to recommend supporting information to a user. A user interface can be provided to enable a user to initiate a process associated with an event. In response to receiving the input, the system can identify variables associated with the request. Based on these variables, the system can retrieve output data of the machine-learning algorithms to identify the supporting information for the user.

Abstract: An autonomous agent creates a first semantic tree from a question and second semantic tree from a candidate answer. The agent identifies, between the first semantic tree and the second semantic tree, common subtrees and calculates a semantic alignment score from a sum of sizes of each of the common subtrees. The agent forms a first syntactic tree for the question and a second syntactic tree for the candidate answer. The agent identifies a number of common syntactic nodes between the first syntactic tree and the second syntactic tree. The agent calculates a syntactic alignment score based on the number of common syntactic nodes. Responsive to determining that a sum of the semantic alignment score and the syntactic alignment score is greater than a threshold, the agent outputs the candidate answer to a device.

Abstract: A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.

Abstract: Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.

Abstract: Discussed herein is a routing mechanism for graphical processing units (GPUs) that are hosted on several host machines in a cloud environment. For a packet transmitted by a GPU of a host machine and received by a network device, the network device determines an incoming port-link of the network device on which the packet was received. The network devices identifies, based on a GPU routing policy, an outgoing port-link of the network device that corresponds to the incoming port-link. The GPU routing policy is preconfigured prior to receiving the packet and establishes a mapping of each incoming port-link of the network device to a unique outgoing port-link of the network device. The packet is forwarded on the outgoing port-link of the network device.

Abstract: Discussed herein are techniques for migrating an application from a source cloud environment (SCE) to a target cloud environment (TCE). Responsive to a request received by an application migration service (AMS) to migrate an application executed in a first compute instance in the SCE to a second compute instance in the TCE, the AMS authenticates credentials of a user with respect to the SCE. Upon the credentials being successfully authenticated, the AMS generates a public key and a private key. The public key is transmitted to a service manager that injects the public key in the application executed in the first compute instance and the private key is assigned to a source agent. The source agent obtains one or more artifacts and configuration information that enable execution of the application based on the private key, which are installed by a target agent in the second compute instance in the TCE.

Abstract: Systems and methods for hot method call graph analysis. An exemplary method can provide a hot method call graph analyzer. The method can receive a generated call graph, the generated call graph comprising a plurality of branches. The method can automatically traverse each branch of the generated call graph. For each traversed branch, the method can generate and store a list of leaf methods of each branch along with a corresponding execution time and number of samples of each leaf method, resulting in plurality of leaf method lists. The method can combine each of the plurality lists of leaf methods in an overall list of leaf methods of the generated call graph, resulting in a complete list of leaf methods of the generated call graph, which can be provided via, for example, a user interface. The systems and method described can additionally perform similar analysis for non-leaf methods.

Abstract: Systems, devices, and methods of the present invention detect rhetoric agreement between texts. In an example, a rhetoric agreement application accesses a multi-part initial query and generates a question communicative discourse tree that represents rhetorical relationships between fragments of the query. The application identifies a sub-discourse tree from the question communicative discourse tree. The application generates a candidate answer communicative discourse tree for each candidate answer of a set of candidate answers. The application computes a level of complementarity between the sub-discourse tree and each candidate answer discourse tree by applying a classification model to the sub-discourse tree and candidate answer communicative discourse trees. The application selects an answer from the candidate answers based on the computed complementarity, thereby building a dialogue structure of an interactive session.

Abstract: Techniques for noise data augmentation for training chatbot systems in natural language processing. In one particular aspect, a method is provided that 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 noise text to generate an augmented training set of utterances; and training the intent classifier using the augmented training set of utterances. The augmenting includes: obtaining the noise text from a list of words, a text corpus, a publication, a dictionary, or any combination thereof irrelevant of original text within the utterances of the training set of utterances, and incorporating the noise text within the utterances relative to the original text in the utterances of the training set of utterances at a predefined augmentation ratio to generate augmented utterances.

