A computer implemented method of generating a machine learning model for interpreting host phage response data comprising receiving datasets and labels for a host phage response, training a machine learning model and using this model to estimate the efficacy of a test phage in inhibiting growth of a test bacteria.

A computer implemented method of generating a machine learning model for interpreting host phage response data comprising receiving datasets and labels for a host phage response, training a machine learning model and using this model to estimate the efficacy of a test phage in inhibiting growth of a test bacteria.

A method for dynamically managing a goal in a care plan of a patient is disclosed. The method includes receiving a selection of a type of the care plan for the patient, responsive to the selection of the type of the care plan, receiving a selection of a goal having a goal type to include in the care plan, generating the care plan including the goal having the goal type, and causing the care plan including the goal to be presented on a computing device of a medical personnel.

Methods and system for privacy preserving information exchange in a network of electronic devices are disclosed. In one embodiment, a method is implemented in an electronic device to serve as a local party for information exchange between the local party and another electronic device to serve as an aggregator. The method includes storing a plurality of values in a 2D vector, where a first dimension of the 2D vector is based on the number of values, and where each position in the first dimension has one unique value. The method further includes transmitting the 2D vector to the aggregator with masking for the aggregator to prevent the aggregator from decoding the 2D vector, where aggregating the masked 2D vector with masked 2D vectors from other local parties allows decoding of the aggregated 2D vector.

Methods and system for privacy preserving information exchange in a network of electronic devices are disclosed. In one embodiment, a method is implemented in an electronic device to serve as a local party for information exchange between the local party and another electronic device to serve as an aggregator. The method includes storing a plurality of values in a 2D vector, where a first dimension of the 2D vector is based on the number of values, and where each position in the first dimension has one unique value. The method further includes transmitting the 2D vector to the aggregator with masking for the aggregator to prevent the aggregator from decoding the 2D vector, where aggregating the masked 2D vector with masked 2D vectors from other local parties allows decoding of the aggregated 2D vector.

An information processing system according to the present invention is an information processing system that sets a weight matrix. The weight matrix is generated by learning using a target matrix that is a matrix including an action status on an item in each of a plurality of setting statuses as an element of a column, includes a weight corresponding to an intersection of items as an element, and is multiplied by the target matrix. The information processing system includes: a similarity degree calculating unit configured to extract, from each column of the target matrix, some elements from among all elements of the column, and calculate a degree of similarity between the items based on the some elements of the each column; and a weight matrix setting unit configured to set the weight matrix that is a sparse matrix including a nonzero element based on the degree of similarity.

An arrangement for automated testing of mobile devices comprising a learning arrangement for learning how to use test devices that do not match with an earlier already defined test case pattern. In the arrangement the learning arrangement generates instructions for performing a set of tasks. The tasks are then executed in the mobile device being tested. The mobile device provides feedback in form of error/success messages, screenshots, source code, return values and similar. Based on the feedback and earlier accumulated information the learning entity can generate a new set of instructions in order to execute the set of tasks successfully.

A method in a telecommunications system including a Data Network, DN, a base station, a connection via the base station from the DN to a User Equipment, UE, executing a UE application producing application data, and an algorithm entity on the DN, wherein the base station transmits network configuration information to a DN application executing on the algorithm entity, the DN application produces and transmits a filtering configuration based on the network configuration information to the UE for use in filtering the application data before transmission to the algorithm entity, allowing the UE to produce application data filtered according to the filtering configuration, and the connection transmits the filtered application data to the algorithm entity.

A method in a telecommunications system including a Data Network, DN, a base station, a connection via the base station from the DN to a User Equipment, UE, executing a UE application producing application data, and an algorithm entity on the DN, wherein the base station transmits network configuration information to a DN application executing on the algorithm entity, the DN application produces and transmits a filtering configuration based on the network configuration information to the UE for use in filtering the application data before transmission to the algorithm entity, allowing the UE to produce application data filtered according to the filtering configuration, and the connection transmits the filtered application data to the algorithm entity.

A method of training a module in an AI system and a method of preventing capture of an AI module in the AI system is disclosed. The AI system includes at least an AI module executing a model, a dataset, and the module adapted to be trained. The method includes receiving input data in the module adapted to be trained, labelling data as good data and bad data in the module adapted to be trained, classifying binarily the labelled good data and the labelled bad data in the module adapted to be trained, inputting the binarily classified data into the AI module, and recording internal behavior of the AI module in response to the binarily classified data on the module adapted to be trained.