A device can receive parking metadata that includes location data indicating that a portion of a vehicle is located outside of a designated parking area (DPA). The device can process the parking metadata to identify values that are to be used when determining actions to perform. The device can obtain supplemental events data associated with events occurring near the DPA. The device can determine the actions to perform based on the parking metadata and the supplemental events data. The device can provide, as one of the actions and to one or more other devices or to the vehicle, a message indicating that the portion of the vehicle is located outside of the DPA. This can cause the one or more other devices or the vehicle to: move the vehicle from the DPA, reposition the vehicle within the DPA, or penalize an owner of the vehicle.

A cross-chain collaborative governance system is configured to perform collaborative service and control governance on cross-chain interoperation between application subchains in a cross-chain alliance. The cross-chain collaborative governance system includes: a cross-chain access application layer configured to make a first application subchain and a second application subchain access the cross-chain collaborative governance system; a credible cross-chain collaborative layer configured to provide collaborative service for cross-chain interoperation between the first application subchain and the second application subchain; and a credible cross-chain governance layer configured to perform control governance on the cross-chain interoperation between the first application subchain and the second application subchain.

Provided are a system for recommending related data based on similarity, and a method thereof, the system including: a data collection device; an event extraction device; a data cleansing device; an event vector generation device; an artificial intelligence learning device; and a similar data recommendation device. The present disclosure is directed to providing a system for recommending related data based on similarity and a method thereof, wherein unstructured open data on a webpage is collected to automatically generate an event label for determining a similarity relation, and an artificial intelligence (AI)-based model is trained to group and recommend semantically similar related data, thereby effectively helping users including data scientists who want to see meaningful results through open data.

A device may receive emergency data, traffic data, network performance data, crime data, and gunshot data associated with a geographical area and may identify a location within the geographical area based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data. The device may determine, based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data for the location, a risk level for the location and may identify an autonomous vehicle based on the risk level, the traffic data, and the network performance data for the location. The device may determine a route for the autonomous vehicle to the location based on the traffic data and the network performance data for the location, and may perform actions based on the autonomous vehicle and the route.

In some implementations, a device may receive data identifying products and encoded data identifying smart tags of the products. The device may map the data and the encoded data to generate encoded product data. The device may receive encoded data provided by smart tags of products received by a store. The device may receive images of the products. The device may compare the encoded data and the encoded product data to identify a set of the products received by the store. The device may correlate the images with the set of the products. The device may process the correlated data to identify locations of the set of the products in the store. The device may generate an instruction to relocate a product to a new location and may provide the instruction to a device, associated with the store, to cause the product to be relocated to the new location.

The present invention relates to an apparatus and a method for predicting the dispersion of hazardous and noxious substances and, more specifically, provides an apparatus and a method for predicting the dispersion of hazardous and noxious substances, the method: checking the components of the hazardous and noxious substances having leaked into the ocean, so as to classify the hazardous and noxious substances into a corresponding classification set among twelve classification sets by means of at least one of vapor pressure, the degradation in water, or density; dividing the classification sets, in which the hazardous and noxious substances are classified, into one dispersion model among an air dispersion model, a seawater dispersion model, and an air/seawater dispersion model according to the dispersion characteristics thereof; acquiring, from a weather center server, the state information of a sea area, which is set to be different according to the divided dispersion models; and predicting a danger radius for the dispersion of the hazardous and noxious substances by using the acquired state information of the sea area, and outputting the same.

The present invention relates to an apparatus and a method for predicting the dispersion of hazardous and noxious substances and, more specifically, provides an apparatus and a method for predicting the dispersion of hazardous and noxious substances, the method: checking the components of the hazardous and noxious substances having leaked into the ocean, so as to classify the hazardous and noxious substances into a corresponding classification set among twelve classification sets by means of at least one of vapor pressure, the degradation in water, or density; dividing the classification sets, in which the hazardous and noxious substances are classified, into one dispersion model among an air dispersion model, a seawater dispersion model, and an air/seawater dispersion model according to the dispersion characteristics thereof; acquiring, from a weather center server, the state information of a sea area, which is set to be different according to the divided dispersion models; and predicting a danger radius for the dispersion of the hazardous and noxious substances by using the acquired state information of the sea area, and outputting the same.

Systems and methods to provide a recommendation for an action based on an application policy are disclosed. The application policy may be associated with an organization. An application policy engine can use an artificial intelligence (AI) engine to execute a machine learning (ML) model. The application policy engine may receive real time video data or audio data, and obtain metadata comprising reference data or environment data. The application policy engine can process the real time video data or audio data using the ML model to infer biometric characteristics associated with a subject. The application policy engine can determine if the application policy was met, conformed to, or missed based on a correlation between the metadata and the inferred biometric characteristics, and provide a corresponding recommendation for an action.

Media, methods, and systems are disclosed for applying a computer-implemented model to a table of computed values to identify one or more anomalies. One or more input forms having a plurality of input form field values is received. The input form field values are automatically parsed into a set of computer-generated candidate standard field values. The set of candidate standard field values are automatically normalized into a corresponding set of normalized field values, based on a computer-automated input normalization model. An automated review model controller is applied to automatically identify a review model to apply to the set of normalized field values, based on certain predetermined target field values. The automatically identified review model is then applied to the set of normalized inputs, and in response to detecting an anomaly, a field value is flagged accordingly.

Media, methods, and systems are disclosed for applying a computer-implemented model to a table of computed values to identify one or more anomalies. One or more input forms having a plurality of input form field values is received. The input form field values are automatically parsed into a set of computer-generated candidate standard field values. The set of candidate standard field values are automatically normalized into a corresponding set of normalized field values, based on a computer-automated input normalization model. An automated review model controller is applied to automatically identify a review model to apply to the set of normalized field values, based on certain predetermined target field values. The automatically identified review model is then applied to the set of normalized inputs, and in response to detecting an anomaly, a field value is flagged accordingly.