Accenture Global Solutions Limited

Samarth Sikand

Sanjay Podder

Vikrant Shyamkant Kaulgud

Vibhu Saujanya Sharma

Neville Dubash

Adam Patten Burden

Anurag Dwarakanath

Manish Ahuja

Raghotham M Rao

Kuntal Dey

Kishore P Durg

Kapil Singi

Venkatesh K Subramanian

Rohit Mehra

Priyavanshi Pathania

Ravi Kiran Velama

Jagadeesh Chandra Bose Rantham Prabhakara

In some examples, recruitment process graph based unsupervised anomaly detection may include obtaining log data associated with a recruitment process for a plurality of candidates, and generating knowledge graphs and graph embeddings. The graph embeddings may be trained to include a plurality of properties such that graph embeddings of genuine candidate hires and fraudulent candidate hires are appropriately spaced in a vector space. The trained graph embeddings may be clustered to generate a plurality of embedding clusters that include a genuine candidate cluster, and a fraudulent candidate cluster. For a new candidate graph embedding for a new candidate, a determination may be made as to whether the new candidate graph embedding belongs to the genuine candidate cluster, to the fraudulent candidate cluster, or to an anomalous cluster, and instructions may be generated to respectively retain or suspend the new candidate.

Recruitment process graph based unsupervised anomaly detection

Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

Computer code generator

In some implementations, a device may receive first energy consumption information relating to a set of hardware components of a computing system. The device may receive second energy consumption information relating to a set of virtual machines associated with the computing system. The device may receive third energy consumption information relating to a set of software elements associated with the computing system. The device may determine an energy consumption of the computing system based on the first energy consumption information, the second energy consumption information, and the third energy consumption information. The device may identify, based on the energy consumption of the computing system, an energy optimization associated with a usage context of the computing system. The device may transmit a set of instructions to alter one or more parameters of the computing system to implement the energy optimization for the computing system.

Energy management for computing systems

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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Jaipur, India

Samarth Sikand