Abstract: An example computing platform is configured to receive configuration data that defines a pipeline for building a deep learning model, the configuration data including data defining an input dataset, data type assignments for a set of input data variables included within the dataset, data transformations that are to be applied to the dataset, and a machine learning process that is to be utilized to train the deep learning model. Based on the received configuration data, the computing platform functions to build the deep learning model by obtaining the input dataset, assigning a data type to data in the dataset, selecting transformation operations for the data in the dataset, splitting the dataset into a sequence of data blocks, applying the transformation operations to each data block to produce a transformed dataset, generating a compressed data structure that includes the transformed datasets, and applying the machine learning process to the transformed datasets.

Abstract: A computing platform may be configured to (i) train an initial model object for a data science model using a machine learning process, (ii) determine that the initial model object exhibits a threshold level of bias, and (iii) thereafter produce an updated version of the initial model object having mitigated bias by (a) identifying a subset of the initial model object's set of input variables that are to be replaced by transformations, (b) producing a post-processed model object by replacing each respective input variable in the identified subset with a respective transformation of the respective input variable that has one or more unknown parameters, (c) producing a parameterized family of the post-processed model object, and (d) selecting, from the parameterized family of the post-processed model object, one given version of the post-processed model object to use as the updated version of the initial model object for the data science model.

Abstract: A framework for interpreting machine learning models is proposed that utilizes interpretability methods to determine the contribution of groups of input variables to the output of the model. Input variables are grouped based on dependencies with other input variables. The groups are identified by processing a training data set with a clustering algorithm. Once the groups of input variables are defined, scores related to each group of input variables for a given instance of the input vector processed by the model are calculated according to one or more algorithms. The algorithms can utilize group Partial Dependence Plot (PDP) values, Shapley Additive Explanations (SHAP) values, and Banzhaf values, and their extensions among others, and a score for each group can be calculated for a given instance of an input vector per group. These scores can then be sorted, ranked, and then combined into one hybrid ranking.

Abstract: A method, system, and computer-readable medium are disclosed for detecting and mitigating bias in a trained machine learning model. The method includes the steps of: training the model based on a training data set; detecting bias in the model relative to a protected class; identifying one or more groups of input variables that contribute to the bias; and mitigating bias in the model. Mitigating the bias is performed by constructing a post-processed score function that either (a) neutralizes or partially neutralizes one or more groups of input variables in the input vector of the model, or (b) utilizes a fair score approximation of the model to project the distributions for the protected class and/or the unprotected class to substantially match. In an embodiment, detecting bias in the trained model is performed by comparing distribution for two or more subpopulations based on a distance metric, such as a Wasserstein distance.

Abstract: A framework for interpreting machine learning models is proposed that utilizes interpretability methods to determine the contribution of groups of input variables to the output of the model. Input variables are grouped based on correlation with other input variables. The groups are identified by processing a training data set with a clustering algorithm. Once the groups of input variables are defined, partial dependent plot (PDP) tables for each group are calculated and stored in a memory, which are used for calculating scores related to each group of input variables for a given instance of the input vector processed by the model. Furthermore, Shapley Additive Explanations (SHAP) values for each group can be calculated by summing the SHAP values of the input variables for a given instance of an input vector per group. These scores can then be sorted, ranked for each interpretability method, and then combined into one hybrid ranking.

Abstract: A method and computer executable program code are disclosed for performing one or more maintenance tasks on a remotely located computer connected to a server computer via a data network.

Abstract: A method and computer executable program code are disclosed for performing one or more maintenance tasks on a remotely located computer connected to a server computer via a data network.

Abstract: A computer program product is embodied on a non-transitory computer readable medium. The computer program product comprises computer code to display a plurality of first indicia presented in a list, where each first indicia indicates a software product, and computer code to display a second indicia associated with a highlighted one of the first indicia. The second indicia comprises information about the software product indicated by the highlighted first indicia. The computer program product additionally comprises computer code to display a third indicia associated with the highlighted first indicia and indicate the availability of a software update for the software product indicated by the highlighted first indicia, and computer code to display a fourth indicia associated with the highlighted first indicia. The fourth indicia facilitates the retrieval of the software update.

