Abstract: According to one embodiment, a method, computer system, and computer program product for software agent synthesis is provided. The present invention may include generating one or more training examples from historical data and software agents; training, using the on the one or more training examples, a language model to synthesize a software agent based on a natural language input from a user; monitoring, using one or more input devices, for one or more natural language user inputs; responsive to identifying one or more natural language user inputs, synthesizing, using the trained language model, one or more software agents based on the one or more natural language user inputs; execute the one or more new software agents to carry out one or more tasks invoked by the one or more natural language user inputs.

Abstract: A method of generating a model for classifying sequential data, the method including receiving the sequential data including records having features; initializing mask weights and classifier weights on the features; processing, iteratively, frames of the sequential data using the model comprising the mask and classifier weights, wherein at each iteration the processing includes generating a current one of the frames, computing a penalty term over a data space of the current frame, and updating the mask weights using the classifier weights on the features and the penalty term; and outputting the machine learning model including updated ones of the mask weights to a service for performing a classification task based on a detection of at least one of the features in test data.

Abstract: A system for smart alerts in a batch system for an IT enterprise. The method includes alert configuration by identifying recent steady state of a batch job and deriving schedules for the steady state. The normal behaviour is then computed within the schedules. The method further includes aggregating the one or more alerts by identifying correlated group of alerts by pruning of one or more jobs and alerts, detecting correlations between the two or more alerts and deriving causality of the grouped alerts. The method finally includes predicting of future alerts of a batch job.

Abstract: A system for smart alerts in a batch system for an IT enterprise. The method includes alert configuration by identifying recent steady state of a batch job and deriving schedules for the steady state. The normal behaviour is then computed within the schedules. The method further includes aggregating the one or more alerts by identifying correlated group of alerts by pruning of one or more jobs and alerts, detecting correlations between the two or more alerts and deriving causality of the grouped alerts. The method finally includes predicting of future alerts of a batch job.