A method includes steps: 1) obtaining monitoring information of different monitoring points in normal state of power equipment; 2) setting faults and obtaining monitoring information of different fault types, positions, monitoring points of the equipment; 3) taking the monitoring information obtained in steps 1) to 2) as training dataset, taking the fault types and positions as labels, inputting the training dataset and the labels to deep CNN for training; 4) collecting monitoring data, performing verification and classification using step 3), obtaining probability values corresponding to each of the labels; 5) taking classification results of different labels as basic probability assignment values, with respect to a monitoring system composed of multiple sensors, taking different sensors as different evidences for decision fusion, performing fusion processing using the DS evidence theory to obtain fault diagnosis result. The invention can intelligently realize fault detection, fault type determination, and fault positioning of the power equipment.

Embodiments of the present systems and methods may provide an intelligent systems framework for analysis of user-generated content from various capture points to determine user intent. For example, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise receiving, at the computer system, data relating to a plurality of aspects of at least one person, including data from at least one of physical or physiological sensors and communicatively connected devices, extracting, at the computer system, from the received data, features relevant to events relating to at least one person, extracting, at the computer system, at least one intent of at least one event relating to at least one person, and performing, at the computer system, an action based on the extracted at least one intent.

Systems, computer-implemented methods and/or computer program products that facilitate management of resources are provided. In one embodiment, a computer-implemented method comprises: employing, by a system operatively coupled to a processor, at least one model to predict respective token needs by a set of processing elements during execution of a workload; and exchanging, by the system, one or more tokens between a subset of the processing elements as a function of the predicted token needs.

Embodiments discussed herein may be generally directed to systems and techniques to generate a quality score based on an observation and an action caused by an actor agent during a testing phase. Embodiments also include determining a temporal difference between the quality score and a previous quality score based on a previous observation and a previous action, determining whether the temporal difference exceeds a threshold value, and generating an attack indication in response to determining the temporal difference exceeds the threshold value.

A method, apparatus, system, and computer program product for training a global machine learning model. A hierarchical structure for nodes in which the global machine learning model is located at a primary node in the hierarchical structure is identified. Authorized nodes in which local data is authorized for use in training in the authorized nodes for a local training of local machine learning models are determined. The machine learning models in the authorized nodes are trained using the local data in the authorized nodes to generate local model updates to weights in the local machine learning models. The local model updates to the weights are propagated upward in the hierarchical structure to the global machine learning model, wherein a node receiving local model updates to the weights from nodes from a lower level aggregates the weights in the local model updates received from the nodes in the lower level.

In an embodiment, a computer-implemented method elicits intents of agents in a social network with imperfect information. In the method, data representing the social network is gathered. The social network includes a plurality of resources and a plurality of agents seeking to alter values of the resources. In an example, the resources may be artists, and the agents may be art institutions. The social network is represented as a graph including nodes representing the plurality of agents. The nodes are connected by edges specifying how resources are transferred between the agents. Based at least in part on the graph, an affinity value and a trajectory value between respective agents in the plurality of agents is determined. Based at least in part on the graph, the affinity value and the trajectory value are input into a trained machine learning model to identify a strategy of the plurality of agents.

Providing expert help to a user comprises providing an application for execution on a mobile device of the user associated with an entity. A computer receives entity rules from the entity, the entity rules include a definition of how communication channels are created. The entity rules are stored in a rules database in association with the user. A help request initiated by the user through the application program and sent by the mobile device, the help request comprising a current context of the user comprising a user ID and a task ID of a current task. Using the entity rules, the current context is transformed into search parameters that are used to search a knowledge repository for experts having profiles that match the current context of the user. The entity rules are used to automatically create a communication channel between the user and the experts matching the current context.

An Internet-of-things (IoT) mechanizes, computerizes, automates, instruments, includes, and connects a broadly dispersed and extensively diverse universe of unrelated “things” to the Internet, e.g., credit cards, home appliances, industrial machinery, airplanes, cars, municipal water pumps, mobile devices, rain gauges, etc. Each thing is assigned a resident local “smart agent”. Or an entity, manifesting remotely only as transaction records and reports, is assigned a virtual smart agent in a network server. These data structures follow, track, record, chart, monitor, characterize, describe, render, and otherwise provide a label and handle on independent things and entities.

A system and method including receiving a plurality of messages generated by a vehicle, each of the messages being associated with an indicated topic; initializing one or more state machines, each state machine preconfigured with a one or more specified state parameters to define a specific scenario, the initializing commencing an execution of the one or more state machines; querying the plurality of messages by the one or more state machines, each state machine querying for messages having an indicated topic associated with the querying state machine; updating, at one or more of a sequence of time intervals of a time period, the one or more state machines based on state transitions determined for each state machine; updating, based on each of the one or more state machines, a scenario record for each of the one or more state machines; and storing the updated scenario record in a data store.

A reasoning engine is disclosed. Contemplated reasoning engines acquire data relating to one or more aspects of various environments. Inference engines within the reasoning engines review the acquire data, historical or current, to generate one or more hypotheses about how the aspects of the environments might be correlated, if at all. The reasoning engine can attempt to validate the hypotheses through controlling acquisition of the environment data.