Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises converting received data from a time-based domain to a frequency-based domain using a signal processing algorithm; identifying transactional noise within the converted data by identifying contextual factors based on a determined pattern, wherein the transactional noise is data associated with an identified fraudulent transaction; filtering the identified transactional noise by removing datapoints within the converted data that reaches a predetermined threshold of signal strength using the signal processing algorithm; and generating a line graph depicting removal of the data that is indicative of the identified transactional noise from the converted data.

Abstract: A method, system, and computer program product for computer vision modeling are provided. The method identifies a set of transactions. A set of categorical behavior transaction types are determined for the set of transactions. The set of categorical behavior transaction types are mapped to a set of colors in a color coordinate system. The method scales color component values of the set of colors in the color coordinate system to generate a pattern of colorized units at intervals along a timespan of the set of transactions. The method generates a fraud detection model based on the set of transactions and the color component values.

Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises converting received data from a time-based domain to a frequency-based domain using a signal processing algorithm; identifying transactional noise within the converted data by identifying contextual factors based on a determined pattern, wherein the transactional noise is data associated with an identified fraudulent transaction; filtering the identified transactional noise by removing datapoints within the converted data that reaches a predetermined threshold of signal strength using the signal processing algorithm; and generating a line graph depicting removal of the data that is indicative of the identified transactional noise from the converted data.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises receiving transactional data from at least two users in a plurality of users; determining a pattern within the received transactional data based on a frequency-based domain conversion, wherein the pattern is associated with a determined periodicity; determining a delay within the received transactional data by identifying contextual factors associated with the received transactional data and measuring an amount of time between each identified contextual factors within a plurality of identified contextual factors using a signal processing algorithm; aligning the received transactional data from the at least two users by placing at least two signal forms associated based on the determined delay within the received transactional data at a same point; and generating a line graph depicting the aligned transactional data.

Abstract: One or more computer processors determine a plurality of models to incorporate a plurality of determined features from a received dataset. The one or more computer processors generate an aggregated prediction utilizing each model, in parallel, in the determined plurality of models subject to stop criteria, wherein stop criteria includes a prediction duration threshold. The one or more computer processors calculate a confidence value for the aggregated prediction.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises determining a pattern within received data based on a periodicity associated with the received data; in response to a calculated signal score associated with the determined pattern of the received data meeting or exceeding a predetermined threshold transaction amount, tracing at least one location associated with the received data; and dynamically suspending an action associated with an account that generated the received data in response to the traced location not being associated with a historical baseline of the account.

Abstract: A method, system, and computer program product for computer vision modeling are provided. The method identifies a set of transactions. A set of categorical behavior transaction types are determined for the set of transactions. The set of categorical behavior transaction types are mapped to a set of colors in a color coordinate system. The method scales color component values of the set of colors in the color coordinate system to generate a pattern of colorized units at intervals along a timespan of the set of transactions. The method generates a fraud detection model based on the set of transactions and the color component values.

Abstract: A method, system and computer program product for obtaining vector representations of code snippets capturing semantic similarity. A first and second training set of code snippets are collected, where the first training set of code snippets implements the same function representing semantic similarity and the second training set of code snippets implements a different function representing semantic dissimilarity. A vector representation of a first and second code snippet from either the first or second training set of code snippets is generated using a machine learning model. A loss value is generated utilizing a loss function that is proportional or inverse to the distance between the first and second vectors in response to receiving the first and second code snippets from the first or second training set of code snippets, respectively. The machine learning model is trained to capture the semantic similarity in the code snippets by minimizing the loss value.

Abstract: A system and method for recommending whether to upgrade dependencies of a software project includes mining historical software data artifacts associated with a plurality of software projects to extract a plurality of metrics and dependency version lineages from the historical software data artifacts, clustering the software projects based on the metrics and the dependency version lineages, extracting target metrics and a target version lineage of a target software project selecting a software project cluster from the plurality of clusters that best matches the target software project, analyzing the metrics for each of the software projects included in the at least one software project cluster to determine that a measurable change to the metrics occurred as a result of upgrading dependencies of the software projects, and recommending which software dependencies of the target software project should be upgraded based on the measurable change to the metrics of the software projects.

