Abstract: A method for detecting deviations between an event log and a process model includes converting the process model into a probability process model, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to an activity in the process model; detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and identifying the differences as the deviations in response to the differences exceeding a predefined threshold; wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model.

Abstract: A method for detecting deviations between an event log and a process model includes converting the process model into a probability process model, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to an activity in the process model; detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and identifying the differences as the deviations in response to the differences exceeding a predefined threshold; wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model.

Abstract: A method for detecting deviations between an event log and a process model includes converting the process model into a probability process model, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to an activity in the process model; detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and identifying the differences as the deviations in response to the differences exceeding a predefined threshold; wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model.

Abstract: A system and method for augmenting or adding to the information used by an in-vitro diagnostic or other diagnostic device to generate results of a test is disclosed. A diagnostic device is capable of generating test results based on a sample, such as a sample provided by a patient, and is also capable of receiving additional information to enhance the results providable by the device. For example, an in-vitro diagnostic device is configured to read a lateral flow assay test, and is configured to receive additional diagnostic, network, test identification, or environmental information over a network. Using both the result of the diagnostic test and the additional information, the disclosed device provides more thorough, accurate, and reliable diagnostic information. The results generated by the device may be communicated to an appropriate remote device to enhance the results obtainable by such tests.

Abstract: A method for detecting deviations between an event log and a process model includes converting the process model into a probability process model, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to an activity in the process model; detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and identifying the differences as the deviations in response to the differences exceeding a predefined threshold; wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model.

Abstract: A method for automatically generating a semantic mapping for a relational database RDB includes obtaining a first semantic mapping from a first RDB to an ontology of linked data; obtaining a schema mapping from the first RDB to a second RDB; and generating a second semantic mapping from the second RDB to the ontology of the linked data based on the first semantic mapping and the schema mapping.

Abstract: Methods, systems, and storage components for utilizing a deep neural network(s) for classifying at least one medical image. A deep neural network (DNN) can be configured by an image processing component to contain at least one revived neuron, where the revived neuron has an adjusted value based on a focus-learning function that is transferred to the DNN by the image processing component, where the focus-learning function provides the adjustment by updating the DNN with data that contains a corrected classification with respect to at least one normal image sample derived from a medical image, and where the correction is based on the focus-learning function comparing an annotation associated with an abnormal image sample derived from the medical image to another annotation associated with the at least one normal image.

Abstract: A method for automatically generating a semantic mapping for a relational database RDB includes obtaining a first semantic mapping from a first RDB to an ontology of linked data; obtaining a schema mapping from the first RDB to a second RDB; and generating a second semantic mapping from the second RDB to the ontology of the linked data based on the first semantic mapping and the schema mapping.

Abstract: This disclosure provides a computer-implemented method for code suggestion. The method comprises collecting a set of runtime context features of a program that is being edited. The method further comprises comparing the set of runtime context features with at least one set of stored context features to find at least one matching set of stored context features. Each of the at least one set of stored context features is extracted from a corresponding code segment. The method further comprises presenting at least one code segment with its set of stored context features matching the set of runtime context features, for the user to choose to add into the program.

Abstract: This disclosure provides a computer-implemented method for automatically creating a macro-service. The method includes: converting source code of an analytic program that includes a set of operation units into a graph representation. Each of the set of operation units performs at least an operation to a data object, and the method further includes performing a query associated with the macro-service on the graph representation to determine a subset of the graph representation. The method further includes generating code for the macro-service based on the determined subset of the graph representation.

Abstract: A method for detecting deviations between an event log and a process model includes converting the process model into a probability process model, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to an activity in the process model; detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and identifying the differences as the deviations in response to the differences exceeding a predefined threshold; wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model.

Abstract: Techniques are described that facilitate automatically providing entities with rephrased versions of standard answers. In one embodiment, a computer-implemented is provided that comprises determining, by a device operatively coupled to a processor, a talking style of a plurality of talking styles that an entity is associated with based on reception of natural language input from the entity proposing a question related to a defined topic. The method further comprises selecting, by the device based on the talking style, an answer rephrasing model from a plurality of answer rephrasing models respectively configured to generate different rephrased versions of a standard answer to the question, and employing, by the device, the answer rephrasing model to generate a rephrased version of the standard that corresponds to the talking style.

