Category: Sponsored Projects

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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). [Extended Abstract, To Appear]
  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). [Extended Abstract, To Appear]
  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]
  • Bhoomika A P [PhD Research Scholar]
  • Karthik Ch [PE, Jan 2023 – present]
  • Keshav Chandak [PE, Jan 2023 – present]
  • 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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Narrative Arc for Effective Learning

Narrative Arc is one of the research projects under the umbrella of Navigated Learning project at Gooru Labs, IIITB.

The Narrative Arc refers to presenting the sequence of learning activities as a narrative to the learner to make learning interesting and to help the learner navigate seamlessly through the learning space. The project has two parts: First is creating the learning pathways automatically given a corpus of learning resources, such that the generated pathways are semantically coherent and pedagogically progressive. Second part is modelling an AI-based automatic conversational agent which makes the learning pathway interesting and adapts the learning pathways according to the users knowledge and preferences. Here, the learning pathway is first presented to the user according to her learning goal, then the conversation agent interacts with the learner to keep the user interested in the learning pathway and to augment her knowledge. The agent also gauges the knowledge of the learner and supports the learner by providing knowledge and if required re-route the learner through a different learning pathway.

Following link has the presentation for the project in RISE 2019 workshop held at IIITB on 14-16 Feb 2019. The title of the presentation is “Narrative Arc Computation towards Digital Empowerment”. Narrative Arc Computation

  • Lead Researcher
  • Current Project Members
    • Mirambika Sikdar(Summer Intern)
  • Previous Project Members
    • Nikhil Bukka Sai
    • Sai Sri Harsha Vallabhuni
    • Rochan Avlur ( Intern)
    • Niharika Chaudhari (Intern)
    • Vibhav Agarwal
    • Abhiramon R
    • Sanket Kutumbe
    • Karan Kumar Gupta
    • Srinivasan P.S
  • Publications
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Navigated Learning

Figure 1: Navigated Learning

Navigated Learning is a new paradigm of learning that aims to balance the three independent requirements: Scale, Personalization and Social Interactions. Please see figure 1 that shows parallels among the concepts that are technological solutions and the three requirements of learning.

This is achieved by representing learning as situated within an abstract “competency space,” and computing semantic embeddings of learning objects and learners into the competency map. The competency map is organized as a progression space– which is a metric space with a partial order. Here, not only is there a notion of “distance” between any two points, but also an element of “progress”. These embeddings can be computed for any semantic object like learning resources, activities, learners, etc. Each point in the space represents a “competency” or a demonstrable skill that can be acquired by the learner.

A primary element of research into Navigated Learning is to construct a competency map for a given subject area of study and to build semantic embedding models for different kinds of objects relevant to the learning process. Semantic embeddings may take different forms depending on the nature of the object. While some objects can be neatly represented as points in the logical space, other objects may be represented by regions, pathways or other contours in the space. In an organizational setting, objects that are embedded onto this space include not just learning resources and learners, but also departments, projects and other organizational elements that require or work with relevant skill sets represented in the competency map.

Navigated learning is manged by a “Learning Navigator” with which every learner interacts. The Learning Navigator (or just, navigator), continuously interacts with the learning map and the learner to perform the following:

Locate: Based on data about their activities and outcomes from formal assessments, the “Locate” module of the navigator embeds learners in the space, and continuously updates their location. Unlike a geographical space, a learner may have acquired several competencies in the competency space. Thus, their location is not identified by a point, but by a data structure called a Skyline, that is detailed in a later section.

Curate: Once a learner’s location is known, based on their stated goals or recently acquired competencies, a set of further candidate competencies are identified. Curating is based on competency modeling principles, that identifies complementary, supplementary and conflicting competencies.

Mediate: This is the logic by which the navigator navigates the learner by making suggestions. Mediation is based on computing an underlying “Narrative Arc” that computes a semantically coherent and meaningful learning sequence individualized for each learner. Mediation also involves suggesting connections with other learners as well as group learning activities.

This project is sponsored by Gooru Learning.

Team Members:

Dr Aparna Lalingkar (PostDoctorate Research Fellow)

Ms Chaitali Diwan (PhD Research Scholar)

Ms Praseeda Kalkur (PhD Research Scholar)

Mr Naman Churiwala (Research Associate)

Mr Prakhar Mishra (MS Research Scholar)

Mr Shyam Kumar VN (MS Research Scholar)

Publications:

Chaitali Diwan, Srinath Srinivasa, and Prasad Ram.Automatic Generation of Coherent Learning Pathways for Open Educational Resources, In Proceedings of the Fourteenth European Conference on Technology Enhanced Learning (EC-TEL 2019), Springer LNCS, Delft, Netherlands, 16-19 September 2019 (to appear)

Aparna Lalingkar, Srinath Srinivasa, PrasadRam (2019), Characterization of Technology-based Mediations for Navigated Learning, Advanced Computing and Communications, Vol 3 (2), June 2019, ACCS  Publications, pp. 33-47. (Paper Link)

Praseeda, Srinath Srinivasa and Prasad Ram “Validating the Myth of Average through Evidences” In: The 12th International Conference on Educational Data Mining, Michel Desmarais, Collin F. Lynch, Agathe Merceron, & Roger Nkambou (eds.) 2019, pp. 631 – 634

Chaitali Diwan, Srinath Srinivasa, and Prasad Ram. Computing Exposition Coherence of Learning Resources, In Proceedings of The 17th International Conference on Ontologies, Databases and Applications of Semantics (ODBASE 2018), Valletta, Malta, October 22-26, 2018, Springer International Publishing.

Lalingkar, A.; Srinivasa, S. & Ram, P. (2018). Deriving Semantics of Learning Mediations, In Proceedings of the 18th IEEE International Conference on Advanced Learning Technologies (ICALT), IEEE, pp. 54-55. (Cited by 1 as per GoogleScholar citation index) (Paper Link)

Sub Projects

Details of Project Hosted

SDG Map showing various states in 2 dimensions is here

The competency map with polylines is hosted in the link here

The corresponding learning map for the learner is hosted in the link here

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