Abstract: A system identifies a first path taken by a learner in a concept space relative to a plurality of concept nodes that are contextualized within the concept space according to an embedding dimension of an embedding mechanism, transforms the first path in the concept space into a second path in a job space comprising a plurality of job nodes that are contextualized within the job space according to the embedding dimension of the (same) embedding mechanism, determines a distance between a first point on the second path and a first job node of the plurality of job nodes, computes an affinity of the learner for a job of the first job node based on the distance, and reports the affinity. Mechanisms for a “reverse” process that transforms a desired affinity for a job into a learning plan with respect to the content mapped in the concept space are also discussed.

Abstract: A content management system generally provides the ability to “weight” various content items by assigning weights to various content, ultimately weighting or skewing a knowledge base towards particular or weighted content during the contextualization process. Weighting nodes and/or weighting a content discovery algorithm may result in content items and concepts represented by the content items being considered more heavily by algorithms creating content groupings. An example method includes generating a knowledge base that includes a plurality of nodes representing a respective plurality of content items and updating the knowledge base based on a received user feedback, where the user feedback corresponds to a weighting for one or more content items. Content groupings are generated based on the updated knowledge base, where the content groupings include subsets of the plurality of content items and the subsets include conceptually similar content items.

Abstract: An example method disclosed herein includes receiving a request for content items relevant to a concept within a first knowledge space, where the first knowledge space includes a plurality of concept groupings of a plurality of nodes representing a plurality of content items. The method further includes identifying a plurality of content items in a second knowledge space relevant to the concept and receiving a selection of one or more of the content items for addition to the first knowledge space. The method further includes adding the selected content items to the first knowledge space.

Abstract: An example method disclosed herein includes receiving a request for content items relevant to a concept within a first knowledge space, where the first knowledge space includes a plurality of concept groupings of a plurality of nodes representing a plurality of content items. The method further includes identifying a plurality of content items in a second knowledge space relevant to the concept and receiving a selection of one or more of the content items for addition to the first knowledge space. The method further includes adding the selected content items to the first knowledge space.

Abstract: An example method disclosed herein includes receiving a request for content items relevant to a concept within a first knowledge space, where the first knowledge space includes a plurality of concept groupings of a plurality of nodes representing a plurality of content items. The method further includes identifying a plurality of content items in a second knowledge space relevant to the concept and receiving a selection of one or more of the content items for addition to the first knowledge space. The method further includes adding the selected content items to the first knowledge space.

Abstract: A method of identifying one or more concepts in a multimedia file includes separating text derived from the multimedia file into sub-portions, extracting features from the text of the sub-portions, and identifying concept clusters for the sub-portions based on the extracted features. The method further includes associating each of the sub-portions with the one or more concepts presented in the sub-portions of text based on the identified concept clusters and presenting, via a user interface, one or more portions of the multimedia file, where the portions of the multimedia file are generated based on the one or more concepts presented in each of the sub-portions of text of the multimedia file.

Abstract: Current inspection processes employed for pipeline networks data acquisition aided with manually locating and recording defects/observations, thus leading labor intensive, prone to error and a time-consuming task thereby resulting in process inefficiencies. Embodiments of the present disclosure provide systems and methods for that leverage artificial intelligence/machine learning models and image processing techniques to automate log and data processing, reports and insights generation thereby reduce dependency on manual analysis, improve annual productivity of survey meterage and bring in process and cost efficiencies into overall asset health management for utilities, thereby enhancing accuracy in defect identification, analysis, classification thereof.

Abstract: Current inspection processes employed for pipeline networks data acquisition aided with manually locating and recording defects/observations, thus leading labor intensive, prone to error and a time-consuming task thereby resulting in process inefficiencies. Embodiments of the present disclosure provide systems and methods for that leverage artificial intelligence/machine learning models and image processing techniques to automate log and data processing, reports and insights generation thereby reduce dependency on manual analysis, improve annual productivity of survey meterage and bring in process and cost efficiencies into overall asset health management for utilities, thereby enhancing accuracy in defect identification, analysis, classification thereof.

Abstract: This disclosure relates generally to systems and methods for data acquisition and asset inspection in presence of magnetic interference. Data acquisition and assets inspection systems in many infrastructures such as railway, power line, and bridges provide inaccurate results in presence of magnetic interference. The proposed system and method proposes UAV based navigation through a dynamic correction path to inspect one or more assets in one or more infrastructures. A plurality of sensors are integrated with the UAV to acquire images of the one or more assets in presence of magnetic field. The acquired images are further processed to segment and detect anomalies in one or more parts of the one or more assets. The detected anomalies are further classified as potential anomalies and non-potential anomalies. The proposed method provides accurate results with reduced processing time.

Abstract: This disclosure relates generally to systems and methods for data acquisition and asset inspection in presence of magnetic interference. Data acquisition and assets inspection systems in many infrastructures such as railway, power line, and bridges provide inaccurate results in presence of magnetic interference. The proposed system and method proposes UAV based navigation through a dynamic correction path to inspect one or more assets in one or more infrastructures. A plurality of sensors are integrated with the UAV to acquire images of the one or more assets in presence of magnetic field. The acquired images are further processed to segment and detect anomalies in one or more parts of the one or more assets. The detected anomalies are further classified as potential anomalies and non-potential anomalies. The proposed method provides accurate results with reduced processing time.