Abstract: Embodiments of the invention are directed to a computer-implemented method. A non-limiting example of the computer-implemented method includes accessing, using a processor system, a process-step sequence that includes a plurality process-steps and a plurality of queue-times. A process-step sequence mining operation is applied to the process-step sequence, wherein the process-step sequence mining operation is operable to make a prediction of an impact of a portion of the process-step sequence on a characteristic of a product generated by the process-step sequence.

Abstract: Techniques for mapping policy documents to regulatory documents to check for compliance between the policies and documents are provided. In one example, a computer-implemented method determining, by a system operatively coupled to a processor, an information input, a control framework, and a document from a first group consisting of a regulatory document and a policy document, wherein the information input is a corpora from a second group consisting of a domain corpora and a global corpora. The computer-implemented method can also comprise mapping, by the system, the received regulatory document or the received policy document to the control framework using a supervised machine learning technique.

Abstract: A method, computer system, and a computer program product for generating a custom corpus is provided. The present invention may include generating a domain graph. The present invention may also include gathering seed data based on the generated domain graph. The present invention may then include identifying domain related data based on the gathered seed data. The present invention may further include querying the domain related data. The present invention may also include creating word embeddings for the domain related data. The present invention may then include evaluating the domain related data.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises ingesting tabular data from at least one modality of a plurality of modalities; simultaneously extracting data and generating a prediction model for a task of a computing device from the extracted data from at least two modalities in the plurality of modalities; generating a data signature based on the generated prediction model from the at least two modalities by leveraging the generated prediction model for ingested tabular data and extracted data; comparing the generated data signature to identified data signatures stored in at least one modality in the plurality of modalities; and performing a task based on the generated data signature and a validation of the comparison of identified data signatures.

Abstract: Tabular data is accessed that contains multiple entries of alphanumeric data. Multiple tokens are generated of the multiple entries of alphanumeric data using a tokenization process. The tokenization process maintains jargon-specific features of the alphanumeric data. Multiple embeddings of the multiple entries of alphanumeric data are generated using the tokens. The embeddings capture similarity of the multiple entries considering all of global features, column features, and row features in the tokens of the tabular data. A neural network is used to predict probabilities for pre-defined classes for the tabular data using the generated embeddings.

Abstract: A computing device includes a processor and a storage device. A wafer asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts identifying and clustering a plurality of assets based on static properties of a wafer asset using a first module of the wafer asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the wafer asset using a second module of the wafer asset modeling module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module of the wafer asset modeling module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the wafer asset and/or an event based on past patterns. Prediction information is stored in the storage device.

Abstract: A method and system are provided to calculate a future behavioral data and identify a relative causal impact of external factors affecting the data. Behavioral data and data for one or more external factors are harvested for a first time period. New behavioral data is harvested for a second time period. New data for the second time period is harvested. Based on a second training algorithm, a forecast time series value of a future behavioral data for a third time period that is after the second time period is calculated. A relative causal impact between each external factor and the predicted time series value of the behavioral data, for the third time period, is identified.

Abstract: A system, method and computer program product, which given in input a question in natural language format, delivers personalized insights related to the answer. Personalized insights are selected among candidate insights mined from the data and ranked based on closeness to (mined) user-preference, relevance to the question, and surprise factor. Two core components include: Question analysis and meaningful insight look up and Multi-dimensional insight ranking. The Question analysis and meaningful insights lookup module performs a semantic analysis of the questions and, uses techniques including “templates” to build new questions which could uncover insights from the data. The Multi-dimensional insight ranking module takes in input a list of insights returned from Question analysis and meaningful insights lookup and rank such insights based on such factors as: relevance to the query, surprise factor, and user preferences.

Abstract: Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding.

Abstract: Systems, methods, and electronic circuits facilitating embedded sensor chips in polymer-based coatings are provided. In one example, a method comprises fabricating an electronic circuit, the electronic circuit comprising one or more semiconductor devices, one or more sensors, and a communication element; encapsulating the electronic circuit within an insulator, resulting in an encapsulated circuit; and dispersing the encapsulated circuit into a lacquer solution comprising a polymer carrier and a solvent.

