Abstract: Method and systems for tagging code include organizing code units in a hierarchical structure that associates the code units with respective description units. The hierarchical structure is filtered to remove un-meaningful description units from the hierarchical structure. Feature tags are generated based on a weighted sum of term frequencies in the description units of the hierarchical structure to associate features with respective code units.

Abstract: Identifying one or more patterns of content consumption across multiple entities and determining an engagement action for a user of an entity based on the patterns may include receiving information associated with content, cross-industry user data associated with consumption of the content, and a given organization user data associated with consumption of the content. A first set of consumption profile vectors associated with the given organization and one or more second set of consumption profile vectors associated respectively with one or more cross-industry organizations may be generated. Information associated with a target user in the given organization may be received. A customized learning plan for the target user in the given organization may be generated based on the first set of consumption profile vectors, the one or more second set of consumption profile vectors, and the information associated with the target user.

Abstract: A method and system of characterizing a job market is provided. The available job postings of a job market are determined. For each job posting, one or more technical skills, a job role level, and one or more professional capabilities related to the job posting are determined. The professional capabilities are aggregated by job dimensions. The determined technical skills and the professional capabilities of each job posting and the aggregated professional capabilities are stored in a database as reference data. A job capability profile for one or more job dimensions is provided based on the stored reference data in the database.

Abstract: Method and systems for tagging code include organizing code units in a hierarchical structure that associates the code units with respective description units. The hierarchical structure is filtered to remove un-meaningful description units from the hierarchical structure. Feature tags are generated based on a weighted sum of term frequencies in the description units of the hierarchical structure to associate features with respective code units.

Abstract: A method for analyzing a virtual discussion. The method may include identifying, with a processing device, a first concept relevant to a first subdiscussion associated with an online discussion. The method may also include identifying a second concept relevant to the first subdiscussion, and determining a relation between the first concept and the second concept.

Abstract: Method and systems for tagging code include building a code structure based description unit association model (CDAM) that associates code units with respective description units and organizes the code units in a hierarchical structure. The CDAM is filtered to remove un-meaningful description units through cross-validation between code units. Feature tags are generated based on a weighted sum of term frequencies in the description units of the CDAM to associate features with respective code units.

Abstract: A method includes reading project data describing a plurality of projects in a project database. The plurality of projects are associated with a plurality of skill sets, where each project of the plurality of projects is associated with a skill set needed to complete the project. A set of principal skill sets is determined, to include a set of frequent skill subsets that each appear in the plurality of skill sets with at least a threshold frequency. A new project is received and associated with a requested skill set. The requested skill set is mapped, using a computer processor, to a corresponding principal skill set of the set of principal skill sets, where the corresponding principal skill set represents the requested skill set. The corresponding principal skill set is used for at least one of staffing the new project and updating organizational staffing plans related to the plurality of projects.

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: 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 method for analyzing a virtual discussion is provided. The method may include identifying, with a processing device, a first concept relevant to a first subdiscussion associated with an online discussion. The method may also include identifying a second concept relevant to the first subdiscussion, and determining a relation between the first concept and the second concept.

Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.

Abstract: According to embodiments, methods, systems, and computer program products are provided for generating material compositions including receiving, by a processor, a plurality of material inputs that collectively form one or more known material compositions, assigning a vector space value to each of the plurality of material inputs, wherein each vector space value is unique for each material input, receiving a first constraint and a second constraint, and generating a transformed material composition based on the vector space value, the first constraint, and the second constraint.

Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.

Abstract: Identifying one or more patterns of content consumption across multiple entities and determining an engagement action for a user of an entity based on the patterns may include receiving information associated with content, cross-industry user data associated with consumption of the content, and a given organization user data associated with consumption of the content. A first set of consumption profile vectors associated with the given organization and one or more second set of consumption profile vectors associated respectively with one or more cross-industry organizations may be generated. Information associated with a target user in the given organization may be received. A customized learning plan for the target user in the given organization may be generated based on the first set of consumption profile vectors, the one or more second set of consumption profile vectors, and the information associated with the target user.

Abstract: According to embodiments, methods, systems, and computer program products are provided for generating material compositions including receiving, by a processor, a plurality of material inputs that collectively form one or more known material compositions, assigning a vector space value to each of the plurality of material inputs, wherein each vector space value is unique for each material input, receiving a first constraint and a second constraint, and generating a transformed material composition based on the vector space value, the first constraint, and the second constraint.

Abstract: Method and systems for tagging code include building a code structure based description unit association model (CDAM) that associates code units with respective description units and organizes the code units in a hierarchical structure. The CDAM is filtered to remove un-meaningful description units through cross-validation between code units. Feature tags are generated based on a weighted sum of term frequencies in the description units of the CDAM to associate features with respective code units.

Abstract: Analyzing task associations may compute artifact associations for a plurality of artifacts based on historical task submission logs. From the computed artifact associations, shared artifacts that have artifact association with targeted artifacts that are associated with planned tasks, may be determined. Task association between the planned tasks may be identified based on the shared artifacts. Task associations may be analyzed to determine one or more of task assignment and task schedule for the planned tasks.

Abstract: Enterprise learning system may receive as input learning goals for a learner. Learning goals may also be specified by job-roles. The system may output course-sequence recommendations, sequence-associations to learning goals and learning-goal recommendations. A semantic analysis may automatically relate learning-goals to learning assets and job-roles. Computational semantic relation-finding may leverage available knowledge-bases. A sequence-recommendation recommends course or learning asset sequences to the learner based on historical usage, the learner's current and/or desired job-roles and learning goals.

Abstract: An apparatus comprises a decoder and an actuator. The decoder is configured to receive a signal comprising a target olfactory pleasantness, to determine one or more combinations of odors which produce the target olfactory pleasantness, and to select a given one of the combinations of odors. The actuator is coupled to the decoder and configured to utilize the given combination of odors to produce an odor with the target olfactory pleasantness.

Abstract: An apparatus comprises a decoder and an actuator. The decoder is configured to receive a signal comprising a target olfactory pleasantness, to determine one or more combinations of odors which produce the target olfactory pleasantness, and to select a given one of the combinations of odors. The actuator is coupled to the decoder and configured to utilize the given combination of odors to produce an odor with the target olfactory pleasantness.