Abstract: Systems, computer-implemented methods, and computer program products that can facilitate a classical and quantum ensemble artificial intelligence model are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an ensemble component that generates an ensemble artificial intelligence model comprising a classical artificial intelligence model and a quantum artificial intelligence model. The computer executable components can further comprise a score component that computes probability scores of a dataset based on the ensemble artificial intelligence model.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate a classical and quantum ensemble artificial intelligence model are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an ensemble component that generates an ensemble artificial intelligence model comprising a classical artificial intelligence model and a quantum artificial intelligence model. The computer executable components can further comprise a score component that computes probability scores of a dataset based on the ensemble artificial intelligence model.

Abstract: A method, system and apparatus for source code auto-suggestion, including receiving and processing source code from a source code repository, extracting one or more features from the source code received from the source code repository, extracting one or more features from the source code within a development environment, comparing the one or more features from the source code received from the source code repository with the one or more features extracted from the source code within the development environment, and providing a segment of source code from the received and processed source code repository according to a similarity to the source code within the development environment.

Abstract: A database comprises historical information of a user's response to previous notifications. The database is accessed to determine a time at which to provide a (new) notification to the user, utilizing at least: a) current user activity status (e.g., determined from measurement information collected from one or more personal devices and/or user calendar events; b) time/day; and c) context information about the notification (e.g., geo-location, indoors/outdoors) including notification type (e.g., calendar entry, email, IM). The user gets the notification via a portable device at the determined time. A machine learning model can select the determined time by discriminating features of the previous notifications for which the user immediately attended versus those that were deferred and/or ignored. Content of the notification can also be altered in view of such discriminating features so as to increase a likelihood the user will immediately attend to the provided notification.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate a classical and quantum ensemble artificial intelligence model are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an ensemble component that generates an ensemble artificial intelligence model comprising a classical artificial intelligence model and a quantum artificial intelligence model. The computer executable components can further comprise a score component that computes probability scores of a dataset based on the ensemble artificial intelligence model.

Abstract: A workflow extraction method, system, and computer program product include analyzing, for each of the design screens, a relatability of one design screen to a previously analyzed design screen in the database and generating a tag that represents a workflow and creating a database linking the tag to a sequence of design screens from a transition graph that details how to move from one of the design screens to another.

Abstract: Techniques are disclosed for generating a voice user interface (VUI) modality within an application that includes graphical user interface (GUI) screens. A GUI screen parser analyzes the GUI screens to determine the various navigational GUI screen paths that are associated with edge objects within multiple GUI screens. Some edge objects are identified as select objects or prompt objects. A natural language processing system generates a select object synonym data structure and a prompt object data structure that may be utilized by a VUI generator to generate VUI data structures that give the application VUI modality.

Abstract: Techniques for optimizing training data within voice user interface (VUI) of an application under development are disclosed. A VUI feedback module synthesizes human speech of a training phrase. This phrase is presented upon a speaker which is simultaneously captured upon a microphone. A speech to text framework converts the synthesized training phrase into text (textualized training phrase). The VUI feedback module compares the textualized training phrase to the actual training phrase and generates a speech training data structure that identifies similarities or dissimilarities between the textualized training phrase and the actual training phrase. This data structure may be utilized by an application developer computing system to identify training data that is most venerable to misinterpretation when a user interacts with the VUI. The VUI may subsequently be adjusted to account for the vulnerabilities to improve operations or user experience of the VUI.

Abstract: Techniques for optimizing training data within voice user interface (VUI) of an application under development are disclosed. A VUI feedback module synthesizes human speech of a training phrase. This phrase is presented upon a speaker which is simultaneously captured upon a microphone. A speech to text framework converts the synthesized training phrase into text (textualized training phrase). The VUI feedback module compares the textualized training phrase to the actual training phrase and generates a speech training data structure that identifies similarities or dissimilarities between the textualized training phrase and the actual training phrase. This data structure may be utilized by an application developer computing system to identify training data that is most venerable to misinterpretation when a user interacts with the VUI. The VUI may subsequently be adjusted to account for the vulnerabilities to improve operations or user experience of the VUI.

Abstract: Techniques are disclosed for generating a voice user interface (VUI) modality within an application that includes graphical user interface (GUI) screens. A GUI screen parser analyzes the GUI screens to determine the various navigational GUI screen paths that are associated with edge objects within multiple GUI screens. Some edge objects are identified as select objects or prompt objects. A natural language processing system generates a select object synonym data structure and a prompt object data structure that may be utilized by a VUI generator to generate VUI data structures that give the application VUI modality.

Abstract: A workflow extraction method, system, and computer program product include analyzing, for each of the design screens, a relatability of one design screen to a previously analyzed design screen in the database and generating a tag that represents a workflow and creating a database linking the tag to a sequence of design screens from a transition graph that details how to move from one of the design screens to another.

