Abstract: A computer-implemented method for a socially connectable agent. A non-limiting example of the computer-implemented method includes receiving, by a processor, input events. The method includes evaluating and enacting rules, by the processor, based on the received input events, where the rule include stochastic elements and temporal operators. The method pushes, by the processor, action events that result from the evaluation and enactment of the rules, and initiates, by the processor, action events.

Abstract: Providing targeted, evidence-based recommendations to improve digital content quality, in one aspect, may include partitioning digital content into a sequence of logical content modules. Quality scores for the logical content modules along a plurality of quality dimensions may be determined. User interactions with the digital content on a user interface are monitored. Raw events generated from user interaction in accessing the digital content are captured. Raw event patterns for the logical content modules are detected. The raw event patterns of user interaction are transformed into a set of dynamic states representing potential user experience and performance. The set of dynamic states are associated with a respective logical content module. The quality dimensions of the logical content modules and associated quality scores are correlated with the dynamic states.

Abstract: A computer system, method, and computer-readable product for providing data for career advice, such as job or education recommendations, from automated review and analysis of career-related data for an individual, which is at least initially obtained from documents, such as resumes and writing samples. For a designated individual, career-related data is obtained from the documents and an initial personality estimate is created for the designated individual based upon, at least, the obtained career-related data. One or more latent factor models for known career-personality matches are then gathered from a database and a questionnaire is provided to the individual to gather further information and augment the personality estimate. The created personality estimate is integrated with the latent factor model(s) to create career advice data.

Abstract: A combination of knowledge representation and retrieval, assessments and personalization for a tutoring platform that provides an automated phased word exposition method for vocabulary learning and enable learners to learn words (or any learning objective) with sufficient proficiency and without excessive repetition. The phased learning objective method implements a moving average model that models the learner across multiple dimensions (e.g. reading, listening, writing, speaking) such that the platform is confident of the learner's understanding, while using minimal assessment doses, and such that the understanding transfers across experiences. The method implemented provides for multiple learning and assessment activities for a learning objective and leverages the same learner model for each learning dimension. Each word of a word set passes through learning and assessment-only phases for statistically significant evidence of learning.

Abstract: An exploration-based career guidance system is disclosed. The career guidance system receives an assessment regarding a candidate and identifies a first set of roles for the candidate based on the assessment. The system receives a selection of a role from among the first set of roles and provides a simulated experience of the selected role and receives a set of interaction data from the simulated experience. The system adjusts the assessment regarding the candidate based on the set of interaction data and identifies a second, different set of roles for the candidate based on the adjusted assessment.

Abstract: Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, selecting a video based on a learning objective, determining types of video interjections using an analytics engine that compares a learner model and the learning objective, determining a location for the video interjections using the analytics engine, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.

Abstract: A computer system, method, and computer-readable product for automatically generating a hierarchy of concepts described in a video using semantic concept matching that assists persons in searching for specific types of content within videos. A video segment is obtained, with the segment having a distinct running time with discrete information related to one or more topics within its content. The discrete information of the video segment is then parsed out and related to a predetermined relevant topic, and then one or more attributes for weighting each discrete information relevant topic within the video segment are determined. A hierarchy of concepts of the video segments is then created, based upon, at least, the weighting of the information topic within the running time of the video segment based upon the presence of the one or more attributes. Other information related to the video segment can also be used to modify the hierarchy.

Abstract: An embodiment includes a method for use in presenting a plurality of concepts to a plurality of students. The method includes developing a first statistical model of one or more modalities effective for teaching at least a given concept of the plurality of concepts across the plurality of students. The method also includes developing a second statistical model of one or more modalities effective for learning by at least a given student of the plurality of students across the plurality of concepts. The method further includes combining the first and second models to determine one or more modalities for presenting at least the given concept to at least the given student; and presenting at least the given concept to at least the given student using the determined one or more modalities.

Abstract: Providing targeted, evidence-based recommendations to improve digital content quality, in one aspect, may include partitioning digital content into a sequence of logical content modules. Quality scores for the logical content modules along a plurality of quality dimensions may be determined. User interactions with the digital content on a user interface are monitored. Raw events generated from user interaction in accessing the digital content are captured. Raw event patterns for the logical content modules are detected. The raw event patterns of user interaction are transformed into a set of dynamic states representing potential user experience and performance. The set of dynamic states are associated with a respective logical content module. The quality dimensions of the logical content modules and associated quality scores are correlated with the dynamic states.

Abstract: A structure having fully aligned via connecting metal lines on different Mx levels. The structure may include a first metal line and a second metal line in a first ILD, a cap covering the first ILD, the second metal line and a portion of the first metal line, a second ILD on the cap, and a via that electrically connects the first metal line to a third metal line, wherein the third metal line is above the first metal line and runs perpendicular to the first metal line, the via is fully aligned to the first metal line and the third metal line, and the via electrically connects the first metal line to the third metal line.

