Accenture Global Solutions Limited

Manish Ahuja

Sanjay Podder

Neville Dubash

Anurag Dwarakanath

Samarth Sikand

Raghotham M Rao

Kishore P Durg

Nisha Ramachandra

Jagadeesh Chandra Bose Rantham Prabhakara

Rekha M Menon

In some examples, human centered computing based digital persona generation may include generating, for a digital persona that is to be generated for a target person, synthetic video files and synthetic audio files that are combined to generate synthetic media files. The digital persona may be generated based on a synthetic media file. An inquiry may be received from a user of the generated digital persona. Another synthetic media file may be used by the digital persona to respond to the inquiry. A real-time emotion of the user may be analyzed based on a text sentiment associated with the inquiry, and a voice sentiment and a facial expression associated with the user. Based on the real-time emotion of the user, a further synthetic media file may be utilized by the digital persona to continue or modify a conversation between the generated digital persona and the user.

Human centered computing based digital persona generation

Systems, apparatuses, and methods are directed towards identifying that an adversarial patch image includes a plurality of pixels. The systems, apparatuses, and methods include dividing the adversarial patch image into a plurality of blocks, that each include a different group of the pixels in which the pixels are contiguous to each other, and assigning a first plurality of colors to the plurality of blocks to assign only one of the first plurality of colors to each pixel of one of the plurality of blocks.

Adversarial patches including pixel blocks for machine learning

A device may receive, from a user device, a request to verify a machine learning (ML) application using a metamorphic testing procedure. The device may determine a type of ML process used by the ML application, and may select one or more metamorphic relations (MRs), to be used for performing the metamorphic testing procedure, based on the type of ML process. The device may receive test data to be used to test the ML application, wherein the test data is based on the one or more MRs, and may perform, by using the one or more MRs and the test data, the metamorphic testing procedure to verify one or more aspects of the ML application. The device may generate a report that indicates whether the one or more aspects of the ML application have been verified and may provide the report for display on an interface of the user device.

Verification by metamorphic testing of applications that utilize artificial intelligence

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Bengaluru, India

Manish Ahuja