Abstract: A method and system are provided for generating a combined prediction using an ensemble machine learning system. The prediction may be used in risk assessment for payroll processing. A data point is received as input to a multitude of trained models. Each model is trained from a respective data subset of a disparate data. A model prediction this generated by each of a multitude of machine learning models. For each respective trained model, a trust score is generated based on a data sparseness metric of the data point and a feature importance vector of the respective model. The model predictions and trust scores are received as input to a meta-model that was trained from the trust score and the model prediction of the multitude of trained models over the respective data subset of the disparate data. A combined prediction is generated using the trained meta-model.

Abstract: Certain aspects of the present disclosure provide techniques for managing a search engine based on search performance metrics. An example method generally includes dividing a set of search history data into a first subset of search history data and a second subset of search history data. The first subset of data is associated with interaction with search results, and the second subset of data is associated with non-interaction with search results. A first quality score is generated for searches in the first subset of data. A second quality score is generated for searches in the second subset of data based on different search intents identified for each temporally related group in the second subset of data. An overall quality score is generated for a search engine, and one or more actions with respect to the search engine are taken based on the overall quality score.

Abstract: A method utilizes a framework for transaction categorization personalization. A transaction record is received. a baseline model is selected from a plurality of machine learning models. An account identifier, corresponding to the transaction record using the baseline model, is selected. The account identifier for the transaction record is presented.

Abstract: Certain aspects of the present disclosure provide techniques for orchestrating a user experience using natural language input. A user experience is orchestrated within an ecosystem of features for which a plurality of corresponding tokens is defined. Natural language describing a desired user experience result is received by a user experience orchestrator. A sequence of tokens corresponding to operations belonging to an ecosystem of features which produce a correct result for the natural language input can be identified by a trained large language model and executed by the user experience orchestrator using a token operator. The output operations determined by the model to produce or be likely to produce the correct result based on the natural language input can be disambiguated, confirmed, and/or executed.

Abstract: A method performs personalized transaction categorization. A transaction record is received, by a server application. In a first stage, sparse raw features are extracted from a transaction record of a transaction and converted into a transaction vector including dense features. In a second stage, the transaction vector is classified into a customized chart of accounts using the dense features to generate adapter model output. The method further includes selecting, an account identifier, corresponding to the transaction record and to an account of the customized chart of accounts, using the adapter model output, and presenting the account identifier for the transaction record.

Abstract: This disclosure provides systems, methods and apparatuses for machine learning. In some implementations, a pre-processing system may identify one or more special features in an input dataset and may generate one or more pruned datasets, respectively, based on the identified special features. The pre-processing system maps each of the pruned datasets to one or more predictions and selectively removes the values associated with one or more special features from the input dataset based on the mapping. In some other implementations, a pre-processing system may map each of the feature sets in an input dataset to multiple predictions in accordance with multiple machine learning algorithms. The pre-processing system evaluates a performance of each of the mappings and selects one of the machine learning algorithms to be used to train the machine learning model based on the performance of each mapping.

Abstract: A method implements a dataset rank metric for measuring dataset relevance. Metadata is identified for a plurality of datasets. A graph structure is generated in storage. The graph structure includes a multitude of nodes connected by a multitude of edges. Each node of the multitude of nodes representing a respective dataset of a multitude of datasets, the multitude of edges connecting the multitude of nodes according to a data lineage determined from metadata of the multitude of datasets. A composite score is generated for each node of the graph. The computer processor iteratively processes the composite scores for the multitude of nodes of the graph to generate a dataset rank for each dataset. The multitude of datasets is presented in an interface, sorted according to the respective dataset rank of each dataset.

Abstract: A system and method that leverage a hybrid bi-directional user experience system that bi-directionally transfers an application session between a first application and a migrated application based on the availability of application features in the migrated application stack.

Abstract: A testing system and method for testing application code against various failure scenarios. The testing system and method generate a test workflow including test source code implementing a series of actions that affect an application component and or an infrastructure component included in application code. The testing system and method execute the test workflow to determine the performance of the application code during one or more failure scenarios caused by the series of actions included in the test workflow. Performance data generated by the test code is analyzed by a performance analysis service or method to identify limitations of the application code and build resiliency patterns that address the limitations and improve the performance of the application code.

Abstract: This disclosure relates to systems and methods for providing user content on a social network. In some aspects, the social network receives, over a communications network from a first computing device associated with a first user of the social network, a transmission including a post to be published on the social network. The social network detects, in the post, goods or services sought or inquired about by the first user, and determines a proximity of the first user. The social network identifies one or more other users of the social network located within a geographical area or the proximity associated with the first user, and presents the post only to the one or more identified users of the social network.