Abstract: The invention provides content delivery systems which comprises a content module that provides downloadable content for offline use wherein the content is personalized for each user accessing the content. The content delivery system comprises usage monitoring module and an evaluation module to monitor and evaluate usage respectively. The system also includes a content personalization module that personalizes content based on the usage.

Abstract: A method and computer executable program code are disclosed for performing one or more maintenance tasks on a remotely located computer connected to a server computer via a data network.

Abstract: A method and computer executable program code are disclosed for performing one or more maintenance tasks on a remotely located computer connected to a server computer via a data network.

Abstract: A method and computer executable program code are disclosed for performing one or more maintenance tasks on a remotely located computer connected to a server computer via a data network.

Abstract: A computer program product is embodied on a non-transitory computer readable medium. The computer program product comprises computer code to display a plurality of first indicia presented in a list, where each first indicia indicates a software product, and computer code to display a second indicia associated with a highlighted one of the first indicia. The second indicia comprises information about the software product indicated by the highlighted first indicia. The computer program product additionally comprises computer code to display a third indicia associated with the highlighted first indicia and indicate the availability of a software update for the software product indicated by the highlighted first indicia, and computer code to display a fourth indicia associated with the highlighted first indicia. The fourth indicia facilitates the retrieval of the software update.

Abstract: A method and computer executable program code are disclosed for performing one or more maintenance tasks on a remotely located computer connected to a server computer via a data network.

Abstract: A computer program product is embodied on a non-transitory computer readable medium. The computer program product comprises computer code to display a plurality of first indicia presented in a list, where each first indicia indicates a software product, and computer code to display a second indicia associated with a highlighted one of the first indicia. The second indicia comprises information about the software product indicated by the highlighted first indicia. The computer program product additionally comprises computer code to display a third indicia associated with the highlighted first indicia and indicate the availability of a software update for the software product indicated by the highlighted first indicia, and computer code to display a fourth indicia associated with the highlighted first indicia. The fourth indicia facilitates the retrieval of the software update.

Abstract: A system and method update client computers of various end users with software updates for software products installed on the client computers. The system includes a service provider computer system, a number of client computers and software vendor computer systems communicating on a common network. The service provider computer system stores in an update database information about the software updates of diverse software vendors, identifying the software products for which software updates are available, information for identifying in the client computers the software products stored thereon, and information for determining for such products, which have software updates available. Users of the client computers connect to the service provider computer and obtain a current version of portions of the database.

Abstract: A system, method and computer program product are provided for uninstalling software on a computer. In use, a plurality of software products identified on a computer is displayed. Further, a first user instruction to uninstall at least a portion of at least one of the software products from the computer is received. Such first user instruction is received via an interface. Still yet, the at least a portion of the at least one software product is uninstalled from the computer, in response to the receipt of the first user instruction. Also, a second user instruction may be received to cancel the uninstallation. Thus, the uninstallation may be cancelled, in response to the receipt of the second user instruction.

Abstract: A system, method and computer program product are provided for uninstalling software on a computer. In use, a plurality of software products identified on a computer is displayed. Further, a first user instruction to uninstall at least a portion of at least one of the software products from the computer is received. Such first user instruction is received via an interface. Still yet, the at least a portion of the at least one software product is uninstalled from the computer, in response to the receipt of the first user instruction. Also, a second user instruction may be received to cancel the uninstallation. Thus, the uninstallation may be cancelled, in response to the receipt of the second user instruction.

Abstract: A system, method, and computer program product are provided for certifying that content available on a host site is free from viruses identified by an anti-virus database. Content configured for publication on a host site is scanned for viruses. The scanned content is loaded onto the host site if no viruses are identified. Further, a certificate is displayed identifying the content on the host site as being scanned for viruses.

Abstract: Described are techniques for clustering a data set of objects. Divide phase processing is performed to partition the data set into two or more partitions forming a hierarchy of the objects. Merge phase processing may be performing using the hierarchy to determine one or more disjoint clusters of objects of the data set. Optional preprocessing may be performed to determine weights for one or more features of an object.