Abstract: A method for heuristics-based link prediction in multiplex networks may include a link prediction computer program: receiving a model for a multiplex network; defining a property matrix that specifies feature for cross-layer correlation; constructing a cross-correlation matrix from the property matrix; applying a count and weight correlation heuristic to the cross-correlation matrix, wherein the count and weight correlation heuristic counts a number of layers that contain a link between two nodes, and weights that count according to cross-layer correlations; applying a correlation weighted heuristic to the cross-correlation matrix; applying a count correlation-weighted heuristic to the cross-correlation matrix that counts the number of layers that contain a link between two nodes and weights that count according to both cross-layer correlations and values resulting from evaluating a monoplex heuristic applied to each layer in the multiplex network, resulting in output values; and identifying a missing link ba

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Abstract: A distributed resource model is described that maintains traits of resources in a distributed computing system. The traits include properties, commands, and events that, along with protocols operating in the distributed computing system, provide real-time access to the traits of resources in the distributed computing system, as well as real-time command of controls for the resources. Controllers manage and publish the traits of resources and provide various types of functionality, such as arbitration, complexity management, fan-out of capabilities, coordination, adaptation, and resource proxying.

Abstract: A method and a system for using a graph neural network framework to implement a link prediction in a multiplex network environment is provided. The method includes: identifying a plurality of layers of a multiplex network, each respective layer including a respective plurality of nodes; for each node included in at least a first layer, providing, by a structural node label and determining a common embedding across all of the plurality of layers and an individual embedding for each individual layer; using a k-nearest approach to select a subset of the plurality of layers for performing link prediction with respect to each layer based on the determined embeddings; and performing a link prediction by determining a respective feed-forward network with respect to each layer included in the selected subset.

Abstract: A method and a system for using a graph neural network framework to implement a link prediction in a multiplex network environment is provided. The method includes: identifying a plurality of layers of a multiplex network, each respective layer including a respective plurality of nodes; for each node included in at least a first layer, providing, by a structural node label and determining a common embedding across all of the plurality of layers and an individual embedding for each individual layer; using a k-nearest approach to select a subset of the plurality of layers for performing link prediction with respect to each layer based on the determined embeddings; and performing a link prediction by determining a respective feed-forward network with respect to each layer included in the selected subset.

Abstract: A method and a system for generating an interpretable embedding that corresponds to a sequence of events is provided. The method includes: receiving information that corresponds to a sequence of events that respectively correspond to interactions between a customer and an organization; determining, for each respective event, a respective product associated with the organization and a respective channel via which the event has occurred; assigning a respective sentiment to each event; computing a respective weight for each event; aggregating the computed weights with respect to the products and the channels; and using the aggregated weights to generate the interpretable embedding for the customer. The interpretable embedding is then usable for generating targeted offers to the customer, handling complaints, and preventing subsequent complaints.

Abstract: A method and a system for using a graph neural network framework to implement a link prediction in a multiplex network environment is provided. The method includes: identifying a plurality of layers of a multiplex network, each respective layer including a respective plurality of nodes; for each node included in at least a first layer, providing, by a structural node label and determining a common embedding across all of the plurality of layers and an individual embedding for each individual layer; using a k-nearest approach to select a subset of the plurality of layers for performing link prediction with respect to each layer based on the determined embeddings; and performing a link prediction by determining a respective feed-forward network with respect to each layer included in the selected subset.

Abstract: A method and a system for performing stochastic sequential assignment of jobs with random arrival times is provided. The method includes receiving a first plurality of jobs in a sequence; and sequentially applying, to each respective job from among the first plurality of jobs, a non-parametric sequential allocation algorithm in order to determine whether to accept the respective job or to decline the respective job. The application of the non-parametric sequential allocation algorithm includes calculating, for each respective job, a corresponding reward value that relates to a reward that is gained when the respective job is accepted; and maximizing an expected cumulative reward value based on the calculated reward values.

