A data collection system in an industrial environment includes a data collector coupled to a plurality of input channels, wherein at least one is connected to a vibration detection facility for detecting a noise pattern from a first industrial machine of a plurality of industrial machines; a data storage structured to store a plurality of noise patterns from the plurality of industrial machines in a library; a data acquisition circuit structured to interpret a plurality of detection values from the collected data; and a data analysis circuit structured to analyze the collected data to determine if the noise pattern from the first industrial machine matches a noise pattern of a second industrial machine stored in the library, wherein if it matches an alarm condition is set to indicate the first industrial machine is experiencing a condition characteristic of the machine performance category of the second industrial machine.

A data collection system in an industrial environment includes a data collector coupled to a plurality of input channels, wherein a collector route determines a subset of the input channels for data collection, the collector route selected based on a data marketplace indicator; a data storage structured to store a plurality of collector routes and collected data that correspond to the input channels, each comprising a different data collection routine for the input channels; a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each corresponding to at least one of the input channels; and a data analysis circuit structured to analyze the collected data from the input channels and evaluate a selected collection routine of the data collector based on the analyzed collected data, wherein the selected collection routine is switched to a second collection routine based on a received data marketplace indicator.

A method of establishing person image attribute model, including: obtaining face detection data and determining face regions of interest; randomly labeling person image attributes of some of the face regions of interest to obtain a training sample; training a person image attribute model according to the training sample; and optimizing the trained person image attribute model to obtain an optimized person image attribute model through an active learning algorithm, according to an unlabeled sample set as output by a trained person image attribute model.

An apparatus, methods, and systems for data collection in a production environment are described. The system may include a data collector communicatively coupled to a plurality of input channels, wherein a first subset of the plurality of input channels are connected to a first set of sensors measuring operational parameters from a production component, a data storage structured to store a plurality of collector routes and collected data, a data acquisition circuit structured to interpret a plurality of detection values from the collected data of the production component, and a data analysis circuit structured to analyze the collected data and evaluate a first collection routine of the data collector based on the analyzed collected data, wherein based on the analyzed collected data the data collector makes a collection routine change.

A computer-implemented method may comprise collecting and storing a plurality of electronic messages and a corresponding plurality of phishing kits, each of which being associated with one or several malicious Uniform Resource Locator (URL) and extracting a set of features from each of the plurality of electronic messages. For each of the extracted set of features, the method may comprise determining a set of optimal scanning parameters using one or more decision trees, trained with a supervised learning algorithm based on programmatically or manually examining or reverse-engineering the source code of the phishing kits, or trained with a supervised learning algorithm based on a function that iteratively requests data from the websites pointed to by the malicious URLs and examines data and codes returned by such requests. These optimal scanning parameters may then be used to scan a malicious URL with a reduced likelihood that a defensive action will be taken to hide the existence of the malicious content pointed to by the malicious URL.

An embodiment of the invention may include a method, computer program product, and system for generating ground truth data for a plurality of cognitive capabilities within an overall cognitive system. The embodiment may include configuring multiple sets of training data. Each set of training data corresponds to a separate cognitive capability. The embodiment may include displaying a set of ground truth curation activities via a user interface. The embodiment may include determining the ground truth curation activities performed for a first type of data for a first duration. The first type of data is selected from the single set of grouped training data. The embodiment may include determining whether the first duration has exceeded a pre-determined threshold. The embodiment may include switching the curation activities to a second type of data. The second type of data is selected from the single set of grouped data.

According to embodiments, an electronic device comprising a processor configured to receive an image including one or more objects, acquire a first one or more of the received one or more objects, acquire one or more reliability measures associated with the acquired one or more first objects, receive an input including information having one or more words, acquire one or more second objects corresponding to at least a part of the one or more words, when there is at least one object corresponding to the one or more second objects among the one or more first object, adjust at least one reliability measure corresponding to the at least one object among the acquired one or more reliability measures, and recognize the one or more first objects by using an image recognition scheme at least based on the one or more reliabilities including the adjusted at least one reliability.

An information processing method includes: obtaining noise region estimation information output from a first converter by a first image including a noise region being input to the first converter; obtaining a second image, on which noise region removal processing has been performed, output from a second converter by the noise region estimation information and the first image being input to the second converter; generating a fourth image including the estimated noise region by using the noise region estimation information and a third image including no noise region and a scene corresponding to the first image; training the first converter by using machine learning in which the first image is reference data and the fourth image is conversion data; and training the second converter by using machine learning in which the third image is reference data and the second image is conversion data.

Disclosed subject matter identifies, characterizes, and mitigates previously unforeseen safety hazards that are likely to be encountered by autonomous conveyances—finding these hazards, assessing their potential safety impact, modifying the design to mitigate them should they occur, disseminating updated design programming to all units, including those under construction or those already in the field, and including those hazard mitigations of high severity that exceed the maximum capabilities of the controller as manufactured. These hazards can include rare, infrequent and unforeseen hazards by monitoring conveyances already in the field, gathering data from autonomous conveyances, such as those using a design being updated, and data obtained from those using other autonomous designs in the field. By obtaining data from non-autonomous conveyances, as supplied by their drivers and operators, reporting real-time via a smartphone application, categories of rare, infrequent or unforeseen hazards can be integrated into modified designs.

Methods, systems, and computer program products for generating concept images of human poses using machine learning models are provided herein. A computer-implemented method includes identifying events from input data by applying a machine learning recognition model to at least a portion of the input data, wherein the identifying comprises (i) detecting multiple entities from the input data and (ii) determining behavioral relationships among at least a portion of the multiple entities; generating, using a machine learning interpretability model and at least a portion of the identified events, images illustrating human poses related to at least a portion of the identified events; outputting at least a portion of the generated images to a user; and updating the machine learning recognition model based at least in part on (i) at least a portion of the generated images and (ii) input from the user.