Abstract: Systems and methods relating to the replacement or removal of sensitive data in images of documents. An initial image of a document with sensitive data is received at an execution module and changes are made based on the execution module's training. The changes include replacing or effectively removing the sensitive data from the image of the document. The resulting sanitized image is then sent to a user for validation of the changes. The feedback from the user is then used in training the execution module to refine its behaviour when applying changes to other initial images of documents. To train the execution module, training data sets of document images with sensitive data manually tagged by users are used. The execution module thus learns to identify sensitive data and its submodules replace that sensitive data with suitable replacement data. The feedback from the user works to improve the resulting sanitized images from the execution module.

Abstract: A distribution of values of time-series data is obtained. Based on the distribution of the values, the time-series data is sampled to generate an anomaly preserving version of the time-series data. Via a trained machine learning model, a reconstructed version of the time-series data is generated based on the anomaly preserving version of the time-series data.

Abstract: Systems and methods for managing an asset portfolio. A system generates a detailed trading schedule that converts a current portfolio into a desired portfolio. The schedule is generated using machine learning and is based on a number of inputs including the current portfolio, a desired portfolio, an execution timeline, as well as user supplied constraints. Once generated, the system evaluates the schedule using one or more market models to determine if the schedule will be feasible given market reactions based on the one or more models. The system iterates the generation/evaluation loop until the best possible schedule is arrived at. In addition, the system may provide recommendations for not only brokers to be used when executing the trades but also trading algorithms that the brokers may use when implementing the schedule.

Abstract: Systems and methods relating to the replacement or removal of sensitive data in images of documents. An initial image of a document with sensitive data is received at an execution module and changes are made based on the execution module's training. The changes include replacing or effectively removing the sensitive data from the image of the document. The resulting sanitized image is then sent to a user for validation of the changes. The feedback from the user is then used in training the execution module to refine its behaviour when applying changes to other initial images of documents. To train the execution module, training data sets of document images with sensitive data manually tagged by users are used. The execution module thus learns to identify sensitive data and its submodules replace that sensitive data with suitable replacement data. The feedback from the user works to improve the resulting sanitized images from the execution module.

Abstract: A computer-implemented method for defining roles, comprising: receiving access usage data comprising identities and respective performed actions; receiving a list of entitlements each allowing the execution of at least one respective action; generating a plurality of groups of actions by regrouping given ones of the identities having associated thereto a same group of the respective performed actions using the access usage data; for each one of the plurality of groups of actions, determining a group of entitlements contained in the list of entitlements that allow the execution of the group of actions; for each one of the plurality of groups of actions, associating thereto the respective group of entitlements, thereby obtaining a plurality of roles; and outputting the plurality of roles.