International Business Machines Corporation

Irene Lizeth Manotas Gutiérrez

Jonathan D Dunne

Vadim Sheinin

Octavian Popescu

Kelley Anders

Mary Swift

Hangu Yeo

Ngoc Phuoc An Vo

Qi Cheng Li

Bong Jun Ko

Keith William Grueneberg

Shiqiang Wang

Xuan-Hong Dang

Elahe Khorasani

Ra'eesa Kabir

A first set of data associated with assets can be received. An ontology graph can be constructed based on the first set of data. A second set of data associated with the assets can be received, the second set of data having a first frequency of sampling. Based on the second set of data, nodes of the ontology graph representing the assets can be characterized. A third set of data associated with the assets can be received, the third set of data having a second frequency of sampling. The third set of data can include real time data associated with the assets. Based on the third set of data and information associated with the assets represented by the ontology graph, a deep learning neural network can be trained to predict a future state of at least one asset of the assets and discover dynamic mutual impact of the assets.

Asset health identification from multi-modality data analysis

A set of values can be selected from a plurality of fields of a table in a database. At least one adverb or adjective can be selected for the set of values. Join paths for values in the set of values can be determined. A structured query language pattern can be determined based, at least in part, on at least one value in the set of values, the at least one adverb or adjective for the set of values, and the join paths for the set of values. The structured query language pattern can be stored to first training data configured, at least in part, for use in machine learning to train a text-to-SQL model, the text-to-SQL model comprising a first artificial neural network and configured to convert first natural language text to a first structured query language query.

Generating training data for a machine learning model that performs text-to-SQL

Various embodiments are provided herein for decreasing a central processing unit (CPU) inference time, thereby shortening processing time of a deep learning model for which run-time complexity is proportional to an output sequence length. A compacted sequence is developed that is used to train the deep learning model. An output of the trained model becomes the compacted sequence on which a post-processing method operates to create an uncompacted version of a predicted sequence.

Reducing inference time processing deep learning models

According to one embodiment, a method, computer system, and computer program product for human-machine interfacing is provided. The present invention may include receiving a personality corpus associated with a personality typology comprising multiple personality types; extracting a plurality of utterances from a user; selecting, by a personality model, a personality type associated with the user based on the utterances and the personality corpus; identifying a compatible personality type of the selected personality type; constructing one or more natural language scripts from a word graph associated with the compatible personality type; and transmitting the one or more natural language scripts to the user.

Personality-enhanced chat discourse responses

A computer system extracts clauses using machine translation. An input sentence in a source language is translated into a translated sentence in a target language using a trained machine translation model, wherein the trained machine translation model inserts a grammatical indicator into a position of the translated sentence that identifies a dependent clause. The input sentence and the translated sentence are aligned to determine a position in the input sentence that corresponds to the position of the grammatical indicator in the translated sentence. The dependent clause is extracted, in the source language, from the input sentence based on the determined position in the input sentence. Embodiments of the present invention further include a method and program product for clause extraction using machine translation in substantially the same manner described above.

Clause extraction using machine translation and natural language processing

Computer-implemented methods, computer program products, and computer systems for mitigating frequency loss may include one or more processors configured for receiving first audio data corresponding to unobstructed user utterances, receiving second audio data corresponding to first obstructed user utterances, generating a frequency loss (FL) model representing frequency loss between the first audio data and the second audio data, receiving third audio data corresponding to one or more second obstructed user utterances, processing the third audio data using the FL model to generate fourth audio data corresponding to a frequency loss mitigated version of the second obstructed user utterances, and transmitting the fourth audio data to a recipient computing device. The first obstructed user utterances are obstructed by a facemask and the one or more second obstructed user utterances is obstructed by the facemask. The FL model may be executed as an audio plugin in a web conferencing program.

Mitigating voice frequency loss

A communication session between two or more computing devices is monitored. One or more share actions, that include one or more media documents, are identified in the communication session responsive to the media documents and based on the monitoring the communication session. Reaction data, that is related to the share actions, in the communication session is analyzed in response the share actions and based on the media documents. One or more relevant media documents are determined based on the analyzing the reaction data. The relevant media documents are analyzed based on the media documents. A first depiction rendered by a first of the devices of the communication session is modified based on the relevant media documents.

Multimedia content differentiation

A user input is analyzed during labeling of a first portion of data to determine a behavior of a user. A labeling accuracy rate of the user is forecasted. A characteristic of a second portion of data to be presented to the user at a second time is determined. The second portion of data is presented to the user. A second label corresponding to the second portion of data is received. A second user input is analyzed to determine a second behavior of the user. The accuracy prediction model is refined. A second labeling accuracy rate of the user is forecasted. The recommendation model is refined.

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White Plains, NY, United States of America

Irene Lizeth Manotas Gutiérrez