Abstract: Methods, apparatus, systems, and articles of manufacture to determine a number of denoising iterations of model output generation are disclosed. An example apparatus includes at least one programmable circuit to execute a model to generate a plurality of outputs based on a text-based prompt, each of the plurality of outputs generated using different numbers of denoising iterations; generate an ordered set of the plurality of outputs based on the number of denoising iterations; determine a plurality of similarities between neighboring outputs in the ordered set of the plurality of outputs; and select a number of denoising iterations based on the plurality of similarities.

Abstract: An embodiment includes generating an input document that includes a plurality of text fields of attribute data. The embodiment also includes extracting a set of candidate features from the attribute data using a feature extraction module that evaluates the attribute data using first and second machine learning models, where the first machine learning model scores terms in the input document and the second machine learning model includes a deep learning model. The embodiment also includes calculating feature-selection values for respective features of the set of candidate features and generating tag information for a remote computing asset using a machine learning classifier that predicts the tag information based on the feature-selection values.

Abstract: An embodiment includes generating an input document that includes a plurality of text fields of attribute data. The embodiment also includes extracting a set of candidate features from the attribute data using a feature extraction module that evaluates the attribute data using first and second machine learning models, where the first machine learning model scores terms in the input document and the second machine learning model includes a deep learning model. The embodiment also includes calculating feature-selection values for respective features of the set of candidate features and generating tag information for a remote computing asset using a machine learning classifier that predicts the tag information based on the feature-selection values.

Abstract: An embodiment includes generating an input document that includes a plurality of text fields of attribute data. The embodiment also includes extracting a set of candidate features from the attribute data using a feature extraction module that evaluates the attribute data using first and second machine learning models, where the first machine learning model scores terms in the input document and the second machine learning model includes a deep learning model. The embodiment also includes calculating feature-selection values for respective features of the set of candidate features and generating tag information for a remote computing asset using a machine learning classifier that predicts the tag information based on the feature-selection values.

Abstract: An embodiment includes calculating an efficacy value of a context assigned to an asset, the efficacy value being based at least in part on a percentage of assets assigned to the context. The embodiment also includes calculating, responsive to the calculated efficacy value being within a predetermined range, a normalized relevance value of a first tag associated with the asset. The embodiment also includes generating relative relevance value for the first tag and a reason code associated with a basis for the relative relevance value of the first tag based at least in part on the normalized relevance value of the first tag. The embodiment also includes initiating, responsive to generating the relative relevance value and the reason code for the first tag, a database command on a training dataset stored in the database that includes the first tag.

Abstract: An embodiment includes calculating an efficacy value of a context assigned to an asset, the efficacy value being based at least in part on a percentage of assets assigned to the context. The embodiment also includes calculating, responsive to the calculated efficacy value being within a predetermined range, a normalized relevance value of a first tag associated with the asset. The embodiment also includes generating relative relevance value for the first tag and a reason code associated with a basis for the relative relevance value of the first tag based at least in part on the normalized relevance value of the first tag. The embodiment also includes initiating, responsive to generating the relative relevance value and the reason code for the first tag, a database command on a training dataset stored in the database that includes the first tag.

Abstract: An embodiment includes generating an input document that includes a plurality of text fields of attribute data. The embodiment also includes extracting a set of candidate features from the attribute data using a feature extraction module that evaluates the attribute data using first and second machine learning models, where the first machine learning model scores terms in the input document and the second machine learning model includes a deep learning model. The embodiment also includes calculating feature-selection values for respective features of the set of candidate features and generating tag information for a remote computing asset using a machine learning classifier that predicts the tag information based on the feature-selection values.

Abstract: A computer implemented method, data processing system, and computer program product for Java class automatic deployment using byte code instrumentation technology. One or more classloaders are instrumented with byte code instrumentation code such that a class loading event is received when a class is loaded. If a determination is made that new byte code instrumentation code needs to be loaded with the loaded class, candidate classloaders that load import classes of the new byte code instrumentation code are determined. A correct classloader from the candidate classloaders to load the new byte code instrumentation code is calculated. The correct classloader is instrumented to have an extended classpath, wherein the new byte code instrumentation code is inserted into the extended classpath of the correct classloader. The class is loaded from the extended classpath and original classpath of the correct classloader.

