Accenture Global Services Gmbh

Accenture Global Solutions Limited

Indian Institute of Technology Kanpur

Venkatesh K Subramanian

Nataraj Kuntagod

Vikrant Shyamkant Kaulgud

Sanjay Podder

Vibhu Saujanya Sharma

Aditya Bhola

Shubhashis Sengupta

Sanjoy Paul

Chethana Dinakar

Alex M Kass

Kunal Verma

Neville Dubash

Anutosh Maitra

Satya Sai Srinivas

Shrikanth Narayanaswamy Chandrasekaran

Aravindan Thoppe Santharam

Santhosh Kumar Shivaram

Satyasai Srinivas Abbabathula

Satish Nagasamudram

Kuntal Dey

Anurag Dwarakanath

Reymonrod Geli Vasquez

Senthil Kumar Kumaresan

Senthil Ramani

Saurabh Bhadkaria

Gary Titus

Samarth Sikand

Nisha Ramachandra

Pradeepkumar Duraisamy Soundrapandian

Deepmala Gupta

Chiranjeeb Ghosh

Rambhau Eknath Rote

Karthik Acharyulu

Subani Basha Nure

Yogesh Kumar Soniwal

Amit Mitra

Subani Bhasha Nure

Sushil Kumar Shah

Chiranjeevi Nalam

Padmaraj Bhat

Malharrao Mujumdar

Venkatraman Vaidhyanathan

Reshma Nuggehally

Sumit Dayal

Preeti Dubey

Yogesh Mallaiah

Subramanian Palanisami

Pradeepkumar Duraisamy

Kaushik Turlapaty

Bruno Marra

Manisha Sriraman

Namrata Maheshwary

A method for detection of a corrupted sensor including providing a first sensor; identifying one or more correlating sensors to the first sensor; determining a correlation between the first sensor and the correlating sensors according to historical sensor values; obtaining a calculated value of the first sensor based on values of the correlating sensors and the correlation; obtaining a measured value of the first sensor; and determining whether the first sensor is corrupted according to a difference between the calculated value and the measured value of the first sensor.

Corrupted sensors detection in sensor consortium

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

A system and method for dynamic risk assessment is disclosed. The system may include a data warehouse, an output device and a processor including a data capturer, a process engine and a rules engine. The data capturer may capture information pertaining to a plurality of risk factors associated with an infection risk corresponding to a plurality of data elements in an environment. The plurality of data elements may pertain to at least one of a space and a person in the space. The process engine may include at least one of a space risk profiler and a person risk profiler. The process engine may determine a risk score and a risk profile associated with the person and the space. Based on the risk profile, the processor may perform at least one of an automatic identification of a mitigation to reduce the infection risk and an automated generation of an alert.

Dynamic risk assessment

A system may receive published messages over an uplink channel. The system may, via channel control logic (CCL), delegate the messages to one or more subject-domain downlink channels (SDDCHs). The CCL may determine to create new SDDCHs to facilitate accurate placement of messages within defined subject-domains corresponding to the various SDDCHs.

Complex system for message downlink channel control

A learning system for automatically transmitting files according to user capabilities is provided. The learning system may include non-transitory memory storing instructions executable to transmit a file to a user device, and a processor circuitry configured to execute the instructions to determine a home base location of a user from at least one of a database storing user information of the user and the user device of the user. The processor circuitry further configured to calculate a travel distance from the home base location of the user to a hub circuitry of the learning system, determine a type of the file and an amount of content of the file based on the travel distance, and transmit, to the user device, the file according to the type of the file and the amount of content of the file.

Location aware learning system for content dispensation for resource-constrained edge devices

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

A device may receive a first sample. The device may identify a plurality of second samples that are similar to the first sample. The plurality of second samples may correspond to service tickets that have previously been processed. The device may obtain or determine log information identifying a plurality of events associated with the first sample. The device may determine respective probabilities of occurrence corresponding to the plurality of second samples. A probability of occurrence, for a particular second sample, may identify a likelihood of receiving a corresponding service ticket within a particular time period of occurrence of the plurality of events. The device may assign ranks to the plurality of second samples based on the respective probabilities of occurrence. The device may provide information identifying the ranks and the plurality of second samples.

