An exemplary system, method, and computer-accessible medium can include, for example, receiving an original dataset(s), receiving a synthetic dataset(s), training a model(s) using the original dataset(s) and the synthetic dataset(s), and evaluating the synthetic dataset(s) based on the training of the model(s). The model(s) can include a first model and a second model, and the first model can be trained using the original dataset(s) and the second model can be trained using the synthetic dataset(s). The synthetic dataset(s) can be evaluated by comparing first results from the training of the first model to second results from the training of the second model.

The present disclosure provides for a method of machine representation and tracking of contract terms over the lifetime of a contract including a step of defining an object model having object model components. Object model components are associated with other object model components where the object model components have object model component types. Further, words of object model components are evaluated to identify whether the words contain one or more core attributes pertaining to details of the contract terms. From the object model components, and the terms they contain, prevailing terms of the contract are evaluated, stored and updated as changes are made to the object model components.

Techniques are provided for performing sentiment analysis on words in a first data set. An example embodiment includes generating a word embedding model including a first plurality of features. A value indicating sentiment for the words in the first data set can be determined using a convolutional neural network (CNN). A second plurality of features are generated based on bigrams identified in the data set. The bigrams can be generated using a co-occurrence graph. The model is updated to include the second plurality of features, and sentiment analysis can be performed on a second data set using the updated model.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include sending a first call to a first node-testing model associated with a first API and receiving a first model output comprising a first model result and a first model-result category. The operations may include identifying a second node-testing model associated with a second API and sending a second call to the second node testing model. The operations may include receiving a second model output comprising a second model result and a second model-result category. The operations may include performing at least one of sending a notification, generating an updated first node-testing model, generating an updated second node-testing model, generating an updated first call, or generating an updated second call.

Systems and methods for censoring text-based data are provided. In some embodiments a censoring system may include at least one processor and at least one non-transitory memory storing application programming interface instructions. The censoring system may be configured to perform operations comprising storing a target pattern type and a computer-based model for identifying a target data pattern corresponding to a target pattern type within text based data. The censoring system may also be configured to receive text-based data by a server, and to retrieve the stored target pattern type to be censored in the text-based data. The censoring system may be configured to identify within the received text-based data, a target data pattern corresponding to the retrieved target pattern type. The censoring system may be configured to censor target characters within the identified target data pattern, and transmit the censored text-based data to a receiving party.

An exemplary system, method, and computer-accessible medium can include, for example, receiving an original dataset(s), receiving a synthetic dataset(s), training a model(s) using the original dataset(s) and the synthetic dataset(s), and evaluating the synthetic dataset(s) based on the training of the model(s). The model(s) can include a first model and a second model, and the first model can be trained using the original dataset(s) and the second model can be trained using the synthetic dataset(s). The synthetic dataset(s) can be evaluated by comparing first results from the training of the first model to second results from the training of the second model.

A scalable system and method for completing a model task using a serverless architecture is disclosed. The system may include memory storing instructions and one or more processors. The method may include receiving a request to complete a model task; retrieving a first model and a first hyperparameter based on the request; provisioning computing resources to a first development instance configured to train the first model based on the first hyperparameter and the model task; training, by the first development instance, an instance of the first model to produce a trained model and terminating said training upon satisfaction of a training criterion; receiving the trained model and a first performance metric; receiving a second performance metric associated with a second model; and terminating the development instance based on a determination that the termination condition is satisfied based on at least one of the first and second performance metrics.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

Systems and methods for transforming legacy models and transforming a model into a neural network model are disclosed. In an embodiment, a method may include receiving input data comprising an input model, an input dataset, and an input command. The method may include applying the input model to the input dataset to generate model output and storing model output and at least one of input model features or a map of the input model. The method may include generating a candidate neural network models with parameters. The method may include tuning the candidate neural network models to the input model. The method may include receiving model output from the candidate neural network models and selecting a neural network model from the candidate neural network models based on the candidate model output and the model selection criteria. In some aspects, the method may include returning the selected neural network model.

Systems and methods for connecting datasets are disclosed. For example, a system may include a memory unit storing instructions and a processor configured to execute the instructions to perform operations. The operations may include receiving a plurality of datasets and a request to identify a cluster of connected datasets among the received plurality of datasets. The operations may include selecting a dataset. In some embodiments, the operations include identifying a data schema of the selected dataset and determining a statistical metric of the selected dataset. The operations may include identifying foreign key scores. The operations may include generating a plurality of edges between the datasets based on the foreign key scores, the data schema, and the statistical metric. The operations may include segmenting and returning datasets based on the plurality of edges.