Abstract: Various embodiments described herein relate to a machine-learning based electronic media analysis software system. The system is configured to detect anomalous and predictive patterns associated with an event. The system is configured to use feature extraction techniques and semi-supervised machine-learning to detect the patterns associated with the event in the electronic media messages, which may indicate a synthetic driven behavior and conversation corresponding to the event.

Abstract: Disclosed herein are embodiments of systems, methods, and products that execute tools to identify non-malicious faults in source codes introduced by engineers and programmers. The tools may execute a machine learning model on the source codes to perform sentiment analysis and pattern analysis on information associated with the source codes to generate annotated source code files identifying anomalies based on the sentiment analysis and the pattern analysis. One or more threat levels are then identified and ranked based on the one or more anomalies and a ranked list of the one or more threat levels is displayed on a graphical user interface of a computer.

Abstract: A computer-implemented adaptive group training method a computer accessing a virtual system and initiating a group training exercise for training a trainee group comprising one or more trainees, the group training exercise including one or more challenges to the virtual system, each of the one or more challenges including a pre-defined sequence of one or more injectable events; the computer controlling subsequent execution of the group training exercise comprising injecting the injectable events; and the computer evaluating performance of the trainee group during the subsequent execution of the group training exercise, including analyzing actions taken by the trainee group in response to each of the injections, and attributing one or more of the actions taken to a trainee.

Abstract: Disclosed herein are embodiments of systems, methods, and products for modernizing and optimizing legacy software. A computing device may perform an automated runtime performance profiling process. The performance profiler may automatically profile the legacy software at runtime, monitor the memory usage and module activities of the legacy software, and pinpoint/identify a subset of inefficient functions in the legacy software that scale poorly or otherwise inefficient. The computing device may further perform a source code analysis and refactoring process. The computing device may parse the source code of the subset of inefficient functions and identify code violations within the source code. The computing device may provide one or more refactoring options to optimize the source code. Each refactoring option may comprise a change to the source code configured to correct the code violations. The computing device may refactor the source code based on a selected refactoring option.

Abstract: A computer-implemented adaptive group training method a computer accessing a virtual system and initiating a group training exercise for training a trainee group comprising one or more trainees, the group training exercise including one or more challenges to the virtual system, each of the one or more challenges including a pre-defined sequence of one or more injectable events; the computer controlling subsequent execution of the group training exercise comprising injecting the injectable events; and the computer evaluating performance of the trainee group during the subsequent execution of the group training exercise, including analyzing actions taken by the trainee group in response to each of the injections, and attributing one or more of the actions taken to a trainee.

Abstract: A computer-implemented adaptive group training method a computer accessing a virtual system and initiating a group training exercise for training a trainee group comprising one or more trainees, the group training exercise including one or more challenges to the virtual system, each of the one or more challenges including a pre-defined sequence of one or more injectable events; the computer controlling subsequent execution of the group training exercise comprising injecting the injectable events; and the computer evaluating performance of the trainee group during the subsequent execution of the group training exercise, including analyzing actions taken by the trainee group in response to each of the injections, and attributing one or more of the actions taken to a trainee.

Abstract: Disclosed herein are embodiments of systems, methods, and products for modernizing and optimizing legacy software. A computing device may perform an automated runtime performance profiling process. The performance profiler may automatically profile the legacy software at runtime, monitor the memory usage and module activities of the legacy software, and pinpoint/identify a subset of inefficient functions in the legacy software that scale poorly or otherwise inefficient. The computing device may further perform a source code analysis and refactoring process. The computing device may parse the source code of the subset of inefficient functions and identify code violations within the source code. The computing device may provide one or more refactoring options to optimize the source code. Each refactoring option may comprise a change to the source code configured to correct the code violations. The computing device may refactor the source code based on a selected refactoring option.

Abstract: A computer-implemented method includes creating, by a computing device, an abstract syntax tree based on a source code file of a software application, the source code file including source code defining operations of the software application. The method also includes traversing, by the computing device, the abstract syntax tree. The method further includes identifying, by the computing device and based on the traversing of the abstract syntax tree, one or more code violations present in the source code. The method also includes generating, by the computing device, at least one refactoring option for the one or more code violations, each refactoring option of the at least one refactoring option representing a change to the source code file that is configured to remediate the associated code violation.

Abstract: Disclosed herein are embodiments of systems, methods, and products that execute tools to identify non-malicious faults in source codes introduced by engineers and programmers. The tools may execute a machine learning model on the source codes to perform sentiment analysis and pattern analysis on information associated with the source codes to generate annotated source code files identifying anomalies based on the sentiment analysis and the pattern analysis. One or more threat levels are then identified and ranked based on the one or more anomalies and a ranked list of the one or more threat levels is displayed on a graphical user interface of a computer.

Abstract: A computer-implemented adaptive group training method a computer accessing a virtual system and initiating a group training exercise for training a trainee group comprising one or more trainees, the group training exercise including one or more challenges to the virtual system, each of the one or more challenges including a pre-defined sequence of one or more injectable events; the computer controlling subsequent execution of the group training exercise comprising injecting the injectable events; and the computer evaluating performance of the trainee group during the subsequent execution of the group training exercise, including analyzing actions taken by the trainee group in response to each of the injections, and attributing one or more of the actions taken to a trainee.

Abstract: A computer-implemented method includes creating, by a computing device, an abstract syntax tree based on a source code file of a software application, the source code file including source code defining operations of the software application. The method also includes traversing, by the computing device, the abstract syntax tree. The method further includes identifying, by the computing device and based on the traversing of the abstract syntax tree, one or more code violations present in the source code. The method also includes generating, by the computing device, at least one refactoring option for the one or more code violations, each refactoring option of the at least one refactoring option representing a change to the source code file that is configured to remediate the associated code violation.

Abstract: A computer-implemented adaptive group training method a computer accessing a virtual system and initiating a group training exercise for training a trainee group comprising one or more trainees, the group training exercise including one or more challenges to the virtual system, each of the one or more challenges including a pre-defined sequence of one or more injectable events; the computer controlling subsequent execution of the group training exercise comprising injecting the injectable events; and the computer evaluating performance of the trainee group during the subsequent execution of the group training exercise, including analyzing actions taken by the trainee group in response to each of the injections, and attributing one or more of the actions taken to a trainee.

Abstract: A computer-implemented method includes creating, by a computing device, an abstract syntax tree based on a source code file of a software application, the source code file including source code defining operations of the software application. The method also includes traversing, by the computing device, the abstract syntax tree. The method further includes identifying, by the computing device and based on the traversing of the abstract syntax tree, one or more code violations present in the source code. The method also includes generating, by the computing device, at least one refactoring option for the one or more code violations, each refactoring option of the at least one refactoring option representing a change to the source code file that is configured to remediate the associated code violation.