Abstract: In one embodiment, a method includes limiting functionality of a remote controlled device during a first period of time where a user of the remote controlled device is not authenticated, receiving, from a separate authentication device, identity information of the user of the remote controlled device and information of the remote controlled device via an authentication process, authenticating the user prior to allowing full functionality of the remote controlled device, sending an indication of the identity of the user to the remote controlled device; and in response to determining during the authenticating that the user is verified as a trainee for the remote controlled device, enabling a trainer/trainee setting that allows a trainer to provide control of the remote controlled device to the trainee without functionality of the remote controlled device being limited.

Abstract: A method for implementing a secure system to prevent adverse drug interactions and repeat prescriptions, for a patient, in a multi-party computing environment. The method includes receiving a patient identifier from a provider, authenticating an access by the provider, and retrieving a second patient identifier, wherein the second patient identifier corresponds to the received patient identifier, wherein the received patient identifier and the second patient identifier are different. The method further includes receiving an input from the provider that corresponds to the patient identifier, and accessing a database that contains a stored private ID, wherein the stored private ID is a combination of the received patient identifier and the retrieved second patient identifier, and wherein the database includes data relating to the patient. The method includes searching the database to obtain a search result based on the received input from the provider, and transmitting the search result to the provider.

Abstract: An apparatus, method, program product, and system are disclosed for cognitive-based incident response. A sensor module captures baseline environment data related to a traffic incident using one or more sensors at a location of the traffic incident. A recommendation module generates and makes available, in real-time, one or more recommendations for responding to the traffic incident using cognitive computing processes based on the baseline environment data, the one or more recommendations comprising precautionary information for managing one or more emergency situations at the traffic incident. An update module continuously captures new environment data for the traffic incident using the one or more sensors, supplements the baseline environment data with the new environment data, and generates and makes available, in real-time, one or more updated recommendations based on the supplemented environment data using the supplemented environment data and the cognitive computing processes.

Abstract: In one embodiment, a system includes a processing circuit and logic integrated with the processing circuit, executable by the processing circuit, or integrated with and executable by the processing circuit. The logic is configured to cause the processing circuit to limit functionality of a remote controlled device during periods of time that a user of the remote controlled device is not authenticated, and to receive identity information of the user of the remote controlled device via an authentication process, with the identity information establishing an identity of the user. Also, the logic is configured to cause the processing circuit to authenticate the user prior to allowing full functionality of the remote controlled device, send an indication of the identity of the user to the remote controlled device, and provide full functionality of the remote controlled device to the user in response to successfully authenticating the user.

Abstract: A first collection including an analytical feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection. Using a forecasting configuration, an analytical feature vector of the third collection is aged to generate a changed analytical feature vector containing analytical feature values expected at a future time. The changed analytical feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A method for implementing a secure system to prevent adverse drug interactions and repeat prescriptions, for a patient, in a multi-party computing environment. The method includes receiving a patient identifier from a provider, authenticating an access by the provider, and retrieving a second patient identifier, wherein the second patient identifier corresponds to the received patient identifier, wherein the received patient identifier and the second patient identifier are different. The method further includes receiving an input from the provider that corresponds to the patient identifier, and accessing a database that contains a stored private ID, wherein the stored private ID is a combination of the received patient identifier and the retrieved second patient identifier, and wherein the database includes data relating to the patient. The method includes searching the database to obtain a search result based on the received input from the provider, and transmitting the search result to the provider.

Abstract: In one embodiment, a method includes limiting functionality of a remote controlled device during a first period of time where a user of the remote controlled device is not authenticated, receiving, from a separate authentication device, identity information of the user of the remote controlled device and information of the remote controlled device via an authentication process, authenticating the user prior to allowing full functionality of the remote controlled device, sending an indication of the identity of the user to the remote controlled device; and in response to determining during the authenticating that the user is verified as a trainee for the remote controlled device, enabling a trainer/trainee setting that allows a trainer to provide control of the remote controlled device to the trainee without functionality of the remote controlled device being limited.

Abstract: An apparatus, method, program product, and system are disclosed for cognitive-based incident response. A sensor module captures baseline environment data related to a traffic incident using one or more sensors at a location of the traffic incident. A recommendation module generates and makes available, in real-time, one or more recommendations for responding to the traffic incident using cognitive computing processes based on the baseline environment data, the one or more recommendations comprising precautionary information for managing one or more emergency situations at the traffic incident. An update module continuously captures new environment data for the traffic incident using the one or more sensors, supplements the baseline environment data with the new environment data, and generates and makes available, in real-time, one or more updated recommendations based on the supplemented environment data using the supplemented environment data and the cognitive computing processes.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by migrating, at least one of a vectors of the second collection with a corresponding vector of a fourth collection. The second and the fourth collections have a property distinct from one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: In one embodiment, a system includes a processing circuit and logic integrated with the processing circuit, executable by the processing circuit, or integrated with and executable by the processing circuit. The logic is configured to cause the processing circuit to limit functionality of a remote controlled device during periods of time that a user of the remote controlled device is not authenticated, and to receive identity information of the user of the remote controlled device via an authentication process, with the identity information establishing an identity of the user. Also, the logic is configured to cause the processing circuit to authenticate the user prior to allowing full functionality of the remote controlled device, send an indication of the identity of the user to the remote controlled device, and provide full functionality of the remote controlled device to the user in response to successfully authenticating the user.

Abstract: A first collection including an analytical feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection. Using a forecasting configuration, an analytical feature vector of the third collection is aged to generate a changed analytical feature vector containing analytical feature values expected at a future time. The changed analytical feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over a feature vectors of the second collection with a corresponding feature vector of a fourth collection. The second and the fourth collections have a property similar to one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a pattern of life (POL) feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by combining a vector of the second collection with a corresponding vector of a different collection. Using a forecasting configuration, a POL feature vector of the third collection is aged to generate a changed POL feature vector containing POL feature values expected at a future time. The changed POL feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including an analytical feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection. Using a forecasting configuration, an analytical feature vector of the third collection is aged to generate a changed analytical feature vector containing analytical feature values expected at a future time. The changed analytical feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over a feature vectors of the second collection with a corresponding feature vector of a fourth collection. The second and the fourth collections have a property similar to one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a pattern of life (POL) feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by combining a vector of the second collection with a corresponding vector of a different collection. Using a forecasting configuration, a POL feature vector of the third collection is aged to generate a changed POL feature vector containing POL feature values expected at a future time. The changed POL feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection. The second and the fourth collections have a property similar to one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a pattern of life (POL) feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection. Using a forecasting configuration, a POL feature vector of the third collection is aged to generate a changed POL feature vector containing POL feature values expected at a future time. The changed POL feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one the vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection, or both. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by migrating, at least one of a vectors of the second collection with a corresponding vector of a fourth collection. The second and the fourth collections have a property distinct from one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.