A system and method for accounting for the impact of concept drift in selecting machine learning training methods to address the identified impact. Pattern recognition is performed on performance metrics of a deployed production model in an Internet-of-Things (IoT) environment to determine the impact that concept drift (data drift) has had on prediction performance. This concurrent analysis is utilized to select one or more approaches for training machine learning models, thereby accounting for the temporal dynamics of concept drift (and its subsequent impact on prediction performance) in a faster and more efficient manner.

A system comprising an assessment engine configured to asynchronously transmit assessment questions to a user and, in response, receive a plurality of personal data from a user. A behavior module communicatively coupled to the assessment engine and operable to generate a behavior profile associated with the user based, at least in part, on the plurality of personal data. The behavior profile configured to identify one or more addiction habits of the user and respective one or more triggers operable to induce the user to desire the one or more addiction habits. A plurality of sensors positioned proximate the user to provide real-time measurements of one or more biological conditions of the user body. A prediction module communicatively coupled to the plurality of sensors, the prediction module operable to predict occurrence of the one or more triggers based on the behavioral profile and the real-time measurements of the one or more biological conditions of the user body.

A method of dispatching power, an electronic device, and a storage medium, which relate to a field of artificial intelligence technology, and in particular to technical fields such as natural language processing and knowledge graph. The method includes: acquiring a power dispatching question information; determining a question semantic information corresponding to the power dispatching question information, wherein the question semantic information includes an identification of a target substation, a question intention, and a target parameter; and determining an answer information according to the question semantic information and a preset rule base, and outputting the answer information, wherein the answer information is used for a power dispatching of the target substation.

By intercepting a natural language communication of a protected party, the communication is monitored, wherein the protected party is a human being. Within the monitored communication using a natural language processing engine, a natural language interaction between the protected party and a second party is detected. To determine an interaction pattern, the natural language interaction is analyzed. The interaction pattern includes data derived from the monitored communication, metadata of the protected party, and metadata of the second party. Using the interaction pattern and an interaction behavior model, an adverse result of the natural language interaction is predicted, wherein the adverse result comprises an economic loss to the protected party. By notifying the protected party, the predicted adverse result is intercepted.

By intercepting a natural language communication of a protected party, the communication is monitored, wherein the protected party is a human being. Within the monitored communication using a natural language processing engine, a natural language interaction between the protected party and a second party is detected. To determine an interaction pattern, the natural language interaction is analyzed. The interaction pattern includes data derived from the monitored communication, metadata of the protected party, and metadata of the second party. Using the interaction pattern and an interaction behavior model, an adverse result of the natural language interaction is predicted, wherein the adverse result comprises an economic loss to the protected party. By notifying the protected party, the predicted adverse result is intercepted.

In some embodiments, a method stores a plurality of identifiers for a plurality of rules. The plurality of rules each include a set of patterns, and a rule and a pattern combination is associated with an identifier in the plurality of identifiers. Information being sent on a network is scanned and the method determines when a pattern in the information matches a pattern for a rule. The method identifies an identifier for the pattern where the identifier identifies a rule and a pattern combination. Then, the method identifies the rule and the pattern combination based on the identifier. The set of patterns for the rule is found in the information based on determining that the rule and the pattern combinations for the rule have been found in the information.

Systems and computerized methods for determining patterns in user activity such that user contextual information can be provided based on the patterns. Historic data associated with a first user is received and entity information is determined based on the historic data. Current location data associated with the first user is received and prediction information is generated based on a combination of the entity information and the current location data for a current time period. Contextual insight information is determined based on a combination of the prediction information with at least one of the current location data, real time data, and contextual data, the contextual insight information including a recommendation of at least one of an activity, a purchase, and an accessory associated with the current location of the first user.

Systems and computerized methods for determining patterns in user activity such that user contextual information can be provided based on the patterns. Historic data associated with a first user is received and entity information is determined based on the historic data. Current location data associated with the first user is received and prediction information is generated based on a combination of the entity information and the current location data for a current time period. Contextual insight information is determined based on a combination of the prediction information with at least one of the current location data, real time data, and contextual data, the contextual insight information including a recommendation of at least one of an activity, a purchase, and an accessory associated with the current location of the first user.

The present disclosure discloses a data annotation method and apparatus, an electronic device and a readable storage medium, and relates to artificial intelligence fields such as deep learning, computer vision and autonomous driving. The method may include: acquiring a detection model, the detection model being trained by using sensor data manually annotated as startup data; performing obstacle detection on to-be-annotated sensor data by using the detection model, the startup data and the to-be-annotated sensor data being a same type of sensor data; performing obstacle tracking and matching according to detection results to obtain obstacle trajectory information; and modifying the detection results according to the obstacle trajectory information, and taking modified detection results as required annotation results. Labor and time costs can be saved by use of the solutions of the present disclosure.

A platform for design of a lighting installation generally includes an automated search engine for retrieving and storing a plurality of lighting objects in a lighting object library and a lighting design environment providing a visual representation of a lighting space containing lighting space objects and lighting objects. The visual representation is based on properties of the lighting space objects and lighting objects obtained from the lighting object library. A plurality of aesthetic filters is configured to permit a designer in a design environment to adjust parameters of the plurality of lighting objects handled in the design environment to provide a desired collective lighting effect using the plurality of lighting objects.