Automated inference of evidence from log information

In some examples, incident prediction and prevention may include ascertaining a plurality of past incidents, clustering the plurality of past incidents to generate a plurality of incidents clusters, and identifying, for each past incident of the plurality of past incidents that is in a respective incidents cluster of the plurality of incidents clusters, a time of occurrence. Incident prediction and prevention may include ascertaining a new incident, assigning the new incident to an incidents cluster of the plurality of incidents clusters, and determining, for the assigned incidents cluster, at least one further predicted incident associated with at least one further corresponding incidents cluster. Further, incident prediction and prevention may include determining a resolution to the at least one further predicted incident, and preventing occurrence of the at least one further predicted incident by executing the determined resolution to the at least one further predicted incident.

Incident prediction and prevention

Various embodiments provide an assessment tool that enables an automated functional assessment of applications for migration to target cloud computing platforms, such as a Platform as a Service (PaaS). The technical capabilities of various types of applications in a traditional non-platform deployment are studied and support for these technical capabilities is evaluated relative to the target platform.

Migration assessment for cloud computing platforms

Workflow test case generation may include receiving an intermediate output of a workflow. The intermediate output of the workflow may reference a plurality of workflow building blocks that provide a specified functionality at each step of the workflow. Workflow definitions of the intermediate output of the workflow may be analyzed to generate a graph structure that represents the workflow. Building block attributes that include an identification of attributes for the plurality of workflow building blocks, and the graph structure may be analyzed to generate building block and attribute information for the workflow. The building block and attribute information and the graph structure may be analyzed to generate test paths for the workflow. The building block and attribute information, the test paths, and a plurality of test case templates may be analyzed to generate test artifacts that include testing to be performed for the workflow.

Workflow test case generation

A system may include one or more server devices. The system may provide a set of questions, to a user of a user device, to characterize an Internet of things system. The system may obtain responses from the user of the user device associated with each of the set of questions. The system may automatically create an Internet of things system architecture that defines the Internet of things system by applying associated Internet of things system architecture rules to the responses.

System for development of IoT system architecture

Technologies are generally described for the display of images by employing monotonic matrix factorization and sub-frame approximation image integration. In some examples, drive signals for a display device may be generated by iteratively applying a monotonic non-negative matrix factorization (NNMF) process to source image data. A given iteration of the monotonic NNMF process may result in approximation image data, partial sum image data, and residue image data, some or all of which may be further processed via subsequent iterations of the monotonic NNMF process. A generated approximation image data may then be displayed during a sub-frame time interval by selective activation of multiple row and column drivers. A series of such displayed approximation image data may effectively correspond to the original source image. In particular, the monotonic NNMF process may allow the generation of non-negative residue image data without the use of element reduction.

Convergent monotonic matrix factorization based entire frame image processing

This disclosure relates to assessing database migrations to cloud computing systems. On example method includes determining, by a migration server including one or more hardware processors, a set of possible transactions associated with a database application based at least in part on a set of application attributes associated with the database application; generating, by the migration server, a set of application requirements associated with the set of possible transactions; and creating, by the migration server, a set of migration recommendations associated with the database application based on the set of application requirements, the set of migration recommendations configured to allow the database application to be migrated to a cloud computing system and to allow the database application to comply with the set of application requirements when executed in the cloud computing system.

Assessing database migrations to cloud computing systems

A method for adapting execution of an application on a mobile device may be performed by a mobile device including a processor and a memory. The method may include receiving an application context, a process context, and one other context. The method also includes analyzing at least one of the application context or the process context together with the one other context. The method also includes dynamically adapting execution of the application on the mobile device based on the analysis. Adapting execution of the application may include transferring processing related to the application to a backend server for processing.

Adaptive architecture for a mobile application based on rich application, process, and resource contexts and deployed in resource constrained environments

Growing community of inventors

Kanpur, India

Venkatesh K Subramanian