Abstract: Low-cost assay test strip readers are disclosed. Such readers enable creation of profiles of analyte reactions detected on an assay test strip utilizing a simple detector fixedly mounted to a body of the reader. The detector may be a single detector, such as a photodetector, which detects an optical signal at a single point. The assay test strip is inserted and/or removed from the test strip reader and the detector detects the optical elements of the strip during such insertion and/or removal. The movement of the test strip with respect to the body enables the detector to scan a length of the test strip, such that a one-dimensional profile of the optical signals can be generated. The reader may convert the detected profile into a displayable indication of analyte concentrations for diagnostic purposes. Moving the test strip relative to an array of detectors enables creation of a two-dimensional profile.

Abstract: Embodiments include methods, systems and computer program products for storing graph data for a directed graph in a relational database. Aspects include creating a plurality of relational tables for the graph data, using a processor on a computer, the plurality of relational tables including adjacency tables and attribute tables. Each row of the attribute tables is dedicated to a subject of the graph data in the dataset and stores a JavaScript Object Notation (JSON) object corresponding to the subject. Each row of the adjacency tables includes a hashtable containing properties and values of the subject for that row.

Abstract: Embodiments include methods, systems and computer program products for storing graph data for a directed graph in a relational database. Aspects include creating a plurality of relational tables for the graph data, using a processor on a computer, the plurality of relational tables including adjacency tables and attribute tables. Each row of the attribute tables is dedicated to a subject of the graph data in the dataset and stores a JavaScript Object Notation (JSON) object corresponding to the subject. Each row of the adjacency tables includes a hashtable containing properties and values of the subject for that row.

Abstract: Certain aspects relate to systems and usage techniques for modular lateral flow assay reader devices that can receive a number of different modules having a barcode scanning input device and optional network connectivity capabilities. A barcode scanning module can provide a simple input method that reduces errors compared to manual data entry. A network connectivity module can enable transmission of test results over a public network for standardizing, tracking and electronically connecting test results from assay reader devices located throughout a network. Such devices can programmatically implement a simplified workflow whereby pressing a single button readies the device for imaging, analyzing, and data storage/transmission and, in some implementations, configures the device to operate in one of a plurality of device operation modes.

Abstract: This disclosure provides a computer-implemented method for automatically creating a macro-service. The method includes: converting source code of an analytic program that includes a set of operation units into a graph representation. Each of the set of operation units performs at least an operation to a data object, and the method further includes performing a query associated with the macro-service on the graph representation to determine a subset of the graph representation. The method further includes generating code for the macro-service based on the determined subset of the graph representation.

Abstract: A dataset regarding a plurality of applications is obtained. A set of parameters is determined from the dataset, comprising at least a sample performance trajectory, a risk factor, and a performance outcome. A maximum likelihood of each performance outcome is determined using a likelihood function, the likelihood function being a mixture model of a trajectory model and an outcome model. The set of parameters is updated according to the maximum likelihood of each performance outcome. A performance trajectory model is built according to the updated set of parameters. The plurality of applications is then grouped into subgroups according to the performance trajectory model, each subgroup containing one or more applications, and each of the one or more applications in a given subgroup having a same or similar trajectory to each other. An alert associated with the applications in at least one of subgroups may be generated.

Abstract: A method for using a plurality of decision engines to produce a single decision comprises: receiving heterogeneous data from the plurality of decision engines comprising a set of class labels from each of the plurality of decision engines, wherein the set of class labels from at least a first decision engine differs from the set of class labels from at least a second decision engine; generating a single set of unified class labels from the heterogeneous data; calculating at least one value corresponding to each of at least a subset of the unified class labels; and performing decision fusion on at least the subset of the unified class labels and corresponding values to produce the single decision.

Abstract: Systems, computer-implemented methods and/or computer program products that facilitate hospital department selection are provided. In one embodiment, a computer-implemented method comprises: employing, by a system operatively coupled to a processor, machine learning to train a model on data, wherein the data comprises patient data for a patient, hospital department designation associated with the patient and clinical data relating to a patient outcome, and wherein the model is trained to evaluate the hospital department designation associated with the patient based on the clinical data relating to the patient outcome; generating, by the system, a classification by classifying the patient into hospital department; and comparing, by the system, the model to the classification to provide a hospital department selection for the patient.