Jpmorgan Chase Bank, N.a.

Shaltiel Eloul

Sean Moran

Fanny Silavong

Jacob Mendel

Yash Satsangi

Antonios Georgiadis

Di Yu

Jacky Ct Chan

Subhashini Tripuraneni

Agathe Lhérondelle

Shafir Balitsky

Tal Dadia

Lucas Newman

Sanket Kamthe

Systems and methods for federated model validation and data verification are disclosed. A method may include: (1) receiving, by a local computer program executed by client system, a federated machine learning model from a federated model server; (2) testing, by the local computer program and using a policy service, the federated machine learning model for vulnerabilities to attacks; (3) accepting, by the local computer program, the federated machine learning model in response to the federated machine learning model passing the testing; (4) training, by the local computer program, the federated machine learning model using input data comprising local data and outputting training parameters; (5) identifying, by the local computer program using the policy service, accidental leakage and/or contamination by comparing the training parameters to the input data; and (6) providing, by the local computer program, the training parameters to the federated model server.

Systems and methods for federated model validation and data verification

A method for automatically identifying proficiencies based on an associated set of codes is provided. The method includes retrieving a file that is associated with a user from a repository, the repository including a source code repository; classifying the file based on a corresponding programming language; determining, by using a model, a proficiency attribute of the user based on the file and a result of the classification; identifying a characteristic of the user based on corresponding activity data from the repository; and generating a profile that corresponds to the user based on the proficiency attribute and the characteristic, the profile including a plurality of proficiency attributes that correspond to the user.

Method and system for proficiency identification

Systems and methods for code repository embedding using attention mechanism for tagging and summarization are disclosed. According to one embodiment, a method for code repository embedding may include: (1) extracting, by a computer program executed by an electronic device, docstring embeddings, code embeddings, and dependency embeddings from scripts in a repository (2) applying, by the computer program, a machine learning algorithm to each of the docstring embeddings, code embeddings, and dependency embeddings; (3) concatenating, by the computer program, outputs of the machine learning algorithm; (4) weighting, by the computer program, the concatenated outputs of the machine learning algorithm using an attention mechanism, resulting in a repository representation comprising an abstract vector; and (5) tagging or summarizing, by the computer program, the script using its repository representation.

Systems and methods for code repository embedding for tagging and summarization tasks using attention on multiple code domains

Systems and methods for streaming classification of distributed ledger-based activities are disclosed. In one embodiment, a method for streaming classification of distributed ledger-based activities may include: receiving, at a detection computer program executed by a server and from a distributed ledger network, information for a plurality of transactions; receiving, from a database and at the detection computer program, node information that identifies a plurality of nodes as involved in illicit transactions, as being involved in licit transactions, or unknown; applying, by the detection computer program, exponential time sampling to sample labels and associated features; training, by the detection computer program, a classifier; receiving, by the detection computer program, a streaming transaction from the distributed ledger network; predicting, by the detection computer program, a classification for the streaming transaction using the trained classifier; and outputting, by the detection computer program, an alert based on the prediction.

Systems and methods for streaming classification of distributed ledger-based activities

Systems and methods for auto-captioning repositories from source code are disclosed. A method for code repository embedding may include a computer program executed by an electronic device: (1) extracting docstring embeddings, code embeddings, and dependency embeddings from scripts in a repository; (2) applying a machine learning algorithm to each of the docstring embeddings, the code embeddings, and the dependency embeddings, wherein outputs of each of the machine learning algorithms comprises a vector; (3) concatenating the vectors into an output embedding vector; (4) weighting the output embedding vector using an attention mechanism, resulting in a repository representation comprising an abstract vector; (5) generating a plurality of tags for the repository representation or the output embedding vector representing the weights of the tags, using a trained neural network; (6) applying a tags-to-caption transformer to the tags or the output embedding vector, resulting in a caption; and (7) outputting the caption.

Systems and methods for auto-captioning repositories from source code

A method for combining an embedding of a graph having a plurality of nodes and edge connections and classifications of distributed ledger-based activities may include: receiving information for historical distributed ledger-based activities comprising identifications of the plurality of nodes in the graph as involved in illicit activities, licit activities, or unknown; applying sampling to sample labels and associated features; training a first classifier based on the plurality of historical distributed ledger-based activities, the node information, and the sample labels; receiving a current distributed ledger-based activity from the distributed ledger network; predicting a classification for the current distributed ledger-based activity using the first classifier; extracting features from the current distributed ledger-based activity; enriching the extracted features based on one or more neighbors of the nodes involved in the current distributed ledger-based activity; reclassifying the current distributed ledger-based activity using a second classifier; and outputting an alert based on the prediction.

Systems and methods for combining graph embedding and random forest classification for improving classification of distributed ledger activities

A method for facilitating automatic assignment of code topics is provided. The method includes accessing a database, the database including data that is associated with a known code topic and data that is associated with an unknown code topic; parsing the database to identify a data file that includes a code snippet, document strings, and/or dependencies. Dividing the identified data file into a training data set and a testing data set; generating a first set of features for each element of the code snippet, document strings, and dependencies, for the training data set; generating a second set of features for the testing data set; identifying, by using a model, a representative feature for the training data set based on frequency distribution of the training data set, the representative feature including a corresponding probability; and determining, by using clustering, a code topic for the data file.

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London, United Kingdom

Shaltiel Eloul