Abstract: A system may receive, from a first device associated with a patient, a request for an ambulance. The system may determine, based on a location associated with the first device, an available ambulance to be dispatched to the patient. The system may provide, to a second device associated with an ambulance operator of the available ambulance, information that identifies the location associated with the first device. The system may identify a hospital to which the patient is to be delivered. The system may provide, to a third device associated with the hospital, information associated with the available ambulance or the patient.

Abstract: A device may evaluate first data to identify a plurality of users, where each user, of the plurality of users, is assigned to complete a portion of a group task. The device may evaluate the first data to select a first user, of the plurality of users, for completion of a particular portion of the group task based on a first set of decision criteria. The device may provide, to the first user, information identifying the particular portion of the group task. The device may receive second data relating to the particular portion of the group task after providing information identifying the particular portion of the group task. The device may evaluate the first data to select a second user, of the plurality of users, based on a second set of decision criteria. The device may provide, to the second user, information associated with the second data.

Abstract: A smart grid for improving the management of a power utility grid is provided. The smart grid as presently disclosed includes using sensors in various portions of the power utility grid, using communications and computing technology to upgrade an electric power grid so that it can operate more efficiently and reliably and support additional services to consumers. The smart grid may include distributed intelligence in the power utility grid (separate from the control center intelligence) including devices that generate data in different sections of the grid, analyze the generated data, and automatically modify the operation of a section of the power grid. Further, the intelligent devices in the power utility grid may cooperate together to analyze and/or control the state of the power grid. Finally, the distributed intelligence may further include distributed storage.

Abstract: A system may include a federated shipping node. The federated shipping node may receive shipping information for a parcel and determine a new shipping state for the parcel based on the shipping information. The federated shipping node may append the new shipping state to a blockchain. In addition, the federated shipping node may store the new shipping state in an off-chain database. A previous shipping state for the parcel may be replaced with the new shipping state in the off-chain database. To display the real-time information related to the parcel, the federated shipping node may receive the new shipping state from the off-chain database, and send the new shipping state to a device for display. To display the historical information related to the parcel, the federated shipping node may receive previous shipping states from the blockchain, and send the previous shipping states to the device for display.

Abstract: A multi-layer compute sizing correction stack may generate prescriptive compute sizing correction tokens for controlling sizing adjustments for computing resources. The input layer of the compute sizing correction stack may generate cleansed utilization data based on historical utilization data received via network connection. A prescriptive engine layer may generate a compute sizing correction trajectory detailing adjustments to sizing for the computing resources. Based on the compute sizing correction trajectory, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.

Abstract: A system includes profile control circuitry that may receive a sovereign onboarding command. The sovereign onboarding command may be issued on behalf of a sovereign associated with a profile. The sovereign onboarding command may update a status value in the profile. The profile may be recorded on a data-tamper-protected distributed ledger. Arbitration circuitry may review the recorded profile status value and ensure that status values are enforced against the sovereign during exchanges.

Abstract: Systems, apparatuses, and methods are directed toward determining emotional states of a subject. For example, the systems, apparatuses, and methods determine one or more previous characteristics of a pupil of an eye based on image data from eye tracking sensors, determine one or more current characteristics of the pupil based on image data from the eye tracking sensors, where the one or more current characteristics include one or more of a current position of the pupil or a current size of the pupil, track movements, based on image data from the eye tracking sensors, in a position of the pupil and changes in a size of the pupil over a time period, automatically predict an emotional state based on a comparison between the one or more previous characteristics and the one or more current characteristics, the tracked movements and the tracked changes in the size and adjust a parameter of one or more hardware devices based on the predicted emotional state.

Abstract: A system includes circuitry for cryptographic data share controls for distributed ledger technology based data constructs. The system may support placement of compute data on to a distributed ledger technology based data construct. The compute data may have multiple layers of encryption to support permissions and coordination of processing operations for application to the compute data. The multiple layers of encryption may include a homomorphic layer to allow sharing of the compute data for processing by a compute party without divulging the content of the compute data with the compute party. While in the homomorphically encrypted form, the homomorphic compute data supports the application of processing operations while maintaining the secrecy of the underlying data.

Abstract: Implementations are directed to providing a semantic sequence based on a sequence associated with the event, and context data provided from a knowledge graph, receiving a set of cases associated with at least one entity included in the event, the set of cases including previous instances of events, and actions performed to resolve the previous instances of events, defining a sub-set of cases from the set of cases based on the context data, for each case of the sub-set of cases, determining a similarity score, each similarity score representing a degree of similarity between the event, and a case of the sub-set of cases, determining an explanation based on features of cases in the sub-set of cases based on the context data, and providing one or more actions based on actions of cases in the sub-set of cases.

Abstract: Systems, methods, and apparatus, including computer programs encoded on computer storage media, for facilitating communication in an industrial control network. A system includes an industrial control network, one or more controller devices, one or more emulators, and an encryption relay processor. Each controller device can be operable to control one or more operational devices connected to the industrial control network. Each emulator can be configured to communicate with a respective controller device, and each emulator can be configured to reference a respective profile that includes information about security capabilities of the respective controller device. The encryption relay processor can be operable to facilitate communication to and from each emulator over the industrial control network.

