Abstract: A device receives and processes current, forecasted, and historical entity information, associated with an entity, to generate processed information. The device calculates an operating enterprise value for the entity based on the processed information and bifurcates the operating enterprise value into a current value associated with current operations of the entity and a future value associated with investments of the entity. The device determines a growth rate based on the current value and the future value, and processes the current value, the future value, and the growth rate, with a first model, to determine underlying drivers of total returns for stakeholders associated with the entity. The device processes the underlying drivers of total returns for stakeholders and revenue, costs, assets, and liabilities associated with the entity, with a second model, to identify analytics data for the entity, and performs actions based on the analytics data identified for the entity.

Abstract: A multi-layer committed compute reservation stack may generate prescriptive reservation matrices for controlling static reservation for computing resources. A transformation layer of the committed compute reservation stack may generate a time-mapping based on historical utilization and tagging data. An iterative analysis layer may determine a consumption-constrained committed compute state of a distribution of static reservation and dynamic requisition that achieves one or more consumption efficiency goals. Once the consumption-constrained committed compute state is determined, the prescriptive engine layer may generate a reservation matrix that may be used to control computing resource static reservation prescriptively.

Abstract: Implementations are directed to receiving a first set of event data from a first data source, the first set of event data including data representative of an occurrence of a real-world event, providing an event ticket, the event ticket including at least a portion of data of the first set of event data, transmitting presentation data to one or more computing devices, the presentation data being based on the event ticket and being processable by the computing device to display a real-time view including one or more graphical representations representative of the real-world event, receiving a second set of event data from a second data source, the second set of event data including data associated with the real-world event, revising the event ticket to include at least a portion of data of the second set of event data to provide a revised event ticket, and transmitting revised presentation data to one or more computing devices, the revised presentation data being based on the revised event ticket and being proc

Abstract: A device may receive information regarding a software environment and/or one or more modules associated with the software environment; receive information regarding one or more compliance requirements; determine one or more smart contracts based on the information regarding the one or more compliance requirements and the information regarding the software environment and/or the one or more modules associated with the software environment; obtain data regarding the software environment and/or the one or more modules; determine, using the one or more smart contracts, whether the data satisfies the one or more compliance requirements; and add at least part of a block, to a blockchain, to indicate whether the data satisfies the one or more compliance requirements, for realization of a reward or penalty associated with the one or more compliance requirements.

Abstract: A digital content communication system for account management and predictive analytics may be provided. The system may include an analytics system that communicates with one or more servers and one or more data stores to provide digital content management in a network.

Abstract: Systems, apparatuses, and methods are directed toward audio and visual mapping. A visual frame associated with an environment may be received. The visual frame may be segmented into a plurality of regions of interest. A first position for a first region of the plurality of regions of interest is determined. A determination may be made that the first position intersects with a projected area associated with a user. The projected area is to include one or more areas that are outside of a current direction of movement of the user. One or more characteristics of a first auditory stimulus are selected based on the first position and the determination that the first position is within the projected area.

Abstract: A device may receive a widget template of a widget. The widget template may include information relating to one or more fields of the widget and content for the one or more fields. The device may receive a stylesheet of the widget. The stylesheet may include information relating to respective style identifiers of the one or more fields. The device may generate a markup object of the widget based on the widget template. The markup object may include respective markup object entries for populating the one or more fields with the content. The device may assign the respective style identifiers to the respective markup object entries, and cause an action to be performed in connection with the markup object.

Abstract: A device receives historical information associated with an individual to be monitored, wherein the historical information includes at least one of information associated with a health history of the individual, health histories of other individuals, activities of the individual, or activities of the other individuals. The device receives monitored information associated with the individual from one or more client devices associated with the individual, and pre-processes the monitored information to generate pre-processed monitored information that is understood by the trained machine learning model. The device processes the pre-processed monitored information, with a trained machine learning model, to identify one or more activities of the individual and one or more deviations from the one or more activities by the individual, and performs one or more actions based on the one or more activities of the individual and/or the one or more deviations from the one or more activities.

Abstract: An example method described herein involves receiving a data input; identifying a plurality of topics in the data input; determining an underrepresented set of data for a first set of topics of the plurality of topics based on a plurality of knowledge graphs associated with the first set of topics; calculating a score for each topic of the first set of topics based on a representative learning technique; determining that the score for a first topic of the first set of topics satisfies a threshold score; selecting a topic specific knowledge graph based on the first topic; identifying representative objects that are similar to objects of the data input based on the topic specific knowledge graph; generating representation data that is similar to the data input based on the representative objects to balance the underrepresented set of data with a set of data associated with a second set of topics of the plurality of topics; and performing an action associated with the representation data.

Abstract: A system may receive a credential token and an access command. The access command may identify access logic to modify a datablock stored in a blockchain. The credential token may be mapped to a role token included in a role-based access control model. The system may determine a role-based access control framework authorizes the access command by identifying an authorization token in the role-based access control model, and the system may determine the role token is mapped to the identified authorization token in the role-based access control model. The system may determine the datablock includes the identified authorization token. The system may validate the identified authorization token. The system may authorize execution of the access logic to modify the datablock in response to the access command being authorized by the role-based access control framework, and validation of the identified authorization token included in the datablock.

