Abstract: Systems, apparatuses, methods, and computer program products are disclosed for managing open issues. An example method includes obtaining a dashboard access request for a portion of open issues managed by the issue management system, the issue management system storing variables associated with each open issue of the open issues; obtaining a dashboard data package based on scores for the portion of open issues, the scores indicating a severity level of the open issues of the portion of open issues and being based on the variables that are stored in the issue management system; and performing an action set using the dashboard data package to manage the portion of open issues.

Abstract: A service provider computing system includes memory having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to obtain a first set of complaint logs corresponding to one or more user complaints, each complaint log including a resolution comments field providing a textual representation of a resolution for a corresponding complaint log, parse the resolution comments field of each complaint log of the first set of complaint logs to identify one or more key terms, and execute a machine-learning predictive model using the one or more key terms to generate, for each complaint log, a prediction indicating whether a corresponding complaint should have compensation, the machine-learning predictive model being a supervised machine learning model configured to accept the parsed resolution comments field for the first set of complaint logs as inputs and to output the prediction for each complaint log.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automating incident severity classification. An example method includes: receiving a historical incident dataset; receiving a new incident dataset including information regarding a set of new incidents; generating a set of predicted severity classifications for the set of new incidents using a machine learning model, the historical incident dataset, and the new incident dataset; determining that a severity classification mismatch between at least one assigned severity classification and a predicted severity classification of the set of predicted severity classifications exists for the new incident dataset; and in response to determining that the severity classification mismatch exists, prompting further investigation of the severity classification mismatch through outputting an indication of the severity classification mismatch.

Abstract: A service provider computing system includes memory having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to obtain a first set of complaint logs corresponding to one or more user complaints, each complaint log including a resolution comments field providing a textual representation of a resolution for a corresponding complaint log, parse the resolution comments field of each complaint log of the first set of complaint logs to identify one or more key terms, and execute a machine-learning predictive model using the one or more key terms to generate, for each complaint log, a prediction indicating whether a corresponding complaint should have compensation, the machine-learning predictive model being a supervised machine learning model configured to accept the parsed resolution comments field for the first set of complaint logs as inputs and to output the prediction for each complaint log.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automating incident severity classification. An example method includes receiving, by communications circuitry, a historical incident dataset, the historical incident dataset including information regarding a set of historical incidents and an assigned severity classification for each historical incident in the set of historical incidents. The example method further includes training, by a model generator and using the historical incident dataset, a machine learning model to classify incident severity. The example method further includes receiving, by the communications circuitry, a new incident dataset, generating, by a prediction engine and using the trained machine learning model and the new incident dataset, a set of predicted severity classifications for the set of new incidents, and outputting, by the communications circuitry, the predicted set of severity classifications for the set of new incidents.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automatically deriving data transformation criteria. An example method includes receiving, by communications circuitry, a source dataset and a target dataset and identifying, by a model generator, a target variable. The example method further includes training, by the model generator, a decision tree for the target variable using the source dataset and the target dataset such that the trained decision tree can predict a value for the target variable from new source data. The example method further includes deriving, by a derivation engine, a set of parameters and pseudocode for producing the target variable from the source dataset.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for managing open issues. An example method includes obtaining a dashboard access request for a portion of open issues managed by the issue management system, the issue management system storing variables associated with each open issue of the open issues; obtaining a dashboard data package based on scores for the portion of open issues, the scores indicating a severity level of the open issues of the portion of open issues and being based on the variables that are stored in the issue management system; and performing an action set using the dashboard data package to manage the portion of open issues.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automatically deriving data transformation criteria. An example method includes receiving, by communications circuitry, a source dataset and a target dataset and identifying, by a model generator, a target variable. The example method further includes training, by the model generator, a decision tree for the target variable using the source dataset and the target dataset such that the trained decision tree can predict a value for the target variable from new source data. The example method further includes deriving, by a derivation engine, a set of parameters and pseudocode for producing the target variable from the source dataset.

Abstract: Techniques are described for automatic creation of optimal auditable entities (AEs) using a machine learning (ML)-based clustering model. The clustering model, when executed on one or more computing devices within an audit system of a company, is configured to automatically cluster the company's business processes into AEs based on similarity analyses of business process attributes. More specifically, in some examples, the clustering model ingests business processes and their corresponding attributes from a database, automatically clusters together business processes to achieve maximum intra-cluster similarity scores, and outputs the final clusters as model AEs. The resulting model AEs may be used as functional units for internal audits of the company's business processes. The resulting model AEs may improve audit efficiency due to the model AEs including only highly similar business processes.

Abstract: Block co-polymers including a hydrophilic block and a hydrophobic poly(amino acid) block which further includes dihydroxyphenyl moieties are provided, as well as methods of making and using the same. Such block copolymers may be used to prepare biocompatible adhesives which display good adhesives properties in aqueous environments, including in in vivo applications.

Abstract: A block co-polymer includes a water soluble carrier block; an amino acid-based or acrylic acid-based block; and the amino acid-based or acrylic acid-based block includes a fluorinated alkyl group or a fluorinated alkylene glycol group.

Abstract: Block co-polymers including a hydrophilic block and a hydrophobic poly(amino acid) block which further includes dihydroxyphenyl moieties are provided, as well as methods of making and using the same. Such block copolymers may be used to prepare biocompatible adhesives which display good adhesives properties in aqueous environments, including in in vivo applications.

Abstract: A block co-polymer includes a water soluble carrier block; an amino acid-based or acrylic acid-based block; and the amino acid-based or acrylic acid-based block includes a fluorinated alkyl group or a fluorinated alkylene glycol group.

Abstract: A centrifugal feeder for rectangular items with one of its upper or lower surfaces having luminescent characteristics which are greater than the other surface. It is desired to orient the items with its elongated axis in the direction of the output and its luminescent surface downward. The feeder has a rotating bowl surrounded by a rotating rim. Items are placed on the rotating bowl by any suitable device. A sweep pushes the items randomly onto the rim and a pair of sweeps then insure that the items are flat and only one layer high. The rim then narrows so that items not disposed with their elongated axis tangentially will over balance and fall off the rim. A sensor then determines the space between items, and if the space exceeds a preselected interval, signals for more items to be placed onto the bowl. A luminescence sensor then determines if the item thereunder is disposed with its greater luminescent surface up and a kick off device kicks off items with the greater luminescent side up.