Abstract: A computer implemented method, data processing system, and computer program product for automatic discovery of the Java classloader delegation hierarchy. One or more classloaders are instrumented with byte code instrumentation code such that a delegation event is received each time a classloader delegates a resource finding method to a parent classloader of the classloader. From each delegation event received, the classloader that generated the delegation event and a parent classloader to which the classloader directly delegates the resource finding method is determined. Based on the delegation events received, the classloaders to which a current classloader in the one or more classloaders can directly delegate are determined. The order of delegation to the classloaders may then be determined.

Abstract: A method for minimizing the message flow wait time for management user exits in a message broker application. A message broker application processes a request in a request and a response message flow. The request message flow generates a request identifier, collects information about the request message flow, and stores the request identifier and information in a global data map. The response message flow uses the request identifier to access the map and read the collected information without having to acquire a lock on the map. The response message flow also collects information about the response message flow, and generates management information about the request based on the information about the request message flow and the information about the response message flow. A dedicated clean up thread in the message broker application is used to remove used items from the global data map.

Abstract: Computer implemented method, system and computer usable program code for end-to-end transaction tracking of long-running processes such as a Business Process Execution Language (BPEL) process in a data processing system, such as a data processing system implemented in a Service-Oriented Architecture (SOA). A computer implemented method for tracking and correlating transactions in a process in a data processing system includes gathering instance identification data and instance state data regarding a transaction and storing the instance identification data and instance state data in association with transaction data regarding the transaction. Asynchronous event data is collected, and transaction performance information for an asynchronous event within the transaction is calculated using the transaction data. A tracking report of the transaction is then generated using the calculated transaction performance information.

Abstract: A computer implemented method, system, and computer usable program code for managing a transaction between a service client and a service provider is provided. A request from a Web services client application is received. A correlation token and a unique transaction handle associated with the correlation token are extracted from the request, forming an extracted correlation token and an extracted transaction handle. A child correlation token is generated using the extracted correlation token as a parent correlation token. A unique transaction handle associated with the child correlation token is generated. A timer associated with the unique transaction handle and child correlation token is started. The request is performed. The timer associated with the unique transaction handle is stopped. The extracted transaction handle is inserted in a response to the request. The response with the extracted transaction handle is transmitted to a Web services client application.

Abstract: A computer implemented method, data processing system, and computer program product for automatic discovery of the Java classloader delegation hierarchy. One or more classloaders are instrumented with byte code instrumentation code such that a delegation event is received each time a classloader delegates a resource finding method to a parent classloader of the classloader. From each delegation event received, the classloader that generated the delegation event and a parent classloader to which the classloader directly delegates the resource finding method is determined. Based on the delegation events received, the classloaders to which a current classloader in the one or more classloaders can directly delegate are determined. The order of delegation to the classloaders may then be determined.

Abstract: A computer implemented method, data processing system, and computer program product for Java class automatic deployment using byte code instrumentation technology. One or more classloaders are instrumented with byte code instrumentation code such that a class loading event is received when a class is loaded. If a determination is made that new byte code instrumentation code needs to be loaded with the loaded class, candidate classloaders that load import classes of the new byte code instrumentation code are determined. A correct classloader from the candidate classloaders to load the new byte code instrumentation code is calculated. The correct classloader is instrumented to have an extended classpath, wherein the new byte code instrumentation code is inserted into the extended classpath of the correct classloader. The class is loaded from the extended classpath and original classpath of the correct classloader.

Abstract: A method for minimizing the message flow wait time for management user exits in a message broker application. A message broker application processes a request in a request and a response message flow. The request message flow generates a request identifier, collects information about the request message flow, and stores the request identifier and information in a global data map. The response message flow uses the request identifier to access the map and read the collected information without having to acquire a lock on the map. The response message flow also collects information about the response message flow, and generates management information about the request based on the information about the request message flow and the information about the response message flow. A dedicated clean up thread in the message broker application is used to remove used items from the global data map.

Abstract: Computer implemented method, system and computer usable program code for end-to-end transaction tracking of long-running processes such as a Business Process Execution Language (BPEL) process in a data processing system, such as a data processing system implemented in a Service-Oriented Architecture (SOA). A computer implemented method for tracking and correlating transactions in a process in a data processing system includes gathering instance identification data and instance state data regarding a transaction and storing the instance identification data and instance state data in association with transaction data regarding the transaction. Asynchronous event data is collected, and transaction performance information for an asynchronous event within the transaction is calculated using the transaction data. A tracking report of the transaction is then generated using the calculated transaction performance information.