Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a correlated prediction for an input data record by generating a correlation matrix based on co-occurrences associated with a plurality of reference non-correlated predictions, generating a simulation matrix comprising a plurality of simulation data records based on a number of simulation instances and the plurality of reference non-correlated predictions, generating a plurality of correlated simulation data records based on the correlation matrix and select ones of the plurality of simulation data records, generating one or more univariates based on the plurality of correlated simulation data records, and determining a correlated prediction based on a comparison of the one or more univariates and a plurality of input non-correlated probabilities associated with the input data record.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis using semi-structured input data. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis using semi-structured input data using at least one of techniques using inferred codified fields and temporally-arranged codified fields.

Abstract: A method comprises: obtaining a current initial image generated by an image generator of an imaging device based on current input signals of sensors of the imaging device; and applying a transformation model to the current initial image to generate a current transformed image, wherein the transformation model is a machine-learning model that has been trained to generate transformed images that more closely resemble reference images generated by a reference image generator.

Abstract: Apparatus and methods for managing disciplinary policies are described herein. In some embodiments, a processor may receive a disciplinary policy and an infraction record. In some embodiments, a processor may determine a resolution datum request, transmit it to a user, and receive a resolution datum. In some embodiments, a processor may communicate an infraction notice to a user and receive a response.

Abstract: Systems and methods are disclosed for predicting diagnoses in medical records. A method includes receiving one or more documents, wherein the one or more documents include medical records. An optical character recognition (OCR) engine is used to extract text from the one or more documents. A natural language processing (NLP) model is used to determine one or more predictions and attention scores for one or more tokens in the one or more documents, wherein each of the one or more tokens represents a word in the extracted text. The one or more tokens are aggregated based on the one or more attention scores to construct sentences. The constructed sentences are presented to a user via a graphical user interface of a device.

Abstract: A composition includes a first component containing at least one polyaspartic acid ester (P1-1) and optionally a polyaspartic acid ester (P1-2) and a mixture of various polyetheraspartic acid esters (P2), and of a second component containing at least one aliphatic polyisocyanate (B1) and at least one isocyanate prepolymer (B2). The composition is particularly suitable as a cover coating composition. Said composition has a good adhesion on polyurea and polyurethane membranes, in particular roof membranes, and has drying times of less than 120 min, an elongation at break of 80-200% and a tensile strength of more than 6 N/mm2.

Abstract: An expandable washer includes a first washer piece comprising a first plurality of steps arranged about a first helical ramp, and a second washer piece comprising a second plurality of steps arranged about a second helical ramp and configured so that at least one step of the second plurality of steps matingly abuts at least one step of the first plurality of steps when the second washer piece is inserted into the first washer piece.

Abstract: Server instantiation or deployment with at least an orchestrated post-deployment configuration service utilizing an exemplary framework providing script orchestration, logging, retry logic and environment-specific infrastructure and service configurations. At least one repository may store configuration scripts (or their equivalent), including first scripts associated with, e.g., a multi-tenant system, vendor, database provider, controller, etc., and second scripts associated with, e.g., a tenant, a database client, customer, etc. After instantiating or installing a server, it may be configured with orchestrated execution to ensure successful first server configuration, and then further configured with orchestrated execution of second scripts to ensure successful subsequent server configuration. Orchestration includes retry logic, logging, and reboot support to repeat or continue script execution after reboot, and the number of scripts series is arbitrary, e.g., there may first, second, third, etc.

Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Abstract: Various embodiments of the present disclosure describe simulation, prediction, and data augmentation techniques for intelligently evaluating predictive risk scores and/or one or more combinations thereof. The techniques include generating, using a risk prediction model, predictive risk scores for an agent dataset. The techniques include generating simulated risk scores for the agent dataset based on the predictive risk scores and a first performance metric for the risk prediction model. The techniques include generating refined risk scores for the agent dataset based on the simulated risk scores and a second performance metric for a target risk refinement model. The techniques include generating return metrics for the target risk refinement model based on one or more iterations of an agent-based simulation enabled by the predictive risk scores, simulated risk scores, and refined risk scores.

