Abstract: A first network entity performing communication in a wireless communication system supporting a network slice, includes: memory storing instructions; and at least one processor, wherein the at least one processor is configured to execute the instructions to cause the first network entity to: receive, from a second network entity, a request to activate or deactivate a network slice instance (NSI) for a geographical coverage area; search for a list comprising at least one of: at least one network slice subnet instance (NSSI), or at least one network slice subnet management entity function (NSSMF); and send a request to a third network entity to activate or deactivate an NSSI, and wherein the at least one NSSI and the at least one NSSMF each support at least one of: the geographical coverage area, or an area code list mapped to the geographical coverage area.

Abstract: Embodiments of the present invention enable a comparison of different machine-learning models based on a single neural network design may be provided. A deep learning architecture for an experimentation framework is represented as a directed acyclic graph with nodes representing neural network layers. Embodiments of the present invention specify a first machine-learning model in a first branch and a second machine-learning model in a second branch of the directed acyclic graph. Each branch has its own optimizer node. Embodiments of the present invention generate source code for the first machine-learning model and the second machine-learning model out of the directed acyclic graph, and train the first machine-learning model and the second machine-learning model simultaneously, thereby enabling the comparison of different machine-learning models.

Abstract: An exclusion database is maintained for software discovery scans where directories within a file system are excluded according to discovered software artifacts, scan time parameters, and pre-defined exclusion definitions associated with software applications. A file system tree having directory-specific attributes for causing software discovery scans to limit scanning within the file system.

Abstract: A computer-implemented method for selecting a dataset from given datasets for updating an artificial intelligence module (AI-module). The given datasets each comprise an input dataset and a corresponding output dataset. The computer-implemented method comprises: obtaining values of parameters for defining different clusters of the given datasets, determining a metric of each given dataset, the metric of each given dataset being dependent on a level of membership of the respective given dataset to one of the clusters and a distance of the respective given dataset to a centroid of the same one of the clusters, and selecting at least one of the given datasets from the given datasets for updating the AI-module on the basis of a comparison of the metrics of the given datasets.

Abstract: A processor may identify, using historical data, an amount of computing resources consumed to remedy the failure with an automatic remedy step. The processor may determine that the amount of consumed computing resources to remedy the failure is less than an amount of computing resources consumed by restarting the process. The processor may perform the automatic remedy step. The processor may identify that the automatic remedy step has failed. The processor may determine a waiting period based on an estimated time to receive a user response to the failure and an estimated load on the computing cluster. The processor may display a generated alert to a user during the waiting period. The processor may identify that no user input has been received during the waiting period. The processor may release computing resources corresponding to the process.

Abstract: Embodiments of the present invention enable a comparison of different machine-learning models based on a single neural network design may be provided. A deep learning architecture for an experimentation framework is represented as a directed acyclic graph with nodes representing neural network layers. Embodiments of the present invention specify a first machine-learning model in a first branch and a second machine-learning model in a second branch of the directed acyclic graph. Each branch has its own optimizer node. Embodiments of the present invention generate source code for the first machine-learning model and the second machine-learning model out of the directed acyclic graph, and train the first machine-learning model and the second machine-learning model simultaneously, thereby enabling the comparison of different machine-learning models.

Abstract: A processor may identify, using historical data, an amount of computing resources consumed to remedy the failure with an automatic remedy step. The processor may determine that the amount of consumed computing resources to remedy the failure is less than an amount of computing resources consumed by restarting the process. The processor may perform the automatic remedy step. The processor may identify that the automatic remedy step has failed. The processor may determine a waiting period based on an estimated time to receive a user response to the failure and an estimated load on the computing cluster. The processor may display a generated alert to a user during the waiting period. The processor may identify that no user input has been received during the waiting period. The processor may release computing resources corresponding to the process.

Abstract: An exclusion database is maintained for software discovery scans where directories within a file system are excluded according to discovered software artifacts, scan time parameters, and pre-defined exclusion definitions associated with software applications. A file system tree having directory-specific attributes for causing software discovery scans to limit scanning within the file system.

Abstract: One or more processors determine that one or more memory locations in a client computing device contain one or more software artifacts that provide a match to a first software signature. One or more processors send instructions not to scan the one or more memory locations against a second software signature.