Abstract: In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can perform an ontology analysis of a schema definition, to determine the types of data, and datasets or entities, associated with that schema; and generate, or update, a model from a reference schema that includes an ontology defined based on relationships between datasets or entities, and their attributes. A reference HUB including one or more schemas can be used to analyze data flows, and further classify or make recommendations such as, for example, transformations enrichments, filtering, or cross-entity data fusion of an input data.

Abstract: Resource allocation limitations include resource limits and resource guarantees. A consumer is vulnerable to interruption by other consumers if using more resources than guaranteed. Resources are designated and/or assigned to consumers based on resource limits and resource guarantees. A constraint programming (CP) solver determines resource limits and resource guarantees that minimize vulnerability and/or vulnerability cost based on resource usage data. A CP data model includes limit elements, guarantee elements, and vulnerability elements. The CP data model further includes guarantee-vulnerability constraints, which relies on exceedance distributions generated from resource usage data for the consumers. The CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

Abstract: In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system provides a programmatic interface, referred to herein in some embodiments as a foreign function interface, by which a user or third-party can define a service, functional and business types, semantic actions, and patterns or predefined complex data flows based on functional and business types, in a declarative manner, to extend the functionality of the system.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for identifying an intent for an utterance based on semantic framing. For an input utterance, a semantic frame is generated. The semantic frame includes semantically relevant grammatical relations and corresponding words identified in the utterance. The semantically relevant grammatical relations define context and relationships of words in the utterance. The semantic frame is used to identify an intent for the utterance, based on an intent model. The intent model maps features to corresponding words for a given intent. The semantic frame is compared to a plurality of intent models, and a best-matching intent model is used to identify the intent for the utterance.

Abstract: Herein are quantitative analytics to increase the accuracy of cardinality estimation without increasing sample size. In an embodiment, a computer selects a few sample values from a multiset. A high-frequency exact count of distinct values that have at least a threshold amount of occurrences in the sample values is counted. A low-frequency exact count of distinct values in the sample that do not have at least the threshold amount of occurrences in the sample is counted. Based on multiple binomial probabilities, an upper bound of a count of missing distinct values in the multiset that are not in the sample is calculated. A total count of distinct values (NDV) in the multiset is estimated based on: a) the high-frequency exact count of distinct values, b) the low-frequency exact count of distinct values, and c) the upper bound of the count of missing distinct values in the multiset that are not in the sample.

Abstract: Methods and system are presented for monitoring database processes to generate machine learning predictions. A plurality of database processes executed on database implementations can be monitored, wherein the monitoring includes determining a start time, an end time, and a number of rows impacted by portions of the database processes, and the monitored database processes generate instances of machine learning data including at least the number of rows impacted and an associated duration of time. Using a machine learning component and the machine learning data, a duration of time can be predicted for a candidate database process for execution on a database implementation.

Abstract: Techniques are provided for cross-region replication of block volume data. The techniques include a method wherein a computer system implements operations including creating a first snapshot of a block volume at a first geographic region and at a first logical time, the block volume including a plurality of partitions, transmitting first snapshot data to an object storage system at a second geographic region, creating a second snapshot of the block volume at the first geographic region and at a second logical time, generating a plurality of deltas, transmitting a plurality of delta data sets corresponding to the plurality of deltas to the object storage system at the second geographic region, generating a checkpoint at least in part by aggregating object metadata associated with the plurality of deltas and the first snapshot, receiving a restore request to generate a restore volume, and generating the restore volume from the checkpoint.

Abstract: A system filters a data set for the efficient execution of queries by removing at least some irrelevant records to generate a reduced data set. The records are determined to be irrelevant based on the values of the records for the particular attribute(s) corresponding to the filtering operation. The system filters out records within a data set that do not include any of the values defined for the particular attribute by a set of queries that are to be executed on the filtered set of records.