Abstract: A device processes, with a model, an application to identify a set of file paths with process identifiers. The device identifies patterns associated with the set of file paths with process identifiers, and determines positions of random elements in each file path of the set of file paths with process identifiers. The device processes the patterns and the positions of the random elements to train a machine learning model, and utilizes the machine learning model to generate a first set of rules to identify files required for execution of the application, and a second set of rules to identify files not required for execution of the application. The device generates a mandatory access control policy based on the first set of rules and the second set of rules, and provides the mandatory access control policy to be implemented by an operating system of a client device.

Abstract: A system for managing personal data stored by an enterprise includes an interface, a permissions database, a processor, and non-transitory computer readable media. The interface is configured to receive a request to access at least some of the personal data, the request defining a purpose for the request. The permissions database that stores a plurality of records that define permissions associated with the personal data. The non-transitory computer readable media in communication with the processor that stores instruction code which, when executed by the processor, causes the processor to locate, within one or more disparate source databases within the enterprise, personal data associated with one or more individuals.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a system to create and employ associative memory maps for analysis of security file and/or logs are disclosed. In one aspect, a method includes the actions of receiving, from an external application, a request for a recommended action; extracting information regarding the entities and relationships between the entities from a data source; constructing an associative memory map from the extracted information; selecting a subgraph from the associative memory map based on a result of employing a vector to search nodes in the associative memory map; identifying the nodes most relevant to the requested recommend action base on a shortest paths of traversal in the selected subgraph of nodes; determining the requested recommended action based on an event identified in the relationships between the identified most relevant nodes; and transmitting the recommended action to the external application.

Abstract: Implementations are directed to methods for detecting and identifying advanced persistent threats (APTs) in networks, including receiving first domain activity data from a first network domain and second domain activity data from a second network domain, including multiple alerts from the respective first and second network domains and where each alert of the multiple alerts results from one or more detected events in the respective first or second network domains. A classification determined for each alert of the multiple alerts with respect to a cyber kill chain. A dependency is then determined for each of one or more pairs of alerts and a graphical visualization of the multiple alerts is generated, where the graphical visualization includes multiple nodes and edges between the nodes, each node corresponding to the cyber kill chain and representing at least one alert, and each edge representing a dependency between alerts.

Abstract: A data platform may receive data files from an electronic data interchange (EDI). The data files may be received in multiple different data formats. The data platform may convert the data files to a common data format, extract data elements from the data files converted to the common data format, and assign the data elements extracted from the data files to file identifiers. The data platform may assign the data elements extracted from the data files to attribute identifiers that identify types of data represented by the data elements, aggregate the data elements to create a standardized data set, and map the data elements in the standardized data set to functions. The data platform may generate values based on mapping the data elements to the functions, determine a metric based on combining the values according to a metric definition, and post the metric to the EDI for consumption.

Abstract: A component analysis platform may communicate with one or more devices to obtain prediction data relating to a type of component. The component analysis platform may process the prediction data to determine a set of predictors for failure of an instance of the component, and may generate a model for failure of the instance of the component based on the set of predictors. The component analysis platform may monitor the instance of the component to obtain component data relating to the instance of the component. The component analysis platform may determine, using the model and based on the component data relating to the instance of the component, a predicted failure for the instance of the component. The component analysis platform may perform a response action related to the predicted failure.

Abstract: Systems and methods for active state synchronization between distributed ledger technology (DLT) platforms are provided. A system may store an origin blockchain compliant with an origin DLT. The system may further store a target blockchain compliant with a target DLT. The target DLT may be different from the origin DLT. The system may include a DLT object synchronizer with access to the origin blockchain and the target blockchain. The DLT object synchronizer may receive, from an exchange node, a request to synchronize an origin instance of a DLT object between the origin blockchain and the target blockchain. The DLT object synchronizer may select a target instance of the DLT object on the target blockchain. The DLT object synchronizer may format origin data from the origin instance for compliance with the target DLT. The DLT object synchronizer may synchronize the origin instance and the target instance.

Abstract: A data pipeline architecture is integrated with an analytics processing stack. The data pipeline architecture may receive incoming data streams from multiple diverse endpoint systems. The data pipeline architecture may include converter interface circuitry with multiple dynamic converters configured to convert the diverse incoming data stream into one or more interchange formats for processing by the analytics processing stack. The analytics processing stack may include multiple layers with insight processing layer circuitry above analysis layer circuitry. The analysis layer circuitry may control analytics models and rule application. The insight processing layer circuitry may monitor output from the analysis layer circuitry and generate insight adjustments responsive to rule changes and analytics model parameter changes produced at the analysis layer circuitry.

Abstract: This disclosure relates to a development and application of a deep-learning neural network (DNN) model for identifying relevance of an information item returned by a search engine in response to a search query by a user, with respect to the search query and a profile for the user. The DNN model includes a set of neural networks arranged to learn correlations between queries, search results, and user profiles using dense numerical word or character embeddings and based on training targets derived from a historical search log containing queries, search results, and user-click data. The DNN model help identifying search results that are relevant to users according to their profiles.

Abstract: A method for controlling access to one or more of a plurality of target systems includes receiving profile data that defines one or more features associated with a plurality of individuals with one or more entitlements of those individuals. Each entitlement is indicative of target system access. The method further includes generating a model that relates the one or more features and the one or more entitlements of the plurality of individuals. Profile data that defines one or more features associated with a target individual is received from a first user management system. A listing that includes one or more entitlements associated with the target individual, and confidence values associated with the one or more entitlements is generated based on the profile data and the model. Each confidence value is indicative of whether the target individual should be granted a corresponding entitlement.