Abstract: Raw sensor data from a device of a user is collected. The raw data does not include location data for the device. The data is preprocessed to identify driving trips in which the user had the device. The trip data is provided to machine-learning models (MLMs) as input and the MLMs provide as output predictions for each trip's estimated speeds, estimated number of hard brakes and a degree of each hard brake, and estimated degrees of distracted driving. A duration, time of day, and calendar date of each trip is identified. The predictions, duration, time of day, and calendar date are modified into four factor values. The four modified factor values are combined into an overall driver characteristic value and provided to a network service associated with the user for purposes of providing or modifying services provided to the user through the network service based on the overall characteristic value.

Abstract: A method for coating a surface, including the steps: a) applying a first layer of a moisture-curable composition onto a surface, b) optionally letting the applied moisture-curable composition cure into a dried first layer, c) applying a second layer of the moisture-curable composition onto the optionally dried first layer, wherein the moisture-curable composition includes: between 20 and 50 wt.-%, preferably between 25 and 45 wt.-%, based on the total weight of the composition, of at least one organic polymer which is liquid at room temperature and contains reactive silane groups, and between 10 and 30 wt.-%, preferably between 15 and 25 wt.-%, based on the total weight of the composition, of at least one polyether PE having between 2 and 6 ether oxygen atoms and is free of hydroxyl groups; and at least one curing catalyst for reactive silane groups. The method yields a multilayer coating with high intercoat adhesion.

Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Abstract: There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing cross-temporal predictive data analysis. In one example, a method includes determining a time-adjusted encoding for each temporal unit of a group of temporal units, processing each time-adjusted encoding using a cross-temporal encoding machine learning model to generate a cross-temporal encoding of the group of temporal units, and performing one or more prediction-based actions based at least in part on the cross-temporal encoding.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for converting a multilabel classification model into a sequence of a plurality of binary classification models based on a plurality of label subgroups associated with the multilabel classification model, where the label subgroups comprise an optimal subgroup size, the optimal subgroup size is generated by optimizing an optimization measure defined by a subgroup size variable and a total inner group correlation measure, and identifying label membership to a particular subgroup by using a mixed integer linear program model.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis using semi-structured input data. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis using semi-structured input data using at least one of techniques using inferred codified fields and temporally-arranged codified fields.

Abstract: The use of a combination of carbon nanotubes and at least one zinc oxide, more particularly an aluminium-doped zinc oxide, for producing a transparent, electrically conductive epoxy resin coating, and an electrostatic dissipative floor system which is sealed with the clear, electrically conductive epoxy resin coating that permits aesthetically attractive, transparently sealed, electrostatic dissipative epoxy resin floor systems, onto which more particularly conductive silica sand is scattered, the floor systems thus being particularly slip-resistant, wherein the structure and color of the sand is well visible through the transparent seal.

Abstract: A method comprises: obtaining a current initial image generated by an image generator of an imaging device based on current input signals of sensors of the imaging device; and applying a transformation model to the current initial image to generate a current transformed image, wherein the transformation model is a machine-learning model that has been trained to generate transformed images that more closely resemble reference images generated by a reference image generator.

Abstract: There is a need for more effective and efficient constrained-optimization-based operational load balancing. In one example, a method comprises determining constraint-satisfying operator-unit mapping arrangements that satisfy an operator unity constraint and an operator capacity constraint; for each constraint-satisfying operator-unit mapping arrangement, determining an arrangement utility measure; processing each arrangement utility measure using an optimization-based ensemble machine learning model that is configured to determine an optimal operator-unit mapping arrangement of the plurality of constraint-satisfying operator-unit mapping arrangements; and initiating the performance of one or more operational load balancing operations based on the optimal operator-unit mapping arrangement.

Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The text includes errors associated with noise in the image. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the errors from the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.