A system for image classification is disclosed that includes a central system configured to provide high reliability image data processing and recognition and a plurality of endpoint systems, each configured to provide image data processing and recognition with a lower reliability than the central system and to generate probability data. A decision switch disposed at each of the plurality of endpoint systems is configured to receive the probability data and to determine whether to deny access, grant access or generate a referral message to the central system, wherein the referral message includes at least a set of image data generated at the endpoint system.

A computing device, including memory, an accelerator device, and a processor. The processor may generate a plurality of data packs that each indicate an update to a variable of one or more variables of a combinatorial cost function. The processor may transmit the plurality of data packs to the accelerator device. The accelerator device may, for each data pack, retrieve a variable value of the variable indicated by the data pack and generate an updated variable value. The accelerator device may generate an updated cost function value based on the updated variable value. The accelerator device may be further configured to determine a transition probability using a Monte Carlo algorithm and may store the updated variable value and the updated cost function value with the transition probability. The accelerator device may output a final updated cost function value to the processor.

A data complementing method implemented by a computer, the method includes: calculating degree of correlation of a data item corresponding to a missing data value with another data item, in a case where the missing data value exists in a plurality of data records including data values corresponding to a plurality of data items, respectively; performing complementation of the missing data value by a recursive method based on a data item value of the other data item, in a case where the degree of correlation is larger than a predetermined correlation threshold; and performing complementation of the missing data value by a statistical method based on a data value other than the missing data value of a data item corresponding to the missing data value, in a case where the degree of correlation is not larger than the correlation threshold.

Disclosed herein are example embodiments of an improved narrative generation system where an analysis service that executes data analysis logic that supports story generation is segregated from an authoring service that executes authoring logic for story generation through an interface. Accordingly, when the authoring service needs analysis from the analysis service, it can invoke the analysis service through the interface. By exposing the analysis service to the authoring service through the shared interface, the details of the logic underlying the analysis service are shielded from the authoring service (and vice versa where the details of the authoring service are shielded from the analysis service). Through parameterization of operating variables, the analysis service can thus be designed as a generalized data analysis service that can operate in a number of different content verticals with respect to a variety of different story types.

A computer-implemented method of improving maintenance of a complex system, the complex system having a plurality of components, the method involving: preparing data across a plurality of data streams from a plurality of data sources; generating a matrix representation of the data; calculating a time proximity of the data; calculating a plurality of corresponding cell values of the matrix representation; matching event information across the plurality of data streams from a plurality of data sources, the plurality of data sources corresponding to the plurality of components, wherein at least one data stream of the plurality of data streams has at least one of low fidelity data and imprecise event generation information; and scoring the imprecise event generation information across the plurality of data streams, thereby providing a score indicating a match quality of the imprecise event generation information.

A computerized-method for generating a dataset for a Machine Learning (ML) model for an increased accurate financial crime detection from an initiation stage of the ML model implementation. The computerized-method includes: retrieval of financial transaction records from a database of financial transaction records to arrange a dataset of financial transaction records, according to preconfigured techniques. Then, processing the records in the dataset; Then, operating feature engineering on preselected anomalous related features to yield probabilistic categorical features and to yield probabilistic numerical features, and then combining the probabilistic categorical features with the probabilistic numerical features to generate a complex features dataset, and providing the probabilistic categorical features, the probabilistic numerical features and the complex features dataset to an ML model, thus, increasing accuracy of detection that is performed right from an initiation stage of the ML model implementation.

The present disclosure relates to systems and methods for characterizing a behavior change of a process. A behavior model that can include a set of behavior parameters can be generated based on behavior data characterizing a prior behavior change of a process. A stimulus parameter for a performance test can be determined based on the set of behavior parameters. An application of the performance test to the process can be controlled based on the stimulus parameter to provide a measure of behavior change of the process. Response data characterizing one or more responses associated with the process during the performance test can be received. The set of behavior parameters can be updated based on the response data to update the behavior model characterizing the behavior change of the process. In some examples, the behavior model can be evaluated to improve or affect a future behavior performance of the process.

A system, method, and computer program product for predicting mismatch contribution in an electronic environment. Embodiments may include modeling, using a processor, a discrete output mismatch contribution problem using sparse logistic regression to generate a mismatch contribution model and applying a cross-validation approach to increase a complexity of the mismatch contribution model. Embodiments may further include computing one or more mismatch contribution values from the mismatch contribution model and defining at least one sizing constraint or determining a worst case result associated with a sampling process based upon, at least in part, the one or more mismatch contribution values.

A system, method, and computer program product for predicting mismatch contribution in an electronic environment. Embodiments may include modeling, using a processor, a discrete output mismatch contribution problem using sparse logistic regression to generate a mismatch contribution model and applying a cross-validation approach to increase a complexity of the mismatch contribution model. Embodiments may further include computing one or more mismatch contribution values from the mismatch contribution model and defining at least one sizing constraint or determining a worst case result associated with a sampling process based upon, at least in part, the one or more mismatch contribution values.

Systems, apparatuses and methods may provide for technology that generates inclusion data in accordance with a Poisson distribution, wherein the inclusion data specifies a number of inclusions for each observation in a set of observations. The technology may also train a first decision tree in a random forest based at least in part on the inclusion data.