Abstract: In some implementations, a device may receive user request data from a user device associated with a user. The device may retrieve pre-defined technical object content data from an enterprise system. The device may retrieve pre-defined interface format data and a pre-defined business rule from a first data structure and a second data structure, respectively, based on the user request data. The device may determine whether a mapping, for generating an enterprise system object, is stored in a third data structure. The device may map the technical object content data to the pre-defined interface format data, based on the mapping and the pre-defined business rule, to generate mapped data when the mapping is stored in a third data structure. The device may generate the enterprise system object based on the mapped data. The device may perform one or more actions based on the enterprise system object.

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: A computer system may access an attributization rule and convert, based on the attributization rule, profile data into attributes for a robust recipient profile. The system may identify, based on tag logic, tags that are associated with the attributes. The tag logic may include instructions to compare the attributes with predetermined attributes associated with the tags. The system may map the identified tags to the recipient identifier. The system may perform communication with a mobile device based on the robust recipient profile and a real-time location of the mobile device. To perform the communication, the system may monitor the plurality of data channels for data comprising real-time location information for the mobile device. The system may determine the mobile device is proximate to a physical object that is associated with the tag and the system may transmit offer information to the mobile device.

Abstract: In some examples, system behavior profiling-based dynamic competency analysis may include identifying a plurality of software generation entities that have contributed to a module of a system, and generating an index to associate each software generation entity of the plurality of software generation entities. Execution links may be extracted from execution traces of the system, and an execution competency list may be generated. A dynamic competency score may be generated for each software generation entity for the system, and an overall dynamic competency score and a combined competency score may be determined. A software generation entity role may be obtained for a new application, and a software generation entity of the plurality of software generation entities may be identified to perform the software generation entity role. Development of the new application may be implemented using the identified software generation entity.

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: A system and method for lead conversion using conversational virtual avatar is disclosed. System comprising processor causes Conversation Virtual Avatar Platform (CVAP) to receive, for first entity, from lead prioritization engine, leads applicable to first entity via lead repository based on scores associated with respective leads. Processor causes CVAP to receive, through conversation management engine (CME) configured in CVAP, from leads, responses to questions pertaining to product attributes and information pertaining to lead.

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: In some examples, migration context and flow graph based migration control may include ascertaining an application that is to be migrated from a physical environment to a cloud environment, and determining a migration issue associated with the migration of the application. Migration context and flow graph based migration control may further include identifying, from a historical issue database, a plurality of historical issues, determining, for the migration issue and the plurality of historical issues, unified proximities, sorting, based on the determined unified proximities, the historical issues, selecting, from the sorted historical issues, a topmost historical issue, and determining, from the topmost historical issue, a resolution associated with the topmost historical issue.

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: Systems and methods for facilitating candidate acquisition are disclosed. The systems may include a candidate acquisition orchestration engine. The engine may include a candidate engagement optimizer, which may receive, from a database storing profiles attributes of a plurality of candidates, an expanded dataset having one or more filtered attributes pertaining to a set of candidates from the plurality of candidates. The optimizer may receive, from an entity intending to engage at least one candidate, inputs associated with preferred parameters for the at least one candidate. The optimizer may process the expanded dataset and the entity inputs through a plurality of classifiers to generate candidate predictions. A bias identification engine may optimize the candidate predictions to remove inherent bias therein so as to generate, for each classifier, optimized candidate predictions. A stack classifier may process the optimized candidate predictions to generate final candidate predictions.

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: A material failure forecasting system accesses historical failure data to forecasts future failures. The failure data of a material is analyzed using text processing techniques to identify failures and suspensions. The text processing techniques provide for identifying failures when fault words are associated with negations. A fault ontology establishes different failure modes that include primary, secondary and tertiary levels which enable identifying a sequence of failures. The failures thus identified are fitted to a data distribution selected from a plurality of data distributions. The parameters from the data distribution are used for simulating a demand profile for the material which considers interchangeability. Similarly failure data of the materials in an equipment can be analyzed and the reliability of the equipment can be estimated.

Abstract: Examples of artificial intelligence-based reasoning explanation are described. In an example implementation, a knowledge model having a plurality of ontologies and a plurality of inferencing rules is generated. Once the knowledge model is generated, based on a real-world problem, a knowledge model from amongst various knowledge models is selected to be used for resolving a real-world problem. The data procured from the real-world problem is clustered and classified into an ontology of the determined knowledge model. Inferencing rules to be used for deconstructing the real-world problem are identified, and a machine reasoning is generated to provide a hypothesis for the problem and an explanation to accompany the hypothesis.

Abstract: A method and system for cloud distributed hybrid data storage and normalization are disclosed. The method may include obtaining a data set comprising data entities. A data entity may comprise data fields each containing a data element. The method may further include determining policy constraint meta-data for each of the data elements based on the storage policy constraint. The policy constraint meta-data may include a first meta-tag indicating the storage policy constraint for the data element. The method may further include determining whether a server satisfies the storage policy constraint based on the first meta-tag for the data element. When the server satisfies the storage policy constraint, the method may further include transmitting the data element to the server to store the data element on the server. When the server fails to satisfy the storage policy constraint, the method may further include, storing the data element on the client.

Abstract: Systems and methods for establishing data augmentation and enrichment within a data repository are disclosed, including a plug-in including an information relation charting engine, an analytical metric study engine, a derived record construction engine, and a configurational layer. The information relation charting engine receives raw data metrics from a production environment, determines a correspondence between field elements associated with each of the source and target schema, and correspondingly generates a mapping. The analytical metric study engine performs analytical analysis on the field elements present in the generated mapping and uses machine learning and previous data log to generate recommendations for the database. The derived record construction engine involves a deep learning model to generate relevant and usable data metrics based on the generated recommendations, which is then mapped to the relational database to generate augmented and enriched database.

Abstract: Systems and methods for profile service management are disclosed. A system generates user data corresponding to plurality of users in an electronic meeting (e-meeting), based on meta data received from one or more data sources. The generated user data is stored in data lake. The system retrieves, from data lake, the user data. Each of plurality of users has corresponding user profile. The system determines, from user data, factual user information for each user of plurality of users from corresponding user profile. Further, the system compares factual user information of plurality of users, to determine set of commonalities between plurality of users. Furthermore, system generates, for each user, integration data comprising factual user information and set of commonalities. Further, system displays, for each user, plurality of meeting objects in a meeting interface associated with the e-meeting, based on the integration data, to provide one or more profile services.