Abstract: Methods and systems are disclosed for detecting threats in voice communications such as telephone calls. Various voice phishing (vishing) detectors detect respective type of threats and can be used or activated individually or in various combinations. A tampering detector utilizes deep scattering spectra and shifted delta cepstra features to detect tampering in the form of voice conversion, speech synthesis, or splicing. A content detector predicts a likelihood that word patterns on an incoming voice signal are indicative of a vishing threat. A spoofing detector authenticates or repudiates a purported speaker based on comparison of voice profiles. The vishing detectors can be provided as an authentication service or embedded in communication equipment. Machine learning and signal processing aspects are disclosed, along with applications to mobile telephony and call centers.

Abstract: Methods and systems are disclosed for detecting threats in voice communications such as telephone calls. Various voice phishing (vishing) detectors detect respective type of threats and can be used or activated individually or in various combinations. A tampering detector utilizes deep scattering spectra and shifted delta cepstra features to detect tampering in the form of voice conversion, speech synthesis, or splicing. A content detector predicts a likelihood that word patterns on an incoming voice signal are indicative of a vishing threat. A spoofing detector authenticates or repudiates a purported speaker based on comparison of voice profiles. The vishing detectors can be provided as an authentication service or embedded in communication equipment. Machine learning and signal processing aspects are disclosed, along with applications to mobile telephony and call centers.

Abstract: Computerized methods are disclosed for automated question generation from source documents through natural language processing, for applications including training and testing. Interleaved selection and transformation phases employ combined semantic-syntactic analysis to progressively refine natural input text into a high density of text fragments having high content value. Non-local semantic content and attributes such as emphasis attributes can be attached to the text fragments. The text fragments are reverse parsed by matching against a precomputed library of combined semantic-syntactic patterns. Once the patterns of each fragment are determined, transformation of fragments into question-answer pairs is performed using question selectors and answer selectors tailored to each pattern. Methods for constructing distractors, both internal and external, are also disclosed. The ecosystem of machine learning components, ontology resources, and process improvement are also described.

Abstract: Computerized methods are disclosed for automated question generation from source documents through natural language processing, for applications including training and testing. Interleaved selection and transformation phases employ combined semantic-syntactic analysis to progressively refine natural input text into a high density of text fragments having high content value. Non-local semantic content and attributes such as emphasis attributes can be attached to the text fragments. The text fragments are reverse parsed by matching against a precomputed library of combined semantic-syntactic patterns. Once the patterns of each fragment are determined, transformation of fragments into question-answer pairs is performed using question selectors and answer selectors tailored to each pattern. Methods for constructing distractors, both internal and external, are also disclosed. The ecosystem of machine learning components, ontology resources, and process improvement are also described.

Abstract: A novel system for automated metadata generation of learning and knowledge Objects are presented. Such system automates the processes of adding descriptive and contextual information to digital learning content, digital documents, and other objects used in learning and knowledge management. It also automates the process of creating associations among higher level objects and classifications systems used to organize content, and it does so in a way that improves the functionality of existing technologies, that can be tuned to meet the needs of a particular organization or community of practice, and that can be extended and refined to take advantage of new information retrieval technologies. It includes methods that handle aggregate digital objects composed of a plurality of other objects and that improves efficiency by caching data and recognizing the relationships among aggregate objects and their components.