Abstract: The present invention relates to a method, system, and computer program product for project adoption documentation generation using machine learning. A method includes receiving a set of project parameters for a set of projects. In an embodiment, a method includes receiving a set of input project parameters for an input project. In an embodiment, a method includes comparing each set of project parameters to the set of input project parameters. In an embodiment, a method includes selecting, responsive to the comparison, a project from the set of projects. In an embodiment, a method includes generating, responsive to the selection, documentation for the input project.

Abstract: The present invention relates to a method, system, and computer program product for project adoption documentation generation using machine learning. A method includes receiving a set of project parameters for a set of projects. In an embodiment, a method includes receiving a set of input project parameters for an input project. In an embodiment, a method includes comparing each set of project parameters to the set of input project parameters. In an embodiment, a method includes selecting, responsive to the comparison, a project from the set of projects. In an embodiment, a method includes generating, responsive to the selection, documentation for the input project.

Abstract: A method, system and computer program product for obtaining vector representations of code snippets capturing semantic similarity. A first and second training set of code snippets are collected, where the first training set of code snippets implements the same function representing semantic similarity and the second training set of code snippets implements a different function representing semantic dissimilarity. A vector representation of a first and second code snippet from either the first or second training set of code snippets is generated using a machine learning model. A loss value is generated utilizing a loss function that is proportional or inverse to the distance between the first and second vectors in response to receiving the first and second code snippets from the first or second training set of code snippets, respectively. The machine learning model is trained to capture the semantic similarity in the code snippets by minimizing the loss value.

Abstract: A system and method for recommending whether to upgrade dependencies of a software project includes mining historical software data artifacts associated with a plurality of software projects to extract a plurality of metrics and dependency version lineages from the historical software data artifacts, clustering the software projects based on the metrics and the dependency version lineages, extracting target metrics and a target version lineage of a target software project selecting a software project cluster from the plurality of clusters that best matches the target software project, analyzing the metrics for each of the software projects included in the at least one software project cluster to determine that a measurable change to the metrics occurred as a result of upgrading dependencies of the software projects, and recommending which software dependencies of the target software project should be upgraded based on the measurable change to the metrics of the software projects.

Abstract: A social networking-based web analytics data processing system having subject matter expert (SME) cognitive capability includes a data report dashboard module, and a rating module. The data report dashboard module includes an electronic hardware controller to generate an initial web analytics data report and to generate at least one inquiry associated with at least one abnormality included in the initial web analytics data report. The rating module detects at least one of a positive ranking and a negative ranking applied to a comment submitted by a user in reply to the at least one inquiry. The social networking-based web analytics data processing system further includes a subject matter expert (SME) identification module that identifies a SME based on at least one of social networking information, community expertise ranking and project stakeholder recognitions.

Abstract: Parties involved in transacting business in an E-marketplace (E-marketplace participants) each identify and submit to the E-marketplace their P3P policy and/or other relevant characteristics related to their privacy policy needs (those that they adhere to, referred to as “privacy policies”; those that they require, referred to as “privacy preferences”, or both). Submitted with the privacy policy is a digital signature that is tied to the owner of the web objects to which the privacy policy pertains. Using a digital signature assures the integrity of the privacy policy since it travels with the privacy policy and thus refers back to the original sender of the policy rather than the middleman (the E-marketplace), and if the document (the privacy policy) to which it is attached has been tampered with, the digital signature will be invalidated.

Abstract: Parties involved in transacting business in an E-marketplace (E-marketplace participants) each identify and submit to the E-marketplace relevant characteristics related to their privacy policy needs. When it is determined that two or more participants are collaborating in a transaction (e.g., a supplier and a shipper; two suppliers; three buyers), the privacy policies of the collaborative group are aggregated to produce a single policy that represents the primary policies of the collaborative transaction being presented by the collaborative group.