Abstract: An approach for training a recurrent neural network to create a model for anomaly detection in the topology of a network is disclosed. The approach comprises, creating an embedding vector for each resource in the network based on applying an embedding algorithm to each resource of the network. A feature vector is then created for each change to a resource in the network based on one or more properties of the change. A recurrent neural network can thus be trained with the embedding vectors and the feature vectors to create a model for anomaly detection in the topology of the network.

Abstract: A method, system, and computer program product are configured to: identify versions of a JAR file used by applications in a system; generate a ranked list of the versions of the JAR file including a highest ranked version; determine a first subset of the applications that use the highest ranked version; determine a second subset of the applications that use a version other than the highest ranked version; for each respective one of the applications in the second subset, identify a respective upgraded version of the JAR file to use with the respective one of the applications based on comparing methods required by the respective one of the applications to sets of methods included in respective ones of the versions of the JAR file; and generate a list including the respective upgraded version of the JAR file identified for each respective one of the applications in the second subset.

Abstract: Method and system are provided for user authentication using original and modified images. The method includes receiving an original image for a user, where the original image is a private image that meets certain configured criteria. The method uses a pre-trained Convolutional Neural Network (CNN) model to extract one or more image features of the original image and feeds the extracted image feature into a Generative Adversarial Network (GAN) image generator to realistically modify the extracted image feature to generate a modified image. The method authenticates a user based on recognition of a presented original image or a presented modified image.

Abstract: A method analyzes applications for modernization. Processor units analyze each application in the applications in isolation from other applications to identify issues in code for the applications and a number of common code components. The processor units create a directed graph structure using the issues identified in the applications and the number of common code components. The directed graph structure includes relationships between the applications, the number of common code components, and the issues identified. The processor units determine a common code component modernization cost for the number of common code components using the directed graph structure. The processor units determine an application modernization cost for a set of the applications using the directed graph structure. The application modernization cost for the set of the applications takes into account the common code component cost for modernizing the number of common code components a single time.

Abstract: Scalable mining method, computer system and computer program product for temporally correlated events that works with input data that is non-transactional in nature and detects high correlation relationships between events with low support thresholds. The scalable mining method manages a large scale of input data by executing the mining process in a distributed manner in a clustered cloud environment via nodes.

Abstract: A method analyzes applications for modernization. Processor units analyze each application in the applications in isolation from other applications to identify issues in code for the applications and a number of common code components. The processor units create a directed graph structure using the issues identified in the applications and the number of common code components. The directed graph structure includes relationships between the applications, the number of common code components, and the issues identified. The processor units determine a common code component modernization cost for the number of common code components using the directed graph structure. The processor units determine an application modernization cost for a set of the applications using the directed graph structure. The application modernization cost for the set of the applications takes into account the common code component cost for modernizing the number of common code components a single time.

Abstract: A method for dynamically generating an optimized processing pipeline for tasks is provided. The method identifies one or more tasks to be executed from defined tasks that are defined declaratively as a number of stages of input data, data transformations, and output data. The method processes the identified tasks to determine dependencies between the tasks based on their defined stages and creates one or more optimized data processing pipelines by performing a dependency resolution procedure on stages of all tasks in parallel using the task dependencies to determine the order of the stages and removing duplication of stages between tasks.

Abstract: Method and system are provided for user authentication using original and modified images. The method includes receiving an original image for a user, where the original image is a private image that meets certain configured criteria. The method uses a pre-trained Convolutional Neural Network (CNN) model to extract one or more image features of the original image and feeds the extracted image feature into a Generative Adversarial Network (GAN) image generator to realistically modify the extracted image feature to generate a modified image. The method authenticates a user based on recognition of a presented original image or a presented modified image.

Abstract: A method for dynamically generating an optimized processing pipeline for tasks is provided. The method identifies one or more tasks to be executed from defined tasks that are defined declaratively as a number of stages of input data, data transformations, and output data. The method processes the identified tasks to determine dependencies between the tasks based on their defined stages and creates one or more optimized data processing pipelines by performing a dependency resolution procedure on stages of all tasks in parallel using the task dependencies to determine the order of the stages and removing duplication of stages between tasks.

Abstract: An approach for training a recurrent neural network to create a model for anomaly detection in the topology of a network is disclosed. The approach comprises, creating an embedding vector for each resource in the network based on applying an embedding algorithm to each resource of the network. A feature vector is then created for each change to a resource in the network based on one or more properties of the change. A recurrent neural network can thus be trained with the embedding vectors and the feature vectors to create a model for anomaly detection in the topology of the network.

