Systems and methods are disclosed for generating a user behavioral avatar. A method may include receiving a request to generate an avatar that performs actions on behalf of a user on a social media platform; identifying a plurality of historical user actions manually taken by the user on the social media platform; generating, based on the plurality of historical user actions, an action profile that represents user tendencies for executing available actions on the social media platform; identifying a plurality of data items in a retrieved backup of at least one computing device associated with the user; classifying the plurality of data items into a plurality of topics; training and executing the avatar to detect, in the social media platform, a social media item that shares at least one topic of the plurality of topics from the backup and perform a user action in accordance with the action profile.

An information processing apparatus includes: an output management information storage unit in which one or more pieces of output management information having an output condition, which is a condition for outputting emotion information and is a condition related to content, and emotion information are stored; an input information accepting unit that accepts input information that is content; an output information acquiring unit that judges whether or not information acquired using the content accepted by the input information accepting unit matches each of the one or more output conditions, and acquires emotion information that is paired with each of the one or more matching output conditions, from the output management information storage unit; and an information output unit that outputs the one or more pieces of emotion information acquired by the output information acquiring unit.

Aspects of the disclosure generally relate to computing devices and/or systems, and may be generally directed to devices, systems, methods, and/or applications for learning an avatar's or an application's operation in various circumstances, storing this knowledge in a knowledgebase (i.e. neural network, graph, sequences, etc.), and/or enabling autonomous operation of the avatar or the application.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reinforcement learning in the field of hygiene. Specifically, the features described relate to selecting actions in a context to be performed by an agent that interacts with an environment by receiving observations, in response, performing actions from a set of actions, wherein the context comprises a bacterial product to be prescribed, wherein the observations comprise data from the mapping of applied bacteria, and wherein the actions comprise data for the prescription of the bacterial product.

A method and an electronic device for selecting influence indicators by using an automatic mechanism are provided. The method includes following steps. Raw data is obtained, where the raw data includes a body-related variable and a plurality of to-be-measured indicators corresponding to the body-related variable. The body-related variable is set as a target parameter. The body-related variable and the to-be-measured indicators are input into a plurality of validation models, and the to-be-measured indicators are sorted according an output result of the validation models to obtain ranking data. Importance of the to-be-measured indicators is calculated by using a screening condition according to the ranking data, so as to select a candidate indicator from the to-be-measured indicators. An influence indicator is determined by calculating a correlation between the candidate indicator and the body-related variable.

A communication system according to the present disclosure includes a camera configured to be able to photograph a user who is a communication partner and a microphone configured to be able to form a beam-forming in a specific direction. The control unit identifies a position of the mouth of a user using an image of the user taken by the camera and controls a position of a head part so that the identified position of the mouth of the user is included in a region of the beam-forming.

Methods and systems for sensor fusion in a production line environment are disclosed. An example system for data collection in an industrial production environment may include an industrial production system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the components; a sensor communication circuit to interpret a plurality of sensor data values in response to a sensed parameter group; and a data analysis circuit to detect an operating condition of the industrial production system based at least in part on a portion of the sensor data values; and a response circuit to modify a production related operating parameter of the industrial production system in response to the detected operating condition.

A non-transitory, computer-readable recording medium stores a program of reinforcement learning by a state-value function. The program causes a computer to execute a process including calculating a temporal difference (TD) error based on an estimated state-value function, the TD error being calculated by giving a perturbation to each component of a feedback coefficient matrix that provides a policy; calculating based on the TD error and the perturbation, an estimated gradient function matrix acquired by estimating a gradient function matrix of the state-value function with respect to the feedback coefficient matrix for a state of a controlled object, when state variation of the controlled object in the reinforcement learning is described by a linear difference equation and an immediate cost or an immediate reward of the controlled object is described in a quadratic form of the state and an input; and updating the feedback coefficient matrix using the estimated gradient function matrix.

An optimization system has a plurality of nodes, each including: a calculation processing unit that calculates an optimum value of a determination variable representing a parameter that controls the nodes by using the ADMM; and a function value calculation unit that receives an input of a value of the determination variable and calculates the value of the objective function based on a calculation model constructed by a learning processing. An arbitrary value of the determination variable is input to the function value calculation unit, and the calculation processing unit substitutes the value of the objective function calculated by the function value calculation unit and the arbitrary value of the determination variable into a second optimization problem by dual transformation of the first optimization problem, thereby repeatedly executing a processing of calculating a value of the dual variable until the value of the dual variable satisfies a predetermined condition.

A system and method for evaluating the performance and usage of a cognitive computing tool which answers questions from users. A log file for these interactions includes the questions, the answers and a confidence rating assigned by the tool to each answer. Questions and answers are analyzed to determine validity, accuracy, and categories by subject matter experts or text analytics tools, and the results are added to the log file. Comments and sentiments from users may be analyzed and added to the log file. Additional data about the users, such as identities, demographics, and locations, may be added. Data from the log file may be presented in a dashboard display as metrics, such as trends and comparisons, describing the usage and performance of the cognitive computing tool. Answers may be displayed as they were presented to the users. Selectable filters may be provided to control the data displayed.