Abstract: A method for preserving privacy with respect to modeling event sequence data is provided. The method includes: receiving information about a sequence of events; modeling the event sequence by a Hawkes process that has an intensity that includes an exogenous base intensity rate and an indigenous component that has an excitation rate and a decay rate; analyzing the received information; and determining estimated values of the exogenous base intensity rate and the excitation rate, such that an accuracy of the estimates corresponds to a length of time over which the sequence of events is observed. Differential privacy is introduced by adding noise to the sequence of events in order to preserve the privacy of individuals associated with the events, and a cost of the differential privacy is expressible as an additional length of observation time required to ensure the accuracy of the estimates.

Abstract: Methods and systems for generating a model to be used for evaluating credit and loan applications are provided. The method includes: training a first model by using all features included in a universe of candidate features; measuring a first metric that relates to an accuracy of the first model and a second metric that relates to a disparity of the first model; constructing a graph based on pairwise correlations of the features; clustering the features into feature sets; estimating a respective disparity contribution associated with each feature set; selecting feature sets to be included in a second model; training, the second model; measuring the first metric and the second metric with respect to the second model; and determining whether the second model satisfies a predetermined accuracy level and a predetermined disparity reduction with respect to the first model.

Abstract: Methods and systems for using images that represent time-series data to forecast corresponding images depicting future values of the time-series data are provided. The method includes: receiving a set of time-series data; converting the set of time-series data into a partial first image that includes a blank region to which future data to be included in the first set of time-series data corresponds; and performing an inpainting operation with respect to the partial first image by generating pixels for filling in the blank region in order to produce an augmented version of the first image. A machine learning algorithm that is trained by using historical time-series data may be used to perform the inpainting operation.

Abstract: A method and a computing apparatus for generating a personal financial plan are provided. The method includes: receiving a first input that includes information about an initial financial state of an individual person; receiving a second input that includes information about a goal financial state of the individual person; receiving a third input about financial habits of the individual person; defining a set of available user actions, each respective available user action being assigned a corresponding probability that indicates a likelihood that the individual person successfully performs the respective available user action; determining, based on the defined set of available user actions and heuristics designed to maximize a likelihood of execution, proposed sequences of potential user actions by which the goal financial state is achievable; and calculating, for each proposed sequence, a respective likelihood score that indicates a corresponding feasibility of a successful completion thereof.

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 and a system for extracting and formatting financial time-series data is provided. The method includes: receiving a first set of tabular data; segmenting the first set of tabular data into a plurality of cells; determining whether each respective cell is a header cell or a content cell; for each header cell, normalizing each of a time period and a metric associated therewith; for each content cell, determining one or more column header cells and one or more row header cells associated. therewith; and generating an output table based on a result thereof.

Abstract: A solution to identify the layout elements in a document is provided. The documents are formulated in an integrated visual space, where they are treated uniformly as a rasterized image. The transformation to image space facilitates exploitation of a spectrum of stylistic cues, such as spacing between the lines and paragraphs. Effective recognition of layout elements is provided by applying a synthetic generator model that creates artificial documents with the required labels and bounding boxes. The generator model is based on a Bayesian Network, offers full flexibility in controlling the document types and labels, and can introduce artificial defects to synthesize low-quality documents. An artificial intelligence model for detecting layouts is trained based on these synthetic documents and the resultant model is used to identify the layout elements in real-world documents. The resulting layout elements, when augmented with the text content, greatly aids in effective information retrieval.

Abstract: Various methods, apparatuses/systems, and media for automatically computing grouped Shapley values for action reason codes are disclosed. A processor utilizes a machine learning model that is configured to output decision data; accesses a database that stores the outputted decision data and data corresponding to groups of related features; establishes a communication link between the machine learning model and the database; directly calculates grouped Shapley values that represent mathematically justifiable measure of importance for the groups of related features by implementing a predefined mathematical algorithm; and outputs reason codes based on the computed grouped Shapley values.

Abstract: A method and a system for solving transactions reconciliation tasks by integrating clustering and optimization are provided. The method includes: receiving first information that relates to a plurality of transactions; extracting, from the first information, a set of transaction-specific features and a set of transaction-specific textual information; generating at least one meta-transaction by clustering a subset of the plurality of transactions based on the extracted set of transaction-specific features and the extracted set of transaction-specific textual information; and matching transactions from within the clustered subset based on the amounts of the transactions.

Abstract: Systems and methods for privacy-preserving summarization of digital activity are disclosed. According to one embodiment in an information processing apparatus comprising at least one computer processor and at least one display, a privacy-preserving digital activity computer program performing the following: (1) capturing a blurred or pixelated screenshot of the at least one display; (2) identifying a plurality of computer application visible in the blurred or pixelated screenshot; (3) identifying a foreground or actively-used application out of the plurality computer applications in the blurred or pixelated screenshot; and (4) logging the visible computer applications and the foreground or actively-used application.