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Abstract: A distributed resource model is described that maintains traits of resources in a distributed computing system. The traits include properties, commands, and events that, along with protocols operating in the distributed computing system, provide real-time access to the traits of resources in the distributed computing system, as well as real-time command of controls for the resources. Controllers manage and publish the traits of resources and provide various types of functionality, such as arbitration, complexity management, fan-out of capabilities, coordination, adaptation, and resource proxying.

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Abstract: A method and a system for increasing accuracy in fraud detection are provided. The method includes: obtaining a first set of transaction data that relates to a first plurality of transactions, each respective transaction from among the first plurality of transactions having a corresponding originating account and a corresponding beneficiary account; determining, for each corresponding beneficiary account, a number of executed transactions associated with the corresponding beneficiary account by computing a respective PageRank value for a transaction graph associated with the corresponding beneficiary account; and when the respective PageRank value of the beneficiary account exceeds a predetermined threshold, determining that the corresponding beneficiary account is unlikely to be a participant in a future fraudulent transaction.

Abstract: Systems and methods for multi-agent based fraud detection are disclosed. A method may include: providing a generator configuration file comprising a plurality of transaction behaviors and a number or proportion of generator agents to act in accordance with each transaction behavior; providing a detector configuration file comprising a detector parameter for a plurality of detector agents to use; generating a first set of test data using the generator agents based on the transaction behavior, wherein the first set of generated test data may include a first set of generated test transactions, and each generated test transactions may include a fraud indicator based on the transaction behavior; training a plurality of detector agents using the first set of generated test data and the detector configuration file, wherein each detector agent outputs a first trained model object; and outputting a first trained detection model based on the first trained model objects.

Abstract: Systems and methods for generating synthetic data using a learned copula are disclosed.

Abstract: A method for creating a realistic agent-based simulation of an over-the-counter market and using data from the real market to calibrate the simulator is provided. The method includes: assigning, to each of a group of agents, a type value that relates to a state of the respective agent; receiving agent-specific data that relates to market-based observations, market-based actions, and market-based rewards; generating a market simulation based on the assigned type values and the received agent-specific data; and calibrating the market simulation based on actual market data.

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Abstract: A distributed resource model is described that maintains traits of resources in a distributed computing system. The traits include properties, commands, and events that, along with protocols operating in the distributed computing system, provide real-time access to the traits of resources in the distributed computing system, as well as real-time command of controls for the resources. Controllers manage and publish the traits of resources and provide various types of functionality, such as arbitration, complexity management, fan-out of capabilities, coordination, adaptation, and resource proxying.

Abstract: A method for heuristics-based link prediction in multiplex networks may include a link prediction computer program: receiving a model for a multiplex network; defining a property matrix that specifies feature for cross-layer correlation; constructing a cross-correlation matrix from the property matrix; applying a count and weight correlation heuristic to the cross-correlation matrix, wherein the count and weight correlation heuristic counts a number of layers that contain a link between two nodes, and weights that count according to cross-layer correlations; applying a correlation weighted heuristic to the cross-correlation matrix; applying a count correlation-weighted heuristic to the cross-correlation matrix that counts the number of layers that contain a link between two nodes and weights that count according to both cross-layer correlations and values resulting from evaluating a monoplex heuristic applied to each layer in the multiplex network, resulting in output values; and identifying a missing link ba

Abstract: Systems and methods for applying game theory for fraud detection. Rather than inspecting every transaction record, embodiments are directed to limiting incoming suspicious transaction records according to a schedule. The schedule may define time windows for various clients and transactions. These time windows may filter down the stream of incoming transaction records to a subset. As a result, fraud may be detected by strategically allocating resources in an optimal way rather than attempting to inspect each and every instance of transaction.

Abstract: Systems and methods for digital document generation using natural language interaction are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for digital document generation using natural language interaction may include: (1) receiving from an electronic device, a natural language command comprising an action to generate digital content, an object, and a data source for the digital content; (2) processing the natural language command to identify the action, the type of digital content, and the data source; (3) identifying a skill in a skill library that is mapped to the action and the object; (4) retrieving data from the data source for the skill; and (5) generating the digital content according to the skill using the data.