Abstract: A system for defining data analytics pipelines with a processor and a memory includes a data source with raw data, a semantic data lake, and a data integration module, wherein the data integration module is configured via computer executable instructions to create semantic annotations that describe a capability and a structure of the raw data of the data source, create or modify a knowledge graph utilizing the semantic annotations, and integrate the raw data and the semantic annotations into the semantic data lake, wherein the raw data are interpretable via the knowledge graph and the semantic annotations.

Abstract: System and method for robust machine learning (ML) includes an attack detector comprising one or more deep neural networks trained using adversarial examples generated from a generative adversarial network (GAN), producing an alertness score based on a likelihood of an input being adversarial. A dynamic ensemble of individually robust ML models of various types and sizes and all being trained to perform an ML-based prediction is dynamically adapted by types and sizes of ML models to be deployed during the inference stage of operation. The adaptive ensemble is responsive to the alertness score received from the attack detector. A data protector module with interpretable neural network models is configured to prescreen training data for the ensemble to detect potential data poisoning or backdoor triggers in initial training data.

Abstract: A method of managing change in a complex system begins with identifying an unsatisfied need to be met by the system. To satisfy the need a proposed change to the system to satisfy the need is represented a high-level representation of the proposed change. The high-level representation is mapped to a low-level executable semantic model, which is used to validate the proposed change and ensure the proposed change meets the identified and does not require additional changes to the system. On a condition that the validating steps determines that additional changes are required the additional changes are represented in the high-level representation of the system; the high-level change is mapped to the low-level executable semantic model and the additional changes are re-validated.

Abstract: A method for testing software applications in a system under test (SUT) includes building a reference model of the SUT that defines a computer-based neural network. The method includes training the reference model using input data and corresponding output data generated by the SUT, selecting an output value within a domain of possible output values of the SUT representing an output that is not represented in the output data used to train the reference model, applying the selected output value to the reference model, and tracing the selected output through the reference model to identify test input values that when input to the reference model, produce the selected output value. The method can further include using the identified test input values to test the system under test.

Abstract: A method for testing software applications in a system under test (SUT) includes building a reference model of the SUT comprising a computer-based neural network, training the reference model using input data and corresponding output data generated by the SUT, selecting an output value within a domain of possible output values of the SUT representing an output that is not represented in the output data used to train the reference model, applying the selected output value to the reference model and tracing the selected output through the reference model to identify test input values that when input to the reference model, produce the selected output value and using the identified test input values to test the system under test.

Abstract: A method of managing change in a complex system begins with identifying an unsatisfied need to be met by the system. To satisfy the need a proposed change to the system to satisfy the need is represented a high-level representation of the proposed change. The high-level representation is mapped to a low-level executable semantic model, which is used to validate the proposed change and ensure the proposed change meets the identified and does not require additional changes to the system. On a condition that the validating steps determines that additional changes are required the additional changes are represented in the high-level representation of the system; the high-level change is mapped to the low-level executable semantic model and the additional changes are re-validated.

Abstract: A method for automatic testing of a piece of software for a mobile device including the following steps: deriving from a description a formalized description, the description includes possible sequences of events of the software and a range for at least one input parameter of the software, the description being used for an implementation of the software; generating from the formalized description a test description; adapting the test description for the mobile device for which the software is to be tested; translating the test specification in a language assigned to the mobile device such that a test described by the test specification can be performed on the mobile device. The method for automatic testing further relates to a corresponding device.

Abstract: A method for automatic testing of a piece of software for a mobile device including the following steps: deriving from a description a formalized description, the description includes possible sequences of events of the software and a range for at least one input parameter of the software, the description being used for an implementation of the software; generating from the formalized description a test description; adapting the test description for the mobile device for which the software is to be tested; translating the test specification in a language assigned to the mobile device such that a test described by the test specification can be performed on the mobile device. The method for automatic testing further relates to a corresponding device.