Abstract: Techniques regarding determining sufficiency of one or more machine learning models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in memory. The computer executable components can comprise a measurement component that measures maximum deviation of a supervised learning model from a reference model over a certification set and an analysis component that determines sufficiency of the supervised learning model based at least in part on the maximum deviation.

Abstract: A computer-implemented method, a computer program product, and a computer system for diagnosing anomalies detected by a black-box machine learning model. A computer determines a local variance of a test sample in a test dataset, where the local variance represents uncertainty of a prediction by the black-box machine learning model. The computer initializes optimal compensations for the test sample, where the optimal compensations are optimal perturbations to test sample values of respective components of a multivariate input variable. The computer determines local gradients for the test sample. Based on the local variance and the local gradients, the computer updates the optimal compensations until convergences of the optimal compensations are reached. Using the optimal compensations, the computer diagnoses the anomalies detected by the black-box machine learning model.

Abstract: A computer-implemented method, a computer program product, and a computer system for diagnosing anomalies detected by a black-box machine learning model. A computer determines a local variance of a test sample in a test dataset, where the local variance represents uncertainty of a prediction by the black-box machine learning model. The computer initializes optimal compensations for the test sample, where the optimal compensations are optimal perturbations to test sample values of respective components of a multivariate input variable. The computer determines local gradients for the test sample. Based on the local variance and the local gradients, the computer updates the optimal compensations until convergences of the optimal compensations are reached. Using the optimal compensations, the computer diagnoses the anomalies detected by the black-box machine learning model.

Abstract: Embodiments for estimating substitutability between skills by combining skill similarities from one or more data sources by a processor. An adjacency of skill similarity of one or more skills of one or more entities may be determined. The adjacency of skill similarity may be used to generate one or more skill clusters. Skill demand of the one or more skill clusters may be forecasted.

Abstract: A system may predict costs for a set of members by building and using a predictive pipeline. The pipeline may be built using a set of historical data for training members. A set of member-level features can be identified by performing empirical testing on the set of historical data. The trained configurable predictive pipeline can generate a set of predictive data for each member, using historical test data for a set of testing members. The system can then generate a predictive report for each set of predictive data.

Abstract: Embodiments of the present invention may provide a risk management application for generating a risk management report. One or more risks associated with a design or construction phase of a project are identified. Project data is also received. Current and future conditions associated with the project are determined and risks that are related to the project are identified. A risk management report is created based on the identified risks.

Abstract: The invention includes various systems, architectures, frameworks and methodologies that can securely enforce a privacy policy. A method is included for securely guaranteeing a privacy policy between two enterprises, comprising: creating a message at a first enterprise, wherein the message includes a request for data concerning a third party and a privacy policy of the first enterprise; signing and certifying the message that the first enterprise has a tamper-proof system with a privacy rules engine and that the privacy policy of the first entity will be enforced by the privacy rules engine of the first enterprise; sending the message to a second enterprise; and running a privacy rules engine at the second enterprise to compare the privacy policy of the first enterprise with a set of privacy rules for the third party.

Abstract: The invention includes various systems, architectures, frameworks and methodologies that can securely enforce a privacy policy. A method is included for securely guaranteeing a privacy policy between two enterprises, comprising: creating a message at a first enterprise, wherein the message includes a request for data concerning a third party and a privacy policy of the first enterprise; signing and certifying the message that the first enterprise has a tamper-proof system with a privacy rules engine and that the privacy policy of the first entity will be enforced by the privacy rules engine of the first enterprise; sending the message to a second enterprise; and running a privacy rules engine at the second enterprise to compare the privacy policy of the first enterprise with a set of privacy rules for the third party.

Abstract: A system, method and computer program product provides a solution to a class of categorization problems using a semi-supervised clustering approach, the method employing performing a Soft Seeded k-means algorithm, which makes effective use of the side information provided by seeds with a wide range of confidence levels, even when they do not provide complete coverage of the pre-defined categories. The semi-supervised clustering is achieved through the introductions of a seed re-assignment penalty measure and model selection measure.

Abstract: Automated mapping of part numbers associated with parts in a bill of materials (BOM) submitted by a BOM originator to internal part numbers assigned to those parts by a BOM receiver is performed by one or more computers connected to one or more networks through one or more network interfaces. A first receive component receives one or more data sets containing historical data on bills of materials received in the past by the BOM receiver. A second receive component receives one or more data sets containing known mappings between internal part numbers used by the BOM receiver, and part numbers used by various BOM originators. A third receive component receives one or more data sets containing information of various parameters and their values describing the parts to which the BOM receiver has assigned internal part numbers.

