Abstract: Methods, systems, and computer program products for suggesting application programming interfaces (APIs) based on feature and context analysis are provided herein. A computer-implemented method includes extracting at least one feature of an input function code based on application of one or more text analytics to one or more inputs pertaining to the input function code; deriving at least one item of context-related information from the input function code; generating a suggestion comprising one or more application programming interfaces, wherein said generating comprises comparing (i) the at least one feature of the input function code and (ii) the at least one item of context-related information to a database, and wherein the database comprises (a) feature data and (b) context-related information pertaining to multiple application programming interfaces; and outputting the suggestion to at least one user.

Abstract: Methods, systems, and computer program products for suggesting APIs based on feature and context analysis are provided herein. A computer-implemented method includes extracting at least one feature of an input function code based on application of one or more text analytics to one or more inputs pertaining to the input function code; deriving at least one item of context-related information from the input function code; generating a suggestion comprising one or more application programming interfaces, wherein said generating comprises comparing (i) the at least one feature of the input function code and (ii) the at least one item of context-related information to a database, and wherein the database comprises (a) feature data and (b) context-related information pertaining to multiple application programming interfaces; and outputting the suggestion to at least one user.

Abstract: In an example, time and frequency domain speech enhancement is implemented on a platform having a programmable device, such a PC or a smartphone running an OS. Echo cancellation is done first in time domain to cancel a dominant portion of the echo. Residual echo is cancelled jointly with noise reduction during a subsequent frequency domain stage. The time domain block uses a dual band, shorter length Adaptive Filter for faster convergence. Non-linear residual echo is cancelled based on an echo estimate and an error signal from the adaptive filters. A controller locates regions that had residual echo suppressed and which do not have speech and injects comfort noise. The controller can be full-duplex and operate non-linearly. An AGC selectively amplifies the frequency bins, based on the Gain function used by the residual echo and noise canceller.

Abstract: In an example, time and frequency domain speech enhancement is implemented on a platform having a programmable device, such a PC or a smartphone running an OS. Echo cancellation is done first in time domain to cancel a dominant portion of the echo. Residual echo is cancelled jointly with noise reduction during a subsequent frequency domain stage. The time domain block uses a dual band, shorter length Adaptive Filter for faster convergence. Non-linear residual echo is cancelled based on an echo estimate and an error signal from the adaptive filters. A controller locates regions that had residual echo suppressed and which do not have speech and injects comfort noise. The controller can be full-duplex and operate non-linearly. An AGC selectively amplifies the frequency bins, based on the Gain function used by the residual echo and noise canceller.

Abstract: In an example, time and frequency domain speech enhancement is implemented on a platform having a programmable device, such a PC or a smartphone running an OS. Echo cancellation is done first in time domain to cancel a dominant portion of the echo. Residual echo is cancelled jointly with noise reduction during a subsequent frequency domain stage. The time domain block uses a dual band, shorter length Adaptive Filter for faster convergence. Non-linear residual echo is cancelled based on an echo estimate and an error signal from the adaptive filters. A controller locates regions that had residual echo suppressed and which do not have speech and injects comfort noise. The controller can be full-duplex and operate non-linearly. An AGC selectively amplifies the frequency bins, based on the Gain function used by the residual echo and noise canceller.

Abstract: In an example, time and frequency domain speech enhancement is implemented on a platform having a programmable device, such a PC or a smartphone running an OS. Echo cancellation is done first in time domain to cancel a dominant portion of the echo. Residual echo is cancelled jointly with noise reduction during a subsequent frequency domain stage. The time domain block uses a dual band, shorter length Adaptive Filter for faster convergence. Non-linear residual echo is cancelled based on an echo estimate and an error signal from the adaptive filters. A controller locates regions that had residual echo suppressed and which do not have speech and injects comfort noise. The controller can be full-duplex and operate non-linearly. An AGC selectively amplifies the frequency bins, based on the Gain function used by the residual echo and noise canceller.

Abstract: Techniques for searching for a user interface (UI) are provided. The techniques include receiving a desired user interface as a query, determining meta-data for the desired user interface, wherein determining the meta-data for the desired user interface comprises converting the desired user interface query to a meta-data query, and using the meta-data query to search for the desired user interface, wherein searching for the desired user interface comprises querying meta-data of one or more existing user interfaces and selecting one or more similar user interfaces.

Abstract: Techniques for facilitating use of a transformer are provided. The techniques include analyzing code of a transformer to compute one or more exception constraints and one or more output constraints, translating the one or more exception constraints and one or more output constraints to state them in terms of one or more input and output meta-model elements, wherein translating the one or more exception constraints and one or more output constraints comprises inferring one or more transformer rules in terms of vocabulary of a meta-model, wherein a transformer rule comprises one of a verification rule and a comprehension rule, and using the one or more inferred transformer rules to facilitate use of a transformer, wherein facilitating use of a transformer comprises supporting model verification and transform comprehension.

Abstract: Techniques for facilitating use of a transformer are provided. The techniques include analyzing code of a transformer to compute one or more exception constraints and one or more output constraints, translating the one or more exception constraints and one or more output constraints to state them in terms of one or more input and output meta-model elements, wherein translating the one or more exception constraints and one or more output constraints comprises inferring one or more transformer rules in terms of vocabulary of a meta-model, wherein a transformer rule comprises one of a verification rule and a comprehension rule, and using the one or more inferred transformer rules to facilitate use of a transformer, wherein facilitating use of a transformer comprises supporting model verification and transform comprehension.

Abstract: Techniques for searching for a user interface (UI) are provided. The techniques include receiving a desired user interface as a query, determining meta-data for the desired user interface, wherein determining the meta-data for the desired user interface comprises converting the desired user interface query to a meta-data query, and using the meta-data query to search for the desired user interface, wherein searching for the desired user interface comprises querying meta-data of one or more existing user interfaces and selecting one or more similar user interfaces.