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: A benefit prediction requirement may be received and associated benefit data from a plurality of sources may be obtained. The benefit prediction requirement may be associated with determining a probability of a benefit being surrendered by a benefit user. Further, a plurality of benefit attributes may be identified and mapped with benefit user data to create a benefit surrender database. From the benefit surrender database, a survival probability and a hazard probability may be determined and benefit assessment data may be created therefrom. Based on the benefit assessment data, a benefit surrender prediction model including a surrender probability may be created and a surrender pattern of the benefit user may be determined. Furthermore, a benefit surrender result may be generated and a remedial action in response to the benefit prediction requirement may be performed.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer-storage media, for prioritizing test cases. Processes may include obtaining test artifacts that were generated based on testing one or more legacy versions of a software application using multiple test cases, generating a risk index based at least on the test artifacts that were generated based on testing the one or more legacy versions of the software application using the multiple test cases, and training an ensemble model that is configured to identify likely unnecessary or redundant test cases in connection with in testing an updated version of the software application, based at least on the risk index.

Abstract: According to an example, with respect to decentralized supply chain for three-dimensional printing based on distributed ledger technology, three-dimensional printing constraints may be ascertained for a three-dimensional part to be printed, and a plurality of attributes for three-dimensional printers may be ascertained. The three-dimensional printing constraints may be compared to each attribute of the plurality of attributes. A determination may be made as to whether the three-dimensional part is printable by at least one three-dimensional printer or not printable by any three-dimensional printer. Costs associated with three-dimensional printing of the three-dimensional part and non-three-dimensional printing based manufacturing of the three-dimensional part may be ascertained. A determination may be made as to whether the three-dimensional part is to be printed by the at least one three-dimensional printer.

Abstract: In some examples, natural language processing (NLP) and artificial intelligence based searching may include identifying named entities in text from a corpus of documents. References in the text may be resolved with the identified named entities. Links between the named entities may be determined, and a bi-direction rootless graph may be generated. Semantic relationships may be determined from text of the named entities, and blacklist keywords may be identified. Machine learning classification may be performed based on a pair of the named entities and a blacklist keyword. A classification may be determined based on the pair of named entities and the blacklist keyword, and a rule may be identified that specifies which named entity in the pair is to be flagged. Further, a node in the graph may be flagged based on an association with the named entity identified according to the rule.

Abstract: Examples of a process optimization system are provided. The system may obtain a query from a user and process data associated with the query from a plurality of data sources. The system may identify a plurality of factors and a target variable from the process data. The system may identify a factor range for the plurality of factors. The system may deconstruct the factor range to identify a plurality of data partitions. The system may identify a data pruning activator based on the plurality of data partitions and a preponderant data partition therefrom. The system may identify a plurality of clusters associated with the preponderant data partition. The system may identify a preponderant cluster from the plurality of clusters. The system may identify a confidence score associated with the preponderant cluster. The system may generate a process optimization result based on the preponderant cluster and the confidence score.

Abstract: A device may receive, from a user device, a request for an automation service for a project. The device may analyze the request to identify one or more input keywords. The device may obtain, by using the one or more input keywords to search one or more data sources, project information for a set of projects and/or automation services information for a set of automation services. The device may determine one or more similar projects and/or one or more similar automation services by analyzing the project information and/or the automation services information. The device may provide, to the user device, a recommendation that identifies one or more automation services associated with the one or more similar projects and/or the one or more similar automation services.

Abstract: Implementations of the present disclosure include providing, by a security platform, graph data defining a graph that is representative of an enterprise network, the graph including nodes and edges between nodes, a set of nodes representing respective assets within the enterprise network, and a node representing a process executed within a system of the enterprise, each edge representing at least a portion of one or more lateral paths between assets in the enterprise network, determining, for each asset, a contribution value indicating a contribution of a respective asset to operation of the process, determining, for each asset, an impact value based on a total value of the process and a respective contribution value of the asset, and implementing one or more remediations based on a set of impact values determined for the assets, each remediation mitigating a cyber-security risk within the enterprise network.

Abstract: Systems and methods for orchestrating a process are disclosed. In an implementation, a system is configured to extract process information associated with the process. Based on the process information, the system is configured to determine a current model of performing the process based on the process information. The system is further configured to retrieve regulatory information associated with the process, wherein the regulatory information is indicative of at least one of a predefined policy, a predefined rule, and a predefined regulation associated with the process. Further, the system is configured to update the current model based on at least one of the process information and the regulatory information for obtaining a predefined outcome of the process.

Abstract: Implementations of the present disclosure include receiving, from an agile security platform, attack graph (AG) data representative of one or more AGs, each AG representing one or more lateral paths within an enterprise network for reaching a target asset from one or more assets within the enterprise network, processing, by a security platform, data from one or more data sources to selectively generate at least one event, the at least one event representing a potential security risk within the enterprise network, and selectively generating, within the security platform, an alert representing the at least one event, the alert being associated with a priority within a set of alerts, the priority being is based on the AG data.

Abstract: Examples of automatically generating natural language pipelines to process an input to generate tags, semantic or syntactic, are described. In an example, on receiving a request to process input data to generate tags, a dependency graph, based on identified dependees and further dependees may be created to satisfy the request, the dependency graph including natural language operations arranged in order of their dependencies on each other. Based on the dependency graph, a pipeline for the tags may be automatically generated, which includes a series of natural language operations such that the operations for dependee tags are processed before any of their associated depender tags.