Abstract: In some examples, machine learning based quantification of performance impact of data irregularities may include generating an irregularity feature vector for each text analytics application of a plurality of text analytics applications. Normalized data associated with a corresponding text analytics application may be generated for each text analytics application and based on minimization of irregularities present in un-normalized data associated with the corresponding text analytics application. An un-normalized data machine learning model may be generated for each text analytics application and based on the un-normalized data associated with the corresponding text analytics application. A normalized data machine learning model may be generated for each text analytics application and based on the normalized data associated with the corresponding text analytics application.

Abstract: A data storage layer provides enhanced data security. In one implementation, the data storage layer allows selective encryption of specific parts of data blocks, with decryption restricted to pre-determined entities. The selective encryption may be applied to parts of data blocks that are stored in blockchains, for instance, to provide fine grained control over which entities with access to the blockchain can decrypt and use the parts of the data blocks with the enhanced security.

Abstract: A device may generate a product delivery map that includes route information that is to be used by a fleet of delivery vehicles for performing a set of deliveries, and a set of location constraints identifying locations that are to be avoided by the fleet of delivery vehicles when performing the set of deliveries. The device may generate a collaborative interactions map that includes a set of collaborative constraints indicating particular supplier organizations that are candidates to engage in collaborative logistics. The device may determine, based on the set of location constraints and the set of collaborative constraints, a set of delivery schedules that are to be used to perform the set of deliveries. The device may provide the set of delivery schedules to one or more devices associated with the delivery organization to allow the fleet of delivery vehicles to perform the set of deliveries.

Abstract: A device may receive one or more images that depict one or more shapes associated with a first object that includes a portal. The device may identify, based on the one or more images, one or more characteristics of the one or more shapes. The device may determine, based on the one or more characteristics of the one or more shapes, one or more attributes of the portal. The device may determine, based on the one or more attributes of the portal, positioning information to be used to position a second object relative to the portal of the first object. The device may output, based on the positioning information, one or more control signals associated with positioning the second object relative to the portal.

Abstract: A system maintains a knowledge layout to support the building of event and analytics models in parity. The system uses the event models to provide a snapshot of the relevant conditions present when a challenge event occurs. The system uses the analytics models to select one or more actions (which may include robotic tasks) to respond to the challenge condition. In some cases, the system may render continued response compulsory until a successful response to the challenge event is achieved.

Abstract: An Artificial Intelligence (AI) based resource identification system enables identifying available resources in response to receiving a request for resources. The request attributes are mapped to the attributes of the resources in a resource pool. Matching index scores are calculated and resources that match the request can be selected based on the matching index scores. If no resources are available suitable alternate resources with lower matching index scores are suggested so that a user has the choice to filter this alternate resources based on a threshold score. Particular suggestions to change one or more of the request attributes can be provided based on analysis of the mapping of the request and the resource attributes which enables the resource identification system to suggest changes to one or more of the skill attributes, level attributes, time attributes or location attributes of the request.

Abstract: A system and method to intelligently formulate automation strategies for technology infrastructure operations are disclosed. The system and method include analyzing infrastructure issue data from support tickets and predicting automation solutions. A cost-benefit analysis is then performed on the automation solutions. Solutions can be ranked and recommended according to the cost-benefit analysis.

Abstract: A vehicle driver monitoring system and method for capturing driver performance parameters is disclosed. The vehicle driver monitoring system and method uses multiple data sources to capture driver performance parameters. Data from one source can be used to validate data from another data source. Additionally, the data from one data source can be used to compensate for missing or corrupt data from the other data source. The vehicle driver monitoring system and method uses a mobile app on a mobile device that can automatically sense when a registered driver is in the vehicle with the mobile device, which helps the vehicle driver monitoring system verify that captured performance parameters are related to the registered driver driving the vehicle. The mobile app can also communicate with the driver in real time.

Abstract: An interactive troubleshooting assistant and method for troubleshooting a system in real time to repair (fix) one or more problems in a system is disclosed. The interactive troubleshooting assistant and method may include receiving multimodal inputs from sensors, wearable devices, a person, etc. that may be input into a feature extractor including attention layers and pre-processing units of a cloud computing system hosted by one or more servers, such as a private cloud system. A pre-processing unit converts the raw multimodal input into a structed form so that an attention layer can give weights to features provided by the pre-processing unit according to their importance. The weighted extracted features may be provided to an actions predictor. The actions predictor generates the most suitable action based on the weighted extracted features generated by the feature extractor based on the multimodal inputs.

Abstract: According to an example, partitioning based migration of systems to container and micro-service based-platforms may include determining, based on an analysis of source code for an application that is to be partitioned, an entity model corresponding to the application, identifying resources associated with the application, and determining a mapping of the identified resources to entities of the entity model. Further, partitioning based migration of systems to container and micro-service based-platforms may include identifying dependencies for each of the mapped resources, generating dependency and control flow metrics for the application, generating affinity values between the mapped resources, generating a resource affinity graph, determining an affinity score between each of the mapped resources, and generating resource clusters that correspond to partitions of the application.