Abstract: An approach is disclosed that receives, at a wearable sensing element worn by a user, sensor data that pertains to the user's physiological functions. Physiological states pertaining to the user are calculated from the received sensor data, with the physiological states including both physical states and mental states. The calculated physiological state is matched to an environmental action states, and environmental actions are responsively performed to change a physical environment of the user.

Abstract: Techniques for optimizing training data within voice user interface (VUI) of an application under development are disclosed. A VUI feedback module synthesizes human speech of a training phrase. This phrase is presented upon a speaker which is simultaneously captured upon a microphone. A speech to text framework converts the synthesized training phrase into text (textualized training phrase). The VUI feedback module compares the textualized training phrase to the actual training phrase and generates a speech training data structure that identifies similarities or dissimilarities between the textualized training phrase and the actual training phrase. This data structure may be utilized by an application developer computing system to identify training data that is most venerable to misinterpretation when a user interacts with the VUI. The VUI may subsequently be adjusted to account for the vulnerabilities to improve operations or user experience of the VUI.

Abstract: Techniques for optimizing training data within voice user interface (VUI) of an application under development are disclosed. A VUI feedback module synthesizes human speech of a training phrase. This phrase is presented upon a speaker which is simultaneously captured upon a microphone. A speech to text framework converts the synthesized training phrase into text (textualized training phrase). The VUI feedback module compares the textualized training phrase to the actual training phrase and generates a speech training data structure that identifies similarities or dissimilarities between the textualized training phrase and the actual training phrase. This data structure may be utilized by an application developer computing system to identify training data that is most venerable to misinterpretation when a user interacts with the VUI. The VUI may subsequently be adjusted to account for the vulnerabilities to improve operations or user experience of the VUI.

Abstract: Techniques are disclosed for identifying which graphical user interface (GUI) screens of an application that is under development would benefit from a voice user interface (VUI). A GUI screen parser analyzes to determine the GUI objects within GUI screens of the application. The parser assigns a speechability score to each analyzed GUI screen. Those GUI screens that have a higher speechability score than a predetermined speechability threshold are indicated as GUI screens that would benefit (e.g., the user experience in interacting with those GUI screens would increase, the number of GUI screens displayed would be reduced, or the like) with the addition of a VUI.

Abstract: Techniques are disclosed for generating a voice user interface (VUI) modality within an application that includes graphical user interface (GUI) screens. A GUI screen parser analyzes the GUI screens to determine the various navigational GUI screen paths that are associated with edge objects within multiple GUI screens. Some edge objects are identified as select objects or prompt objects. A natural language processing system generates a select object synonym data structure and a prompt object data structure that may be utilized by a VUI generator to generate VUI data structures that give the application VUI modality.

Abstract: Techniques are disclosed for generating a voice user interface (VUI) modality within an application that includes graphical user interface (GUI) screens. A GUI screen parser analyzes the GUI screens to determine the various navigational GUI screen paths that are associated with edge objects within multiple GUI screens. Some edge objects are identified as select objects or prompt objects. A natural language processing system generates a select object synonym data structure and a prompt object data structure that may be utilized by a VUI generator to generate VUI data structures that give the application VUI modality.

Abstract: Techniques are disclosed for identifying which graphical user interface (GUI) screens of an application that is under development would benefit from a voice user interface (VUI). A GUI screen parser analyzes to determine the GUI objects within GUI screens of the application. The parser assigns a speechability score to each analyzed GUI screen. Those GUI screens that have a higher speechability score than a predetermined speechability threshold are indicated as GUI screens that would benefit (e.g., the user experience in interacting with those GUI screens would increase, the number of GUI screens displayed would be reduced, or the like) with the addition of a VUI.

Abstract: Techniques are disclosed for identifying which graphical user interface (GUI) screens of an application that is under development would benefit from a voice user interface (VUI). A GUI screen parser analyzes to determine the GUI objects within GUI screens of the application. The parser assigns a speechability score to each analyzed GUI screen. Those GUI screens that have a higher speechability score than a predetermined speechability threshold are indicated as GUI screens that would benefit (e.g., the user experience in interacting with those GUI screens would increase, the number of GUI screens displayed would be reduced, or the like) with the addition of a VUI.

Abstract: Techniques are disclosed for identifying which graphical user interface (GUI) screens of an application that is under development would benefit from a voice user interface (VUI). A GUI screen parser analyzes to determine the GUI objects within GUI screens of the application. The parser assigns a speechability score to each analyzed GUI screen. Those GUI screens that have a higher speechability score than a predetermined speechability threshold are indicated as GUI screens that would benefit (e.g., the user experience in interacting with those GUI screens would increase, the number of GUI screens displayed would be reduced, or the like) with the addition of a VUI.