Category: Projects

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SDG 2D Visualization

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IndicNLP

IndicNLP project focuses on building an knowledge management framework for oral community knowledge in low-resource and colloquial Kannada language.

Background

Knowledge in rural communities is largely created, preserved, and is transferred verbally, and it is limited. This information is valuable to these communities, and managing and making it available digitally with state-of-the-art approaches enriches awareness and collective knowledge of people of these communities. The large amounts of data and information produced on the Internet are inaccessible to the population in these rural communities due to factors like lack of infrastructure, connectivity, and limited literacy. Knowledge internal to rural communities is also not conserved and made available in any global Big Data information systems. Artificial Intelligence (AI) technologies such as Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) provide substantial assistance when vast quantities of data, like Big Data, are available to build solutions. In the case of low-resource languages like Kannada and rural colloquial dialects, publicly available corpora are significantly less. Building state-of-the-art AI solutions is challenging in this context, and we address this problem in this work. Knowledge management in rural communities requires a low-cost and efficient approach that social workers can use. Organizations such as Namma Halli Radio have collected an audio corpus of a few hours containing community interactions spoken in colloquial language. We propose an architecture for oral knowledge management for rural communities speaking colloquial Kannada using audio recordings.

Funding Agency

Mphasis F1 foundation

Publications

M. Aparna, Sharath Srivatsa, G. Sai Madhavan, T. B. Dinesh, and Srinath Srinivasa. AI-based Assistance for Management of Oral Community Knowledge in Low-Resource and Colloquial Kannada language. International Conference on Big-Data-Analytics in Astronomy, Science and Engineering, BASE 2023, Springer LNCS. [to-appear]

Sharath Srivatsa, Aparna M, Sai Madhavan G, and Srinath Srinivasa. 2024. Knowledge Management Framework Over Low Resource Indian Colloquial Language Audio Contents. In Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD) (CODS-COMAD ’24). Association for Computing Machinery, New York, NY, USA, 553–557. https://doi.org/10.1145/3632410.3632483 

Aparna M and Srinath Srinivasa. 2023. Active learning for Named Entity Recognition in Kannada. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.24580582.v1

Media Mentions

Demo

Graama-Kannada Audio Search webapp : http://103.156.19.244:33035/,
(username : guest, password : guest123)

Graama-Kannada demo video:

People

Research Scholars

Project Students

  • Goutham U R
  • Ram Sai Koushik Polisetti
  • Sai Madhavan G
  • Kappagantula Lakshmi Abhigna
  • Manuj Malik
  • Debmalya Sen
  • Vikram Adithya C P
  • Venumula Sai Sumanth Reddy

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IUDX Data Trusts

Developing a policy-based consent service for Data Trusts in collaboration with IUDX and IISc.

Funding Agency

IUDX

Publications

Ayappane, Balambiga, et al. “Extensible Consent Management Architectures for Data Trusts.” arXiv preprint arXiv:2309.16789 (2023).

B. Ayapane, R. Vaidyanathan, S. Srinivasa, S. Upadhyaya, and S. Vivek, “Consent Service Architecture for Policy-Based Consent Management in Data Trusts,” in ACM CODS-COMAD, 2024 (To appear)

Team Details

  1. Balambiga Ayappane (MS scholar) 
  2. Rohith Vaidyanathan (MS scholar) 
  3. Prof. V Sridhar (collaborator) 
  4. Asilata Karandikar (collaborating PhD Scholar) 
  5. Prof. Srinivas Vivek (collaborator) 
  6. Santosh Kumar Upadhyaya (collaborating PhD scholar)
  7. Prof. Srinath Srinivasa (PI)
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Big Data Analytics for Policy Support

Spotlight Data Story: Intervention Dashboard for Maternal Deaths

The primary focus of Big Data Analytics for Policy Support project is to develop a sophisticated data management system that consolidates diverse documents from various government departments into a unified access point. This centralized hub serves as a valuable resource for policymakers, domain experts, and data analysts, allowing them to easily search and analyze documents through visual dashboards. The front-facing website of this work can be accessed at Karnataka Data Lake: Policy Research using Big Data Analytics.

The other research direction of the project focuses on creating a comprehensive model library, which becomes a crucial asset for policymakers. This library not only aids in making informed decisions based on the current state but also looks ahead to anticipate future scenarios related to policy interventions. The ultimate aim of the Policy Support System (PSS) is to provide policymakers with a powerful tool that addresses current challenges related to Sustainable Development Goals (SDGs) while also proactively navigating the evolving landscape of policy dynamics.