Abstract: A current data set with multiple records is fed into a data analysis model. The current data set is limited to data occurring in a current time window with a predetermined window size. The model is run on the current data set and a current data prediction result is generated. Limited historical data sets having multiple records are selected. Each record has values for several features. Each historical data set is limited to data occurring in a historical time window having the window size. A historical class label distribution is determined for the historical data sets and an upper and lower control limit are determined for the historical data sets using the historical class label distribution. A current class label distribution having a mean value is determined for the current prediction result. An alert is provided when the mean value is not between the upper and lower control limit.

Abstract: A processor may identify, using historical data, an amount of computing resources consumed to remedy the failure with an automatic remedy step. The processor may determine that the amount of consumed computing resources to remedy the failure is less than an amount of computing resources consumed by restarting the process. The processor may perform the automatic remedy step. The processor may identify that the automatic remedy step has failed. The processor may determine a waiting period based on an estimated time to receive a user response to the failure and an estimated load on the computing cluster. The processor may display a generated alert to a user during the waiting period. The processor may identify that no user input has been received during the waiting period. The processor may release computing resources corresponding to the process.

Abstract: A processor may identify, using historical data, an amount of computing resources consumed to remedy the failure with an automatic remedy step. The processor may determine that the amount of consumed computing resources to remedy the failure is less than an amount of computing resources consumed by restarting the process. The processor may perform the automatic remedy step. The processor may identify that the automatic remedy step has failed. The processor may determine a waiting period based on an estimated time to receive a user response to the failure and an estimated load on the computing cluster. The processor may display a generated alert to a user during the waiting period. The processor may identify that no user input has been received during the waiting period. The processor may release computing resources corresponding to the process.

Abstract: A method for improving a machine learning model may be provided. The method comprises selecting a model quality metric of the machine learning model, determining a threshold value for a model quality value relating to the model quality metric using an X control chart method based on cross validation with a number of folds, equal to a number of possible model quality values, and on determining that the model quality value is below the determined threshold value, retraining the machine learning model with a new set of training data.

Abstract: A method for improving a machine learning model may be provided. The method comprises selecting a model quality metric of the machine learning model, determining a threshold value for a model quality value relating to the model quality metric using an X control chart method based on cross validation with a number of folds, equal to a number of possible model quality values, and on determining that the model quality value is below the determined threshold value, retraining the machine learning model with a new set of training data.

Abstract: A current data set with multiple records is fed into a data analysis model. The current data set is limited to data occurring in a current time window with a predetermined window size. The model is run on the current data set and a current data prediction result is generated. Limited historical data sets having multiple records are selected. Each record has values for several features. Each historical data set is limited to data occurring in a historical time window having the window size. A historical class label distribution is determined for the historical data sets and an upper and lower control limit are determined for the historical data sets using the historical class label distribution. A current class label distribution having a mean value is determined for the current prediction result. An alert is provided when the mean value is not between the upper and lower control limit.

Abstract: Described herein is an approach for automatically determining the semantic relatedness of documents to semantic concepts. A first text mining analysis extracts a set of reference concepts from reference documents. A second text mining analysis extracts a set of test concepts from test documents that include a mixture of new concepts and reference concepts. An extended co-occurrence matrix is computed that indicates a frequency of co-occurrence (RCCF) of each new and each reference concept in the test documents with all other new and reference concepts. The extended co-occurrence matrix is used for computing a new concept relatedness score (NCRS) for the new concepts. A document similarity score (DSS) is computed for each of the test documents by aggregating, inter alia, the NCRS of each new concept with the RCCF of each reference concept. The DSS represents the semantic relatedness of the test document to the totality of the reference concepts.

Abstract: Described herein is an approach for automatically determining the semantic relatedness of documents to semantic concepts. A first text mining analysis extracts a set of reference concepts from reference documents. A second text mining analysis extracts a set of test concepts from test documents that include a mixture of new concepts and reference concepts. An extended co-occurrence matrix is computed that indicates a frequency of co-occurrence (RCCF) of each new and each reference concept in the test documents with all other new and reference concepts. The extended co-occurrence matrix is used for computing a new concept relatedness score (NCRS) for the new concepts. A document similarity score (DSS) is computed for each of the test documents by aggregating, inter alia, the NCRS of each new concept with the RCCF of each reference concept. The DSS represents the semantic relatedness of the test document to the totality of the reference concepts.

Abstract: One or more processors determine that one or more memory locations in a client computing device contain one or more software artifacts that provide a match to a first software signature. One or more processors send instructions not to scan the one or more memory locations against a second software signature.