Abstract: Techniques are described for component design based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing use of one or more components of the system by one or more individuals. The sensor data from various instances of the component may be aggregated and analyzed to determine update(s) to the design of the component. For example, the aggregate sensor data may be analyzed to identify portions of the component frequently associated with movements by users (e.g., fidgeting, adjustments). The identified portion(s) can be presented graphically in a design view used to specify design modification(s) for the component. In some implementations, the aggregate sensor data is provided as input to a model that is trained, using machine learning, to output design modification(s) for the component based on the input aggregate sensor data.

Abstract: A multi-layer tier requisition stack may generate prescriptive tier requisition tokens for controlling requisition of database-compute resources at database-compute tiers. The input layer of the tier requisition stack may obtain historical data and database-compute tolerance data. The coefficient layer may be used to determine activity coefficients for each data type within the historical data. The activity coefficients may then be combined to determine an overall activity factor. The tolerance layer may be used to select an initial database-compute tier based on the activity factor. The tolerance layer may then increase from the initial database compute tier to an adjusted database-compute tier while accommodating tolerances within the database-compute tolerance data. The requisition layer may generate a tier requisition token based on the adjusted database-compute tier and/or finalization directives obtained at the requisition layer.

Abstract: A system includes a multi-layer throughput optimization (TPO) stack to generate a token containing prescriptions for rightsizing database service throughput.

Abstract: Implementations of the present disclosure include providing graph data defining a graph that is representative of an enterprise network, the graph including nodes and edges between nodes, each node representing an asset within the enterprise network, and each edge representing one or more lateral attack paths between assets in the enterprise network, determining, for each node, an incoming value based on attributes of a set of incoming edges and an outgoing value based on attributes of a set of outgoing edges, the attributes including a number of edges and semantic types of the edges, at least one cardinality value of each node being determined based on one or more of the incoming value and the outgoing value of the node, receiving input representative of filter parameters, generating a sub-graph based on attributes of the nodes and the filter parameters, and displaying, by the visualization platform, the sub-graph in a display.

Abstract: A system may facilitate a distributed ledger technology (DLT) record based (for example, blockchain-based) ballot organization. A node configured to support an organizer role may generate a ballot that designates answers, and in some cases, conditions for valid vote-value transfers. The organizer may distribute vote-value to one or more voters. The voters may then commit portions (including null portions) of the received vote value to answers using committed tokens configured to bind the voter to a particular vote-value without divulging the particular vote value while in a cryptographic form.

Abstract: A device may receive data associated with one or more healthcare organizations. The data may relate to a performance of one or more processes or operations of the one or more healthcare organizations. The device may process the data using one or more techniques to permit mapping of the data to a healthcare operating model. The device may map the data to the healthcare operating model. The healthcare operating model may be used to perform one or more analyses of the one or more processes or operations of the one or more healthcare organizations. The device may perform one or more analyses of the data to identify one or more deficiencies related to the one or more processes or operations. The device may perform one or more actions to positively impact the performance of the one or more processes or operations of the one or more healthcare organizations.

Abstract: A device may receive first information relating to a first set of transactions and a first set of chargebacks associated with the first set of transactions; process the first information to generate a processed data set; train a model to perform classification of the first set of chargebacks, where the model is to receive, as input, information relating to transactions and at least one chargeback, and where the model is to output information identifying a classification of the at least one chargeback; receive second information identifying a second set of transactions and a second set of chargebacks associated with the second set of transactions, where the second information is received from multiple, different sources; determine a classification of the second set of chargebacks using the model and based on the second information; and perform an action based on the classification of the second set of chargebacks.

Abstract: A system includes a security controller located within a central-zone in communication with a provisioned agent operating on an edge-zone device. The security controller may implement controller-blind security control on the edge-zone device by making security authorization determinations using a secure-operation confidence analysis performed by the provisioned agent. The provisioned agent, operating within the edge-zone, may have access to un-sanitized data, which may be unavailable to the security controller in the central-zone. The provisioned agent may access condition-descriptors via a secure-operation confidence assignment matrix. Using the condition-descriptors, the provisioned agent determine feedback, which may include a secure-operation confidence coefficient, based on the un-sanitized data to which the security controller is blind.

Abstract: A system and method for transforming unstructured text into structured form is disclosed. The system and method include converting an input word sequence (e.g., sentence) into tagged output which can be then easily be converted into a structured format. The system may include a bidirectional recurrent neural network that can generate multiple labels of individual words or phrases. In some embodiments, a customized learning loss equation involving set similarity is used to generate the multiple labels.

Abstract: A network communication stack running on relational processing circuitry performs control and maintenance actions on records from a database server managed by repository control circuitry. The database interaction layer of the communication stack accesses the records on the database server. The database interaction layer passes the accessed records to the data processing layer for parsing and storage as tabular entries. An operator may perform manipulations on the tabular entries using a command interface generated by the command layer of the communication stack using locally-defined interface parameters that are independent of characteristics of the database server. The data processing layer recompiles manipulated entries into an altered record. The database interaction layer sends the altered record back to the database server.