The Architecture Diagram of Big Data Analytics for Policy Support

Publications

2024

2023

2022

2021

People

Faculty

Research Scholars

Project Associates

Project Students

  • Nithin Raj
  • Omkar Lavangad
  • Phani Tirthala
  • Himesh Krishna Osuri
  • Anish Sai Potluri
  • Ayush Tiwari
  • Sowmith Nandan R
  • Arpitha Malavalli
  • Saiakash Konidena
  • Arjun Dutta
  • Akshay Girish Thite
  • Umang Okate
  • Gaurav Nilesh Tirodkar
  • Jasvin James Manjaly
  • Bhavil Sharma
  • Mallangi Siva Charan Reddy
  • Nimish Gaurish Sinai Khandeparkar
  • Gramya Gupta
  • Manish Reddy Koppula
  • Swadi Korattiparambil

Interns

  • Shreya Subramaniyam
  • Ojas Hegde
  • Greeshma Chandra Shekar
  • Nandan N
  • Sachin Tiwari
  • Siddharth Gautam

Research Site

Karnataka Data Lake: Policy Research using Big Data Analytics

Media Mentions

https://www.newindianexpress.com/cities/bengaluru/2022/jul/04/bengaluru-to-get-rs-5-crore-data-lake-forbetter-governance-improved-planning-2472616.html

Talks & Videos

  • Big-data-analytics in Astronomy, Science, and Engineering (BASE) 2021
  • MLN-DIVE Lecture Series: Srinath Srinivasa

Title: Diffusion and Perturbation Modeling for Policy Interventions

Date: June 11, 2022

https://psu.mediaspace.kaltura.com/media/MLN-DIVE+lecture+seriesA+Srinath+Srinivasa/1_mm4wvj58

  • Bangalore Tech Summit 16th – 18th November 2022
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Responsible Autonomy

Designing computational models of multi-agent autonomous systems which can act responsibly.

Funding Agency

Machine Intelligence and Robotics (MINRO)

Center for Internet of Ethical Things (CIET)

Publications

  1. Janvi Chhabra, Jayati Deshmukh and Srinath Srinivasa. 2024. Modelling the Dynamics of Subjective Identity in Allocation Games. In Proceedings of the 2024 International Conference on Autonomous Agents and Multiagent Systems (AAMAS ’24). [link]
  2. Karthik Sama, Jayati Deshmukh and Srinath Srinivasa. 2024. Social Identities and Responsible Agency. In Proceedings of the 2024 International Conference on Autonomous Agents and Multiagent Systems (AAMAS ’24). [link]
  3. Sama Sai Karthik, Jayati Deshmukh, Srinath Srinivasa. 2024. Transcending To Notions. Preprint. https://arxiv.org/abs/2401.12159 [link]
  4. Janvi Chhabra, Jayati Deshmukh, Srinath Srinivasa. 2024. Modelling the Dynamics of Identity and Fairness in Ultimatum Game. Preprint. https://arxiv.org/abs/2401.11881 [link]
  5. Jayati Deshmukh, Nikitha Adivi, and Srinath Srinivasa. 2023. Resolving the Dilemma of Responsibility in Multi-agent Flow Networks. In International Conference on Practical Applications of Agents and Multi-Agent Systems. Springer, 76–87. DOI: http://dx.doi.org/https://doi.org/10.1007/978-3-031-37616-0_7
  6. Janvi Chhabra, Karthik Sama, Jayati Deshmukh, and Srinath Srinivasa. 2023. When Extrinsic Payoffs Meet Intrinsic Expectations. In International Conference on Practical Applications of Agents and Multi-Agent Systems. Springer, 40–51. DOI: http://dx.doi.org/https://doi.org/10.1007/978-3-031-37616-0_4
  7. Jayati Deshmukh. 2023. Emergent Responsible Autonomy in Multi-Agent
    Systems. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (AAMAS ’23). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 3029–3031. https://dl.acm.org/doi/abs/10.5555/3545946.3599161
  8. Jayati Deshmukh, Nikitha Adivi, and Srinath Srinivasa. 2023. Modeling Application Scenarios for Responsible Autonomy Using Computational Transcendence. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (AAMAS ’23). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2496–2498. https://dl.acm.org/doi/abs/10.5555/3545946.3598980
  9. Janvi Chhabra, Karthik Sama, Jayati Deshmukh, and Srinath Srinivasa. 2023. Comparative Modeling of Ethical Constructs in Autonomous Decision Making. (2023). DOI: http://dx.doi.org/https://doi.org/10.36227/techrxiv.21802302.v1
  10. Jayati Deshmukh and Srinath Srinivasa. 2022. Computational Transcendence: Responsibility and agency. Frontiers in Robotics and AI 9 (2022). DOI: http://dx.doi.org/https://doi.org/10.3389/frobt.2022.977303
  11. Srinath Srinivasa and Jayati Deshmukh. 2022. AI and the Sense of Self. arXiv preprint arXiv:2201.05576 (2022). DOI: http://dx.doi.org/https://doi.org/10.48550/arXiv.2201.05576
  12. Jayati Deshmukh, Srinath Srinivasa, and Sridhar Mandyam. 2021. What Keeps a Vibrant Population Together? Complex Syst. 30, 3 (2021), 347–373. DOI: http://dx.doi.org/https://doi.org/10.25088/ComplexSystems.30.3.347
  13. Srinath Srinivasa and Jayati Deshmukh. 2020. The Evolution of Computational Agency. In Novel Approaches to Information Systems Design. IGI Global, 1–19. DOI: http://dx.doi.org/https://doi.org/10.4018/978-1-7998-2975-1.ch001

