Abstract: A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Abstract: A system includes circuitry for rewriting blockchains in a non-tamper-evident or tamper-evident operation using a key secret held in portions by multiple individually untrusted parties. The blockchains may include a series of blocks secured by integrity codes that may prevent non-tamper-evident rewrites by non-trusted parties that are not in possession of the key secret or individually-untrusted parties in possession of only a portion of the key secret. In some cases, multiple individually-untrusted parties may combine their portions into the key secret. As a group, the multiple individually-untrusted parties may perform non-tamper-evident operation with respect to at least one integrity code within the blockchain.

Abstract: Knowledge graph systems are disclosed for enhancing a knowledge graph by generating a new node. The knowledge graph system converts a knowledge graph into an embedding space, and selects a region of interest from within the embedding space. The knowledge graph system further identifies, from the region of interest, one or more gap regions, and calculates a center for each gap region. A node is generated for each gap region, and the information represented by the node is added to the original knowledge graph to generate an updated knowledge graph.

Abstract: This disclosure is directed to a computer system and method to assist in identifying data feature intersection or overlap between private datasets without revealing any specific data items or data features in the datasets. Various technical components including natural language processing, lexical optimization, and encryption and key management technologies such as homomorphic encryption and secret sharing and coding, are integrated into the disclosed system and method to achieve the data feature intersection identification. Such a system and method may be employed in circumstances where data feature intersection is important for collaborative efforts between entities.

Abstract: Aspects of the present disclosure provide an environmental probe configured to be at least partially inserted into the ground at a location and to provide defensible space monitoring and maintenance for a home or other structure. The environmental probe may include one or more environmental sensors configured to perform various environmental measurements to generate environmental measurement data. The environmental probe (e.g., a processor of the environmental probe) may compare the environmental measurement data to one or more thresholds to determine an alert state associated with the location. The environmental probe may include one or more wireless interfaces configured to enable communication with a remote device, such as a smart hub device or other environmental probes. The environmental probe may transmit an indicator of the alert state to the remote device to enable performance of one or more operations based on the alert state.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include accessing rules that each relate one or more values of the feature vectors to a respective label of a plurality of labels. The actions further include, based on the rules, generating heuristics that each identify related values of the feature vectors. The actions further include, for each of the heuristics, generating a matrix that reflects a similarity of the feature vectors. The actions further include, based on the matrices that each reflects a respective similarity of the feature vectors, generating clusters that each include a subset of the feature vectors. The actions further include, for each cluster, determining a label of the plurality of labels.

Abstract: The present disclosure describes a method of controlling a manufacturing system using multivariate time series, the method comprising: recording data from one or more devices in the manufacturing system; storing the recorded data in a data storage as a plurality of time series, wherein each time series has a first recorded value corresponding to a first time and a final recorded value corresponding to an end of the time series; interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, wherein the missing values fall between the first and end time of the respective time series; storing the interpolated values as prediction data in a prediction storage, wherein the interpolated values include the uncertainty of each interpolated value; loading the recorded data that fall within a second time window from the data storage; loading prediction data from the prediction storage that fall within the second time window and for which no recorded data are available;

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are described that enable identifying an insurance information associated with a property, retrieving one or more compliance rules related to the insurance information, automatically determining, based on the one or more compliance rules, whether the insurance information has a compliant status or a non-compliant status, in response to determining that the insurance information has the non-compliant status, processing the insurance information, based on one or more exception rules, to generate an exception waiver including a plurality of waiver parameters, applying the plurality of waiver parameters to a waiver model to determine a waiver confidence score, and based on the waiver confidence score selectively generate an insurance certificate for the property.

Abstract: Methods, systems, and apparatus for implementing a hexadecimal to quantum computation translation. In one aspect, a method includes obtaining one or more hexadecimal data inputs; applying a quantum computation translation operation to each hexadecimal data input to generate one or more corresponding sequences of quantum computations; implementing the one or more sequences of quantum computations using quantum computing hardware to obtain one or more corresponding sequence of measurement results; and providing the one or more sequences of measurement results as respective representations of the one or more hexadecimal data inputs.

Abstract: In some implementations, a device may receive an image that depicts handwritten text. The device may determine that a section of the image includes the handwritten text. The device may analyze, using a first image processing technique, the section to identify subsections of the section that include individual words of the handwritten text. The device may reconfigure, using a second image processing technique, the subsections to create preprocessed word images associated with the individual words. The device may analyze, using a word recognition model, the preprocessed word images to generate digitized words that are associated with the preprocessed word images. The device may verify, based on a reference data structure, that the digitized words correspond to recognized words of the word recognition model. The device may generate, based on verifying the digitized words, digital text according to a sequence of the digitized words in the section.