Abstract: A sharded, permissioned, distributed ledger may reduce the amount of work and communication required by each participant, thus possibly avoiding scalability bottlenecks that may be inherent in previous distributed ledger implementations and possibly enabling the use of additional resources to translate to increased throughput. A sharded, permissioned, distributed ledger may be made up of multiple shards, each of which may also be a distributed ledger and which may operate in parallel. Participation within a sharded, permissioned, distributed ledger may be allowed only with permission of an authority. A sharded, permissioned, distributed ledger may include a plurality of nodes, each including a dispatcher configured to receive transaction requests from clients and to forward received requests to verifiers configured to append transactions to individual ones of the shards.

Abstract: To execute function-step-based graph queries on a graph engine that has its own graph query language, rather than re-implementing an existing infrastructure to support function-step-based graph protocols, function-step-based graph queries are transformed to the graph query language that is understood by the graph engine. The existing infrastructure computes the results of the transformed queries. Result sets are then transformed to function-based-based result sets, which are returned to customers. In this manner, the graph engine supports function-step-based graph query workloads without implementation of the function-step-based graph protocol.

Abstract: Systems and methods for unsupervised training and evaluation of anomaly detection models are described. In some embodiments, an unsupervised process comprises generating an approximation of a data distribution for a training dataset including varying values for a metric of a computing resource. The process further determines, based on the size of the training dataset, a first quantile probability and a second quantile probability that represent an interval for covering a prescribed proportion of values for the metric within a prescribed confidence level. The process further trains a lower limit of the anomaly detection model using a first quantile that represents the first quantile probability in the approximation of the data distribution and an upper limit using a second quantile that represents the second quantile probability in the approximation. The trained upper and lower limits may be used to monitor input data for anomalous behavior and, if detected, trigger responsive action(s).

Abstract: Techniques are disclosed for generating network security policies for different versions of a component of an application deployed in a computing environment where the different versions have potentially different network requirements and the different versions operate together at the same time in the computing environment. The disclosed techniques include capabilities for enabling different versions of a component of a containerized application to co-exist at the same time on different computing nodes in a cluster of nodes in a containerized environment that deploys and executes the application. The techniques additionally include capabilities for enabling different network policies to be generated for the different versions of the component, where each component has potentially different network requirements. The techniques provide a mechanism to create precise, per-component network policies, while respecting the overall coarse-grained policies of the containerized application.

Abstract: Systems, methods, and other embodiments associated with patching database objects while a database system is online are described. In one embodiment, a patch command is defined and identifies a database code object, wherein the patch command is configured to modify code of the database code object. A serialized lock is requested from the database system for the database code object, wherein the serialized lock prohibits executing applications of the database system from accessing the database code object. In response to receiving the serialized lock from the database system, the code of the database code object is modified with a patch code to generate a modified database code object. The modified database code object is compiled and the serialized lock on the database code object is released to allow other executing applications to call and access the database code object.

Abstract: In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can include a software development component and graphical user interface, referred to herein in some embodiments as a pipeline editor, or Lambda Studio IDE, that provides a visual environment for use with the system, including providing real-time recommendations for performing semantic actions on data accessed from an input HUB, based on an understanding of the meaning or semantics associated with the data.

Abstract: In an embodiment, a computer obtains a mapping of a relational schema of a database to a graph data model. The relational schema identifies vertex table(s) that correspond to vertex type(s) in the graph data model and edge table(s) that correspond to edge type(s) in the graph data model. Each edge type is associated with a source vertex type and a target vertex type. Based on that mapping, a forward compressed sparse row (CSR) representation is populated for forward traversal of edges of a same edge type. Each edge originates at a source vertex and terminates at a target vertex. Based on the forward CSR representation, a reverse CSR representation of the edge type is populated for reverse traversal of the edges of the edge type. Acceleration occurs in two ways. Values calculated for the forward CSR are reused for the reverse CSR. Elastic and inelastic scaling may occur.

Abstract: Systems, computer-implemented methods, and computer-readable media for facilitating resource balancing based on resource capacities and resource assignments are disclosed. Electronic communications, received via interfaces, from monitoring devices to identify resource descriptions of resources may be monitored. A resource descriptions data store may be updated to associate each entity of the entities and resource capacities of each resource type of resource types. A first electronic communication, from resource-controlling systems, may be detected. Model data from a model data store may be accessed based on the identified resource descriptions. A first model may be identified based on the model data. A resources assessment corresponding may be generated based on whether a threshold is satisfied based on the first model, a first resource capacity of a first resource type, and the first electronic communication.

