The disclosure relates to an artificial intelligence (AI) system using a machine learning algorithm such as deep learning, and an application thereof. In particular, an electronic apparatus, a document displaying method thereof, and a non-transitory computer readable recording medium are provided. An electronic apparatus according to an embodiment of the disclosure includes a display unit displaying a document, a microphone receiving a user voice, and a processor configured to acquire at least one topic from contents included in a plurality of pages constituting the document, recognize a voice input through the microphone, match the recognized voice with one of the acquired at least one topic, and control the display unit to display a page including the matched topic.

Data are maintained in a distributed computing system that describe a graph. The graph represents relationships among items. The graph has a plurality of vertices that represent the items and a plurality of edges connecting the plurality of vertices. At least one vertex of the plurality of vertices includes a set of label values indicating the at least one vertex's strength of association with a label from a set of labels. The set of labels describe possible characteristics of an item represented by the at least one vertex. At least one edge of the plurality of edges includes a set of label weights for influencing label values that traverse the at least one edge. A label propagation algorithm is executed for a plurality of the vertices in the graph in parallel for a series of synchronized iterations to propagate labels through the graph.

Data are maintained in a distributed computing system that describe a graph. The graph represents relationships among items. The graph has a plurality of vertices that represent the items and a plurality of edges connecting the plurality of vertices. At least one vertex of the plurality of vertices includes a set of label values indicating the at least one vertex's strength of association with a label from a set of labels. The set of labels describe possible characteristics of an item represented by the at least one vertex. At least one edge of the plurality of edges includes a set of label weights for influencing label values that traverse the at least one edge. A label propagation algorithm is executed for a plurality of the vertices in the graph in parallel for a series of synchronized iterations to propagate labels through the graph.

The optimization processing apparatus is an apparatus for assigning actions on a per-user basis. The optimization processing apparatus includes: a data obtainment unit that obtains constraint information on a per-action basis and user information on a per-user basis; a gain function estimation unit estimates, for each user, a prediction function and a reliability degree function based on the constraint information and the user information, and estimates a gain function from the prediction function and the reliability degree function; and an assignment processing unit that assigns the actions on a per-user basis based on the estimated gain functions. The gain function estimation unit corrects, for each user, the gain function of the user in a case where a set condition is satisfied.

Machine learning object-searching methods and apparatuses are disclosed. The method comprises: selecting a state from a set of states of a target object-searching scene as a first state; obtaining a target optimal object-searching strategy whose initial state is the first state for searching for a target object; performing strategy learning by taking the target optimal object-searching strategy as a learning target to obtain an object-searching strategy by which a robot searches for the target object, and adding the obtained object-searching strategy into an object-searching strategy pool; determining whether the obtained object-searching strategy is consistent with the target optimal object-searching strategy; if yes, determining that the strategy learning in which the first state is taken as the initial state of the object-searching strategy is completed; and if not, returning to the step of selecting a state from a set of states of a target object-searching scene.

The information processing device inputs data into a machine learning model, acquires a first value output from the machine learning model in response to the inputting, a second value output from the machine learning model based on a variable obtained by modifying a latent variable that is calculated by the machine learning model in response to the inputting, and information entropy of the latent variable, and trains the machine learning model based on the first value, the second value and the information entropy of the latent variable.

Systems, methods, and computer-readable media are provided for generating a unique ID for a sensor in a network. Once the sensor is installed on a component of the network, the sensor can send attributes of the sensor to a control server of the network. The attributes of the sensor can include at least one unique identifier of the sensor or the host component of the sensor. The control server can determine a hash value using a one-way hash function and a secret key, send the hash value to the sensor, and designate the hash value as a sensor ID of the sensor. In response to receiving the sensor ID, the sensor can incorporate the sensor ID in subsequent communication messages. Other components of the network can verify the validity of the sensor using a hash of the at least one unique identifier of the sensor and the secret key.

Systems, methods, and computer-readable media are provided for generating a unique ID for a sensor in a network. Once the sensor is installed on a component of the network, the sensor can send attributes of the sensor to a control server of the network. The attributes of the sensor can include at least one unique identifier of the sensor or the host component of the sensor. The control server can determine a hash value using a one-way hash function and a secret key, send the hash value to the sensor, and designate the hash value as a sensor ID of the sensor. In response to receiving the sensor ID, the sensor can incorporate the sensor ID in subsequent communication messages. Other components of the network can verify the validity of the sensor using a hash of the at least one unique identifier of the sensor and the secret key.

A method includes receiving a data processing request at a computing system. The data processing request identifies data to be compared to sets of criteria according to a predefined sequence of the sets that is defined by a non-variant logic process. The method also includes determining whether the request is to be processed according to a variant logic process that defines a modified sequence of the criteria sets than the non-variant logic process. The method also includes dynamically altering the predefined sequence of the criteria sets to the modified sequence responsive to determining that the request is to be processed using the variant logic process, comparing the data identified by the request with the criteria sets according to the modified sequence, and processing the data according to the criteria sets of criteria in the modified sequence.

A graph searching apparatus 10 includes: a generation unit 11 for selecting a plurality of vertices based on an adjacency relationship of vertices included in a graph and generating a frontier matrix in which different labels are respectively set for elements corresponding to the selected vertices; and a classification unit 12 for classifying the vertices using the frontier matrix and an adjacency matrix representing the adjacency relationship of the vertices included in the graph.