Abstract: Blockchain distribution of computer data is disclosed. Computer data can comprise computer code, a computer code segment, a computer command, or a block of computer data, which can be employed by a device to patch software, change a device state, or synchronize data between devices. Blockchain distribution can provide benefits in a heterogeneous device environment, facilitate ad hoc device synchronization, and embody a distributed patch and communications network. Devices can receive a blockchain block from another device and, in some embodiments, enable other devices to access the block from the device. In some embodiments, devices can discard irrelevant blocks, however, an entire blockchain can be reconstructed where partial blockchains can be received from more than one device. Additionally, checkpoint blocks can enable devices to navigate the blockchain efficiently by skipping over known irrelevant blocks.

Abstract: A system may receive published messages over an uplink channel. The system may, via channel control logic (CCL), delegate the messages to one or more subject-domain downlink channels (SDDCHs). The CCL may determine to create new SDDCHs to facilitate accurate placement of messages within defined subject-domains corresponding to the various SDDCHs.

Abstract: This disclosure provides methods and systems for predicting missing links and previously unknown numerals in a knowledge graph. A jointly trained multi-task machine learning model is disclosed for integrating a symbolic pipeline for predicting missing links and a regression numerical pipeline for predicting numerals with prediction uncertainty. The two prediction pipelines share a jointly trained embedding space of entities and relationships of the knowledge graph. The numerical pipeline additionally includes a second-layer multi-task regression neural network containing multiple regression neural networks for parallel numerical prediction tasks with a cross stich network allowing for information/model parameter sharing between the various parallel numerical prediction tasks.

Abstract: A system and method of automating payment processing is disclosed. Payment documents can be processed using an array of tools including artificial intelligence integrated with the end-user's own ERP architecture. The proposed implementations maintain a logical segregation of the documents, which may include the processing of receipts, validation of documents against ERP information, tax analysis, posting of documents, as well as real-time monitoring and support of settlement and accounts payable operations. Embodiments of this approach utilize enhanced optical character recognition (OCR) techniques in conjunction with robotic process automation (RPA) and Machine Learning to produce a platform capable of enabling these processes to run end-to-end with little to no human intervention or error.

Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.

Abstract: A device receives an infrastructure design document that represents a network infrastructure design. The device causes the infrastructure design document to be displayed via a first interface of a geographic information system (GIS) tool that is to be used during an inspection of a site, where the inspection includes inspecting structural components that are to support equipment of a network. The device receives, from a user device, feedback data that is based on the inspection. The device causes the feedback data to be integrated into the GIS tool. The device receives, from another user device, instructions that are to be used to update the infrastructure design document. The device updates the infrastructure design document based on the set of instructions and performs actions that allow the infrastructure design document to be used when implementing the network infrastructure design.

Abstract: Implementations for receiving, by the design assistant, an initial set of requirements, each requirement in the initial set of requirements being manually provided by a user, providing, by the design assistant and using a knowledge graph, a first set of questions based on the initial set of requirements and content determined from at least one design document in a set of design documents, filtering, by the design assistant, one or more questions from the first set of questions to provide a second set of questions, providing, by the design assistant, at least a portion of the second set of questions as output to the user to elicit one or more responses from the user, and editing, by the design assistant, the initial set of requirements based on the one or more responses to provide a set of requirements, the set of requirements for the to-be-designed product.

Abstract: Systems and methods that offer significant improvements to current content processing management systems for heterogeneous and widely distributed content sources are disclosed. The proposed systems and methods are configured to provide a framework and libraries of extensible components that together are designed enable creation of solutions to acquire data from one or more content repositories, possibly distributed around the world across a wide range of operating systems and hardware, process said content, and publish the resulting processed information to a search engine or other target application. The proposed embodiments offer an improved architecture that incorporates manager nodes and worker (processing) nodes, where worker nodes are configured to scan and process data, while manager nodes are configured to handle all allocation of work (including throttling) and control state and failover. Such an arrangement enables the system to perform with greater scalability and reliability.

Abstract: A device may receive historical risk data identifying historical risks associated with entities, and historical compliance data identifying historical compliance actions performed by the entities. The device may train a machine learning model with the historical risk data and the historical compliance data to generate a structured semantic model, and may receive entity risk data identifying new and existing risks associated with an entity. The device may receive entity compliance data identifying new and existing compliance actions performed by the entity, and may process the entity risk data and the entity compliance data, with the structured semantic model, to determine risk and compliance insights for the entity. The risk and compliance insights may include insights associated with a key performance indicator, a compliance issue, a regulatory issue, an operational risk, a compliance risk, or a qualification of controls. The device may perform actions based on the risk and compliance insights.

Abstract: Embodiments provide systems, methods, and computer-readable storage media for automated and objective testing of applications or processes. Graphical representations of the application may be analyzed to derive attribute data and identify flows (e.g., possible processing paths that may be accessed during utilization of the application by a user). Test cases may be automatically generated based on the attribute data and the identified flows. Additionally, testing scripts for testing the portions of the application corresponding to each identified flow may be generated using machine learning logic. Once generated, the testing scripts may be executed against the application to test different portions of the application functionality (or processes). Execution of the testing scripts may be monitored to generate feedback used to train the machine learning logic. Reports may be generated based on the monitoring and provided to users to enable the users to resolve any errors encountered during the testing.