Abstract: Topology information including a plurality of snapshots of a network topology associated with respective points in time for a network can be received by an apparatus. Each snapshot is represented as a graph of nodes each corresponding to a network resource and having a node identifier. The graph for each snapshot is modified by replacing nodes representing network resources having the same role in the network with a single aggregated node. Feature learning is performed based on the modified graphs representing the plurality of snapshots, and determines a feature representation for each node in the modified graphs. An identifier for each node in the plurality of snapshots is associated with the corresponding feature representation for use in the correlation of network resources. Node identifiers for nodes in the same aggregated node in a modified graph are associated with the same feature representation.

Abstract: Scalable mining method, computer system and computer program product for temporally correlated events that works with input data that is non-transactional in nature and detects high correlation relationships between events with low support thresholds. The scalable mining method manages a large scale of input data by executing the mining process in a distributed manner in a clustered cloud environment via nodes.

Abstract: Topology information including a plurality of snapshots of a network topology associated with respective points in time for a network can be received by an apparatus. Each snapshot is represented as a graph of nodes each corresponding to a network resource and having a node identifier. The graph for each snapshot is modified by replacing nodes representing network resources having the same role in the network with a single aggregated node. Feature learning is performed based on the modified graphs representing the plurality of snapshots, and determines a feature representation for each node in the modified graphs. An identifier for each node in the plurality of snapshots is associated with the corresponding feature representation for use in the correlation of network resources. Node identifiers for nodes in the same aggregated node in a modified graph are associated with the same feature representation.

Abstract: A method and system are provided for retraining an analytic model. The method includes building, by a processor, a Markov chain for the analytic model. The Markov chain has only two states that consist of an alarm state and a no alarm state. The method further includes updating, by the processor, the Markov chain with observed states, for each of a plurality of timestamps evaluated during a burn-in period. The method also includes updating, by the processor, state transition probabilities within the Markov chain, for each of a plurality of timestamps evaluated after the burn-in period. The method additionally includes generating, by the processor, a signal for causing the model to be retrained, responsive to any of the state transition probabilities representing a probability of greater than 0.5 of seeing the alarm state in a previous interval and again in a current interval.

Abstract: In a method for visualizing cyclical patterns in metric data, receiving, by one or more processors, data relating to a computer environment. The method further includes creating, by one or more processors, a visualization, wherein the visualization presents the data in a first format. The method further includes causing, by one or more processors, the visualization to be displayed in a user interface. The method further includes receiving, by one or more processors, an indication of a user interaction in the user interface, wherein the user interaction indicates an alternative visualization of the data, wherein the alternative visualization presents the data in a second format that includes a fragment of the received data. The method further includes creating, by one or more processors, the alternative visualization.

Abstract: Deploying a model for anomaly detection in time series data. A period of data is received. A model of the period of data is received. It is determined that the model fits a part of the period of data and that the fitted part of the period of data includes the most recent data. A reduced model for the part of the period of data that fit the received model is built. The reduced model is deployed.

Abstract: A method and system for improving virtual keyboard input is provided. The method includes presenting a GUI comprising a character display portion and a virtual touch screen activated keyboard. The virtual touch screen activated keyboard internally includes currently viewable alphabetical character keys and a free sketch area for entering specialized characters not currently viewable via the virtual keyboard. A selection for an alphabetical character is received via the virtual touch screen activated keyboard and presented via the display portion. A user defined motion sketching a character associated with the specialized characters is detected from a user via the free sketch area. The character is converted into one of the specialized characters and presented to the user.

Abstract: Deploying a model for anomaly detection in time series data. A period of data is received. A model of the period of data is received. It is determined that the model fits a part of the period of data and that the fitted part of the period of data includes the most recent data. A reduced model for the part of the period of data that fit the received model is built. The reduced model is deployed.

Abstract: Anomaly detection is provided. A first component of a first data resource of a plurality of data resources is identified. Each data resource of the plurality of data resources includes one or more components. A score of the first component is determined based, at least in part, on underlying data of the first component and underlying data of one or more other components of data resources of the plurality of data resources that correspond to the first component. An interest level of the first data resource is determined. A relationship between the score of the first component and the interest level of the first data resource is modeled.

Abstract: A method of identifying topics which a user requires help with when using a software program is described. For each of a plurality of help documents, the help document is associated with a set of topics and their relative prevalence within the help document. User access to the help documents is tracked during use of the software program. Topics in relation to which help was required during use of the software program are identified based on an amount of access to one or more of the help documents and the relative prevalence of topics within the accessed help documents.