Team Details

  1. Jayati Deshmukh [PhD Research Scholar]
  2. Janvi Chhabra [RE, PE, IMTech thesis, Jan 2022 – June 2023]
  3. Sama Sai Karthik [RE, PE, IMTech thesis, Jan 2022 – June 2023]
  4. Nikitha AN [PE, Aug 2022 – Dec 2022]

Videos

Samvaad talk on August 22nd, 2022

Talk at Jijnasa 2021

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Traffic Modeling and Simulation

Major research directions of the project are:

  • Simulations of traffic scenarios (like one-way, adding new roads, parking spots etc).
  • Experiments on Adaptive Traffic Lights.
  • Simulations to optimize traffic flows at network level.

Funding Agency

MINRO

Team Details

  • Jayati Deshmukh [PhD Research Scholar]
  • Karthik Ch [PE, Jan 2023 – May 2023]
  • Keshav Chandak [PE, Jan 2023 – May 2023]
  • Shubhanshu Agrawal [Summer Intern, PE, May 2022 – Dec 2022]
  • Manas Agrawal [PE, Aug 2022 – Dec 2022]
  • Shivankar Pilligundla [PE, Aug 2022 – Dec 2022]
  • Prakhar Rastogi [Summer Intern, May 2022 – July 2022]
  • Vijay Jaisankar [PE, Jan 2022 – May 2022]
  • Vignesh Bondugula [PE, Jan 2022 – May 2022]
  • Siva Jagadesh M [PE, Jan 2022 – May 2022]
  • Shashank Reddy [PE, Aug-Dec 2021]
  • Gayathri Venkatesh [PE, Aug-Dec 2021]
  • Manasa Kashyap [PE, Aug-Dec 2021]
  • Sriranjan S. [Summer intern, May-Jul 2021]
  • Ayush Yadav [PE, Jan-Apr 2021]
  • Rahul Murali Shankar [PE, Jan-Apr 2021]
  • Pratik Shastri [PE and IMTech thesis, Aug 2020-Jun 2021]
  • Divyanshu Khandelwal [PE, Aug-Dec 2020]

Code

Performing traffic interventions and running traffic simulations.

Activities

Demo presented at SUMO User Conference 2022. Won Best presentation based on live audience voting.

Demo of the project at Bangalore Tech Summit (BTS) 2020.

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Precision Learning for Enterprise

Overview:

Organizational learning is fast becoming an integral element of the daily operations of organizations given the pace of change in technology and market dynamics. Organizational learning is the process of creating, retaining, and transferring knowledge within an organization. An organization improves over time as it gains experience. From this experience, it is able to create knowledge. This knowledge is broad, covering any topic that could better an organization. Organisational learning is an essential element for the survival of any organisation in this fast paced technological and business landscape.

A concept of “learning organizations” has been a focus of enormous research and managerial interest since the early 1990s. Peter Senge, identifies five key disciplines for organizational learning:

  1. Systems Thinking
  2. Shared Vision
  3. Mental Models
  4. Team Learning
  5. Personal Mastery

While the concept of learning organizations elicited enormous interest in its early years, the above principles of learning organizations were found to be very challenging to manage, in an environment of fast changes and churn in the workplace.

In recent times, a new paradigm called precision learning is gaining a lot of research attention. Precision learning entails the use of “Big Data” techniques to identify expertise and knowledge gaps in a given community or organization, and mediate between them. The result is a continuous, on-going process of learning catering to a population of diverse interests, skills and learning goals.

Navigated Learning is a precision learning model being developed at the Web Science lab at IIIT Bangalore. Navigated learning implements precision learning, by computing a variety of semantic embeddings. This project explores Navigated Learning models for enterprise settings.

Expected Social Impact:

Navigated learning is part of the research focus called “Digital Empowerment” being pursued by the Web Science lab. The underlying social challenge being addressed is the need for informal and continuous upskilling required for overall social empowerment. Current models of technology enabled learning have not gained enough engagement and traction from users to pursue their upskilling activities in a sustained manner. One of the reasons we believe for this, is the dearth of context, relevance and social support during the learning process. Navigated learning is expected to cater to this need, and we hope that it becomes as popular as, and an empowering alternative to streaming video and gaming apps.

