Accenture Global Solutions Limited

Accenture Global Services Gmbh

Neville Dubash

Sanjay Podder

Janardan Misra

Anurag Dwarakanath

Nisha Ramachandra

Divya Rawat

Bargav Jayaraman

Aditya Priyadarshi

Anitha Chandran

Parvathy Vijay Raghavan

Shalini Chaudhari

Bindu Madhav Tummalapalli

Bhanu Anand

Shubhashis Sengupta

Kishore P Durg

Bhaskar Ghosh

Venkatesh K Subramanian

Milind Ravikiran Savagaonkar

Samarth Sikand

Raghotham M Rao

Manish Ahuja

Kuntal Dey

Vikrant Shyamkant Kaulgud

Roshni Ramesh Ramnani

Priyavanshi Pathania

Annervaz K M

Harshawardhan Madhukar Wabgaonkar

Dipin Era

Tirupal Rao Ravilla

Sumitraj Ganapat Patil

Jagadeesh Chandra Bose Rantham Prabhakara

Subani Basha Nure

Rakesh Thimmaiah

Nikhil Chandrakant Khedkar

Jayati Deshmukh

Karthik Acharyulu

Shrikanth N C

Chaitra Hareesh

Reeja Jose

Prashant Sahadev Sawant

Kaushik Turlapaty

Subramanian Palanisami

Rashmi Singh

Venkat Surya

Sachin Hanumantappa Waddar

Padmaraj Bhat

Bruno Marra

Rekha M Menon

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

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

In some examples, system behavior profiling-based dynamic competency analysis may include identifying a plurality of software generation entities that have contributed to a module of a system, and generating an index to associate each software generation entity of the plurality of software generation entities. Execution links may be extracted from execution traces of the system, and an execution competency list may be generated. A dynamic competency score may be generated for each software generation entity for the system, and an overall dynamic competency score and a combined competency score may be determined. A software generation entity role may be obtained for a new application, and a software generation entity of the plurality of software generation entities may be identified to perform the software generation entity role. Development of the new application may be implemented using the identified software generation entity.

System behavior profiling-based dynamic competency analysis

A device may identify a first concept hierarchy of a first multimedia presentation and a second concept hierarchy of a second multimedia presentation. The device may determine a set of concepts associated with the first concept hierarchy and the second concept hierarchy and may determine a plurality of similarity scores associated with the set of concepts. The device may generate a new concept hierarchy based on the plurality of similarity scores, the first concept hierarchy, and the second concept hierarchy by: performing a first process to merge a concept with an additional concept; performing a second process to position a concept sequentially before or after an additional concept; performing a third process to position a concept and a different concept to sequentially follow an additional concept; and performing a fourth process to position a concept to sequentially follow an additional concept and a different concept.

Facilitating merging of concept hierarchies

In some examples, Blockchain intelligent security implementation may include determining whether a Blockchain transaction has been initiated, generating, based on a determination that the Blockchain transaction has been initiated, a password, and storing the generated password. The stored password may be forwarded to a user associated with the Blockchain transaction. A further password may be received from the user associated with the Blockchain transaction, and validated, based on comparison of the stored password to the further password. Based on the validation of the further password, the Blockchain transaction may be processed.

Blockchain intelligent security implementation

A device may receive first information that identifies an input associated with a virtual agent application executing on a user device. The virtual agent application may provide an interface for a project involving a plurality of user devices. The device may determine, based on the first information that identifies the input, a first response based on second information. The device may determine, based on at least one of the first information that identifies the input or the first response and without user input, a second response. The device may provide, to the virtual agent application of the user device, fourth information that identifies at least one of the first response or the second response.

Platform for supporting multiple virtual agent applications

In some examples, natural language unification based robotic agent control may include ascertaining, by a robotic agent, an image of an object or an environment, and ascertaining a plurality of natural language insights for the image. A semantic relatedness may be determined between each insight of the plurality of insights, and a semantic relatedness graph may be generated for the plurality of insights. For each insight of the plurality of insights, at least one central concept may be identified. Based on the semantic relatedness graph and the identified at least one central concept, the plurality of insights may be clustered to generate at least one insights cluster. For insights included in the least one insights cluster, a unified insight may be generated. Further, an operation associated with the robotic agent, the object, or the environment may be controlled by the robotic agent and based on the unified insight.