Abstract: A method for using a graph neural network framework to improve learning and predicting in a multiplex network environment is provided. The method includes: identifying a plurality of layers of a multiplex network; estimating, for each layer, a corresponding probability of selecting the layer as being a relevant layer for training with respect to an application; estimating, for each layer, a corresponding loss associated with selecting the layer as being relevant; calculating, for each layer based on the corresponding probability and the corresponding loss, a corresponding regret associated with selecting the layer as being relevant; determining, for each layer based on the calculated corresponding regret, whether to select the layer as being relevant; and training the multiplex network with respect to the application by aggregating information obtained from layers that have been determined as being relevant layers.

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: Systems and methods for privacy-preserving inventory matching are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for inventory matching may include: (1) receiving, from each of a plurality of clients, a masked submission comprising an identification of at least one security to buy or sell and a desired quantity to buy or sell; (2) aggregating the masked submissions resulting in a sum of the desired quantities to buy or sell; (3) matching at least two of the clients to conduct a transaction based on aggregation and their respective masked submissions; and (4) conducing the transaction between the matched clients.

Abstract: A method and a computing apparatus for allocating space in a building are provided. The method includes: receiving a first input that includes information that relates to a plurality of persons that intend to occupy the building; receiving a second input that includes information that relates to building specifications; receiving a third input that includes information that relates to constraints that are mandatory and preferences that are not mandatory; and determining, based on each of the first input, the second input, and the third input, an allocation of each person to a corresponding space within the building and a respective schedule for each person to occupy the corresponding space. The determination of the allocation and the respective schedule may be effected by applying an artificial intelligence (AI) algorithm that is configured to satisfy all of the mandatory constraints and to optimize a satisfaction of the preferences.

Abstract: A method for optimizing personnel utilization is provided. The method includes: accessing first task management planning information that relates to a first project that has not been completed; using the accessed first task management planning information to identify a plurality of tasks to be performed in connection with the first project and to identify a plurality of persons to be assigned to respective tasks; retrieving historical task management information that relates to at least one project that has been completed; and adjusting at least a first portion of the first task management planning information based on the retrieved historical task management information.

Abstract: A solution to identify the layout elements in a document is provided. The documents are formulated in an integrated visual space, where they are treated uniformly as a rasterized image. The transformation to image space facilitates exploitation of a spectrum of stylistic cues, such as spacing between the lines and paragraphs. Effective recognition of layout elements is provided by applying a synthetic generator model that creates artificial documents with the required labels and bounding boxes. The generator model is based on a Bayesian Network, offers full flexibility in controlling the document types and labels, and can introduce artificial defects to synthesize low-quality documents. An artificial intelligence model for detecting layouts is trained based on these synthetic documents and the resultant model is used to identify the layout elements in real-world documents. The resulting layout elements, when augmented with the text content, greatly aids in effective information retrieval.

Abstract: A method for providing a data-driven tool for individualized financial planning is provided. The method includes: receiving first information that relates to a user, the first information including at least one financial goal of the user; applying, to the first information, a machine learning algorithm that is uses historical data that relates to financial outcomes; calculating, based on an output of the machine learning algorithm, a probability that the at least one financial goal of the user is achievable; and determining, based on an output the machine learning algorithm, a proposed sequence of actions to be taken by the user with respect to achieving the at least one financial goal. The method may further include applying an artificial intelligence (AI) algorithm that uses a Monte Carlo tree search (MCTS) technique with respect to potential user actions.

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 for generating schedules for a group of persons within a common workspace is provided. The method includes: receiving a first user input that relates to employee preferences; receiving a second user input that relates to at least one manager preference; receiving a third user input that relates to business constraints; generating, based on the received first, second, and third user inputs, a respective schedule for each corresponding group member; and outputting each respective schedule to a user interface for display thereon. The method may also include generating a set of metrics that corresponds to the employee preferences.

Abstract: A method for generating a recommendation for a potential investment is provided. The method includes: receiving first information that relates to each respective company from among a plurality of companies; generating, for each respective company based on the first information, a first vector representation; receiving second information that relates to each respective investor from among a plurality of investors; generating, for each respective investor based on the second information, a second vector representation; using the first vector representations and the second vector representations as inputs to an artificial intelligence-based algorithm in order to calculate a respective similarity metric for each respective company-investor pair; generating the recommendation for the potential investment based on the calculated similarity metrics; and generating an explanation for the recommendation.