Abstract: A computer-implemented method for a socially connectable agent. A non-limiting example of the computer-implemented method includes receiving, by a processor, input events. The method includes evaluating and enacting rules, by the processor, based on the received input events, where the rule include stochastic elements and temporal operators. The method pushes, by the processor, action events that result from the evaluation and enactment of the rules, and initiates, by the processor, action events.

Abstract: A computer system, method, and computer-readable product for automatically generating a hierarchy of concepts described in a video using semantic concept matching that assists persons in searching for specific types of content within videos. A video segment is obtained, with the segment having a distinct running time with discrete information related to one or more topics within its content. The discrete information of the video segment is then parsed out and related to a predetermined relevant topic, and then one or more attributes for weighting each discrete information relevant topic within the video segment are determined. A hierarchy of concepts of the video segments is then created, based upon, at least, the weighting of the information topic within the running time of the video segment based upon the presence of the one or more attributes. Other information related to the video segment can also be used to modify the hierarchy.

Abstract: Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, selecting a video based on a learning objective, determining types of video interjections using an analytics engine that compares a learner model and the learning objective, determining a location for the video interjections using the analytics engine, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.

Abstract: A computer system, method, and computer-readable product for automatically generating a hierarchy of concepts described in a video using semantic concept matching that assists persons in searching for specific types of content within videos. A video segment is obtained, with the segment having a distinct running time with discrete information related to one or more topics within its content. The discrete information of the video segment is then parsed out and related to a predetermined relevant topic, and then one or more attributes for weighting each discrete information relevant topic within the video segment are determined. A hierarchy of concepts of the video segments is then created, based upon, at least, the weighting of the information topic within the running time of the video segment based upon the presence of the one or more attributes. Other information related to the video segment can also be used to modify the hierarchy.

Abstract: Providing targeted, evidence-based recommendations to improve digital content quality, in one aspect, may include partitioning digital content into a sequence of logical content modules. Quality scores for the logical content modules along a plurality of quality dimensions may be determined. User interactions with the digital content on a user interface are monitored. Raw events generated from user interaction in accessing the digital content are captured. Raw event patterns for the logical content modules are detected. The raw event patterns of user interaction are transformed into a set of dynamic states representing potential user experience and performance. The set of dynamic states are associated with a respective logical content module. The quality dimensions of the logical content modules and associated quality scores are correlated with the dynamic states.

Abstract: Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, generating and storing educational content metadata on a timeline for the videos, selecting a video based on a user specific learning objective, determining types of video interjections using an analytics engine that compares a user learner model, the user learning objective and the metadata, determining a location for the video interjections using the analytics engine that compares the user learner model, the learning objective, the metadata and timeline, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.

Abstract: A combination of knowledge representation and retrieval, assessments and personalization for a tutoring platform that provides an automated phased word exposition method for vocabulary learning and enable learners to learn words (or any learning objective) with sufficient proficiency and without excessive repetition. The phased learning objective method implements a moving average model that models the learner across multiple dimensions (e.g. reading, listening, writing, speaking) such that the platform is confident of the learner's understanding, while using minimal assessment doses, and such that the understanding transfers across experiences. The method implemented provides for multiple learning and assessment activities for a learning objective and leverages the same learner model for each learning dimension. Each word of a word set passes through learning and assessment-only phases for statistically significant evidence of learning.

Abstract: An embodiment includes a method for use in presenting a plurality of concepts to a plurality of students. The method includes developing a first statistical model of one or more modalities effective for teaching at least a given concept of the plurality of concepts across the plurality of students. The method also includes developing a second statistical model of one or more modalities effective for learning by at least a given student of the plurality of students across the plurality of concepts. The method further includes combining the first and second models to determine one or more modalities for presenting at least the given concept to at least the given student; and presenting at least the given concept to at least the given student using the determined one or more modalities.

Abstract: A structure having fully aligned via connecting metal lines on different Mx levels. The structure may include a first metal line and a second metal line in a first ILD, a cap covering the first ILD, the second metal line and a portion of the first metal line, a second ILD on the cap, and a via that electrically connects the first metal line to a third metal line, wherein the third metal line is above the first metal line and runs perpendicular to the first metal line, the via is fully aligned to the first metal line and the third metal line, and the via electrically connects the first metal line to the third metal line.

Abstract: A structure having fully aligned via connecting metal lines on different Mx levels. The structure may include a first metal line and a second metal line in a first ILD, a cap covering the first ILD, the second metal line and a portion of the first metal line, a second ILD on the cap, and a via that electrically connects the first metal line to a third metal line, wherein the third metal line is above the first metal line and runs perpendicular to the first metal line, the via is fully aligned to the first metal line and the third metal line, and the via electrically connects the first metal line to the third metal line.