Abstract: A method includes receiving a set of execution paths for a directed acyclic graph. The directed acyclic graph includes multiple nodes and multiple edges. The nodes include sets of executable code. The edges represent an operational relationship between at least two nodes. The execution paths include a subset of the nodes connected by a sequence of edges. The method further includes setting a current training level to a maximum training level. The method further includes constructing a transition probability set for the current training level and adding the transition probability set to a transition probability dictionary. The method further includes storing the transition probability dictionary as a final transition probability dictionary.

Abstract: A system and method implementing K-anonymity processing of a data record to protect sensitive information, while still revealing useful information. The system and method performing K-anonymity processing of categories in the data record, and choosing to mask the data of the category that produces the highest anonymity score. The system and method repeats the process until a K-value of the data record is achieved.

Abstract: A system is configured to train a machine learning tree network using path based features, such as leaf nodes or connections between nodes. A first machine learning tree network model, for example, may be trained using a first set of training data, and used to generate predictions for a second set of training data. The path based features are determined from the first machine learning tree network model when generating the predictions for the second set of training data. The path based features may then be used to train a second machine learning tree network model, e.g., using logistic regression.

Abstract: Data of prior online banking sessions is logged or stored and analyzed to identify online banking action patterns and pages or screens of an online banking website used to execute the actions. User profile data associated with executed actions is also identified. User profile data may involve the computing device used to access the site, location and/or temporal data such as date, time and frequency. When profile data of a current online banking session is received, rules dictating how the user interface of the online banking website can be selectively modified are accessed and applied to identify a page or screen to be presented to the user thus bypassing at least one intermediate page or screen that would have otherwise been presented to the user navigating the online banking website according to its pre-defined structure.

Abstract: At least one processor may obtain a document comprising text tokens. The at least one processor may determine, based on a pre-trained language model, word embeddings corresponding to the text tokens. The at least one processor may determine, based on the word embeddings, named entities corresponding to the text tokens; and one or more accuracy predictions corresponding to the named entities. The at least one processor may compare the one or more accuracy predictions with at least one threshold. The at least one processor may associate, based on the comparing, the named entities with one or more confidence levels. The at last one processor may deliver the named entities and the one or more confidence levels.

Abstract: A federation link is used to facilitate bi-directional identity federation between software applications. The federation link is created to include user and account identity information for software applications having respective authentication providers. The federation link is created by one of the software applications and shared, for example, with the authentication provider of the other software application. The federation link can be utilized by both software applications to facilitate automated user authentication when navigating in either direction between the software applications.

Abstract: A method for controlling software agent workers for migrating data between databases. The method monitors resource utilization of at least one of the databases during the migration of the data and compares the monitored resource utilization to a desired resource utilization range. When the comparison indicates that the monitored resource utilization is less than the desired resource utilization range, the method additively deploys additional software agent workers. When the comparison indicates that the monitored resource utilization is greater than the desired resource utilization range, the method multiplicatively removes software agent workers.

Abstract: A transaction tracking service (TTS). The TTS includes a transaction repository configured to store transaction records organized within a transaction groups. The TTS also includes a computer processor operatively connected to the transaction repository. The computer processor is configured to execute instructions to make a first determination that a first transaction record of the plurality of transaction records is unassigned, generate, based on the first determination, an unassigned transaction list (UTL) comprising a first set of transaction data associated with the first transaction record, and transmit the UTL to a client device operatively connected to the TTS.

Abstract: Aspects of the present disclosure provide techniques for image-based document search. Embodiments include receiving an image of a document and providing the image of the document as input to a machine learning model, where the machine learning model generates separate embeddings of a plurality of patches of the image of the document and the machine learning model generates an embedding of the image of the document based on the separate embeddings of the plurality of patches. Embodiments include determining a compact embedding of the image of the document based on applying a dimensionality reduction technique to the embedding of the image of the document generated by the machine learning model. Embodiments include performing a search for relevant documents based on the compact embedding of the image of the document. Embodiments include performing one or more actions based on one or more relevant documents identified through the search.

Abstract: A sequence of data entry screens are configured to collect the data from a user. The method and system receive data entered by a user into a data entry screen. The method and system then determine metrics of the collected data, and ranks the collected data and the data entry screens based on the determined metrics. The ranking is then used to display the next best screen in the sequence for collecting data.