A machine learning device, which learns an operation program of a robot, includes a state observation unit which observes as a state variable at least one of a shaking of an arm of the robot and a length of an operation trajectory of the arm of the robot; a determination data obtaining unit which obtains as determination data a cycle time in which the robot performs processing; and a learning unit which learns the operation program of the robot based on an output of the state observation unit and an output of the determination data obtaining unit.

Provided is a method for controlling privileges for an intelligent industrial assistant. The method may include receiving a first input from a user. The first input may include a first natural language input and first biometric data. An identity of the user may be determined based on the first biometric data. A role associated with the user may be determined based on the identity of the user. A request of the user may be determined based on the natural language input. The request may be associated with at least one acceptable role. A determination may be made whether the role associated with the user matches at least one of the acceptable role(s). A system and computer program product are also disclosed.

An identification device has a processor configured to carry out plural identification processing by which an individual is identified based on plural acquired data different from each other indicating the individual and, when the identification of the individual by one or more identification processing of the plural identification processing fails and the identification of the individual by one or more other identification processing of the plural identification processing succeeds, learn the at least one identification processing by which the identification of the individual fails.

Systems, methods, and apparatuses for generating and using machine learning models using genetic data. A set of input features for training the machine learning model can be identified and used to train the model based on training samples, e.g., for which one or more labels are known. As examples, the input features can include aligned variables (e.g., derived from sequences aligned to a population level or individual references) and/or non-aligned variables (e.g., sequence content). The features can be classified into different groups based on the underlying genetic data or intermediate values resulting from a processing of the underlying genetic data. Features can be selected from a feature space for creating a feature vector for training a model. The selection and creation of feature vectors can be performed iteratively to train many models as part of a search for optimal features and an optimal model.

Systems, methods, and apparatuses for generating and using machine learning models using genetic data. A set of input features for training the machine learning model can be identified and used to train the model based on training samples, e.g., for which one or more labels are known. As examples, the input features can include aligned variables (e.g., derived from sequences aligned to a population level or individual references) and/or non-aligned variables (e.g., sequence content). The features can be classified into different groups based on the underlying genetic data or intermediate values resulting from a processing of the underlying genetic data. Features can be selected from a feature space for creating a feature vector for training a model. The selection and creation of feature vectors can be performed iteratively to train many models as part of a search for optimal features and an optimal model.

A three-dimensional track planning method based on improved particle swarm optimization algorithm is disclosed. During the process of searching an optimal three-dimensional track in a track space, Different inertia weights are set in different particle swarm iterative evolution stages. A maximum inertia weight is used to make global convergence in a set early stage of evolution, and a minimum inertia weight is used to make local convergence in a set late stage of evolution. Disturbance mutation operation in a motion process of particles is added based on swarm diversity. Infeasible particles are selected based on constraints. Constraint violation functions of infeasible particles are compared, and infeasible particles with small constraint violation functions are kept. The disclosure makes full use of all particles, so that the infeasible solutions can also provide help for the overall optimization of the swarm, and ensures the reliability and efficiency of track planning

In one embodiment, a method includes receiving a user input from a first user at the first client system, determining that the user input is a sharing request to share content, determining multiple second users the sharing request is directed to, determining, for each second user, modalities associated with the respective second user based on the content, a user profile associated with the respective second user, and modalities supported by a second client system the respective second user is currently engaged with, the respective second user being associated with two or more second client systems, and sending, to one or more second client systems currently associated with the second users, instructions for accessing the content based on the determined modalities for each second user.

A system that incorporates teachings of the present disclosure may include, for example, a synthesis engine having a controller, and a storage medium for storing instructions to be executed by the controller. The instructions, when executed by the controller, can cause the controller to retrieve collected information associated with a behavior of an individual, synthesize from the information a measure of a mood of the individual to interact with others, and transmit the measure to a system associated with the individual to manage requests between the individual and the entity. The measure of the mood of the individual can indicate an availability of the individual and a receptiveness of the individual to accept a request to interact with an entity. The measure can be described by a plurality of dimensions. Other embodiments are disclosed.

A recording medium records a simulation program for causing a computer to execute a process including: arranging an agent, which has cognitive information and behaves in a virtual space based on the cognitive information, in the virtual space in which a plurality of guidance displays is set; when, with respect to each of the plurality of guidance displays, the agent enters within a first range of the respective guidance displays, and first guidance information corresponding to the first range is related with a destination of the agent, changing cognitive information up to the destination of the agent; and when, with respect to each of the plurality of guidance displays, the agent enters within a second range different from the first range, and second guidance information corresponding to the second range is related with the destination of the agent, changing cognitive information not directly related with the destination of the agent.

Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving, at a local device from a remote device, query data representative of a question relating to source content of a source document, and determining whether one or more pre-determined questions stored in a question-answer cache maintained at the local device matches the query data according to one or more matching criteria. The method further includes obtaining from the question-answer cache, in response to a determination that at least one of the pre-determined questions matches the query data received from the remote device, at least one answer data item, associated with at least one pre-determined question, corresponding to an answer to the question relating to the source content.