Abstract: The present invention discloses methods and systems for improving asset operation based on identification of significant changes in sensor combinations in related events.

Abstract: An example method comprises performing for each class from a plurality of classes: constructing binary training set for the class, the binary training set including labeled cases for that class from the main training set other labeled cases from the main training set; training classifier for the class on the binary training set; computing a local calibration threshold using scores of the labeled cases in the binary training set; and adjusting all scores of the label cases in the binary training set with the local calibration threshold to meet a global decision threshold. The method also comprises determining, with the processor, a global hierarchical calibration threshold by using the adjusted scores for all classes to optimize a performance measurement of all trained classifiers. The method further comprises classifying, with the processor, a new case by using a previously trained classifier, a local calibration threshold, and the global hierarchical calibration threshold.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises receiving a training dataset of cases, where each of a plurality of classes is associated with a set of labeled cases in the training dataset. The method also comprises defining a proper subset of classes in the training dataset, and training a first classifier model on the proper subset of classes in the training dataset. The method further comprises testing the first classifier model on at least one class in the training dataset that was excluded from the proper subset, and determining a performance measurement of the first classifier model.

Abstract: The present invention discloses methods and systems for improving asset operation based on identification of significant changes in sensor combinations in related events.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating a group of most frequent elements in a dataset, calculating features of each of the most frequent elements in the dataset, applying a trained model to the features of each of the most frequent elements, and generating a list of predicted relevant elements from the list of most frequent elements. The method further comprises determining at least one element-chain group for each predicted relevant element and a group score for the element-chain-group, ordering a plurality of element-chain groups for the dataset based on the group score for each of the element-chain groups, and identifying a predetermined number of element-chain groups to be outputted to a user.

Abstract: An example method comprises performing for each class from a plurality of classes: constructing binary training set for the class, the binary training set including labeled cases for that class from the main training set other labeled cases from the main training set; training classifier for the class on the binary training set; computing a local calibration threshold using scores of the labeled cases in the binary training set; and adjusting all scores of the label cases in the binary training set with the local calibration threshold to meet a global decision threshold. The method also comprises determining, with the processor, a global hierarchical calibration threshold by using the adjusted scores for all classes to optimize a performance measurement of all trained classifiers. The method further comprises classifying, with the processor, a new case by using a previously trained classifier, a local calibration threshold, and the global hierarchical calibration threshold.

Abstract: Examples herein involve generating a spiral visualization of items of a dataset. Dimensions of item indicators corresponding to items of a dataset are analyzed. Starting coordinates for a spiral path for a spiral visualization of the items are determined based on the dimensions of the item indicators and a display area of a display device. A spiral path may be calculated based on the starting coordinates and the dimensions of the item indicators. The spiral visualization may be generated by rendering the item indicators on the spiral path to present a first item indicator of the item indicators from a corresponding first item of the list at the starting coordinates and a last item indicator of the item indicators of a corresponding last item on the list on the spiral path toward the center of the spiral.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises receiving a training dataset of cases, where each of a plurality of classes is associated with a set of labeled cases in the training dataset. The method also comprises defining a proper subset of classes in the training dataset, and training a first classifier model on the proper subset of classes in the training dataset. The method further comprises testing the first classifier model on at least one class in the training dataset that was excluded from the proper subset, and determining a performance measurement of the first classifier model.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating a group of most frequent elements in a dataset, calculating features of each of the most frequent elements in the dataset, applying a trained model to the features of each of the most frequent elements, and generating a list of predicted relevant elements from the list of most frequent elements. The method further comprises determining at least one element-chain group for each predicted relevant element and a group score for the element-chain-group, ordering a plurality of element-chain groups for the dataset based on the group score for each of the element-chain groups, and identifying a predetermined number of element-chain groups to be outputted to a user.

Abstract: A tag is set as a tag in focus in response to detecting a user selection of the tag. An item in an item view of a user interface is associated with the tag in focus in response to the user selecting the item.

