Abstract: Systems and methods for fueling (or charging) communication, for example between a hydrogen fueling station and a hydrogen powered vehicle (or an electric vehicle and charging station) may utilize near field communication as well as vehicle to infrastructure communication. Safety information, fueling or charging information, payment information, and other information may be transmitted, and the redundant nature of the communication permits fault recovery and improved process monitoring. In this manner, fueling and/or recharging is made safer, faster, and more efficient.

Abstract: Embodiments described herein provide a method for text summarization. The method includes receiving a training dataset having at least an uncompressed text, a compressed text, and one or more information entities accompanying the compressed text. The method also includes generating, using a perturber model, a perturbed text with the one or more information entities being inserted into the compressed text. The method further includes training the perturber model based on a first training objective, and generating, using the trained perturber model, a perturbed summary in response to an input of a reference summary. The method further includes generating, via an editor model, a predicted summary by removing information from the perturbed summary conditioned on a source document of the reference summary, and training the editor model based on a second training objective.

Abstract: Embodiments described herein provide a document summarization framework that employs an ensemble of summarization models, each of which is a modified version of a base summarization model to control hallucination. For example, a base summarization model may first be trained on a full training data set. The trained base summarization model is then fine-tuned using a first filtered subset of the training data which contains noisy data, resulting in an “anti-expert” model. The parameters of the anti-expert model are subtracted from the parameters of the trained base model to produce a final summarization model which yields robust factual performance.

Abstract: A one-time passphrase is transmitted from an authentication system to a personal communication device of a user. The one-time passphrase includes common but incongruous words. The user is prompted to verbalize the one-time passphrase to a processor-implemented, conversational user interface. Utterances from the user are received by a conversational user interface, and the utterances are communicated from the conversational user interface to the authentication system via a trusted communication channel. The authentication system determines, using speech recognition, presence or non-presence of the one-time passphrase within the received utterances. The authentication system authenticates the user in response to detecting presence of the one-time passphrase within the received utterances.

Abstract: Embodiments described herein provide a query-focused summarization model that employs a single or dual encoder model. A two-step approach may be adopted that first extracts parts of the source document and then synthesizes the extracted segments into a final summary. In another embodiment, an end-to-end approach may be adopted that splits the source document into overlapping segments, and then concatenates encodings into a single embedding sequence for the decoder to output a summary.

Abstract: An apparatus (100) that automatically generates suggested responses to an incoming natural language communication includes: a classifier (170) that has been trained to predict one or more style attributes exhibited by natural language communications; a generative natural language model (180) that has been trained to generate responses to natural language communications; and at least one processor which executes computer program code from at least one memory, wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to perform defined operations.

Abstract: Embodiments described herein provide an alignment-based pre-training mechanism for protein prediction. Specifically, the protein prediction model takes as input features derived from multiple sequence alignments (MSAs), which cluster proteins with related sequences. Features derived from MSAs, such as position specific scoring matrices and hidden Markov model (HMM) profiles, have long known to be useful features for predicting the structure of a protein. Thus, in order to predict profiles derived from MSAs from a single protein in the alignment, the neural network learns information about that protein's structure using HMM profiles derived from MSAs as labels during pre-training (rather than as input features in a downstream task).

Abstract: A system is provided to receive a request for insights based on reviews for a product, wherein the request includes information input by a user relating to configuration information, calibration information, and desired feature information, and wherein the configuration information includes a relevance weight for at least one of a plurality of attributes for each review. The system assigns, based on the relevance weight, a normalized relevance weight for each review. The system generates, by a trained model based on the user-input information and the normalized relevance weight, quantitative and qualitative insights for the reviews. The system displays the quantitative and qualitative insights. The system modifies, by the user, a rating of a displayed qualitative insight term. The system executes the trained model based on the modified rating, and generates and displays updated quantitative and qualitative insights for the reviews.

Abstract: Embodiments of the present invention provide a system for automatically extracting conversational structure from a voice record based on lexical and acoustic features. The system also aggregates business-relevant statistics and entities from a collection of spoken conversations. The system may infer a coarse-level conversational structure based on fine-level activities identified from extracted acoustic features. The system improves significantly over previous systems by extracting structure based on lexical and acoustic features. This enables extracting conversational structure on a larger scale and finer level of detail than previous systems, and can feed an analytics and business intelligence platform, e.g. for customer service phone calls. During operation, the system obtains a voice record. The system then extracts a lexical feature using automatic speech recognition (ASR). The system extracts an acoustic feature.

Abstract: Embodiments of the present invention provide a system for automatically extracting conversational structure from a voice record based on lexical and acoustic features. The system also aggregates business-relevant statistics and entities from a collection of spoken conversations. The system may infer a coarse-level conversational structure based on fine-level activities identified from extracted acoustic features. The system improves significantly over previous systems by extracting structure based on lexical and acoustic features. This enables extracting conversational structure on a larger scale and finer level of detail than previous systems, and can feed an analytics and business intelligence platform, e.g. for customer service phone calls. During operation, the system obtains a voice record. The system then extracts a lexical feature using automatic speech recognition (ASR). The system extracts an acoustic feature.

Abstract: One embodiment of the present invention provides a system for creating a health/wellness program on a generic health/wellness platform. During operation, the system receives, at the generic health/wellness platform, a set of definitions for the health/wellness program, constructs a program model for the health/wellness program, generates a program instance to be executed on the generic health/wellness platform, and associates the program instance to a number of health/wellness modules provided by the health/wellness platform.

Abstract: One embodiment of the present invention provides a system for automatically reporting progress in completing objectives and goals of a plan. During operation, the system receives data indicating user selection and/or configuration of a plan with one or more goals, objectives, and/or milestones. The system obtains data generated by sensors in a sensing device and/or a mobile device. The sensors generate physiological data or data from detecting activity or environment associated with the user. Next, the system analyzes the data to determine whether the user has completed an objective, milestone, or goal of the plan. If the system determines that the user has completed an objective, milestone, or goal of the plan, the system pushes an alert to the mobile device indicating that the user has completed the objective, milestone, or goal.

Abstract: One embodiment of the present invention provides a system for automatically reporting progress in completing objectives and goals of a plan. During operation, the system receives data indicating user selection and/or configuration of a plan with one or more goals, objectives, and/or milestones. The system obtains data generated by sensors in a sensing device and/or a mobile device. The sensors generate physiological data or data from detecting activity or environment associated with the user. Next, the system analyzes the data to determine whether the user has completed an objective, milestone, or goal of the plan. If the system determines that the user has completed an objective, milestone, or goal of the plan, the system pushes an alert to the mobile device indicating that the user has completed the objective, milestone, or goal.

Abstract: One embodiment of the present invention provides a system for creating a health/wellness program on a generic health/wellness platform. During operation, the system receives, at the generic health/wellness platform, a set of definitions for the health/wellness program, constructs a program model for the health/wellness program, generates a program instance to be executed on the generic health/wellness platform, and associates the program instance to a number of health/wellness modules provided by the health/wellness platform.