Since this is under applied research in cognitive computing “Precision Learning for Enterprise portal” will be delivered in which the concept of Navigated Learning is applied.

This project is sponsored by Mphasis under the Mphasis Center of Excellence for Cognitive Computing.

Team Members:

Dr Aparna Lalingkar, Postdoctorate Research Fellow

Mr Prakhar Mishra, MS Research Scholar

Mr Shyam Kumar VN, MS Research Scholar

Current Members:

Jagatdeep Pattanaik, MTech Student

Puneeth Sarma, IMTech Student

Rohit Katlaa, IMTech Student 

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An Open Architecture for Smart Cities

Sponsor and Collaborator: Siemens India

Time Frame: May 2015 — Apr2016

Status: Active

Smart cities are an upcoming area of growth in the region and provide a large gamut of technical challenges in the area of wide area distributed sensing and processing. A significant part of these challenges can be addressed by leveraging upcoming technologies in the fog computing area where edge devices not only collect data and provide control signals but perform local optimizations based on global optimization requirements. However, to implement rapid growth of smart campus/city requires applications. These applications may be on different devices, software, middleware etc. As a result, this heterogeneity is a huge challenge at present to integrate to available system, collect data etc. Hence, one needs to develop a platform agnostic to above complex heterogeneous environment.

This provides an opportunity for both Siemens and IIIT Bangalore to create an open platform to share data and expose relevant APIs using which smart applications can be built by anyone.

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Inferencing in the Large: Semantic Integration of Open-Data Tables

Inferencing in the Large (ITL) is a research problem encompassing knowledge extraction, knowledge organisation and knowledge retrieval from open structured data, especially from the Indian Open Government Data.

With vast amounts of tabular data freely available under several Open-Data initiatives, consumption of information depends upon the perspectives of the consumer. These perspectives can be viewed as various contexts the data can be placed in and analysed. Extraction and Organisation of these contexts are non-trivial and we address the problem using semantic integration of open structured data. A collection of open datasets can map to similar contexts (themes) and a single table can map to different themes. ITL presents a model that semantically integrates and aggregates open data in a data mesh of applicable inter-related contexts. Sandesh 1.0 are Sandesh-RDF (v 2.0) are implementations of ITL using open government data from the Indian Open Government Data portal. We use the Linked Open Data (LOD) to associate semantics to the data. The MWF (Many Worlds on a Frame) knowledge framework has been implemented using RDF N-Quads to represent the knowledge extraction in Sandesh-RDF (v 2.0). Sandesh-RDF queries the knowledge graph created from the N-Quads which is the semantic representation of data from data.gov.in. The previous version of Sandesh used the default SQLlite implementation of the MWF framework.

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Reach

Sponsors and Collaborators: Horizon 2020, European Commission

Time Frame: Jan 2016 – Dec 2020

Status – Active

The REACH project aims to develop solution to avail the provision for high speed Internet access in rural India using unlicensed TV white space spectrum and designing the Geolocation database for it. With the wide increase of population and use of Internet in India, the efficient utilization and management of spectrum is needed. The utilization of TV white space spectrum is emerging as a best alternative to fulfill this need since there are many unused channel in TV spectrum due to migration from analog to digital transmission technology.

At IIIT-B, we are working on Distributed Algorithms for Spectrum Assignment for White Space Devices. Spectrum assignment for devices in white-space spectrum is challenging due to the fact that, white-space spectrum has temporal and spatial variations and is most often fragmented. We have created an autonomous agent model for spectrum assignment of white space devices at a given location. Each white space device (WSD) acts autonomously out of self-interest, choosing a strategy from its bag of strategies. It obtains a payoff based on its choice and choices made by all other agents. WSDs interact with each other using a central shared memory located at a “Master” device. Based on payoffs received by different strategies, WSDs evolve their strategic profile over time. This has the effect of “demographic changes” in the population. The system is said to have reached a state of equilibrium (or, in a state of evolutionary best-response) when the demographic profile stabilises. The system is trained on different load profiles to compute their respective evolutionary best responses.

Project Outcomes

  1. Chaitali Diwan. Autonomous Spectrum Assignment of White Space devices. MTech thesis. June 2016.
  2. Chaitali Diwan, Srinath Srinivasa, Bala Murali Krishna. Autonomous Spectrum Assignment of White Space Devices. Proceedings of the 12th EAI International Conference on Cognitive Radio Oriented Wireless Networks. Lisbon, Portugal. September 2017.
  3. Simulation dashboard for autonomous spectrum allocation algorithm

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