Abstract: Examples herein involve generating a spiral visualization of items of a dataset. Dimensions of item indicators corresponding to items of a dataset are analyzed. Starting coordinates for a spiral path for a spiral visualization of the items are determined based on the dimensions of the item indicators and a display area of a display device. A spiral path may be calculated based on the starting coordinates and the dimensions of the item indicators. The spiral visualization may be generated by rendering the item indicators on the spiral path to present a first item indicator of the item indicators from a corresponding first item of the list at the starting coordinates and a last item indicator of the item indicators of a corresponding last item on the list on the spiral path toward the center of the spiral.

Abstract: According to an example, intent based clustering may include generating a plurality of clusters based on an analysis of categories of features of data used to generate the clusters with respect to an order of each of the features by determining whether a number of samples of the data for a category of the categories meets a specified criterion.

Abstract: According to an example, intent based clustering may include classifying objects based on training objects, and clustering the objects to determine initial clusters. The classification and initial clustering may be used to determine modified clusters.

Abstract: An interactive system to detect malware is provided to interactively analyze web proxy log data. The log data is progressively processed to compute analytics for different context settings. The system has a context module, an interaction module and a plurality of analytics modules. When a change of the context setting (filter, weights etc.) is requested, the processing and calculation of analytics for the current context setting is paused and subsequently restarted for the now changed context setting. An analytics interface provided via a graphical user interface is updated upon the change of context settings.

Abstract: According to an example, term chain clustering may include receiving a set of training cases from a known category, and receiving a set of unlabeled cases that are to be analyzed with respect to the known category. A plurality of terms of the set of training cases from the known category, and the set of unlabeled cases that are to be analyzed, may be analyzed using a term scoring function to generate a score for each of the plurality of terms. A highest scoring term may be selected from the analyzed terms based on the score for each of the plurality of terms. A selected set that includes cases from the set of unlabeled cases that include the highest scoring term may be generated.

Abstract: In at least some examples, a system comprises a processor and a memory coupled to the processor. The memory stores an image defect visibility predictor that, when executed by the processor, compares an original image with a defect image and outputs a predicted defect visibility image (PDVI) that accounts for defect masking by the original image.

Abstract: A malfunction diagnosis system of a printer is provided. A printer controller receives malfunction-related input by the user from an input device, which user input specifies a print defect perceptible to the user. It generates image data representing a test print job in response to the user input specifying the perceptible print defect, and forwards the test print job image data to the printing unit to produce a corresponding test printout. Measurement data output from a sensing device as measured on the test printout is received and analyzed, and information indicative of a cause for the print defect based on the analyzed measurement data output from the sensing device is output.

Abstract: A method includes obtaining, by executing a module stored on a non-transitory computer-readable storage device, approximately-zero polynomials for each of multiple classes. The method further includes evaluating the approximately-zero polynomials for each class on a plurality of points to compute distances from each point to each of the classes. The method also includes scaling the approximately-zero polynomials based on the distances and classifying the points based on the scaled approximately-zero polynomials.

Abstract: Method and system embodiments of the present invention are directed to automated identification of regions within digitally-encoded images that correspond to objects and features of scenes captured in the digitally-encoded images, a process referred to as ‘perceptual segmentation’ of an image. Regions or segments within an image are first identified by any of various region-identifying or segmentation methods. For each region or segment, features of pixels within the region or segment are employed to compute one or more segment features. The segment features are used, in turn, to identify the region or segment as belonging to a particular type of region or segment, and the region is then accordingly labeled or tagged as a region or segment of the determined type.

Abstract: According to one example, there is provided a method of determining a workflow in a production environment that comprises a plurality of production resources. The method comprises receiving a job request and determining, based on the contents of the job request and on a stored workflow history of job requests previously processed in the production environment, a predicted workflow to be used when processing the received job request.