Various embodiments relate generally to data science and data analysis, computer software and systems, including a subset of intermediary executable instructions constituting an communication interface between various software and/or hardware platforms, and, more specifically, to an automated application interface governance platform to automate development, maintenance, and governance functions for application interfaces, such as harmonizing, validating, and/or replicating application program interfaces (“APIs”). For example, a method may include identifying a subset of application interfaces, synthesizing a data structure for each application interface, analyzing the data structure against other data structures to identify duplicative portions among multiple data structures, substituting a reference to a location into a portion of multiple application interfaces. Optionally, the method may include evaluating interoperability of multiple application interfaces to validate collective operation of a subset of application interfaces.

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.

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 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 receiving a call to an API node. The operations may include determining that the call is associated with the first version of the API. The operations may include determining that the API node is associated with a second version of the API. The operations may include translating the call into a translated call using a translation model, the translated call being associated with the second version of the API.

A computer-implemented method includes generating from a corpus a knowledge graph comprising a plurality of nodes interconnected by a plurality of edges. Each node of the plurality of nodes represents an entity extracted from the corpus. Each edge of the plurality of edges represents a relationship between corresponding entities extracted from the corpus. The knowledge graph includes a plurality of source passages extracted from the corpus from which the plurality of edges was generated. A generative language model is trained, for each edge of the plurality of edges, to associate two or more related entities by utilizing the knowledge graph. Using the trained generative language model, one or more passages representing the edge of the knowledge graph are generated for each edge of the knowledge graph.

A language model is fine-tuned by extracting terminology terms from a text document. The method comprises identifying a text snippet, identifying candidate multi-word expressions using part of speech tags, and determining a specificity score value for each of the candidate multi-word expressions. Moreover, the method comprises determining a topic similarity score value for each of the candidate multi-word expressions, selecting remaining expressions from the candidate multi-word expressions using a function of a specificity value and a topic similarity value of each of the candidate multi-word expressions, adding a noun comprised in the text snippet to the remaining expressions depending on a correlation function, labeling the remaining multi-word expressions, and fine-tuning an existing pre-trained transformer-based language model using as training data the identified text snippet marked with the labeled remaining expressions.

Systems and methods for generating synthetic video are disclosed. For example, a system may include a memory unit and a processor configured to execute the instructions to perform operations. The operations may include receiving video data, normalizing image frames, generating difference images, and generating an image sequence generator model. The operations may include training an autoencoder model using difference images, the autoencoder comprising an encoder model and a decoder model. The operations may include identifying a seed image frame and generating a seed difference image from the seed image frame. The operations may include generating, by the image sequence generator model, synthetic difference images based on the seed difference image. In some aspects, the operations may include using the decoder model to synthetic normalized image frames from the synthetic difference images. The operations may include generating synthetic video by adding background to the synthetic normalized image frames.

A computer-implemented method for training a global federated learning model using an aggregator server includes training multiple local models at respective local nodes. Each local node selects a set of attributes from its training dataset for training its local model. Each local node generates an anonymized training dataset by using a syntactic anonymization method, and by selecting quasi-identifying attributes from training attributes, and generalizing the quasi-identifying attributes using a syntactic algorithm. Further, each local node computes a syntactic mapping based on equivalence classes produced in the anonymized training dataset. The aggregator server computes a union of mappings received from all the local nodes. Further, federated learning includes training the global federated learning model by iteratively sending, by the local nodes to the aggregator server, parameter updates computed over the local models. The aggregator server aggregates the received parameter updates, and sends the aggregated parameters to the local nodes.

An image collating device that collates a first image and a second image includes a frequency characteristic acquiring unit, a frequency characteristic synthesizing unit, and a determining unit. The frequency characteristic acquiring unit acquires a frequency characteristic of the first image and a frequency characteristic of the second image. The frequency characteristic synthesizing unit generates a synthesized frequency characteristic by synthesizing the frequency characteristic of the first image and the frequency characteristic of the second image. The determining unit calculates a score indicating a degree to which the synthesized frequency characteristic is a wave having a single period, and collates the first image and the second image based on the score.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.