Abstract: A method is provided for creating a semantic model for submitting search queries thereto. The method includes an act of receiving data from one or more input sources in an entity and relationship capture service of a situational awareness engine. The method further includes an act of extracting entities and relationships between the entities in two or more extraction services, where the two or more extraction services include at least two of a table-to-graph service, an event-to-graph service, a sensor-to-graph service, a text-to-graph service, and an image-to-graph service. The method includes an act of generating a semantic model based on fusion and labeling the extracted data provided by the at least two extraction services, where the semantic model can receive a search query and respond to the search query based on the generated semantic model.

Abstract: A computing device includes a processor and a storage device. A wafer asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts identifying and clustering a plurality of assets based on static properties of a wafer asset using a first module of the wafer asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the wafer asset using a second module of the wafer asset modeling module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module of the wafer asset modeling module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the wafer asset and/or an event based on past patterns. Prediction information is stored in the storage device.

Abstract: A computing device includes a processor and a storage device. A vehicle asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts of identifying and clustering a plurality of assets based on static properties of a vehicle asset using a first module of the vehicle asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the vehicle asset using a second module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the vehicle asset and/or an event based on past patterns. Prediction information is stored in the storage device.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises ingesting tabular data from at least one modality of a plurality of modalities; simultaneously extracting data and generating a prediction model for a task of a computing device from the extracted data from at least two modalities in the plurality of modalities; generating a data signature based on the generated prediction model from the at least two modalities by leveraging the generated prediction model for ingested tabular data and extracted data; comparing the generated data signature to identified data signatures stored in at least one modality in the plurality of modalities; and performing a task based on the generated data signature and a validation of the comparison of identified data signatures.

Abstract: A video stream control method, system, and a computer program product include analyzing a video stream to identify a section of the video stream where a condition will occur, delivering a notification that the condition will occur in the section of the video stream and modifying the video stream before the section of the video stream is played, and selecting a type of delivery of the section of the video stream based on a set of rules factoring the condition and user data at a time of the notification.

Abstract: Techniques for mapping policy documents to regulatory documents to check for compliance between the policies and documents are provided. In one example, a computer-implemented method determining, by a system operatively coupled to a processor, an information input, a control framework, and a document from a first group consisting of a regulatory document and a policy document, wherein the information input is a corpora from a second group consisting of a domain corpora and a global corpora. The computer-implemented method can also comprise mapping, by the system, the received regulatory document or the received policy document to the control framework using a supervised machine learning technique.

Abstract: Techniques for mapping policy documents to regulatory documents to check for compliance between the policies and documents are provided. In one example, a computer-implemented method determining, by a system operatively coupled to a processor, an information input, a control framework, and a document from a first group consisting of a regulatory document and a policy document, wherein the information input is a corpora from a second group consisting of a domain corpora and a global corpora. The computer-implemented method can also comprise mapping, by the system, the received regulatory document or the received policy document to the control framework using a supervised machine learning technique.

Abstract: Systems, methods, and electronic circuits facilitating embedded sensor chips in polymer-based coatings are provided. In one example, a method comprises fabricating an electronic circuit, the electronic circuit comprising one or more semiconductor devices, one or more sensors, and a communication element; encapsulating the electronic circuit within an insulator, resulting in an encapsulated circuit; and dispersing the encapsulated circuit into a lacquer solution comprising a polymer carrier and a solvent.

Abstract: A method, computer system, and a computer program product for generating a custom corpus is provided. The present invention may include generating a domain graph. The present invention may also include gathering seed data based on the generated domain graph. The present invention may then include identifying domain related data based on the gathered seed data. The present invention may further include querying the domain related data. The present invention may also include creating word embeddings for the domain related data. The present invention may then include evaluating the domain related data.

Abstract: Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding.