International Business Machines Corporation

Joydeep Mondal

Sarthak Ahuja

Sudhanshu Shekhar Singh

Amanda Klabzuba

Kuntal Dey

Seema Nagar

Abhijit Mishra

Kushal Mukherjee

John Arthur Medicke

Vinayak Sastri

David Glenn George

Shrihari Vasudevan

Michael W Ticknor

Michael Peran

Daniel Augenstern

Richard H Zhou

A method, system and computer program product for assessing similarity between two job description documents. Job description documents consist of sentences framed in a particular manner, where the sentences are represented as a set of actions, an object corresponding to each action and a set of attributes corresponding to the object. The two job description documents are parsed to generate a first and a second set of an action-object-attribute triplet representation, where the first set of the action-object-attribute triplet representation is associated with the first job description document and the second set of the action-object-attribute triplet representation is associated with the second job description document. A similarity score between the first and second sets of action-object-attribute triplet representations is then calculated by hierarchically matching the first and second sets of action-object-attribute triplet representations across the job description documents. In this manner, similar job positions/job descriptions may be more accurately identified.

Creating and using triplet representations to assess similarity between job description documents

Methods, computer program products, and systems are presented. The methods include, for instance: training of a machine learning model for predicting a rating in certain content rating systems based on training data. The machine learning model includes a plurality of maturity classifiers corresponding to individual features of the previously rated contents. An input content is obtained and features of the input content are extracted by use of respective content analysis tools and subsequently classified by the maturity classifiers of the machine learning model.

Dynamic content rating assistant

Methods, systems and computer program products for generating virtual reality (VR)-based interactive audience simulations are provided herein. A computer-implemented method includes determining one or more situational and location characteristics for a given performance by a user, generating a VR-based simulated audience for the given performance based at least in part on the determined situational and location characteristics, presenting the VR-based simulated audience to a user during the given performance utilizing a VR headset, utilizing one or more sensors to measure one or more aspects of the given performance before the VR-based simulated audience, and generating real-time feedback adjusting the VR-based simulated audience presented to the user utilizing the VR headset based at least in part on the measured aspects of the given performance.

Virtual-reality based interactive audience simulation

Embodiments for recommending exemplars of a data-set by a processor. A selected number of exemplars may be labeled from one or more classes in a data-set. One or more class exemplars for each of the one or more classes in the data-set may be recommended according to similarities between the selected number of labeled exemplars and remaining data of the data-set.

Recommending exemplars of an unlabeled data-set

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New Delhi, India

Joydeep Mondal