Abstract: A system and method for reasoning about concepts, relations and rules having a semantic network comprising at least one node from a predetermined set of node types, at least one link from a predetermined set of link types, and zero or more rules from a predetermined set of rule types, a subset of the rule types being matching rule types, each node and each link being associated with a set of zero or more rules; a network reasoning data structure having a reasoning type database having at least one regular expression, each of the regular expressions being a class of sequences having at least three node types and two link types, wherein the network reasoning data structure further has a context being a set of rules; and a reasoning engine having an activator for activating one or more activated paths in the semantic network, the set of activated paths having a common starting node in the semantic network, wherein the reasoning engine further has a validator for selecting a subset of the activated paths being va

Abstract: A method of sharing telematics data for a vehicle with service providers can include receiving the telematics data for the vehicle, where the telematics data dynamically changes over time, and comparing the telematics data with a privacy policy associated with the vehicle. The privacy policy can specify rules for selectively releasing items of the telematics data to one or more service providers. Data items of the telematics data can be selectively provided to the service providers according to the comparing step.

Abstract: A system, method and computer program product provides a solution to a class of categorization problems using a semi-supervised clustering approach, the method employing performing a Soft Seeded k-means algorithm, which makes effective use of the side information provided by seeds with a wide range of confidence levels, even when they do not provide complete coverage of the pre-defined categories. The semi-supervised clustering is achieved through the introductions of a seed re-assignment penalty measure and model selection measure.

Abstract: Automated mapping of part numbers associated with parts in a bill of materials (BOM) to internal part numbers assigned by a BOM receiver. Historical data on bills of materials, known part number mappings, and parametric data describing the parts associated with bills of materials re received. Prediction models, for predicting BOM receiver internal part numbers, are learned from the historical BOM data, known part number mappings data and parametric data. The learned models are applied to unmapped BOM originator part numbers to assign BOM receiver internal part numbers.

Abstract: A system and method for reasoning about concepts, relations and rules having a semantic network comprising at least one node from a predetermined set of node types, at least one link from a predetermined set of link types, and zero or more rules from a predetermined set of rule types, a subset of the rule types being matching rule types, each node and each link being associated with a set of zero or more rules; a network reasoning data structure having a reasoning type database having at least one regular expression, each of the regular expressions being a class of sequences having at least three node types and two link types, wherein the network reasoning data structure further has a context being a set of rules; and a reasoning engine having an activator for activating one or more activated paths in the semantic network, the set of activated paths having a common starting node in the semantic network, wherein the reasoning engine further has a validator for selecting a subset of the activated paths being va

Abstract: An exemplary embodiment of the present invention includes a method to enforce privacy preferences on exchanges of personal data of a data-subject. The method comprises the steps of: specifying data-subject authorization rule sets having subject constraints, receiving a request message from a requester and a requester privacy statement, comparing the requester privacy statement to the subject constraints, and releasing the data-subject data in a response message to the requester only if the subject constraints are satisfied. The requester privacy statement includes purpose, retention, recipient, and access information, wherein the purpose information specifies the purpose for which the requested data is acquired, the retention information specifies a retention policy for the requested data, the recipient information specifies the recipients of the requested data, and the access information specifies whether the requested data should be accessing to the data-subject after the data has been released.

Abstract: A method of sharing telematics data for a vehicle with service providers can include receiving the telematics data for the vehicle, where the telematics data dynamically changes over time, and comparing the telematics data with a privacy policy associated with the vehicle. The privacy policy can specify rules for selectively releasing items of the telematics data to one or more service providers. Data items of the telematics data can be selectively provided to the service providers according to the comparing step.

Abstract: The invention includes various systems, architectures, frameworks and methodologies that can securely enforce a privacy policy. A method is included for securely guaranteeing a privacy policy between two enterprises, comprising: creating a message at a first enterprise, wherein the message includes a request for data concerning a third party and a privacy policy of the first enterprise; signing and certifying the message that the first enterprise has a tamper-proof system with a privacy rules engine and that the privacy policy of the first entity will be enforced by the privacy rules engine of the first enterprise; sending the message to a second enterprise; and running a privacy rules engine at the second enterprise to compare the privacy policy of the first enterprise with a set of privacy rules for the third party.

Abstract: A system and method for reasoning about concepts, relations and rules having a semantic network comprising at least one node from a predetermined set of node types, at least one link from a predetermined set of link types, and zero or more rules from a predetermined set of rule types, a subset of the rule types being matching rule types, each node and each link being associated with a set of zero or more rules; a network reasoning data structure having a reasoning type database having at least one regular expression, each of the regular expressions being a class of sequences having at least three node types and two link types, wherein the network reasoning data structure further has a context being a set of rules; and a reasoning engine having an activator for activating one or more activated paths in the semantic network, the set of activated paths having a common starting node in the semantic network, wherein the reasoning engine further has a validator for selecting a subset of the activated paths being va

Abstract: Automated mapping of part numbers associated with parts in a bill of materials (BOM) submitted by a BOM originator to internal part numbers assigned to those parts by a BOM receiver is performed by one or more computers connected to one or more networks through one or more network interfaces. A first receive component receives one or more data sets containing historical data on bills of materials received in the past by the BOM receiver. A second receive component receives one or more data sets containing known mappings between internal part numbers used by the BOM receiver, and part numbers used by various BOM originators. A third receive component receives one or more data sets containing information of various parameters and their values describing the parts to which the BOM receiver has assigned internal part numbers.