Natural language unification based robotic agent control

In some examples, natural language eminence based robotic agent control may include ascertaining, by a robotic agent, an image of an object or an environment, and ascertaining a plurality of natural language insights for the image. For each insight of the plurality of insights, an eminence score may be generated, and each insight of the plurality of insights may be ranked according to the eminence scores. An operation associated with the robotic agent, the object, or the environment may be controlled by the robotic agent and based on a highest ranked insight.

Natural language eminence based robotic agent control

A device may obtain test case information for a set of test cases. The test case information may include test case description information, test case environment information, and/or test case defect information. The device may determine a set of field-level similarity scores by using a set of similarity analysis techniques to analyze a set of test case field groups associated with the test case information. The device may determine a set of overall similarity scores for a set of test case groups by using a machine learning technique to analyze the set of field-level similarity scores. The device may update a data structure that stores the test case information to establish one or more associations between the test case information and the set of overall similarity scores. The device may process a request from a user device using information included in the updated data structure.

Using similarity analysis and machine learning techniques to manage test case information

A device may receive information associated with first and second bug reports to be classified as duplicate or non-duplicate bug reports. The device may identify first and second descriptions associated with the first and second bug reports, respectively. The first and second descriptions may be different descriptions having a shared description type. The device may identify a neural network for encoding the first and second descriptions, based on the shared description type. The device may encode the first description into a first vector using the neural network, and may encode the second description into a second vector using the neural network. The device may classify the first and second bug reports as duplicate or non-duplicate bug reports based on the first vector and the second vector. The device may perform an action based on classifying the first and second bug reports as duplicate or non-duplicate bug reports.

Duplicate and similar bug report detection and retrieval using neural networks

A device may determine probabilities for test scripts associated with a test to be executed on a software element, where a respective probability is associated with a respective test script, indicates a likelihood that the respective test script will be unsuccessful in a test cycle, and is determined based on historical test results, associated with the software element, for the respective test script. The device may generate, based on the probabilities, a test script execution order, of the test scripts, for the test cycle, and may execute, based on the test script execution order, the test on the software element in the test cycle. The device may dynamically generate, based on results for the test in the test cycle, an updated test script execution order, and may execute, based on the updated test script execution order, the test on the software element in the test cycle.

Generating a test script execution order

A device may obtain a document. The device may identify a skip value for the document. The skip value may relate to a quantity of words or a quantity of characters that are to be skipped in an n-gram. The device may determine one or more skip n-grams using the skip value for the document. A skip n-gram, of the one or more skip n-grams, may include a sequence of one or more words or one or more characters with a set of occurrences in the document. The sequence of one or more words or one or more characters may include a skip value quantity of words or characters within the sequence. The device may extract one or more terms from the document based on the one or more skip n-grams. The device may provide information identifying the one or more terms.

Automated term extraction

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

A device may receive test scripts that include first information identifying first elements of user interfaces or second information identifying test steps. The test scripts may be written in first text or first program code. The device may process the first text or the first program code of the test scripts. The device may identify the first elements on the user interfaces. The first elements may be identified without using second program code associated with the user interfaces. The first elements may be identified based on a type of the first elements, second text associated with the first elements, or a relationship between the first elements and second elements. The device may identify positions for the first elements. The positions may permit the device to interact with the first elements to perform the test steps. The device may perform the test steps to test the user interfaces.

Device based visual test automation

A device may receive information identifying a first set of instructions. The first set of instructions may identify an action to perform to test a first program. The device may identify a second set of instructions, related to testing a second program, that can be used in association with the first set of instructions. The first test may be similar to the second test. The device may identify multiple steps, of the first set of instructions, that can be combined to form a third set of instructions. The third set of instructions may be used to test the first program or a third program. The device may generate program code in a first programming language to perform the action. The first programming language may be different than a second programming language used to write the first set of instructions. The device may perform the action.

Test automation using multiple programming languages

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Mumbai, India

Neville Dubash