Abstract: The present embodiments relate to connecting a network device to a wireless access point in a network environment based on an association affinity between the network device and the wireless access device. A wireless access point in a network environment can receive a request for a first network device to connect to any wireless access point in the network environment that includes a signal strength metric. The wireless access point can determine whether the first network device corresponds with a prioritized wireless access point using weighted averages based on a historical derived proximity of the wireless access points in the network environment and a historical connectivity to wireless access points in the network environment. A connection prioritization action can be performed to connect the first network device with the prioritized wireless access point in the network environment based on the determination of whether the first network device corresponds with the prioritized wireless access point.

Abstract: Techniques are disclosed for enabling attested end-to-end encryption for transporting data between devices. In one example, a destination device receives a policy profile that includes an origination key and a destination key, and the origination key corresponds to a public transfer key of a source device. The destination device verifies the policy profile based on the destination key corresponding to a public transfer key of the source device. The destination device receives a signed encrypted data encryption key from the source device. The destination device receives encrypted data from the source device. The destination device verifies the signed encrypted data encryption key originated from the source device based on the signed encrypted data key being signed with a private attestation identity key that corresponds to a public attestation identity key of the source device. The destination device decrypts encrypted data using a private transfer key of the destination device.

Abstract: Techniques are disclosed for automatically inferring software-defined network policies from the observed workload in a computing environment. The disclosed techniques include monitoring network traffic flow originating from network interfaces corresponding to containers that execute components of an application, recording details of a new network connection or a change in the existing network connection, obtaining information concerning the components of the application, identifying metadata for a component involved in the new network connection or the change in an existing network connection based on a comparison of the details of the new network connection or a change in the existing network connection and the information concerning the components of the application, generating a network policy for the component using at least the metadata for the component, and integrating the network policy for the component into a deployment package for the application.

Abstract: Techniques are described for triggering conditional automated social posts. According to an embodiment, a set of one or more conditions is received through a user interface by a system executing on one or more computing devices. The system monitors one or more social media channels for target content that has been posted on at least one social media channel of the one or more social media channels. In response to detecting, by the system executing on one or more computing devices, that the target content has been posted on at least one social media channel of the one or more social media channels, the system determines whether the set of one or more conditions are satisfied. In response to determining that the set of one or more conditions are satisfied, the system triggers an action responsive to the target content.

Abstract: Herein are techniques to generate candidate rulesets for machine learning (ML) explainability (MLX) for black-box ML models. In an embodiment, an ML model generates classifications that each associates a distinct example with a label. A decision tree that, based on the classifications, contains tree nodes is received or generated. Each node contains label(s), a condition that identifies a feature of examples, and a split value for the feature. When a node has child nodes, the feature and the split value that are identified by the condition of the node are set to maximize information gain of the child nodes. Candidate rules are generated by traversing the tree. Each rule is built from a combination of nodes in a tree traversal path. Each rule contains a condition of at least one node and is assigned to a rule level. Candidate rules are subsequently optimized into an optimal ruleset for actual use.

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: Described herein are systems and methods for providing custom component compilation within a cloud-based content hub environment. In accordance with an embodiment, embodiments can allow developers to work with a variety of technologies and optimize a page or site for runtime performance, including in some instances the use of a custom (component) compiler, for use during compilation of a particular component of a website.

Abstract: Systems and methods for supporting rollback of changes made to target systems via an integration platform. Systems and methods can support reverting changes made to target applications via an integration platform (e.g., integration platform as a service (iPaaS)) with the help of a rollback activity. In order to aid the development and design of integration orchestrations, the integration platform can provide suggested rollback actions. In case of a failure in integration runtime, this rollback activity will revert changes made to the target applications prior to the point of fault. This helps ensure that systems remain in consistent state.