Abstract: Implementations are directed to receiving delivery order data for delivery of the tangible asset, the delivery order data including a unique identifier associated with a mobile device of a recipient, receiving evidence data from the mobile device of the recipient, providing an order record stored in a distributed ledger system, the order record including at least a portion of the delivery order data, and the evidence data, determining that the tangible asset is to be handed over to the recipient, and in response: collecting delivery evidence data, providing the delivery evidence data for comparison to the evidence data, and receiving a validation result based on the comparison, and selectively indicating that the tangible asset has been handed over to the recipient at least partially based on the validation result, and updating the order record stored in the distributed ledger system.

Abstract: A computer-implemented method and a system parse and aggregate unstructured data objects. The method includes obtaining the unstructured data objects from description fields of records in a database comprising client tickets that are created for application maintenance, transforming the obtained unstructured data objects to create transformed data objects, identifying a number of criteria attributes of the client tickets, where the number of criteria attributes are determined according to at least part of the transformed data objects, word importance, word sentiment, a user input, or client ticket priorities, or any combinations, and generating a plurality of ticket reports by aggregating the client tickets according to ranking orders of numbers of tickets for the client tickets and the identified criteria attributes.

Abstract: The present disclosure relates to systems, methods, and computer readable medium for automatically managing API documentation and code generation. The disclosure provides an IDE plug-in that may import a machine readable reference documentation file from a documentation database, and create automatically generated code from the machine readable reference documentation file. Generally, the IDE plug-in may then allow a developer to validate their project back against the machine readable reference documentation file to ensure that all expected code is present in the project. This may allow the developer to ensure that the project properly meets the functional objectives intended for the API, before their implementation is deployed.

Abstract: A device may receive issue resolution information, associated with a cognitive model, including an item of issue resolution information that describes an issue and a resolution to the issue. The device may assign the item of issue resolution information to a domain hierarchy, where the assigning is associated with a first user. The device may generate a question and an answer corresponding to the item of issue resolution information, where the generating of the question and the answer is associated with a second user. The device may approve the question and the answer, where the approving is associated with a third user. The device may generate a question/answer (QA) pair for the question and the answer. The device may create a data corpus including the QA pair, and provide the data corpus to cause the cognitive model to be trained based on the data corpus.

Abstract: A system maintains a knowledge layout to support the analysis of active events and determination of epicenter and aftershock nodes via an event reach stack. At an input layer of the event reach stack, the system may receive active event data. At a semantic layer, the system may parse the active event data to determine event phrases. Based on the event phrases, the system may identify epicenter nodes directly affected by the active event. At an analytic model layer, the system may successively determine aftershock nodes by traversing the knowledge layout outward from the epicenter nodes. The system then directs the response to the active event to the aftershock and epicenter nodes, via action at a focus response layer of the event reach stack.

Abstract: A distributed data storage layer supports biometric identification systems. The biometric identity system includes hardware and software improvements for capturing, retrieving, and verifying identity based on securely stored biometric data in the distributed data storage layer. As a result, the biometric identity system provides increased individual security and reliable identification.

Abstract: A system for determining a vulnerability of source code includes a processor; and non-transitory computer readable media that includes instruction code that causes the processor to receive source code and a selection of one or more code analyzers for detecting vulnerability issues in the source code. The processor executes the one or more code analyzer to generate initial vulnerability data. The initial vulnerability data specifies one or more vulnerable code sections in the source code. The processor communicates the initial vulnerability data to a vulnerability analyzing engine. The vulnerability analyzing engine is configured to identify one or more code sections of the one or more code sections of the initial vulnerability data that correspond to false positives.

Abstract: A device receives virtual network function (VNF) data identifying a VNF to be implemented in a network and configures the VNF data with hardware and software requirements for deploying the VNF in a non-production environment, wherein configuring the VNF data with the hardware and software requirements generates a configured VNF. The device deploys the configured VNF in the non-production environment and performs one or more tests on the configured VNF deployed in the non-production environment. The device receives one or more test results based on performing the one or more tests on the configured VNF, and updates the configured VNF, based on the one or more test results, to generate an updated VNF. The device causes the updated VNF to be deployed in the network.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to screen candidate pharmaceutical molecules or compounds for pharmaceutical uses (e.g., treating diseases or conditions). The screening may be based on structural similarity between the candidate pharmaceutical molecules and various pharmaceutical targets (e.g., proteins, nucleic acids, etc.). In aspects, one or more machine learning (ML) models may be trained to assign a candidate pharmaceutical molecule to one of multiple clusters based on chemical/physical properties of the candidate pharmaceutical molecule and chemical/physical properties of pharmaceutical targets associated with the clusters. The pharmaceutical targets associated with the cluster may be scored based on comparisons between the candidate pharmaceutical molecule and the pharmaceutical target, and a subset of the pharmaceutical targets may be identified based on the scores.

Abstract: Implementations include providing, by the PKG platform, an initial knowledge graph based on user-specific data associated with a user, and a domain-specific knowledge graph, receiving, by the PKG platform, data representative of at least one answer provided from the user to a respective question, providing, by the PKG platform, an expanded knowledge graph based on the initial knowledge graph, the expanded knowledge graph including one or more nodes and respective edges based on the data, generating, by the PKG platform, a weighted knowledge graph based a groundtruth knowledge graph, and a targeted knowledge graph, the groundtruth knowledge graph including one or more true answers, and the targeted knowledge graph including the at least one answer provided from the user, and generating, by the PKG platform, the hyper-personalized knowledge graph (hpKG) based on the weighted knowledge graph, the hpKG being unique to the user within a domain.