Abstract: Implementations of the present disclosure include providing a state graph representative of a set of action states within a network, each action state representing an attack that can be performed by an adversary within the network, determining a path stealthiness value for each attack path of a set of attack paths within the network, path stealthiness values being determined based on a mapping that maps each action state to one or more technique-tactic pairs and one or more security controls, determining a path hardness value for each attack path of the set of attack paths within the network, path hardness values being determined based on a state correlation matrix that correlates action states relative to each other, and a decay factor that represents a reduction in effort required to repeatedly perform an action of an action state, and selectively generating one or more alerts based on one or more of path stealthiness values and path hardness values.

Abstract: A device may receive site data identifying a site associated with a telecommunications network. The device may determine handover data identifying handovers of user equipment associated with the site. The device may receive traffic data identifying traffic generated by the user equipment associated with the site. The device may calculate, based on the site data, the handover data, and the traffic data, a plurality of key performance indicators (KPIs) for the site, for a predefined time period, and if the site is decommissioned, wherein the plurality of KPIs include one or more of: resource block utilization data, determining KPI satisfaction data identifying whether one or more of the plurality of KPIs satisfy one or more of a plurality of KPI thresholds, and performing one or more actions based on the KPI satisfaction data.

Abstract: A cloud resource control circuitry may provide an automatic, self-managed, and a real-time elasticity in an active cloud stack including a software stack. The cloud resource control circuitry particularly adjusts cloud computing resources allocated to the active cloud stack based on real-time remote monitoring of resource utilization of the software stack. The resource adjustment may be performed in a stepwise fashion, in a pipelined manner as to the various layers of a software stack, and using hierarchical approach as to levels of cloud computing resources (e.g., task/level, server level, virtual machine/cloud level), to provide a more efficient cloud computing environment.

Abstract: A device may receive video data associated with a video, and may identify image data associated with the video data and that corresponds to a frame of the video. The device may process the image data, with an image analysis model, to determine an image compliance score associated with a policy and with context of the image data, and may identify text data associated with the video data. The device may process the text data, with a text analysis model, to determine a text compliance score associated with the policy and with context of the text data, and may determine, based on the image compliance score and the text compliance score, an aggregate compliance score associated with the frame. The device may determine whether the aggregate compliance score satisfies a threshold associated with the policy, and may perform actions based on whether the aggregate compliance score satisfies the threshold.

Abstract: In some implementations, a device may receive a first document from a first client device. The device may parse the first document into first sentences. The device may receive a second document from a second client device. The device may parse the second document into second sentences. The device may select a first sentence of the first document and a second sentence of the second document. The device may convert the first sentence into a first vector and the second sentence into a second vector. The device may determine a similarity score associated with the first vector and the second vector. The device may determine, based on the similarity score, that a first word set in the first sentence and a second word set in the second sentence correspond to a reference annotation. The device may perform an action associated with the reference annotation.

Abstract: A device may receive first data associated with a delivery of an item or service. The first data may be received from a system in association with an order being placed for the item or service. The device may receive second data associated with scheduling the delivery from another device. A portion of the second data may include natural language text data, or natural language audio data. The device may process the first data and the second data using a processing technique to identify information related to scheduling the delivery. The device may perform an action related to the delivery. The action may include scheduling the delivery based on a result of processing the first data and the second data, monitoring the first data and the second data, or modifying the delivery based on monitoring the first data and the second data.

Abstract: The document generally describes hardware for computing multiple orders of statistical moments. In one aspect, a system includes multiple stages of compute units. A first stage includes a first sequence of compute units includes a first compute unit configured to compute a first raw statistical moment for a first portion of data points in the time series of data points and one or more first additional compute units that are each configured to compute a respective first statistical moment for the first portion of data points. Each additional stage includes a second sequence of compute units for computing statistical moments for a respective second portion of the time series of data points. Each additional stage includes a second compute unit configured to compute the first raw statistical moment for the respective second portion of the time series of data points and one or more second additional compute units.

Abstract: Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support private querying and exchanging of domain information, such as Domain Name System (DNS) information, between multiple devices. To illustrate, two devices may generate and exchange a list domain information queries and a list of domain information items, respectively. The lists may be encrypted by respective private keys of the devices. Each device may double-encrypt the respective received list using the respective private key, reorder the double-encrypted list, and transmit the reordered double-encrypted list to the other device. The reordered double-encrypted list of domain information items may be compared to the double-encrypted list of domain information queries to identify indices of domain information queries that match domain information items while maintaining privacy of the domain information queries and domain information items at the devices.

Abstract: Systems, methods, and computer-readable storage media facilitating automated testing of datasets including natural language data are disclosed. In the disclosed embodiments, rule sets may be used to condition and transform an input dataset into a format that is suitable for use with one or more artificial intelligence processes configured to extract parameters and classification information from the input dataset. The parameters and classes derived by the artificial intelligence processes may then be used to automatically generate various testing tools (e.g., scripts, test conditions, etc.) that may be executed against a test dataset, such as program code or other types of data.